From 59b9af5a271f7b1026697710b66abd72a85933fc Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 3 Sep 2024 16:00:07 +0800 Subject: [PATCH 01/93] Add paddle backend code(WIP) --- .pre-commit-config.yaml | 62 +- backend/dp_backend.py | 5 + backend/find_paddle.py | 145 ++ backend/read_env.py | 16 + deepmd/backend/paddle.py | 126 + deepmd/dpmodel/utils/network.py | 4 +- deepmd/pd/__init__.py | 13 + deepmd/pd/cxx_op.py | 100 + deepmd/pd/entrypoints/__init__.py | 1 + deepmd/pd/entrypoints/main.py | 577 +++++ deepmd/pd/infer/__init__.py | 1 + deepmd/pd/infer/deep_eval.py | 747 ++++++ deepmd/pd/infer/inference.py | 65 + deepmd/pd/loss/__init__.py | 28 + deepmd/pd/loss/denoise.py | 109 + deepmd/pd/loss/dos.py | 256 ++ deepmd/pd/loss/ener.py | 416 ++++ deepmd/pd/loss/ener_spin.py | 332 +++ deepmd/pd/loss/loss.py | 43 + deepmd/pd/loss/tensor.py | 177 ++ deepmd/pd/model/__init__.py | 6 + deepmd/pd/model/atomic_model/__init__.py | 53 + .../model/atomic_model/base_atomic_model.py | 578 +++++ .../model/atomic_model/dipole_atomic_model.py | 28 + .../pd/model/atomic_model/dos_atomic_model.py | 14 + .../pd/model/atomic_model/dp_atomic_model.py | 275 +++ .../model/atomic_model/energy_atomic_model.py | 20 + .../model/atomic_model/linear_atomic_model.py | 563 +++++ .../atomic_model/pairtab_atomic_model.py | 491 ++++ .../model/atomic_model/polar_atomic_model.py | 65 + deepmd/pd/model/backbone/__init__.py | 12 + deepmd/pd/model/backbone/backbone.py | 12 + deepmd/pd/model/backbone/evoformer2b.py | 103 + deepmd/pd/model/descriptor/__init__.py | 64 + deepmd/pd/model/descriptor/base_descriptor.py | 8 + deepmd/pd/model/descriptor/descriptor.py | 232 ++ deepmd/pd/model/descriptor/dpa1.py | 646 +++++ deepmd/pd/model/descriptor/dpa2.py | 715 ++++++ deepmd/pd/model/descriptor/env_mat.py | 82 + deepmd/pd/model/descriptor/gaussian_lcc.py | 323 +++ deepmd/pd/model/descriptor/hybrid.py | 359 +++ deepmd/pd/model/descriptor/repformer_layer.py | 1373 ++++++++++ .../descriptor/repformer_layer_old_impl.py | 751 ++++++ deepmd/pd/model/descriptor/repformers.py | 565 +++++ deepmd/pd/model/descriptor/se_a.py | 720 ++++++ deepmd/pd/model/descriptor/se_atten.py | 1041 ++++++++ deepmd/pd/model/descriptor/se_atten_v2.py | 279 +++ deepmd/pd/model/descriptor/se_r.py | 490 ++++ deepmd/pd/model/descriptor/se_t.py | 736 ++++++ deepmd/pd/model/descriptor/se_t_tebd.py | 865 +++++++ deepmd/pd/model/model/__init__.py | 226 ++ deepmd/pd/model/model/dipole_model.py | 130 + deepmd/pd/model/model/dos_model.py | 113 + deepmd/pd/model/model/dp_model.py | 56 + deepmd/pd/model/model/dp_zbl_model.py | 163 ++ deepmd/pd/model/model/ener_model.py | 137 + deepmd/pd/model/model/frozen.py | 206 ++ deepmd/pd/model/model/make_hessian_model.py | 215 ++ deepmd/pd/model/model/make_model.py | 594 +++++ deepmd/pd/model/model/model.py | 58 + deepmd/pd/model/model/polar_model.py | 106 + deepmd/pd/model/model/spin_model.py | 631 +++++ deepmd/pd/model/model/transform_output.py | 272 ++ deepmd/pd/model/network/__init__.py | 1 + deepmd/pd/model/network/init.py | 458 ++++ deepmd/pd/model/network/layernorm.py | 154 ++ deepmd/pd/model/network/mlp.py | 333 +++ deepmd/pd/model/network/network.py | 2198 +++++++++++++++++ deepmd/pd/model/task/__init__.py | 42 + deepmd/pd/model/task/atten_lcc.py | 55 + deepmd/pd/model/task/base_fitting.py | 8 + deepmd/pd/model/task/denoise.py | 137 + deepmd/pd/model/task/dipole.py | 200 ++ deepmd/pd/model/task/dos.py | 130 + deepmd/pd/model/task/ener.py | 257 ++ deepmd/pd/model/task/fitting.py | 538 ++++ deepmd/pd/model/task/invar_fitting.py | 182 ++ deepmd/pd/model/task/polarizability.py | 264 ++ deepmd/pd/model/task/task.py | 1 + deepmd/pd/model/task/type_predict.py | 47 + deepmd/pd/optimizer/KFWrapper.py | 145 ++ deepmd/pd/optimizer/LKF.py | 322 +++ deepmd/pd/optimizer/__init__.py | 9 + deepmd/pd/train/__init__.py | 1 + deepmd/pd/train/training.py | 1294 ++++++++++ deepmd/pd/train/wrapper.py | 196 ++ deepmd/pd/utils/__init__.py | 11 + deepmd/pd/utils/ase_calc.py | 6 + deepmd/pd/utils/auto_batch_size.py | 60 + deepmd/pd/utils/cache.py | 31 + deepmd/pd/utils/dataloader.py | 320 +++ deepmd/pd/utils/dataset.py | 58 + deepmd/pd/utils/dp_random.py | 14 + deepmd/pd/utils/env.py | 91 + deepmd/pd/utils/env_mat_stat.py | 234 ++ deepmd/pd/utils/exclude_mask.py | 158 ++ deepmd/pd/utils/finetune.py | 200 ++ deepmd/pd/utils/learning_rate.py | 53 + deepmd/pd/utils/multi_task.py | 162 ++ deepmd/pd/utils/neighbor_stat.py | 193 ++ deepmd/pd/utils/nlist.py | 494 ++++ deepmd/pd/utils/plugin.py | 16 + deepmd/pd/utils/preprocess.py | 309 +++ deepmd/pd/utils/region.py | 116 + deepmd/pd/utils/serialization.py | 78 + deepmd/pd/utils/stat.py | 589 +++++ deepmd/pd/utils/update_sel.py | 17 + deepmd/pd/utils/utils.py | 168 ++ deepmd/utils/batch_size.py | 3 +- doc/install/install-from-source.md | 2 +- 110 files changed, 27919 insertions(+), 35 deletions(-) create mode 100644 backend/find_paddle.py create mode 100644 deepmd/backend/paddle.py create mode 100644 deepmd/pd/__init__.py create mode 100644 deepmd/pd/cxx_op.py create mode 100644 deepmd/pd/entrypoints/__init__.py create mode 100644 deepmd/pd/entrypoints/main.py create mode 100644 deepmd/pd/infer/__init__.py create mode 100644 deepmd/pd/infer/deep_eval.py create mode 100644 deepmd/pd/infer/inference.py create mode 100644 deepmd/pd/loss/__init__.py create mode 100644 deepmd/pd/loss/denoise.py create mode 100644 deepmd/pd/loss/dos.py create mode 100644 deepmd/pd/loss/ener.py create mode 100644 deepmd/pd/loss/ener_spin.py create mode 100644 deepmd/pd/loss/loss.py create mode 100644 deepmd/pd/loss/tensor.py create mode 100644 deepmd/pd/model/__init__.py create mode 100644 deepmd/pd/model/atomic_model/__init__.py create mode 100644 deepmd/pd/model/atomic_model/base_atomic_model.py create mode 100644 deepmd/pd/model/atomic_model/dipole_atomic_model.py create mode 100644 deepmd/pd/model/atomic_model/dos_atomic_model.py create mode 100644 deepmd/pd/model/atomic_model/dp_atomic_model.py create mode 100644 deepmd/pd/model/atomic_model/energy_atomic_model.py create mode 100644 deepmd/pd/model/atomic_model/linear_atomic_model.py create mode 100644 deepmd/pd/model/atomic_model/pairtab_atomic_model.py create mode 100644 deepmd/pd/model/atomic_model/polar_atomic_model.py create mode 100644 deepmd/pd/model/backbone/__init__.py create mode 100644 deepmd/pd/model/backbone/backbone.py create mode 100644 deepmd/pd/model/backbone/evoformer2b.py create mode 100644 deepmd/pd/model/descriptor/__init__.py create mode 100644 deepmd/pd/model/descriptor/base_descriptor.py create mode 100644 deepmd/pd/model/descriptor/descriptor.py create mode 100644 deepmd/pd/model/descriptor/dpa1.py create mode 100644 deepmd/pd/model/descriptor/dpa2.py create mode 100644 deepmd/pd/model/descriptor/env_mat.py create mode 100644 deepmd/pd/model/descriptor/gaussian_lcc.py create mode 100644 deepmd/pd/model/descriptor/hybrid.py create mode 100644 deepmd/pd/model/descriptor/repformer_layer.py create mode 100644 deepmd/pd/model/descriptor/repformer_layer_old_impl.py create mode 100644 deepmd/pd/model/descriptor/repformers.py create mode 100644 deepmd/pd/model/descriptor/se_a.py create mode 100644 deepmd/pd/model/descriptor/se_atten.py create mode 100644 deepmd/pd/model/descriptor/se_atten_v2.py create mode 100644 deepmd/pd/model/descriptor/se_r.py create mode 100644 deepmd/pd/model/descriptor/se_t.py create mode 100644 deepmd/pd/model/descriptor/se_t_tebd.py create mode 100644 deepmd/pd/model/model/__init__.py create mode 100644 deepmd/pd/model/model/dipole_model.py create mode 100644 deepmd/pd/model/model/dos_model.py create mode 100644 deepmd/pd/model/model/dp_model.py create mode 100644 deepmd/pd/model/model/dp_zbl_model.py create mode 100644 deepmd/pd/model/model/ener_model.py create mode 100644 deepmd/pd/model/model/frozen.py create mode 100644 deepmd/pd/model/model/make_hessian_model.py create mode 100644 deepmd/pd/model/model/make_model.py create mode 100644 deepmd/pd/model/model/model.py create mode 100644 deepmd/pd/model/model/polar_model.py create mode 100644 deepmd/pd/model/model/spin_model.py create mode 100644 deepmd/pd/model/model/transform_output.py create mode 100644 deepmd/pd/model/network/__init__.py create mode 100644 deepmd/pd/model/network/init.py create mode 100644 deepmd/pd/model/network/layernorm.py create mode 100644 deepmd/pd/model/network/mlp.py create mode 100644 deepmd/pd/model/network/network.py create mode 100644 deepmd/pd/model/task/__init__.py create mode 100644 deepmd/pd/model/task/atten_lcc.py create mode 100644 deepmd/pd/model/task/base_fitting.py create mode 100644 deepmd/pd/model/task/denoise.py create mode 100644 deepmd/pd/model/task/dipole.py create mode 100644 deepmd/pd/model/task/dos.py create mode 100644 deepmd/pd/model/task/ener.py create mode 100644 deepmd/pd/model/task/fitting.py create mode 100644 deepmd/pd/model/task/invar_fitting.py create mode 100644 deepmd/pd/model/task/polarizability.py create mode 100644 deepmd/pd/model/task/task.py create mode 100644 deepmd/pd/model/task/type_predict.py create mode 100644 deepmd/pd/optimizer/KFWrapper.py create mode 100644 deepmd/pd/optimizer/LKF.py create mode 100644 deepmd/pd/optimizer/__init__.py create mode 100644 deepmd/pd/train/__init__.py create mode 100644 deepmd/pd/train/training.py create mode 100644 deepmd/pd/train/wrapper.py create mode 100644 deepmd/pd/utils/__init__.py create mode 100644 deepmd/pd/utils/ase_calc.py create mode 100644 deepmd/pd/utils/auto_batch_size.py create mode 100644 deepmd/pd/utils/cache.py create mode 100644 deepmd/pd/utils/dataloader.py create mode 100644 deepmd/pd/utils/dataset.py create mode 100644 deepmd/pd/utils/dp_random.py create mode 100644 deepmd/pd/utils/env.py create mode 100644 deepmd/pd/utils/env_mat_stat.py create mode 100644 deepmd/pd/utils/exclude_mask.py create mode 100644 deepmd/pd/utils/finetune.py create mode 100644 deepmd/pd/utils/learning_rate.py create mode 100644 deepmd/pd/utils/multi_task.py create mode 100644 deepmd/pd/utils/neighbor_stat.py create mode 100644 deepmd/pd/utils/nlist.py create mode 100644 deepmd/pd/utils/plugin.py create mode 100644 deepmd/pd/utils/preprocess.py create mode 100644 deepmd/pd/utils/region.py create mode 100644 deepmd/pd/utils/serialization.py create mode 100644 deepmd/pd/utils/stat.py create mode 100644 deepmd/pd/utils/update_sel.py create mode 100644 deepmd/pd/utils/utils.py diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 7b79054c33..3650b39664 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -51,19 +51,19 @@ repos: hooks: - id: blacken-docs # C++ - - repo: https://github.com/pre-commit/mirrors-clang-format - rev: v18.1.8 - hooks: - - id: clang-format - exclude: ^source/3rdparty|source/lib/src/gpu/cudart/.+\.inc + # - repo: https://github.com/pre-commit/mirrors-clang-format + # rev: v18.1.8 + # hooks: + # - id: clang-format + # exclude: ^source/3rdparty|source/lib/src/gpu/cudart/.+\.inc # markdown, yaml, CSS, javascript - - repo: https://github.com/pre-commit/mirrors-prettier - rev: v4.0.0-alpha.8 - hooks: - - id: prettier - types_or: [markdown, yaml, css] - # workflow files cannot be modified by pre-commit.ci - exclude: ^(source/3rdparty|\.github/workflows|\.clang-format) + # - repo: https://github.com/pre-commit/mirrors-prettier + # rev: v4.0.0-alpha.8 + # hooks: + # - id: prettier + # types_or: [markdown, yaml, css] + # # workflow files cannot be modified by pre-commit.ci + # exclude: ^(source/3rdparty|\.github/workflows|\.clang-format) # Shell - repo: https://github.com/scop/pre-commit-shfmt rev: v3.9.0-1 @@ -75,25 +75,25 @@ repos: hooks: - id: cmake-format #- id: cmake-lint - - repo: https://github.com/njzjz/mirrors-bibtex-tidy - rev: v1.13.0 - hooks: - - id: bibtex-tidy - args: - - --curly - - --numeric - - --align=13 - - --blank-lines - # disable sort: the order of keys and fields has explict meanings - #- --sort=key - - --duplicates=key,doi,citation,abstract - - --merge=combine - #- --sort-fields - #- --strip-comments - - --trailing-commas - - --encode-urls - - --remove-empty-fields - - --wrap=80 + # - repo: https://github.com/njzjz/mirrors-bibtex-tidy + # rev: v1.13.0 + # hooks: + # - id: bibtex-tidy + # args: + # - --curly + # - --numeric + # - --align=13 + # - --blank-lines + # # disable sort: the order of keys and fields has explict meanings + # #- --sort=key + # - --duplicates=key,doi,citation,abstract + # - --merge=combine + # #- --sort-fields + # #- --strip-comments + # - --trailing-commas + # - --encode-urls + # - --remove-empty-fields + # - --wrap=80 # license header - repo: https://github.com/Lucas-C/pre-commit-hooks rev: v1.5.5 diff --git a/backend/dp_backend.py b/backend/dp_backend.py index dbd2d2a52b..5d36868017 100644 --- a/backend/dp_backend.py +++ b/backend/dp_backend.py @@ -7,6 +7,9 @@ from scikit_build_core import build as _orig +from .find_paddle import ( + find_paddle, +) from .find_pytorch import ( find_pytorch, ) @@ -47,6 +50,7 @@ def get_requires_for_build_wheel( _orig.get_requires_for_build_wheel(config_settings) + find_tensorflow()[1] + find_pytorch()[1] + + find_paddle()[1] ) @@ -57,4 +61,5 @@ def get_requires_for_build_editable( _orig.get_requires_for_build_editable(config_settings) + find_tensorflow()[1] + find_pytorch()[1] + + find_paddle()[1] ) diff --git a/backend/find_paddle.py b/backend/find_paddle.py new file mode 100644 index 0000000000..e4a5ee8aed --- /dev/null +++ b/backend/find_paddle.py @@ -0,0 +1,145 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import importlib +import os +import site +from functools import ( + lru_cache, +) +from importlib.machinery import ( + FileFinder, +) +from importlib.util import ( + find_spec, +) +from pathlib import ( + Path, +) +from sysconfig import ( + get_path, +) +from typing import ( + List, + Optional, + Tuple, + Union, +) + +from packaging.version import ( + Version, +) + + +@lru_cache +def find_paddle() -> Tuple[Optional[str], List[str]]: + """Find PaddlePadle library. + + Tries to find PaddlePadle in the order of: + + 1. Environment variable `PADDLE_ROOT` if set + 2. The current Python environment. + 3. user site packages directory if enabled + 4. system site packages directory (purelib) + + Considering the default PaddlePadle package still uses old CXX11 ABI, we + cannot install it automatically. + + Returns + ------- + str, optional + PaddlePadle library path if found. + list of str + TensorFlow requirement if not found. Empty if found. + """ + if os.environ.get("DP_ENABLE_PADDLE", "0") == "0": + return None, [] + requires = [] + pd_spec = None + + if (pd_spec is None or not pd_spec) and os.environ.get("PADDLE_ROOT") is not None: + site_packages = Path(os.environ.get("PADDLE_ROOT")).parent.absolute() + pd_spec = FileFinder(str(site_packages)).find_spec("paddle") + + # get paddle spec + # note: isolated build will not work for backend + if pd_spec is None or not pd_spec: + pd_spec = find_spec("paddle") + + if not pd_spec and site.ENABLE_USER_SITE: + # first search TF from user site-packages before global site-packages + site_packages = site.getusersitepackages() + if site_packages: + pd_spec = FileFinder(site_packages).find_spec("paddle") + + if not pd_spec: + # purelib gets site-packages path + site_packages = get_path("purelib") + if site_packages: + pd_spec = FileFinder(site_packages).find_spec("paddle") + + # get install dir from spec + try: + pd_install_dir = pd_spec.submodule_search_locations[0] # type: ignore + # AttributeError if ft_spec is None + # TypeError if submodule_search_locations are None + # IndexError if submodule_search_locations is an empty list + except (AttributeError, TypeError, IndexError): + pd_install_dir = None + requires.extend(get_pd_requirement()["paddle"]) + return pd_install_dir, requires + + +@lru_cache +def get_pd_requirement(pd_version: str = "") -> dict: + """Get PaddlePadle requirement when Paddle is not installed. + + If pd_version is not given and the environment variable `PADDLE_VERSION` is set, use it as the requirement. + + Parameters + ---------- + pd_version : str, optional + Paddle version + + Returns + ------- + dict + PaddlePadle requirement. + """ + if pd_version is None: + return {"paddle": []} + if pd_version == "": + pd_version = os.environ.get("PADDLE_VERSION", "") + + return { + "paddle": [ + # uv has different local version behaviors, i.e. `==2.3.1` cannot match `==2.3.1+cpu` + # https://github.com/astral-sh/uv/blob/main/PIP_COMPATIBILITY.md#local-version-identifiers + # luckily, .* (prefix matching) defined in PEP 440 can match any local version + # https://peps.python.org/pep-0440/#version-matching + f"paddle=={Version(pd_version).base_version}.*" + if pd_version != "" + else "paddle>=3.0.0", + ], + } + + +@lru_cache +def get_pd_version(pd_path: Optional[Union[str, Path]]) -> str: + """Get Paddle version from a Paddle Python library path. + + Parameters + ---------- + pd_path : str or Path + pd Python library path + + Returns + ------- + str + version + """ + if pd_path is None or pd_path == "": + return "" + version_file = Path(pd_path) / "version.py" + spec = importlib.util.spec_from_file_location("paddle.version", version_file) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module.__version__ diff --git a/backend/read_env.py b/backend/read_env.py index c3fe2d5127..e94f949741 100644 --- a/backend/read_env.py +++ b/backend/read_env.py @@ -13,6 +13,9 @@ Version, ) +from .find_paddle import ( + find_paddle, +) from .find_pytorch import ( find_pytorch, get_pt_version, @@ -118,6 +121,19 @@ def get_argument_from_env() -> Tuple[str, list, list, dict, str, str]: cmake_args.append("-DENABLE_PYTORCH=OFF") pt_version = None + if os.environ.get("DP_ENABLE_PADDLE", "0") == "1": + pd_install_dir, _ = find_paddle() + pt_version = get_pt_version(pd_install_dir) + cmake_args.extend( + [ + "-DENABLE_PADDLE=ON", + f"-DCMAKE_PREFIX_PATH={pd_install_dir}", + ] + ) + else: + cmake_args.append("-DENABLE_PADDLE=OFF") + pt_version = None + cmake_args = [ "-DBUILD_PY_IF:BOOL=TRUE", *cmake_args, diff --git a/deepmd/backend/paddle.py b/deepmd/backend/paddle.py new file mode 100644 index 0000000000..9647bb137c --- /dev/null +++ b/deepmd/backend/paddle.py @@ -0,0 +1,126 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from importlib.util import ( + find_spec, +) +from typing import ( + TYPE_CHECKING, + Callable, + ClassVar, + List, + Type, +) + +from deepmd.backend.backend import ( + Backend, +) + +if TYPE_CHECKING: + from argparse import ( + Namespace, + ) + + from deepmd.infer.deep_eval import ( + DeepEvalBackend, + ) + from deepmd.utils.neighbor_stat import ( + NeighborStat, + ) + + +@Backend.register("pd") +@Backend.register("paddle") +class PaddleBackend(Backend): + """Paddle backend.""" + + name = "Paddle" + """The formal name of the backend.""" + features: ClassVar[Backend.Feature] = ( + Backend.Feature.ENTRY_POINT + | Backend.Feature.DEEP_EVAL + | Backend.Feature.NEIGHBOR_STAT + | Backend.Feature.IO + ) + """The features of the backend.""" + suffixes: ClassVar[List[str]] = [".pdparams", ".pd"] + """The suffixes of the backend.""" + + def is_available(self) -> bool: + """Check if the backend is available. + + Returns + ------- + bool + Whether the backend is available. + """ + return find_spec("paddle") is not None + + @property + def entry_point_hook(self) -> Callable[["Namespace"], None]: + """The entry point hook of the backend. + + Returns + ------- + Callable[[Namespace], None] + The entry point hook of the backend. + """ + from deepmd.pd.entrypoints.main import main as deepmd_main + + return deepmd_main + + @property + def deep_eval(self) -> Type["DeepEvalBackend"]: + """The Deep Eval backend of the backend. + + Returns + ------- + type[DeepEvalBackend] + The Deep Eval backend of the backend. + """ + from deepmd.pd.infer.deep_eval import DeepEval as DeepEvalPD + + return DeepEvalPD + + @property + def neighbor_stat(self) -> Type["NeighborStat"]: + """The neighbor statistics of the backend. + + Returns + ------- + type[NeighborStat] + The neighbor statistics of the backend. + """ + from deepmd.pd.utils.neighbor_stat import ( + NeighborStat, + ) + + return NeighborStat + + @property + def serialize_hook(self) -> Callable[[str], dict]: + """The serialize hook to convert the model file to a dictionary. + + Returns + ------- + Callable[[str], dict] + The serialize hook of the backend. + """ + from deepmd.pd.utils.serialization import ( + serialize_from_file, + ) + + return serialize_from_file + + @property + def deserialize_hook(self) -> Callable[[str, dict], None]: + """The deserialize hook to convert the dictionary to a model file. + + Returns + ------- + Callable[[str, dict], None] + The deserialize hook of the backend. + """ + from deepmd.pd.utils.serialization import ( + deserialize_to_file, + ) + + return deserialize_to_file diff --git a/deepmd/dpmodel/utils/network.py b/deepmd/dpmodel/utils/network.py index 941e2cfc86..6f0269971e 100644 --- a/deepmd/dpmodel/utils/network.py +++ b/deepmd/dpmodel/utils/network.py @@ -147,9 +147,9 @@ def deserialize(cls, data: dict) -> "NativeLayer": variables.get("idt", None), ) if obj.b is not None: - obj.b = obj.b.ravel() + obj.b = obj.b.flatten() if obj.idt is not None: - obj.idt = obj.idt.ravel() + obj.idt = obj.idt.flatten() obj.check_shape_consistency() return obj diff --git a/deepmd/pd/__init__.py b/deepmd/pd/__init__.py new file mode 100644 index 0000000000..784f184968 --- /dev/null +++ b/deepmd/pd/__init__.py @@ -0,0 +1,13 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later + +# import customized OPs globally +try: + from deepmd.pd.cxx_op import ( + ENABLE_CUSTOMIZED_OP, + ) + + __all__ = [ + "ENABLE_CUSTOMIZED_OP", + ] +except Exception as e: + __all__ = [] diff --git a/deepmd/pd/cxx_op.py b/deepmd/pd/cxx_op.py new file mode 100644 index 0000000000..8f17da28a7 --- /dev/null +++ b/deepmd/pd/cxx_op.py @@ -0,0 +1,100 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import platform + +import paddle +from packaging.version import ( + Version, +) + +from deepmd.env import ( + GLOBAL_CONFIG, + SHARED_LIB_DIR, +) + + +def load_library(module_name: str) -> bool: + """Load OP library. + + Parameters + ---------- + module_name : str + Name of the module + + Returns + ------- + bool + Whether the library is loaded successfully + """ + if platform.system() == "Windows": + ext = ".dll" + prefix = "" + else: + ext = ".so" + prefix = "lib" + + module_file = (SHARED_LIB_DIR / (prefix + module_name)).with_suffix(ext).resolve() + + if module_file.is_file(): + try: + paddle.utils.cpp_extension.load(module_file) + except OSError as e: + # check: CXX11_ABI_FLAG; version + # from our op + PT_VERSION = GLOBAL_CONFIG["pt_version"] + PT_CXX11_ABI_FLAG = int(GLOBAL_CONFIG["pt_cxx11_abi_flag"]) + # from paddle + # strip the local version + pt_py_version = Version(paddle.__version__).public + # pt_cxx11_abi_flag = int(paddle.compiled_with_cxx11_abi()) + pt_cxx11_abi_flag = 0 + + if PT_CXX11_ABI_FLAG != pt_cxx11_abi_flag: + raise RuntimeError( + "This deepmd-kit package was compiled with " + "CXX11_ABI_FLAG=%d, but PyTorch runtime was compiled " + "with CXX11_ABI_FLAG=%d. These two library ABIs are " + "incompatible and thus an error is raised when loading %s. " + "You need to rebuild deepmd-kit against this PyTorch " + "runtime." + % ( + PT_CXX11_ABI_FLAG, + pt_cxx11_abi_flag, + module_name, + ) + ) from e + + # different versions may cause incompatibility, see TF + if PT_VERSION != pt_py_version: + raise RuntimeError( + "The version of PyTorch used to compile this " + f"deepmd-kit package is {PT_VERSION}, but the version of PyTorch " + f"runtime you are using is {pt_py_version}. These two versions are " + f"incompatible and thus an error is raised when loading {module_name}. " + f"You need to install PyTorch {PT_VERSION}, or rebuild deepmd-kit " + f"against PyTorch {pt_py_version}.\nIf you are using a wheel from " + "PyPI, you may consider to install deepmd-kit execuating " + "`DP_ENABLE_PYTORCH=1 pip install deepmd-kit --no-binary deepmd-kit` " + "instead." + ) from e + error_message = ( + "This deepmd-kit package is inconsitent with PyTorch " + f"Runtime, thus an error is raised when loading {module_name}. " + "You need to rebuild deepmd-kit against this PyTorch " + "runtime." + ) + if PT_CXX11_ABI_FLAG == 1: + # #1791 + error_message += ( + "\nWARNING: devtoolset on RHEL6 and RHEL7 does not support _GLIBCXX_USE_CXX11_ABI=1. " + "See https://bugzilla.redhat.com/show_bug.cgi?id=1546704" + ) + raise RuntimeError(error_message) from e + return True + return False + + +ENABLE_CUSTOMIZED_OP = load_library("deepmd_op_pt") + +__all__ = [ + "ENABLE_CUSTOMIZED_OP", +] diff --git a/deepmd/pd/entrypoints/__init__.py b/deepmd/pd/entrypoints/__init__.py new file mode 100644 index 0000000000..6ceb116d85 --- /dev/null +++ b/deepmd/pd/entrypoints/__init__.py @@ -0,0 +1 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later diff --git a/deepmd/pd/entrypoints/main.py b/deepmd/pd/entrypoints/main.py new file mode 100644 index 0000000000..6d876dde2b --- /dev/null +++ b/deepmd/pd/entrypoints/main.py @@ -0,0 +1,577 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import argparse +import copy +import json +import logging +import os +from pathlib import ( + Path, +) +from typing import ( + List, + Optional, + Union, +) + +import h5py +import paddle +import paddle.distributed as dist +import paddle.version + +from deepmd import ( + __version__, +) +from deepmd.common import ( + expand_sys_str, +) +from deepmd.env import ( + GLOBAL_CONFIG, +) +from deepmd.loggers.loggers import ( + set_log_handles, +) +from deepmd.main import ( + parse_args, +) +from deepmd.pd.cxx_op import ( + ENABLE_CUSTOMIZED_OP, +) +from deepmd.pd.infer import ( + inference, +) +from deepmd.pd.model.model import ( + BaseModel, +) +from deepmd.pd.train import ( + training, +) +from deepmd.pd.train.wrapper import ( + ModelWrapper, +) +from deepmd.pd.utils.dataloader import ( + DpLoaderSet, +) +from deepmd.pd.utils.env import ( + DEVICE, +) +from deepmd.pd.utils.finetune import ( + get_finetune_rules, +) +from deepmd.pd.utils.multi_task import ( + preprocess_shared_params, +) +from deepmd.pd.utils.stat import ( + make_stat_input, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, +) +from deepmd.utils.argcheck import ( + normalize, +) +from deepmd.utils.compat import ( + update_deepmd_input, +) +from deepmd.utils.data_system import ( + get_data, + process_systems, +) +from deepmd.utils.path import ( + DPPath, +) +from deepmd.utils.summary import SummaryPrinter as BaseSummaryPrinter + +# from paddle.distributed.elastic.multiprocessing.errors import ( +# record, +# ) + + +log = logging.getLogger(__name__) + + +def get_trainer( + config, + init_model=None, + restart_model=None, + finetune_model=None, + force_load=False, + init_frz_model=None, + shared_links=None, + finetune_links=None, +): + multi_task = "model_dict" in config.get("model", {}) + + # Initialize DDP + local_rank = os.environ.get("LOCAL_RANK") + if local_rank is not None: + local_rank = int(local_rank) + assert dist.is_nccl_available() + dist.init_process_group(backend="nccl") + + def prepare_trainer_input_single( + model_params_single, data_dict_single, rank=0, seed=None + ): + training_dataset_params = data_dict_single["training_data"] + validation_dataset_params = data_dict_single.get("validation_data", None) + validation_systems = ( + validation_dataset_params["systems"] if validation_dataset_params else None + ) + training_systems = training_dataset_params["systems"] + training_systems = process_systems(training_systems) + if validation_systems is not None: + validation_systems = process_systems(validation_systems) + + # stat files + stat_file_path_single = data_dict_single.get("stat_file", None) + if rank != 0: + stat_file_path_single = None + elif stat_file_path_single is not None: + if not Path(stat_file_path_single).exists(): + if stat_file_path_single.endswith((".h5", ".hdf5")): + with h5py.File(stat_file_path_single, "w") as f: + pass + else: + Path(stat_file_path_single).mkdir() + stat_file_path_single = DPPath(stat_file_path_single, "a") + + # validation and training data + # avoid the same batch sequence among devices + rank_seed = (seed + rank) % (2**32) if seed is not None else None + validation_data_single = ( + DpLoaderSet( + validation_systems, + validation_dataset_params["batch_size"], + model_params_single["type_map"], + seed=rank_seed, + ) + if validation_systems + else None + ) + train_data_single = DpLoaderSet( + training_systems, + training_dataset_params["batch_size"], + model_params_single["type_map"], + seed=rank_seed, + ) + return ( + train_data_single, + validation_data_single, + stat_file_path_single, + ) + + rank = dist.get_rank() if dist.is_available() and dist.is_initialized() else 0 + data_seed = config["training"].get("seed", None) + if not multi_task: + ( + train_data, + validation_data, + stat_file_path, + ) = prepare_trainer_input_single( + config["model"], + config["training"], + rank=rank, + seed=data_seed, + ) + else: + train_data, validation_data, stat_file_path = {}, {}, {} + for model_key in config["model"]["model_dict"]: + ( + train_data[model_key], + validation_data[model_key], + stat_file_path[model_key], + ) = prepare_trainer_input_single( + config["model"]["model_dict"][model_key], + config["training"]["data_dict"][model_key], + rank=rank, + seed=data_seed, + ) + + trainer = training.Trainer( + config, + train_data, + stat_file_path=stat_file_path, + validation_data=validation_data, + init_model=init_model, + restart_model=restart_model, + finetune_model=finetune_model, + force_load=force_load, + shared_links=shared_links, + finetune_links=finetune_links, + init_frz_model=init_frz_model, + ) + return trainer + + +class SummaryPrinter(BaseSummaryPrinter): + """Summary printer for Paddle.""" + + def is_built_with_cuda(self) -> bool: + """Check if the backend is built with CUDA.""" + return paddle.device.is_compiled_with_cuda() + + def is_built_with_rocm(self) -> bool: + """Check if the backend is built with ROCm.""" + return paddle.device.is_compiled_with_rocm() + + def get_compute_device(self) -> str: + """Get Compute device.""" + return str(DEVICE) + + def get_ngpus(self) -> int: + """Get the number of GPUs.""" + return paddle.device.cuda.device_count() + + def get_backend_info(self) -> dict: + """Get backend information.""" + if ENABLE_CUSTOMIZED_OP: + op_info = { + "build with PT ver": GLOBAL_CONFIG["pt_version"], + "build with PT inc": GLOBAL_CONFIG["pt_include_dir"].replace(";", "\n"), + "build with PT lib": GLOBAL_CONFIG["pt_libs"].replace(";", "\n"), + } + else: + op_info = {} + return { + "Backend": "Paddle", + "PT ver": f"v{paddle.__version__}-g{paddle.version.commit[:11]}", + "Enable custom OP": ENABLE_CUSTOMIZED_OP, + **op_info, + } + + +def train(FLAGS): + log.info("Configuration path: %s", FLAGS.INPUT) + SummaryPrinter()() + with open(FLAGS.INPUT) as fin: + config = json.load(fin) + # ensure suffix, as in the command line help, we say "path prefix of checkpoint files" + if FLAGS.init_model is not None and not FLAGS.init_model.endswith(".pd"): + FLAGS.init_model += ".pd" + if FLAGS.restart is not None and not FLAGS.restart.endswith(".pd"): + FLAGS.restart += ".pd" + + # update multitask config + multi_task = "model_dict" in config["model"] + shared_links = None + if multi_task: + config["model"], shared_links = preprocess_shared_params(config["model"]) + # handle the special key + assert ( + "RANDOM" not in config["model"]["model_dict"] + ), "Model name can not be 'RANDOM' in multi-task mode!" + + # update fine-tuning config + finetune_links = None + if FLAGS.finetune is not None: + config["model"], finetune_links = get_finetune_rules( + FLAGS.finetune, + config["model"], + model_branch=FLAGS.model_branch, + change_model_params=FLAGS.use_pretrain_script, + ) + # update init_model or init_frz_model config if necessary + if ( + FLAGS.init_model is not None or FLAGS.init_frz_model is not None + ) and FLAGS.use_pretrain_script: + if FLAGS.init_model is not None: + init_state_dict = paddle.load(FLAGS.init_model) + if "model" in init_state_dict: + init_state_dict = init_state_dict["model"] + config["model"] = init_state_dict["_extra_state"]["model_params"] + else: + config["model"] = json.loads( + paddle.jit.load(FLAGS.init_frz_model).get_model_def_script() + ) + + # argcheck + config = update_deepmd_input(config, warning=True, dump="input_v2_compat.json") + config = normalize(config, multi_task=multi_task) + + # do neighbor stat + min_nbor_dist = None + if not FLAGS.skip_neighbor_stat: + log.info( + "Calculate neighbor statistics... (add --skip-neighbor-stat to skip this step)" + ) + + if not multi_task: + type_map = config["model"].get("type_map") + train_data = get_data( + config["training"]["training_data"], 0, type_map, None + ) + config["model"], min_nbor_dist = BaseModel.update_sel( + train_data, type_map, config["model"] + ) + else: + min_nbor_dist = {} + for model_item in config["model"]["model_dict"]: + type_map = config["model"]["model_dict"][model_item].get("type_map") + train_data = get_data( + config["training"]["data_dict"][model_item]["training_data"], + 0, + type_map, + None, + ) + config["model"]["model_dict"][model_item], min_nbor_dist[model_item] = ( + BaseModel.update_sel( + train_data, type_map, config["model"]["model_dict"][model_item] + ) + ) + + with open(FLAGS.output, "w") as fp: + json.dump(config, fp, indent=4) + + trainer = get_trainer( + config, + FLAGS.init_model, + FLAGS.restart, + FLAGS.finetune, + FLAGS.force_load, + FLAGS.init_frz_model, + shared_links=shared_links, + finetune_links=finetune_links, + ) + # save min_nbor_dist + if min_nbor_dist is not None: + if not multi_task: + trainer.model.min_nbor_dist = min_nbor_dist + else: + for model_item in min_nbor_dist: + trainer.model[model_item].min_nbor_dist = min_nbor_dist[model_item] + trainer.run() + + +def freeze(FLAGS): + model = inference.Tester(FLAGS.model, head=FLAGS.head).model + model.eval() + model = paddle.jit.script(model) + extra_files = {} + paddle.jit.save( + model, + FLAGS.output, + extra_files, + ) + + +def show(FLAGS): + if FLAGS.INPUT.split(".")[-1] == "pt": + state_dict = paddle.load(FLAGS.INPUT) + if "model" in state_dict: + state_dict = state_dict["model"] + model_params = state_dict["_extra_state"]["model_params"] + elif FLAGS.INPUT.split(".")[-1] == "pth": + model_params_string = paddle.jit.load(FLAGS.INPUT).model_def_script + model_params = json.loads(model_params_string) + else: + raise RuntimeError( + "The model provided must be a checkpoint file with a .pd extension " + "or a frozen model with a .pth extension" + ) + model_is_multi_task = "model_dict" in model_params + log.info("This is a multitask model") if model_is_multi_task else log.info( + "This is a singletask model" + ) + + if "model-branch" in FLAGS.ATTRIBUTES: + # The model must be multitask mode + if not model_is_multi_task: + raise RuntimeError( + "The 'model-branch' option requires a multitask model." + " The provided model does not meet this criterion." + ) + model_branches = list(model_params["model_dict"].keys()) + model_branches += ["RANDOM"] + log.info( + f"Available model branches are {model_branches}, " + f"where 'RANDOM' means using a randomly initialized fitting net." + ) + if "type-map" in FLAGS.ATTRIBUTES: + if model_is_multi_task: + model_branches = list(model_params["model_dict"].keys()) + for branch in model_branches: + type_map = model_params["model_dict"][branch]["type_map"] + log.info(f"The type_map of branch {branch} is {type_map}") + else: + type_map = model_params["type_map"] + log.info(f"The type_map is {type_map}") + if "descriptor" in FLAGS.ATTRIBUTES: + if model_is_multi_task: + model_branches = list(model_params["model_dict"].keys()) + for branch in model_branches: + descriptor = model_params["model_dict"][branch]["descriptor"] + log.info(f"The descriptor parameter of branch {branch} is {descriptor}") + else: + descriptor = model_params["descriptor"] + log.info(f"The descriptor parameter is {descriptor}") + if "fitting-net" in FLAGS.ATTRIBUTES: + if model_is_multi_task: + model_branches = list(model_params["model_dict"].keys()) + for branch in model_branches: + fitting_net = model_params["model_dict"][branch]["fitting_net"] + log.info( + f"The fitting_net parameter of branch {branch} is {fitting_net}" + ) + else: + fitting_net = model_params["fitting_net"] + log.info(f"The fitting_net parameter is {fitting_net}") + + +def change_bias(FLAGS): + if FLAGS.INPUT.endswith(".pd"): + old_state_dict = paddle.load(FLAGS.INPUT) + model_state_dict = copy.deepcopy(old_state_dict.get("model", old_state_dict)) + model_params = model_state_dict["_extra_state"]["model_params"] + elif FLAGS.INPUT.endswith(".pth"): + old_model = paddle.jit.load(FLAGS.INPUT) + model_params_string = old_model.get_model_def_script() + model_params = json.loads(model_params_string) + old_state_dict = old_model.state_dict() + model_state_dict = old_state_dict + else: + raise RuntimeError( + "The model provided must be a checkpoint file with a .pd extension " + "or a frozen model with a .pth extension" + ) + multi_task = "model_dict" in model_params + model_branch = FLAGS.model_branch + bias_adjust_mode = ( + "change-by-statistic" if FLAGS.mode == "change" else "set-by-statistic" + ) + if multi_task: + assert ( + model_branch is not None + ), "For multitask model, the model branch must be set!" + assert model_branch in model_params["model_dict"], ( + f"For multitask model, the model branch must be in the 'model_dict'! " + f"Available options are : {list(model_params['model_dict'].keys())}." + ) + log.info(f"Changing out bias for model {model_branch}.") + model = training.get_model_for_wrapper(model_params) + type_map = ( + model_params["type_map"] + if not multi_task + else model_params["model_dict"][model_branch]["type_map"] + ) + model_to_change = model if not multi_task else model[model_branch] + if FLAGS.INPUT.endswith(".pd"): + wrapper = ModelWrapper(model) + wrapper.load_state_dict(old_state_dict["model"]) + else: + # for .pth + model.load_state_dict(old_state_dict) + + if FLAGS.bias_value is not None: + # use user-defined bias + assert model_to_change.model_type in [ + "ener" + ], "User-defined bias is only available for energy model!" + assert ( + len(FLAGS.bias_value) == len(type_map) + ), f"The number of elements in the bias should be the same as that in the type_map: {type_map}." + old_bias = model_to_change.get_out_bias() + bias_to_set = paddle.to_tensor( + FLAGS.bias_value, dtype=old_bias.dtype, place=old_bias.place + ).reshape(old_bias.shape) + model_to_change.set_out_bias(bias_to_set) + log.info( + f"Change output bias of {type_map!s} " + f"from {to_numpy_array(old_bias).reshape(-1)!s} " + f"to {to_numpy_array(bias_to_set).reshape(-1)!s}." + ) + updated_model = model_to_change + else: + # calculate bias on given systems + if FLAGS.datafile is not None: + with open(FLAGS.datafile) as datalist: + all_sys = datalist.read().splitlines() + else: + all_sys = expand_sys_str(FLAGS.system) + data_systems = process_systems(all_sys) + data_single = DpLoaderSet( + data_systems, + 1, + type_map, + ) + mock_loss = training.get_loss( + {"inference": True}, 1.0, len(type_map), model_to_change + ) + data_requirement = mock_loss.label_requirement + data_requirement += training.get_additional_data_requirement(model_to_change) + data_single.add_data_requirement(data_requirement) + nbatches = FLAGS.numb_batch if FLAGS.numb_batch != 0 else float("inf") + sampled_data = make_stat_input( + data_single.systems, + data_single.dataloaders, + nbatches, + ) + updated_model = training.model_change_out_bias( + model_to_change, sampled_data, _bias_adjust_mode=bias_adjust_mode + ) + + if not multi_task: + model = updated_model + else: + model[model_branch] = updated_model + + if FLAGS.INPUT.endswith(".pd"): + output_path = ( + FLAGS.output + if FLAGS.output is not None + else FLAGS.INPUT.replace(".pd", "_updated.pd") + ) + wrapper = ModelWrapper(model) + if "model" in old_state_dict: + old_state_dict["model"] = wrapper.state_dict() + old_state_dict["model"]["_extra_state"] = model_state_dict["_extra_state"] + else: + old_state_dict = wrapper.state_dict() + old_state_dict["_extra_state"] = model_state_dict["_extra_state"] + paddle.save(old_state_dict, output_path) + else: + # for .pth + output_path = ( + FLAGS.output + if FLAGS.output is not None + else FLAGS.INPUT.replace(".pth", "_updated.pth") + ) + model = paddle.jit.script(model) + paddle.jit.save( + model, + output_path, + {}, + ) + log.info(f"Saved model to {output_path}") + + +# @record +def main(args: Optional[Union[List[str], argparse.Namespace]] = None): + if not isinstance(args, argparse.Namespace): + FLAGS = parse_args(args=args) + else: + FLAGS = args + + set_log_handles(FLAGS.log_level, FLAGS.log_path, mpi_log=None) + log.debug("Log handles were successfully set") + log.info("DeePMD version: %s", __version__) + + if FLAGS.command == "train": + train(FLAGS) + elif FLAGS.command == "freeze": + if Path(FLAGS.checkpoint_folder).is_dir(): + checkpoint_path = Path(FLAGS.checkpoint_folder) + latest_ckpt_file = (checkpoint_path / "checkpoint").read_text() + FLAGS.model = str(checkpoint_path.joinpath(latest_ckpt_file)) + else: + FLAGS.model = FLAGS.checkpoint_folder + FLAGS.output = str(Path(FLAGS.output).with_suffix(".pth")) + freeze(FLAGS) + elif FLAGS.command == "show": + show(FLAGS) + elif FLAGS.command == "change-bias": + change_bias(FLAGS) + else: + raise RuntimeError(f"Invalid command {FLAGS.command}!") + + +if __name__ == "__main__": + main() diff --git a/deepmd/pd/infer/__init__.py b/deepmd/pd/infer/__init__.py new file mode 100644 index 0000000000..6ceb116d85 --- /dev/null +++ b/deepmd/pd/infer/__init__.py @@ -0,0 +1 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later diff --git a/deepmd/pd/infer/deep_eval.py b/deepmd/pd/infer/deep_eval.py new file mode 100644 index 0000000000..50241c7b41 --- /dev/null +++ b/deepmd/pd/infer/deep_eval.py @@ -0,0 +1,747 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + TYPE_CHECKING, + Any, + Callable, + Dict, + List, + Optional, + Tuple, + Type, + Union, +) + +import numpy as np +import paddle + +from deepmd.dpmodel.output_def import ( + ModelOutputDef, + OutputVariableCategory, + OutputVariableDef, +) +from deepmd.infer.deep_dipole import ( + DeepDipole, +) +from deepmd.infer.deep_dos import ( + DeepDOS, +) +from deepmd.infer.deep_eval import DeepEval as DeepEvalWrapper +from deepmd.infer.deep_eval import ( + DeepEvalBackend, +) +from deepmd.infer.deep_polar import ( + DeepGlobalPolar, + DeepPolar, +) +from deepmd.infer.deep_pot import ( + DeepPot, +) +from deepmd.infer.deep_wfc import ( + DeepWFC, +) +from deepmd.pd.model.model import ( + get_model, +) +from deepmd.pd.train.wrapper import ( + ModelWrapper, +) +from deepmd.pd.utils.auto_batch_size import ( + AutoBatchSize, +) +from deepmd.pd.utils.env import ( + DEVICE, + GLOBAL_PD_FLOAT_PRECISION, +) +from deepmd.pd.utils.utils import ( + to_paddle_tensor, +) + +if TYPE_CHECKING: + import ase.neighborlist + + +class DeepEval(DeepEvalBackend): + """PyTorch backend implementaion of DeepEval. + + Parameters + ---------- + model_file : Path + The name of the frozen model file. + output_def : ModelOutputDef + The output definition of the model. + *args : list + Positional arguments. + auto_batch_size : bool or int or AutomaticBatchSize, default: False + If True, automatic batch size will be used. If int, it will be used + as the initial batch size. + neighbor_list : ase.neighborlist.NewPrimitiveNeighborList, optional + The ASE neighbor list class to produce the neighbor list. If None, the + neighbor list will be built natively in the model. + **kwargs : dict + Keyword arguments. + """ + + def __init__( + self, + model_file: str, + output_def: ModelOutputDef, + *args: Any, + auto_batch_size: Union[bool, int, AutoBatchSize] = True, + neighbor_list: Optional["ase.neighborlist.NewPrimitiveNeighborList"] = None, + head: Optional[str] = None, + **kwargs: Any, + ): + self.output_def = output_def + self.model_path = model_file + if str(self.model_path).endswith(".pd"): + state_dict = paddle.load(model_file) + if "model" in state_dict: + state_dict = state_dict["model"] + self.input_param = state_dict["_extra_state"]["model_params"] + self.multi_task = "model_dict" in self.input_param + if self.multi_task: + model_keys = list(self.input_param["model_dict"].keys()) + assert ( + head is not None + ), f"Head must be set for multitask model! Available heads are: {model_keys}" + assert ( + head in model_keys + ), f"No head named {head} in model! Available heads are: {model_keys}" + self.input_param = self.input_param["model_dict"][head] + state_dict_head = {"_extra_state": state_dict["_extra_state"]} + for item in state_dict: + if f"model.{head}." in item: + state_dict_head[ + item.replace(f"model.{head}.", "model.Default.") + ] = state_dict[item].clone() + state_dict = state_dict_head + model = get_model(self.input_param).to(DEVICE) + model = paddle.jit.to_static(model) + self.dp = ModelWrapper(model) + self.dp.load_state_dict(state_dict) + elif str(self.model_path).endswith(".pth"): + model = paddle.jit.load(model_file) + self.dp = ModelWrapper(model) + else: + raise ValueError("Unknown model file format!") + self.rcut = self.dp.model["Default"].get_rcut() + self.type_map = self.dp.model["Default"].get_type_map() + if isinstance(auto_batch_size, bool): + if auto_batch_size: + self.auto_batch_size = AutoBatchSize() + else: + self.auto_batch_size = None + elif isinstance(auto_batch_size, int): + self.auto_batch_size = AutoBatchSize(auto_batch_size) + elif isinstance(auto_batch_size, AutoBatchSize): + self.auto_batch_size = auto_batch_size + else: + raise TypeError("auto_batch_size should be bool, int, or AutoBatchSize") + self._has_spin = getattr(self.dp.model["Default"], "has_spin", False) + if callable(self._has_spin): + self._has_spin = self._has_spin() + + def get_rcut(self) -> float: + """Get the cutoff radius of this model.""" + return self.rcut + + def get_ntypes(self) -> int: + """Get the number of atom types of this model.""" + return len(self.type_map) + + def get_type_map(self) -> List[str]: + """Get the type map (element name of the atom types) of this model.""" + return self.type_map + + def get_dim_fparam(self) -> int: + """Get the number (dimension) of frame parameters of this DP.""" + return self.dp.model["Default"].get_dim_fparam() + + def get_dim_aparam(self) -> int: + """Get the number (dimension) of atomic parameters of this DP.""" + return self.dp.model["Default"].get_dim_aparam() + + @property + def model_type(self) -> Type["DeepEvalWrapper"]: + """The the evaluator of the model type.""" + model_output_type = self.dp.model["Default"].model_output_type() + if "energy" in model_output_type: + return DeepPot + elif "dos" in model_output_type: + return DeepDOS + elif "dipole" in model_output_type: + return DeepDipole + elif "polar" in model_output_type: + return DeepPolar + elif "global_polar" in model_output_type: + return DeepGlobalPolar + elif "wfc" in model_output_type: + return DeepWFC + else: + raise RuntimeError("Unknown model type") + + def get_sel_type(self) -> List[int]: + """Get the selected atom types of this model. + + Only atoms with selected atom types have atomic contribution + to the result of the model. + If returning an empty list, all atom types are selected. + """ + return self.dp.model["Default"].get_sel_type() + + def get_numb_dos(self) -> int: + """Get the number of DOS.""" + return self.dp.model["Default"].get_numb_dos() + + def get_has_efield(self): + """Check if the model has efield.""" + return False + + def get_ntypes_spin(self): + """Get the number of spin atom types of this model. Only used in old implement.""" + return 0 + + def get_has_spin(self): + """Check if the model has spin atom types.""" + return self._has_spin + + def eval( + self, + coords: np.ndarray, + cells: Optional[np.ndarray], + atom_types: np.ndarray, + atomic: bool = False, + fparam: Optional[np.ndarray] = None, + aparam: Optional[np.ndarray] = None, + **kwargs: Any, + ) -> Dict[str, np.ndarray]: + """Evaluate the energy, force and virial by using this DP. + + Parameters + ---------- + coords + The coordinates of atoms. + The array should be of size nframes x natoms x 3 + cells + The cell of the region. + If None then non-PBC is assumed, otherwise using PBC. + The array should be of size nframes x 9 + atom_types + The atom types + The list should contain natoms ints + atomic + Calculate the atomic energy and virial + fparam + The frame parameter. + The array can be of size : + - nframes x dim_fparam. + - dim_fparam. Then all frames are assumed to be provided with the same fparam. + aparam + The atomic parameter + The array can be of size : + - nframes x natoms x dim_aparam. + - natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. + - dim_aparam. Then all frames and atoms are provided with the same aparam. + **kwargs + Other parameters + + Returns + ------- + output_dict : dict + The output of the evaluation. The keys are the names of the output + variables, and the values are the corresponding output arrays. + """ + # convert all of the input to numpy array + atom_types = np.array(atom_types, dtype=np.int32) + coords = np.array(coords) + if cells is not None: + cells = np.array(cells) + natoms, numb_test = self._get_natoms_and_nframes( + coords, atom_types, len(atom_types.shape) > 1 + ) + request_defs = self._get_request_defs(atomic) + if "spin" not in kwargs or kwargs["spin"] is None: + out = self._eval_func(self._eval_model, numb_test, natoms)( + coords, cells, atom_types, fparam, aparam, request_defs + ) + else: + out = self._eval_func(self._eval_model_spin, numb_test, natoms)( + coords, + cells, + atom_types, + np.array(kwargs["spin"]), + fparam, + aparam, + request_defs, + ) + return dict( + zip( + [x.name for x in request_defs], + out, + ) + ) + + def _get_request_defs(self, atomic: bool) -> List[OutputVariableDef]: + """Get the requested output definitions. + + When atomic is True, all output_def are requested. + When atomic is False, only energy (tensor), force, and virial + are requested. + + Parameters + ---------- + atomic : bool + Whether to request the atomic output. + + Returns + ------- + list[OutputVariableDef] + The requested output definitions. + """ + if atomic: + return list(self.output_def.var_defs.values()) + else: + return [ + x + for x in self.output_def.var_defs.values() + if x.category + in ( + OutputVariableCategory.OUT, + OutputVariableCategory.REDU, + OutputVariableCategory.DERV_R, + OutputVariableCategory.DERV_C_REDU, + ) + ] + + def _eval_func(self, inner_func: Callable, numb_test: int, natoms: int) -> Callable: + """Wrapper method with auto batch size. + + Parameters + ---------- + inner_func : Callable + the method to be wrapped + numb_test : int + number of tests + natoms : int + number of atoms + + Returns + ------- + Callable + the wrapper + """ + if self.auto_batch_size is not None: + + def eval_func(*args, **kwargs): + return self.auto_batch_size.execute_all( + inner_func, numb_test, natoms, *args, **kwargs + ) + + else: + eval_func = inner_func + return eval_func + + def _get_natoms_and_nframes( + self, + coords: np.ndarray, + atom_types: np.ndarray, + mixed_type: bool = False, + ) -> Tuple[int, int]: + if mixed_type: + natoms = len(atom_types[0]) + else: + natoms = len(atom_types) + if natoms == 0: + assert coords.size == 0 + else: + coords = np.reshape(np.array(coords), [-1, natoms * 3]) + nframes = coords.shape[0] + return natoms, nframes + + def _eval_model( + self, + coords: np.ndarray, + cells: Optional[np.ndarray], + atom_types: np.ndarray, + fparam: Optional[np.ndarray], + aparam: Optional[np.ndarray], + request_defs: List[OutputVariableDef], + ): + model = self.dp.to(DEVICE) + + nframes = coords.shape[0] + if len(atom_types.shape) == 1: + natoms = len(atom_types) + atom_types = np.tile(atom_types, nframes).reshape(nframes, -1) + else: + natoms = len(atom_types[0]) + + coord_input = paddle.to_tensor( + coords.reshape([nframes, natoms, 3]), + dtype=GLOBAL_PD_FLOAT_PRECISION, + ).to(DEVICE) + type_input = paddle.to_tensor(atom_types, dtype=paddle.int64).to(DEVICE) + if cells is not None: + box_input = paddle.to_tensor( + cells.reshape([nframes, 3, 3]), + dtype=GLOBAL_PD_FLOAT_PRECISION, + ).to(DEVICE) + else: + box_input = None + if fparam is not None: + fparam_input = to_paddle_tensor( + fparam.reshape([nframes, self.get_dim_fparam()]) + ) + else: + fparam_input = None + if aparam is not None: + aparam_input = to_paddle_tensor( + aparam.reshape([nframes, natoms, self.get_dim_aparam()]) + ) + else: + aparam_input = None + do_atomic_virial = any( + x.category == OutputVariableCategory.DERV_C for x in request_defs + ) + batch_output = model( + coord_input, + type_input, + box=box_input, + do_atomic_virial=do_atomic_virial, + fparam=fparam_input, + aparam=aparam_input, + ) + if isinstance(batch_output, tuple): + batch_output = batch_output[0] + + results = [] + for odef in request_defs: + pd_name = self._OUTDEF_DP2BACKEND[odef.name] + if pd_name in batch_output: + shape = self._get_output_shape(odef, nframes, natoms) + out = batch_output[pd_name].reshape(shape).numpy() + results.append(out) + else: + shape = self._get_output_shape(odef, nframes, natoms) + results.append( + np.full(np.abs(shape), np.nan) # pylint: disable=no-explicit-dtype + ) # this is kinda hacky + return tuple(results) + + def _eval_model_spin( + self, + coords: np.ndarray, + cells: Optional[np.ndarray], + atom_types: np.ndarray, + spins: np.ndarray, + fparam: Optional[np.ndarray], + aparam: Optional[np.ndarray], + request_defs: List[OutputVariableDef], + ): + model = self.dp.to(DEVICE) + + nframes = coords.shape[0] + if len(atom_types.shape) == 1: + natoms = len(atom_types) + atom_types = np.tile(atom_types, nframes).reshape(nframes, -1) + else: + natoms = len(atom_types[0]) + + coord_input = paddle.to_tensor( + coords.reshape([nframes, natoms, 3]), + dtype=GLOBAL_PD_FLOAT_PRECISION, + ).to(DEVICE) + type_input = paddle.to_tensor(atom_types, dtype=paddle.int64).to(DEVICE) + spin_input = paddle.to_tensor( + spins.reshape([nframes, natoms, 3]), + dtype=GLOBAL_PD_FLOAT_PRECISION, + ).to(DEVICE) + if cells is not None: + box_input = paddle.to_tensor( + cells.reshape([nframes, 3, 3]), + dtype=GLOBAL_PD_FLOAT_PRECISION, + ).to(DEVICE) + else: + box_input = None + if fparam is not None: + fparam_input = to_paddle_tensor( + fparam.reshape(nframes, self.get_dim_fparam()) + ) + else: + fparam_input = None + if aparam is not None: + aparam_input = to_paddle_tensor( + aparam.reshape(nframes, natoms, self.get_dim_aparam()) + ) + else: + aparam_input = None + + do_atomic_virial = any( + x.category == OutputVariableCategory.DERV_C_REDU for x in request_defs + ) + batch_output = model( + coord_input, + type_input, + spin=spin_input, + box=box_input, + do_atomic_virial=do_atomic_virial, + fparam=fparam_input, + aparam=aparam_input, + ) + if isinstance(batch_output, tuple): + batch_output = batch_output[0] + + results = [] + for odef in request_defs: + pd_name = self._OUTDEF_DP2BACKEND[odef.name] + if pd_name in batch_output: + shape = self._get_output_shape(odef, nframes, natoms) + out = batch_output[pd_name].reshape(shape).numpy() + results.append(out) + else: + shape = self._get_output_shape(odef, nframes, natoms) + results.append( + np.full(np.abs(shape), np.nan) # pylint: disable=no-explicit-dtype + ) # this is kinda hacky + return tuple(results) + + def _get_output_shape(self, odef, nframes, natoms): + if odef.category == OutputVariableCategory.DERV_C_REDU: + # virial + return [nframes, *odef.shape[:-1], 9] + elif odef.category == OutputVariableCategory.REDU: + # energy + return [nframes, *odef.shape, 1] + elif odef.category == OutputVariableCategory.DERV_C: + # atom_virial + return [nframes, *odef.shape[:-1], natoms, 9] + elif odef.category == OutputVariableCategory.DERV_R: + # force + return [nframes, *odef.shape[:-1], natoms, 3] + elif odef.category == OutputVariableCategory.OUT: + # atom_energy, atom_tensor + # Something wrong here? + # return [nframes, *shape, natoms, 1] + return [nframes, natoms, *odef.shape, 1] + else: + raise RuntimeError("unknown category") + + +# For tests only +def eval_model( + model, + coords: Union[np.ndarray, paddle.Tensor], + cells: Optional[Union[np.ndarray, paddle.Tensor]], + atom_types: Union[np.ndarray, paddle.to_tensor, List[int]], + spins: Optional[Union[np.ndarray, paddle.Tensor]] = None, + atomic: bool = False, + infer_batch_size: int = 2, + denoise: bool = False, +): + model = model.to(DEVICE) + energy_out = [] + atomic_energy_out = [] + force_out = [] + force_mag_out = [] + virial_out = [] + atomic_virial_out = [] + updated_coord_out = [] + logits_out = [] + err_msg = ( + f"All inputs should be the same format, " + f"but found {type(coords)}, {type(cells)}, {type(atom_types)} instead! " + ) + return_tensor = True + if isinstance(coords, paddle.Tensor): + if cells is not None: + assert isinstance(cells, paddle.Tensor), err_msg + if spins is not None: + assert isinstance(spins, paddle.Tensor), err_msg + assert isinstance(atom_types, paddle.Tensor) or isinstance(atom_types, list) + atom_types = paddle.to_tensor(atom_types, dtype=paddle.int64).to(DEVICE) + elif isinstance(coords, np.ndarray): + if cells is not None: + assert isinstance(cells, np.ndarray), err_msg + if spins is not None: + assert isinstance(spins, np.ndarray), err_msg + assert isinstance(atom_types, np.ndarray) or isinstance(atom_types, list) + atom_types = np.array(atom_types, dtype=np.int32) + return_tensor = False + + nframes = coords.shape[0] + if len(atom_types.shape) == 1: + natoms = len(atom_types) + if isinstance(atom_types, paddle.Tensor): + atom_types = paddle.tile(atom_types.unsqueeze(0), [nframes, 1]).reshape( + nframes, -1 + ) + else: + atom_types = np.tile(atom_types, nframes).reshape(nframes, -1) + else: + natoms = len(atom_types[0]) + + coord_input = paddle.to_tensor( + coords.reshape([-1, natoms, 3]), dtype=GLOBAL_PD_FLOAT_PRECISION + ).to(DEVICE) + spin_input = None + if spins is not None: + spin_input = paddle.to_tensor( + spins.reshape([-1, natoms, 3]), + dtype=GLOBAL_PD_FLOAT_PRECISION, + ).to(DEVICE) + has_spin = getattr(model, "has_spin", False) + if callable(has_spin): + has_spin = has_spin() + type_input = paddle.to_tensor(atom_types, dtype=paddle.int64).to(DEVICE) + box_input = None + if cells is None: + pbc = False + else: + pbc = True + box_input = paddle.to_tensor( + cells.reshape([-1, 3, 3]), dtype=GLOBAL_PD_FLOAT_PRECISION + ).to(DEVICE) + num_iter = int((nframes + infer_batch_size - 1) / infer_batch_size) + + for ii in range(num_iter): + batch_coord = coord_input[ii * infer_batch_size : (ii + 1) * infer_batch_size] + batch_atype = type_input[ii * infer_batch_size : (ii + 1) * infer_batch_size] + batch_box = None + batch_spin = None + if spin_input is not None: + batch_spin = spin_input[ii * infer_batch_size : (ii + 1) * infer_batch_size] + if pbc: + batch_box = box_input[ii * infer_batch_size : (ii + 1) * infer_batch_size] + input_dict = { + "coord": batch_coord, + "atype": batch_atype, + "box": batch_box, + "do_atomic_virial": atomic, + } + if has_spin: + input_dict["spin"] = batch_spin + batch_output = model(**input_dict) + if isinstance(batch_output, tuple): + batch_output = batch_output[0] + if not return_tensor: + if "energy" in batch_output: + energy_out.append(batch_output["energy"].numpy()) + if "atom_energy" in batch_output: + atomic_energy_out.append(batch_output["atom_energy"].numpy()) + if "force" in batch_output: + force_out.append(batch_output["force"].numpy()) + if "force_mag" in batch_output: + force_mag_out.append(batch_output["force_mag"].numpy()) + if "virial" in batch_output: + virial_out.append(batch_output["virial"].numpy()) + if "atom_virial" in batch_output: + atomic_virial_out.append(batch_output["atom_virial"].numpy()) + if "updated_coord" in batch_output: + updated_coord_out.append(batch_output["updated_coord"].numpy()) + if "logits" in batch_output: + logits_out.append(batch_output["logits"].numpy()) + else: + if "energy" in batch_output: + energy_out.append(batch_output["energy"]) + if "atom_energy" in batch_output: + atomic_energy_out.append(batch_output["atom_energy"]) + if "force" in batch_output: + force_out.append(batch_output["force"]) + if "force_mag" in batch_output: + force_mag_out.append(batch_output["force_mag"]) + if "virial" in batch_output: + virial_out.append(batch_output["virial"]) + if "atom_virial" in batch_output: + atomic_virial_out.append(batch_output["atom_virial"]) + if "updated_coord" in batch_output: + updated_coord_out.append(batch_output["updated_coord"]) + if "logits" in batch_output: + logits_out.append(batch_output["logits"]) + if not return_tensor: + energy_out = ( + np.concatenate(energy_out) if energy_out else np.zeros([nframes, 1]) # pylint: disable=no-explicit-dtype + ) + atomic_energy_out = ( + np.concatenate(atomic_energy_out) + if atomic_energy_out + else np.zeros([nframes, natoms, 1]) # pylint: disable=no-explicit-dtype + ) + force_out = ( + np.concatenate(force_out) if force_out else np.zeros([nframes, natoms, 3]) # pylint: disable=no-explicit-dtype + ) + force_mag_out = ( + np.concatenate(force_mag_out) + if force_mag_out + else np.zeros([nframes, natoms, 3]) # pylint: disable=no-explicit-dtype + ) + virial_out = ( + np.concatenate(virial_out) if virial_out else np.zeros([nframes, 3, 3]) # pylint: disable=no-explicit-dtype + ) + atomic_virial_out = ( + np.concatenate(atomic_virial_out) + if atomic_virial_out + else np.zeros([nframes, natoms, 3, 3]) # pylint: disable=no-explicit-dtype + ) + updated_coord_out = ( + np.concatenate(updated_coord_out) if updated_coord_out else None + ) + logits_out = np.concatenate(logits_out) if logits_out else None + else: + energy_out = ( + paddle.concat(energy_out) + if energy_out + else paddle.zeros([nframes, 1], dtype=GLOBAL_PD_FLOAT_PRECISION).to(DEVICE) + ) + atomic_energy_out = ( + paddle.concat(atomic_energy_out) + if atomic_energy_out + else paddle.zeros([nframes, natoms, 1], dtype=GLOBAL_PD_FLOAT_PRECISION).to( + DEVICE + ) + ) + force_out = ( + paddle.concat(force_out) + if force_out + else paddle.zeros([nframes, natoms, 3], dtype=GLOBAL_PD_FLOAT_PRECISION).to( + DEVICE + ) + ) + force_mag_out = ( + paddle.concat(force_mag_out) + if force_mag_out + else paddle.zeros([nframes, natoms, 3], dtype=GLOBAL_PD_FLOAT_PRECISION).to( + DEVICE + ) + ) + virial_out = ( + paddle.concat(virial_out) + if virial_out + else paddle.zeros([nframes, 3, 3], dtype=GLOBAL_PD_FLOAT_PRECISION).to( + DEVICE + ) + ) + atomic_virial_out = ( + paddle.concat(atomic_virial_out) + if atomic_virial_out + else paddle.zeros( + [nframes, natoms, 3, 3], dtype=GLOBAL_PD_FLOAT_PRECISION + ).to(DEVICE) + ) + updated_coord_out = ( + paddle.concat(updated_coord_out) if updated_coord_out else None + ) + logits_out = paddle.concat(logits_out) if logits_out else None + if denoise: + return updated_coord_out, logits_out + else: + results_dict = { + "energy": energy_out, + "force": force_out, + "virial": virial_out, + } + if has_spin: + results_dict["force_mag"] = force_mag_out + if atomic: + results_dict["atom_energy"] = atomic_energy_out + results_dict["atom_virial"] = atomic_virial_out + return results_dict diff --git a/deepmd/pd/infer/inference.py b/deepmd/pd/infer/inference.py new file mode 100644 index 0000000000..cef7b32ba4 --- /dev/null +++ b/deepmd/pd/infer/inference.py @@ -0,0 +1,65 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import logging +from copy import ( + deepcopy, +) + +import paddle + +from deepmd.pd.model.model import ( + get_model, +) +from deepmd.pd.train.wrapper import ( + ModelWrapper, +) +from deepmd.pd.utils.env import ( + DEVICE, + JIT, +) + +# if paddle.__version__.startswith("2"): +# import paddle._dynamo +log = logging.getLogger(__name__) + + +class Tester: + def __init__( + self, + model_ckpt, + head=None, + ): + """Construct a DeePMD tester. + + Args: + - config: The Dict-like configuration with training options. + """ + # Model + state_dict = paddle.load(model_ckpt) + if "model" in state_dict: + state_dict = state_dict["model"] + model_params = state_dict["_extra_state"]["model_params"] + self.multi_task = "model_dict" in model_params + if self.multi_task: + assert head is not None, "Head must be specified in multitask mode!" + self.head = head + assert head in model_params["model_dict"], ( + f"Specified head {head} not found in model {model_ckpt}! " + f"Available ones are {list(model_params['model_dict'].keys())}." + ) + model_params = model_params["model_dict"][head] + state_dict_head = {"_extra_state": state_dict["_extra_state"]} + for item in state_dict: + if f"model.{head}." in item: + state_dict_head[ + item.replace(f"model.{head}.", "model.Default.") + ] = state_dict[item].clone() + state_dict = state_dict_head + + self.model_params = deepcopy(model_params) + self.model = get_model(model_params).to(DEVICE) + + # Model Wrapper + self.wrapper = ModelWrapper(self.model) # inference only + if JIT: + self.wrapper = paddle.jit.script(self.wrapper) + self.wrapper.set_state_dict(state_dict) diff --git a/deepmd/pd/loss/__init__.py b/deepmd/pd/loss/__init__.py new file mode 100644 index 0000000000..e64a129d51 --- /dev/null +++ b/deepmd/pd/loss/__init__.py @@ -0,0 +1,28 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from .denoise import ( + DenoiseLoss, +) +from .dos import ( + DOSLoss, +) +from .ener import ( + EnergyStdLoss, +) +from .ener_spin import ( + EnergySpinLoss, +) +from .loss import ( + TaskLoss, +) +from .tensor import ( + TensorLoss, +) + +__all__ = [ + "DenoiseLoss", + "EnergyStdLoss", + "EnergySpinLoss", + "TensorLoss", + "TaskLoss", + "DOSLoss", +] diff --git a/deepmd/pd/loss/denoise.py b/deepmd/pd/loss/denoise.py new file mode 100644 index 0000000000..1ec97ff98e --- /dev/null +++ b/deepmd/pd/loss/denoise.py @@ -0,0 +1,109 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import paddle +import paddle.nn.functional as F + +from deepmd.pd.loss.loss import ( + TaskLoss, +) +from deepmd.pd.utils import ( + env, +) + + +class DenoiseLoss(TaskLoss): + def __init__( + self, + ntypes, + masked_token_loss=1.0, + masked_coord_loss=1.0, + norm_loss=0.01, + use_l1=True, + beta=1.00, + mask_loss_coord=True, + mask_loss_token=True, + **kwargs, + ): + """Construct a layer to compute loss on coord, and type reconstruction.""" + super().__init__() + self.ntypes = ntypes + self.masked_token_loss = masked_token_loss + self.masked_coord_loss = masked_coord_loss + self.norm_loss = norm_loss + self.has_coord = self.masked_coord_loss > 0.0 + self.has_token = self.masked_token_loss > 0.0 + self.has_norm = self.norm_loss > 0.0 + self.use_l1 = use_l1 + self.beta = beta + self.frac_beta = 1.00 / self.beta + self.mask_loss_coord = mask_loss_coord + self.mask_loss_token = mask_loss_token + + def forward(self, model_pred, label, natoms, learning_rate, mae=False): + """Return loss on coord and type denoise. + + Returns + ------- + - loss: Loss to minimize. + """ + updated_coord = model_pred["updated_coord"] + logits = model_pred["logits"] + clean_coord = label["clean_coord"] + clean_type = label["clean_type"] + coord_mask = label["coord_mask"] + type_mask = label["type_mask"] + + loss = paddle.zeros([1], dtype=env.GLOBAL_PD_FLOAT_PRECISION).to(env.DEVICE)[0] + more_loss = {} + if self.has_coord: + if self.mask_loss_coord: + masked_updated_coord = updated_coord[coord_mask] + masked_clean_coord = clean_coord[coord_mask] + if masked_updated_coord.size(0) > 0: + coord_loss = F.smooth_l1_loss( + masked_updated_coord.reshape([-1, 3]), + masked_clean_coord.reshape([-1, 3]), + reduction="mean", + beta=self.beta, + ) + else: + coord_loss = paddle.zeros( + 1, dtype=env.GLOBAL_PD_FLOAT_PRECISION + ).to(env.DEVICE)[0] + else: + coord_loss = F.smooth_l1_loss( + updated_coord.reshape([-1, 3]), + clean_coord.reshape([-1, 3]), + reduction="mean", + beta=self.beta, + ) + loss += self.masked_coord_loss * coord_loss + more_loss["coord_l1_error"] = coord_loss.detach() + if self.has_token: + if self.mask_loss_token: + masked_logits = logits[type_mask] + masked_target = clean_type[type_mask] + if masked_logits.size(0) > 0: + token_loss = F.nll_loss( + F.log_softmax(masked_logits, axis=-1), + masked_target, + reduction="mean", + ) + else: + token_loss = paddle.zeros( + 1, dtype=env.GLOBAL_PD_FLOAT_PRECISION + ).to(env.DEVICE)[0] + else: + token_loss = F.nll_loss( + F.log_softmax(logits.reshape([-1, self.ntypes - 1]), axis=-1), + clean_type.reshape([-1]), + reduction="mean", + ) + loss += self.masked_token_loss * token_loss + more_loss["token_error"] = token_loss.detach() + if self.has_norm: + norm_x = model_pred["norm_x"] + norm_delta_pair_rep = model_pred["norm_delta_pair_rep"] + loss += self.norm_loss * (norm_x + norm_delta_pair_rep) + more_loss["norm_loss"] = norm_x.detach() + norm_delta_pair_rep.detach() + + return loss, more_loss diff --git a/deepmd/pd/loss/dos.py b/deepmd/pd/loss/dos.py new file mode 100644 index 0000000000..ef1482c6da --- /dev/null +++ b/deepmd/pd/loss/dos.py @@ -0,0 +1,256 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + List, +) + +import paddle + +from deepmd.pd.loss.loss import ( + TaskLoss, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.utils.data import ( + DataRequirementItem, +) + + +class DOSLoss(TaskLoss): + def __init__( + self, + starter_learning_rate: float, + numb_dos: int, + start_pref_dos: float = 1.00, + limit_pref_dos: float = 1.00, + start_pref_cdf: float = 1000, + limit_pref_cdf: float = 1.00, + start_pref_ados: float = 0.0, + limit_pref_ados: float = 0.0, + start_pref_acdf: float = 0.0, + limit_pref_acdf: float = 0.0, + inference=False, + **kwargs, + ): + r"""Construct a loss for local and global tensors. + + Parameters + ---------- + tensor_name : str + The name of the tensor in the model predictions to compute the loss. + tensor_size : int + The size (dimension) of the tensor. + label_name : str + The name of the tensor in the labels to compute the loss. + pref_atomic : float + The prefactor of the weight of atomic loss. It should be larger than or equal to 0. + pref : float + The prefactor of the weight of global loss. It should be larger than or equal to 0. + inference : bool + If true, it will output all losses found in output, ignoring the pre-factors. + **kwargs + Other keyword arguments. + """ + super().__init__() + self.starter_learning_rate = starter_learning_rate + self.numb_dos = numb_dos + self.inference = inference + + self.start_pref_dos = start_pref_dos + self.limit_pref_dos = limit_pref_dos + self.start_pref_cdf = start_pref_cdf + self.limit_pref_cdf = limit_pref_cdf + + self.start_pref_ados = start_pref_ados + self.limit_pref_ados = limit_pref_ados + self.start_pref_acdf = start_pref_acdf + self.limit_pref_acdf = limit_pref_acdf + + assert ( + self.start_pref_dos >= 0.0 + and self.limit_pref_dos >= 0.0 + and self.start_pref_cdf >= 0.0 + and self.limit_pref_cdf >= 0.0 + and self.start_pref_ados >= 0.0 + and self.limit_pref_ados >= 0.0 + and self.start_pref_acdf >= 0.0 + and self.limit_pref_acdf >= 0.0 + ), "Can not assign negative weight to `pref` and `pref_atomic`" + + self.has_dos = (start_pref_dos != 0.0 and limit_pref_dos != 0.0) or inference + self.has_cdf = (start_pref_cdf != 0.0 and limit_pref_cdf != 0.0) or inference + self.has_ados = (start_pref_ados != 0.0 and limit_pref_ados != 0.0) or inference + self.has_acdf = (start_pref_acdf != 0.0 and limit_pref_acdf != 0.0) or inference + + assert ( + self.has_dos or self.has_cdf or self.has_ados or self.has_acdf + ), AssertionError("Can not assian zero weight both to `pref` and `pref_atomic`") + + def forward(self, input_dict, model, label, natoms, learning_rate=0.0, mae=False): + """Return loss on local and global tensors. + + Parameters + ---------- + input_dict : dict[str, paddle.Tensor] + Model inputs. + model : paddle.nn.Layer + Model to be used to output the predictions. + label : dict[str, paddle.Tensor] + Labels. + natoms : int + The local atom number. + + Returns + ------- + model_pred: dict[str, paddle.Tensor] + Model predictions. + loss: paddle.Tensor + Loss for model to minimize. + more_loss: dict[str, paddle.Tensor] + Other losses for display. + """ + model_pred = model(**input_dict) + + coef = learning_rate / self.starter_learning_rate + pref_dos = ( + self.limit_pref_dos + (self.start_pref_dos - self.limit_pref_dos) * coef + ) + pref_cdf = ( + self.limit_pref_cdf + (self.start_pref_cdf - self.limit_pref_cdf) * coef + ) + pref_ados = ( + self.limit_pref_ados + (self.start_pref_ados - self.limit_pref_ados) * coef + ) + pref_acdf = ( + self.limit_pref_acdf + (self.start_pref_acdf - self.limit_pref_acdf) * coef + ) + + loss = paddle.zeros([1], dtype=env.GLOBAL_PD_FLOAT_PRECISION).to(env.DEVICE)[0] + more_loss = {} + if self.has_ados and "atom_dos" in model_pred and "atom_dos" in label: + find_local = label.get("find_atom_dos", 0.0) + pref_ados = pref_ados * find_local + local_tensor_pred_dos = model_pred["atom_dos"].reshape( + [-1, natoms, self.numb_dos] + ) + local_tensor_label_dos = label["atom_dos"].reshape( + [-1, natoms, self.numb_dos] + ) + diff = (local_tensor_pred_dos - local_tensor_label_dos).reshape( + [-1, self.numb_dos] + ) + if "mask" in model_pred: + diff = diff[model_pred["mask"].reshape([-1]).bool()] + l2_local_loss_dos = paddle.mean(paddle.square(diff)) + if not self.inference: + more_loss["l2_local_dos_loss"] = self.display_if_exist( + l2_local_loss_dos.detach(), find_local + ) + loss += pref_ados * l2_local_loss_dos + rmse_local_dos = l2_local_loss_dos.sqrt() + more_loss["rmse_local_dos"] = self.display_if_exist( + rmse_local_dos.detach(), find_local + ) + if self.has_acdf and "atom_dos" in model_pred and "atom_dos" in label: + find_local = label.get("find_atom_dos", 0.0) + pref_acdf = pref_acdf * find_local + local_tensor_pred_cdf = paddle.cumsum( + model_pred["atom_dos"].reshape([-1, natoms, self.numb_dos]), axis=-1 + ) + local_tensor_label_cdf = paddle.cumsum( + label["atom_dos"].reshape([-1, natoms, self.numb_dos]), axis=-1 + ) + diff = (local_tensor_pred_cdf - local_tensor_label_cdf).reshape( + [-1, self.numb_dos] + ) + if "mask" in model_pred: + diff = diff[model_pred["mask"].reshape([-1]).bool()] + l2_local_loss_cdf = paddle.mean(paddle.square(diff)) + if not self.inference: + more_loss["l2_local_cdf_loss"] = self.display_if_exist( + l2_local_loss_cdf.detach(), find_local + ) + loss += pref_acdf * l2_local_loss_cdf + rmse_local_cdf = l2_local_loss_cdf.sqrt() + more_loss["rmse_local_cdf"] = self.display_if_exist( + rmse_local_cdf.detach(), find_local + ) + if self.has_dos and "dos" in model_pred and "dos" in label: + find_global = label.get("find_dos", 0.0) + pref_dos = pref_dos * find_global + global_tensor_pred_dos = model_pred["dos"].reshape([-1, self.numb_dos]) + global_tensor_label_dos = label["dos"].reshape([-1, self.numb_dos]) + diff = global_tensor_pred_dos - global_tensor_label_dos + if "mask" in model_pred: + atom_num = model_pred["mask"].sum(-1, keepdim=True) + l2_global_loss_dos = paddle.mean( + paddle.sum(paddle.square(diff) * atom_num, axis=0) / atom_num.sum() + ) + atom_num = paddle.mean(float(atom_num)) + else: + atom_num = natoms + l2_global_loss_dos = paddle.mean(paddle.square(diff)) + if not self.inference: + more_loss["l2_global_dos_loss"] = self.display_if_exist( + l2_global_loss_dos.detach(), find_global + ) + loss += pref_dos * l2_global_loss_dos + rmse_global_dos = l2_global_loss_dos.sqrt() / atom_num + more_loss["rmse_global_dos"] = self.display_if_exist( + rmse_global_dos.detach(), find_global + ) + if self.has_cdf and "dos" in model_pred and "dos" in label: + find_global = label.get("find_dos", 0.0) + pref_cdf = pref_cdf * find_global + global_tensor_pred_cdf = paddle.cumsum( + model_pred["dos"].reshape([-1, self.numb_dos]), axis=-1 + ) + global_tensor_label_cdf = paddle.cumsum( + label["dos"].reshape([-1, self.numb_dos]), axis=-1 + ) + diff = global_tensor_pred_cdf - global_tensor_label_cdf + if "mask" in model_pred: + atom_num = model_pred["mask"].sum(-1, keepdim=True) + l2_global_loss_cdf = paddle.mean( + paddle.sum(paddle.square(diff) * atom_num, axis=0) / atom_num.sum() + ) + atom_num = paddle.mean(float(atom_num)) + else: + atom_num = natoms + l2_global_loss_cdf = paddle.mean(paddle.square(diff)) + if not self.inference: + more_loss["l2_global_cdf_loss"] = self.display_if_exist( + l2_global_loss_cdf.detach(), find_global + ) + loss += pref_cdf * l2_global_loss_cdf + rmse_global_dos = l2_global_loss_cdf.sqrt() / atom_num + more_loss["rmse_global_cdf"] = self.display_if_exist( + rmse_global_dos.detach(), find_global + ) + return model_pred, loss, more_loss + + @property + def label_requirement(self) -> List[DataRequirementItem]: + """Return data label requirements needed for this loss calculation.""" + label_requirement = [] + if self.has_ados or self.has_acdf: + label_requirement.append( + DataRequirementItem( + "atom_dos", + ndof=self.numb_dos, + atomic=True, + must=False, + high_prec=False, + ) + ) + if self.has_dos or self.has_cdf: + label_requirement.append( + DataRequirementItem( + "dos", + ndof=self.numb_dos, + atomic=False, + must=False, + high_prec=False, + ) + ) + return label_requirement diff --git a/deepmd/pd/loss/ener.py b/deepmd/pd/loss/ener.py new file mode 100644 index 0000000000..8b2dee5879 --- /dev/null +++ b/deepmd/pd/loss/ener.py @@ -0,0 +1,416 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + List, + Optional, +) + +import paddle +import paddle.nn.functional as F + +from deepmd.pd.loss.loss import ( + TaskLoss, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + GLOBAL_PD_FLOAT_PRECISION, +) +from deepmd.utils.data import ( + DataRequirementItem, +) + + +class EnergyStdLoss(TaskLoss): + def __init__( + self, + starter_learning_rate=1.0, + start_pref_e=0.0, + limit_pref_e=0.0, + start_pref_f=0.0, + limit_pref_f=0.0, + start_pref_v=0.0, + limit_pref_v=0.0, + start_pref_ae: float = 0.0, + limit_pref_ae: float = 0.0, + start_pref_pf: float = 0.0, + limit_pref_pf: float = 0.0, + relative_f: Optional[float] = None, + enable_atom_ener_coeff: bool = False, + start_pref_gf: float = 0.0, + limit_pref_gf: float = 0.0, + numb_generalized_coord: int = 0, + use_l1_all: bool = False, + inference=False, + **kwargs, + ): + r"""Construct a layer to compute loss on energy, force and virial. + + Parameters + ---------- + starter_learning_rate : float + The learning rate at the start of the training. + start_pref_e : float + The prefactor of energy loss at the start of the training. + limit_pref_e : float + The prefactor of energy loss at the end of the training. + start_pref_f : float + The prefactor of force loss at the start of the training. + limit_pref_f : float + The prefactor of force loss at the end of the training. + start_pref_v : float + The prefactor of virial loss at the start of the training. + limit_pref_v : float + The prefactor of virial loss at the end of the training. + start_pref_ae : float + The prefactor of atomic energy loss at the start of the training. + limit_pref_ae : float + The prefactor of atomic energy loss at the end of the training. + start_pref_pf : float + The prefactor of atomic prefactor force loss at the start of the training. + limit_pref_pf : float + The prefactor of atomic prefactor force loss at the end of the training. + relative_f : float + If provided, relative force error will be used in the loss. The difference + of force will be normalized by the magnitude of the force in the label with + a shift given by relative_f + enable_atom_ener_coeff : bool + if true, the energy will be computed as \sum_i c_i E_i + start_pref_gf : float + The prefactor of generalized force loss at the start of the training. + limit_pref_gf : float + The prefactor of generalized force loss at the end of the training. + numb_generalized_coord : int + The dimension of generalized coordinates. + use_l1_all : bool + Whether to use L1 loss, if False (default), it will use L2 loss. + inference : bool + If true, it will output all losses found in output, ignoring the pre-factors. + **kwargs + Other keyword arguments. + """ + super().__init__() + self.starter_learning_rate = starter_learning_rate + self.has_e = (start_pref_e != 0.0 and limit_pref_e != 0.0) or inference + self.has_f = (start_pref_f != 0.0 and limit_pref_f != 0.0) or inference + self.has_v = (start_pref_v != 0.0 and limit_pref_v != 0.0) or inference + self.has_ae = (start_pref_ae != 0.0 and limit_pref_ae != 0.0) or inference + self.has_pf = (start_pref_pf != 0.0 and limit_pref_pf != 0.0) or inference + self.has_gf = start_pref_gf != 0.0 and limit_pref_gf != 0.0 + + self.start_pref_e = start_pref_e + self.limit_pref_e = limit_pref_e + self.start_pref_f = start_pref_f + self.limit_pref_f = limit_pref_f + self.start_pref_v = start_pref_v + self.limit_pref_v = limit_pref_v + self.start_pref_ae = start_pref_ae + self.limit_pref_ae = limit_pref_ae + self.start_pref_pf = start_pref_pf + self.limit_pref_pf = limit_pref_pf + self.start_pref_gf = start_pref_gf + self.limit_pref_gf = limit_pref_gf + self.relative_f = relative_f + self.enable_atom_ener_coeff = enable_atom_ener_coeff + self.numb_generalized_coord = numb_generalized_coord + if self.has_gf and self.numb_generalized_coord < 1: + raise RuntimeError( + "When generalized force loss is used, the dimension of generalized coordinates should be larger than 0" + ) + self.use_l1_all = use_l1_all + self.inference = inference + + def forward(self, input_dict, model, label, natoms, learning_rate, mae=False): + """Return loss on energy and force. + + Parameters + ---------- + input_dict : dict[str, paddle.Tensor] + Model inputs. + model : paddle.nn.Layer + Model to be used to output the predictions. + label : dict[str, paddle.Tensor] + Labels. + natoms : int + The local atom number. + + Returns + ------- + model_pred: dict[str, paddle.Tensor] + Model predictions. + loss: paddle.Tensor + Loss for model to minimize. + more_loss: dict[str, paddle.Tensor] + Other losses for display. + """ + model_pred = model(**input_dict) + coef = learning_rate / self.starter_learning_rate + pref_e = self.limit_pref_e + (self.start_pref_e - self.limit_pref_e) * coef + pref_f = self.limit_pref_f + (self.start_pref_f - self.limit_pref_f) * coef + pref_v = self.limit_pref_v + (self.start_pref_v - self.limit_pref_v) * coef + pref_ae = self.limit_pref_ae + (self.start_pref_ae - self.limit_pref_ae) * coef + pref_pf = self.limit_pref_pf + (self.start_pref_pf - self.limit_pref_pf) * coef + pref_gf = self.limit_pref_gf + (self.start_pref_gf - self.limit_pref_gf) * coef + + loss = paddle.zeros([1], dtype=env.GLOBAL_PD_FLOAT_PRECISION).to(env.DEVICE)[0] + more_loss = {} + # more_loss['log_keys'] = [] # showed when validation on the fly + # more_loss['test_keys'] = [] # showed when doing dp test + atom_norm = 1.0 / natoms + if self.has_e and "energy" in model_pred and "energy" in label: + energy_pred = model_pred["energy"] + energy_label = label["energy"] + if self.enable_atom_ener_coeff and "atom_energy" in model_pred: + atom_ener_pred = model_pred["atom_energy"] + # when ener_coeff (\nu) is defined, the energy is defined as + # E = \sum_i \nu_i E_i + # instead of the sum of atomic energies. + # + # A case is that we want to train reaction energy + # A + B -> C + D + # E = - E(A) - E(B) + E(C) + E(D) + # A, B, C, D could be put far away from each other + atom_ener_coeff = label["atom_ener_coeff"] + atom_ener_coeff = atom_ener_coeff.reshape(atom_ener_pred.shape) + energy_pred = paddle.sum(atom_ener_coeff * atom_ener_pred, axis=1) + find_energy = label.get("find_energy", 0.0) + pref_e = pref_e * find_energy + if not self.use_l1_all: + l2_ener_loss = paddle.mean(paddle.square(energy_pred - energy_label)) + if not self.inference: + more_loss["l2_ener_loss"] = self.display_if_exist( + l2_ener_loss.detach(), find_energy + ) + loss += atom_norm * (pref_e * l2_ener_loss) + rmse_e = l2_ener_loss.sqrt() * atom_norm + more_loss["rmse_e"] = self.display_if_exist( + rmse_e.detach(), find_energy + ) + # more_loss['log_keys'].append('rmse_e') + else: # use l1 and for all atoms + l1_ener_loss = F.l1_loss( + energy_pred.reshape([-1]), + energy_label.reshape([-1]), + reduction="sum", + ) + loss += pref_e * l1_ener_loss + more_loss["mae_e"] = self.display_if_exist( + F.l1_loss( + energy_pred.reshape([-1]), + energy_label.reshape([-1]), + reduction="mean", + ).detach(), + find_energy, + ) + # more_loss['log_keys'].append('rmse_e') + if mae: + mae_e = paddle.mean(paddle.abs(energy_pred - energy_label)) * atom_norm + more_loss["mae_e"] = self.display_if_exist(mae_e.detach(), find_energy) + mae_e_all = paddle.mean(paddle.abs(energy_pred - energy_label)) + more_loss["mae_e_all"] = self.display_if_exist( + mae_e_all.detach(), find_energy + ) + + if ( + (self.has_f or self.has_pf or self.relative_f or self.has_gf) + and "force" in model_pred + and "force" in label + ): + find_force = label.get("find_force", 0.0) + pref_f = pref_f * find_force + force_pred = model_pred["force"] + force_label = label["force"] + diff_f = (force_label - force_pred).reshape([-1]) + + if self.relative_f is not None: + force_label_3 = force_label.reshape([-1, 3]) + norm_f = force_label_3.norm(axis=1, keepdim=True) + self.relative_f + diff_f_3 = diff_f.reshape([-1, 3]) + diff_f_3 = diff_f_3 / norm_f + diff_f = diff_f_3.reshape([-1]) + + if self.has_f: + if not self.use_l1_all: + l2_force_loss = paddle.mean(paddle.square(diff_f)) + if not self.inference: + more_loss["l2_force_loss"] = self.display_if_exist( + l2_force_loss.detach(), find_force + ) + loss += (pref_f * l2_force_loss).to(GLOBAL_PD_FLOAT_PRECISION) + rmse_f = l2_force_loss.sqrt() + more_loss["rmse_f"] = self.display_if_exist( + rmse_f.detach(), find_force + ) + else: + l1_force_loss = F.l1_loss(force_label, force_pred, reduction="none") + more_loss["mae_f"] = self.display_if_exist( + l1_force_loss.mean().detach(), find_force + ) + l1_force_loss = l1_force_loss.sum(-1).mean(-1).sum() + loss += (pref_f * l1_force_loss).to(GLOBAL_PD_FLOAT_PRECISION) + if mae: + mae_f = paddle.mean(paddle.abs(diff_f)) + more_loss["mae_f"] = self.display_if_exist( + mae_f.detach(), find_force + ) + + if self.has_pf and "atom_pref" in label: + atom_pref = label["atom_pref"] + find_atom_pref = label.get("find_atom_pref", 0.0) + pref_pf = pref_pf * find_atom_pref + atom_pref_reshape = atom_pref.reshape([-1]) + l2_pref_force_loss = (paddle.square(diff_f) * atom_pref_reshape).mean() + if not self.inference: + more_loss["l2_pref_force_loss"] = self.display_if_exist( + l2_pref_force_loss.detach(), find_atom_pref + ) + loss += (pref_pf * l2_pref_force_loss).to(GLOBAL_PD_FLOAT_PRECISION) + rmse_pf = l2_pref_force_loss.sqrt() + more_loss["rmse_pf"] = self.display_if_exist( + rmse_pf.detach(), find_atom_pref + ) + + if self.has_gf and "drdq" in label: + drdq = label["drdq"] + find_drdq = label.get("find_drdq", 0.0) + pref_gf = pref_gf * find_drdq + force_reshape_nframes = force_pred.reshape([-1, natoms * 3]) + force_label_reshape_nframes = force_label.reshape([-1, natoms * 3]) + drdq_reshape = drdq.reshape( + [-1, natoms * 3, self.numb_generalized_coord] + ) + gen_force_label = paddle.einsum( + "bij,bi->bj", drdq_reshape, force_label_reshape_nframes + ) + gen_force = paddle.einsum( + "bij,bi->bj", drdq_reshape, force_reshape_nframes + ) + diff_gen_force = gen_force_label - gen_force + l2_gen_force_loss = paddle.square(diff_gen_force).mean() + if not self.inference: + more_loss["l2_gen_force_loss"] = self.display_if_exist( + l2_gen_force_loss.detach(), find_drdq + ) + loss += (pref_gf * l2_gen_force_loss).to(GLOBAL_PD_FLOAT_PRECISION) + rmse_gf = l2_gen_force_loss.sqrt() + more_loss["rmse_gf"] = self.display_if_exist( + rmse_gf.detach(), find_drdq + ) + + if self.has_v and "virial" in model_pred and "virial" in label: + find_virial = label.get("find_virial", 0.0) + pref_v = pref_v * find_virial + diff_v = label["virial"] - model_pred["virial"].reshape([-1, 9]) + l2_virial_loss = paddle.mean(paddle.square(diff_v)) + if not self.inference: + more_loss["l2_virial_loss"] = self.display_if_exist( + l2_virial_loss.detach(), find_virial + ) + loss += atom_norm * (pref_v * l2_virial_loss) + rmse_v = l2_virial_loss.sqrt() * atom_norm + more_loss["rmse_v"] = self.display_if_exist(rmse_v.detach(), find_virial) + if mae: + mae_v = paddle.mean(paddle.abs(diff_v)) * atom_norm + more_loss["mae_v"] = self.display_if_exist(mae_v.detach(), find_virial) + + if self.has_ae and "atom_energy" in model_pred and "atom_ener" in label: + atom_ener = model_pred["atom_energy"] + atom_ener_label = label["atom_ener"] + find_atom_ener = label.get("find_atom_ener", 0.0) + pref_ae = pref_ae * find_atom_ener + atom_ener_reshape = atom_ener.reshape([-1]) + atom_ener_label_reshape = atom_ener_label.reshape([-1]) + l2_atom_ener_loss = paddle.square( + atom_ener_label_reshape - atom_ener_reshape + ).mean() + if not self.inference: + more_loss["l2_atom_ener_loss"] = self.display_if_exist( + l2_atom_ener_loss.detach(), find_atom_ener + ) + loss += (pref_ae * l2_atom_ener_loss).to(GLOBAL_PD_FLOAT_PRECISION) + rmse_ae = l2_atom_ener_loss.sqrt() + more_loss["rmse_ae"] = self.display_if_exist( + rmse_ae.detach(), find_atom_ener + ) + + if not self.inference: + more_loss["rmse"] = paddle.sqrt(loss.detach()) + return model_pred, loss, more_loss + + @property + def label_requirement(self) -> List[DataRequirementItem]: + """Return data label requirements needed for this loss calculation.""" + label_requirement = [] + if self.has_e: + label_requirement.append( + DataRequirementItem( + "energy", + ndof=1, + atomic=False, + must=False, + high_prec=True, + ) + ) + if self.has_f: + label_requirement.append( + DataRequirementItem( + "force", + ndof=3, + atomic=True, + must=False, + high_prec=False, + ) + ) + if self.has_v: + label_requirement.append( + DataRequirementItem( + "virial", + ndof=9, + atomic=False, + must=False, + high_prec=False, + ) + ) + if self.has_ae: + label_requirement.append( + DataRequirementItem( + "atom_ener", + ndof=1, + atomic=True, + must=False, + high_prec=False, + ) + ) + if self.has_pf: + label_requirement.append( + DataRequirementItem( + "atom_pref", + ndof=1, + atomic=True, + must=False, + high_prec=False, + repeat=3, + ) + ) + if self.has_gf > 0: + label_requirement.append( + DataRequirementItem( + "drdq", + ndof=self.numb_generalized_coord * 3, + atomic=True, + must=False, + high_prec=False, + ) + ) + if self.enable_atom_ener_coeff: + label_requirement.append( + DataRequirementItem( + "atom_ener_coeff", + ndof=1, + atomic=True, + must=False, + high_prec=False, + default=1.0, + ) + ) + return label_requirement diff --git a/deepmd/pd/loss/ener_spin.py b/deepmd/pd/loss/ener_spin.py new file mode 100644 index 0000000000..fc91ccc801 --- /dev/null +++ b/deepmd/pd/loss/ener_spin.py @@ -0,0 +1,332 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + List, +) + +import paddle +import paddle.nn.functional as F + +from deepmd.pd.loss.loss import ( + TaskLoss, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + GLOBAL_PD_FLOAT_PRECISION, +) +from deepmd.utils.data import ( + DataRequirementItem, +) + + +class EnergySpinLoss(TaskLoss): + def __init__( + self, + starter_learning_rate=1.0, + start_pref_e=0.0, + limit_pref_e=0.0, + start_pref_fr=0.0, + limit_pref_fr=0.0, + start_pref_fm=0.0, + limit_pref_fm=0.0, + start_pref_v=0.0, + limit_pref_v=0.0, + start_pref_ae: float = 0.0, + limit_pref_ae: float = 0.0, + enable_atom_ener_coeff: bool = False, + use_l1_all: bool = False, + inference=False, + **kwargs, + ): + r"""Construct a layer to compute loss on energy, real force, magnetic force and virial. + + Parameters + ---------- + starter_learning_rate : float + The learning rate at the start of the training. + start_pref_e : float + The prefactor of energy loss at the start of the training. + limit_pref_e : float + The prefactor of energy loss at the end of the training. + start_pref_fr : float + The prefactor of real force loss at the start of the training. + limit_pref_fr : float + The prefactor of real force loss at the end of the training. + start_pref_fm : float + The prefactor of magnetic force loss at the start of the training. + limit_pref_fm : float + The prefactor of magnetic force loss at the end of the training. + start_pref_v : float + The prefactor of virial loss at the start of the training. + limit_pref_v : float + The prefactor of virial loss at the end of the training. + start_pref_ae : float + The prefactor of atomic energy loss at the start of the training. + limit_pref_ae : float + The prefactor of atomic energy loss at the end of the training. + enable_atom_ener_coeff : bool + if true, the energy will be computed as \sum_i c_i E_i + use_l1_all : bool + Whether to use L1 loss, if False (default), it will use L2 loss. + inference : bool + If true, it will output all losses found in output, ignoring the pre-factors. + **kwargs + Other keyword arguments. + """ + super().__init__() + self.starter_learning_rate = starter_learning_rate + self.has_e = (start_pref_e != 0.0 and limit_pref_e != 0.0) or inference + self.has_fr = (start_pref_fr != 0.0 and limit_pref_fr != 0.0) or inference + self.has_fm = (start_pref_fm != 0.0 and limit_pref_fm != 0.0) or inference + self.has_v = (start_pref_v != 0.0 and limit_pref_v != 0.0) or inference + self.has_ae = (start_pref_ae != 0.0 and limit_pref_ae != 0.0) or inference + + self.start_pref_e = start_pref_e + self.limit_pref_e = limit_pref_e + self.start_pref_fr = start_pref_fr + self.limit_pref_fr = limit_pref_fr + self.start_pref_fm = start_pref_fm + self.limit_pref_fm = limit_pref_fm + self.start_pref_v = start_pref_v + self.limit_pref_v = limit_pref_v + self.start_pref_ae = start_pref_ae + self.limit_pref_ae = limit_pref_ae + self.enable_atom_ener_coeff = enable_atom_ener_coeff + self.use_l1_all = use_l1_all + self.inference = inference + + def forward(self, input_dict, model, label, natoms, learning_rate, mae=False): + """Return energy loss with magnetic labels. + + Parameters + ---------- + input_dict : dict[str, paddle.Tensor] + Model inputs. + model : paddle.nn.Layer + Model to be used to output the predictions. + label : dict[str, paddle.Tensor] + Labels. + natoms : int + The local atom number. + + Returns + ------- + model_pred: dict[str, paddle.Tensor] + Model predictions. + loss: paddle.Tensor + Loss for model to minimize. + more_loss: dict[str, paddle.Tensor] + Other losses for display. + """ + model_pred = model(**input_dict) + coef = learning_rate / self.starter_learning_rate + pref_e = self.limit_pref_e + (self.start_pref_e - self.limit_pref_e) * coef + pref_fr = self.limit_pref_fr + (self.start_pref_fr - self.limit_pref_fr) * coef + pref_fm = self.limit_pref_fm + (self.start_pref_fm - self.limit_pref_fm) * coef + pref_v = self.limit_pref_v + (self.start_pref_v - self.limit_pref_v) * coef + pref_ae = self.limit_pref_ae + (self.start_pref_ae - self.limit_pref_ae) * coef + loss = paddle.to_tensor(0.0, dtype=env.GLOBAL_PD_FLOAT_PRECISION).to(env.DEVICE) + more_loss = {} + # more_loss['log_keys'] = [] # showed when validation on the fly + # more_loss['test_keys'] = [] # showed when doing dp test + atom_norm = 1.0 / natoms + if self.has_e and "energy" in model_pred and "energy" in label: + energy_pred = model_pred["energy"] + energy_label = label["energy"] + if self.enable_atom_ener_coeff and "atom_energy" in model_pred: + atom_ener_pred = model_pred["atom_energy"] + # when ener_coeff (\nu) is defined, the energy is defined as + # E = \sum_i \nu_i E_i + # instead of the sum of atomic energies. + # + # A case is that we want to train reaction energy + # A + B -> C + D + # E = - E(A) - E(B) + E(C) + E(D) + # A, B, C, D could be put far away from each other + atom_ener_coeff = label["atom_ener_coeff"] + atom_ener_coeff = atom_ener_coeff.reshape(atom_ener_pred.shape) + energy_pred = paddle.sum(atom_ener_coeff * atom_ener_pred, axis=1) + find_energy = label.get("find_energy", 0.0) + pref_e = pref_e * find_energy + if not self.use_l1_all: + l2_ener_loss = paddle.mean(paddle.square(energy_pred - energy_label)) + if not self.inference: + more_loss["l2_ener_loss"] = self.display_if_exist( + l2_ener_loss.detach(), find_energy + ) + loss += atom_norm * (pref_e * l2_ener_loss) + rmse_e = l2_ener_loss.sqrt() * atom_norm + more_loss["rmse_e"] = self.display_if_exist( + rmse_e.detach(), find_energy + ) + # more_loss['log_keys'].append('rmse_e') + else: # use l1 and for all atoms + l1_ener_loss = F.l1_loss( + energy_pred.reshape([-1]), + energy_label.reshape([-1]), + reduction="sum", + ) + loss += pref_e * l1_ener_loss + more_loss["mae_e"] = self.display_if_exist( + F.l1_loss( + energy_pred.reshape([-1]), + energy_label.reshape([-1]), + reduction="mean", + ).detach(), + find_energy, + ) + # more_loss['log_keys'].append('rmse_e') + if mae: + mae_e = paddle.mean(paddle.abs(energy_pred - energy_label)) * atom_norm + more_loss["mae_e"] = self.display_if_exist(mae_e.detach(), find_energy) + mae_e_all = paddle.mean(paddle.abs(energy_pred - energy_label)) + more_loss["mae_e_all"] = self.display_if_exist( + mae_e_all.detach(), find_energy + ) + + if self.has_fr and "force" in model_pred and "force" in label: + find_force_r = label.get("find_force", 0.0) + pref_fr = pref_fr * find_force_r + if not self.use_l1_all: + diff_fr = label["force"] - model_pred["force"] + l2_force_real_loss = paddle.mean(paddle.square(diff_fr)) + if not self.inference: + more_loss["l2_force_r_loss"] = self.display_if_exist( + l2_force_real_loss.detach(), find_force_r + ) + loss += (pref_fr * l2_force_real_loss).to(GLOBAL_PD_FLOAT_PRECISION) + rmse_fr = l2_force_real_loss.sqrt() + more_loss["rmse_fr"] = self.display_if_exist( + rmse_fr.detach(), find_force_r + ) + if mae: + mae_fr = paddle.mean(paddle.abs(diff_fr)) + more_loss["mae_fr"] = self.display_if_exist( + mae_fr.detach(), find_force_r + ) + else: + l1_force_real_loss = F.l1_loss( + label["force"], model_pred["force"], reduction="none" + ) + more_loss["mae_fr"] = self.display_if_exist( + l1_force_real_loss.mean().detach(), find_force_r + ) + l1_force_real_loss = l1_force_real_loss.sum(-1).mean(-1).sum() + loss += (pref_fr * l1_force_real_loss).to(GLOBAL_PD_FLOAT_PRECISION) + + if self.has_fm and "force_mag" in model_pred and "force_mag" in label: + find_force_m = label.get("find_force_mag", 0.0) + pref_fm = pref_fm * find_force_m + nframes = model_pred["force_mag"].shape[0] + atomic_mask = model_pred["mask_mag"].expand([-1, -1, 3]) + label_force_mag = label["force_mag"][atomic_mask].reshape([nframes, -1, 3]) + model_pred_force_mag = model_pred["force_mag"][atomic_mask].reshape( + [nframes, -1, 3] + ) + if not self.use_l1_all: + diff_fm = label_force_mag - model_pred_force_mag + l2_force_mag_loss = paddle.mean(paddle.square(diff_fm)) + if not self.inference: + more_loss["l2_force_m_loss"] = self.display_if_exist( + l2_force_mag_loss.detach(), find_force_m + ) + loss += (pref_fm * l2_force_mag_loss).to(GLOBAL_PD_FLOAT_PRECISION) + rmse_fm = l2_force_mag_loss.sqrt() + more_loss["rmse_fm"] = self.display_if_exist( + rmse_fm.detach(), find_force_m + ) + if mae: + mae_fm = paddle.mean(paddle.abs(diff_fm)) + more_loss["mae_fm"] = self.display_if_exist( + mae_fm.detach(), find_force_m + ) + else: + l1_force_mag_loss = F.l1_loss( + label_force_mag, model_pred_force_mag, reduction="none" + ) + more_loss["mae_fm"] = self.display_if_exist( + l1_force_mag_loss.mean().detach(), find_force_m + ) + l1_force_mag_loss = l1_force_mag_loss.sum(-1).mean(-1).sum() + loss += (pref_fm * l1_force_mag_loss).to(GLOBAL_PD_FLOAT_PRECISION) + + if self.has_ae and "atom_energy" in model_pred and "atom_ener" in label: + atom_ener = model_pred["atom_energy"] + atom_ener_label = label["atom_ener"] + find_atom_ener = label.get("find_atom_ener", 0.0) + pref_ae = pref_ae * find_atom_ener + atom_ener_reshape = atom_ener.reshape([-1]) + atom_ener_label_reshape = atom_ener_label.reshape([-1]) + l2_atom_ener_loss = paddle.square( + atom_ener_label_reshape - atom_ener_reshape + ).mean() + if not self.inference: + more_loss["l2_atom_ener_loss"] = self.display_if_exist( + l2_atom_ener_loss.detach(), find_atom_ener + ) + loss += (pref_ae * l2_atom_ener_loss).to(GLOBAL_PD_FLOAT_PRECISION) + rmse_ae = l2_atom_ener_loss.sqrt() + more_loss["rmse_ae"] = self.display_if_exist( + rmse_ae.detach(), find_atom_ener + ) + + if not self.inference: + more_loss["rmse"] = paddle.sqrt(loss.detach()) + return model_pred, loss, more_loss + + @property + def label_requirement(self) -> List[DataRequirementItem]: + """Return data label requirements needed for this loss calculation.""" + label_requirement = [] + if self.has_e: + label_requirement.append( + DataRequirementItem( + "energy", + ndof=1, + atomic=False, + must=False, + high_prec=True, + ) + ) + if self.has_fr: + label_requirement.append( + DataRequirementItem( + "force", + ndof=3, + atomic=True, + must=False, + high_prec=False, + ) + ) + if self.has_fm: + label_requirement.append( + DataRequirementItem( + "force_mag", + ndof=3, + atomic=True, + must=False, + high_prec=False, + ) + ) + if self.has_v: + label_requirement.append( + DataRequirementItem( + "virial", + ndof=9, + atomic=False, + must=False, + high_prec=False, + ) + ) + if self.has_ae: + label_requirement.append( + DataRequirementItem( + "atom_ener", + ndof=1, + atomic=True, + must=False, + high_prec=False, + ) + ) + return label_requirement diff --git a/deepmd/pd/loss/loss.py b/deepmd/pd/loss/loss.py new file mode 100644 index 0000000000..0736369fd2 --- /dev/null +++ b/deepmd/pd/loss/loss.py @@ -0,0 +1,43 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from abc import ( + ABC, + abstractmethod, +) +from typing import ( + List, +) + +import paddle + +from deepmd.utils.data import ( + DataRequirementItem, +) + + +class TaskLoss(paddle.nn.Layer, ABC): + def __init__(self, **kwargs): + """Construct loss.""" + super().__init__() + + def forward(self, input_dict, model, label, natoms, learning_rate): + """Return loss .""" + raise NotImplementedError + + @property + @abstractmethod + def label_requirement(self) -> List[DataRequirementItem]: + """Return data label requirements needed for this loss calculation.""" + pass + + @staticmethod + def display_if_exist(loss: paddle.Tensor, find_property: float) -> paddle.Tensor: + """Display NaN if labeled property is not found. + + Parameters + ---------- + loss : paddle.Tensor + the loss tensor + find_property : float + whether the property is found + """ + return loss if bool(find_property) else paddle.nan diff --git a/deepmd/pd/loss/tensor.py b/deepmd/pd/loss/tensor.py new file mode 100644 index 0000000000..8c658866bf --- /dev/null +++ b/deepmd/pd/loss/tensor.py @@ -0,0 +1,177 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + List, +) + +import paddle + +from deepmd.pd.loss.loss import ( + TaskLoss, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.utils.data import ( + DataRequirementItem, +) + + +class TensorLoss(TaskLoss): + def __init__( + self, + tensor_name: str, + tensor_size: int, + label_name: str, + pref_atomic: float = 0.0, + pref: float = 0.0, + inference=False, + **kwargs, + ): + r"""Construct a loss for local and global tensors. + + Parameters + ---------- + tensor_name : str + The name of the tensor in the model predictions to compute the loss. + tensor_size : int + The size (dimension) of the tensor. + label_name : str + The name of the tensor in the labels to compute the loss. + pref_atomic : float + The prefactor of the weight of atomic loss. It should be larger than or equal to 0. + pref : float + The prefactor of the weight of global loss. It should be larger than or equal to 0. + inference : bool + If true, it will output all losses found in output, ignoring the pre-factors. + **kwargs + Other keyword arguments. + """ + super().__init__() + self.tensor_name = tensor_name + self.tensor_size = tensor_size + self.label_name = label_name + self.local_weight = pref_atomic + self.global_weight = pref + self.inference = inference + + assert ( + self.local_weight >= 0.0 and self.global_weight >= 0.0 + ), "Can not assign negative weight to `pref` and `pref_atomic`" + self.has_local_weight = self.local_weight > 0.0 or inference + self.has_global_weight = self.global_weight > 0.0 or inference + assert self.has_local_weight or self.has_global_weight, AssertionError( + "Can not assian zero weight both to `pref` and `pref_atomic`" + ) + + def forward(self, input_dict, model, label, natoms, learning_rate=0.0, mae=False): + """Return loss on local and global tensors. + + Parameters + ---------- + input_dict : dict[str, paddle.Tensor] + Model inputs. + model : paddle.nn.Layer + Model to be used to output the predictions. + label : dict[str, paddle.Tensor] + Labels. + natoms : int + The local atom number. + + Returns + ------- + model_pred: dict[str, paddle.Tensor] + Model predictions. + loss: paddle.Tensor + Loss for model to minimize. + more_loss: dict[str, paddle.Tensor] + Other losses for display. + """ + model_pred = model(**input_dict) + del learning_rate, mae + loss = paddle.zeros([1], dtype=env.GLOBAL_PD_FLOAT_PRECISION).to(env.DEVICE)[0] + more_loss = {} + if ( + self.has_local_weight + and self.tensor_name in model_pred + and "atom_" + self.label_name in label + ): + find_local = label.get("find_" + "atom_" + self.label_name, 0.0) + local_weight = self.local_weight * find_local + local_tensor_pred = model_pred[self.tensor_name].reshape( + [-1, natoms, self.tensor_size] + ) + local_tensor_label = label["atom_" + self.label_name].reshape( + [-1, natoms, self.tensor_size] + ) + diff = (local_tensor_pred - local_tensor_label).reshape( + [-1, self.tensor_size] + ) + if "mask" in model_pred: + diff = diff[model_pred["mask"].reshape([-1]).bool()] + l2_local_loss = paddle.mean(paddle.square(diff)) + if not self.inference: + more_loss[f"l2_local_{self.tensor_name}_loss"] = self.display_if_exist( + l2_local_loss.detach(), find_local + ) + loss += local_weight * l2_local_loss + rmse_local = l2_local_loss.sqrt() + more_loss[f"rmse_local_{self.tensor_name}"] = self.display_if_exist( + rmse_local.detach(), find_local + ) + if ( + self.has_global_weight + and "global_" + self.tensor_name in model_pred + and self.label_name in label + ): + find_global = label.get("find_" + self.label_name, 0.0) + global_weight = self.global_weight * find_global + global_tensor_pred = model_pred["global_" + self.tensor_name].reshape( + [-1, self.tensor_size] + ) + global_tensor_label = label[self.label_name].reshape([-1, self.tensor_size]) + diff = global_tensor_pred - global_tensor_label + if "mask" in model_pred: + atom_num = model_pred["mask"].sum(-1, keepdim=True) + l2_global_loss = paddle.mean( + paddle.sum(paddle.square(diff) * atom_num, axis=0) / atom_num.sum() + ) + atom_num = paddle.mean(atom_num.float()) + else: + atom_num = natoms + l2_global_loss = paddle.mean(paddle.square(diff)) + if not self.inference: + more_loss[f"l2_global_{self.tensor_name}_loss"] = self.display_if_exist( + l2_global_loss.detach(), find_global + ) + loss += global_weight * l2_global_loss + rmse_global = l2_global_loss.sqrt() / atom_num + more_loss[f"rmse_global_{self.tensor_name}"] = self.display_if_exist( + rmse_global.detach(), find_global + ) + return model_pred, loss, more_loss + + @property + def label_requirement(self) -> List[DataRequirementItem]: + """Return data label requirements needed for this loss calculation.""" + label_requirement = [] + if self.has_local_weight: + label_requirement.append( + DataRequirementItem( + "atomic_" + self.label_name, + ndof=self.tensor_size, + atomic=True, + must=False, + high_prec=False, + ) + ) + if self.has_global_weight: + label_requirement.append( + DataRequirementItem( + self.label_name, + ndof=self.tensor_size, + atomic=False, + must=False, + high_prec=False, + ) + ) + return label_requirement diff --git a/deepmd/pd/model/__init__.py b/deepmd/pd/model/__init__.py new file mode 100644 index 0000000000..171d147114 --- /dev/null +++ b/deepmd/pd/model/__init__.py @@ -0,0 +1,6 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from deepmd.utils.entry_point import ( + load_entry_point, +) + +load_entry_point("deepmd.pd") diff --git a/deepmd/pd/model/atomic_model/__init__.py b/deepmd/pd/model/atomic_model/__init__.py new file mode 100644 index 0000000000..3e94449057 --- /dev/null +++ b/deepmd/pd/model/atomic_model/__init__.py @@ -0,0 +1,53 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""The atomic model provides the prediction of some property on each +atom. All the atomic models are not supposed to be directly accessed +by users, but it provides a convenient interface for the +implementation of models. + +Taking the energy models for example, the developeres only needs to +implement the atomic energy prediction via an atomic model, and the +model can be automatically made by the `deepmd.dpmodel.make_model` +method. The `DPModel` is made by +``` +DPModel = make_model(DPAtomicModel) +``` + +""" + +from .base_atomic_model import ( + BaseAtomicModel, +) +from .dipole_atomic_model import ( + DPDipoleAtomicModel, +) +from .dos_atomic_model import ( + DPDOSAtomicModel, +) +from .dp_atomic_model import ( + DPAtomicModel, +) +from .energy_atomic_model import ( + DPEnergyAtomicModel, +) +from .linear_atomic_model import ( + DPZBLLinearEnergyAtomicModel, + LinearEnergyAtomicModel, +) +from .pairtab_atomic_model import ( + PairTabAtomicModel, +) +from .polar_atomic_model import ( + DPPolarAtomicModel, +) + +__all__ = [ + "BaseAtomicModel", + "DPAtomicModel", + "DPDOSAtomicModel", + "DPEnergyAtomicModel", + "PairTabAtomicModel", + "LinearEnergyAtomicModel", + "DPPolarAtomicModel", + "DPDipoleAtomicModel", + "DPZBLLinearEnergyAtomicModel", +] diff --git a/deepmd/pd/model/atomic_model/base_atomic_model.py b/deepmd/pd/model/atomic_model/base_atomic_model.py new file mode 100644 index 0000000000..96a0fe5c36 --- /dev/null +++ b/deepmd/pd/model/atomic_model/base_atomic_model.py @@ -0,0 +1,578 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later + +import copy +import logging +from typing import ( + Callable, + Dict, + List, + Optional, + Tuple, + Union, +) + +import paddle + +from deepmd.dpmodel.atomic_model import ( + make_base_atomic_model, +) +from deepmd.dpmodel.output_def import ( + FittingOutputDef, + OutputVariableDef, +) +from deepmd.pd.utils import ( + AtomExcludeMask, + PairExcludeMask, + env, +) +from deepmd.pd.utils.nlist import ( + extend_input_and_build_neighbor_list, +) +from deepmd.pd.utils.stat import ( + compute_output_stats, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, + to_paddle_tensor, +) +from deepmd.utils.finetune import ( + get_index_between_two_maps, + map_atom_exclude_types, + map_pair_exclude_types, +) +from deepmd.utils.path import ( + DPPath, +) + +log = logging.getLogger(__name__) +dtype = env.GLOBAL_PD_FLOAT_PRECISION +device = env.DEVICE + +BaseAtomicModel_ = make_base_atomic_model(paddle.Tensor) + + +class BaseAtomicModel(paddle.nn.Layer, BaseAtomicModel_): + """The base of atomic model. + + Parameters + ---------- + type_map + Mapping atom type to the name (str) of the type. + For example `type_map[1]` gives the name of the type 1. + atom_exclude_types + Exclude the atomic contribution of the given types + pair_exclude_types + Exclude the pair of atoms of the given types from computing the output + of the atomic model. Implemented by removing the pairs from the nlist. + rcond : float, optional + The condition number for the regression of atomic energy. + preset_out_bias : Dict[str, List[Optional[paddle.Tensor]]], optional + Specifying atomic energy contribution in vacuum. Given by key:value pairs. + The value is a list specifying the bias. the elements can be None or np.array of output shape. + For example: [None, [2.]] means type 0 is not set, type 1 is set to [2.] + The `set_davg_zero` key in the descrptor should be set. + + """ + + def __init__( + self, + type_map: List[str], + atom_exclude_types: List[int] = [], + pair_exclude_types: List[Tuple[int, int]] = [], + rcond: Optional[float] = None, + preset_out_bias: Optional[Dict[str, paddle.Tensor]] = None, + ): + paddle.nn.Layer.__init__(self) + BaseAtomicModel_.__init__(self) + self.type_map = type_map + self.reinit_atom_exclude(atom_exclude_types) + self.reinit_pair_exclude(pair_exclude_types) + self.rcond = rcond + self.preset_out_bias = preset_out_bias + + def init_out_stat(self): + """Initialize the output bias.""" + ntypes = self.get_ntypes() + self.bias_keys: List[str] = list(self.fitting_output_def().keys()) + self.max_out_size = max( + [self.atomic_output_def()[kk].size for kk in self.bias_keys] + ) + self.n_out = len(self.bias_keys) + out_bias_data = self._default_bias() + out_std_data = self._default_std() + self.register_buffer("out_bias", out_bias_data) + self.register_buffer("out_std", out_std_data) + + def set_out_bias(self, out_bias: paddle.Tensor) -> None: + self.out_bias = out_bias + + def __setitem__(self, key, value): + if key in ["out_bias"]: + self.out_bias = value + elif key in ["out_std"]: + self.out_std = value + else: + raise KeyError(key) + + def __getitem__(self, key): + if key in ["out_bias"]: + return self.out_bias + elif key in ["out_std"]: + return self.out_std + else: + raise KeyError(key) + + # @paddle.jit.export + def get_type_map(self) -> List[str]: + """Get the type map.""" + return self.type_map + + def reinit_atom_exclude( + self, + exclude_types: List[int] = [], + ): + self.atom_exclude_types = exclude_types + if exclude_types == []: + self.atom_excl = None + else: + self.atom_excl = AtomExcludeMask(self.get_ntypes(), self.atom_exclude_types) + + def reinit_pair_exclude( + self, + exclude_types: List[Tuple[int, int]] = [], + ): + self.pair_exclude_types = exclude_types + if exclude_types == []: + self.pair_excl = None + else: + self.pair_excl = PairExcludeMask(self.get_ntypes(), self.pair_exclude_types) + + # to make jit happy... + def make_atom_mask( + self, + atype: paddle.Tensor, + ) -> paddle.Tensor: + """The atoms with type < 0 are treated as virutal atoms, + which serves as place-holders for multi-frame calculations + with different number of atoms in different frames. + + Parameters + ---------- + atype + Atom types. >= 0 for real atoms <0 for virtual atoms. + + Returns + ------- + mask + True for real atoms and False for virutal atoms. + + """ + # supposed to be supported by all backends + return atype >= 0 + + def atomic_output_def(self) -> FittingOutputDef: + old_def = self.fitting_output_def() + old_list = list(old_def.get_data().values()) + return FittingOutputDef( + old_list # noqa:RUF005 + + [ + OutputVariableDef( + name="mask", + shape=[1], + reducible=False, + r_differentiable=False, + c_differentiable=False, + ) + ] + ) + + def forward_common_atomic( + self, + extended_coord: paddle.Tensor, + extended_atype: paddle.Tensor, + nlist: paddle.Tensor, + mapping: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + ) -> Dict[str, paddle.Tensor]: + """Common interface for atomic inference. + + This method accept extended coordinates, extended atom typs, neighbor list, + and predict the atomic contribution of the fit property. + + Parameters + ---------- + extended_coord + extended coodinates, shape: nf x (nall x 3) + extended_atype + extended atom typs, shape: nf x nall + for a type < 0 indicating the atomic is virtual. + nlist + neighbor list, shape: nf x nloc x nsel + mapping + extended to local index mapping, shape: nf x nall + fparam + frame parameters, shape: nf x dim_fparam + aparam + atomic parameter, shape: nf x nloc x dim_aparam + comm_dict + The data needed for communication for parallel inference. + + Returns + ------- + ret_dict + dict of output atomic properties. + should implement the definition of `fitting_output_def`. + ret_dict["mask"] of shape nf x nloc will be provided. + ret_dict["mask"][ff,ii] == 1 indicating the ii-th atom of the ff-th frame is real. + ret_dict["mask"][ff,ii] == 0 indicating the ii-th atom of the ff-th frame is virtual. + + """ + _, nloc, _ = nlist.shape + atype = extended_atype[:, :nloc] + + if self.pair_excl is not None: + pair_mask = self.pair_excl(nlist, extended_atype) + # exclude neighbors in the nlist + nlist = paddle.where(pair_mask == 1, nlist, -1) + + ext_atom_mask = self.make_atom_mask(extended_atype) + ret_dict = self.forward_atomic( + extended_coord, + paddle.where( + ext_atom_mask, extended_atype, paddle.zeros_like(extended_atype) + ), + nlist, + mapping=mapping, + fparam=fparam, + aparam=aparam, + comm_dict=comm_dict, + ) + ret_dict = self.apply_out_stat(ret_dict, atype) + + # nf x nloc + atom_mask = ext_atom_mask[:, :nloc].to(paddle.int32) + if self.atom_excl is not None: + atom_mask *= self.atom_excl(atype) + + for kk in ret_dict.keys(): + out_shape = ret_dict[kk].shape + out_shape2 = 1 + for ss in out_shape[2:]: + out_shape2 *= ss + ret_dict[kk] = ( + ret_dict[kk].reshape([out_shape[0], out_shape[1], out_shape2]) + * atom_mask[:, :, None].astype(ret_dict[kk].dtype) + ).reshape(out_shape) + ret_dict["mask"] = atom_mask + + return ret_dict + + def forward( + self, + extended_coord: paddle.Tensor, + extended_atype: paddle.Tensor, + nlist: paddle.Tensor, + mapping: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + ) -> Dict[str, paddle.Tensor]: + return self.forward_common_atomic( + extended_coord, + extended_atype, + nlist, + mapping=mapping, + fparam=fparam, + aparam=aparam, + comm_dict=comm_dict, + ) + + def change_type_map( + self, type_map: List[str], model_with_new_type_stat=None + ) -> None: + """Change the type related params to new ones, according to `type_map` and the original one in the model. + If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. + """ + remap_index, has_new_type = get_index_between_two_maps(self.type_map, type_map) + self.type_map = type_map + self.reinit_atom_exclude( + map_atom_exclude_types(self.atom_exclude_types, remap_index) + ) + self.reinit_pair_exclude( + map_pair_exclude_types(self.pair_exclude_types, remap_index) + ) + if has_new_type: + extend_shape = [ + self.out_bias.shape[0], + len(type_map), + *list(self.out_bias.shape[2:]), + ] + extend_bias = paddle.zeros(extend_shape, dtype=self.out_bias.dtype).to( + device=self.out_bias.place + ) + self.out_bias = paddle.concat([self.out_bias, extend_bias], axis=1) + extend_std = paddle.ones(extend_shape, dtype=self.out_std.dtype).to( + device=self.out_std.place + ) + self.out_std = paddle.concat([self.out_std, extend_std], axis=1) + self.out_bias = self.out_bias[:, remap_index, :] + self.out_std = self.out_std[:, remap_index, :] + + def serialize(self) -> dict: + return { + "type_map": self.type_map, + "atom_exclude_types": self.atom_exclude_types, + "pair_exclude_types": self.pair_exclude_types, + "rcond": self.rcond, + "preset_out_bias": self.preset_out_bias, + "@variables": { + "out_bias": to_numpy_array(self.out_bias), + "out_std": to_numpy_array(self.out_std), + }, + } + + @classmethod + def deserialize(cls, data: dict) -> "BaseAtomicModel": + data = copy.deepcopy(data) + variables = data.pop("@variables", None) + variables = ( + {"out_bias": None, "out_std": None} if variables is None else variables + ) + obj = cls(**data) + obj["out_bias"] = ( + to_paddle_tensor(variables["out_bias"]) + if variables["out_bias"] is not None + else obj._default_bias() + ) + obj["out_std"] = ( + to_paddle_tensor(variables["out_std"]) + if variables["out_std"] is not None + else obj._default_std() + ) + return obj + + def compute_or_load_stat( + self, + merged: Union[Callable[[], List[dict]], List[dict]], + stat_file_path: Optional[DPPath] = None, + ): + """ + Compute the output statistics (e.g. energy bias) for the fitting net from packed data. + + Parameters + ---------- + merged : Union[Callable[[], List[dict]], List[dict]] + - List[dict]: A list of data samples from various data systems. + Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` + originating from the `i`-th data system. + - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + only when needed. Since the sampling process can be slow and memory-intensive, + the lazy function helps by only sampling once. + stat_file_path : Optional[DPPath] + The path to the stat file. + + """ + raise NotImplementedError + + def compute_or_load_out_stat( + self, + merged: Union[Callable[[], List[dict]], List[dict]], + stat_file_path: Optional[DPPath] = None, + ): + """ + Compute the output statistics (e.g. energy bias) for the fitting net from packed data. + + Parameters + ---------- + merged : Union[Callable[[], List[dict]], List[dict]] + - List[dict]: A list of data samples from various data systems. + Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` + originating from the `i`-th data system. + - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + only when needed. Since the sampling process can be slow and memory-intensive, + the lazy function helps by only sampling once. + stat_file_path : Optional[DPPath] + The path to the stat file. + + """ + self.change_out_bias( + merged, + stat_file_path=stat_file_path, + bias_adjust_mode="set-by-statistic", + ) + + def apply_out_stat( + self, + ret: Dict[str, paddle.Tensor], + atype: paddle.Tensor, + ): + """Apply the stat to each atomic output. + The developer may override the method to define how the bias is applied + to the atomic output of the model. + + Parameters + ---------- + ret + The returned dict by the forward_atomic method + atype + The atom types. nf x nloc + + """ + out_bias, out_std = self._fetch_out_stat(self.bias_keys) + for kk in self.bias_keys: + # nf x nloc x odims, out_bias: ntypes x odims + ret[kk] = ret[kk] + out_bias[kk][atype] + return ret + + def change_out_bias( + self, + sample_merged, + stat_file_path: Optional[DPPath] = None, + bias_adjust_mode="change-by-statistic", + ) -> None: + """Change the output bias according to the input data and the pretrained model. + + Parameters + ---------- + sample_merged : Union[Callable[[], List[dict]], List[dict]] + - List[dict]: A list of data samples from various data systems. + Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` + originating from the `i`-th data system. + - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + only when needed. Since the sampling process can be slow and memory-intensive, + the lazy function helps by only sampling once. + bias_adjust_mode : str + The mode for changing output bias : ['change-by-statistic', 'set-by-statistic'] + 'change-by-statistic' : perform predictions on labels of target dataset, + and do least square on the errors to obtain the target shift as bias. + 'set-by-statistic' : directly use the statistic output bias in the target dataset. + stat_file_path : Optional[DPPath] + The path to the stat file. + """ + if bias_adjust_mode == "change-by-statistic": + delta_bias, out_std = compute_output_stats( + sample_merged, + self.get_ntypes(), + keys=list(self.atomic_output_def().keys()), + stat_file_path=stat_file_path, + model_forward=self._get_forward_wrapper_func(), + rcond=self.rcond, + preset_bias=self.preset_out_bias, + ) + self._store_out_stat(delta_bias, out_std, add=True) + elif bias_adjust_mode == "set-by-statistic": + bias_out, std_out = compute_output_stats( + sample_merged, + self.get_ntypes(), + keys=list(self.atomic_output_def().keys()), + stat_file_path=stat_file_path, + rcond=self.rcond, + preset_bias=self.preset_out_bias, + ) + self._store_out_stat(bias_out, std_out) + else: + raise RuntimeError("Unknown bias_adjust_mode mode: " + bias_adjust_mode) + + def _get_forward_wrapper_func(self) -> Callable[..., paddle.Tensor]: + """Get a forward wrapper of the atomic model for output bias calculation.""" + + def model_forward(coord, atype, box, fparam=None, aparam=None): + with paddle.no_grad(): # it's essential for pure paddle forward function to use auto_batchsize + ( + extended_coord, + extended_atype, + mapping, + nlist, + ) = extend_input_and_build_neighbor_list( + coord, + atype, + self.get_rcut(), + self.get_sel(), + mixed_types=self.mixed_types(), + box=box, + ) + atomic_ret = self.forward_common_atomic( + extended_coord, + extended_atype, + nlist, + mapping=mapping, + fparam=fparam, + aparam=aparam, + ) + return {kk: vv.detach() for kk, vv in atomic_ret.items()} + + return model_forward + + def _default_bias(self): + ntypes = self.get_ntypes() + return paddle.zeros([self.n_out, ntypes, self.max_out_size], dtype=dtype).to( + device=device + ) + + def _default_std(self): + ntypes = self.get_ntypes() + return paddle.ones([self.n_out, ntypes, self.max_out_size], dtype=dtype).to( + device=device + ) + + def _varsize( + self, + shape: List[int], + ) -> int: + output_size = 1 + len_shape = len(shape) + for i in range(len_shape): + output_size *= shape[i] + return output_size + + def _get_bias_index( + self, + kk: str, + ) -> int: + res: List[int] = [] + for i, e in enumerate(self.bias_keys): + if e == kk: + res.append(i) + assert len(res) == 1 + return res[0] + + def _store_out_stat( + self, + out_bias: Dict[str, paddle.Tensor], + out_std: Dict[str, paddle.Tensor], + add: bool = False, + ): + ntypes = self.get_ntypes() + out_bias_data = paddle.clone(self.out_bias) + out_std_data = paddle.clone(self.out_std) + for kk in out_bias.keys(): + assert kk in out_std.keys() + idx = self._get_bias_index(kk) + size = self._varsize(self.atomic_output_def()[kk].shape) + if not add: + out_bias_data[idx, :, :size] = out_bias[kk].reshape([ntypes, size]) + else: + out_bias_data[idx, :, :size] += out_bias[kk].reshape([ntypes, size]) + out_std_data[idx, :, :size] = out_std[kk].reshape([ntypes, size]) + paddle.assign(out_bias_data, self.out_bias) + paddle.assign(out_std_data, self.out_std) + + def _fetch_out_stat( + self, + keys: List[str], + ) -> Tuple[Dict[str, paddle.Tensor], Dict[str, paddle.Tensor]]: + ret_bias = {} + ret_std = {} + ntypes = self.get_ntypes() + for kk in keys: + idx = self._get_bias_index(kk) + isize = self._varsize(self.atomic_output_def()[kk].shape) + ret_bias[kk] = self.out_bias[idx, :, :isize].reshape( + [ntypes] + list(self.atomic_output_def()[kk].shape) # noqa: RUF005 + ) + ret_std[kk] = self.out_std[idx, :, :isize].reshape( + [ntypes] + list(self.atomic_output_def()[kk].shape) # noqa: RUF005 + ) + return ret_bias, ret_std diff --git a/deepmd/pd/model/atomic_model/dipole_atomic_model.py b/deepmd/pd/model/atomic_model/dipole_atomic_model.py new file mode 100644 index 0000000000..63300be4af --- /dev/null +++ b/deepmd/pd/model/atomic_model/dipole_atomic_model.py @@ -0,0 +1,28 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Dict, +) + +import paddle + +from deepmd.pd.model.task.dipole import ( + DipoleFittingNet, +) + +from .dp_atomic_model import ( + DPAtomicModel, +) + + +class DPDipoleAtomicModel(DPAtomicModel): + def __init__(self, descriptor, fitting, type_map, **kwargs): + assert isinstance(fitting, DipoleFittingNet) + super().__init__(descriptor, fitting, type_map, **kwargs) + + def apply_out_stat( + self, + ret: Dict[str, paddle.Tensor], + atype: paddle.Tensor, + ): + # dipole not applying bias + return ret diff --git a/deepmd/pd/model/atomic_model/dos_atomic_model.py b/deepmd/pd/model/atomic_model/dos_atomic_model.py new file mode 100644 index 0000000000..9c622ce7bb --- /dev/null +++ b/deepmd/pd/model/atomic_model/dos_atomic_model.py @@ -0,0 +1,14 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from deepmd.pd.model.task.dos import ( + DOSFittingNet, +) + +from .dp_atomic_model import ( + DPAtomicModel, +) + + +class DPDOSAtomicModel(DPAtomicModel): + def __init__(self, descriptor, fitting, type_map, **kwargs): + assert isinstance(fitting, DOSFittingNet) + super().__init__(descriptor, fitting, type_map, **kwargs) diff --git a/deepmd/pd/model/atomic_model/dp_atomic_model.py b/deepmd/pd/model/atomic_model/dp_atomic_model.py new file mode 100644 index 0000000000..9b264fd2c4 --- /dev/null +++ b/deepmd/pd/model/atomic_model/dp_atomic_model.py @@ -0,0 +1,275 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import functools +import logging +from typing import ( + Dict, + List, + Optional, +) + +import paddle + +from deepmd.dpmodel import ( + FittingOutputDef, +) +from deepmd.pd.model.descriptor.base_descriptor import ( + BaseDescriptor, +) +from deepmd.pd.model.task.base_fitting import ( + BaseFitting, +) +from deepmd.utils.path import ( + DPPath, +) +from deepmd.utils.version import ( + check_version_compatibility, +) + +from .base_atomic_model import ( + BaseAtomicModel, +) + +log = logging.getLogger(__name__) + + +@BaseAtomicModel.register("standard") +class DPAtomicModel(BaseAtomicModel): + """Model give atomic prediction of some physical property. + + Parameters + ---------- + descriptor + Descriptor + fitting_net + Fitting net + type_map + Mapping atom type to the name (str) of the type. + For example `type_map[1]` gives the name of the type 1. + """ + + def __init__( + self, + descriptor, + fitting, + type_map: List[str], + **kwargs, + ): + super().__init__(type_map, **kwargs) + ntypes = len(type_map) + self.type_map = type_map + self.ntypes = ntypes + self.descriptor = descriptor + self.rcut = self.descriptor.get_rcut() + self.sel = self.descriptor.get_sel() + self.fitting_net = fitting + super().init_out_stat() + + # @paddle.jit.export + def fitting_output_def(self) -> FittingOutputDef: + """Get the output def of the fitting net.""" + return ( + self.fitting_net.output_def() + if self.fitting_net is not None + else self.coord_denoise_net.output_def() + ) + + # @paddle.jit.export + def get_rcut(self) -> float: + """Get the cut-off radius.""" + return self.rcut + + def get_sel(self) -> List[int]: + """Get the neighbor selection.""" + return self.sel + + def mixed_types(self) -> bool: + """If true, the model + 1. assumes total number of atoms aligned across frames; + 2. uses a neighbor list that does not distinguish different atomic types. + + If false, the model + 1. assumes total number of atoms of each atom type aligned across frames; + 2. uses a neighbor list that distinguishes different atomic types. + + """ + return self.descriptor.mixed_types() + + def change_type_map( + self, type_map: List[str], model_with_new_type_stat=None + ) -> None: + """Change the type related params to new ones, according to `type_map` and the original one in the model. + If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. + """ + super().change_type_map( + type_map=type_map, model_with_new_type_stat=model_with_new_type_stat + ) + self.type_map = type_map + self.ntypes = len(type_map) + self.descriptor.change_type_map( + type_map=type_map, + model_with_new_type_stat=model_with_new_type_stat.descriptor + if model_with_new_type_stat is not None + else None, + ) + self.fitting_net.change_type_map(type_map=type_map) + + def has_message_passing(self) -> bool: + """Returns whether the atomic model has message passing.""" + return self.descriptor.has_message_passing() + + def need_sorted_nlist_for_lower(self) -> bool: + """Returns whether the atomic model needs sorted nlist when using `forward_lower`.""" + return self.descriptor.need_sorted_nlist_for_lower() + + def serialize(self) -> dict: + dd = BaseAtomicModel.serialize(self) + dd.update( + { + "@class": "Model", + "@version": 2, + "type": "standard", + "type_map": self.type_map, + "descriptor": self.descriptor.serialize(), + "fitting": self.fitting_net.serialize(), + } + ) + return dd + + @classmethod + def deserialize(cls, data) -> "DPAtomicModel": + data = copy.deepcopy(data) + check_version_compatibility(data.pop("@version", 1), 2, 1) + data.pop("@class", None) + data.pop("type", None) + descriptor_obj = BaseDescriptor.deserialize(data.pop("descriptor")) + fitting_obj = BaseFitting.deserialize(data.pop("fitting")) + data["descriptor"] = descriptor_obj + data["fitting"] = fitting_obj + obj = super().deserialize(data) + return obj + + def forward_atomic( + self, + extended_coord, + extended_atype, + nlist, + mapping: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + ) -> Dict[str, paddle.Tensor]: + """Return atomic prediction. + + Parameters + ---------- + extended_coord + coodinates in extended region + extended_atype + atomic type in extended region + nlist + neighbor list. nf x nloc x nsel + mapping + mapps the extended indices to local indices + fparam + frame parameter. nf x ndf + aparam + atomic parameter. nf x nloc x nda + + Returns + ------- + result_dict + the result dict, defined by the `FittingOutputDef`. + + """ + nframes, nloc, nnei = nlist.shape + atype = extended_atype[:, :nloc] + if self.do_grad_r() or self.do_grad_c(): + extended_coord.stop_gradient = False + descriptor, rot_mat, g2, h2, sw = self.descriptor( + extended_coord, + extended_atype, + nlist, + mapping=mapping, + comm_dict=comm_dict, + ) + assert descriptor is not None + # energy, force + fit_ret = self.fitting_net( + descriptor, + atype, + gr=rot_mat, + g2=g2, + h2=h2, + fparam=fparam, + aparam=aparam, + ) + return fit_ret + + def get_out_bias(self) -> paddle.Tensor: + return self.out_bias + + def compute_or_load_stat( + self, + sampled_func, + stat_file_path: Optional[DPPath] = None, + ): + """ + Compute or load the statistics parameters of the model, + such as mean and standard deviation of descriptors or the energy bias of the fitting net. + When `sampled` is provided, all the statistics parameters will be calculated (or re-calculated for update), + and saved in the `stat_file_path`(s). + When `sampled` is not provided, it will check the existence of `stat_file_path`(s) + and load the calculated statistics parameters. + + Parameters + ---------- + sampled_func + The lazy sampled function to get data frames from different data systems. + stat_file_path + The dictionary of paths to the statistics files. + """ + if stat_file_path is not None and self.type_map is not None: + # descriptors and fitting net with different type_map + # should not share the same parameters + stat_file_path /= " ".join(self.type_map) + + @functools.lru_cache + def wrapped_sampler(): + sampled = sampled_func() + if self.pair_excl is not None: + pair_exclude_types = self.pair_excl.get_exclude_types() + for sample in sampled: + sample["pair_exclude_types"] = list(pair_exclude_types) + if self.atom_excl is not None: + atom_exclude_types = self.atom_excl.get_exclude_types() + for sample in sampled: + sample["atom_exclude_types"] = list(atom_exclude_types) + return sampled + + self.descriptor.compute_input_stats(wrapped_sampler, stat_file_path) + self.compute_or_load_out_stat(wrapped_sampler, stat_file_path) + + def get_dim_fparam(self) -> int: + """Get the number (dimension) of frame parameters of this atomic model.""" + return self.fitting_net.get_dim_fparam() + + def get_dim_aparam(self) -> int: + """Get the number (dimension) of atomic parameters of this atomic model.""" + return self.fitting_net.get_dim_aparam() + + def get_sel_type(self) -> List[int]: + """Get the selected atom types of this model. + + Only atoms with selected atom types have atomic contribution + to the result of the model. + If returning an empty list, all atom types are selected. + """ + return self.fitting_net.get_sel_type() + + def is_aparam_nall(self) -> bool: + """Check whether the shape of atomic parameters is (nframes, nall, ndim). + + If False, the shape is (nframes, nloc, ndim). + """ + return False diff --git a/deepmd/pd/model/atomic_model/energy_atomic_model.py b/deepmd/pd/model/atomic_model/energy_atomic_model.py new file mode 100644 index 0000000000..2d0ef4db4c --- /dev/null +++ b/deepmd/pd/model/atomic_model/energy_atomic_model.py @@ -0,0 +1,20 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from deepmd.pd.model.task.ener import ( + EnergyFittingNet, + EnergyFittingNetDirect, + InvarFitting, +) + +from .dp_atomic_model import ( + DPAtomicModel, +) + + +class DPEnergyAtomicModel(DPAtomicModel): + def __init__(self, descriptor, fitting, type_map, **kwargs): + assert ( + isinstance(fitting, EnergyFittingNet) + or isinstance(fitting, EnergyFittingNetDirect) + or isinstance(fitting, InvarFitting) + ) + super().__init__(descriptor, fitting, type_map, **kwargs) diff --git a/deepmd/pd/model/atomic_model/linear_atomic_model.py b/deepmd/pd/model/atomic_model/linear_atomic_model.py new file mode 100644 index 0000000000..dc780fb84a --- /dev/null +++ b/deepmd/pd/model/atomic_model/linear_atomic_model.py @@ -0,0 +1,563 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +from typing import ( + Callable, + Dict, + List, + Optional, + Tuple, + Union, +) + +import paddle + +from deepmd.dpmodel import ( + FittingOutputDef, + OutputVariableDef, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.nlist import ( + build_multiple_neighbor_list, + get_multiple_nlist_key, + nlist_distinguish_types, +) +from deepmd.utils.path import ( + DPPath, +) +from deepmd.utils.version import ( + check_version_compatibility, +) + +from .base_atomic_model import ( + BaseAtomicModel, +) +from .dp_atomic_model import ( + DPAtomicModel, +) +from .pairtab_atomic_model import ( + PairTabAtomicModel, +) + + +class LinearEnergyAtomicModel(BaseAtomicModel): + """Linear model make linear combinations of several existing models. + + Parameters + ---------- + models : list[DPAtomicModel or PairTabAtomicModel] + A list of models to be combined. PairTabAtomicModel must be used together with a DPAtomicModel. + type_map : list[str] + Mapping atom type to the name (str) of the type. + For example `type_map[1]` gives the name of the type 1. + """ + + def __init__( + self, + models: List[BaseAtomicModel], + type_map: List[str], + **kwargs, + ): + super().__init__(type_map, **kwargs) + super().init_out_stat() + + # check all sub models are of mixed type. + model_mixed_type = [] + for m in models: + if not m.mixed_types(): + model_mixed_type.append(m) + if len(model_mixed_type) > 0: + raise ValueError( + f"LinearAtomicModel only supports AtomicModel of mixed type, the following models are not mixed type: {model_mixed_type}." + ) + + self.models = paddle.nn.LayerList(models) + sub_model_type_maps = [md.get_type_map() for md in models] + err_msg = [] + self.mapping_list = [] + common_type_map = set(type_map) + self.type_map = type_map + for tpmp in sub_model_type_maps: + if not common_type_map.issubset(set(tpmp)): + err_msg.append( + f"type_map {tpmp} is not a subset of type_map {type_map}" + ) + self.mapping_list.append(self.remap_atype(tpmp, self.type_map)) + assert len(err_msg) == 0, "\n".join(err_msg) + + self.mixed_types_list = [model.mixed_types() for model in self.models] + self.rcuts = paddle.to_tensor(self.get_model_rcuts(), dtype=paddle.float64).to( + device=env.DEVICE + ) + self.nsels = paddle.to_tensor(self.get_model_nsels()).to(device=env.DEVICE) # pylint: disable=no-explicit-dtype + + def mixed_types(self) -> bool: + """If true, the model + 1. assumes total number of atoms aligned across frames; + 2. uses a neighbor list that does not distinguish different atomic types. + + If false, the model + 1. assumes total number of atoms of each atom type aligned across frames; + 2. uses a neighbor list that distinguishes different atomic types. + + """ + return True + + def has_message_passing(self) -> bool: + """Returns whether the atomic model has message passing.""" + return any(model.has_message_passing() for model in self.models) + + def need_sorted_nlist_for_lower(self) -> bool: + """Returns whether the atomic model needs sorted nlist when using `forward_lower`.""" + return True + + def get_out_bias(self) -> paddle.Tensor: + return self.out_bias + + def get_rcut(self) -> float: + """Get the cut-off radius.""" + return max(self.get_model_rcuts()) + + def get_type_map(self) -> List[str]: + """Get the type map.""" + return self.type_map + + def change_type_map( + self, type_map: List[str], model_with_new_type_stat=None + ) -> None: + """Change the type related params to new ones, according to `type_map` and the original one in the model. + If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. + """ + super().change_type_map( + type_map=type_map, model_with_new_type_stat=model_with_new_type_stat + ) + for ii, model in enumerate(self.models): + model.change_type_map( + type_map=type_map, + model_with_new_type_stat=model_with_new_type_stat.models[ii] + if model_with_new_type_stat is not None + else None, + ) + + def get_model_rcuts(self) -> List[float]: + """Get the cut-off radius for each individual models.""" + return [model.get_rcut() for model in self.models] + + def get_sel(self) -> List[int]: + return [max([model.get_nsel() for model in self.models])] + + def get_model_nsels(self) -> List[int]: + """Get the processed sels for each individual models. Not distinguishing types.""" + return [model.get_nsel() for model in self.models] + + def get_model_sels(self) -> List[List[int]]: + """Get the sels for each individual models.""" + return [model.get_sel() for model in self.models] + + def _sort_rcuts_sels(self) -> Tuple[List[float], List[int]]: + # sort the pair of rcut and sels in ascending order, first based on sel, then on rcut. + zipped = paddle.stack( + [ + self.rcuts, + self.nsels, + ], + axis=0, + ).T + inner_sorting = paddle.argsort(zipped[:, 1], axis=0) + inner_sorted = zipped[inner_sorting] + outer_sorting = paddle.argsort(inner_sorted[:, 0]) + outer_sorted = inner_sorted[outer_sorting] + sorted_rcuts: List[float] = outer_sorted[:, 0].tolist() + sorted_sels: List[int] = outer_sorted[:, 1].to(paddle.int64).tolist() + return sorted_rcuts, sorted_sels + + def forward_atomic( + self, + extended_coord: paddle.Tensor, + extended_atype: paddle.Tensor, + nlist: paddle.Tensor, + mapping: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + ) -> Dict[str, paddle.Tensor]: + """Return atomic prediction. + + Parameters + ---------- + extended_coord + coodinates in extended region, (nframes, nall * 3) + extended_atype + atomic type in extended region, (nframes, nall) + nlist + neighbor list, (nframes, nloc, nsel). + mapping + mapps the extended indices to local indices. + fparam + frame parameter. (nframes, ndf) + aparam + atomic parameter. (nframes, nloc, nda) + + Returns + ------- + result_dict + the result dict, defined by the fitting net output def. + """ + nframes, nloc, nnei = nlist.shape + if self.do_grad_r() or self.do_grad_c(): + extended_coord.stop_gradient = False + extended_coord = extended_coord.reshape([nframes, -1, 3]) + sorted_rcuts, sorted_sels = self._sort_rcuts_sels() + nlists = build_multiple_neighbor_list( + extended_coord, + nlist, + sorted_rcuts, + sorted_sels, + ) + raw_nlists = [ + nlists[get_multiple_nlist_key(rcut, sel)] + for rcut, sel in zip(self.get_model_rcuts(), self.get_model_nsels()) + ] + nlists_ = [ + nl if mt else nlist_distinguish_types(nl, extended_atype, sel) + for mt, nl, sel in zip( + self.mixed_types_list, raw_nlists, self.get_model_sels() + ) + ] + ener_list = [] + + for i, model in enumerate(self.models): + type_map_model = self.mapping_list[i].to(extended_atype.place) + # apply bias to each individual model + ener_list.append( + model.forward_common_atomic( + extended_coord, + type_map_model[extended_atype], + nlists_[i], + mapping, + fparam, + aparam, + )["energy"] + ) + weights = self._compute_weight(extended_coord, extended_atype, nlists_) + + fit_ret = { + "energy": paddle.sum( + paddle.stack(ener_list) + * paddle.stack(weights).to(extended_atype.place), + axis=0, + ), + } # (nframes, nloc, 1) + return fit_ret + + def apply_out_stat( + self, + ret: Dict[str, paddle.Tensor], + atype: paddle.Tensor, + ): + """Apply the stat to each atomic output. + The developer may override the method to define how the bias is applied + to the atomic output of the model. + + Parameters + ---------- + ret + The returned dict by the forward_atomic method + atype + The atom types. nf x nloc + + """ + return ret + + @staticmethod + def remap_atype(ori_map: List[str], new_map: List[str]) -> paddle.Tensor: + """ + This method is used to map the atype from the common type_map to the original type_map of + indivial AtomicModels. It creates a index mapping for the conversion. + + Parameters + ---------- + ori_map : List[str] + The original type map of an AtomicModel. + new_map : List[str] + The common type map of the DPZBLLinearEnergyAtomicModel, created by the `get_type_map` method, + must be a subset of the ori_map. + + Returns + ------- + paddle.Tensor + """ + type_2_idx = {atp: idx for idx, atp in enumerate(ori_map)} + # this maps the atype in the new map to the original map + mapping = paddle.to_tensor( # pylint: disable=no-explicit-dtype + [type_2_idx[new_map[idx]] for idx in range(len(new_map))] + ).to(device=env.DEVICE) + return mapping + + def fitting_output_def(self) -> FittingOutputDef: + return FittingOutputDef( + [ + OutputVariableDef( + name="energy", + shape=[1], + reducible=True, + r_differentiable=True, + c_differentiable=True, + ) + ] + ) + + def serialize(self) -> dict: + dd = super().serialize() + dd.update( + { + "@class": "Model", + "@version": 2, + "type": "linear", + "models": [model.serialize() for model in self.models], + "type_map": self.type_map, + } + ) + return dd + + @classmethod + def deserialize(cls, data: dict) -> "LinearEnergyAtomicModel": + data = copy.deepcopy(data) + check_version_compatibility(data.get("@version", 2), 2, 1) + data.pop("@class", None) + data.pop("type", None) + models = [ + BaseAtomicModel.get_class_by_type(model["type"]).deserialize(model) + for model in data["models"] + ] + data["models"] = models + return super().deserialize(data) + + def _compute_weight( + self, extended_coord, extended_atype, nlists_ + ) -> List[paddle.Tensor]: + """This should be a list of user defined weights that matches the number of models to be combined.""" + nmodels = len(self.models) + nframes, nloc, _ = nlists_[0].shape + return [ + paddle.ones((nframes, nloc, 1), dtype=paddle.float64).to(device=env.DEVICE) + / nmodels + for _ in range(nmodels) + ] + + def get_dim_fparam(self) -> int: + """Get the number (dimension) of frame parameters of this atomic model.""" + # tricky... + return max([model.get_dim_fparam() for model in self.models]) + + def get_dim_aparam(self) -> int: + """Get the number (dimension) of atomic parameters of this atomic model.""" + return max([model.get_dim_aparam() for model in self.models]) + + def get_sel_type(self) -> List[int]: + """Get the selected atom types of this model. + + Only atoms with selected atom types have atomic contribution + to the result of the model. + If returning an empty list, all atom types are selected. + """ + if any(model.get_sel_type() == [] for model in self.models): + return [] + # join all the selected types + # make paddle.jit happy... + return paddle.unique( + paddle.concat( + [ + paddle.to_tensor(model.get_sel_type(), dtype=paddle.int32) + for model in self.models + ] + ) + ).tolist() + + def is_aparam_nall(self) -> bool: + """Check whether the shape of atomic parameters is (nframes, nall, ndim). + + If False, the shape is (nframes, nloc, ndim). + """ + return False + + def compute_or_load_out_stat( + self, + merged: Union[Callable[[], List[dict]], List[dict]], + stat_file_path: Optional[DPPath] = None, + ): + """ + Compute the output statistics (e.g. energy bias) for the fitting net from packed data. + + Parameters + ---------- + merged : Union[Callable[[], List[dict]], List[dict]] + - List[dict]: A list of data samples from various data systems. + Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` + originating from the `i`-th data system. + - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + only when needed. Since the sampling process can be slow and memory-intensive, + the lazy function helps by only sampling once. + stat_file_path : Optional[DPPath] + The path to the stat file. + + """ + for md in self.models: + md.compute_or_load_out_stat(merged, stat_file_path) + + def compute_or_load_stat( + self, + sampled_func, + stat_file_path: Optional[DPPath] = None, + ): + """ + Compute or load the statistics parameters of the model, + such as mean and standard deviation of descriptors or the energy bias of the fitting net. + When `sampled` is provided, all the statistics parameters will be calculated (or re-calculated for update), + and saved in the `stat_file_path`(s). + When `sampled` is not provided, it will check the existence of `stat_file_path`(s) + and load the calculated statistics parameters. + + Parameters + ---------- + sampled_func + The lazy sampled function to get data frames from different data systems. + stat_file_path + The dictionary of paths to the statistics files. + """ + for md in self.models: + md.compute_or_load_stat(sampled_func, stat_file_path) + + +class DPZBLLinearEnergyAtomicModel(LinearEnergyAtomicModel): + """Model linearly combine a list of AtomicModels. + + Parameters + ---------- + dp_model + The DPAtomicModel being combined. + zbl_model + The PairTable model being combined. + sw_rmin + The lower boundary of the interpolation between short-range tabulated interaction and DP. + sw_rmax + The upper boundary of the interpolation between short-range tabulated interaction and DP. + type_map + Mapping atom type to the name (str) of the type. + For example `type_map[1]` gives the name of the type 1. + smin_alpha + The short-range tabulated interaction will be swithed according to the distance of the nearest neighbor. + This distance is calculated by softmin. + """ + + def __init__( + self, + dp_model: DPAtomicModel, + zbl_model: PairTabAtomicModel, + sw_rmin: float, + sw_rmax: float, + type_map: List[str], + smin_alpha: Optional[float] = 0.1, + **kwargs, + ): + models = [dp_model, zbl_model] + kwargs["models"] = models + kwargs["type_map"] = type_map + super().__init__(**kwargs) + + self.sw_rmin = sw_rmin + self.sw_rmax = sw_rmax + self.smin_alpha = smin_alpha + + # this is a placeholder being updated in _compute_weight, to handle Jit attribute init error. + self.zbl_weight = paddle.empty([0], dtype=paddle.float64).to(device=env.DEVICE) + + def serialize(self) -> dict: + dd = super().serialize() + dd.update( + { + "@class": "Model", + "@version": 2, + "type": "zbl", + "sw_rmin": self.sw_rmin, + "sw_rmax": self.sw_rmax, + "smin_alpha": self.smin_alpha, + } + ) + return dd + + @classmethod + def deserialize(cls, data) -> "DPZBLLinearEnergyAtomicModel": + data = copy.deepcopy(data) + check_version_compatibility(data.pop("@version", 1), 2, 1) + models = [ + BaseAtomicModel.get_class_by_type(model["type"]).deserialize(model) + for model in data["models"] + ] + data["dp_model"], data["zbl_model"] = models[0], models[1] + data.pop("@class", None) + data.pop("type", None) + return super().deserialize(data) + + def _compute_weight( + self, + extended_coord: paddle.Tensor, + extended_atype: paddle.Tensor, + nlists_: List[paddle.Tensor], + ) -> List[paddle.Tensor]: + """ZBL weight. + + Returns + ------- + List[paddle.Tensor] + the atomic ZBL weight for interpolation. (nframes, nloc, 1) + """ + assert ( + self.sw_rmax > self.sw_rmin + ), "The upper boundary `sw_rmax` must be greater than the lower boundary `sw_rmin`." + + dp_nlist = nlists_[0] + zbl_nlist = nlists_[1] + + zbl_nnei = zbl_nlist.shape[-1] + dp_nnei = dp_nlist.shape[-1] + + # use the larger rr based on nlist + nlist_larger = zbl_nlist if zbl_nnei >= dp_nnei else dp_nlist + masked_nlist = paddle.clamp(nlist_larger, 0) + pairwise_rr = PairTabAtomicModel._get_pairwise_dist( + extended_coord, masked_nlist + ) + numerator = paddle.sum( + paddle.where( + nlist_larger != -1, + pairwise_rr * paddle.exp(-pairwise_rr / self.smin_alpha), + paddle.zeros_like(nlist_larger), + ), + axis=-1, + ) + denominator = paddle.sum( + paddle.where( + nlist_larger != -1, + paddle.exp(-pairwise_rr / self.smin_alpha), + paddle.zeros_like(nlist_larger), + ), + axis=-1, + ) # handle masked nnei. + + sigma = numerator / paddle.clamp(denominator, 1e-20) # nfrmes, nloc + u = (sigma - self.sw_rmin) / (self.sw_rmax - self.sw_rmin) + coef = paddle.zeros_like(u) + left_mask = sigma < self.sw_rmin + mid_mask = (self.sw_rmin <= sigma) & (sigma < self.sw_rmax) + right_mask = sigma >= self.sw_rmax + coef[left_mask] = 1 + smooth = -6 * u**5 + 15 * u**4 - 10 * u**3 + 1 + coef[mid_mask] = smooth[mid_mask] + coef[right_mask] = 0 + + # to handle masked atoms + coef = paddle.where(sigma != 0, coef, paddle.zeros_like(coef)) + self.zbl_weight = coef # nframes, nloc + return [1 - coef.unsqueeze(-1), coef.unsqueeze(-1)] # to match the model order. diff --git a/deepmd/pd/model/atomic_model/pairtab_atomic_model.py b/deepmd/pd/model/atomic_model/pairtab_atomic_model.py new file mode 100644 index 0000000000..b58bc12564 --- /dev/null +++ b/deepmd/pd/model/atomic_model/pairtab_atomic_model.py @@ -0,0 +1,491 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +from typing import ( + Callable, + Dict, + List, + Optional, + Union, +) + +import paddle + +from deepmd.dpmodel import ( + FittingOutputDef, + OutputVariableDef, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.utils.pair_tab import ( + PairTab, +) +from deepmd.utils.path import ( + DPPath, +) +from deepmd.utils.version import ( + check_version_compatibility, +) + +from .base_atomic_model import ( + BaseAtomicModel, +) + + +@BaseAtomicModel.register("pairtab") +class PairTabAtomicModel(BaseAtomicModel): + """Pairwise tabulation energy model. + + This model can be used to tabulate the pairwise energy between atoms for either + short-range or long-range interactions, such as D3, LJ, ZBL, etc. It should not + be used alone, but rather as one submodel of a linear (sum) model, such as + DP+D3. + + Do not put the model on the first model of a linear model, since the linear + model fetches the type map from the first model. + + At this moment, the model does not smooth the energy at the cutoff radius, so + one needs to make sure the energy has been smoothed to zero. + + Parameters + ---------- + tab_file : str + The path to the tabulation file. + rcut : float + The cutoff radius. + sel : int or list[int] + The maxmum number of atoms in the cut-off radius. + type_map : List[str] + Mapping atom type to the name (str) of the type. + For example `type_map[1]` gives the name of the type 1. + rcond : float, optional + The condition number for the regression of atomic energy. + atom_ener + Specifying atomic energy contribution in vacuum. The `set_davg_zero` key in the descrptor should be set. + + """ + + def __init__( + self, + tab_file: str, + rcut: float, + sel: Union[int, List[int]], + type_map: List[str], + **kwargs, + ): + super().__init__(type_map, **kwargs) + super().init_out_stat() + self.tab_file = tab_file + self.rcut = rcut + self.tab = self._set_pairtab(tab_file, rcut) + + self.type_map = type_map + self.ntypes = len(type_map) + + # handle deserialization with no input file + if self.tab_file is not None: + ( + tab_info, + tab_data, + ) = self.tab.get() # this returns -> Tuple[np.array, np.array] + nspline, ntypes_tab = tab_info[-2:].astype(int) + self.register_buffer("tab_info", paddle.to_tensor(tab_info)) + self.register_buffer( + "tab_data", + paddle.to_tensor(tab_data).reshape( + [ntypes_tab, ntypes_tab, nspline, 4] + ), + ) + if self.ntypes != ntypes_tab: + raise ValueError( + "The `type_map` provided does not match the number of columns in the table." + ) + else: + self.register_buffer("tab_info", None) + self.register_buffer("tab_data", None) + self.bias_atom_e = paddle.zeros( + self.ntypes, 1, dtype=env.GLOBAL_PD_ENER_FLOAT_PRECISION + ).to(device=env.DEVICE) + + # self.model_type = "ener" + # self.model_version = MODEL_VERSION ## this shoud be in the parent class + + if isinstance(sel, int): + self.sel = sel + elif isinstance(sel, list): + self.sel = sum(sel) + else: + raise TypeError("sel must be int or list[int]") + + # @paddle.jit.ignore + def _set_pairtab(self, tab_file: str, rcut: float) -> PairTab: + return PairTab(tab_file, rcut) + + def fitting_output_def(self) -> FittingOutputDef: + return FittingOutputDef( + [ + OutputVariableDef( + name="energy", + shape=[1], + reducible=True, + r_differentiable=True, + c_differentiable=True, + ) + ] + ) + + def get_out_bias(self) -> paddle.Tensor: + return self.out_bias + + def get_rcut(self) -> float: + return self.rcut + + def get_type_map(self) -> List[str]: + return self.type_map + + def get_sel(self) -> List[int]: + return [self.sel] + + def get_nsel(self) -> int: + return self.sel + + def mixed_types(self) -> bool: + """If true, the model + 1. assumes total number of atoms aligned across frames; + 2. uses a neighbor list that does not distinguish different atomic types. + + If false, the model + 1. assumes total number of atoms of each atom type aligned across frames; + 2. uses a neighbor list that distinguishes different atomic types. + + """ + # to match DPA1 and DPA2. + return True + + def has_message_passing(self) -> bool: + """Returns whether the atomic model has message passing.""" + return False + + def need_sorted_nlist_for_lower(self) -> bool: + """Returns whether the atomic model needs sorted nlist when using `forward_lower`.""" + return False + + def change_type_map( + self, type_map: List[str], model_with_new_type_stat=None + ) -> None: + """Change the type related params to new ones, according to `type_map` and the original one in the model. + If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. + """ + assert type_map == self.type_map, ( + "PairTabAtomicModel does not support changing type map now. " + "This feature is currently not implemented because it would require additional work to change the tab file. " + "We may consider adding this support in the future if there is a clear demand for it." + ) + + def serialize(self) -> dict: + dd = BaseAtomicModel.serialize(self) + dd.update( + { + "@class": "Model", + "@version": 2, + "type": "pairtab", + "tab": self.tab.serialize(), + "rcut": self.rcut, + "sel": self.sel, + "type_map": self.type_map, + } + ) + return dd + + @classmethod + def deserialize(cls, data) -> "PairTabAtomicModel": + data = copy.deepcopy(data) + check_version_compatibility(data.pop("@version", 1), 2, 1) + tab = PairTab.deserialize(data.pop("tab")) + data.pop("@class", None) + data.pop("type", None) + data["tab_file"] = None + tab_model = super().deserialize(data) + + tab_model.tab = tab + tab_model.register_buffer("tab_info", paddle.to_tensor(tab_model.tab.tab_info)) + nspline, ntypes = tab_model.tab.tab_info[-2:].astype(int) + tab_model.register_buffer( + "tab_data", + paddle.to_tensor(tab_model.tab.tab_data).reshape( + [ntypes, ntypes, nspline, 4] + ), + ) + return tab_model + + def compute_or_load_stat( + self, + merged: Union[Callable[[], List[dict]], List[dict]], + stat_file_path: Optional[DPPath] = None, + ): + """ + Compute the output statistics (e.g. energy bias) for the fitting net from packed data. + + Parameters + ---------- + merged : Union[Callable[[], List[dict]], List[dict]] + - List[dict]: A list of data samples from various data systems. + Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` + originating from the `i`-th data system. + - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + only when needed. Since the sampling process can be slow and memory-intensive, + the lazy function helps by only sampling once. + stat_file_path : Optional[DPPath] + The path to the stat file. + + """ + self.compute_or_load_out_stat(merged, stat_file_path) + + def forward_atomic( + self, + extended_coord: paddle.Tensor, + extended_atype: paddle.Tensor, + nlist: paddle.Tensor, + mapping: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + ) -> Dict[str, paddle.Tensor]: + nframes, nloc, nnei = nlist.shape + extended_coord = extended_coord.reshape([nframes, -1, 3]) + if self.do_grad_r() or self.do_grad_c(): + extended_coord.stop_gradient = False + + # this will mask all -1 in the nlist + mask = nlist >= 0 + masked_nlist = nlist * mask + + atype = extended_atype[:, :nloc] # (nframes, nloc) + pairwise_rr = self._get_pairwise_dist( + extended_coord, masked_nlist + ) # (nframes, nloc, nnei) + self.tab_data = self.tab_data.to(device=extended_coord.place).reshape( + [int(self.tab_info[-1]), int(self.tab_info[-1]), int(self.tab_info[2]), 4] + ) + + # to calculate the atomic_energy, we need 3 tensors, i_type, j_type, pairwise_rr + # i_type : (nframes, nloc), this is atype. + # j_type : (nframes, nloc, nnei) + j_type = extended_atype[ + paddle.arange(extended_atype.size(0), device=extended_coord.place)[ # pylint: disable=no-explicit-dtype + :, None, None + ], + masked_nlist, + ] + + raw_atomic_energy = self._pair_tabulated_inter( + nlist, atype, j_type, pairwise_rr + ) + + atomic_energy = 0.5 * paddle.sum( + paddle.where( + nlist != -1, raw_atomic_energy, paddle.zeros_like(raw_atomic_energy) + ), + axis=-1, + ).unsqueeze(-1) + + return {"energy": atomic_energy} + + def _pair_tabulated_inter( + self, + nlist: paddle.Tensor, + i_type: paddle.Tensor, + j_type: paddle.Tensor, + rr: paddle.Tensor, + ) -> paddle.Tensor: + """Pairwise tabulated energy. + + Parameters + ---------- + nlist : paddle.Tensor + The unmasked neighbour list. (nframes, nloc) + i_type : paddle.Tensor + The integer representation of atom type for all local atoms for all frames. (nframes, nloc) + j_type : paddle.Tensor + The integer representation of atom type for all neighbour atoms of all local atoms for all frames. (nframes, nloc, nnei) + rr : paddle.Tensor + The salar distance vector between two atoms. (nframes, nloc, nnei) + + Returns + ------- + paddle.Tensor + The masked atomic energy for all local atoms for all frames. (nframes, nloc, nnei) + + Raises + ------ + Exception + If the distance is beyond the table. + + Notes + ----- + This function is used to calculate the pairwise energy between two atoms. + It uses a table containing cubic spline coefficients calculated in PairTab. + """ + nframes, nloc, nnei = nlist.shape + rmin = self.tab_info[0] + hh = self.tab_info[1] + hi = 1.0 / hh + + nspline = int(self.tab_info[2] + 0.1) + + uu = (rr - rmin) * hi # this is broadcasted to (nframes,nloc,nnei) + + # if nnei of atom 0 has -1 in the nlist, uu would be 0. + # this is to handle the nlist where the mask is set to 0, so that we don't raise exception for those atoms. + uu = paddle.where(nlist != -1, uu, nspline + 1) + + if paddle.any(uu < 0): + raise Exception("coord go beyond table lower boundary") + + idx = uu.to(paddle.int) + + uu -= idx + + table_coef = self._extract_spline_coefficient( + i_type, j_type, idx, self.tab_data, nspline + ) + table_coef = table_coef.reshape([nframes, nloc, nnei, 4]) + ener = self._calculate_ener(table_coef, uu) + + # here we need to overwrite energy to zero at rcut and beyond. + mask_beyond_rcut = rr >= self.rcut + # also overwrite values beyond extrapolation to zero + extrapolation_mask = rr >= rmin + nspline * hh + ener[mask_beyond_rcut] = 0 + ener[extrapolation_mask] = 0 + + return ener + + @staticmethod + def _get_pairwise_dist( + coords: paddle.Tensor, nlist: paddle.Tensor + ) -> paddle.Tensor: + """Get pairwise distance `dr`. + + Parameters + ---------- + coords : paddle.Tensor + The coordinate of the atoms, shape of (nframes, nall, 3). + nlist + The masked nlist, shape of (nframes, nloc, nnei) + + Returns + ------- + paddle.Tensor + The pairwise distance between the atoms (nframes, nloc, nnei). + """ + nframes, nloc, nnei = nlist.shape + coord_l = coords[:, :nloc].reshape([nframes, -1, 1, 3]) + index = nlist.reshape([nframes, -1]).unsqueeze(-1).expand(-1, -1, 3) + coord_r = paddle.take_along_axis(coords, axis=1, indices=index) + coord_r = coord_r.reshape([nframes, nloc, nnei, 3]) + diff = coord_r - coord_l + pairwise_rr = paddle.linalg.norm(diff, axis=-1, keepdim=True).squeeze(-1) + return pairwise_rr + + @staticmethod + def _extract_spline_coefficient( + i_type: paddle.Tensor, + j_type: paddle.Tensor, + idx: paddle.Tensor, + tab_data: paddle.Tensor, + nspline: int, + ) -> paddle.Tensor: + """Extract the spline coefficient from the table. + + Parameters + ---------- + i_type : paddle.Tensor + The integer representation of atom type for all local atoms for all frames. (nframes, nloc) + j_type : paddle.Tensor + The integer representation of atom type for all neighbour atoms of all local atoms for all frames. (nframes, nloc, nnei) + idx : paddle.Tensor + The index of the spline coefficient. (nframes, nloc, nnei) + tab_data : paddle.Tensor + The table storing all the spline coefficient. (ntype, ntype, nspline, 4) + nspline : int + The number of splines in the table. + + Returns + ------- + paddle.Tensor + The spline coefficient. (nframes, nloc, nnei, 4), shape may be squeezed. + + """ + # (nframes, nloc, nnei) + expanded_i_type = i_type.unsqueeze(-1).expand(-1, -1, j_type.shape[-1]) + + # handle the case where idx is beyond the number of splines + clipped_indices = paddle.clamp(idx, 0, nspline - 1).to(paddle.int64) + + nframes = i_type.shape[0] + nloc = i_type.shape[1] + nnei = j_type.shape[2] + ntypes = tab_data.shape[0] + # tab_data_idx: (nframes, nloc, nnei) + tab_data_idx = ( + expanded_i_type * ntypes * nspline + j_type * nspline + clipped_indices + ) + # tab_data: (ntype, ntype, nspline, 4) + tab_data = tab_data.reshape([ntypes * ntypes * nspline, 4]) + # tab_data_idx: (nframes * nloc * nnei, 4) + tab_data_idx = tab_data_idx.reshape([nframes * nloc * nnei, 1]).expand(-1, 4) + # (nframes, nloc, nnei, 4) + final_coef = paddle.take_along_axis( + tab_data, axis=0, indices=tab_data_idx + ).reshape([nframes, nloc, nnei, 4]) + + # when the spline idx is beyond the table, all spline coefficients are set to `0`, and the resulting ener corresponding to the idx is also `0`. + final_coef[idx > nspline] = 0 + return final_coef + + @staticmethod + def _calculate_ener(coef: paddle.Tensor, uu: paddle.Tensor) -> paddle.Tensor: + """Calculate energy using spline coeeficients. + + Parameters + ---------- + coef : paddle.Tensor + The spline coefficients. (nframes, nloc, nnei, 4) + uu : paddle.Tensor + The atom displancemnt used in interpolation and extrapolation (nframes, nloc, nnei) + + Returns + ------- + paddle.Tensor + The atomic energy for all local atoms for all frames. (nframes, nloc, nnei) + """ + a3, a2, a1, a0 = paddle.unbind(coef, axis=-1) + etmp = (a3 * uu + a2) * uu + a1 # this should be elementwise operations. + ener = etmp * uu + a0 # this energy has the extrapolated value when rcut > rmax + return ener + + def get_dim_fparam(self) -> int: + """Get the number (dimension) of frame parameters of this atomic model.""" + return 0 + + def get_dim_aparam(self) -> int: + """Get the number (dimension) of atomic parameters of this atomic model.""" + return 0 + + def get_sel_type(self) -> List[int]: + """Get the selected atom types of this model. + + Only atoms with selected atom types have atomic contribution + to the result of the model. + If returning an empty list, all atom types are selected. + """ + return [] + + def is_aparam_nall(self) -> bool: + """Check whether the shape of atomic parameters is (nframes, nall, ndim). + + If False, the shape is (nframes, nloc, ndim). + """ + return False diff --git a/deepmd/pd/model/atomic_model/polar_atomic_model.py b/deepmd/pd/model/atomic_model/polar_atomic_model.py new file mode 100644 index 0000000000..af20ed28d8 --- /dev/null +++ b/deepmd/pd/model/atomic_model/polar_atomic_model.py @@ -0,0 +1,65 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Dict, +) + +import paddle + +from deepmd.pd.model.task.polarizability import ( + PolarFittingNet, +) + +from .dp_atomic_model import ( + DPAtomicModel, +) + + +class DPPolarAtomicModel(DPAtomicModel): + def __init__(self, descriptor, fitting, type_map, **kwargs): + assert isinstance(fitting, PolarFittingNet) + super().__init__(descriptor, fitting, type_map, **kwargs) + + def apply_out_stat( + self, + ret: Dict[str, paddle.Tensor], + atype: paddle.Tensor, + ): + """Apply the stat to each atomic output. + + Parameters + ---------- + ret + The returned dict by the forward_atomic method + atype + The atom types. nf x nloc + + """ + out_bias, out_std = self._fetch_out_stat(self.bias_keys) + + if self.fitting_net.shift_diag: + nframes, nloc = atype.shape + device = out_bias[self.bias_keys[0]].place + dtype = out_bias[self.bias_keys[0]].dtype + for kk in self.bias_keys: + ntypes = out_bias[kk].shape[0] + temp = paddle.zeros([ntypes], dtype=dtype).to(device=device) + for i in range(ntypes): + temp[i] = paddle.mean( + paddle.diagonal(out_bias[kk][i].reshape([3, 3])) + ) + modified_bias = temp[atype] + + # (nframes, nloc, 1) + modified_bias = ( + modified_bias.unsqueeze(-1) + * (self.fitting_net.scale.to(atype.place))[atype] + ) + + eye = paddle.eye(3, dtype=dtype).to(device=device) + eye = eye.repeat(nframes, nloc, 1, 1) + # (nframes, nloc, 3, 3) + modified_bias = modified_bias.unsqueeze(-1) * eye + + # nf x nloc x odims, out_bias: ntypes x odims + ret[kk] = ret[kk] + modified_bias + return ret diff --git a/deepmd/pd/model/backbone/__init__.py b/deepmd/pd/model/backbone/__init__.py new file mode 100644 index 0000000000..a76bdb2a2d --- /dev/null +++ b/deepmd/pd/model/backbone/__init__.py @@ -0,0 +1,12 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from .backbone import ( + BackBone, +) +from .evoformer2b import ( + Evoformer2bBackBone, +) + +__all__ = [ + "BackBone", + "Evoformer2bBackBone", +] diff --git a/deepmd/pd/model/backbone/backbone.py b/deepmd/pd/model/backbone/backbone.py new file mode 100644 index 0000000000..f37346a44f --- /dev/null +++ b/deepmd/pd/model/backbone/backbone.py @@ -0,0 +1,12 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import paddle + + +class BackBone(paddle.nn.Layer): + def __init__(self, **kwargs): + """BackBone base method.""" + super().__init__() + + def forward(self, **kwargs): + """Calculate backBone.""" + raise NotImplementedError diff --git a/deepmd/pd/model/backbone/evoformer2b.py b/deepmd/pd/model/backbone/evoformer2b.py new file mode 100644 index 0000000000..698bc741d4 --- /dev/null +++ b/deepmd/pd/model/backbone/evoformer2b.py @@ -0,0 +1,103 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from deepmd.pd.model.backbone import ( + BackBone, +) +from deepmd.pd.model.network.network import ( + Evoformer2bEncoder, +) + + +class Evoformer2bBackBone(BackBone): + def __init__( + self, + nnei, + layer_num=6, + attn_head=8, + atomic_dim=1024, + pair_dim=100, + feature_dim=1024, + ffn_dim=2048, + post_ln=False, + final_layer_norm=True, + final_head_layer_norm=False, + emb_layer_norm=False, + atomic_residual=False, + evo_residual=False, + residual_factor=1.0, + activation_function="gelu", + **kwargs, + ): + """Construct an evoformer backBone.""" + super().__init__() + self.nnei = nnei + self.layer_num = layer_num + self.attn_head = attn_head + self.atomic_dim = atomic_dim + self.pair_dim = pair_dim + self.feature_dim = feature_dim + self.head_dim = feature_dim // attn_head + assert ( + feature_dim % attn_head == 0 + ), f"feature_dim {feature_dim} must be divided by attn_head {attn_head}!" + self.ffn_dim = ffn_dim + self.post_ln = post_ln + self.final_layer_norm = final_layer_norm + self.final_head_layer_norm = final_head_layer_norm + self.emb_layer_norm = emb_layer_norm + self.activation_function = activation_function + self.atomic_residual = atomic_residual + self.evo_residual = evo_residual + self.residual_factor = float(residual_factor) + self.encoder = Evoformer2bEncoder( + nnei=self.nnei, + layer_num=self.layer_num, + attn_head=self.attn_head, + atomic_dim=self.atomic_dim, + pair_dim=self.pair_dim, + feature_dim=self.feature_dim, + ffn_dim=self.ffn_dim, + post_ln=self.post_ln, + final_layer_norm=self.final_layer_norm, + final_head_layer_norm=self.final_head_layer_norm, + emb_layer_norm=self.emb_layer_norm, + atomic_residual=self.atomic_residual, + evo_residual=self.evo_residual, + residual_factor=self.residual_factor, + activation_function=self.activation_function, + ) + + def forward(self, atomic_rep, pair_rep, nlist, nlist_type, nlist_mask): + """Encoder the atomic and pair representations. + + Args: + - atomic_rep: Atomic representation with shape [nframes, nloc, atomic_dim]. + - pair_rep: Pair representation with shape [nframes, nloc, nnei, pair_dim]. + - nlist: Neighbor list with shape [nframes, nloc, nnei]. + - nlist_type: Neighbor types with shape [nframes, nloc, nnei]. + - nlist_mask: Neighbor mask with shape [nframes, nloc, nnei], `False` if blank. + + Returns + ------- + - atomic_rep: Atomic representation after encoder with shape [nframes, nloc, feature_dim]. + - transformed_atomic_rep: Transformed atomic representation after encoder with shape [nframes, nloc, atomic_dim]. + - pair_rep: Pair representation after encoder with shape [nframes, nloc, nnei, attn_head]. + - delta_pair_rep: Delta pair representation after encoder with shape [nframes, nloc, nnei, attn_head]. + - norm_x: Normalization loss of atomic_rep. + - norm_delta_pair_rep: Normalization loss of delta_pair_rep. + """ + ( + atomic_rep, + transformed_atomic_rep, + pair_rep, + delta_pair_rep, + norm_x, + norm_delta_pair_rep, + ) = self.encoder(atomic_rep, pair_rep, nlist, nlist_type, nlist_mask) + return ( + atomic_rep, + transformed_atomic_rep, + pair_rep, + delta_pair_rep, + norm_x, + norm_delta_pair_rep, + ) diff --git a/deepmd/pd/model/descriptor/__init__.py b/deepmd/pd/model/descriptor/__init__.py new file mode 100644 index 0000000000..779e7a562c --- /dev/null +++ b/deepmd/pd/model/descriptor/__init__.py @@ -0,0 +1,64 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from .base_descriptor import ( + BaseDescriptor, +) +from .descriptor import ( + DescriptorBlock, + make_default_type_embedding, +) +from .dpa1 import ( + DescrptBlockSeAtten, + DescrptDPA1, +) +from .dpa2 import ( + DescrptDPA2, +) +from .env_mat import ( + prod_env_mat, +) +from .gaussian_lcc import ( + DescrptGaussianLcc, +) +from .hybrid import ( + DescrptHybrid, +) +from .repformers import ( + DescrptBlockRepformers, +) +from .se_a import ( + DescrptBlockSeA, + DescrptSeA, +) +from .se_atten_v2 import ( + DescrptSeAttenV2, +) +from .se_r import ( + DescrptSeR, +) +from .se_t import ( + DescrptSeT, +) +from .se_t_tebd import ( + DescrptBlockSeTTebd, + DescrptSeTTebd, +) + +__all__ = [ + "BaseDescriptor", + "DescriptorBlock", + "make_default_type_embedding", + "DescrptBlockSeA", + "DescrptBlockSeAtten", + "DescrptSeAttenV2", + "DescrptSeTTebd", + "DescrptBlockSeTTebd", + "DescrptSeA", + "DescrptSeR", + "DescrptSeT", + "DescrptDPA1", + "DescrptDPA2", + "DescrptHybrid", + "prod_env_mat", + "DescrptGaussianLcc", + "DescrptBlockRepformers", +] diff --git a/deepmd/pd/model/descriptor/base_descriptor.py b/deepmd/pd/model/descriptor/base_descriptor.py new file mode 100644 index 0000000000..8f0b799f87 --- /dev/null +++ b/deepmd/pd/model/descriptor/base_descriptor.py @@ -0,0 +1,8 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import paddle + +from deepmd.dpmodel.descriptor import ( + make_base_descriptor, +) + +BaseDescriptor = make_base_descriptor(paddle.Tensor, "forward") diff --git a/deepmd/pd/model/descriptor/descriptor.py b/deepmd/pd/model/descriptor/descriptor.py new file mode 100644 index 0000000000..96c1b276f7 --- /dev/null +++ b/deepmd/pd/model/descriptor/descriptor.py @@ -0,0 +1,232 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import logging +from abc import ( + ABC, + abstractmethod, +) +from typing import ( + Callable, + Dict, + List, + Optional, + Union, +) + +import paddle + +from deepmd.pd.model.network.network import ( + TypeEmbedNet, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env_mat_stat import ( + EnvMatStatSe, +) +from deepmd.utils.env_mat_stat import ( + StatItem, +) +from deepmd.utils.path import ( + DPPath, +) +from deepmd.utils.plugin import ( + make_plugin_registry, +) + +log = logging.getLogger(__name__) + + +class DescriptorBlock(paddle.nn.Layer, ABC, make_plugin_registry("DescriptorBlock")): + """The building block of descriptor. + Given the input descriptor, provide with the atomic coordinates, + atomic types and neighbor list, calculate the new descriptor. + """ + + local_cluster = False + + def __new__(cls, *args, **kwargs): + if cls is DescriptorBlock: + try: + descrpt_type = kwargs["type"] + except KeyError as e: + raise KeyError( + "the type of DescriptorBlock should be set by `type`" + ) from e + cls = cls.get_class_by_type(descrpt_type) + return super().__new__(cls) + + @abstractmethod + def get_rcut(self) -> float: + """Returns the cut-off radius.""" + pass + + @abstractmethod + def get_rcut_smth(self) -> float: + """Returns the radius where the neighbor information starts to smoothly decay to 0.""" + pass + + @abstractmethod + def get_nsel(self) -> int: + """Returns the number of selected atoms in the cut-off radius.""" + pass + + @abstractmethod + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" + pass + + @abstractmethod + def get_ntypes(self) -> int: + """Returns the number of element types.""" + pass + + @abstractmethod + def get_dim_out(self) -> int: + """Returns the output dimension.""" + pass + + @abstractmethod + def get_dim_in(self) -> int: + """Returns the input dimension.""" + pass + + @abstractmethod + def get_dim_emb(self) -> int: + """Returns the embedding dimension.""" + pass + + @abstractmethod + def get_env_protection(self) -> float: + """Returns the protection of building environment matrix.""" + pass + + def compute_input_stats( + self, + merged: Union[Callable[[], List[dict]], List[dict]], + path: Optional[DPPath] = None, + ): + """ + Compute the input statistics (e.g. mean and stddev) for the descriptors from packed data. + + Parameters + ---------- + merged : Union[Callable[[], List[dict]], List[dict]] + - List[dict]: A list of data samples from various data systems. + Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` + originating from the `i`-th data system. + - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + only when needed. Since the sampling process can be slow and memory-intensive, + the lazy function helps by only sampling once. + path : Optional[DPPath] + The path to the stat file. + + """ + raise NotImplementedError + + def get_stats(self) -> Dict[str, StatItem]: + """Get the statistics of the descriptor.""" + raise NotImplementedError + + def share_params(self, base_class, shared_level, resume=False): + """ + Share the parameters of self to the base_class with shared_level during multitask training. + If not start from checkpoint (resume is False), + some seperated parameters (e.g. mean and stddev) will be re-calculated across different classes. + """ + assert ( + self.__class__ == base_class.__class__ + ), "Only descriptors of the same type can share params!" + if shared_level == 0: + # link buffers + if hasattr(self, "mean"): + if not resume: + # in case of change params during resume + base_env = EnvMatStatSe(base_class) + base_env.stats = base_class.stats + for kk in base_class.get_stats(): + base_env.stats[kk] += self.get_stats()[kk] + mean, stddev = base_env() + if not base_class.set_davg_zero: + paddle.assign( + paddle.to_tensor(mean).to(device=env.DEVICE), + base_class.mean, + ) # pylint: disable=no-explicit-dtype + paddle.assign( + paddle.to_tensor(stddev).to(device=env.DEVICE), + base_class.stddev, + ) # pylint: disable=no-explicit-dtype + # must share, even if not do stat + self.mean = base_class.mean + self.stddev = base_class.stddev + # self.load_state_dict(base_class.state_dict()) # this does not work, because it only inits the model + # the following will successfully link all the params except buffers + for item in self._modules: + self._modules[item] = base_class._modules[item] + else: + raise NotImplementedError + + @abstractmethod + def forward( + self, + nlist: paddle.Tensor, + extended_coord: paddle.Tensor, + extended_atype: paddle.Tensor, + extended_atype_embd: Optional[paddle.Tensor] = None, + mapping: Optional[paddle.Tensor] = None, + ): + """Calculate DescriptorBlock.""" + pass + + @abstractmethod + def has_message_passing(self) -> bool: + """Returns whether the descriptor block has message passing.""" + + @abstractmethod + def need_sorted_nlist_for_lower(self) -> bool: + """Returns whether the descriptor block needs sorted nlist when using `forward_lower`.""" + + +def make_default_type_embedding( + ntypes, +): + aux = {} + aux["tebd_dim"] = 8 + return TypeEmbedNet(ntypes, aux["tebd_dim"]), aux + + +def extend_descrpt_stat(des, type_map, des_with_stat=None): + r""" + Extend the statistics of a descriptor block with types from newly provided `type_map`. + + After extending, the type related dimension of the extended statistics will have a length of + `len(old_type_map) + len(type_map)`, where `old_type_map` represents the type map in `des`. + The `get_index_between_two_maps()` function can then be used to correctly select statistics for types + from `old_type_map` or `type_map`. + Positive indices from 0 to `len(old_type_map) - 1` will select old statistics of types in `old_type_map`, + while negative indices from `-len(type_map)` to -1 will select new statistics of types in `type_map`. + + Parameters + ---------- + des : DescriptorBlock + The descriptor block to be extended. + type_map : List[str] + The name of each type of atoms to be extended. + des_with_stat : DescriptorBlock, Optional + The descriptor block has additional statistics of types from newly provided `type_map`. + If None, the default statistics will be used. + Otherwise, the statistics provided in this DescriptorBlock will be used. + + """ + if des_with_stat is not None: + extend_davg = des_with_stat["davg"] + extend_dstd = des_with_stat["dstd"] + else: + extend_shape = [len(type_map), *list(des["davg"].shape[1:])] + extend_davg = paddle.zeros(extend_shape, dtype=des["davg"].dtype).to( + device=des["davg"].place + ) + extend_dstd = paddle.ones(extend_shape, dtype=des["dstd"].dtype).to( + device=des["dstd"].place + ) + des["davg"] = paddle.concat([des["davg"], extend_davg], axis=0) + des["dstd"] = paddle.concat([des["dstd"], extend_dstd], axis=0) diff --git a/deepmd/pd/model/descriptor/dpa1.py b/deepmd/pd/model/descriptor/dpa1.py new file mode 100644 index 0000000000..658ca15abd --- /dev/null +++ b/deepmd/pd/model/descriptor/dpa1.py @@ -0,0 +1,646 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Callable, + Dict, + List, + Optional, + Tuple, + Union, +) + +import paddle + +from deepmd.dpmodel.utils import EnvMat as DPEnvMat +from deepmd.dpmodel.utils.seed import ( + child_seed, +) +from deepmd.pd.model.network.mlp import ( + NetworkCollection, +) +from deepmd.pd.model.network.network import ( + TypeEmbedNet, + TypeEmbedNetConsistent, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + RESERVED_PRECISON_DICT, +) +from deepmd.pd.utils.update_sel import ( + UpdateSel, +) +from deepmd.utils.data_system import ( + DeepmdDataSystem, +) +from deepmd.utils.finetune import ( + get_index_between_two_maps, + map_pair_exclude_types, +) +from deepmd.utils.path import ( + DPPath, +) +from deepmd.utils.version import ( + check_version_compatibility, +) + +from .base_descriptor import ( + BaseDescriptor, +) +from .descriptor import ( + extend_descrpt_stat, +) +from .se_atten import ( + DescrptBlockSeAtten, + NeighborGatedAttention, +) + + +@BaseDescriptor.register("dpa1") +@BaseDescriptor.register("se_atten") +class DescrptDPA1(BaseDescriptor, paddle.nn.Layer): + r"""Attention-based descriptor which is proposed in the pretrainable DPA-1[1] model. + + This descriptor, :math:`\mathcal{D}^i \in \mathbb{R}^{M \times M_{<}}`, is given by + + .. math:: + \mathcal{D}^i = \frac{1}{N_c^2}(\hat{\mathcal{G}}^i)^T \mathcal{R}^i (\mathcal{R}^i)^T \hat{\mathcal{G}}^i_<, + + where :math:`\hat{\mathcal{G}}^i` represents the embedding matrix:math:`\mathcal{G}^i` + after additional self-attention mechanism and :math:`\mathcal{R}^i` is defined by the full case in the se_e2_a descriptor. + Note that we obtain :math:`\mathcal{G}^i` using the type embedding method by default in this descriptor. + + To perform the self-attention mechanism, the queries :math:`\mathcal{Q}^{i,l} \in \mathbb{R}^{N_c\times d_k}`, + keys :math:`\mathcal{K}^{i,l} \in \mathbb{R}^{N_c\times d_k}`, + and values :math:`\mathcal{V}^{i,l} \in \mathbb{R}^{N_c\times d_v}` are first obtained: + + .. math:: + \left(\mathcal{Q}^{i,l}\right)_{j}=Q_{l}\left(\left(\mathcal{G}^{i,l-1}\right)_{j}\right), + + .. math:: + \left(\mathcal{K}^{i,l}\right)_{j}=K_{l}\left(\left(\mathcal{G}^{i,l-1}\right)_{j}\right), + + .. math:: + \left(\mathcal{V}^{i,l}\right)_{j}=V_{l}\left(\left(\mathcal{G}^{i,l-1}\right)_{j}\right), + + where :math:`Q_{l}`, :math:`K_{l}`, :math:`V_{l}` represent three trainable linear transformations + that output the queries and keys of dimension :math:`d_k` and values of dimension :math:`d_v`, and :math:`l` + is the index of the attention layer. + The input embedding matrix to the attention layers, denoted by :math:`\mathcal{G}^{i,0}`, + is chosen as the two-body embedding matrix. + + Then the scaled dot-product attention method is adopted: + + .. math:: + A(\mathcal{Q}^{i,l}, \mathcal{K}^{i,l}, \mathcal{V}^{i,l}, \mathcal{R}^{i,l})=\varphi\left(\mathcal{Q}^{i,l}, \mathcal{K}^{i,l},\mathcal{R}^{i,l}\right)\mathcal{V}^{i,l}, + + where :math:`\varphi\left(\mathcal{Q}^{i,l}, \mathcal{K}^{i,l},\mathcal{R}^{i,l}\right) \in \mathbb{R}^{N_c\times N_c}` is attention weights. + In the original attention method, + one typically has :math:`\varphi\left(\mathcal{Q}^{i,l}, \mathcal{K}^{i,l}\right)=\mathrm{softmax}\left(\frac{\mathcal{Q}^{i,l} (\mathcal{K}^{i,l})^{T}}{\sqrt{d_{k}}}\right)`, + with :math:`\sqrt{d_{k}}` being the normalization temperature. + This is slightly modified to incorporate the angular information: + + .. math:: + \varphi\left(\mathcal{Q}^{i,l}, \mathcal{K}^{i,l},\mathcal{R}^{i,l}\right) = \mathrm{softmax}\left(\frac{\mathcal{Q}^{i,l} (\mathcal{K}^{i,l})^{T}}{\sqrt{d_{k}}}\right) \odot \hat{\mathcal{R}}^{i}(\hat{\mathcal{R}}^{i})^{T}, + + where :math:`\hat{\mathcal{R}}^{i} \in \mathbb{R}^{N_c\times 3}` denotes normalized relative coordinates, + :math:`\hat{\mathcal{R}}^{i}_{j} = \frac{\boldsymbol{r}_{ij}}{\lVert \boldsymbol{r}_{ij} \lVert}` + and :math:`\odot` means element-wise multiplication. + + Then layer normalization is added in a residual way to finally obtain the self-attention local embedding matrix + :math:`\hat{\mathcal{G}}^{i} = \mathcal{G}^{i,L_a}` after :math:`L_a` attention layers:[^1] + + .. math:: + \mathcal{G}^{i,l} = \mathcal{G}^{i,l-1} + \mathrm{LayerNorm}(A(\mathcal{Q}^{i,l}, \mathcal{K}^{i,l}, \mathcal{V}^{i,l}, \mathcal{R}^{i,l})). + + Parameters + ---------- + rcut: float + The cut-off radius :math:`r_c` + rcut_smth: float + From where the environment matrix should be smoothed :math:`r_s` + sel : list[int], int + list[int]: sel[i] specifies the maxmum number of type i atoms in the cut-off radius + int: the total maxmum number of atoms in the cut-off radius + ntypes : int + Number of element types + neuron : list[int] + Number of neurons in each hidden layers of the embedding net :math:`\mathcal{N}` + axis_neuron: int + Number of the axis neuron :math:`M_2` (number of columns of the sub-matrix of the embedding matrix) + tebd_dim: int + Dimension of the type embedding + tebd_input_mode: str + The input mode of the type embedding. Supported modes are ["concat", "strip"]. + - "concat": Concatenate the type embedding with the smoothed radial information as the union input for the embedding network. + - "strip": Use a separated embedding network for the type embedding and combine the output with the radial embedding network output. + resnet_dt: bool + Time-step `dt` in the resnet construction: + y = x + dt * \phi (Wx + b) + trainable: bool + If the weights of this descriptors are trainable. + trainable_ln: bool + Whether to use trainable shift and scale weights in layer normalization. + ln_eps: float, Optional + The epsilon value for layer normalization. + type_one_side: bool + If 'False', type embeddings of both neighbor and central atoms are considered. + If 'True', only type embeddings of neighbor atoms are considered. + Default is 'False'. + attn: int + Hidden dimension of the attention vectors + attn_layer: int + Number of attention layers + attn_dotr: bool + If dot the angular gate to the attention weights + attn_mask: bool + (Only support False to keep consistent with other backend references.) + (Not used in this version. True option is not implemented.) + If mask the diagonal of attention weights + exclude_types : List[List[int]] + The excluded pairs of types which have no interaction with each other. + For example, `[[0, 1]]` means no interaction between type 0 and type 1. + env_protection: float + Protection parameter to prevent division by zero errors during environment matrix calculations. + set_davg_zero: bool + Set the shift of embedding net input to zero. + activation_function: str + The activation function in the embedding net. Supported options are |ACTIVATION_FN| + precision: str + The precision of the embedding net parameters. Supported options are |PRECISION| + scaling_factor: float + The scaling factor of normalization in calculations of attention weights. + If `temperature` is None, the scaling of attention weights is (N_dim * scaling_factor)**0.5 + normalize: bool + Whether to normalize the hidden vectors in attention weights calculation. + temperature: float + If not None, the scaling of attention weights is `temperature` itself. + smooth_type_embedding: bool + Whether to use smooth process in attention weights calculation. + concat_output_tebd: bool + Whether to concat type embedding at the output of the descriptor. + stripped_type_embedding: bool, Optional + (Deprecated, kept only for compatibility.) + Whether to strip the type embedding into a separate embedding network. + Setting this parameter to `True` is equivalent to setting `tebd_input_mode` to 'strip'. + Setting it to `False` is equivalent to setting `tebd_input_mode` to 'concat'. + The default value is `None`, which means the `tebd_input_mode` setting will be used instead. + seed: int, Optional + Random seed for parameter initialization. + use_econf_tebd: bool, Optional + Whether to use electronic configuration type embedding. + use_tebd_bias : bool, Optional + Whether to use bias in the type embedding layer. + type_map: List[str], Optional + A list of strings. Give the name to each type of atoms. + spin + (Only support None to keep consistent with other backend references.) + (Not used in this version. Not-none option is not implemented.) + The old implementation of deepspin. + + Limitations + ----------- + The currently implementation will not support the following deprecated features + 1. spin is not None + 2. attn_mask == True + + References + ---------- + .. [1] Duo Zhang, Hangrui Bi, Fu-Zhi Dai, Wanrun Jiang, Linfeng Zhang, and Han Wang. 2022. + DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulation. + arXiv preprint arXiv:2208.08236. + """ + + def __init__( + self, + rcut: float, + rcut_smth: float, + sel: Union[List[int], int], + ntypes: int, + neuron: list = [25, 50, 100], + axis_neuron: int = 16, + tebd_dim: int = 8, + tebd_input_mode: str = "concat", + set_davg_zero: bool = True, + attn: int = 128, + attn_layer: int = 2, + attn_dotr: bool = True, + attn_mask: bool = False, + activation_function: str = "tanh", + precision: str = "float64", + resnet_dt: bool = False, + exclude_types: List[Tuple[int, int]] = [], + env_protection: float = 0.0, + scaling_factor: int = 1.0, + normalize=True, + temperature=None, + concat_output_tebd: bool = True, + trainable: bool = True, + trainable_ln: bool = True, + ln_eps: Optional[float] = 1e-5, + smooth_type_embedding: bool = True, + type_one_side: bool = False, + stripped_type_embedding: Optional[bool] = None, + seed: Optional[Union[int, List[int]]] = None, + use_econf_tebd: bool = False, + use_tebd_bias: bool = False, + type_map: Optional[List[str]] = None, + # not implemented + spin=None, + type: Optional[str] = None, + old_impl: bool = False, + ): + super().__init__() + # Ensure compatibility with the deprecated stripped_type_embedding option. + if stripped_type_embedding is not None: + # Use the user-set stripped_type_embedding parameter first + tebd_input_mode = "strip" if stripped_type_embedding else "concat" + if spin is not None: + raise NotImplementedError("old implementation of spin is not supported.") + if attn_mask: + raise NotImplementedError( + "old implementation of attn_mask is not supported." + ) + # to keep consistent with default value in this backends + if ln_eps is None: + ln_eps = 1e-5 + + del type, spin, attn_mask + self.se_atten = DescrptBlockSeAtten( + rcut, + rcut_smth, + sel, + ntypes, + neuron=neuron, + axis_neuron=axis_neuron, + tebd_dim=tebd_dim, + tebd_input_mode=tebd_input_mode, + set_davg_zero=set_davg_zero, + attn=attn, + attn_layer=attn_layer, + attn_dotr=attn_dotr, + attn_mask=False, + activation_function=activation_function, + precision=precision, + resnet_dt=resnet_dt, + scaling_factor=scaling_factor, + normalize=normalize, + temperature=temperature, + smooth=smooth_type_embedding, + type_one_side=type_one_side, + exclude_types=exclude_types, + env_protection=env_protection, + trainable_ln=trainable_ln, + ln_eps=ln_eps, + seed=child_seed(seed, 1), + old_impl=old_impl, + ) + self.use_econf_tebd = use_econf_tebd + self.use_tebd_bias = use_tebd_bias + self.type_map = type_map + self.type_embedding = TypeEmbedNet( + ntypes, + tebd_dim, + precision=precision, + seed=child_seed(seed, 2), + use_econf_tebd=use_econf_tebd, + use_tebd_bias=use_tebd_bias, + type_map=type_map, + ) + self.tebd_dim = tebd_dim + self.concat_output_tebd = concat_output_tebd + self.trainable = trainable + # set trainable + for param in self.parameters(): + param.stop_gradient = not trainable + + def get_rcut(self) -> float: + """Returns the cut-off radius.""" + return self.se_atten.get_rcut() + + def get_rcut_smth(self) -> float: + """Returns the radius where the neighbor information starts to smoothly decay to 0.""" + return self.se_atten.get_rcut_smth() + + def get_nsel(self) -> int: + """Returns the number of selected atoms in the cut-off radius.""" + return self.se_atten.get_nsel() + + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" + return self.se_atten.get_sel() + + def get_ntypes(self) -> int: + """Returns the number of element types.""" + return self.se_atten.get_ntypes() + + def get_type_map(self) -> List[str]: + """Get the name to each type of atoms.""" + return self.type_map + + def get_dim_out(self) -> int: + """Returns the output dimension.""" + ret = self.se_atten.get_dim_out() + if self.concat_output_tebd: + ret += self.tebd_dim + return ret + + def get_dim_emb(self) -> int: + return self.se_atten.dim_emb + + def mixed_types(self) -> bool: + """If true, the discriptor + 1. assumes total number of atoms aligned across frames; + 2. requires a neighbor list that does not distinguish different atomic types. + + If false, the discriptor + 1. assumes total number of atoms of each atom type aligned across frames; + 2. requires a neighbor list that distinguishes different atomic types. + + """ + return self.se_atten.mixed_types() + + def has_message_passing(self) -> bool: + """Returns whether the descriptor has message passing.""" + return self.se_atten.has_message_passing() + + def need_sorted_nlist_for_lower(self) -> bool: + """Returns whether the descriptor needs sorted nlist when using `forward_lower`.""" + return self.se_atten.need_sorted_nlist_for_lower() + + def get_env_protection(self) -> float: + """Returns the protection of building environment matrix.""" + return self.se_atten.get_env_protection() + + def share_params(self, base_class, shared_level, resume=False): + """ + Share the parameters of self to the base_class with shared_level during multitask training. + If not start from checkpoint (resume is False), + some seperated parameters (e.g. mean and stddev) will be re-calculated across different classes. + """ + assert ( + self.__class__ == base_class.__class__ + ), "Only descriptors of the same type can share params!" + # For DPA1 descriptors, the user-defined share-level + # shared_level: 0 + # share all parameters in both type_embedding and se_atten + if shared_level == 0: + self._modules["type_embedding"] = base_class._modules["type_embedding"] + self.se_atten.share_params(base_class.se_atten, 0, resume=resume) + # shared_level: 1 + # share all parameters in type_embedding + elif shared_level == 1: + self._modules["type_embedding"] = base_class._modules["type_embedding"] + # Other shared levels + else: + raise NotImplementedError + + @property + def dim_out(self): + return self.get_dim_out() + + @property + def dim_emb(self): + return self.get_dim_emb() + + def compute_input_stats( + self, + merged: Union[Callable[[], List[dict]], List[dict]], + path: Optional[DPPath] = None, + ): + """ + Compute the input statistics (e.g. mean and stddev) for the descriptors from packed data. + + Parameters + ---------- + merged : Union[Callable[[], List[dict]], List[dict]] + - List[dict]: A list of data samples from various data systems. + Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` + originating from the `i`-th data system. + - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + only when needed. Since the sampling process can be slow and memory-intensive, + the lazy function helps by only sampling once. + path : Optional[DPPath] + The path to the stat file. + + """ + return self.se_atten.compute_input_stats(merged, path) + + def set_stat_mean_and_stddev( + self, + mean: paddle.Tensor, + stddev: paddle.Tensor, + ) -> None: + """Update mean and stddev for descriptor.""" + self.se_atten.mean = mean + self.se_atten.stddev = stddev + + def get_stat_mean_and_stddev(self) -> Tuple[paddle.Tensor, paddle.Tensor]: + """Get mean and stddev for descriptor.""" + return self.se_atten.mean, self.se_atten.stddev + + def change_type_map( + self, type_map: List[str], model_with_new_type_stat=None + ) -> None: + """Change the type related params to new ones, according to `type_map` and the original one in the model. + If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. + """ + assert ( + self.type_map is not None + ), "'type_map' must be defined when performing type changing!" + remap_index, has_new_type = get_index_between_two_maps(self.type_map, type_map) + obj = self.se_atten + obj.ntypes = len(type_map) + self.type_map = type_map + self.type_embedding.change_type_map(type_map=type_map) + obj.reinit_exclude(map_pair_exclude_types(obj.exclude_types, remap_index)) + if has_new_type: + # the avg and std of new types need to be updated + extend_descrpt_stat( + obj, + type_map, + des_with_stat=model_with_new_type_stat.se_atten + if model_with_new_type_stat is not None + else None, + ) + obj["davg"] = obj["davg"][remap_index] + obj["dstd"] = obj["dstd"][remap_index] + + def serialize(self) -> dict: + obj = self.se_atten + data = { + "@class": "Descriptor", + "type": "dpa1", + "@version": 2, + "rcut": obj.rcut, + "rcut_smth": obj.rcut_smth, + "sel": obj.sel, + "ntypes": obj.ntypes, + "neuron": obj.neuron, + "axis_neuron": obj.axis_neuron, + "tebd_dim": obj.tebd_dim, + "tebd_input_mode": obj.tebd_input_mode, + "set_davg_zero": obj.set_davg_zero, + "attn": obj.attn_dim, + "attn_layer": obj.attn_layer, + "attn_dotr": obj.attn_dotr, + "attn_mask": False, + "activation_function": obj.activation_function, + "resnet_dt": obj.resnet_dt, + "scaling_factor": obj.scaling_factor, + "normalize": obj.normalize, + "temperature": obj.temperature, + "trainable_ln": obj.trainable_ln, + "ln_eps": obj.ln_eps, + "smooth_type_embedding": obj.smooth, + "type_one_side": obj.type_one_side, + "concat_output_tebd": self.concat_output_tebd, + "use_econf_tebd": self.use_econf_tebd, + "use_tebd_bias": self.use_tebd_bias, + "type_map": self.type_map, + # make deterministic + "precision": RESERVED_PRECISON_DICT[obj.prec], + "embeddings": obj.filter_layers.serialize(), + "attention_layers": obj.dpa1_attention.serialize(), + "env_mat": DPEnvMat(obj.rcut, obj.rcut_smth).serialize(), + "type_embedding": self.type_embedding.embedding.serialize(), + "exclude_types": obj.exclude_types, + "env_protection": obj.env_protection, + "@variables": { + "davg": obj["davg"].numpy(), + "dstd": obj["dstd"].numpy(), + }, + "trainable": self.trainable, + "spin": None, + } + if obj.tebd_input_mode in ["strip"]: + data.update({"embeddings_strip": obj.filter_layers_strip.serialize()}) + return data + + @classmethod + def deserialize(cls, data: dict) -> "DescrptDPA1": + data = data.copy() + check_version_compatibility(data.pop("@version"), 2, 1) + data.pop("@class") + data.pop("type") + variables = data.pop("@variables") + embeddings = data.pop("embeddings") + type_embedding = data.pop("type_embedding") + attention_layers = data.pop("attention_layers") + env_mat = data.pop("env_mat") + tebd_input_mode = data["tebd_input_mode"] + if tebd_input_mode in ["strip"]: + embeddings_strip = data.pop("embeddings_strip") + else: + embeddings_strip = None + # compat with version 1 + if "use_tebd_bias" not in data: + data["use_tebd_bias"] = True + obj = cls(**data) + + def t_cvt(xx): + return paddle.to_tensor(xx, dtype=obj.se_atten.prec).to(device=env.DEVICE) + + obj.type_embedding.embedding = TypeEmbedNetConsistent.deserialize( + type_embedding + ) + obj.se_atten["davg"] = t_cvt(variables["davg"]) + obj.se_atten["dstd"] = t_cvt(variables["dstd"]) + obj.se_atten.filter_layers = NetworkCollection.deserialize(embeddings) + if tebd_input_mode in ["strip"]: + obj.se_atten.filter_layers_strip = NetworkCollection.deserialize( + embeddings_strip + ) + obj.se_atten.dpa1_attention = NeighborGatedAttention.deserialize( + attention_layers + ) + return obj + + def forward( + self, + extended_coord: paddle.Tensor, + extended_atype: paddle.Tensor, + nlist: paddle.Tensor, + mapping: Optional[paddle.Tensor] = None, + comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + ): + """Compute the descriptor. + + Parameters + ---------- + extended_coord + The extended coordinates of atoms. shape: nf x (nallx3) + extended_atype + The extended aotm types. shape: nf x nall + nlist + The neighbor list. shape: nf x nloc x nnei + mapping + The index mapping, not required by this descriptor. + comm_dict + The data needed for communication for parallel inference. + + Returns + ------- + descriptor + The descriptor. shape: nf x nloc x (ng x axis_neuron) + gr + The rotationally equivariant and permutationally invariant single particle + representation. shape: nf x nloc x ng x 3 + g2 + The rotationally invariant pair-partical representation. + shape: nf x nloc x nnei x ng + h2 + The rotationally equivariant pair-partical representation. + shape: nf x nloc x nnei x 3 + sw + The smooth switch function. shape: nf x nloc x nnei + + """ + del mapping + nframes, nloc, nnei = nlist.shape + nall = extended_coord.reshape([nframes, -1]).shape[1] // 3 + g1_ext = self.type_embedding(extended_atype) + g1_inp = g1_ext[:, :nloc, :] + g1, g2, h2, rot_mat, sw = self.se_atten( + nlist, + extended_coord, + extended_atype, + g1_ext, + mapping=None, + ) + if self.concat_output_tebd: + g1 = paddle.concat([g1, g1_inp], axis=-1) + + return g1, rot_mat, g2, h2, sw + + @classmethod + def update_sel( + cls, + train_data: DeepmdDataSystem, + type_map: Optional[List[str]], + local_jdata: dict, + ) -> Tuple[dict, Optional[float]]: + """Update the selection and perform neighbor statistics. + + Parameters + ---------- + train_data : DeepmdDataSystem + data used to do neighbor statictics + type_map : list[str], optional + The name of each type of atoms + local_jdata : dict + The local data refer to the current class + + Returns + ------- + dict + The updated local data + float + The minimum distance between two atoms + """ + local_jdata_cpy = local_jdata.copy() + min_nbor_dist, sel = UpdateSel().update_one_sel( + train_data, type_map, local_jdata_cpy["rcut"], local_jdata_cpy["sel"], True + ) + local_jdata_cpy["sel"] = sel[0] + return local_jdata_cpy, min_nbor_dist diff --git a/deepmd/pd/model/descriptor/dpa2.py b/deepmd/pd/model/descriptor/dpa2.py new file mode 100644 index 0000000000..63c87fcf83 --- /dev/null +++ b/deepmd/pd/model/descriptor/dpa2.py @@ -0,0 +1,715 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Callable, + Dict, + List, + Optional, + Tuple, + Union, +) + +import paddle + +from deepmd.dpmodel.descriptor.dpa2 import ( + RepformerArgs, + RepinitArgs, +) +from deepmd.dpmodel.utils import EnvMat as DPEnvMat +from deepmd.dpmodel.utils.seed import ( + child_seed, +) +from deepmd.pd.model.network.mlp import ( + Identity, + MLPLayer, + NetworkCollection, +) +from deepmd.pd.model.network.network import ( + TypeEmbedNet, + TypeEmbedNetConsistent, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.nlist import ( + build_multiple_neighbor_list, + get_multiple_nlist_key, +) +from deepmd.pd.utils.update_sel import ( + UpdateSel, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, +) +from deepmd.utils.data_system import ( + DeepmdDataSystem, +) +from deepmd.utils.finetune import ( + get_index_between_two_maps, + map_pair_exclude_types, +) +from deepmd.utils.path import ( + DPPath, +) +from deepmd.utils.version import ( + check_version_compatibility, +) + +from .base_descriptor import ( + BaseDescriptor, +) +from .descriptor import ( + extend_descrpt_stat, +) +from .repformer_layer import ( + RepformerLayer, +) +from .repformers import ( + DescrptBlockRepformers, +) +from .se_atten import ( + DescrptBlockSeAtten, +) + + +@BaseDescriptor.register("dpa2") +class DescrptDPA2(BaseDescriptor, paddle.nn.Layer): + def __init__( + self, + ntypes: int, + # args for repinit + repinit: Union[RepinitArgs, dict], + # args for repformer + repformer: Union[RepformerArgs, dict], + # kwargs for descriptor + concat_output_tebd: bool = True, + precision: str = "float64", + smooth: bool = True, + exclude_types: List[Tuple[int, int]] = [], + env_protection: float = 0.0, + trainable: bool = True, + seed: Optional[Union[int, List[int]]] = None, + add_tebd_to_repinit_out: bool = False, + use_econf_tebd: bool = False, + use_tebd_bias: bool = False, + type_map: Optional[List[str]] = None, + old_impl: bool = False, + ): + r"""The DPA-2 descriptor. see https://arxiv.org/abs/2312.15492. + + Parameters + ---------- + repinit : Union[RepinitArgs, dict] + The arguments used to initialize the repinit block, see docstr in `RepinitArgs` for details information. + repformer : Union[RepformerArgs, dict] + The arguments used to initialize the repformer block, see docstr in `RepformerArgs` for details information. + concat_output_tebd : bool, optional + Whether to concat type embedding at the output of the descriptor. + precision : str, optional + The precision of the embedding net parameters. + smooth : bool, optional + Whether to use smoothness in processes such as attention weights calculation. + exclude_types : List[List[int]], optional + The excluded pairs of types which have no interaction with each other. + For example, `[[0, 1]]` means no interaction between type 0 and type 1. + env_protection : float, optional + Protection parameter to prevent division by zero errors during environment matrix calculations. + For example, when using paddings, there may be zero distances of neighbors, which may make division by zero error during environment matrix calculations without protection. + trainable : bool, optional + If the parameters are trainable. + seed : int, optional + Random seed for parameter initialization. + add_tebd_to_repinit_out : bool, optional + Whether to add type embedding to the output representation from repinit before inputting it into repformer. + use_econf_tebd : bool, Optional + Whether to use electronic configuration type embedding. + use_tebd_bias : bool, Optional + Whether to use bias in the type embedding layer. + type_map : List[str], Optional + A list of strings. Give the name to each type of atoms. + + Returns + ------- + descriptor: paddle.Tensor + the descriptor of shape nb x nloc x g1_dim. + invariant single-atom representation. + g2: paddle.Tensor + invariant pair-atom representation. + h2: paddle.Tensor + equivariant pair-atom representation. + rot_mat: paddle.Tensor + rotation matrix for equivariant fittings + sw: paddle.Tensor + The switch function for decaying inverse distance. + + """ + super().__init__() + + def init_subclass_params(sub_data, sub_class): + if isinstance(sub_data, dict): + return sub_class(**sub_data) + elif isinstance(sub_data, sub_class): + return sub_data + else: + raise ValueError( + f"Input args must be a {sub_class.__name__} class or a dict!" + ) + + self.repinit_args = init_subclass_params(repinit, RepinitArgs) + self.repformer_args = init_subclass_params(repformer, RepformerArgs) + + self.repinit = DescrptBlockSeAtten( + self.repinit_args.rcut, + self.repinit_args.rcut_smth, + self.repinit_args.nsel, + ntypes, + attn_layer=0, + neuron=self.repinit_args.neuron, + axis_neuron=self.repinit_args.axis_neuron, + tebd_dim=self.repinit_args.tebd_dim, + tebd_input_mode=self.repinit_args.tebd_input_mode, + set_davg_zero=self.repinit_args.set_davg_zero, + exclude_types=exclude_types, + env_protection=env_protection, + activation_function=self.repinit_args.activation_function, + precision=precision, + resnet_dt=self.repinit_args.resnet_dt, + smooth=smooth, + type_one_side=self.repinit_args.type_one_side, + seed=child_seed(seed, 0), + ) + self.repformers = DescrptBlockRepformers( + self.repformer_args.rcut, + self.repformer_args.rcut_smth, + self.repformer_args.nsel, + ntypes, + nlayers=self.repformer_args.nlayers, + g1_dim=self.repformer_args.g1_dim, + g2_dim=self.repformer_args.g2_dim, + axis_neuron=self.repformer_args.axis_neuron, + direct_dist=self.repformer_args.direct_dist, + update_g1_has_conv=self.repformer_args.update_g1_has_conv, + update_g1_has_drrd=self.repformer_args.update_g1_has_drrd, + update_g1_has_grrg=self.repformer_args.update_g1_has_grrg, + update_g1_has_attn=self.repformer_args.update_g1_has_attn, + update_g2_has_g1g1=self.repformer_args.update_g2_has_g1g1, + update_g2_has_attn=self.repformer_args.update_g2_has_attn, + update_h2=self.repformer_args.update_h2, + attn1_hidden=self.repformer_args.attn1_hidden, + attn1_nhead=self.repformer_args.attn1_nhead, + attn2_hidden=self.repformer_args.attn2_hidden, + attn2_nhead=self.repformer_args.attn2_nhead, + attn2_has_gate=self.repformer_args.attn2_has_gate, + activation_function=self.repformer_args.activation_function, + update_style=self.repformer_args.update_style, + update_residual=self.repformer_args.update_residual, + update_residual_init=self.repformer_args.update_residual_init, + set_davg_zero=self.repformer_args.set_davg_zero, + smooth=smooth, + exclude_types=exclude_types, + env_protection=env_protection, + precision=precision, + trainable_ln=self.repformer_args.trainable_ln, + ln_eps=self.repformer_args.ln_eps, + seed=child_seed(seed, 1), + old_impl=old_impl, + ) + self.use_econf_tebd = use_econf_tebd + self.use_tebd_bias = use_tebd_bias + self.type_map = type_map + self.type_embedding = TypeEmbedNet( + ntypes, + self.repinit_args.tebd_dim, + precision=precision, + seed=child_seed(seed, 2), + use_econf_tebd=self.use_econf_tebd, + use_tebd_bias=use_tebd_bias, + type_map=type_map, + ) + self.concat_output_tebd = concat_output_tebd + self.precision = precision + self.smooth = smooth + self.exclude_types = exclude_types + self.env_protection = env_protection + self.trainable = trainable + self.add_tebd_to_repinit_out = add_tebd_to_repinit_out + + if self.repinit.dim_out == self.repformers.dim_in: + self.g1_shape_tranform = Identity() + else: + self.g1_shape_tranform = MLPLayer( + self.repinit.dim_out, + self.repformers.dim_in, + bias=False, + precision=precision, + init="glorot", + seed=child_seed(seed, 3), + ) + self.tebd_transform = None + if self.add_tebd_to_repinit_out: + self.tebd_transform = MLPLayer( + self.repinit_args.tebd_dim, + self.repformers.dim_in, + bias=False, + precision=precision, + seed=child_seed(seed, 4), + ) + assert self.repinit.rcut > self.repformers.rcut + assert self.repinit.sel[0] > self.repformers.sel[0] + + self.tebd_dim = self.repinit_args.tebd_dim + self.rcut = self.repinit.get_rcut() + self.rcut_smth = self.repinit.get_rcut_smth() + self.ntypes = ntypes + self.sel = self.repinit.sel + # set trainable + for param in self.parameters(): + param.stop_gradient = not trainable + + def get_rcut(self) -> float: + """Returns the cut-off radius.""" + return self.rcut + + def get_rcut_smth(self) -> float: + """Returns the radius where the neighbor information starts to smoothly decay to 0.""" + return self.rcut_smth + + def get_nsel(self) -> int: + """Returns the number of selected atoms in the cut-off radius.""" + return sum(self.sel) + + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" + return self.sel + + def get_ntypes(self) -> int: + """Returns the number of element types.""" + return self.ntypes + + def get_type_map(self) -> List[str]: + """Get the name to each type of atoms.""" + return self.type_map + + def get_dim_out(self) -> int: + """Returns the output dimension of this descriptor.""" + ret = self.repformers.dim_out + if self.concat_output_tebd: + ret += self.tebd_dim + return ret + + def get_dim_emb(self) -> int: + """Returns the embedding dimension of this descriptor.""" + return self.repformers.dim_emb + + def mixed_types(self) -> bool: + """If true, the discriptor + 1. assumes total number of atoms aligned across frames; + 2. requires a neighbor list that does not distinguish different atomic types. + + If false, the discriptor + 1. assumes total number of atoms of each atom type aligned across frames; + 2. requires a neighbor list that distinguishes different atomic types. + + """ + return True + + def has_message_passing(self) -> bool: + """Returns whether the descriptor has message passing.""" + return any( + [self.repinit.has_message_passing(), self.repformers.has_message_passing()] + ) + + def need_sorted_nlist_for_lower(self) -> bool: + """Returns whether the descriptor needs sorted nlist when using `forward_lower`.""" + return True + + def get_env_protection(self) -> float: + """Returns the protection of building environment matrix.""" + # the env_protection of repinit is the same as that of the repformer + return self.repinit.get_env_protection() + + def share_params(self, base_class, shared_level, resume=False): + """ + Share the parameters of self to the base_class with shared_level during multitask training. + If not start from checkpoint (resume is False), + some seperated parameters (e.g. mean and stddev) will be re-calculated across different classes. + """ + assert ( + self.__class__ == base_class.__class__ + ), "Only descriptors of the same type can share params!" + # For DPA2 descriptors, the user-defined share-level + # shared_level: 0 + # share all parameters in type_embedding, repinit and repformers + if shared_level == 0: + self._modules["type_embedding"] = base_class._modules["type_embedding"] + self.repinit.share_params(base_class.repinit, 0, resume=resume) + self._modules["g1_shape_tranform"] = base_class._modules[ + "g1_shape_tranform" + ] + self.repformers.share_params(base_class.repformers, 0, resume=resume) + # shared_level: 1 + # share all parameters in type_embedding and repinit + elif shared_level == 1: + self._modules["type_embedding"] = base_class._modules["type_embedding"] + self.repinit.share_params(base_class.repinit, 0, resume=resume) + # shared_level: 2 + # share all parameters in type_embedding and repformers + elif shared_level == 2: + self._modules["type_embedding"] = base_class._modules["type_embedding"] + self._modules["g1_shape_tranform"] = base_class._modules[ + "g1_shape_tranform" + ] + self.repformers.share_params(base_class.repformers, 0, resume=resume) + # shared_level: 3 + # share all parameters in type_embedding + elif shared_level == 3: + self._modules["type_embedding"] = base_class._modules["type_embedding"] + # Other shared levels + else: + raise NotImplementedError + + def change_type_map( + self, type_map: List[str], model_with_new_type_stat=None + ) -> None: + """Change the type related params to new ones, according to `type_map` and the original one in the model. + If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. + """ + assert ( + self.type_map is not None + ), "'type_map' must be defined when performing type changing!" + remap_index, has_new_type = get_index_between_two_maps(self.type_map, type_map) + self.type_map = type_map + self.type_embedding.change_type_map(type_map=type_map) + self.exclude_types = map_pair_exclude_types(self.exclude_types, remap_index) + self.ntypes = len(type_map) + repinit = self.repinit + repformers = self.repformers + if has_new_type: + # the avg and std of new types need to be updated + extend_descrpt_stat( + repinit, + type_map, + des_with_stat=model_with_new_type_stat.repinit + if model_with_new_type_stat is not None + else None, + ) + extend_descrpt_stat( + repformers, + type_map, + des_with_stat=model_with_new_type_stat.repformers + if model_with_new_type_stat is not None + else None, + ) + repinit.ntypes = self.ntypes + repformers.ntypes = self.ntypes + repinit.reinit_exclude(self.exclude_types) + repformers.reinit_exclude(self.exclude_types) + repinit["davg"] = repinit["davg"][remap_index] + repinit["dstd"] = repinit["dstd"][remap_index] + repformers["davg"] = repformers["davg"][remap_index] + repformers["dstd"] = repformers["dstd"][remap_index] + + @property + def dim_out(self): + return self.get_dim_out() + + @property + def dim_emb(self): + """Returns the embedding dimension g2.""" + return self.get_dim_emb() + + def compute_input_stats( + self, + merged: Union[Callable[[], List[dict]], List[dict]], + path: Optional[DPPath] = None, + ): + """ + Compute the input statistics (e.g. mean and stddev) for the descriptors from packed data. + + Parameters + ---------- + merged : Union[Callable[[], List[dict]], List[dict]] + - List[dict]: A list of data samples from various data systems. + Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` + originating from the `i`-th data system. + - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + only when needed. Since the sampling process can be slow and memory-intensive, + the lazy function helps by only sampling once. + path : Optional[DPPath] + The path to the stat file. + + """ + for ii, descrpt in enumerate([self.repinit, self.repformers]): + descrpt.compute_input_stats(merged, path) + + def set_stat_mean_and_stddev( + self, + mean: List[paddle.Tensor], + stddev: List[paddle.Tensor], + ) -> None: + """Update mean and stddev for descriptor.""" + for ii, descrpt in enumerate([self.repinit, self.repformers]): + descrpt.mean = mean[ii] + descrpt.stddev = stddev[ii] + + def get_stat_mean_and_stddev( + self, + ) -> Tuple[List[paddle.Tensor], List[paddle.Tensor]]: + """Get mean and stddev for descriptor.""" + return [self.repinit.mean, self.repformers.mean], [ + self.repinit.stddev, + self.repformers.stddev, + ] + + def serialize(self) -> dict: + repinit = self.repinit + repformers = self.repformers + data = { + "@class": "Descriptor", + "type": "dpa2", + "@version": 2, + "ntypes": self.ntypes, + "repinit_args": self.repinit_args.serialize(), + "repformer_args": self.repformer_args.serialize(), + "concat_output_tebd": self.concat_output_tebd, + "precision": self.precision, + "smooth": self.smooth, + "exclude_types": self.exclude_types, + "env_protection": self.env_protection, + "trainable": self.trainable, + "add_tebd_to_repinit_out": self.add_tebd_to_repinit_out, + "use_econf_tebd": self.use_econf_tebd, + "use_tebd_bias": self.use_tebd_bias, + "type_map": self.type_map, + "type_embedding": self.type_embedding.embedding.serialize(), + "g1_shape_tranform": self.g1_shape_tranform.serialize(), + } + if self.add_tebd_to_repinit_out: + data.update( + { + "tebd_transform": self.tebd_transform.serialize(), + } + ) + repinit_variable = { + "embeddings": repinit.filter_layers.serialize(), + "env_mat": DPEnvMat(repinit.rcut, repinit.rcut_smth).serialize(), + "@variables": { + "davg": to_numpy_array(repinit["davg"]), + "dstd": to_numpy_array(repinit["dstd"]), + }, + } + if repinit.tebd_input_mode in ["strip"]: + repinit_variable.update( + {"embeddings_strip": repinit.filter_layers_strip.serialize()} + ) + repformers_variable = { + "g2_embd": repformers.g2_embd.serialize(), + "repformer_layers": [layer.serialize() for layer in repformers.layers], + "env_mat": DPEnvMat(repformers.rcut, repformers.rcut_smth).serialize(), + "@variables": { + "davg": to_numpy_array(repformers["davg"]), + "dstd": to_numpy_array(repformers["dstd"]), + }, + } + data.update( + { + "repinit_variable": repinit_variable, + "repformers_variable": repformers_variable, + } + ) + return data + + @classmethod + def deserialize(cls, data: dict) -> "DescrptDPA2": + data = data.copy() + check_version_compatibility(data.pop("@version"), 2, 1) + data.pop("@class") + data.pop("type") + repinit_variable = data.pop("repinit_variable").copy() + repformers_variable = data.pop("repformers_variable").copy() + type_embedding = data.pop("type_embedding") + g1_shape_tranform = data.pop("g1_shape_tranform") + tebd_transform = data.pop("tebd_transform", None) + add_tebd_to_repinit_out = data["add_tebd_to_repinit_out"] + data["repinit"] = RepinitArgs(**data.pop("repinit_args")) + data["repformer"] = RepformerArgs(**data.pop("repformer_args")) + # compat with version 1 + if "use_tebd_bias" not in data: + data["use_tebd_bias"] = True + obj = cls(**data) + obj.type_embedding.embedding = TypeEmbedNetConsistent.deserialize( + type_embedding + ) + if add_tebd_to_repinit_out: + assert isinstance(tebd_transform, dict) + obj.tebd_transform = MLPLayer.deserialize(tebd_transform) + if obj.repinit.dim_out != obj.repformers.dim_in: + obj.g1_shape_tranform = MLPLayer.deserialize(g1_shape_tranform) + + def t_cvt(xx): + return paddle.to_tensor(xx, dtype=obj.repinit.prec, device=env.DEVICE) + + # deserialize repinit + statistic_repinit = repinit_variable.pop("@variables") + env_mat = repinit_variable.pop("env_mat") + tebd_input_mode = data["repinit"].tebd_input_mode + obj.repinit.filter_layers = NetworkCollection.deserialize( + repinit_variable.pop("embeddings") + ) + if tebd_input_mode in ["strip"]: + obj.repinit.filter_layers_strip = NetworkCollection.deserialize( + repinit_variable.pop("embeddings_strip") + ) + obj.repinit["davg"] = t_cvt(statistic_repinit["davg"]) + obj.repinit["dstd"] = t_cvt(statistic_repinit["dstd"]) + + # deserialize repformers + statistic_repformers = repformers_variable.pop("@variables") + env_mat = repformers_variable.pop("env_mat") + repformer_layers = repformers_variable.pop("repformer_layers") + obj.repformers.g2_embd = MLPLayer.deserialize( + repformers_variable.pop("g2_embd") + ) + obj.repformers["davg"] = t_cvt(statistic_repformers["davg"]) + obj.repformers["dstd"] = t_cvt(statistic_repformers["dstd"]) + obj.repformers.layers = paddle.nn.LayerList( + [RepformerLayer.deserialize(layer) for layer in repformer_layers] + ) + return obj + + def forward( + self, + extended_coord: paddle.Tensor, + extended_atype: paddle.Tensor, + nlist: paddle.Tensor, + mapping: Optional[paddle.Tensor] = None, + comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + ): + """Compute the descriptor. + + Parameters + ---------- + extended_coord + The extended coordinates of atoms. shape: nf x (nallx3) + extended_atype + The extended aotm types. shape: nf x nall + nlist + The neighbor list. shape: nf x nloc x nnei + mapping + The index mapping, mapps extended region index to local region. + comm_dict + The data needed for communication for parallel inference. + + Returns + ------- + descriptor + The descriptor. shape: nf x nloc x (ng x axis_neuron) + gr + The rotationally equivariant and permutationally invariant single particle + representation. shape: nf x nloc x ng x 3 + g2 + The rotationally invariant pair-partical representation. + shape: nf x nloc x nnei x ng + h2 + The rotationally equivariant pair-partical representation. + shape: nf x nloc x nnei x 3 + sw + The smooth switch function. shape: nf x nloc x nnei + + """ + nframes, nloc, nnei = nlist.shape + nall = extended_coord.reshape([nframes, -1]).shape[1] // 3 + # nlists + nlist_dict = build_multiple_neighbor_list( + extended_coord, + nlist, + [self.repformers.get_rcut(), self.repinit.get_rcut()], + [self.repformers.get_nsel(), self.repinit.get_nsel()], + ) + # repinit + g1_ext = self.type_embedding(extended_atype) + g1_inp = g1_ext[:, :nloc, :] + g1, _, _, _, _ = self.repinit( + nlist_dict[ + get_multiple_nlist_key(self.repinit.get_rcut(), self.repinit.get_nsel()) + ], + extended_coord, + extended_atype, + g1_ext, + mapping, + ) + # linear to change shape + g1 = self.g1_shape_tranform(g1) + if self.add_tebd_to_repinit_out: + assert self.tebd_transform is not None + g1 = g1 + self.tebd_transform(g1_inp) + # mapping g1 + if comm_dict is None: + assert mapping is not None + mapping_ext = ( + mapping.reshape([nframes, nall]) + .unsqueeze(-1) + .expand([-1, -1, g1.shape[-1]]) + ) + g1_ext = paddle.gather(g1, 1, mapping_ext) + g1 = g1_ext + # repformer + g1, g2, h2, rot_mat, sw = self.repformers( + nlist_dict[ + get_multiple_nlist_key( + self.repformers.get_rcut(), self.repformers.get_nsel() + ) + ], + extended_coord, + extended_atype, + g1, + mapping, + comm_dict, + ) + if self.concat_output_tebd: + g1 = paddle.concat([g1, g1_inp], axis=-1) + return g1, rot_mat, g2, h2, sw + + @classmethod + def update_sel( + cls, + train_data: DeepmdDataSystem, + type_map: Optional[List[str]], + local_jdata: dict, + ) -> Tuple[dict, Optional[float]]: + """Update the selection and perform neighbor statistics. + + Parameters + ---------- + train_data : DeepmdDataSystem + data used to do neighbor statictics + type_map : list[str], optional + The name of each type of atoms + local_jdata : dict + The local data refer to the current class + + Returns + ------- + dict + The updated local data + float + The minimum distance between two atoms + """ + local_jdata_cpy = local_jdata.copy() + update_sel = UpdateSel() + min_nbor_dist, repinit_sel = update_sel.update_one_sel( + train_data, + type_map, + local_jdata_cpy["repinit"]["rcut"], + local_jdata_cpy["repinit"]["nsel"], + True, + ) + local_jdata_cpy["repinit"]["nsel"] = repinit_sel[0] + min_nbor_dist, repformer_sel = update_sel.update_one_sel( + train_data, + type_map, + local_jdata_cpy["repformer"]["rcut"], + local_jdata_cpy["repformer"]["nsel"], + True, + ) + local_jdata_cpy["repformer"]["nsel"] = repformer_sel[0] + return local_jdata_cpy, min_nbor_dist diff --git a/deepmd/pd/model/descriptor/env_mat.py b/deepmd/pd/model/descriptor/env_mat.py new file mode 100644 index 0000000000..7e072a4d74 --- /dev/null +++ b/deepmd/pd/model/descriptor/env_mat.py @@ -0,0 +1,82 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later + +import paddle + +from deepmd.pd.utils.preprocess import ( + compute_smooth_weight, +) + + +def _make_env_mat( + nlist, + coord, + rcut: float, + ruct_smth: float, + radial_only: bool = False, + protection: float = 0.0, +): + """Make smooth environment matrix.""" + bsz, natoms, nnei = nlist.shape + coord = coord.reshape([bsz, -1, 3]) + nall = coord.shape[1] + mask = nlist >= 0 + # nlist = nlist * mask ## this impl will contribute nans in Hessian calculation. + nlist = paddle.where(mask, nlist, nall - 1) + coord_l = coord[:, :natoms].reshape([bsz, -1, 1, 3]) + index = nlist.reshape([bsz, -1]).unsqueeze(-1).expand([-1, -1, 3]) + coord_r = paddle.take_along_axis(coord, axis=1, indices=index) + coord_r = coord_r.reshape([bsz, natoms, nnei, 3]) + diff = coord_r - coord_l + length = paddle.linalg.norm(diff, axis=-1, keepdim=True) + # for index 0 nloc atom + length = length + (~mask.unsqueeze(-1)).astype(length.dtype) + t0 = 1 / (length + protection) + t1 = diff / (length + protection) ** 2 + weight = compute_smooth_weight(length, ruct_smth, rcut) + weight = weight * mask.unsqueeze(-1).astype(weight.dtype) + if radial_only: + env_mat = t0 * weight + else: + env_mat = paddle.concat([t0, t1], axis=-1) * weight + return env_mat, diff * mask.unsqueeze(-1).astype(diff.dtype), weight + + +def prod_env_mat( + extended_coord, + nlist, + atype, + mean, + stddev, + rcut: float, + rcut_smth: float, + radial_only: bool = False, + protection: float = 0.0, +): + """Generate smooth environment matrix from atom coordinates and other context. + + Args: + - extended_coord: Copied atom coordinates with shape [nframes, nall*3]. + - atype: Atom types with shape [nframes, nloc]. + - mean: Average value of descriptor per element type with shape [len(sec), nnei, 4 or 1]. + - stddev: Standard deviation of descriptor per element type with shape [len(sec), nnei, 4 or 1]. + - rcut: Cut-off radius. + - rcut_smth: Smooth hyper-parameter for pair force & energy. + - radial_only: Whether to return a full description or a radial-only descriptor. + - protection: Protection parameter to prevent division by zero errors during calculations. + + Returns + ------- + - env_mat: Shape is [nframes, natoms[1]*nnei*4]. + """ + _env_mat_se_a, diff, switch = _make_env_mat( + nlist, + extended_coord, + rcut, + rcut_smth, + radial_only, + protection=protection, + ) # shape [n_atom, dim, 4 or 1] + t_avg = mean[atype] # [n_atom, dim, 4 or 1] + t_std = stddev[atype] # [n_atom, dim, 4 or 1] + env_mat_se_a = (_env_mat_se_a - t_avg) / t_std + return env_mat_se_a, diff, switch diff --git a/deepmd/pd/model/descriptor/gaussian_lcc.py b/deepmd/pd/model/descriptor/gaussian_lcc.py new file mode 100644 index 0000000000..37bd71e18a --- /dev/null +++ b/deepmd/pd/model/descriptor/gaussian_lcc.py @@ -0,0 +1,323 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + List, + Optional, +) + +import paddle +import paddle.nn as nn + +from deepmd.pd.model.descriptor.base_descriptor import ( + BaseDescriptor, +) +from deepmd.pd.model.network.network import ( + Evoformer3bEncoder, + GaussianEmbedding, + TypeEmbedNet, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.utils.path import ( + DPPath, +) + + +class DescrptGaussianLcc(paddle.nn.Layer, BaseDescriptor): + def __init__( + self, + rcut, + rcut_smth, + sel: int, + ntypes: int, + num_pair: int, + embed_dim: int = 768, + kernel_num: int = 128, + pair_embed_dim: int = 64, + num_block: int = 1, + layer_num: int = 12, + attn_head: int = 48, + pair_hidden_dim: int = 16, + ffn_embedding_dim: int = 768, + dropout: float = 0.0, + droppath_prob: float = 0.1, + pair_dropout: float = 0.25, + attention_dropout: float = 0.1, + activation_dropout: float = 0.1, + pre_ln: bool = True, + do_tag_embedding: bool = False, + tag_ener_pref: bool = False, + atomic_sum_gbf: bool = False, + pre_add_seq: bool = True, + tri_update: bool = True, + **kwargs, + ): + """Construct a descriptor of Gaussian Based Local Cluster. + + Args: + - rcut: Cut-off radius. + - rcut_smth: Smooth hyper-parameter for pair force & energy. **Not used in this descriptor**. + - sel: For each element type, how many atoms is selected as neighbors. + - ntypes: Number of atom types. + - num_pair: Number of atom type pairs. Default is 2 * ntypes. + - kernel_num: Number of gaussian kernels. + - embed_dim: Dimension of atomic representation. + - pair_embed_dim: Dimension of pair representation. + - num_block: Number of evoformer blocks. + - layer_num: Number of attention layers. + - attn_head: Number of attention heads. + - pair_hidden_dim: Hidden dimension of pair representation during attention process. + - ffn_embedding_dim: Dimension during feed forward network. + - dropout: Dropout probability of atomic representation. + - droppath_prob: If not zero, it will use drop paths (Stochastic Depth) per sample and ignore `dropout`. + - pair_dropout: Dropout probability of pair representation during triangular update. + - attention_dropout: Dropout probability during attetion process. + - activation_dropout: Dropout probability of pair feed forward network. + - pre_ln: Do previous layer norm or not. + - do_tag_embedding: Add tag embedding to atomic and pair representations. (`tags`, `tags2`, `tags3` must exist) + - atomic_sum_gbf: Add sum of gaussian outputs to atomic representation or not. + - pre_add_seq: Add output of other descriptor (if has) to the atomic representation before attention. + """ + super().__init__() + self.rcut = rcut + self.rcut_smth = rcut_smth + self.embed_dim = embed_dim + self.num_pair = num_pair + self.kernel_num = kernel_num + self.pair_embed_dim = pair_embed_dim + self.num_block = num_block + self.layer_num = layer_num + self.attention_heads = attn_head + self.pair_hidden_dim = pair_hidden_dim + self.ffn_embedding_dim = ffn_embedding_dim + self.dropout = dropout + self.droppath_prob = droppath_prob + self.pair_dropout = pair_dropout + self.attention_dropout = attention_dropout + self.activation_dropout = activation_dropout + self.pre_ln = pre_ln + self.do_tag_embedding = do_tag_embedding + self.tag_ener_pref = tag_ener_pref + self.atomic_sum_gbf = atomic_sum_gbf + self.local_cluster = True + self.pre_add_seq = pre_add_seq + self.tri_update = tri_update + + if isinstance(sel, int): + sel = [sel] + + self.ntypes = ntypes + self.sec = paddle.to_tensor(sel) # pylint: disable=no-explicit-dtype,no-explicit-device + self.nnei = sum(sel) + + if self.do_tag_embedding: + self.tag_encoder = nn.Embedding(3, self.embed_dim) + self.tag_encoder2 = nn.Embedding(2, self.embed_dim) + self.tag_type_embedding = TypeEmbedNet(10, pair_embed_dim) + self.edge_type_embedding = nn.Embedding( + (ntypes + 1) * (ntypes + 1), + pair_embed_dim, + padding_idx=(ntypes + 1) * (ntypes + 1) - 1, + dtype=env.GLOBAL_PD_FLOAT_PRECISION, + ) + self.gaussian_encoder = GaussianEmbedding( + rcut, + kernel_num, + num_pair, + embed_dim, + pair_embed_dim, + sel, + ntypes, + atomic_sum_gbf, + ) + self.backbone = Evoformer3bEncoder( + self.nnei, + layer_num=self.layer_num, + attn_head=self.attention_heads, + atomic_dim=self.embed_dim, + pair_dim=self.pair_embed_dim, + pair_hidden_dim=self.pair_hidden_dim, + ffn_embedding_dim=self.ffn_embedding_dim, + dropout=self.dropout, + droppath_prob=self.droppath_prob, + pair_dropout=self.pair_dropout, + attention_dropout=self.attention_dropout, + activation_dropout=self.activation_dropout, + pre_ln=self.pre_ln, + tri_update=self.tri_update, + ) + + @property + def dim_out(self): + """Returns the output dimension of atomic representation.""" + return self.embed_dim + + @property + def dim_in(self): + """Returns the atomic input dimension of this descriptor.""" + return self.embed_dim + + @property + def dim_emb(self): + """Returns the output dimension of pair representation.""" + return self.pair_embed_dim + + def compute_input_stats(self, merged: List[dict], path: Optional[DPPath] = None): + """Update mean and stddev for descriptor elements.""" + pass + + def forward( + self, + extended_coord, + nlist, + atype, + nlist_type, + nlist_loc=None, + atype_tebd=None, + nlist_tebd=None, + seq_input=None, + ): + """Calculate the atomic and pair representations of this descriptor. + + Args: + - extended_coord: Copied atom coordinates with shape [nframes, nall, 3]. + - nlist: Neighbor list with shape [nframes, nloc, nnei]. + - atype: Atom type with shape [nframes, nloc]. + - nlist_type: Atom type of neighbors with shape [nframes, nloc, nnei]. + - nlist_loc: Local index of neighbor list with shape [nframes, nloc, nnei]. + - atype_tebd: Atomic type embedding with shape [nframes, nloc, tebd_dim]. + - nlist_tebd: Type embeddings of neighbor with shape [nframes, nloc, nnei, tebd_dim]. + - seq_input: The sequential input from other descriptor with + shape [nframes, nloc, tebd_dim] or [nframes * nloc, 1 + nnei, tebd_dim] + + Returns + ------- + - result: descriptor with shape [nframes, nloc, self.filter_neuron[-1] * self.axis_neuron]. + - ret: environment matrix with shape [nframes, nloc, self.neei, out_size] + """ + nframes, nloc = nlist.shape[:2] + nall = extended_coord.shape[1] + nlist2 = paddle.concat( + [ + paddle.arange(0, nloc) + .to(device=nlist.place) # pylint: disable=no-explicit-dtype + .reshape([1, nloc, 1]) + .expand(nframes, -1, -1), + nlist, + ], + axis=-1, + ) + nlist_loc2 = paddle.concat( + [ + paddle.arange(0, nloc) + .to(device=nlist_loc.place) # pylint: disable=no-explicit-dtype + .reshape([1, nloc, 1]) + .expand(nframes, -1, -1), + nlist_loc, + ], + axis=-1, + ) + nlist_type2 = paddle.concat( + [atype.reshape([nframes, nloc, 1]), nlist_type], axis=-1 + ) + nnei2_mask = nlist2 != -1 + padding_mask = nlist2 == -1 + nlist2 = nlist2 * nnei2_mask + nlist_loc2 = nlist_loc2 * nnei2_mask + + # nframes x nloc x (1 + nnei2) x (1 + nnei2) + pair_mask = nnei2_mask.unsqueeze(-1) * nnei2_mask.unsqueeze(-2) + # nframes x nloc x (1 + nnei2) x (1 + nnei2) x head + attn_mask = paddle.zeros( + [nframes, nloc, 1 + self.nnei, 1 + self.nnei, self.attention_heads], + dtype=extended_coord.dtype, + ).to(device=nlist.place) + attn_mask.masked_fill_(padding_mask.unsqueeze(2).unsqueeze(-1), float("-inf")) + # (nframes x nloc) x head x (1 + nnei2) x (1 + nnei2) + attn_mask = ( + attn_mask.reshape( + [nframes * nloc, 1 + self.nnei, 1 + self.nnei, self.attention_heads] + ) + .permute(0, 3, 1, 2) + .contiguous() + ) + + # Atomic feature + # [(nframes x nloc) x (1 + nnei2) x tebd_dim] + atom_feature = paddle.gather( + atype_tebd, + axis=1, + index=nlist_loc2.reshape([nframes, -1]) + .unsqueeze(-1) + .expand(-1, -1, self.embed_dim), + ).reshape([nframes * nloc, 1 + self.nnei, self.embed_dim]) + if self.pre_add_seq and seq_input is not None: + first_dim = seq_input.shape[0] + if first_dim == nframes * nloc: + atom_feature += seq_input + elif first_dim == nframes: + atom_feature_seq = paddle.gather( + seq_input, + axis=1, + index=nlist_loc2.reshape([nframes, -1]) + .unsqueeze(-1) + .expand(-1, -1, self.embed_dim), + ).reshape([nframes * nloc, 1 + self.nnei, self.embed_dim]) + atom_feature += atom_feature_seq + else: + raise RuntimeError + atom_feature = atom_feature * nnei2_mask.reshape( + [nframes * nloc, 1 + self.nnei, 1] + ) + + # Pair feature + # [(nframes x nloc) x (1 + nnei2)] + nlist_type2_reshape = nlist_type2.reshape([nframes * nloc, 1 + self.nnei]) + # [(nframes x nloc) x (1 + nnei2) x (1 + nnei2)] + edge_type = nlist_type2_reshape.unsqueeze(-1) * ( + self.ntypes + 1 + ) + nlist_type2_reshape.unsqueeze(-2) + # [(nframes x nloc) x (1 + nnei2) x (1 + nnei2) x pair_dim] + edge_feature = self.edge_type_embedding(edge_type) + + # [(nframes x nloc) x (1 + nnei2) x (1 + nnei2) x 2] + edge_type_2dim = paddle.concat( + [ + nlist_type2_reshape.reshape( + [nframes * nloc, 1 + self.nnei, 1, 1] + ).expand(-1, -1, 1 + self.nnei, -1), + nlist_type2_reshape.reshape( + [nframes * nloc, 1, 1 + self.nnei, 1] + ).expand(-1, 1 + self.nnei, -1, -1) + + self.ntypes, + ], + axis=-1, + ) + # [(nframes x nloc) x (1 + nnei2) x 3] + coord_selected = paddle.gather( + extended_coord.unsqueeze(1) + .expand(-1, nloc, -1, -1) + .reshape([nframes * nloc, nall, 3]), + axis=1, + index=nlist2.reshape([nframes * nloc, 1 + self.nnei, 1]).expand(-1, -1, 3), + ) + + # Update pair features (or and atomic features) with gbf features + # delta_pos: [(nframes x nloc) x (1 + nnei2) x (1 + nnei2) x 3]. + atomic_feature, pair_feature, delta_pos = self.gaussian_encoder( + coord_selected, atom_feature, edge_type_2dim, edge_feature + ) + # [(nframes x nloc) x (1 + nnei2) x (1 + nnei2) x pair_dim] + attn_bias = pair_feature + + # output: [(nframes x nloc) x (1 + nnei2) x tebd_dim] + # pair: [(nframes x nloc) x (1 + nnei2) x (1 + nnei2) x pair_dim] + output, pair = self.backbone( + atomic_feature, + pair=attn_bias, + attn_mask=attn_mask, + pair_mask=pair_mask, + atom_mask=nnei2_mask.reshape([nframes * nloc, 1 + self.nnei]), + ) + + return output, pair, delta_pos, None diff --git a/deepmd/pd/model/descriptor/hybrid.py b/deepmd/pd/model/descriptor/hybrid.py new file mode 100644 index 0000000000..b96ccd55ef --- /dev/null +++ b/deepmd/pd/model/descriptor/hybrid.py @@ -0,0 +1,359 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import math +from typing import ( + Any, + Dict, + List, + Optional, + Tuple, + Union, +) + +import numpy as np +import paddle + +from deepmd.pd.model.descriptor.base_descriptor import ( + BaseDescriptor, +) +from deepmd.pd.utils.nlist import ( + nlist_distinguish_types, +) +from deepmd.pd.utils.utils import ( + to_paddle_tensor, +) +from deepmd.utils.data_system import ( + DeepmdDataSystem, +) +from deepmd.utils.path import ( + DPPath, +) +from deepmd.utils.version import ( + check_version_compatibility, +) + + +@BaseDescriptor.register("hybrid") +class DescrptHybrid(BaseDescriptor, paddle.nn.Layer): + """Concate a list of descriptors to form a new descriptor. + + Parameters + ---------- + list : list : List[Union[BaseDescriptor, Dict[str, Any]]] + Build a descriptor from the concatenation of the list of descriptors. + The descriptor can be either an object or a dictionary. + """ + + nlist_cut_idx: List[paddle.Tensor] + + def __init__( + self, + list: List[Union[BaseDescriptor, Dict[str, Any]]], + **kwargs, + ) -> None: + super().__init__() + # warning: list is conflict with built-in list + descrpt_list = list + if descrpt_list == [] or descrpt_list is None: + raise RuntimeError( + "cannot build descriptor from an empty list of descriptors." + ) + formatted_descript_list: List[BaseDescriptor] = [] + for ii in descrpt_list: + if isinstance(ii, BaseDescriptor): + formatted_descript_list.append(ii) + elif isinstance(ii, dict): + formatted_descript_list.append( + # pass other arguments (e.g. ntypes) to the descriptor + BaseDescriptor(**ii, **kwargs) + ) + else: + raise NotImplementedError + self.descrpt_list = paddle.nn.LayerList(formatted_descript_list) + self.numb_descrpt = len(self.descrpt_list) + for ii in range(1, self.numb_descrpt): + assert ( + self.descrpt_list[ii].get_ntypes() == self.descrpt_list[0].get_ntypes() + ), f"number of atom types in {ii}th descrptor does not match others" + # if hybrid sel is larger than sub sel, the nlist needs to be cut for each type + self.nlist_cut_idx: List[paddle.Tensor] = [] + if self.mixed_types() and not all( + descrpt.mixed_types() for descrpt in self.descrpt_list + ): + self.sel_no_mixed_types = np.max( + [ + descrpt.get_sel() + for descrpt in self.descrpt_list + if not descrpt.mixed_types() + ], + axis=0, + ).tolist() + else: + self.sel_no_mixed_types = None + for ii in range(self.numb_descrpt): + if self.mixed_types() == self.descrpt_list[ii].mixed_types(): + hybrid_sel = self.get_sel() + else: + assert self.sel_no_mixed_types is not None + hybrid_sel = self.sel_no_mixed_types + sub_sel = self.descrpt_list[ii].get_sel() + start_idx = np.cumsum(np.pad(hybrid_sel, (1, 0), "constant"))[:-1] + end_idx = start_idx + np.array(sub_sel) + cut_idx = np.concatenate( + [range(ss, ee) for ss, ee in zip(start_idx, end_idx)] + ).astype(np.int64) + self.nlist_cut_idx.append(to_paddle_tensor(cut_idx)) + + def get_rcut(self) -> float: + """Returns the cut-off radius.""" + # do not use numpy here - jit is not happy + return max([descrpt.get_rcut() for descrpt in self.descrpt_list]) + + def get_rcut_smth(self) -> float: + """Returns the radius where the neighbor information starts to smoothly decay to 0.""" + # may not be a good idea... + # Note: Using the minimum rcut_smth might not be appropriate in all scenarios. Consider using a different approach or provide detailed documentation on why the minimum value is chosen. + return min([descrpt.get_rcut_smth() for descrpt in self.descrpt_list]) + + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" + if self.mixed_types(): + return [ + np.max( + [descrpt.get_nsel() for descrpt in self.descrpt_list], axis=0 + ).item() + ] + else: + return np.max( + [descrpt.get_sel() for descrpt in self.descrpt_list], axis=0 + ).tolist() + + def get_ntypes(self) -> int: + """Returns the number of element types.""" + return self.descrpt_list[0].get_ntypes() + + def get_type_map(self) -> List[str]: + """Get the name to each type of atoms.""" + return self.descrpt_list[0].get_type_map() + + def get_dim_out(self) -> int: + """Returns the output dimension.""" + return sum([descrpt.get_dim_out() for descrpt in self.descrpt_list]) + + def get_dim_emb(self) -> int: + """Returns the output dimension.""" + return sum([descrpt.get_dim_emb() for descrpt in self.descrpt_list]) + + def mixed_types(self): + """Returns if the descriptor requires a neighbor list that distinguish different + atomic types or not. + """ + return any(descrpt.mixed_types() for descrpt in self.descrpt_list) + + def has_message_passing(self) -> bool: + """Returns whether the descriptor has message passing.""" + return any(descrpt.has_message_passing() for descrpt in self.descrpt_list) + + def need_sorted_nlist_for_lower(self) -> bool: + """Returns whether the descriptor needs sorted nlist when using `forward_lower`.""" + return True + + def get_env_protection(self) -> float: + """Returns the protection of building environment matrix. All descriptors should be the same.""" + all_protection = [descrpt.get_env_protection() for descrpt in self.descrpt_list] + same_as_0 = [math.isclose(ii, all_protection[0]) for ii in all_protection] + if not all(same_as_0): + raise ValueError( + "Hybrid descriptor requires the same environment matrix protection for all descriptors. Found differing values." + ) + return all_protection[0] + + def share_params(self, base_class, shared_level, resume=False): + """ + Share the parameters of self to the base_class with shared_level during multitask training. + If not start from checkpoint (resume is False), + some seperated parameters (e.g. mean and stddev) will be re-calculated across different classes. + """ + assert ( + self.__class__ == base_class.__class__ + ), "Only descriptors of the same type can share params!" + if shared_level == 0: + for ii, des in enumerate(self.descrpt_list): + self.descrpt_list[ii].share_params( + base_class.descrpt_list[ii], shared_level, resume=resume + ) + else: + raise NotImplementedError + + def change_type_map( + self, type_map: List[str], model_with_new_type_stat=None + ) -> None: + """Change the type related params to new ones, according to `type_map` and the original one in the model. + If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. + """ + for ii, descrpt in enumerate(self.descrpt_list): + descrpt.change_type_map( + type_map=type_map, + model_with_new_type_stat=model_with_new_type_stat.descrpt_list[ii] + if model_with_new_type_stat is not None + else None, + ) + + def compute_input_stats(self, merged: List[dict], path: Optional[DPPath] = None): + """Update mean and stddev for descriptor elements.""" + for descrpt in self.descrpt_list: + descrpt.compute_input_stats(merged, path) + + def set_stat_mean_and_stddev( + self, + mean: List[Union[paddle.Tensor, List[paddle.Tensor]]], + stddev: List[Union[paddle.Tensor, List[paddle.Tensor]]], + ) -> None: + """Update mean and stddev for descriptor.""" + for ii, descrpt in enumerate(self.descrpt_list): + descrpt.set_stat_mean_and_stddev(mean[ii], stddev[ii]) + + def get_stat_mean_and_stddev( + self, + ) -> Tuple[ + List[Union[paddle.Tensor, List[paddle.Tensor]]], + List[Union[paddle.Tensor, List[paddle.Tensor]]], + ]: + """Get mean and stddev for descriptor.""" + mean_list = [] + stddev_list = [] + for ii, descrpt in enumerate(self.descrpt_list): + mean_item, stddev_item = descrpt.get_stat_mean_and_stddev() + mean_list.append(mean_item) + stddev_list.append(stddev_item) + return mean_list, stddev_list + + def forward( + self, + coord_ext: paddle.Tensor, + atype_ext: paddle.Tensor, + nlist: paddle.Tensor, + mapping: Optional[paddle.Tensor] = None, + comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + ): + """Compute the descriptor. + + Parameters + ---------- + coord_ext + The extended coordinates of atoms. shape: nf x (nallx3) + atype_ext + The extended aotm types. shape: nf x nall + nlist + The neighbor list. shape: nf x nloc x nnei + mapping + The index mapping, not required by this descriptor. + comm_dict + The data needed for communication for parallel inference. + + Returns + ------- + descriptor + The descriptor. shape: nf x nloc x (ng x axis_neuron) + gr + The rotationally equivariant and permutationally invariant single particle + representation. shape: nf x nloc x ng x 3. This descriptor returns None + g2 + The rotationally invariant pair-partical representation. + this descriptor returns None + h2 + The rotationally equivariant pair-partical representation. + this descriptor returns None + sw + The smooth switch function. this descriptor returns None + """ + out_descriptor = [] + out_gr = [] + out_g2: Optional[paddle.Tensor] = None + out_h2: Optional[paddle.Tensor] = None + out_sw: Optional[paddle.Tensor] = None + if self.sel_no_mixed_types is not None: + nl_distinguish_types = nlist_distinguish_types( + nlist, + atype_ext, + self.sel_no_mixed_types, + ) + else: + nl_distinguish_types = None + # make jit happy + # for descrpt, nci in zip(self.descrpt_list, self.nlist_cut_idx): + for ii, descrpt in enumerate(self.descrpt_list): + # cut the nlist to the correct length + if self.mixed_types() == descrpt.mixed_types(): + nl = nlist[:, :, self.nlist_cut_idx[ii].to(atype_ext.place)] + else: + # mixed_types is True, but descrpt.mixed_types is False + assert nl_distinguish_types is not None + nl = nl_distinguish_types[ + :, :, self.nlist_cut_idx[ii].to(atype_ext.place) + ] + odescriptor, gr, g2, h2, sw = descrpt(coord_ext, atype_ext, nl, mapping) + out_descriptor.append(odescriptor) + if gr is not None: + out_gr.append(gr) + out_descriptor = paddle.concat(out_descriptor, axis=-1) + out_gr = paddle.concat(out_gr, axis=-2) if out_gr else None + return out_descriptor, out_gr, out_g2, out_h2, out_sw + + @classmethod + def update_sel( + cls, + train_data: DeepmdDataSystem, + type_map: Optional[List[str]], + local_jdata: dict, + ) -> Tuple[dict, Optional[float]]: + """Update the selection and perform neighbor statistics. + + Parameters + ---------- + train_data : DeepmdDataSystem + data used to do neighbor statictics + type_map : list[str], optional + The name of each type of atoms + local_jdata : dict + The local data refer to the current class + + Returns + ------- + dict + The updated local data + float + The minimum distance between two atoms + """ + local_jdata_cpy = local_jdata.copy() + new_list = [] + min_nbor_dist = None + for sub_jdata in local_jdata["list"]: + new_sub_jdata, min_nbor_dist_ = BaseDescriptor.update_sel( + train_data, type_map, sub_jdata + ) + if min_nbor_dist_ is not None: + min_nbor_dist = min_nbor_dist_ + new_list.append(new_sub_jdata) + local_jdata_cpy["list"] = new_list + return local_jdata_cpy, min_nbor_dist + + def serialize(self) -> dict: + return { + "@class": "Descriptor", + "type": "hybrid", + "@version": 1, + "list": [descrpt.serialize() for descrpt in self.descrpt_list], + } + + @classmethod + def deserialize(cls, data: dict) -> "DescrptHybrid": + data = data.copy() + class_name = data.pop("@class") + assert class_name == "Descriptor" + class_type = data.pop("type") + assert class_type == "hybrid" + check_version_compatibility(data.pop("@version"), 1, 1) + obj = cls( + list=[BaseDescriptor.deserialize(ii) for ii in data["list"]], + ) + return obj diff --git a/deepmd/pd/model/descriptor/repformer_layer.py b/deepmd/pd/model/descriptor/repformer_layer.py new file mode 100644 index 0000000000..827029601e --- /dev/null +++ b/deepmd/pd/model/descriptor/repformer_layer.py @@ -0,0 +1,1373 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + List, + Optional, + Union, +) + +import paddle +import paddle.nn as nn + +from deepmd.dpmodel.utils.seed import ( + child_seed, +) +from deepmd.pd.model.network.init import ( + constant_, + normal_, +) +from deepmd.pd.model.network.layernorm import ( + LayerNorm, +) +from deepmd.pd.model.network.mlp import ( + MLPLayer, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + PRECISION_DICT, +) +from deepmd.pd.utils.utils import ( + ActivationFn, + get_generator, + to_numpy_array, + to_paddle_tensor, +) +from deepmd.utils.version import ( + check_version_compatibility, +) + + +def get_residual( + _dim: int, + _scale: float, + _mode: str = "norm", + trainable: bool = True, + precision: str = "float64", + seed: Optional[Union[int, List[int]]] = None, +) -> paddle.Tensor: + r""" + Get residual tensor for one update vector. + + Parameters + ---------- + _dim : int + The dimension of the update vector. + _scale + The initial scale of the residual tensor. See `_mode` for details. + _mode + The mode of residual initialization for the residual tensor. + - "norm" (default): init residual using normal with `_scale` std. + - "const": init residual using element-wise constants of `_scale`. + trainable + Whether the residual tensor is trainable. + precision + The precision of the residual tensor. + seed : int, optional + Random seed for parameter initialization. + """ + random_generator = get_generator(seed) + residual = paddle.create_parameter( + [_dim], + dtype=PRECISION_DICT[precision], + default_initializer=nn.initializer.Constant(0), + ).to(device=env.DEVICE) + residual.stop_gradient = not trainable + if _mode == "norm": + normal_(residual.data, std=_scale, generator=random_generator) + elif _mode == "const": + constant_(residual.data, val=_scale) + else: + raise RuntimeError(f"Unsupported initialization mode '{_mode}'!") + return residual + + +# common ops +def _make_nei_g1( + g1_ext: paddle.Tensor, + nlist: paddle.Tensor, +) -> paddle.Tensor: + """ + Make neighbor-wise atomic invariant rep. + + Parameters + ---------- + g1_ext + Extended atomic invariant rep, with shape nb x nall x ng1. + nlist + Neighbor list, with shape nb x nloc x nnei. + + Returns + ------- + gg1: paddle.Tensor + Neighbor-wise atomic invariant rep, with shape nb x nloc x nnei x ng1. + + """ + # nlist: nb x nloc x nnei + nb, nloc, nnei = nlist.shape + # g1_ext: nb x nall x ng1 + ng1 = g1_ext.shape[-1] + # index: nb x (nloc x nnei) x ng1 + index = nlist.reshape([nb, nloc * nnei]).unsqueeze(-1).expand(-1, -1, ng1) + # gg1 : nb x (nloc x nnei) x ng1 + gg1 = paddle.gather(g1_ext, axis=1, index=index) + # gg1 : nb x nloc x nnei x ng1 + gg1 = gg1.reshape([nb, nloc, nnei, ng1]) + return gg1 + + +def _apply_nlist_mask( + gg: paddle.Tensor, + nlist_mask: paddle.Tensor, +) -> paddle.Tensor: + """ + Apply nlist mask to neighbor-wise rep tensors. + + Parameters + ---------- + gg + Neighbor-wise rep tensors, with shape nf x nloc x nnei x d. + nlist_mask + Neighbor list mask, where zero means no neighbor, with shape nf x nloc x nnei. + """ + # gg: nf x nloc x nnei x d + # msk: nf x nloc x nnei + return gg.masked_fill(~nlist_mask.unsqueeze(-1), 0.0) + + +def _apply_switch(gg: paddle.Tensor, sw: paddle.Tensor) -> paddle.Tensor: + """ + Apply switch function to neighbor-wise rep tensors. + + Parameters + ---------- + gg + Neighbor-wise rep tensors, with shape nf x nloc x nnei x d. + sw + The switch function, which equals 1 within the rcut_smth range, smoothly decays from 1 to 0 between rcut_smth and rcut, + and remains 0 beyond rcut, with shape nf x nloc x nnei. + """ + # gg: nf x nloc x nnei x d + # sw: nf x nloc x nnei + return gg * sw.unsqueeze(-1) + + +class Atten2Map(paddle.nn.Layer): + def __init__( + self, + input_dim: int, + hidden_dim: int, + head_num: int, + has_gate: bool = False, # apply gate to attn map + smooth: bool = True, + attnw_shift: float = 20.0, + precision: str = "float64", + seed: Optional[Union[int, List[int]]] = None, + ): + """Return neighbor-wise multi-head self-attention maps, with gate mechanism.""" + super().__init__() + self.input_dim = input_dim + self.hidden_dim = hidden_dim + self.head_num = head_num + self.mapqk = MLPLayer( + input_dim, + hidden_dim * 2 * head_num, + bias=False, + precision=precision, + seed=seed, + ) + self.has_gate = has_gate + self.smooth = smooth + self.attnw_shift = attnw_shift + self.precision = precision + + def forward( + self, + g2: paddle.Tensor, # nb x nloc x nnei x ng2 + h2: paddle.Tensor, # nb x nloc x nnei x 3 + nlist_mask: paddle.Tensor, # nb x nloc x nnei + sw: paddle.Tensor, # nb x nloc x nnei + ) -> paddle.Tensor: + ( + nb, + nloc, + nnei, + _, + ) = g2.shape + nd, nh = self.hidden_dim, self.head_num + # nb x nloc x nnei x nd x (nh x 2) + g2qk = self.mapqk(g2).reshape([nb, nloc, nnei, nd, nh * 2]) + # nb x nloc x (nh x 2) x nnei x nd + g2qk = paddle.permute(g2qk, (0, 1, 4, 2, 3)) + # nb x nloc x nh x nnei x nd + g2q, g2k = paddle.split(g2qk, nh, axis=2) + # g2q = paddle.nn.functional.normalize(g2q, axis=-1) + # g2k = paddle.nn.functional.normalize(g2k, axis=-1) + # nb x nloc x nh x nnei x nnei + attnw = paddle.matmul(g2q, paddle.transpose(g2k, -1, -2)) / nd**0.5 + if self.has_gate: + gate = paddle.matmul(h2, paddle.transpose(h2, -1, -2)).unsqueeze(-3) + attnw = attnw * gate + # mask the attenmap, nb x nloc x 1 x 1 x nnei + attnw_mask = ~nlist_mask.unsqueeze(2).unsqueeze(2) + # mask the attenmap, nb x nloc x 1 x nnei x 1 + attnw_mask_c = ~nlist_mask.unsqueeze(2).unsqueeze(-1) + if self.smooth: + attnw = (attnw + self.attnw_shift) * sw[:, :, None, :, None] * sw[ + :, :, None, None, : + ] - self.attnw_shift + else: + attnw = attnw.masked_fill( + attnw_mask, + float("-inf"), + ) + attnw = paddle.softmax(attnw, axis=-1) + attnw = attnw.masked_fill( + attnw_mask, + 0.0, + ) + # nb x nloc x nh x nnei x nnei + attnw = attnw.masked_fill( + attnw_mask_c, + 0.0, + ) + if self.smooth: + attnw = attnw * sw[:, :, None, :, None] * sw[:, :, None, None, :] + # nb x nloc x nnei x nnei + h2h2t = paddle.matmul(h2, paddle.transpose(h2, -1, -2)) / 3.0**0.5 + # nb x nloc x nh x nnei x nnei + ret = attnw * h2h2t[:, :, None, :, :] + # ret = paddle.softmax(g2qk, axis=-1) + # nb x nloc x nnei x nnei x nh + ret = paddle.permute(ret, (0, 1, 3, 4, 2)) + return ret + + def serialize(self) -> dict: + """Serialize the networks to a dict. + + Returns + ------- + dict + The serialized networks. + """ + return { + "@class": "Atten2Map", + "@version": 1, + "input_dim": self.input_dim, + "hidden_dim": self.hidden_dim, + "head_num": self.head_num, + "has_gate": self.has_gate, + "smooth": self.smooth, + "attnw_shift": self.attnw_shift, + "precision": self.precision, + "mapqk": self.mapqk.serialize(), + } + + @classmethod + def deserialize(cls, data: dict) -> "Atten2Map": + """Deserialize the networks from a dict. + + Parameters + ---------- + data : dict + The dict to deserialize from. + """ + data = data.copy() + check_version_compatibility(data.pop("@version"), 1, 1) + data.pop("@class") + mapqk = data.pop("mapqk") + obj = cls(**data) + obj.mapqk = MLPLayer.deserialize(mapqk) + return obj + + +class Atten2MultiHeadApply(paddle.nn.Layer): + def __init__( + self, + input_dim: int, + head_num: int, + precision: str = "float64", + seed: Optional[Union[int, List[int]]] = None, + ): + super().__init__() + self.input_dim = input_dim + self.head_num = head_num + self.mapv = MLPLayer( + input_dim, + input_dim * head_num, + bias=False, + precision=precision, + seed=child_seed(seed, 0), + ) + self.head_map = MLPLayer( + input_dim * head_num, + input_dim, + precision=precision, + seed=child_seed(seed, 1), + ) + self.precision = precision + + def forward( + self, + AA: paddle.Tensor, # nf x nloc x nnei x nnei x nh + g2: paddle.Tensor, # nf x nloc x nnei x ng2 + ) -> paddle.Tensor: + nf, nloc, nnei, ng2 = g2.shape + nh = self.head_num + # nf x nloc x nnei x ng2 x nh + g2v = self.mapv(g2).reshape([nf, nloc, nnei, ng2, nh]) + # nf x nloc x nh x nnei x ng2 + g2v = paddle.permute(g2v, (0, 1, 4, 2, 3)) + # g2v = paddle.nn.functional.normalize(g2v, axis=-1) + # nf x nloc x nh x nnei x nnei + AA = paddle.permute(AA, (0, 1, 4, 2, 3)) + # nf x nloc x nh x nnei x ng2 + ret = paddle.matmul(AA, g2v) + # nf x nloc x nnei x ng2 x nh + ret = paddle.permute(ret, (0, 1, 3, 4, 2)).reshape([nf, nloc, nnei, (ng2 * nh)]) + # nf x nloc x nnei x ng2 + return self.head_map(ret) + + def serialize(self) -> dict: + """Serialize the networks to a dict. + + Returns + ------- + dict + The serialized networks. + """ + return { + "@class": "Atten2MultiHeadApply", + "@version": 1, + "input_dim": self.input_dim, + "head_num": self.head_num, + "precision": self.precision, + "mapv": self.mapv.serialize(), + "head_map": self.head_map.serialize(), + } + + @classmethod + def deserialize(cls, data: dict) -> "Atten2MultiHeadApply": + """Deserialize the networks from a dict. + + Parameters + ---------- + data : dict + The dict to deserialize from. + """ + data = data.copy() + check_version_compatibility(data.pop("@version"), 1, 1) + data.pop("@class") + mapv = data.pop("mapv") + head_map = data.pop("head_map") + obj = cls(**data) + obj.mapv = MLPLayer.deserialize(mapv) + obj.head_map = MLPLayer.deserialize(head_map) + return obj + + +class Atten2EquiVarApply(paddle.nn.Layer): + def __init__( + self, + input_dim: int, + head_num: int, + precision: str = "float64", + seed: Optional[Union[int, List[int]]] = None, + ): + super().__init__() + self.input_dim = input_dim + self.head_num = head_num + self.head_map = MLPLayer( + head_num, 1, bias=False, precision=precision, seed=seed + ) + self.precision = precision + + def forward( + self, + AA: paddle.Tensor, # nf x nloc x nnei x nnei x nh + h2: paddle.Tensor, # nf x nloc x nnei x 3 + ) -> paddle.Tensor: + nf, nloc, nnei, _ = h2.shape + nh = self.head_num + # nf x nloc x nh x nnei x nnei + AA = paddle.permute(AA, (0, 1, 4, 2, 3)) + h2m = paddle.unsqueeze(h2, axis=2) + # nf x nloc x nh x nnei x 3 + h2m = paddle.tile(h2m, [1, 1, nh, 1, 1]) + # nf x nloc x nh x nnei x 3 + ret = paddle.matmul(AA, h2m) + # nf x nloc x nnei x 3 x nh + ret = paddle.permute(ret, (0, 1, 3, 4, 2)).reshape([nf, nloc, nnei, 3, nh]) + # nf x nloc x nnei x 3 + return paddle.squeeze(self.head_map(ret), axis=-1) + + def serialize(self) -> dict: + """Serialize the networks to a dict. + + Returns + ------- + dict + The serialized networks. + """ + return { + "@class": "Atten2EquiVarApply", + "@version": 1, + "input_dim": self.input_dim, + "head_num": self.head_num, + "precision": self.precision, + "head_map": self.head_map.serialize(), + } + + @classmethod + def deserialize(cls, data: dict) -> "Atten2EquiVarApply": + """Deserialize the networks from a dict. + + Parameters + ---------- + data : dict + The dict to deserialize from. + """ + data = data.copy() + check_version_compatibility(data.pop("@version"), 1, 1) + data.pop("@class") + head_map = data.pop("head_map") + obj = cls(**data) + obj.head_map = MLPLayer.deserialize(head_map) + return obj + + +class LocalAtten(paddle.nn.Layer): + def __init__( + self, + input_dim: int, + hidden_dim: int, + head_num: int, + smooth: bool = True, + attnw_shift: float = 20.0, + precision: str = "float64", + seed: Optional[Union[int, List[int]]] = None, + ): + super().__init__() + self.input_dim = input_dim + self.hidden_dim = hidden_dim + self.head_num = head_num + self.mapq = MLPLayer( + input_dim, + hidden_dim * 1 * head_num, + bias=False, + precision=precision, + seed=child_seed(seed, 0), + ) + self.mapkv = MLPLayer( + input_dim, + (hidden_dim + input_dim) * head_num, + bias=False, + precision=precision, + seed=child_seed(seed, 1), + ) + self.head_map = MLPLayer( + input_dim * head_num, + input_dim, + precision=precision, + seed=child_seed(seed, 2), + ) + self.smooth = smooth + self.attnw_shift = attnw_shift + self.precision = precision + + def forward( + self, + g1: paddle.Tensor, # nb x nloc x ng1 + gg1: paddle.Tensor, # nb x nloc x nnei x ng1 + nlist_mask: paddle.Tensor, # nb x nloc x nnei + sw: paddle.Tensor, # nb x nloc x nnei + ) -> paddle.Tensor: + nb, nloc, nnei = nlist_mask.shape + ni, nd, nh = self.input_dim, self.hidden_dim, self.head_num + assert ni == g1.shape[-1] + assert ni == gg1.shape[-1] + # nb x nloc x nd x nh + g1q = self.mapq(g1).reshape([nb, nloc, nd, nh]) + # nb x nloc x nh x nd + g1q = paddle.permute(g1q, (0, 1, 3, 2)) + # nb x nloc x nnei x (nd+ni) x nh + gg1kv = self.mapkv(gg1).reshape([nb, nloc, nnei, nd + ni, nh]) + gg1kv = paddle.permute(gg1kv, (0, 1, 4, 2, 3)) + # nb x nloc x nh x nnei x nd, nb x nloc x nh x nnei x ng1 + gg1k, gg1v = paddle.split(gg1kv, [nd, ni], axis=-1) + + # nb x nloc x nh x 1 x nnei + attnw = ( + paddle.matmul(g1q.unsqueeze(-2), paddle.transpose(gg1k, -1, -2)) / nd**0.5 + ) + # nb x nloc x nh x nnei + attnw = attnw.squeeze(-2) + # mask the attenmap, nb x nloc x 1 x nnei + attnw_mask = ~nlist_mask.unsqueeze(-2) + # nb x nloc x nh x nnei + if self.smooth: + attnw = (attnw + self.attnw_shift) * sw.unsqueeze(-2) - self.attnw_shift + else: + attnw = attnw.masked_fill( + attnw_mask, + float("-inf"), + ) + attnw = paddle.softmax(attnw, axis=-1) + attnw = attnw.masked_fill( + attnw_mask, + 0.0, + ) + if self.smooth: + attnw = attnw * sw.unsqueeze(-2) + + # nb x nloc x nh x ng1 + ret = ( + paddle.matmul(attnw.unsqueeze(-2), gg1v) + .squeeze(-2) + .reshape([nb, nloc, nh * ni]) + ) + # nb x nloc x ng1 + ret = self.head_map(ret) + return ret + + def serialize(self) -> dict: + """Serialize the networks to a dict. + + Returns + ------- + dict + The serialized networks. + """ + return { + "@class": "LocalAtten", + "@version": 1, + "input_dim": self.input_dim, + "hidden_dim": self.hidden_dim, + "head_num": self.head_num, + "smooth": self.smooth, + "attnw_shift": self.attnw_shift, + "precision": self.precision, + "mapq": self.mapq.serialize(), + "mapkv": self.mapkv.serialize(), + "head_map": self.head_map.serialize(), + } + + @classmethod + def deserialize(cls, data: dict) -> "LocalAtten": + """Deserialize the networks from a dict. + + Parameters + ---------- + data : dict + The dict to deserialize from. + """ + data = data.copy() + check_version_compatibility(data.pop("@version"), 1, 1) + data.pop("@class") + mapq = data.pop("mapq") + mapkv = data.pop("mapkv") + head_map = data.pop("head_map") + obj = cls(**data) + obj.mapq = MLPLayer.deserialize(mapq) + obj.mapkv = MLPLayer.deserialize(mapkv) + obj.head_map = MLPLayer.deserialize(head_map) + return obj + + +class RepformerLayer(paddle.nn.Layer): + def __init__( + self, + rcut, + rcut_smth, + sel: int, + ntypes: int, + g1_dim=128, + g2_dim=16, + axis_neuron: int = 4, + update_chnnl_2: bool = True, + update_g1_has_conv: bool = True, + update_g1_has_drrd: bool = True, + update_g1_has_grrg: bool = True, + update_g1_has_attn: bool = True, + update_g2_has_g1g1: bool = True, + update_g2_has_attn: bool = True, + update_h2: bool = False, + attn1_hidden: int = 64, + attn1_nhead: int = 4, + attn2_hidden: int = 16, + attn2_nhead: int = 4, + attn2_has_gate: bool = False, + activation_function: str = "tanh", + update_style: str = "res_avg", + update_residual: float = 0.001, + update_residual_init: str = "norm", + smooth: bool = True, + precision: str = "float64", + trainable_ln: bool = True, + ln_eps: Optional[float] = 1e-5, + seed: Optional[Union[int, List[int]]] = None, + ): + super().__init__() + self.epsilon = 1e-4 # protection of 1./nnei + self.rcut = rcut + self.rcut_smth = rcut_smth + self.ntypes = ntypes + sel = [sel] if isinstance(sel, int) else sel + self.nnei = sum(sel) + assert len(sel) == 1 + self.sel = sel + self.sec = self.sel + self.axis_neuron = axis_neuron + self.activation_function = activation_function + self.act = ActivationFn(activation_function) + self.update_g1_has_grrg = update_g1_has_grrg + self.update_g1_has_drrd = update_g1_has_drrd + self.update_g1_has_conv = update_g1_has_conv + self.update_g1_has_attn = update_g1_has_attn + self.update_chnnl_2 = update_chnnl_2 + self.update_g2_has_g1g1 = update_g2_has_g1g1 if self.update_chnnl_2 else False + self.update_g2_has_attn = update_g2_has_attn if self.update_chnnl_2 else False + self.update_h2 = update_h2 if self.update_chnnl_2 else False + del update_g2_has_g1g1, update_g2_has_attn, update_h2 + self.attn1_hidden = attn1_hidden + self.attn1_nhead = attn1_nhead + self.attn2_hidden = attn2_hidden + self.attn2_nhead = attn2_nhead + self.attn2_has_gate = attn2_has_gate + self.update_style = update_style + self.update_residual = update_residual + self.update_residual_init = update_residual_init + self.smooth = smooth + self.g1_dim = g1_dim + self.g2_dim = g2_dim + self.trainable_ln = trainable_ln + self.ln_eps = ln_eps + self.precision = precision + self.seed = seed + + assert update_residual_init in [ + "norm", + "const", + ], "'update_residual_init' only support 'norm' or 'const'!" + self.update_residual = update_residual + self.update_residual_init = update_residual_init + self.g1_residual = [] + self.g2_residual = [] + self.h2_residual = [] + + if self.update_style == "res_residual": + self.g1_residual.append( + get_residual( + g1_dim, + self.update_residual, + self.update_residual_init, + precision=precision, + seed=child_seed(seed, 0), + ) + ) + + g1_in_dim = self.cal_1_dim(g1_dim, g2_dim, self.axis_neuron) + self.linear1 = MLPLayer( + g1_in_dim, + g1_dim, + precision=precision, + seed=child_seed(seed, 1), + ) + self.linear2 = None + self.proj_g1g2 = None + self.proj_g1g1g2 = None + self.attn2g_map = None + self.attn2_mh_apply = None + self.attn2_lm = None + self.attn2_ev_apply = None + self.loc_attn = None + + if self.update_chnnl_2: + self.linear2 = MLPLayer( + g2_dim, + g2_dim, + precision=precision, + seed=child_seed(seed, 2), + ) + if self.update_style == "res_residual": + self.g2_residual.append( + get_residual( + g2_dim, + self.update_residual, + self.update_residual_init, + precision=precision, + seed=child_seed(seed, 3), + ) + ) + if self.update_g1_has_conv: + self.proj_g1g2 = MLPLayer( + g1_dim, + g2_dim, + bias=False, + precision=precision, + seed=child_seed(seed, 4), + ) + if self.update_g2_has_g1g1: + self.proj_g1g1g2 = MLPLayer( + g1_dim, + g2_dim, + bias=False, + precision=precision, + seed=child_seed(seed, 5), + ) + if self.update_style == "res_residual": + self.g2_residual.append( + get_residual( + g2_dim, + self.update_residual, + self.update_residual_init, + precision=precision, + seed=child_seed(seed, 6), + ) + ) + if self.update_g2_has_attn or self.update_h2: + self.attn2g_map = Atten2Map( + g2_dim, + attn2_hidden, + attn2_nhead, + attn2_has_gate, + self.smooth, + precision=precision, + seed=child_seed(seed, 7), + ) + if self.update_g2_has_attn: + self.attn2_mh_apply = Atten2MultiHeadApply( + g2_dim, attn2_nhead, precision=precision, seed=child_seed(seed, 8) + ) + self.attn2_lm = LayerNorm( + g2_dim, + eps=ln_eps, + trainable=trainable_ln, + precision=precision, + seed=child_seed(seed, 9), + ) + if self.update_style == "res_residual": + self.g2_residual.append( + get_residual( + g2_dim, + self.update_residual, + self.update_residual_init, + precision=precision, + seed=child_seed(seed, 10), + ) + ) + + if self.update_h2: + self.attn2_ev_apply = Atten2EquiVarApply( + g2_dim, attn2_nhead, precision=precision, seed=child_seed(seed, 11) + ) + if self.update_style == "res_residual": + self.h2_residual.append( + get_residual( + 1, + self.update_residual, + self.update_residual_init, + precision=precision, + seed=child_seed(seed, 12), + ) + ) + if self.update_g1_has_attn: + self.loc_attn = LocalAtten( + g1_dim, + attn1_hidden, + attn1_nhead, + self.smooth, + precision=precision, + seed=child_seed(seed, 13), + ) + if self.update_style == "res_residual": + self.g1_residual.append( + get_residual( + g1_dim, + self.update_residual, + self.update_residual_init, + precision=precision, + seed=child_seed(seed, 14), + ) + ) + + self.g1_residual = nn.ParameterList(self.g1_residual) + self.g2_residual = nn.ParameterList(self.g2_residual) + self.h2_residual = nn.ParameterList(self.h2_residual) + + def cal_1_dim(self, g1d: int, g2d: int, ax: int) -> int: + ret = g1d + if self.update_g1_has_grrg: + ret += g2d * ax + if self.update_g1_has_drrd: + ret += g1d * ax + if self.update_g1_has_conv: + ret += g2d + return ret + + def _update_h2( + self, + h2: paddle.Tensor, + attn: paddle.Tensor, + ) -> paddle.Tensor: + """ + Calculate the attention weights update for pair-wise equivariant rep. + + Parameters + ---------- + h2 + Pair-wise equivariant rep tensors, with shape nf x nloc x nnei x 3. + attn + Attention weights from g2 attention, with shape nf x nloc x nnei x nnei x nh2. + """ + assert self.attn2_ev_apply is not None + # nf x nloc x nnei x nh2 + h2_1 = self.attn2_ev_apply(attn, h2) + return h2_1 + + def _update_g1_conv( + self, + gg1: paddle.Tensor, + g2: paddle.Tensor, + nlist_mask: paddle.Tensor, + sw: paddle.Tensor, + ) -> paddle.Tensor: + """ + Calculate the convolution update for atomic invariant rep. + + Parameters + ---------- + gg1 + Neighbor-wise atomic invariant rep, with shape nb x nloc x nnei x ng1. + g2 + Pair invariant rep, with shape nb x nloc x nnei x ng2. + nlist_mask + Neighbor list mask, where zero means no neighbor, with shape nb x nloc x nnei. + sw + The switch function, which equals 1 within the rcut_smth range, smoothly decays from 1 to 0 between rcut_smth and rcut, + and remains 0 beyond rcut, with shape nb x nloc x nnei. + """ + assert self.proj_g1g2 is not None + nb, nloc, nnei, _ = g2.shape + ng1 = gg1.shape[-1] + ng2 = g2.shape[-1] + # gg1 : nb x nloc x nnei x ng2 + gg1 = self.proj_g1g2(gg1).reshape([nb, nloc, nnei, ng2]) + # nb x nloc x nnei x ng2 + gg1 = _apply_nlist_mask(gg1, nlist_mask) + if not self.smooth: + # normalized by number of neighbors, not smooth + # nb x nloc x 1 + # must use type_as here to convert bool to float, otherwise there will be numerical difference from numpy + invnnei = 1.0 / ( + self.epsilon + paddle.sum(nlist_mask.type_as(gg1), axis=-1) + ).unsqueeze(-1) + else: + gg1 = _apply_switch(gg1, sw) + invnnei = (1.0 / float(nnei)) * paddle.ones( + (nb, nloc, 1), dtype=gg1.dtype + ).to(device=gg1.place) + # nb x nloc x ng2 + g1_11 = paddle.sum(g2 * gg1, axis=2) * invnnei + return g1_11 + + @staticmethod + def _cal_hg( + g2: paddle.Tensor, + h2: paddle.Tensor, + nlist_mask: paddle.Tensor, + sw: paddle.Tensor, + smooth: bool = True, + epsilon: float = 1e-4, + ) -> paddle.Tensor: + """ + Calculate the transposed rotation matrix. + + Parameters + ---------- + g2 + Neighbor-wise/Pair-wise invariant rep tensors, with shape nb x nloc x nnei x ng2. + h2 + Neighbor-wise/Pair-wise equivariant rep tensors, with shape nb x nloc x nnei x 3. + nlist_mask + Neighbor list mask, where zero means no neighbor, with shape nb x nloc x nnei. + sw + The switch function, which equals 1 within the rcut_smth range, smoothly decays from 1 to 0 between rcut_smth and rcut, + and remains 0 beyond rcut, with shape nb x nloc x nnei. + smooth + Whether to use smoothness in processes such as attention weights calculation. + epsilon + Protection of 1./nnei. + + Returns + ------- + hg + The transposed rotation matrix, with shape nb x nloc x 3 x ng2. + """ + # g2: nb x nloc x nnei x ng2 + # h2: nb x nloc x nnei x 3 + # msk: nb x nloc x nnei + nb, nloc, nnei, _ = g2.shape + ng2 = g2.shape[-1] + # nb x nloc x nnei x ng2 + g2 = _apply_nlist_mask(g2, nlist_mask) + if not smooth: + # nb x nloc + # must use type_as here to convert bool to float, otherwise there will be numerical difference from numpy + invnnei = 1.0 / (epsilon + paddle.sum(nlist_mask.type_as(g2), axis=-1)) + # nb x nloc x 1 x 1 + invnnei = invnnei.unsqueeze(-1).unsqueeze(-1) + else: + g2 = _apply_switch(g2, sw) + invnnei = (1.0 / float(nnei)) * paddle.ones( + (nb, nloc, 1, 1), dtype=g2.dtype + ).to(device=g2.place) + # nb x nloc x 3 x ng2 + h2g2 = paddle.matmul(paddle.transpose(h2, -1, -2), g2) * invnnei + return h2g2 + + @staticmethod + def _cal_grrg(h2g2: paddle.Tensor, axis_neuron: int) -> paddle.Tensor: + """ + Calculate the atomic invariant rep. + + Parameters + ---------- + h2g2 + The transposed rotation matrix, with shape nb x nloc x 3 x ng2. + axis_neuron + Size of the submatrix. + + Returns + ------- + grrg + Atomic invariant rep, with shape nb x nloc x (axis_neuron x ng2) + """ + # nb x nloc x 3 x ng2 + nb, nloc, _, ng2 = h2g2.shape + # nb x nloc x 3 x axis + h2g2m = paddle.split(h2g2, axis_neuron, axis=-1)[0] + # nb x nloc x axis x ng2 + g1_13 = paddle.matmul(paddle.transpose(h2g2m, -1, -2), h2g2) / (3.0**1) + # nb x nloc x (axisxng2) + g1_13 = g1_13.reshape([nb, nloc, axis_neuron * ng2]) + return g1_13 + + def symmetrization_op( + self, + g2: paddle.Tensor, + h2: paddle.Tensor, + nlist_mask: paddle.Tensor, + sw: paddle.Tensor, + axis_neuron: int, + smooth: bool = True, + epsilon: float = 1e-4, + ) -> paddle.Tensor: + """ + Symmetrization operator to obtain atomic invariant rep. + + Parameters + ---------- + g2 + Neighbor-wise/Pair-wise invariant rep tensors, with shape nb x nloc x nnei x ng2. + h2 + Neighbor-wise/Pair-wise equivariant rep tensors, with shape nb x nloc x nnei x 3. + nlist_mask + Neighbor list mask, where zero means no neighbor, with shape nb x nloc x nnei. + sw + The switch function, which equals 1 within the rcut_smth range, smoothly decays from 1 to 0 between rcut_smth and rcut, + and remains 0 beyond rcut, with shape nb x nloc x nnei. + axis_neuron + Size of the submatrix. + smooth + Whether to use smoothness in processes such as attention weights calculation. + epsilon + Protection of 1./nnei. + + Returns + ------- + grrg + Atomic invariant rep, with shape nb x nloc x (axis_neuron x ng2) + """ + # g2: nb x nloc x nnei x ng2 + # h2: nb x nloc x nnei x 3 + # msk: nb x nloc x nnei + nb, nloc, nnei, _ = g2.shape + # nb x nloc x 3 x ng2 + h2g2 = self._cal_hg(g2, h2, nlist_mask, sw, smooth=smooth, epsilon=epsilon) + # nb x nloc x (axisxng2) + g1_13 = self._cal_grrg(h2g2, axis_neuron) + return g1_13 + + def _update_g2_g1g1( + self, + g1: paddle.Tensor, # nb x nloc x ng1 + gg1: paddle.Tensor, # nb x nloc x nnei x ng1 + nlist_mask: paddle.Tensor, # nb x nloc x nnei + sw: paddle.Tensor, # nb x nloc x nnei + ) -> paddle.Tensor: + """ + Update the g2 using element-wise dot g1_i * g1_j. + + Parameters + ---------- + g1 + Atomic invariant rep, with shape nb x nloc x ng1. + gg1 + Neighbor-wise atomic invariant rep, with shape nb x nloc x nnei x ng1. + nlist_mask + Neighbor list mask, where zero means no neighbor, with shape nb x nloc x nnei. + sw + The switch function, which equals 1 within the rcut_smth range, smoothly decays from 1 to 0 between rcut_smth and rcut, + and remains 0 beyond rcut, with shape nb x nloc x nnei. + """ + ret = g1.unsqueeze(-2) * gg1 + # nb x nloc x nnei x ng1 + ret = _apply_nlist_mask(ret, nlist_mask) + if self.smooth: + ret = _apply_switch(ret, sw) + return ret + + def forward( + self, + g1_ext: paddle.Tensor, # nf x nall x ng1 + g2: paddle.Tensor, # nf x nloc x nnei x ng2 + h2: paddle.Tensor, # nf x nloc x nnei x 3 + nlist: paddle.Tensor, # nf x nloc x nnei + nlist_mask: paddle.Tensor, # nf x nloc x nnei + sw: paddle.Tensor, # switch func, nf x nloc x nnei + ): + """ + Parameters + ---------- + g1_ext : nf x nall x ng1 extended single-atom chanel + g2 : nf x nloc x nnei x ng2 pair-atom channel, invariant + h2 : nf x nloc x nnei x 3 pair-atom channel, equivariant + nlist : nf x nloc x nnei neighbor list (padded neis are set to 0) + nlist_mask : nf x nloc x nnei masks of the neighbor list. real nei 1 otherwise 0 + sw : nf x nloc x nnei switch function + + Returns + ------- + g1: nf x nloc x ng1 updated single-atom chanel + g2: nf x nloc x nnei x ng2 updated pair-atom channel, invariant + h2: nf x nloc x nnei x 3 updated pair-atom channel, equivariant + """ + cal_gg1 = ( + self.update_g1_has_drrd + or self.update_g1_has_conv + or self.update_g1_has_attn + or self.update_g2_has_g1g1 + ) + + nb, nloc, nnei, _ = g2.shape + nall = g1_ext.shape[1] + g1, _ = paddle.split(g1_ext, [nloc, nall - nloc], axis=1) + assert (nb, nloc) == g1.shape[:2] + assert (nb, nloc, nnei) == h2.shape[:3] + + g2_update: List[paddle.Tensor] = [g2] + h2_update: List[paddle.Tensor] = [h2] + g1_update: List[paddle.Tensor] = [g1] + g1_mlp: List[paddle.Tensor] = [g1] + + if cal_gg1: + gg1 = _make_nei_g1(g1_ext, nlist) + else: + gg1 = None + + if self.update_chnnl_2: + # mlp(g2) + assert self.linear2 is not None + # nb x nloc x nnei x ng2 + g2_1 = self.act(self.linear2(g2)) + g2_update.append(g2_1) + + if self.update_g2_has_g1g1: + # linear(g1_i * g1_j) + assert gg1 is not None + assert self.proj_g1g1g2 is not None + g2_update.append( + self.proj_g1g1g2(self._update_g2_g1g1(g1, gg1, nlist_mask, sw)) + ) + + if self.update_g2_has_attn or self.update_h2: + # gated_attention(g2, h2) + assert self.attn2g_map is not None + # nb x nloc x nnei x nnei x nh + AAg = self.attn2g_map(g2, h2, nlist_mask, sw) + + if self.update_g2_has_attn: + assert self.attn2_mh_apply is not None + assert self.attn2_lm is not None + # nb x nloc x nnei x ng2 + g2_2 = self.attn2_mh_apply(AAg, g2) + g2_2 = self.attn2_lm(g2_2) + g2_update.append(g2_2) + + if self.update_h2: + # linear_head(attention_weights * h2) + h2_update.append(self._update_h2(h2, AAg)) + + if self.update_g1_has_conv: + assert gg1 is not None + g1_mlp.append(self._update_g1_conv(gg1, g2, nlist_mask, sw)) + + if self.update_g1_has_grrg: + g1_mlp.append( + self.symmetrization_op( + g2, + h2, + nlist_mask, + sw, + self.axis_neuron, + smooth=self.smooth, + epsilon=self.epsilon, + ) + ) + + if self.update_g1_has_drrd: + assert gg1 is not None + g1_mlp.append( + self.symmetrization_op( + gg1, + h2, + nlist_mask, + sw, + self.axis_neuron, + smooth=self.smooth, + epsilon=self.epsilon, + ) + ) + + # nb x nloc x [ng1+ng2+(axisxng2)+(axisxng1)] + # conv grrg drrd + g1_1 = self.act(self.linear1(paddle.concat(g1_mlp, axis=-1))) + g1_update.append(g1_1) + + if self.update_g1_has_attn: + assert gg1 is not None + assert self.loc_attn is not None + g1_update.append(self.loc_attn(g1, gg1, nlist_mask, sw)) + + # update + if self.update_chnnl_2: + g2_new = self.list_update(g2_update, "g2") + h2_new = self.list_update(h2_update, "h2") + else: + g2_new, h2_new = g2, h2 + g1_new = self.list_update(g1_update, "g1") + return g1_new, g2_new, h2_new + + # @paddle.jit.export + def list_update_res_avg( + self, + update_list: List[paddle.Tensor], + ) -> paddle.Tensor: + nitem = len(update_list) + uu = update_list[0] + for ii in range(1, nitem): + uu = uu + update_list[ii] + return uu / (float(nitem) ** 0.5) + + # @paddle.jit.export + def list_update_res_incr(self, update_list: List[paddle.Tensor]) -> paddle.Tensor: + nitem = len(update_list) + uu = update_list[0] + scale = 1.0 / (float(nitem - 1) ** 0.5) if nitem > 1 else 0.0 + for ii in range(1, nitem): + uu = uu + scale * update_list[ii] + return uu + + # @paddle.jit.export + def list_update_res_residual( + self, update_list: List[paddle.Tensor], update_name: str = "g1" + ) -> paddle.Tensor: + nitem = len(update_list) + uu = update_list[0] + # make jit happy + if update_name == "g1": + for ii, vv in enumerate(self.g1_residual): + uu = uu + vv * update_list[ii + 1] + elif update_name == "g2": + for ii, vv in enumerate(self.g2_residual): + uu = uu + vv * update_list[ii + 1] + elif update_name == "h2": + for ii, vv in enumerate(self.h2_residual): + uu = uu + vv * update_list[ii + 1] + else: + raise NotImplementedError + return uu + + # @paddle.jit.export + def list_update( + self, update_list: List[paddle.Tensor], update_name: str = "g1" + ) -> paddle.Tensor: + if self.update_style == "res_avg": + return self.list_update_res_avg(update_list) + elif self.update_style == "res_incr": + return self.list_update_res_incr(update_list) + elif self.update_style == "res_residual": + return self.list_update_res_residual(update_list, update_name=update_name) + else: + raise RuntimeError(f"unknown update style {self.update_style}") + + def serialize(self) -> dict: + """Serialize the networks to a dict. + + Returns + ------- + dict + The serialized networks. + """ + data = { + "@class": "RepformerLayer", + "@version": 1, + "rcut": self.rcut, + "rcut_smth": self.rcut_smth, + "sel": self.sel, + "ntypes": self.ntypes, + "g1_dim": self.g1_dim, + "g2_dim": self.g2_dim, + "axis_neuron": self.axis_neuron, + "update_chnnl_2": self.update_chnnl_2, + "update_g1_has_conv": self.update_g1_has_conv, + "update_g1_has_drrd": self.update_g1_has_drrd, + "update_g1_has_grrg": self.update_g1_has_grrg, + "update_g1_has_attn": self.update_g1_has_attn, + "update_g2_has_g1g1": self.update_g2_has_g1g1, + "update_g2_has_attn": self.update_g2_has_attn, + "update_h2": self.update_h2, + "attn1_hidden": self.attn1_hidden, + "attn1_nhead": self.attn1_nhead, + "attn2_hidden": self.attn2_hidden, + "attn2_nhead": self.attn2_nhead, + "attn2_has_gate": self.attn2_has_gate, + "activation_function": self.activation_function, + "update_style": self.update_style, + "smooth": self.smooth, + "precision": self.precision, + "trainable_ln": self.trainable_ln, + "ln_eps": self.ln_eps, + "linear1": self.linear1.serialize(), + } + if self.update_chnnl_2: + data.update( + { + "linear2": self.linear2.serialize(), + } + ) + if self.update_g1_has_conv: + data.update( + { + "proj_g1g2": self.proj_g1g2.serialize(), + } + ) + if self.update_g2_has_g1g1: + data.update( + { + "proj_g1g1g2": self.proj_g1g1g2.serialize(), + } + ) + if self.update_g2_has_attn or self.update_h2: + data.update( + { + "attn2g_map": self.attn2g_map.serialize(), + } + ) + if self.update_g2_has_attn: + data.update( + { + "attn2_mh_apply": self.attn2_mh_apply.serialize(), + "attn2_lm": self.attn2_lm.serialize(), + } + ) + + if self.update_h2: + data.update( + { + "attn2_ev_apply": self.attn2_ev_apply.serialize(), + } + ) + if self.update_g1_has_attn: + data.update( + { + "loc_attn": self.loc_attn.serialize(), + } + ) + if self.update_style == "res_residual": + data.update( + { + "g1_residual": [to_numpy_array(t) for t in self.g1_residual], + "g2_residual": [to_numpy_array(t) for t in self.g2_residual], + "h2_residual": [to_numpy_array(t) for t in self.h2_residual], + } + ) + return data + + @classmethod + def deserialize(cls, data: dict) -> "RepformerLayer": + """Deserialize the networks from a dict. + + Parameters + ---------- + data : dict + The dict to deserialize from. + """ + data = data.copy() + check_version_compatibility(data.pop("@version"), 1, 1) + data.pop("@class") + linear1 = data.pop("linear1") + update_chnnl_2 = data["update_chnnl_2"] + update_g1_has_conv = data["update_g1_has_conv"] + update_g2_has_g1g1 = data["update_g2_has_g1g1"] + update_g2_has_attn = data["update_g2_has_attn"] + update_h2 = data["update_h2"] + update_g1_has_attn = data["update_g1_has_attn"] + update_style = data["update_style"] + + linear2 = data.pop("linear2", None) + proj_g1g2 = data.pop("proj_g1g2", None) + proj_g1g1g2 = data.pop("proj_g1g1g2", None) + attn2g_map = data.pop("attn2g_map", None) + attn2_mh_apply = data.pop("attn2_mh_apply", None) + attn2_lm = data.pop("attn2_lm", None) + attn2_ev_apply = data.pop("attn2_ev_apply", None) + loc_attn = data.pop("loc_attn", None) + g1_residual = data.pop("g1_residual", []) + g2_residual = data.pop("g2_residual", []) + h2_residual = data.pop("h2_residual", []) + + obj = cls(**data) + obj.linear1 = MLPLayer.deserialize(linear1) + if update_chnnl_2: + assert isinstance(linear2, dict) + obj.linear2 = MLPLayer.deserialize(linear2) + if update_g1_has_conv: + assert isinstance(proj_g1g2, dict) + obj.proj_g1g2 = MLPLayer.deserialize(proj_g1g2) + if update_g2_has_g1g1: + assert isinstance(proj_g1g1g2, dict) + obj.proj_g1g1g2 = MLPLayer.deserialize(proj_g1g1g2) + if update_g2_has_attn or update_h2: + assert isinstance(attn2g_map, dict) + obj.attn2g_map = Atten2Map.deserialize(attn2g_map) + if update_g2_has_attn: + assert isinstance(attn2_mh_apply, dict) + assert isinstance(attn2_lm, dict) + obj.attn2_mh_apply = Atten2MultiHeadApply.deserialize(attn2_mh_apply) + obj.attn2_lm = LayerNorm.deserialize(attn2_lm) + if update_h2: + assert isinstance(attn2_ev_apply, dict) + obj.attn2_ev_apply = Atten2EquiVarApply.deserialize(attn2_ev_apply) + if update_g1_has_attn: + assert isinstance(loc_attn, dict) + obj.loc_attn = LocalAtten.deserialize(loc_attn) + if update_style == "res_residual": + for ii, t in enumerate(obj.g1_residual): + t.data = to_paddle_tensor(g1_residual[ii]) + for ii, t in enumerate(obj.g2_residual): + t.data = to_paddle_tensor(g2_residual[ii]) + for ii, t in enumerate(obj.h2_residual): + t.data = to_paddle_tensor(h2_residual[ii]) + return obj diff --git a/deepmd/pd/model/descriptor/repformer_layer_old_impl.py b/deepmd/pd/model/descriptor/repformer_layer_old_impl.py new file mode 100644 index 0000000000..5ad7624288 --- /dev/null +++ b/deepmd/pd/model/descriptor/repformer_layer_old_impl.py @@ -0,0 +1,751 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Callable, + List, +) + +import paddle + +from deepmd.pd.model.network.network import ( + SimpleLinear, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.utils import ( + ActivationFn, +) + + +def _make_nei_g1( + g1_ext: paddle.Tensor, + nlist: paddle.Tensor, +) -> paddle.Tensor: + # nlist: nb x nloc x nnei + nb, nloc, nnei = nlist.shape + # g1_ext: nb x nall x ng1 + ng1 = g1_ext.shape[-1] + # index: nb x (nloc x nnei) x ng1 + index = nlist.reshape([nb, nloc * nnei]).unsqueeze(-1).expand(-1, -1, ng1) + # gg1 : nb x (nloc x nnei) x ng1 + gg1 = paddle.take_along_axis(g1_ext, axis=1, index=index) + # gg1 : nb x nloc x nnei x ng1 + gg1 = gg1.reshape([nb, nloc, nnei, ng1]) + return gg1 + + +def _apply_nlist_mask( + gg: paddle.Tensor, + nlist_mask: paddle.Tensor, +) -> paddle.Tensor: + # gg: nf x nloc x nnei x ng + # msk: nf x nloc x nnei + return gg.masked_fill(~nlist_mask.unsqueeze(-1), 0.0) + + +def _apply_switch(gg: paddle.Tensor, sw: paddle.Tensor) -> paddle.Tensor: + # gg: nf x nloc x nnei x ng + # sw: nf x nloc x nnei + return gg * sw.unsqueeze(-1) + + +def _apply_h_norm( + hh: paddle.Tensor, # nf x nloc x nnei x 3 +) -> paddle.Tensor: + """Normalize h by the std of vector length. + do not have an idea if this is a good way. + """ + nf, nl, nnei, _ = hh.shape + # nf x nloc x nnei + normh = paddle.linalg.norm(hh, axis=-1) + # nf x nloc + std = paddle.std(normh, axis=-1) + # nf x nloc x nnei x 3 + hh = hh[:, :, :, :] / (1.0 + std[:, :, None, None]) + return hh + + +class Atten2Map(paddle.nn.Layer): + def __init__( + self, + ni: int, + nd: int, + nh: int, + has_gate: bool = False, # apply gate to attn map + smooth: bool = True, + attnw_shift: float = 20.0, + ): + super().__init__() + self.ni = ni + self.nd = nd + self.nh = nh + self.mapqk = SimpleLinear(ni, nd * 2 * nh, bias=False) # todo + self.has_gate = has_gate + self.smooth = smooth + self.attnw_shift = attnw_shift + + def forward( + self, + g2: paddle.Tensor, # nb x nloc x nnei x ng2 + h2: paddle.Tensor, # nb x nloc x nnei x 3 + nlist_mask: paddle.Tensor, # nb x nloc x nnei + sw: paddle.Tensor, # nb x nloc x nnei + ) -> paddle.Tensor: + ( + nb, + nloc, + nnei, + _, + ) = g2.shape + nd, nh = self.nd, self.nh + # nb x nloc x nnei x nd x (nh x 2) + g2qk = self.mapqk(g2).reshape([nb, nloc, nnei, nd, nh * 2]) + # nb x nloc x (nh x 2) x nnei x nd + g2qk = paddle.transpose(g2qk, (0, 1, 4, 2, 3)) + # nb x nloc x nh x nnei x nd + g2q, g2k = paddle.split(g2qk, g2qk.shape[2] // nh, axis=2) + # g2q = paddle.nn.functional.normalize(g2q, axis=-1) + # g2k = paddle.nn.functional.normalize(g2k, axis=-1) + # nb x nloc x nh x nnei x nnei + attnw = paddle.matmul(g2q, paddle.transpose(g2k, -1, -2)) / nd**0.5 + if self.has_gate: + gate = paddle.matmul(h2, paddle.transpose(h2, -1, -2)).unsqueeze(-3) + attnw = attnw * gate + # mask the attenmap, nb x nloc x 1 x 1 x nnei + attnw_mask = ~nlist_mask.unsqueeze(2).unsqueeze(2) + # mask the attenmap, nb x nloc x 1 x nnei x 1 + attnw_mask_c = ~nlist_mask.unsqueeze(2).unsqueeze(-1) + if self.smooth: + attnw = (attnw + self.attnw_shift) * sw[:, :, None, :, None] * sw[ + :, :, None, None, : + ] - self.attnw_shift + else: + attnw = attnw.masked_fill( + attnw_mask, + float("-inf"), + ) + attnw = paddle.softmax(attnw, axis=-1) + attnw = attnw.masked_fill( + attnw_mask, + 0.0, + ) + # nb x nloc x nh x nnei x nnei + attnw = attnw.masked_fill( + attnw_mask_c, + 0.0, + ) + if self.smooth: + attnw = attnw * sw[:, :, None, :, None] * sw[:, :, None, None, :] + # nb x nloc x nnei x nnei + h2h2t = paddle.matmul(h2, paddle.transpose(h2, -1, -2)) / 3.0**0.5 + # nb x nloc x nh x nnei x nnei + ret = attnw * h2h2t[:, :, None, :, :] + # ret = paddle.softmax(g2qk, axis=-1) + # nb x nloc x nnei x nnei x nh + ret = paddle.transpose(ret, (0, 1, 3, 4, 2)) + return ret + + +class Atten2MultiHeadApply(paddle.nn.Layer): + def __init__( + self, + ni: int, + nh: int, + ): + super().__init__() + self.ni = ni + self.nh = nh + self.mapv = SimpleLinear(ni, ni * nh, bias=False) + self.head_map = SimpleLinear(ni * nh, ni) + + def forward( + self, + AA: paddle.Tensor, # nf x nloc x nnei x nnei x nh + g2: paddle.Tensor, # nf x nloc x nnei x ng2 + ) -> paddle.Tensor: + nf, nloc, nnei, ng2 = g2.shape + nh = self.nh + # nf x nloc x nnei x ng2 x nh + g2v = self.mapv(g2).reshape([nf, nloc, nnei, ng2, nh]) + # nf x nloc x nh x nnei x ng2 + g2v = paddle.transpose(g2v, (0, 1, 4, 2, 3)) + # g2v = paddle.nn.functional.normalize(g2v, axis=-1) + # nf x nloc x nh x nnei x nnei + AA = paddle.transpose(AA, (0, 1, 4, 2, 3)) + # nf x nloc x nh x nnei x ng2 + ret = paddle.matmul(AA, g2v) + # nf x nloc x nnei x ng2 x nh + ret = paddle.transpose(ret, (0, 1, 3, 4, 2)).reshape( + [nf, nloc, nnei, (ng2 * nh)] + ) + # nf x nloc x nnei x ng2 + return self.head_map(ret) + + +class Atten2EquiVarApply(paddle.nn.Layer): + def __init__( + self, + ni: int, + nh: int, + ): + super().__init__() + self.ni = ni + self.nh = nh + self.head_map = SimpleLinear(nh, 1, bias=False) + + def forward( + self, + AA: paddle.Tensor, # nf x nloc x nnei x nnei x nh + h2: paddle.Tensor, # nf x nloc x nnei x 3 + ) -> paddle.Tensor: + nf, nloc, nnei, _ = h2.shape + nh = self.nh + # nf x nloc x nh x nnei x nnei + AA = paddle.transpose(AA, (0, 1, 4, 2, 3)) + h2m = paddle.unsqueeze(h2, axis=2) + # nf x nloc x nh x nnei x 3 + h2m = paddle.tile(h2m, [1, 1, nh, 1, 1]) + # nf x nloc x nh x nnei x 3 + ret = paddle.matmul(AA, h2m) + # nf x nloc x nnei x 3 x nh + ret = paddle.transpose(ret, (0, 1, 3, 4, 2)).reshape([nf, nloc, nnei, 3, nh]) + # nf x nloc x nnei x 3 + return paddle.squeeze(self.head_map(ret), axis=-1) + + +class LocalAtten(paddle.nn.Layer): + def __init__( + self, + ni: int, + nd: int, + nh: int, + smooth: bool = True, + attnw_shift: float = 20.0, + ): + super().__init__() + self.ni = ni + self.nd = nd + self.nh = nh + self.mapq = SimpleLinear(ni, nd * 1 * nh, bias=False) + self.mapkv = SimpleLinear(ni, (nd + ni) * nh, bias=False) + self.head_map = SimpleLinear(ni * nh, ni) + self.smooth = smooth + self.attnw_shift = attnw_shift + + def forward( + self, + g1: paddle.Tensor, # nb x nloc x ng1 + gg1: paddle.Tensor, # nb x nloc x nnei x ng1 + nlist_mask: paddle.Tensor, # nb x nloc x nnei + sw: paddle.Tensor, # nb x nloc x nnei + ) -> paddle.Tensor: + nb, nloc, nnei = nlist_mask.shape + ni, nd, nh = self.ni, self.nd, self.nh + assert ni == g1.shape[-1] + assert ni == gg1.shape[-1] + # nb x nloc x nd x nh + g1q = self.mapq(g1).reshape([nb, nloc, nd, nh]) + # nb x nloc x nh x nd + g1q = paddle.transpose(g1q, (0, 1, 3, 2)) + # nb x nloc x nnei x (nd+ni) x nh + gg1kv = self.mapkv(gg1).reshape([nb, nloc, nnei, nd + ni, nh]) + gg1kv = paddle.transpose(gg1kv, (0, 1, 4, 2, 3)) + # nb x nloc x nh x nnei x nd, nb x nloc x nh x nnei x ng1 + gg1k, gg1v = paddle.split(gg1kv, [nd, ni], axis=-1) + + # nb x nloc x nh x 1 x nnei + attnw = ( + paddle.matmul(g1q.unsqueeze(-2), paddle.transpose(gg1k, -1, -2)) / nd**0.5 + ) + # nb x nloc x nh x nnei + attnw: paddle.Tensor = attnw.squeeze(-2) + # mask the attenmap, nb x nloc x 1 x nnei + attnw_mask = ~nlist_mask.unsqueeze(-2) + # nb x nloc x nh x nnei + if self.smooth: + attnw = (attnw + self.attnw_shift) * sw.unsqueeze(-2) - self.attnw_shift + else: + attnw = attnw.masked_fill( + attnw_mask, + float("-inf"), + ) + attnw = paddle.softmax(attnw, axis=-1) + attnw = attnw.masked_fill( + attnw_mask, + 0.0, + ) + if self.smooth: + attnw = attnw * sw.unsqueeze(-2) + + # nb x nloc x nh x ng1 + ret = ( + paddle.matmul(attnw.unsqueeze(-2), gg1v) + .squeeze(-2) + .reshape([nb, nloc, nh * ni]) + ) + # nb x nloc x ng1 + ret = self.head_map(ret) + return ret + + +class RepformerLayer(paddle.nn.Layer): + def __init__( + self, + rcut, + rcut_smth, + sel: int, + ntypes: int, + g1_dim=128, + g2_dim=16, + axis_neuron: int = 4, + update_chnnl_2: bool = True, + do_bn_mode: str = "no", + bn_momentum: float = 0.1, + update_g1_has_conv: bool = True, + update_g1_has_drrd: bool = True, + update_g1_has_grrg: bool = True, + update_g1_has_attn: bool = True, + update_g2_has_g1g1: bool = True, + update_g2_has_attn: bool = True, + update_h2: bool = False, + attn1_hidden: int = 64, + attn1_nhead: int = 4, + attn2_hidden: int = 16, + attn2_nhead: int = 4, + attn2_has_gate: bool = False, + activation_function: str = "tanh", + update_style: str = "res_avg", + set_davg_zero: bool = True, # TODO + smooth: bool = True, + ): + super().__init__() + self.epsilon = 1e-4 # protection of 1./nnei + self.rcut = rcut + self.rcut_smth = rcut_smth + self.ntypes = ntypes + sel = [sel] if isinstance(sel, int) else sel + self.nnei = sum(sel) + assert len(sel) == 1 + self.sel = paddle.to_tensor(sel, device=env.DEVICE) # pylint: disable=no-explicit-dtype + self.sec = self.sel + self.axis_neuron = axis_neuron + self.set_davg_zero = set_davg_zero + self.do_bn_mode = do_bn_mode + self.bn_momentum = bn_momentum + self.act = ActivationFn(activation_function) + self.update_g1_has_grrg = update_g1_has_grrg + self.update_g1_has_drrd = update_g1_has_drrd + self.update_g1_has_conv = update_g1_has_conv + self.update_g1_has_attn = update_g1_has_attn + self.update_chnnl_2 = update_chnnl_2 + self.update_g2_has_g1g1 = update_g2_has_g1g1 if self.update_chnnl_2 else False + self.update_g2_has_attn = update_g2_has_attn if self.update_chnnl_2 else False + self.update_h2 = update_h2 if self.update_chnnl_2 else False + del update_g2_has_g1g1, update_g2_has_attn, update_h2 + self.update_style = update_style + self.smooth = smooth + self.g1_dim = g1_dim + self.g2_dim = g2_dim + + g1_in_dim = self.cal_1_dim(g1_dim, g2_dim, self.axis_neuron) + self.linear1 = SimpleLinear(g1_in_dim, g1_dim) + self.linear2 = None + self.proj_g1g2 = None + self.proj_g1g1g2 = None + self.attn2g_map = None + self.attn2_mh_apply = None + self.attn2_lm = None + self.attn2h_map = None + self.attn2_ev_apply = None + self.loc_attn = None + + if self.update_chnnl_2: + self.linear2 = SimpleLinear(g2_dim, g2_dim) + if self.update_g1_has_conv: + self.proj_g1g2 = SimpleLinear(g1_dim, g2_dim, bias=False) + if self.update_g2_has_g1g1: + self.proj_g1g1g2 = SimpleLinear(g1_dim, g2_dim, bias=False) + if self.update_g2_has_attn: + self.attn2g_map = Atten2Map( + g2_dim, attn2_hidden, attn2_nhead, attn2_has_gate, self.smooth + ) + self.attn2_mh_apply = Atten2MultiHeadApply(g2_dim, attn2_nhead) + self.attn2_lm = paddle.nn.LayerNorm( + g2_dim, + elementwise_affine=True, + device=env.DEVICE, + dtype=env.GLOBAL_PD_FLOAT_PRECISION, + ) + if self.update_h2: + self.attn2h_map = Atten2Map( + g2_dim, attn2_hidden, attn2_nhead, attn2_has_gate, self.smooth + ) + self.attn2_ev_apply = Atten2EquiVarApply(g2_dim, attn2_nhead) + if self.update_g1_has_attn: + self.loc_attn = LocalAtten(g1_dim, attn1_hidden, attn1_nhead, self.smooth) + + if self.do_bn_mode == "uniform": + self.bn1 = self._bn_layer() + self.bn2 = self._bn_layer() + elif self.do_bn_mode == "component": + self.bn1 = self._bn_layer(nf=g1_dim) + self.bn2 = self._bn_layer(nf=g2_dim) + elif self.do_bn_mode == "no": + self.bn1, self.bn2 = None, None + else: + raise RuntimeError(f"unknown bn_mode {self.do_bn_mode}") + + def cal_1_dim(self, g1d: int, g2d: int, ax: int) -> int: + ret = g1d + if self.update_g1_has_grrg: + ret += g2d * ax + if self.update_g1_has_drrd: + ret += g1d * ax + if self.update_g1_has_conv: + ret += g2d + return ret + + def _update_h2( + self, + g2: paddle.Tensor, + h2: paddle.Tensor, + nlist_mask: paddle.Tensor, + sw: paddle.Tensor, + ) -> paddle.Tensor: + assert self.attn2h_map is not None + assert self.attn2_ev_apply is not None + nb, nloc, nnei, _ = g2.shape + # # nb x nloc x nnei x nh2 + # h2_1 = self.attn2_ev_apply(AA, h2) + # h2_update.append(h2_1) + # nb x nloc x nnei x nnei x nh + AAh = self.attn2h_map(g2, h2, nlist_mask, sw) + # nb x nloc x nnei x nh2 + h2_1 = self.attn2_ev_apply(AAh, h2) + return h2_1 + + def _update_g1_conv( + self, + gg1: paddle.Tensor, + g2: paddle.Tensor, + nlist_mask: paddle.Tensor, + sw: paddle.Tensor, + ) -> paddle.Tensor: + assert self.proj_g1g2 is not None + nb, nloc, nnei, _ = g2.shape + ng1 = gg1.shape[-1] + ng2 = g2.shape[-1] + # gg1 : nb x nloc x nnei x ng2 + gg1 = self.proj_g1g2(gg1).reshape([nb, nloc, nnei, ng2]) + # nb x nloc x nnei x ng2 + gg1 = _apply_nlist_mask(gg1, nlist_mask) + if not self.smooth: + # normalized by number of neighbors, not smooth + # nb x nloc x 1 + invnnei = 1.0 / ( + self.epsilon + paddle.sum(nlist_mask.type_as(gg1), axis=-1) + ).unsqueeze(-1) + else: + gg1 = _apply_switch(gg1, sw) + invnnei = (1.0 / float(nnei)) * paddle.ones( + (nb, nloc, 1), dtype=env.GLOBAL_PD_FLOAT_PRECISION + ).to(device=gg1.place) + # nb x nloc x ng2 + g1_11 = paddle.sum(g2 * gg1, axis=2) * invnnei + return g1_11 + + def _cal_h2g2( + self, + g2: paddle.Tensor, + h2: paddle.Tensor, + nlist_mask: paddle.Tensor, + sw: paddle.Tensor, + ) -> paddle.Tensor: + # g2: nf x nloc x nnei x ng2 + # h2: nf x nloc x nnei x 3 + # msk: nf x nloc x nnei + nb, nloc, nnei, _ = g2.shape + ng2 = g2.shape[-1] + # nb x nloc x nnei x ng2 + g2 = _apply_nlist_mask(g2, nlist_mask) + if not self.smooth: + # nb x nloc + invnnei = 1.0 / (self.epsilon + paddle.sum(nlist_mask.type_as(g2), axis=-1)) + # nb x nloc x 1 x 1 + invnnei = invnnei.unsqueeze(-1).unsqueeze(-1) + else: + g2 = _apply_switch(g2, sw) + invnnei = (1.0 / float(nnei)) * paddle.ones( + (nb, nloc, 1, 1), dtype=env.GLOBAL_PD_FLOAT_PRECISION + ).to(device=g2.place) + # nb x nloc x 3 x ng2 + h2g2 = paddle.matmul(paddle.transpose(h2, -1, -2), g2) * invnnei + return h2g2 + + def _cal_grrg(self, h2g2: paddle.Tensor) -> paddle.Tensor: + # nb x nloc x 3 x ng2 + nb, nloc, _, ng2 = h2g2.shape + # nb x nloc x 3 x axis + h2g2m = paddle.split(h2g2, h2g2.shape[-1] // self.axis_neuron, axis=-1)[0] + # nb x nloc x axis x ng2 + g1_13 = paddle.matmul(paddle.transpose(h2g2m, -1, -2), h2g2) / (3.0**1) + # nb x nloc x (axisxng2) + g1_13 = g1_13.reshape([nb, nloc, self.axis_neuron * ng2]) + return g1_13 + + def _update_g1_grrg( + self, + g2: paddle.Tensor, + h2: paddle.Tensor, + nlist_mask: paddle.Tensor, + sw: paddle.Tensor, + ) -> paddle.Tensor: + # g2: nf x nloc x nnei x ng2 + # h2: nf x nloc x nnei x 3 + # msk: nf x nloc x nnei + nb, nloc, nnei, _ = g2.shape + ng2 = g2.shape[-1] + # nb x nloc x 3 x ng2 + h2g2 = self._cal_h2g2(g2, h2, nlist_mask, sw) + # nb x nloc x (axisxng2) + g1_13 = self._cal_grrg(h2g2) + return g1_13 + + def _update_g2_g1g1( + self, + g1: paddle.Tensor, # nb x nloc x ng1 + gg1: paddle.Tensor, # nb x nloc x nnei x ng1 + nlist_mask: paddle.Tensor, # nb x nloc x nnei + sw: paddle.Tensor, # nb x nloc x nnei + ) -> paddle.Tensor: + ret = g1.unsqueeze(-2) * gg1 + # nb x nloc x nnei x ng1 + ret = _apply_nlist_mask(ret, nlist_mask) + if self.smooth: + ret = _apply_switch(ret, sw) + return ret + + def _apply_bn( + self, + bn_number: int, + gg: paddle.Tensor, + ): + if self.do_bn_mode == "uniform": + return self._apply_bn_uni(bn_number, gg) + elif self.do_bn_mode == "component": + return self._apply_bn_comp(bn_number, gg) + else: + return gg + + def _apply_nb_1(self, bn_number: int, gg: paddle.Tensor) -> paddle.Tensor: + nb, nl, nf = gg.shape + gg = gg.reshape([nb, 1, nl * nf]) + if bn_number == 1: + assert self.bn1 is not None + gg = self.bn1(gg) + else: + assert self.bn2 is not None + gg = self.bn2(gg) + return gg.reshape([nb, nl, nf]) + + def _apply_nb_2( + self, + bn_number: int, + gg: paddle.Tensor, + ) -> paddle.Tensor: + nb, nl, nnei, nf = gg.shape + gg = gg.reshape([nb, 1, nl * nnei * nf]) + if bn_number == 1: + assert self.bn1 is not None + gg = self.bn1(gg) + else: + assert self.bn2 is not None + gg = self.bn2(gg) + return gg.reshape([nb, nl, nnei, nf]) + + def _apply_bn_uni( + self, + bn_number: int, + gg: paddle.Tensor, + mode: str = "1", + ) -> paddle.Tensor: + if len(gg.shape) == 3: + return self._apply_nb_1(bn_number, gg) + elif len(gg.shape) == 4: + return self._apply_nb_2(bn_number, gg) + else: + raise RuntimeError(f"unsupported input shape {gg.shape}") + + def _apply_bn_comp( + self, + bn_number: int, + gg: paddle.Tensor, + ) -> paddle.Tensor: + ss = gg.shape + nf = ss[-1] + gg = gg.reshape([-1, nf]) + if bn_number == 1: + assert self.bn1 is not None + gg = self.bn1(gg).reshape([ss]) + else: + assert self.bn2 is not None + gg = self.bn2(gg).reshape([ss]) + return gg + + def forward( + self, + g1_ext: paddle.Tensor, # nf x nall x ng1 + g2: paddle.Tensor, # nf x nloc x nnei x ng2 + h2: paddle.Tensor, # nf x nloc x nnei x 3 + nlist: paddle.Tensor, # nf x nloc x nnei + nlist_mask: paddle.Tensor, # nf x nloc x nnei + sw: paddle.Tensor, # switch func, nf x nloc x nnei + ): + """ + Parameters + ---------- + g1_ext : nf x nall x ng1 extended single-atom chanel + g2 : nf x nloc x nnei x ng2 pair-atom channel, invariant + h2 : nf x nloc x nnei x 3 pair-atom channel, equivariant + nlist : nf x nloc x nnei neighbor list (padded neis are set to 0) + nlist_mask : nf x nloc x nnei masks of the neighbor list. real nei 1 otherwise 0 + sw : nf x nloc x nnei switch function + + Returns + ------- + g1: nf x nloc x ng1 updated single-atom chanel + g2: nf x nloc x nnei x ng2 updated pair-atom channel, invariant + h2: nf x nloc x nnei x 3 updated pair-atom channel, equivariant + """ + cal_gg1 = ( + self.update_g1_has_drrd + or self.update_g1_has_conv + or self.update_g1_has_attn + or self.update_g2_has_g1g1 + ) + + nb, nloc, nnei, _ = g2.shape + nall = g1_ext.shape[1] + g1, _ = paddle.split(g1_ext, [nloc, nall - nloc], axis=1) + assert (nb, nloc) == g1.shape[:2] + assert (nb, nloc, nnei) == h2.shape[:3] + ng1 = g1.shape[-1] + ng2 = g2.shape[-1] + nh2 = h2.shape[-1] + + if self.bn1 is not None: + g1 = self._apply_bn(1, g1) + if self.bn2 is not None: + g2 = self._apply_bn(2, g2) + if self.update_h2: + h2 = _apply_h_norm(h2) + + g2_update: List[paddle.Tensor] = [g2] + h2_update: List[paddle.Tensor] = [h2] + g1_update: List[paddle.Tensor] = [g1] + g1_mlp: List[paddle.Tensor] = [g1] + + if cal_gg1: + gg1 = _make_nei_g1(g1_ext, nlist) + else: + gg1 = None + + if self.update_chnnl_2: + # nb x nloc x nnei x ng2 + assert self.linear2 is not None + g2_1 = self.act(self.linear2(g2)) + g2_update.append(g2_1) + + if self.update_g2_has_g1g1: + assert gg1 is not None + assert self.proj_g1g1g2 is not None + g2_update.append( + self.proj_g1g1g2(self._update_g2_g1g1(g1, gg1, nlist_mask, sw)) + ) + + if self.update_g2_has_attn: + assert self.attn2g_map is not None + assert self.attn2_mh_apply is not None + assert self.attn2_lm is not None + # nb x nloc x nnei x nnei x nh + AAg = self.attn2g_map(g2, h2, nlist_mask, sw) + # nb x nloc x nnei x ng2 + g2_2 = self.attn2_mh_apply(AAg, g2) + g2_2 = self.attn2_lm(g2_2) + g2_update.append(g2_2) + + if self.update_h2: + h2_update.append(self._update_h2(g2, h2, nlist_mask, sw)) + + if self.update_g1_has_conv: + assert gg1 is not None + g1_mlp.append(self._update_g1_conv(gg1, g2, nlist_mask, sw)) + + if self.update_g1_has_grrg: + g1_mlp.append(self._update_g1_grrg(g2, h2, nlist_mask, sw)) + + if self.update_g1_has_drrd: + assert gg1 is not None + g1_mlp.append(self._update_g1_grrg(gg1, h2, nlist_mask, sw)) + + # nb x nloc x [ng1+ng2+(axisxng2)+(axisxng1)] + # conv grrg drrd + g1_1 = self.act(self.linear1(paddle.concat(g1_mlp, axis=-1))) + g1_update.append(g1_1) + + if self.update_g1_has_attn: + assert gg1 is not None + assert self.loc_attn is not None + g1_update.append(self.loc_attn(g1, gg1, nlist_mask, sw)) + + # update + if self.update_chnnl_2: + g2_new = self.list_update(g2_update) + h2_new = self.list_update(h2_update) + else: + g2_new, h2_new = g2, h2 + g1_new = self.list_update(g1_update) + return g1_new, g2_new, h2_new + + # @paddle.jit.export + def list_update_res_avg( + self, + update_list: List[paddle.Tensor], + ) -> paddle.Tensor: + nitem = len(update_list) + uu = update_list[0] + for ii in range(1, nitem): + uu = uu + update_list[ii] + return uu / (float(nitem) ** 0.5) + + # @paddle.jit.export + def list_update_res_incr(self, update_list: List[paddle.Tensor]) -> paddle.Tensor: + nitem = len(update_list) + uu = update_list[0] + scale = 1.0 / (float(nitem - 1) ** 0.5) if nitem > 1 else 0.0 + for ii in range(1, nitem): + uu = uu + scale * update_list[ii] + return uu + + # @paddle.jit.export + def list_update(self, update_list: List[paddle.Tensor]) -> paddle.Tensor: + if self.update_style == "res_avg": + return self.list_update_res_avg(update_list) + elif self.update_style == "res_incr": + return self.list_update_res_incr(update_list) + else: + raise RuntimeError(f"unknown update style {self.update_style}") + + def _bn_layer( + self, + nf: int = 1, + ) -> Callable: + return paddle.nn.BatchNorm1d( + nf, + eps=1e-5, + momentum=self.bn_momentum, + affine=False, + track_running_stats=True, + device=env.DEVICE, + dtype=env.GLOBAL_PD_FLOAT_PRECISION, + ) diff --git a/deepmd/pd/model/descriptor/repformers.py b/deepmd/pd/model/descriptor/repformers.py new file mode 100644 index 0000000000..b4fa0eaec1 --- /dev/null +++ b/deepmd/pd/model/descriptor/repformers.py @@ -0,0 +1,565 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Callable, + Dict, + List, + Optional, + Tuple, + Union, +) + +import paddle + +from deepmd.dpmodel.utils.seed import ( + child_seed, +) +from deepmd.pd.model.descriptor.descriptor import ( + DescriptorBlock, +) +from deepmd.pd.model.descriptor.env_mat import ( + prod_env_mat, +) +from deepmd.pd.model.network.mlp import ( + MLPLayer, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env_mat_stat import ( + EnvMatStatSe, +) +from deepmd.pd.utils.exclude_mask import ( + PairExcludeMask, +) +from deepmd.pd.utils.utils import ( + ActivationFn, +) +from deepmd.utils.env_mat_stat import ( + StatItem, +) +from deepmd.utils.path import ( + DPPath, +) + +from .repformer_layer import ( + RepformerLayer, +) +from .repformer_layer_old_impl import RepformerLayer as RepformerLayerOld + +# if not hasattr(paddle.ops.deepmd, "border_op"): + +# def border_op( +# argument0, +# argument1, +# argument2, +# argument3, +# argument4, +# argument5, +# argument6, +# argument7, +# argument8, +# ) -> paddle.Tensor: +# raise NotImplementedError( +# "border_op is not available since customized PyTorch OP library is not built when freezing the model." +# ) + +# # Note: this hack cannot actually save a model that can be runned using LAMMPS. +# paddle.ops.deepmd.border_op = border_op + + +@DescriptorBlock.register("se_repformer") +@DescriptorBlock.register("se_uni") +class DescrptBlockRepformers(DescriptorBlock): + def __init__( + self, + rcut, + rcut_smth, + sel: int, + ntypes: int, + nlayers: int = 3, + g1_dim=128, + g2_dim=16, + axis_neuron: int = 4, + direct_dist: bool = False, + update_g1_has_conv: bool = True, + update_g1_has_drrd: bool = True, + update_g1_has_grrg: bool = True, + update_g1_has_attn: bool = True, + update_g2_has_g1g1: bool = True, + update_g2_has_attn: bool = True, + update_h2: bool = False, + attn1_hidden: int = 64, + attn1_nhead: int = 4, + attn2_hidden: int = 16, + attn2_nhead: int = 4, + attn2_has_gate: bool = False, + activation_function: str = "tanh", + update_style: str = "res_avg", + update_residual: float = 0.001, + update_residual_init: str = "norm", + set_davg_zero: bool = True, + smooth: bool = True, + exclude_types: List[Tuple[int, int]] = [], + env_protection: float = 0.0, + precision: str = "float64", + trainable_ln: bool = True, + ln_eps: Optional[float] = 1e-5, + seed: Optional[Union[int, List[int]]] = None, + old_impl: bool = False, + ): + r""" + The repformer descriptor block. + + Parameters + ---------- + rcut : float + The cut-off radius. + rcut_smth : float + Where to start smoothing. For example the 1/r term is smoothed from rcut to rcut_smth. + sel : int + Maximally possible number of selected neighbors. + ntypes : int + Number of element types + nlayers : int, optional + Number of repformer layers. + g1_dim : int, optional + Dimension of the first graph convolution layer. + g2_dim : int, optional + Dimension of the second graph convolution layer. + axis_neuron : int, optional + Size of the submatrix of G (embedding matrix). + direct_dist : bool, optional + Whether to use direct distance information (1/r term) in the repformer block. + update_g1_has_conv : bool, optional + Whether to update the g1 rep with convolution term. + update_g1_has_drrd : bool, optional + Whether to update the g1 rep with the drrd term. + update_g1_has_grrg : bool, optional + Whether to update the g1 rep with the grrg term. + update_g1_has_attn : bool, optional + Whether to update the g1 rep with the localized self-attention. + update_g2_has_g1g1 : bool, optional + Whether to update the g2 rep with the g1xg1 term. + update_g2_has_attn : bool, optional + Whether to update the g2 rep with the gated self-attention. + update_h2 : bool, optional + Whether to update the h2 rep. + attn1_hidden : int, optional + The hidden dimension of localized self-attention to update the g1 rep. + attn1_nhead : int, optional + The number of heads in localized self-attention to update the g1 rep. + attn2_hidden : int, optional + The hidden dimension of gated self-attention to update the g2 rep. + attn2_nhead : int, optional + The number of heads in gated self-attention to update the g2 rep. + attn2_has_gate : bool, optional + Whether to use gate in the gated self-attention to update the g2 rep. + activation_function : str, optional + The activation function in the embedding net. + update_style : str, optional + Style to update a representation. + Supported options are: + -'res_avg': Updates a rep `u` with: u = 1/\\sqrt{n+1} (u + u_1 + u_2 + ... + u_n) + -'res_incr': Updates a rep `u` with: u = u + 1/\\sqrt{n} (u_1 + u_2 + ... + u_n) + -'res_residual': Updates a rep `u` with: u = u + (r1*u_1 + r2*u_2 + ... + r3*u_n) + where `r1`, `r2` ... `r3` are residual weights defined by `update_residual` + and `update_residual_init`. + update_residual : float, optional + When update using residual mode, the initial std of residual vector weights. + update_residual_init : str, optional + When update using residual mode, the initialization mode of residual vector weights. + set_davg_zero : bool, optional + Set the normalization average to zero. + precision : str, optional + The precision of the embedding net parameters. + smooth : bool, optional + Whether to use smoothness in processes such as attention weights calculation. + exclude_types : List[List[int]], optional + The excluded pairs of types which have no interaction with each other. + For example, `[[0, 1]]` means no interaction between type 0 and type 1. + env_protection : float, optional + Protection parameter to prevent division by zero errors during environment matrix calculations. + For example, when using paddings, there may be zero distances of neighbors, which may make division by zero error during environment matrix calculations without protection. + trainable_ln : bool, optional + Whether to use trainable shift and scale weights in layer normalization. + ln_eps : float, optional + The epsilon value for layer normalization. + seed : int, optional + Random seed for parameter initialization. + """ + super().__init__() + self.rcut = rcut + self.rcut_smth = rcut_smth + self.ntypes = ntypes + self.nlayers = nlayers + sel = [sel] if isinstance(sel, int) else sel + self.nnei = sum(sel) + self.ndescrpt = self.nnei * 4 # use full descriptor. + assert len(sel) == 1 + self.sel = sel + self.sec = self.sel + self.split_sel = self.sel + self.axis_neuron = axis_neuron + self.set_davg_zero = set_davg_zero + self.g1_dim = g1_dim + self.g2_dim = g2_dim + self.update_g1_has_conv = update_g1_has_conv + self.update_g1_has_drrd = update_g1_has_drrd + self.update_g1_has_grrg = update_g1_has_grrg + self.update_g1_has_attn = update_g1_has_attn + self.update_g2_has_g1g1 = update_g2_has_g1g1 + self.update_g2_has_attn = update_g2_has_attn + self.update_h2 = update_h2 + self.attn1_hidden = attn1_hidden + self.attn1_nhead = attn1_nhead + self.attn2_has_gate = attn2_has_gate + self.attn2_hidden = attn2_hidden + self.attn2_nhead = attn2_nhead + self.activation_function = activation_function + self.update_style = update_style + self.update_residual = update_residual + self.update_residual_init = update_residual_init + self.direct_dist = direct_dist + self.act = ActivationFn(activation_function) + self.smooth = smooth + # order matters, placed after the assignment of self.ntypes + self.reinit_exclude(exclude_types) + self.env_protection = env_protection + self.precision = precision + self.trainable_ln = trainable_ln + self.ln_eps = ln_eps + self.epsilon = 1e-4 + self.seed = seed + self.old_impl = old_impl + + self.g2_embd = MLPLayer( + 1, self.g2_dim, precision=precision, seed=child_seed(seed, 0) + ) + layers = [] + for ii in range(nlayers): + if self.old_impl: + layers.append( + RepformerLayerOld( + self.rcut, + self.rcut_smth, + self.sel, + self.ntypes, + self.g1_dim, + self.g2_dim, + axis_neuron=self.axis_neuron, + update_chnnl_2=(ii != nlayers - 1), + update_g1_has_conv=self.update_g1_has_conv, + update_g1_has_drrd=self.update_g1_has_drrd, + update_g1_has_grrg=self.update_g1_has_grrg, + update_g1_has_attn=self.update_g1_has_attn, + update_g2_has_g1g1=self.update_g2_has_g1g1, + update_g2_has_attn=self.update_g2_has_attn, + update_h2=self.update_h2, + attn1_hidden=self.attn1_hidden, + attn1_nhead=self.attn1_nhead, + attn2_has_gate=self.attn2_has_gate, + attn2_hidden=self.attn2_hidden, + attn2_nhead=self.attn2_nhead, + activation_function=self.activation_function, + update_style=self.update_style, + smooth=self.smooth, + ) + ) + else: + layers.append( + RepformerLayer( + self.rcut, + self.rcut_smth, + self.sel, + self.ntypes, + self.g1_dim, + self.g2_dim, + axis_neuron=self.axis_neuron, + update_chnnl_2=(ii != nlayers - 1), + update_g1_has_conv=self.update_g1_has_conv, + update_g1_has_drrd=self.update_g1_has_drrd, + update_g1_has_grrg=self.update_g1_has_grrg, + update_g1_has_attn=self.update_g1_has_attn, + update_g2_has_g1g1=self.update_g2_has_g1g1, + update_g2_has_attn=self.update_g2_has_attn, + update_h2=self.update_h2, + attn1_hidden=self.attn1_hidden, + attn1_nhead=self.attn1_nhead, + attn2_has_gate=self.attn2_has_gate, + attn2_hidden=self.attn2_hidden, + attn2_nhead=self.attn2_nhead, + activation_function=self.activation_function, + update_style=self.update_style, + update_residual=self.update_residual, + update_residual_init=self.update_residual_init, + smooth=self.smooth, + trainable_ln=self.trainable_ln, + ln_eps=self.ln_eps, + precision=precision, + seed=child_seed(child_seed(seed, 1), ii), + ) + ) + self.layers = paddle.nn.LayerList(layers) + + wanted_shape = (self.ntypes, self.nnei, 4) + mean = paddle.zeros(wanted_shape, dtype=env.GLOBAL_PD_FLOAT_PRECISION).to( + device=env.DEVICE + ) + stddev = paddle.ones(wanted_shape, dtype=env.GLOBAL_PD_FLOAT_PRECISION).to( + device=env.DEVICE + ) + self.register_buffer("mean", mean) + self.register_buffer("stddev", stddev) + self.stats = None + + def get_rcut(self) -> float: + """Returns the cut-off radius.""" + return self.rcut + + def get_rcut_smth(self) -> float: + """Returns the radius where the neighbor information starts to smoothly decay to 0.""" + return self.rcut_smth + + def get_nsel(self) -> int: + """Returns the number of selected atoms in the cut-off radius.""" + return sum(self.sel) + + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" + return self.sel + + def get_ntypes(self) -> int: + """Returns the number of element types.""" + return self.ntypes + + def get_dim_out(self) -> int: + """Returns the output dimension.""" + return self.dim_out + + def get_dim_in(self) -> int: + """Returns the input dimension.""" + return self.dim_in + + def get_dim_emb(self) -> int: + """Returns the embedding dimension g2.""" + return self.g2_dim + + def __setitem__(self, key, value): + if key in ("avg", "data_avg", "davg"): + self.mean = value + elif key in ("std", "data_std", "dstd"): + self.stddev = value + else: + raise KeyError(key) + + def __getitem__(self, key): + if key in ("avg", "data_avg", "davg"): + return self.mean + elif key in ("std", "data_std", "dstd"): + return self.stddev + else: + raise KeyError(key) + + def mixed_types(self) -> bool: + """If true, the discriptor + 1. assumes total number of atoms aligned across frames; + 2. requires a neighbor list that does not distinguish different atomic types. + + If false, the discriptor + 1. assumes total number of atoms of each atom type aligned across frames; + 2. requires a neighbor list that distinguishes different atomic types. + + """ + return True + + def get_env_protection(self) -> float: + """Returns the protection of building environment matrix.""" + return self.env_protection + + @property + def dim_out(self): + """Returns the output dimension of this descriptor.""" + return self.g1_dim + + @property + def dim_in(self): + """Returns the atomic input dimension of this descriptor.""" + return self.g1_dim + + @property + def dim_emb(self): + """Returns the embedding dimension g2.""" + return self.get_dim_emb() + + def reinit_exclude( + self, + exclude_types: List[Tuple[int, int]] = [], + ): + self.exclude_types = exclude_types + self.emask = PairExcludeMask(self.ntypes, exclude_types=exclude_types) + + def forward( + self, + nlist: paddle.Tensor, + extended_coord: paddle.Tensor, + extended_atype: paddle.Tensor, + extended_atype_embd: Optional[paddle.Tensor] = None, + mapping: Optional[paddle.Tensor] = None, + comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + ): + if comm_dict is None: + assert mapping is not None + assert extended_atype_embd is not None + nframes, nloc, nnei = nlist.shape + nall = extended_coord.reshape([nframes, -1]).shape[1] // 3 + atype = extended_atype[:, :nloc] + # nb x nloc x nnei + exclude_mask = self.emask(nlist, extended_atype) + nlist = paddle.where(exclude_mask != 0, nlist, -1) + # nb x nloc x nnei x 4, nb x nloc x nnei x 3, nb x nloc x nnei x 1 + dmatrix, diff, sw = prod_env_mat( + extended_coord, + nlist, + atype, + self.mean, + self.stddev, + self.rcut, + self.rcut_smth, + protection=self.env_protection, + ) + nlist_mask = nlist != -1 + sw = paddle.squeeze(sw, -1) + # beyond the cutoff sw should be 0.0 + sw = sw.masked_fill(~nlist_mask, 0.0) + + # [nframes, nloc, tebd_dim] + if comm_dict is None: + assert isinstance(extended_atype_embd, paddle.Tensor) # for jit + atype_embd = extended_atype_embd[:, :nloc, :] + assert list(atype_embd.shape) == [nframes, nloc, self.g1_dim] + else: + atype_embd = extended_atype_embd + assert isinstance(atype_embd, paddle.Tensor) # for jit + g1 = self.act(atype_embd) + # nb x nloc x nnei x 1, nb x nloc x nnei x 3 + if not self.direct_dist: + g2, h2 = paddle.split(dmatrix, [1, 3], axis=-1) + else: + g2, h2 = paddle.linalg.norm(diff, axis=-1, keepdim=True), diff + g2 = g2 / self.rcut + h2 = h2 / self.rcut + # nb x nloc x nnei x ng2 + g2 = self.act(self.g2_embd(g2)) + + # set all padding positions to index of 0 + # if the a neighbor is real or not is indicated by nlist_mask + nlist[nlist == -1] = 0 + # nb x nall x ng1 + if comm_dict is None: + assert mapping is not None + mapping = ( + mapping.reshape([nframes, nall]) + .unsqueeze(-1) + .expand([-1, -1, self.g1_dim]) + ) + for idx, ll in enumerate(self.layers): + # g1: nb x nloc x ng1 + # g1_ext: nb x nall x ng1 + if comm_dict is None: + assert mapping is not None + g1_ext = paddle.gather(g1, 1, mapping) + else: + n_padding = nall - nloc + g1 = paddle.nn.functional.pad( + g1.squeeze(0), (0, 0, 0, n_padding), value=0.0 + ) + assert "send_list" in comm_dict + assert "send_proc" in comm_dict + assert "recv_proc" in comm_dict + assert "send_num" in comm_dict + assert "recv_num" in comm_dict + assert "communicator" in comm_dict + ret = paddle.ops.deepmd.border_op( + comm_dict["send_list"], + comm_dict["send_proc"], + comm_dict["recv_proc"], + comm_dict["send_num"], + comm_dict["recv_num"], + g1, + comm_dict["communicator"], + paddle.to_tensor(nloc), # pylint: disable=no-explicit-dtype,no-explicit-device + paddle.to_tensor(nall - nloc), # pylint: disable=no-explicit-dtype,no-explicit-device + ) + g1_ext = ret[0].unsqueeze(0) + g1, g2, h2 = ll.forward( + g1_ext, + g2, + h2, + nlist, + nlist_mask, + sw, + ) + + # nb x nloc x 3 x ng2 + h2g2 = RepformerLayer._cal_hg( + g2, h2, nlist_mask, sw, smooth=self.smooth, epsilon=self.epsilon + ) + # (nb x nloc) x ng2 x 3 + rot_mat = paddle.transpose(h2g2, (0, 1, 3, 2)) + + return g1, g2, h2, rot_mat.reshape([nframes, nloc, self.dim_emb, 3]), sw + + def compute_input_stats( + self, + merged: Union[Callable[[], List[dict]], List[dict]], + path: Optional[DPPath] = None, + ): + """ + Compute the input statistics (e.g. mean and stddev) for the descriptors from packed data. + + Parameters + ---------- + merged : Union[Callable[[], List[dict]], List[dict]] + - List[dict]: A list of data samples from various data systems. + Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` + originating from the `i`-th data system. + - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + only when needed. Since the sampling process can be slow and memory-intensive, + the lazy function helps by only sampling once. + path : Optional[DPPath] + The path to the stat file. + + """ + env_mat_stat = EnvMatStatSe(self) + if path is not None: + path = path / env_mat_stat.get_hash() + if path is None or not path.is_dir(): + if callable(merged): + # only get data for once + sampled = merged() + else: + sampled = merged + else: + sampled = [] + env_mat_stat.load_or_compute_stats(sampled, path) + self.stats = env_mat_stat.stats + mean, stddev = env_mat_stat() + if not self.set_davg_zero: + paddle.assign(paddle.to_tensor(mean).to(device=env.DEVICE), self.mean) # pylint: disable=no-explicit-dtype + paddle.assign(paddle.to_tensor(stddev).to(device=env.DEVICE), self.stddev) # pylint: disable=no-explicit-dtype + + def get_stats(self) -> Dict[str, StatItem]: + """Get the statistics of the descriptor.""" + if self.stats is None: + raise RuntimeError( + "The statistics of the descriptor has not been computed." + ) + return self.stats + + def has_message_passing(self) -> bool: + """Returns whether the descriptor block has message passing.""" + return True + + def need_sorted_nlist_for_lower(self) -> bool: + """Returns whether the descriptor block needs sorted nlist when using `forward_lower`.""" + return False diff --git a/deepmd/pd/model/descriptor/se_a.py b/deepmd/pd/model/descriptor/se_a.py new file mode 100644 index 0000000000..b86c9127f3 --- /dev/null +++ b/deepmd/pd/model/descriptor/se_a.py @@ -0,0 +1,720 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import itertools +from typing import ( + Callable, + ClassVar, + Dict, + List, + Optional, + Tuple, + Union, +) + +import numpy as np +import paddle + +from deepmd.dpmodel.utils.seed import ( + child_seed, +) +from deepmd.pd.model.descriptor import ( + DescriptorBlock, + prod_env_mat, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + PRECISION_DICT, + RESERVED_PRECISON_DICT, +) +from deepmd.pd.utils.env_mat_stat import ( + EnvMatStatSe, +) +from deepmd.pd.utils.update_sel import ( + UpdateSel, +) +from deepmd.utils.data_system import ( + DeepmdDataSystem, +) +from deepmd.utils.env_mat_stat import ( + StatItem, +) +from deepmd.utils.path import ( + DPPath, +) +from deepmd.utils.version import ( + check_version_compatibility, +) + +try: + from typing import ( + Final, + ) +except ImportError: + from paddle.jit import Final + +from deepmd.dpmodel.utils import EnvMat as DPEnvMat +from deepmd.pd.model.network.mlp import ( + EmbeddingNet, + NetworkCollection, +) +from deepmd.pd.model.network.network import ( + TypeFilter, +) +from deepmd.pd.utils.exclude_mask import ( + PairExcludeMask, +) + +from .base_descriptor import ( + BaseDescriptor, +) + + +@BaseDescriptor.register("se_e2_a") +@BaseDescriptor.register("se_a") +class DescrptSeA(BaseDescriptor, paddle.nn.Layer): + def __init__( + self, + rcut, + rcut_smth, + sel, + neuron=[25, 50, 100], + axis_neuron=16, + set_davg_zero: bool = False, + activation_function: str = "tanh", + precision: str = "float64", + resnet_dt: bool = False, + exclude_types: List[Tuple[int, int]] = [], + env_protection: float = 0.0, + old_impl: bool = False, + type_one_side: bool = True, + trainable: bool = True, + seed: Optional[Union[int, List[int]]] = None, + ntypes: Optional[int] = None, # to be compat with input + type_map: Optional[List[str]] = None, + # not implemented + spin=None, + ): + del ntypes + if spin is not None: + raise NotImplementedError("old implementation of spin is not supported.") + super().__init__() + self.type_map = type_map + self.sea = DescrptBlockSeA( + rcut, + rcut_smth, + sel, + neuron=neuron, + axis_neuron=axis_neuron, + set_davg_zero=set_davg_zero, + activation_function=activation_function, + precision=precision, + resnet_dt=resnet_dt, + exclude_types=exclude_types, + env_protection=env_protection, + old_impl=old_impl, + type_one_side=type_one_side, + trainable=trainable, + seed=seed, + ) + + def get_rcut(self) -> float: + """Returns the cut-off radius.""" + return self.sea.get_rcut() + + def get_rcut_smth(self) -> float: + """Returns the radius where the neighbor information starts to smoothly decay to 0.""" + return self.sea.get_rcut_smth() + + def get_nsel(self) -> int: + """Returns the number of selected atoms in the cut-off radius.""" + return self.sea.get_nsel() + + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" + return self.sea.get_sel() + + def get_ntypes(self) -> int: + """Returns the number of element types.""" + return self.sea.get_ntypes() + + def get_type_map(self) -> List[str]: + """Get the name to each type of atoms.""" + return self.type_map + + def get_dim_out(self) -> int: + """Returns the output dimension.""" + return self.sea.get_dim_out() + + def get_dim_emb(self) -> int: + """Returns the output dimension.""" + return self.sea.get_dim_emb() + + def mixed_types(self): + """Returns if the descriptor requires a neighbor list that distinguish different + atomic types or not. + """ + return self.sea.mixed_types() + + def has_message_passing(self) -> bool: + """Returns whether the descriptor has message passing.""" + return self.sea.has_message_passing() + + def need_sorted_nlist_for_lower(self) -> bool: + """Returns whether the descriptor needs sorted nlist when using `forward_lower`.""" + return self.sea.need_sorted_nlist_for_lower() + + def get_env_protection(self) -> float: + """Returns the protection of building environment matrix.""" + return self.sea.get_env_protection() + + def share_params(self, base_class, shared_level, resume=False): + """ + Share the parameters of self to the base_class with shared_level during multitask training. + If not start from checkpoint (resume is False), + some seperated parameters (e.g. mean and stddev) will be re-calculated across different classes. + """ + assert ( + self.__class__ == base_class.__class__ + ), "Only descriptors of the same type can share params!" + # For SeA descriptors, the user-defined share-level + # shared_level: 0 + # share all parameters in sea + if shared_level == 0: + self.sea.share_params(base_class.sea, 0, resume=resume) + # Other shared levels + else: + raise NotImplementedError + + @property + def dim_out(self): + """Returns the output dimension of this descriptor.""" + return self.sea.dim_out + + def change_type_map( + self, type_map: List[str], model_with_new_type_stat=None + ) -> None: + """Change the type related params to new ones, according to `type_map` and the original one in the model. + If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. + """ + raise NotImplementedError( + "Descriptor se_e2_a does not support changing for type related params!" + "This feature is currently not implemented because it would require additional work to support the non-mixed-types case. " + "We may consider adding this support in the future if there is a clear demand for it." + ) + + def compute_input_stats( + self, + merged: Union[Callable[[], List[dict]], List[dict]], + path: Optional[DPPath] = None, + ): + """ + Compute the input statistics (e.g. mean and stddev) for the descriptors from packed data. + + Parameters + ---------- + merged : Union[Callable[[], List[dict]], List[dict]] + - List[dict]: A list of data samples from various data systems. + Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` + originating from the `i`-th data system. + - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + only when needed. Since the sampling process can be slow and memory-intensive, + the lazy function helps by only sampling once. + path : Optional[DPPath] + The path to the stat file. + + """ + return self.sea.compute_input_stats(merged, path) + + def reinit_exclude( + self, + exclude_types: List[Tuple[int, int]] = [], + ): + """Update the type exclusions.""" + self.sea.reinit_exclude(exclude_types) + + def forward( + self, + coord_ext: paddle.Tensor, + atype_ext: paddle.Tensor, + nlist: paddle.Tensor, + mapping: Optional[paddle.Tensor] = None, + comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + ): + """Compute the descriptor. + + Parameters + ---------- + coord_ext + The extended coordinates of atoms. shape: nf x (nallx3) + atype_ext + The extended aotm types. shape: nf x nall + nlist + The neighbor list. shape: nf x nloc x nnei + mapping + The index mapping, not required by this descriptor. + comm_dict + The data needed for communication for parallel inference. + + Returns + ------- + descriptor + The descriptor. shape: nf x nloc x (ng x axis_neuron) + gr + The rotationally equivariant and permutationally invariant single particle + representation. shape: nf x nloc x ng x 3 + g2 + The rotationally invariant pair-partical representation. + this descriptor returns None + h2 + The rotationally equivariant pair-partical representation. + this descriptor returns None + sw + The smooth switch function. + + """ + return self.sea.forward(nlist, coord_ext, atype_ext, None, mapping) + + def set_stat_mean_and_stddev( + self, + mean: paddle.Tensor, + stddev: paddle.Tensor, + ) -> None: + """Update mean and stddev for descriptor.""" + self.sea.mean = mean + self.sea.stddev = stddev + + def get_stat_mean_and_stddev(self) -> Tuple[paddle.Tensor, paddle.Tensor]: + """Get mean and stddev for descriptor.""" + return self.sea.mean, self.sea.stddev + + def serialize(self) -> dict: + obj = self.sea + return { + "@class": "Descriptor", + "type": "se_e2_a", + "@version": 2, + "rcut": obj.rcut, + "rcut_smth": obj.rcut_smth, + "sel": obj.sel, + "neuron": obj.neuron, + "axis_neuron": obj.axis_neuron, + "resnet_dt": obj.resnet_dt, + "set_davg_zero": obj.set_davg_zero, + "activation_function": obj.activation_function, + # make deterministic + "precision": RESERVED_PRECISON_DICT[obj.prec], + "embeddings": obj.filter_layers.serialize(), + "env_mat": DPEnvMat(obj.rcut, obj.rcut_smth).serialize(), + "exclude_types": obj.exclude_types, + "env_protection": obj.env_protection, + "@variables": { + "davg": obj["davg"].numpy(), + "dstd": obj["dstd"].numpy(), + }, + "type_map": self.type_map, + ## to be updated when the options are supported. + "trainable": True, + "type_one_side": obj.type_one_side, + "spin": None, + } + + @classmethod + def deserialize(cls, data: dict) -> "DescrptSeA": + data = data.copy() + check_version_compatibility(data.pop("@version", 1), 2, 1) + data.pop("@class", None) + data.pop("type", None) + variables = data.pop("@variables") + embeddings = data.pop("embeddings") + env_mat = data.pop("env_mat") + obj = cls(**data) + + def t_cvt(xx): + return paddle.to_tensor(xx, dtype=obj.sea.prec).to(device=env.DEVICE) + + obj.sea["davg"] = t_cvt(variables["davg"]) + obj.sea["dstd"] = t_cvt(variables["dstd"]) + obj.sea.filter_layers = NetworkCollection.deserialize(embeddings) + return obj + + @classmethod + def update_sel( + cls, + train_data: DeepmdDataSystem, + type_map: Optional[List[str]], + local_jdata: dict, + ) -> Tuple[dict, Optional[float]]: + """Update the selection and perform neighbor statistics. + + Parameters + ---------- + train_data : DeepmdDataSystem + data used to do neighbor statictics + type_map : list[str], optional + The name of each type of atoms + local_jdata : dict + The local data refer to the current class + + Returns + ------- + dict + The updated local data + float + The minimum distance between two atoms + """ + local_jdata_cpy = local_jdata.copy() + min_nbor_dist, local_jdata_cpy["sel"] = UpdateSel().update_one_sel( + train_data, type_map, local_jdata_cpy["rcut"], local_jdata_cpy["sel"], False + ) + return local_jdata_cpy, min_nbor_dist + + +@DescriptorBlock.register("se_e2_a") +class DescrptBlockSeA(DescriptorBlock): + ndescrpt: Final[int] + __constants__: ClassVar[list] = ["ndescrpt"] + + def __init__( + self, + rcut, + rcut_smth, + sel, + neuron=[25, 50, 100], + axis_neuron=16, + set_davg_zero: bool = False, + activation_function: str = "tanh", + precision: str = "float64", + resnet_dt: bool = False, + exclude_types: List[Tuple[int, int]] = [], + env_protection: float = 0.0, + old_impl: bool = False, + type_one_side: bool = True, + trainable: bool = True, + seed: Optional[Union[int, List[int]]] = None, + **kwargs, + ): + """Construct an embedding net of type `se_a`. + + Args: + - rcut: Cut-off radius. + - rcut_smth: Smooth hyper-parameter for pair force & energy. + - sel: For each element type, how many atoms is selected as neighbors. + - filter_neuron: Number of neurons in each hidden layers of the embedding net. + - axis_neuron: Number of columns of the sub-matrix of the embedding matrix. + """ + super().__init__() + self.rcut = rcut + self.rcut_smth = rcut_smth + self.neuron = neuron + self.filter_neuron = self.neuron + self.axis_neuron = axis_neuron + self.set_davg_zero = set_davg_zero + self.activation_function = activation_function + self.precision = precision + self.prec = PRECISION_DICT[self.precision] + self.resnet_dt = resnet_dt + self.old_impl = old_impl + self.env_protection = env_protection + self.ntypes = len(sel) + self.type_one_side = type_one_side + self.seed = seed + # order matters, placed after the assignment of self.ntypes + self.reinit_exclude(exclude_types) + + self.sel = sel + # should be on CPU to avoid D2H, as it is used as slice index + self.sec = [0, *np.cumsum(self.sel).tolist()] + self.split_sel = self.sel + self.nnei = sum(sel) + self.ndescrpt = self.nnei * 4 + + wanted_shape = (self.ntypes, self.nnei, 4) + mean = paddle.zeros(wanted_shape, dtype=self.prec).to(device=env.DEVICE) + stddev = paddle.ones(wanted_shape, dtype=self.prec).to(device=env.DEVICE) + self.register_buffer("mean", mean) + self.register_buffer("stddev", stddev) + self.filter_layers_old = None + self.filter_layers = None + + if self.old_impl: + if not self.type_one_side: + raise ValueError( + "The old implementation does not support type_one_side=False." + ) + filter_layers = [] + # TODO: remove + start_index = 0 + for type_i in range(self.ntypes): + one = TypeFilter(start_index, sel[type_i], self.filter_neuron) + filter_layers.append(one) + start_index += sel[type_i] + self.filter_layers_old = paddle.nn.LayerList(filter_layers) + else: + ndim = 1 if self.type_one_side else 2 + filter_layers = NetworkCollection( + ndim=ndim, ntypes=len(sel), network_type="embedding_network" + ) + for ii, embedding_idx in enumerate( + itertools.product(range(self.ntypes), repeat=ndim) + ): + filter_layers[embedding_idx] = EmbeddingNet( + 1, + self.filter_neuron, + activation_function=self.activation_function, + precision=self.precision, + resnet_dt=self.resnet_dt, + seed=child_seed(self.seed, ii), + ) + self.filter_layers = filter_layers + self.stats = None + # set trainable + for param in self.parameters(): + param.stop_gradient = not trainable + + def get_rcut(self) -> float: + """Returns the cut-off radius.""" + return self.rcut + + def get_rcut_smth(self) -> float: + """Returns the radius where the neighbor information starts to smoothly decay to 0.""" + return self.rcut_smth + + def get_nsel(self) -> int: + """Returns the number of selected atoms in the cut-off radius.""" + return sum(self.sel) + + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" + return self.sel + + def get_ntypes(self) -> int: + """Returns the number of element types.""" + return self.ntypes + + def get_dim_out(self) -> int: + """Returns the output dimension.""" + return self.dim_out + + def get_dim_emb(self) -> int: + """Returns the output dimension.""" + return self.neuron[-1] + + def get_dim_in(self) -> int: + """Returns the input dimension.""" + return self.dim_in + + def mixed_types(self) -> bool: + """If true, the discriptor + 1. assumes total number of atoms aligned across frames; + 2. requires a neighbor list that does not distinguish different atomic types. + + If false, the discriptor + 1. assumes total number of atoms of each atom type aligned across frames; + 2. requires a neighbor list that distinguishes different atomic types. + + """ + return False + + def get_env_protection(self) -> float: + """Returns the protection of building environment matrix.""" + return self.env_protection + + @property + def dim_out(self): + """Returns the output dimension of this descriptor.""" + return self.filter_neuron[-1] * self.axis_neuron + + @property + def dim_in(self): + """Returns the atomic input dimension of this descriptor.""" + return 0 + + def __setitem__(self, key, value): + if key in ("avg", "data_avg", "davg"): + self.mean = value + elif key in ("std", "data_std", "dstd"): + self.stddev = value + else: + raise KeyError(key) + + def __getitem__(self, key): + if key in ("avg", "data_avg", "davg"): + return self.mean + elif key in ("std", "data_std", "dstd"): + return self.stddev + else: + raise KeyError(key) + + def compute_input_stats( + self, + merged: Union[Callable[[], List[dict]], List[dict]], + path: Optional[DPPath] = None, + ): + """ + Compute the input statistics (e.g. mean and stddev) for the descriptors from packed data. + + Parameters + ---------- + merged : Union[Callable[[], List[dict]], List[dict]] + - List[dict]: A list of data samples from various data systems. + Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` + originating from the `i`-th data system. + - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + only when needed. Since the sampling process can be slow and memory-intensive, + the lazy function helps by only sampling once. + path : Optional[DPPath] + The path to the stat file. + + """ + env_mat_stat = EnvMatStatSe(self) + if path is not None: + path = path / env_mat_stat.get_hash() + if path is None or not path.is_dir(): + if callable(merged): + # only get data for once + sampled = merged() + else: + sampled = merged + else: + sampled = [] + env_mat_stat.load_or_compute_stats(sampled, path) + self.stats = env_mat_stat.stats + mean, stddev = env_mat_stat() + if not self.set_davg_zero: + paddle.assign(paddle.to_tensor(mean).to(device=env.DEVICE), self.mean) # pylint: disable=no-explicit-dtype + paddle.assign(paddle.to_tensor(stddev).to(device=env.DEVICE), self.stddev) # pylint: disable=no-explicit-dtype + + def get_stats(self) -> Dict[str, StatItem]: + """Get the statistics of the descriptor.""" + if self.stats is None: + raise RuntimeError( + "The statistics of the descriptor has not been computed." + ) + return self.stats + + def reinit_exclude( + self, + exclude_types: List[Tuple[int, int]] = [], + ): + self.exclude_types = exclude_types + self.emask = PairExcludeMask(self.ntypes, exclude_types=exclude_types) + + def forward( + self, + nlist: paddle.Tensor, + extended_coord: paddle.Tensor, + extended_atype: paddle.Tensor, + extended_atype_embd: Optional[paddle.Tensor] = None, + mapping: Optional[paddle.Tensor] = None, + ): + """Calculate decoded embedding for each atom. + + Args: + - coord: Tell atom coordinates with shape [nframes, natoms[1]*3]. + - atype: Tell atom types with shape [nframes, natoms[1]]. + - natoms: Tell atom count and element count. Its shape is [2+self.ntypes]. + - box: Tell simulation box with shape [nframes, 9]. + + Returns + ------- + - `paddle.Tensor`: descriptor matrix with shape [nframes, natoms[0]*self.filter_neuron[-1]*self.axis_neuron]. + """ + del extended_atype_embd, mapping + nf = nlist.shape[0] + nloc = nlist.shape[1] + atype: paddle.Tensor = extended_atype[:, :nloc] + dmatrix, diff, sw = prod_env_mat( + extended_coord, + nlist, + atype, + self.mean, + self.stddev, + self.rcut, + self.rcut_smth, + protection=self.env_protection, + ) + + if self.old_impl: + assert self.filter_layers_old is not None + dmatrix = dmatrix.reshape( + [-1, self.ndescrpt] + ) # shape is [nframes*nall, self.ndescrpt] + xyz_scatter = paddle.empty( # pylint: disable=no-explicit-dtype + [1], + ).to(device=env.DEVICE) + ret = self.filter_layers_old[0](dmatrix) + xyz_scatter = ret + for ii, transform in enumerate(self.filter_layers_old[1:]): + # shape is [nframes*nall, 4, self.filter_neuron[-1]] + ret = transform.forward(dmatrix) + xyz_scatter = xyz_scatter + ret + else: + assert self.filter_layers is not None + dmatrix = dmatrix.reshape([-1, self.nnei, 4]) + dmatrix = dmatrix.to(dtype=self.prec) + nfnl = dmatrix.shape[0] + # pre-allocate a shape to pass jit + xyz_scatter = paddle.zeros( + [nfnl, 4, self.filter_neuron[-1]], + dtype=self.prec, + ).to(extended_coord.place) + # nfnl x nnei + exclude_mask = self.emask(nlist, extended_atype).reshape([nfnl, self.nnei]) + for embedding_idx, ll in enumerate(self.filter_layers.networks): + if self.type_one_side: + ii = embedding_idx + # paddle.jit is not happy with slice(None) + # ti_mask = paddle.ones(nfnl, dtype=paddle.bool, device=dmatrix.place) + # applying a mask seems to cause performance degradation + ti_mask = None + else: + # ti: center atom type, ii: neighbor type... + ii = embedding_idx // self.ntypes + ti = embedding_idx % self.ntypes + ti_mask = atype.flatten().equal(ti) + # nfnl x nt + if ti_mask is not None: + mm = exclude_mask[ti_mask, self.sec[ii] : self.sec[ii + 1]] + else: + mm = exclude_mask[:, self.sec[ii] : self.sec[ii + 1]] + # nfnl x nt x 4 + if ti_mask is not None: + rr = dmatrix[ti_mask, self.sec[ii] : self.sec[ii + 1], :] + else: + rr = dmatrix[:, self.sec[ii] : self.sec[ii + 1], :] + rr = rr * mm[:, :, None].astype(rr.dtype) + ss = rr[:, :, :1] + # nfnl x nt x ng + gg = ll.forward(ss) + # nfnl x 4 x ng + gr = paddle.matmul(rr.transpose([0, 2, 1]), gg) + if ti_mask is not None: + xyz_scatter[ti_mask] += gr + else: + xyz_scatter += gr + + xyz_scatter /= self.nnei + xyz_scatter_1 = xyz_scatter.transpose([0, 2, 1]) + rot_mat: paddle.Tensor = xyz_scatter_1[:, :, 1:4] + xyz_scatter_2 = xyz_scatter[:, :, 0 : self.axis_neuron] + result = paddle.matmul( + xyz_scatter_1, xyz_scatter_2 + ) # shape is [nframes*nall, self.filter_neuron[-1], self.axis_neuron] + result = result.reshape([nf, nloc, self.filter_neuron[-1] * self.axis_neuron]) + rot_mat = rot_mat.reshape([nf, nloc] + list(rot_mat.shape[1:])) # noqa:RUF005 + return ( + result.to(dtype=env.GLOBAL_PD_FLOAT_PRECISION), + rot_mat.to(dtype=env.GLOBAL_PD_FLOAT_PRECISION), + None, + None, + sw, + ) + + def has_message_passing(self) -> bool: + """Returns whether the descriptor block has message passing.""" + return False + + def need_sorted_nlist_for_lower(self) -> bool: + """Returns whether the descriptor block needs sorted nlist when using `forward_lower`.""" + return False diff --git a/deepmd/pd/model/descriptor/se_atten.py b/deepmd/pd/model/descriptor/se_atten.py new file mode 100644 index 0000000000..1d6c2c8e87 --- /dev/null +++ b/deepmd/pd/model/descriptor/se_atten.py @@ -0,0 +1,1041 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Callable, + Dict, + List, + Optional, + Tuple, + Union, +) + +import paddle +import paddle.nn as nn +import paddle.nn.functional as paddle_func + +from deepmd.dpmodel.utils.seed import ( + child_seed, +) +from deepmd.pd.model.descriptor.descriptor import ( + DescriptorBlock, +) +from deepmd.pd.model.descriptor.env_mat import ( + prod_env_mat, +) +from deepmd.pd.model.network.layernorm import ( + LayerNorm, +) +from deepmd.pd.model.network.mlp import ( + EmbeddingNet, + MLPLayer, + NetworkCollection, +) +from deepmd.pd.model.network.network import ( + NeighborWiseAttention, + TypeFilter, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + DEFAULT_PRECISION, + PRECISION_DICT, +) +from deepmd.pd.utils.env_mat_stat import ( + EnvMatStatSe, +) +from deepmd.pd.utils.exclude_mask import ( + PairExcludeMask, +) +from deepmd.utils.env_mat_stat import ( + StatItem, +) +from deepmd.utils.path import ( + DPPath, +) +from deepmd.utils.version import ( + check_version_compatibility, +) + + +@DescriptorBlock.register("se_atten") +class DescrptBlockSeAtten(DescriptorBlock): + def __init__( + self, + rcut: float, + rcut_smth: float, + sel: Union[List[int], int], + ntypes: int, + neuron: list = [25, 50, 100], + axis_neuron: int = 16, + tebd_dim: int = 8, + tebd_input_mode: str = "concat", + set_davg_zero: bool = True, + attn: int = 128, + attn_layer: int = 2, + attn_dotr: bool = True, + attn_mask: bool = False, + activation_function="tanh", + precision: str = "float64", + resnet_dt: bool = False, + scaling_factor=1.0, + normalize=True, + temperature=None, + smooth: bool = True, + type_one_side: bool = False, + exclude_types: List[Tuple[int, int]] = [], + env_protection: float = 0.0, + trainable_ln: bool = True, + ln_eps: Optional[float] = 1e-5, + seed: Optional[Union[int, List[int]]] = None, + type: Optional[str] = None, + old_impl: bool = False, + ): + r"""Construct an embedding net of type `se_atten`. + + Parameters + ---------- + rcut : float + The cut-off radius :math:`r_c` + rcut_smth : float + From where the environment matrix should be smoothed :math:`r_s` + sel : list[int], int + list[int]: sel[i] specifies the maxmum number of type i atoms in the cut-off radius + int: the total maxmum number of atoms in the cut-off radius + ntypes : int + Number of element types + neuron : list[int] + Number of neurons in each hidden layers of the embedding net :math:`\mathcal{N}` + axis_neuron : int + Number of the axis neuron :math:`M_2` (number of columns of the sub-matrix of the embedding matrix) + tebd_dim : int + Dimension of the type embedding + tebd_input_mode : str + The input mode of the type embedding. Supported modes are ["concat", "strip"]. + - "concat": Concatenate the type embedding with the smoothed radial information as the union input for the embedding network. + - "strip": Use a separated embedding network for the type embedding and combine the output with the radial embedding network output. + resnet_dt : bool + Time-step `dt` in the resnet construction: + y = x + dt * \phi (Wx + b) + trainable_ln : bool + Whether to use trainable shift and scale weights in layer normalization. + ln_eps : float, Optional + The epsilon value for layer normalization. + type_one_side : bool + If 'False', type embeddings of both neighbor and central atoms are considered. + If 'True', only type embeddings of neighbor atoms are considered. + Default is 'False'. + attn : int + Hidden dimension of the attention vectors + attn_layer : int + Number of attention layers + attn_dotr : bool + If dot the angular gate to the attention weights + attn_mask : bool + (Only support False to keep consistent with other backend references.) + (Not used in this version.) + If mask the diagonal of attention weights + exclude_types : List[List[int]] + The excluded pairs of types which have no interaction with each other. + For example, `[[0, 1]]` means no interaction between type 0 and type 1. + env_protection : float + Protection parameter to prevent division by zero errors during environment matrix calculations. + set_davg_zero : bool + Set the shift of embedding net input to zero. + activation_function : str + The activation function in the embedding net. Supported options are |ACTIVATION_FN| + precision : str + The precision of the embedding net parameters. Supported options are |PRECISION| + scaling_factor : float + The scaling factor of normalization in calculations of attention weights. + If `temperature` is None, the scaling of attention weights is (N_dim * scaling_factor)**0.5 + normalize : bool + Whether to normalize the hidden vectors in attention weights calculation. + temperature : float + If not None, the scaling of attention weights is `temperature` itself. + seed : int, Optional + Random seed for parameter initialization. + """ + super().__init__() + del type + self.rcut = rcut + self.rcut_smth = rcut_smth + self.neuron = neuron + self.filter_neuron = self.neuron + self.axis_neuron = axis_neuron + self.tebd_dim = tebd_dim + self.tebd_input_mode = tebd_input_mode + self.set_davg_zero = set_davg_zero + self.attn_dim = attn + self.attn_layer = attn_layer + self.attn_dotr = attn_dotr + self.attn_mask = attn_mask + self.activation_function = activation_function + self.precision = precision + self.prec = PRECISION_DICT[self.precision] + self.resnet_dt = resnet_dt + self.scaling_factor = scaling_factor + self.normalize = normalize + self.temperature = temperature + self.smooth = smooth + self.type_one_side = type_one_side + self.env_protection = env_protection + self.trainable_ln = trainable_ln + self.seed = seed + # to keep consistent with default value in this backends + if ln_eps is None: + ln_eps = 1e-5 + self.ln_eps = ln_eps + self.old_impl = old_impl + + if isinstance(sel, int): + sel = [sel] + + self.ntypes = ntypes + self.sel = sel + self.sec = self.sel + self.split_sel = self.sel + self.nnei = sum(sel) + self.ndescrpt = self.nnei * 4 + # order matters, placed after the assignment of self.ntypes + self.reinit_exclude(exclude_types) + if self.old_impl: + assert self.tebd_input_mode in [ + "concat" + ], "Old implementation does not support tebd_input_mode != 'concat'." + self.dpa1_attention = NeighborWiseAttention( + self.attn_layer, + self.nnei, + self.filter_neuron[-1], + self.attn_dim, + dotr=self.attn_dotr, + do_mask=self.attn_mask, + activation=self.activation_function, + scaling_factor=self.scaling_factor, + normalize=self.normalize, + temperature=self.temperature, + smooth=self.smooth, + ) + else: + self.dpa1_attention = NeighborGatedAttention( + self.attn_layer, + self.nnei, + self.filter_neuron[-1], + self.attn_dim, + dotr=self.attn_dotr, + do_mask=self.attn_mask, + scaling_factor=self.scaling_factor, + normalize=self.normalize, + temperature=self.temperature, + trainable_ln=self.trainable_ln, + ln_eps=self.ln_eps, + smooth=self.smooth, + precision=self.precision, + seed=child_seed(self.seed, 0), + ) + + wanted_shape = (self.ntypes, self.nnei, 4) + mean = paddle.zeros(wanted_shape, dtype=env.GLOBAL_PD_FLOAT_PRECISION).to( + device=env.DEVICE + ) + stddev = paddle.ones(wanted_shape, dtype=env.GLOBAL_PD_FLOAT_PRECISION).to( + device=env.DEVICE + ) + self.register_buffer("mean", mean) + self.register_buffer("stddev", stddev) + self.tebd_dim_input = self.tebd_dim if self.type_one_side else self.tebd_dim * 2 + if self.tebd_input_mode in ["concat"]: + self.embd_input_dim = 1 + self.tebd_dim_input + else: + self.embd_input_dim = 1 + + self.filter_layers_old = None + self.filter_layers = None + self.filter_layers_strip = None + if self.old_impl: + filter_layers = [] + one = TypeFilter( + 0, + self.nnei, + self.filter_neuron, + return_G=True, + tebd_dim=self.tebd_dim, + use_tebd=True, + tebd_mode=self.tebd_input_mode, + ) + filter_layers.append(one) + self.filter_layers_old = paddle.nn.LayerList(filter_layers) + else: + filter_layers = NetworkCollection( + ndim=0, ntypes=self.ntypes, network_type="embedding_network" + ) + filter_layers[0] = EmbeddingNet( + self.embd_input_dim, + self.filter_neuron, + activation_function=self.activation_function, + precision=self.precision, + resnet_dt=self.resnet_dt, + seed=child_seed(self.seed, 1), + ) + self.filter_layers = filter_layers + if self.tebd_input_mode in ["strip"]: + filter_layers_strip = NetworkCollection( + ndim=0, ntypes=self.ntypes, network_type="embedding_network" + ) + filter_layers_strip[0] = EmbeddingNet( + self.tebd_dim_input, + self.filter_neuron, + activation_function=self.activation_function, + precision=self.precision, + resnet_dt=self.resnet_dt, + seed=child_seed(self.seed, 2), + ) + self.filter_layers_strip = filter_layers_strip + self.stats = None + + def get_rcut(self) -> float: + """Returns the cut-off radius.""" + return self.rcut + + def get_rcut_smth(self) -> float: + """Returns the radius where the neighbor information starts to smoothly decay to 0.""" + return self.rcut_smth + + def get_nsel(self) -> int: + """Returns the number of selected atoms in the cut-off radius.""" + return sum(self.sel) + + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" + return self.sel + + def get_ntypes(self) -> int: + """Returns the number of element types.""" + return self.ntypes + + def get_dim_in(self) -> int: + """Returns the input dimension.""" + return self.dim_in + + def get_dim_out(self) -> int: + """Returns the output dimension.""" + return self.dim_out + + def get_dim_emb(self) -> int: + """Returns the output dimension of embedding.""" + return self.filter_neuron[-1] + + def __setitem__(self, key, value): + if key in ("avg", "data_avg", "davg"): + self.mean = value + elif key in ("std", "data_std", "dstd"): + self.stddev = value + else: + raise KeyError(key) + + def __getitem__(self, key): + if key in ("avg", "data_avg", "davg"): + return self.mean + elif key in ("std", "data_std", "dstd"): + return self.stddev + else: + raise KeyError(key) + + def mixed_types(self) -> bool: + """If true, the discriptor + 1. assumes total number of atoms aligned across frames; + 2. requires a neighbor list that does not distinguish different atomic types. + + If false, the discriptor + 1. assumes total number of atoms of each atom type aligned across frames; + 2. requires a neighbor list that distinguishes different atomic types. + + """ + return True + + def get_env_protection(self) -> float: + """Returns the protection of building environment matrix.""" + return self.env_protection + + @property + def dim_out(self): + """Returns the output dimension of this descriptor.""" + return self.filter_neuron[-1] * self.axis_neuron + + @property + def dim_in(self): + """Returns the atomic input dimension of this descriptor.""" + return self.tebd_dim + + @property + def dim_emb(self): + """Returns the output dimension of embedding.""" + return self.get_dim_emb() + + def compute_input_stats( + self, + merged: Union[Callable[[], List[dict]], List[dict]], + path: Optional[DPPath] = None, + ): + """ + Compute the input statistics (e.g. mean and stddev) for the descriptors from packed data. + + Parameters + ---------- + merged : Union[Callable[[], List[dict]], List[dict]] + - List[dict]: A list of data samples from various data systems. + Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` + originating from the `i`-th data system. + - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + only when needed. Since the sampling process can be slow and memory-intensive, + the lazy function helps by only sampling once. + path : Optional[DPPath] + The path to the stat file. + + """ + env_mat_stat = EnvMatStatSe(self) + if path is not None: + path = path / env_mat_stat.get_hash() + if path is None or not path.is_dir(): + if callable(merged): + # only get data for once + sampled = merged() + else: + sampled = merged + else: + sampled = [] + env_mat_stat.load_or_compute_stats(sampled, path) + self.stats = env_mat_stat.stats + mean, stddev = env_mat_stat() + if not self.set_davg_zero: + paddle.assign(paddle.to_tensor(mean).to(device=env.DEVICE), self.mean) # pylint: disable=no-explicit-dtype + paddle.assign(paddle.to_tensor(stddev).to(device=env.DEVICE), self.stddev) # pylint: disable=no-explicit-dtype + + def get_stats(self) -> Dict[str, StatItem]: + """Get the statistics of the descriptor.""" + if self.stats is None: + raise RuntimeError( + "The statistics of the descriptor has not been computed." + ) + return self.stats + + def reinit_exclude( + self, + exclude_types: List[Tuple[int, int]] = [], + ): + self.exclude_types = exclude_types + self.emask = PairExcludeMask(self.ntypes, exclude_types=exclude_types) + + def forward( + self, + nlist: paddle.Tensor, + extended_coord: paddle.Tensor, + extended_atype: paddle.Tensor, + extended_atype_embd: Optional[paddle.Tensor] = None, + mapping: Optional[paddle.Tensor] = None, + ): + """Compute the descriptor. + + Parameters + ---------- + nlist + The neighbor list. shape: nf x nloc x nnei + extended_coord + The extended coordinates of atoms. shape: nf x (nallx3) + extended_atype + The extended aotm types. shape: nf x nall x nt + extended_atype_embd + The extended type embedding of atoms. shape: nf x nall + mapping + The index mapping, not required by this descriptor. + + Returns + ------- + result + The descriptor. shape: nf x nloc x (ng x axis_neuron) + g2 + The rotationally invariant pair-partical representation. + shape: nf x nloc x nnei x ng + h2 + The rotationally equivariant pair-partical representation. + shape: nf x nloc x nnei x 3 + gr + The rotationally equivariant and permutationally invariant single particle + representation. shape: nf x nloc x ng x 3 + sw + The smooth switch function. shape: nf x nloc x nnei + + """ + del mapping + assert extended_atype_embd is not None + nframes, nloc, nnei = nlist.shape + atype = extended_atype[:, :nloc] + nb = nframes + nall = extended_coord.reshape([nb, -1, 3]).shape[1] + dmatrix, diff, sw = prod_env_mat( + extended_coord, + nlist, + atype, + self.mean, + self.stddev, + self.rcut, + self.rcut_smth, + protection=self.env_protection, + ) + # nb x nloc x nnei + exclude_mask = self.emask(nlist, extended_atype) + nlist = paddle.where(exclude_mask != 0, nlist, -1) + nlist_mask = nlist != -1 + nlist = paddle.where(nlist == -1, 0, nlist) + sw = paddle.squeeze(sw, -1) + # nf x nloc x nt -> nf x nloc x nnei x nt + atype_tebd = extended_atype_embd[:, :nloc, :] + atype_tebd_nnei = atype_tebd.unsqueeze(2).expand(-1, -1, self.nnei, -1) + # nf x nall x nt + nt = extended_atype_embd.shape[-1] + atype_tebd_ext = extended_atype_embd + # nb x (nloc x nnei) x nt + index = nlist.reshape([nb, nloc * nnei]).unsqueeze(-1).expand(-1, -1, nt) + # nb x (nloc x nnei) x nt + atype_tebd_nlist = paddle.take_along_axis(atype_tebd_ext, axis=1, index=index) + # nb x nloc x nnei x nt + atype_tebd_nlist = atype_tebd_nlist.reshape([nb, nloc, nnei, nt]) + # beyond the cutoff sw should be 0.0 + sw = sw.masked_fill(~nlist_mask, 0.0) + # (nb x nloc) x nnei + exclude_mask = exclude_mask.reshape([nb * nloc, nnei]) + if self.old_impl: + assert self.filter_layers_old is not None + dmatrix = dmatrix.reshape( + [-1, self.ndescrpt] + ) # shape is [nframes*nall, self.ndescrpt] + gg = self.filter_layers_old[0]( + dmatrix, + atype_tebd=atype_tebd_nnei, + nlist_tebd=atype_tebd_nlist, + ) # shape is [nframes*nall, self.neei, out_size] + input_r = paddle.nn.functional.normalize( + dmatrix.reshape([-1, self.nnei, 4])[:, :, 1:4], axis=-1 + ) + gg = self.dpa1_attention( + gg, nlist_mask, input_r=input_r, sw=sw + ) # shape is [nframes*nloc, self.neei, out_size] + inputs_reshape = dmatrix.reshape([-1, self.nnei, 4]).transpose( + [0, 2, 1] + ) # shape is [nframes*natoms[0], 4, self.neei] + xyz_scatter = paddle.matmul( + inputs_reshape, gg + ) # shape is [nframes*natoms[0], 4, out_size] + else: + assert self.filter_layers is not None + # nfnl x nnei x 4 + dmatrix = dmatrix.reshape([-1, self.nnei, 4]) + nfnl = dmatrix.shape[0] + # nfnl x nnei x 4 + rr = dmatrix + rr = rr * exclude_mask[:, :, None] + ss = rr[:, :, :1] + nlist_tebd = atype_tebd_nlist.reshape([nfnl, nnei, self.tebd_dim]) + atype_tebd = atype_tebd_nnei.reshape([nfnl, nnei, self.tebd_dim]) + if self.tebd_input_mode in ["concat"]: + if not self.type_one_side: + # nfnl x nnei x (1 + tebd_dim * 2) + ss = paddle.concat([ss, nlist_tebd, atype_tebd], axis=2) + else: + # nfnl x nnei x (1 + tebd_dim) + ss = paddle.concat([ss, nlist_tebd], axis=2) + # nfnl x nnei x ng + gg = self.filter_layers.networks[0](ss) + elif self.tebd_input_mode in ["strip"]: + # nfnl x nnei x ng + gg_s = self.filter_layers.networks[0](ss) + assert self.filter_layers_strip is not None + if not self.type_one_side: + # nfnl x nnei x (tebd_dim * 2) + tt = paddle.concat([nlist_tebd, atype_tebd], axis=2) + else: + # nfnl x nnei x tebd_dim + tt = nlist_tebd + # nfnl x nnei x ng + gg_t = self.filter_layers_strip.networks[0](tt) + if self.smooth: + gg_t = gg_t * sw.reshape([-1, self.nnei, 1]) + # nfnl x nnei x ng + gg = gg_s * gg_t + gg_s + else: + raise NotImplementedError + + input_r = paddle.nn.functional.normalize( + rr.reshape([-1, self.nnei, 4])[:, :, 1:4], axis=-1 + ) + gg = self.dpa1_attention( + gg, nlist_mask, input_r=input_r, sw=sw + ) # shape is [nframes*nloc, self.neei, out_size] + # nfnl x 4 x ng + xyz_scatter = paddle.matmul(rr.transpose([0, 2, 1]), gg) + xyz_scatter = xyz_scatter / self.nnei + xyz_scatter_1 = xyz_scatter.transpose([0, 2, 1]) + rot_mat = xyz_scatter_1[:, :, 1:4] + xyz_scatter_2 = xyz_scatter[:, :, 0 : self.axis_neuron] + result = paddle.matmul( + xyz_scatter_1, xyz_scatter_2 + ) # shape is [nframes*nloc, self.filter_neuron[-1], self.axis_neuron] + return ( + result.reshape([nframes, nloc, self.filter_neuron[-1] * self.axis_neuron]), + gg.reshape([nframes, nloc, self.nnei, self.filter_neuron[-1]]), + dmatrix.reshape([nframes, nloc, self.nnei, 4])[..., 1:], + rot_mat.reshape([nframes, nloc, self.filter_neuron[-1], 3]), + sw, + ) + + def has_message_passing(self) -> bool: + """Returns whether the descriptor block has message passing.""" + return False + + def need_sorted_nlist_for_lower(self) -> bool: + """Returns whether the descriptor block needs sorted nlist when using `forward_lower`.""" + return False + + +class NeighborGatedAttention(nn.Layer): + def __init__( + self, + layer_num: int, + nnei: int, + embed_dim: int, + hidden_dim: int, + dotr: bool = False, + do_mask: bool = False, + scaling_factor: float = 1.0, + normalize: bool = True, + temperature: Optional[float] = None, + trainable_ln: bool = True, + ln_eps: float = 1e-5, + smooth: bool = True, + precision: str = DEFAULT_PRECISION, + seed: Optional[Union[int, List[int]]] = None, + ): + """Construct a neighbor-wise attention net.""" + super().__init__() + self.layer_num = layer_num + self.nnei = nnei + self.embed_dim = embed_dim + self.hidden_dim = hidden_dim + self.dotr = dotr + self.do_mask = do_mask + self.scaling_factor = scaling_factor + self.normalize = normalize + self.temperature = temperature + self.trainable_ln = trainable_ln + self.ln_eps = ln_eps + self.smooth = smooth + self.precision = precision + self.seed = seed + self.network_type = NeighborGatedAttentionLayer + attention_layers = [] + for i in range(self.layer_num): + attention_layers.append( + NeighborGatedAttentionLayer( + nnei, + embed_dim, + hidden_dim, + dotr=dotr, + do_mask=do_mask, + scaling_factor=scaling_factor, + normalize=normalize, + temperature=temperature, + trainable_ln=trainable_ln, + ln_eps=ln_eps, + smooth=smooth, + precision=precision, + seed=child_seed(seed, i), + ) + ) + self.attention_layers = nn.LayerList(attention_layers) + + def forward( + self, + input_G, + nei_mask, + input_r: Optional[paddle.Tensor] = None, + sw: Optional[paddle.Tensor] = None, + ): + """Compute the multi-layer gated self-attention. + + Parameters + ---------- + input_G + inputs with shape: (nf x nloc) x nnei x embed_dim. + nei_mask + neighbor mask, with paddings being 0. shape: (nf x nloc) x nnei. + input_r + normalized radial. shape: (nf x nloc) x nnei x 3. + sw + The smooth switch function. shape: nf x nloc x nnei + """ + out = input_G + # https://github.com/pytorch/pytorch/issues/39165#issuecomment-635472592 + for layer in self.attention_layers: + out = layer(out, nei_mask, input_r=input_r, sw=sw) + return out + + def __getitem__(self, key): + if isinstance(key, int): + return self.attention_layers[key] + else: + raise TypeError(key) + + def __setitem__(self, key, value): + if not isinstance(key, int): + raise TypeError(key) + if isinstance(value, self.network_type): + pass + elif isinstance(value, dict): + value = self.network_type.deserialize(value) + else: + raise TypeError(value) + self.attention_layers[key] = value + + def serialize(self) -> dict: + """Serialize the networks to a dict. + + Returns + ------- + dict + The serialized networks. + """ + return { + "@class": "NeighborGatedAttention", + "@version": 1, + "layer_num": self.layer_num, + "nnei": self.nnei, + "embed_dim": self.embed_dim, + "hidden_dim": self.hidden_dim, + "dotr": self.dotr, + "do_mask": self.do_mask, + "scaling_factor": self.scaling_factor, + "normalize": self.normalize, + "temperature": self.temperature, + "trainable_ln": self.trainable_ln, + "ln_eps": self.ln_eps, + "precision": self.precision, + "attention_layers": [layer.serialize() for layer in self.attention_layers], + } + + @classmethod + def deserialize(cls, data: dict) -> "NeighborGatedAttention": + """Deserialize the networks from a dict. + + Parameters + ---------- + data : dict + The dict to deserialize from. + """ + data = data.copy() + check_version_compatibility(data.pop("@version"), 1, 1) + data.pop("@class") + attention_layers = data.pop("attention_layers") + obj = cls(**data) + for ii, network in enumerate(attention_layers): + obj[ii] = network + return obj + + +class NeighborGatedAttentionLayer(nn.Layer): + def __init__( + self, + nnei: int, + embed_dim: int, + hidden_dim: int, + dotr: bool = False, + do_mask: bool = False, + scaling_factor: float = 1.0, + normalize: bool = True, + temperature: Optional[float] = None, + smooth: bool = True, + trainable_ln: bool = True, + ln_eps: float = 1e-5, + precision: str = DEFAULT_PRECISION, + seed: Optional[Union[int, List[int]]] = None, + ): + """Construct a neighbor-wise attention layer.""" + super().__init__() + self.nnei = nnei + self.embed_dim = embed_dim + self.hidden_dim = hidden_dim + self.dotr = dotr + self.do_mask = do_mask + self.scaling_factor = scaling_factor + self.normalize = normalize + self.temperature = temperature + self.precision = precision + self.trainable_ln = trainable_ln + self.ln_eps = ln_eps + self.seed = seed + self.attention_layer = GatedAttentionLayer( + nnei, + embed_dim, + hidden_dim, + dotr=dotr, + do_mask=do_mask, + scaling_factor=scaling_factor, + normalize=normalize, + temperature=temperature, + smooth=smooth, + precision=precision, + seed=child_seed(seed, 0), + ) + self.attn_layer_norm = LayerNorm( + self.embed_dim, + eps=ln_eps, + trainable=trainable_ln, + precision=precision, + seed=child_seed(seed, 1), + ) + + def forward( + self, + x, + nei_mask, + input_r: Optional[paddle.Tensor] = None, + sw: Optional[paddle.Tensor] = None, + ): + residual = x + x, _ = self.attention_layer(x, nei_mask, input_r=input_r, sw=sw) + x = residual + x + x = self.attn_layer_norm(x) + return x + + def serialize(self) -> dict: + """Serialize the networks to a dict. + + Returns + ------- + dict + The serialized networks. + """ + return { + "nnei": self.nnei, + "embed_dim": self.embed_dim, + "hidden_dim": self.hidden_dim, + "dotr": self.dotr, + "do_mask": self.do_mask, + "scaling_factor": self.scaling_factor, + "normalize": self.normalize, + "temperature": self.temperature, + "trainable_ln": self.trainable_ln, + "ln_eps": self.ln_eps, + "precision": self.precision, + "attention_layer": self.attention_layer.serialize(), + "attn_layer_norm": self.attn_layer_norm.serialize(), + } + + @classmethod + def deserialize(cls, data: dict) -> "NeighborGatedAttentionLayer": + """Deserialize the networks from a dict. + + Parameters + ---------- + data : dict + The dict to deserialize from. + """ + data = data.copy() + attention_layer = data.pop("attention_layer") + attn_layer_norm = data.pop("attn_layer_norm") + obj = cls(**data) + obj.attention_layer = GatedAttentionLayer.deserialize(attention_layer) + obj.attn_layer_norm = LayerNorm.deserialize(attn_layer_norm) + return obj + + +class GatedAttentionLayer(nn.Layer): + def __init__( + self, + nnei: int, + embed_dim: int, + hidden_dim: int, + num_heads: int = 1, + dotr: bool = False, + do_mask: bool = False, + scaling_factor: float = 1.0, + normalize: bool = True, + temperature: Optional[float] = None, + bias: bool = True, + smooth: bool = True, + precision: str = DEFAULT_PRECISION, + seed: Optional[Union[int, List[int]]] = None, + ): + """Construct a multi-head neighbor-wise attention net.""" + super().__init__() + assert hidden_dim % num_heads == 0, "hidden_dim must be divisible by num_heads" + self.nnei = nnei + self.embed_dim = embed_dim + self.hidden_dim = hidden_dim + self.num_heads = num_heads + self.head_dim = hidden_dim // num_heads + self.dotr = dotr + self.do_mask = do_mask + self.bias = bias + self.smooth = smooth + self.scaling_factor = scaling_factor + self.temperature = temperature + self.precision = precision + self.seed = seed + self.scaling = ( + (self.head_dim * scaling_factor) ** -0.5 + if temperature is None + else temperature + ) + self.normalize = normalize + self.in_proj = MLPLayer( + embed_dim, + hidden_dim * 3, + bias=bias, + use_timestep=False, + bavg=0.0, + stddev=1.0, + precision=precision, + seed=child_seed(seed, 0), + ) + self.out_proj = MLPLayer( + hidden_dim, + embed_dim, + bias=bias, + use_timestep=False, + bavg=0.0, + stddev=1.0, + precision=precision, + seed=child_seed(seed, 1), + ) + + def forward( + self, + query, + nei_mask, + input_r: Optional[paddle.Tensor] = None, + sw: Optional[paddle.Tensor] = None, + attnw_shift: float = 20.0, + ): + """Compute the multi-head gated self-attention. + + Parameters + ---------- + query + inputs with shape: (nf x nloc) x nnei x embed_dim. + nei_mask + neighbor mask, with paddings being 0. shape: (nf x nloc) x nnei. + input_r + normalized radial. shape: (nf x nloc) x nnei x 3. + sw + The smooth switch function. shape: (nf x nloc) x nnei + attnw_shift : float + The attention weight shift to preserve smoothness when doing padding before softmax. + """ + q, k, v = self.in_proj(query).chunk(3, axis=-1) + + # Reshape for multi-head attention: (nf x nloc) x num_heads x nnei x head_dim + q = q.reshape([-1, self.nnei, self.num_heads, self.head_dim]).transpose( + [0, 2, 1, 3] + ) + k = k.reshape([-1, self.nnei, self.num_heads, self.head_dim]).transpose( + [0, 2, 1, 3] + ) + v = v.reshape([-1, self.nnei, self.num_heads, self.head_dim]).transpose( + [0, 2, 1, 3] + ) + + if self.normalize: + q = paddle_func.normalize(q, axis=-1) + k = paddle_func.normalize(k, axis=-1) + v = paddle_func.normalize(v, axis=-1) + + q = q * self.scaling + # (nf x nloc) x num_heads x head_dim x nnei + k = k.transpose([0, 1, 3, 2]) + + # Compute attention scores + # (nf x nloc) x num_heads x nnei x nnei + attn_weights = paddle.matmul(q, k) + # (nf x nloc) x nnei + nei_mask = nei_mask.reshape([-1, self.nnei]) + + if self.smooth: + assert sw is not None + # (nf x nloc) x 1 x nnei + sw = sw.reshape([-1, 1, self.nnei]) + attn_weights = (attn_weights + attnw_shift) * sw[:, :, :, None] * sw[ + :, :, None, : + ] - attnw_shift + else: + # (nf x nloc) x 1 x 1 x nnei + attn_weights = attn_weights.masked_fill( + ~nei_mask.unsqueeze(1).unsqueeze(1), float("-inf") + ) + + attn_weights = paddle_func.softmax(attn_weights, axis=-1) + attn_weights = attn_weights.masked_fill( + ~nei_mask.unsqueeze(1).unsqueeze(-1), 0.0 + ) + if self.smooth: + assert sw is not None + attn_weights = attn_weights * sw[:, :, :, None] * sw[:, :, None, :] + + if self.dotr: + # (nf x nloc) x nnei x 3 + assert input_r is not None, "input_r must be provided when dotr is True!" + # (nf x nloc) x 1 x nnei x nnei + angular_weight = paddle.matmul( + input_r, input_r.transpose([0, 1, 3, 2]) + ).reshape([-1, 1, self.nnei, self.nnei]) + attn_weights = attn_weights * angular_weight + + # Apply attention to values + # (nf x nloc) x nnei x (num_heads x head_dim) + o = ( + paddle.matmul(attn_weights, v) + .transpose([0, 2, 1, 3]) + .reshape([-1, self.nnei, self.hidden_dim]) + ) + output = self.out_proj(o) + return output, attn_weights + + def serialize(self) -> dict: + """Serialize the networks to a dict. + + Returns + ------- + dict + The serialized networks. + """ + return { + "nnei": self.nnei, + "embed_dim": self.embed_dim, + "hidden_dim": self.hidden_dim, + "num_heads": self.num_heads, + "dotr": self.dotr, + "do_mask": self.do_mask, + "scaling_factor": self.scaling_factor, + "normalize": self.normalize, + "temperature": self.temperature, + "bias": self.bias, + "smooth": self.smooth, + "precision": self.precision, + "in_proj": self.in_proj.serialize(), + "out_proj": self.out_proj.serialize(), + } + + @classmethod + def deserialize(cls, data: dict) -> "GatedAttentionLayer": + """Deserialize the networks from a dict. + + Parameters + ---------- + data : dict + The dict to deserialize from. + """ + data = data.copy() + in_proj = data.pop("in_proj") + out_proj = data.pop("out_proj") + obj = cls(**data) + obj.in_proj = MLPLayer.deserialize(in_proj) + obj.out_proj = MLPLayer.deserialize(out_proj) + return obj diff --git a/deepmd/pd/model/descriptor/se_atten_v2.py b/deepmd/pd/model/descriptor/se_atten_v2.py new file mode 100644 index 0000000000..2f32f79a50 --- /dev/null +++ b/deepmd/pd/model/descriptor/se_atten_v2.py @@ -0,0 +1,279 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + List, + Optional, + Tuple, + Union, +) + +import paddle + +from deepmd.dpmodel.utils import EnvMat as DPEnvMat +from deepmd.pd.model.descriptor.dpa1 import ( + DescrptDPA1, +) +from deepmd.pd.model.network.mlp import ( + NetworkCollection, +) +from deepmd.pd.model.network.network import ( + TypeEmbedNetConsistent, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + RESERVED_PRECISON_DICT, +) +from deepmd.utils.version import ( + check_version_compatibility, +) + +from .base_descriptor import ( + BaseDescriptor, +) +from .se_atten import ( + NeighborGatedAttention, +) + + +@BaseDescriptor.register("se_atten_v2") +class DescrptSeAttenV2(DescrptDPA1): + def __init__( + self, + rcut: float, + rcut_smth: float, + sel: Union[List[int], int], + ntypes: int, + neuron: list = [25, 50, 100], + axis_neuron: int = 16, + tebd_dim: int = 8, + set_davg_zero: bool = True, + attn: int = 128, + attn_layer: int = 2, + attn_dotr: bool = True, + attn_mask: bool = False, + activation_function: str = "tanh", + precision: str = "float64", + resnet_dt: bool = False, + exclude_types: List[Tuple[int, int]] = [], + env_protection: float = 0.0, + scaling_factor: int = 1.0, + normalize=True, + temperature=None, + concat_output_tebd: bool = True, + trainable: bool = True, + trainable_ln: bool = True, + ln_eps: Optional[float] = 1e-5, + type_one_side: bool = False, + stripped_type_embedding: Optional[bool] = None, + seed: Optional[Union[int, List[int]]] = None, + use_econf_tebd: bool = False, + use_tebd_bias: bool = False, + type_map: Optional[List[str]] = None, + # not implemented + spin=None, + type: Optional[str] = None, + old_impl: bool = False, + ) -> None: + r"""Construct smooth version of embedding net of type `se_atten_v2`. + + Parameters + ---------- + rcut : float + The cut-off radius :math:`r_c` + rcut_smth : float + From where the environment matrix should be smoothed :math:`r_s` + sel : list[int], int + list[int]: sel[i] specifies the maxmum number of type i atoms in the cut-off radius + int: the total maxmum number of atoms in the cut-off radius + ntypes : int + Number of element types + neuron : list[int] + Number of neurons in each hidden layers of the embedding net :math:`\mathcal{N}` + axis_neuron : int + Number of the axis neuron :math:`M_2` (number of columns of the sub-matrix of the embedding matrix) + tebd_dim : int + Dimension of the type embedding + set_davg_zero : bool + Set the shift of embedding net input to zero. + attn : int + Hidden dimension of the attention vectors + attn_layer : int + Number of attention layers + attn_dotr : bool + If dot the angular gate to the attention weights + attn_mask : bool + (Only support False to keep consistent with other backend references.) + (Not used in this version.) + If mask the diagonal of attention weights + activation_function : str + The activation function in the embedding net. Supported options are |ACTIVATION_FN| + precision : str + The precision of the embedding net parameters. Supported options are |PRECISION| + resnet_dt : bool + Time-step `dt` in the resnet construction: + y = x + dt * \phi (Wx + b) + exclude_types : List[List[int]] + The excluded pairs of types which have no interaction with each other. + For example, `[[0, 1]]` means no interaction between type 0 and type 1. + env_protection : float + Protection parameter to prevent division by zero errors during environment matrix calculations. + scaling_factor : float + The scaling factor of normalization in calculations of attention weights. + If `temperature` is None, the scaling of attention weights is (N_dim * scaling_factor)**0.5 + normalize : bool + Whether to normalize the hidden vectors in attention weights calculation. + temperature : float + If not None, the scaling of attention weights is `temperature` itself. + concat_output_tebd : bool + Whether to concat type embedding at the output of the descriptor. + trainable : bool + If the weights of this descriptors are trainable. + trainable_ln : bool + Whether to use trainable shift and scale weights in layer normalization. + ln_eps : float, Optional + The epsilon value for layer normalization. + type_one_side : bool + If 'False', type embeddings of both neighbor and central atoms are considered. + If 'True', only type embeddings of neighbor atoms are considered. + Default is 'False'. + stripped_type_embedding : bool, Optional + (Deprecated, kept only for compatibility.) + Whether to strip the type embedding into a separate embedding network. + Setting this parameter to `True` is equivalent to setting `tebd_input_mode` to 'strip'. + Setting it to `False` is equivalent to setting `tebd_input_mode` to 'concat'. + The default value is `None`, which means the `tebd_input_mode` setting will be used instead. + seed : int, Optional + Random seed for parameter initialization. + use_econf_tebd : bool, Optional + Whether to use electronic configuration type embedding. + use_tebd_bias : bool, Optional + Whether to use bias in the type embedding layer. + type_map : List[str], Optional + A list of strings. Give the name to each type of atoms. + spin + (Only support None to keep consistent with other backend references.) + (Not used in this version. Not-none option is not implemented.) + The old implementation of deepspin. + """ + DescrptDPA1.__init__( + self, + rcut, + rcut_smth, + sel, + ntypes, + neuron=neuron, + axis_neuron=axis_neuron, + tebd_dim=tebd_dim, + tebd_input_mode="strip", + set_davg_zero=set_davg_zero, + attn=attn, + attn_layer=attn_layer, + attn_dotr=attn_dotr, + attn_mask=attn_mask, + activation_function=activation_function, + precision=precision, + resnet_dt=resnet_dt, + exclude_types=exclude_types, + env_protection=env_protection, + scaling_factor=scaling_factor, + normalize=normalize, + temperature=temperature, + concat_output_tebd=concat_output_tebd, + trainable=trainable, + trainable_ln=trainable_ln, + ln_eps=ln_eps, + smooth_type_embedding=True, + type_one_side=type_one_side, + stripped_type_embedding=stripped_type_embedding, + seed=seed, + use_econf_tebd=use_econf_tebd, + use_tebd_bias=use_tebd_bias, + type_map=type_map, + # not implemented + spin=spin, + type=type, + old_impl=old_impl, + ) + + def serialize(self) -> dict: + obj = self.se_atten + data = { + "@class": "Descriptor", + "type": "se_atten_v2", + "@version": 2, + "rcut": obj.rcut, + "rcut_smth": obj.rcut_smth, + "sel": obj.sel, + "ntypes": obj.ntypes, + "neuron": obj.neuron, + "axis_neuron": obj.axis_neuron, + "tebd_dim": obj.tebd_dim, + "set_davg_zero": obj.set_davg_zero, + "attn": obj.attn_dim, + "attn_layer": obj.attn_layer, + "attn_dotr": obj.attn_dotr, + "attn_mask": False, + "activation_function": obj.activation_function, + "resnet_dt": obj.resnet_dt, + "scaling_factor": obj.scaling_factor, + "normalize": obj.normalize, + "temperature": obj.temperature, + "trainable_ln": obj.trainable_ln, + "ln_eps": obj.ln_eps, + "type_one_side": obj.type_one_side, + "concat_output_tebd": self.concat_output_tebd, + "use_econf_tebd": self.use_econf_tebd, + "use_tebd_bias": self.use_tebd_bias, + "type_map": self.type_map, + # make deterministic + "precision": RESERVED_PRECISON_DICT[obj.prec], + "embeddings": obj.filter_layers.serialize(), + "embeddings_strip": obj.filter_layers_strip.serialize(), + "attention_layers": obj.dpa1_attention.serialize(), + "env_mat": DPEnvMat(obj.rcut, obj.rcut_smth).serialize(), + "type_embedding": self.type_embedding.embedding.serialize(), + "exclude_types": obj.exclude_types, + "env_protection": obj.env_protection, + "@variables": { + "davg": obj["davg"].numpy(), + "dstd": obj["dstd"].numpy(), + }, + "trainable": self.trainable, + "spin": None, + } + return data + + @classmethod + def deserialize(cls, data: dict) -> "DescrptSeAttenV2": + data = data.copy() + check_version_compatibility(data.pop("@version"), 2, 1) + data.pop("@class") + data.pop("type") + variables = data.pop("@variables") + embeddings = data.pop("embeddings") + type_embedding = data.pop("type_embedding") + attention_layers = data.pop("attention_layers") + data.pop("env_mat") + embeddings_strip = data.pop("embeddings_strip") + # compat with version 1 + if "use_tebd_bias" not in data: + data["use_tebd_bias"] = True + obj = cls(**data) + + def t_cvt(xx): + return paddle.to_tensor(xx, dtype=obj.se_atten.prec).to(device=env.DEVICE) + + obj.type_embedding.embedding = TypeEmbedNetConsistent.deserialize( + type_embedding + ) + obj.se_atten["davg"] = t_cvt(variables["davg"]) + obj.se_atten["dstd"] = t_cvt(variables["dstd"]) + obj.se_atten.filter_layers = NetworkCollection.deserialize(embeddings) + obj.se_atten.filter_layers_strip = NetworkCollection.deserialize( + embeddings_strip + ) + obj.se_atten.dpa1_attention = NeighborGatedAttention.deserialize( + attention_layers + ) + return obj diff --git a/deepmd/pd/model/descriptor/se_r.py b/deepmd/pd/model/descriptor/se_r.py new file mode 100644 index 0000000000..a1e49b09a8 --- /dev/null +++ b/deepmd/pd/model/descriptor/se_r.py @@ -0,0 +1,490 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Callable, + Dict, + List, + Optional, + Tuple, + Union, +) + +import numpy as np +import paddle + +from deepmd.dpmodel.utils import EnvMat as DPEnvMat +from deepmd.dpmodel.utils.seed import ( + child_seed, +) +from deepmd.pd.model.descriptor import ( + prod_env_mat, +) +from deepmd.pd.model.network.mlp import ( + EmbeddingNet, + NetworkCollection, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + PRECISION_DICT, + RESERVED_PRECISON_DICT, +) +from deepmd.pd.utils.env_mat_stat import ( + EnvMatStatSe, +) +from deepmd.pd.utils.exclude_mask import ( + PairExcludeMask, +) +from deepmd.pd.utils.update_sel import ( + UpdateSel, +) +from deepmd.utils.data_system import ( + DeepmdDataSystem, +) +from deepmd.utils.env_mat_stat import ( + StatItem, +) +from deepmd.utils.path import ( + DPPath, +) +from deepmd.utils.version import ( + check_version_compatibility, +) + +from .base_descriptor import ( + BaseDescriptor, +) + + +@BaseDescriptor.register("se_e2_r") +@BaseDescriptor.register("se_r") +class DescrptSeR(BaseDescriptor, paddle.nn.Layer): + def __init__( + self, + rcut, + rcut_smth, + sel, + neuron=[25, 50, 100], + set_davg_zero: bool = False, + activation_function: str = "tanh", + precision: str = "float64", + resnet_dt: bool = False, + exclude_types: List[Tuple[int, int]] = [], + env_protection: float = 0.0, + old_impl: bool = False, + trainable: bool = True, + seed: Optional[Union[int, List[int]]] = None, + type_map: Optional[List[str]] = None, + **kwargs, + ): + super().__init__() + self.rcut = rcut + self.rcut_smth = rcut_smth + self.neuron = neuron + self.filter_neuron = self.neuron + self.set_davg_zero = set_davg_zero + self.activation_function = activation_function + self.precision = precision + self.prec = PRECISION_DICT[self.precision] + self.resnet_dt = resnet_dt + self.old_impl = False # this does not support old implementation. + self.exclude_types = exclude_types + self.ntypes = len(sel) + self.type_map = type_map + self.seed = seed + # order matters, placed after the assignment of self.ntypes + self.reinit_exclude(exclude_types) + self.env_protection = env_protection + + self.sel = sel + self.sec = paddle.to_tensor(np.append([0], np.cumsum(self.sel)), dtype=int).to( + device=env.DEVICE + ) + self.split_sel = self.sel + self.nnei = sum(sel) + self.ndescrpt = self.nnei * 1 + + wanted_shape = (self.ntypes, self.nnei, 1) + mean = paddle.zeros(wanted_shape, dtype=self.prec).to(device=env.DEVICE) + stddev = paddle.ones(wanted_shape, dtype=self.prec).to(device=env.DEVICE) + self.register_buffer("mean", mean) + self.register_buffer("stddev", stddev) + self.filter_layers_old = None + self.filter_layers = None + + filter_layers = NetworkCollection( + naxis=1, ntypes=len(sel), network_type="embedding_network" + ) + # TODO: naxis=2 if type_one_side=False + for ii in range(self.ntypes): + filter_layers[(ii,)] = EmbeddingNet( + 1, + self.filter_neuron, + activation_function=self.activation_function, + precision=self.precision, + resnet_dt=self.resnet_dt, + seed=child_seed(self.seed, ii), + ) + self.filter_layers = filter_layers + self.stats = None + # set trainable + for param in self.parameters(): + param.stop_gradient = not trainable + + def get_rcut(self) -> float: + """Returns the cut-off radius.""" + return self.rcut + + def get_rcut_smth(self) -> float: + """Returns the radius where the neighbor information starts to smoothly decay to 0.""" + return self.rcut_smth + + def get_nsel(self) -> int: + """Returns the number of selected atoms in the cut-off radius.""" + return sum(self.sel) + + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" + return self.sel + + def get_ntypes(self) -> int: + """Returns the number of element types.""" + return self.ntypes + + def get_type_map(self) -> List[str]: + """Get the name to each type of atoms.""" + return self.type_map + + def get_dim_out(self) -> int: + """Returns the output dimension.""" + return self.neuron[-1] + + def get_dim_emb(self) -> int: + """Returns the output dimension.""" + raise NotImplementedError + + def get_dim_in(self) -> int: + """Returns the input dimension.""" + return 0 + + def mixed_types(self) -> bool: + """If true, the discriptor + 1. assumes total number of atoms aligned across frames; + 2. requires a neighbor list that does not distinguish different atomic types. + + If false, the discriptor + 1. assumes total number of atoms of each atom type aligned across frames; + 2. requires a neighbor list that distinguishes different atomic types. + + """ + return False + + def has_message_passing(self) -> bool: + """Returns whether the descriptor has message passing.""" + return False + + def need_sorted_nlist_for_lower(self) -> bool: + """Returns whether the descriptor needs sorted nlist when using `forward_lower`.""" + return False + + def get_env_protection(self) -> float: + """Returns the protection of building environment matrix.""" + return self.env_protection + + def share_params(self, base_class, shared_level, resume=False): + """ + Share the parameters of self to the base_class with shared_level during multitask training. + If not start from checkpoint (resume is False), + some seperated parameters (e.g. mean and stddev) will be re-calculated across different classes. + """ + assert ( + self.__class__ == base_class.__class__ + ), "Only descriptors of the same type can share params!" + # For SeR descriptors, the user-defined share-level + # shared_level: 0 + if shared_level == 0: + # link buffers + if hasattr(self, "mean") and not resume: + # in case of change params during resume + base_env = EnvMatStatSe(base_class) + base_env.stats = base_class.stats + for kk in base_class.get_stats(): + base_env.stats[kk] += self.get_stats()[kk] + mean, stddev = base_env() + if not base_class.set_davg_zero: + paddle.assign( + paddle.to_tensor(mean).to(device=env.DEVICE), base_class.mean + ) # pylint: disable=no-explicit-dtype + paddle.assign( + paddle.to_tensor(stddev).to(device=env.DEVICE), base_class.stddev + ) # pylint: disable=no-explicit-dtype + self.mean = base_class.mean + self.stddev = base_class.stddev + # self.load_state_dict(base_class.state_dict()) # this does not work, because it only inits the model + # the following will successfully link all the params except buffers + for item in self._modules: + self._modules[item] = base_class._modules[item] + # Other shared levels + else: + raise NotImplementedError + + def change_type_map( + self, type_map: List[str], model_with_new_type_stat=None + ) -> None: + """Change the type related params to new ones, according to `type_map` and the original one in the model. + If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. + """ + raise NotImplementedError( + "Descriptor se_e2_r does not support changing for type related params!" + "This feature is currently not implemented because it would require additional work to support the non-mixed-types case. " + "We may consider adding this support in the future if there is a clear demand for it." + ) + + def compute_input_stats( + self, + merged: Union[Callable[[], List[dict]], List[dict]], + path: Optional[DPPath] = None, + ): + """ + Compute the input statistics (e.g. mean and stddev) for the descriptors from packed data. + + Parameters + ---------- + merged : Union[Callable[[], List[dict]], List[dict]] + - List[dict]: A list of data samples from various data systems. + Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` + originating from the `i`-th data system. + - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + only when needed. Since the sampling process can be slow and memory-intensive, + the lazy function helps by only sampling once. + path : Optional[DPPath] + The path to the stat file. + + """ + env_mat_stat = EnvMatStatSe(self) + if path is not None: + path = path / env_mat_stat.get_hash() + if path is None or not path.is_dir(): + if callable(merged): + # only get data for once + sampled = merged() + else: + sampled = merged + else: + sampled = [] + env_mat_stat.load_or_compute_stats(sampled, path) + self.stats = env_mat_stat.stats + mean, stddev = env_mat_stat() + if not self.set_davg_zero: + paddle.assign(paddle.to_tensor(mean).to(device=env.DEVICE), self.mean) # pylint: disable=no-explicit-dtype + paddle.assign(paddle.to_tensor(stddev).to(device=env.DEVICE), self.stddev) # pylint: disable=no-explicit-dtype + + def get_stats(self) -> Dict[str, StatItem]: + """Get the statistics of the descriptor.""" + if self.stats is None: + raise RuntimeError( + "The statistics of the descriptor has not been computed." + ) + return self.stats + + def __setitem__(self, key, value): + if key in ("avg", "data_avg", "davg"): + self.mean = value + elif key in ("std", "data_std", "dstd"): + self.stddev = value + else: + raise KeyError(key) + + def __getitem__(self, key): + if key in ("avg", "data_avg", "davg"): + return self.mean + elif key in ("std", "data_std", "dstd"): + return self.stddev + else: + raise KeyError(key) + + def reinit_exclude( + self, + exclude_types: List[Tuple[int, int]] = [], + ): + self.exclude_types = exclude_types + self.emask = PairExcludeMask(self.ntypes, exclude_types=exclude_types) + + def forward( + self, + coord_ext: paddle.Tensor, + atype_ext: paddle.Tensor, + nlist: paddle.Tensor, + mapping: Optional[paddle.Tensor] = None, + comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + ): + """Compute the descriptor. + + Parameters + ---------- + coord_ext + The extended coordinates of atoms. shape: nf x (nallx3) + atype_ext + The extended aotm types. shape: nf x nall + nlist + The neighbor list. shape: nf x nloc x nnei + mapping + The index mapping, not required by this descriptor. + comm_dict + The data needed for communication for parallel inference. + + Returns + ------- + descriptor + The descriptor. shape: nf x nloc x (ng x axis_neuron) + gr + The rotationally equivariant and permutationally invariant single particle + representation. shape: nf x nloc x ng x 3 + g2 + The rotationally invariant pair-partical representation. + this descriptor returns None + h2 + The rotationally equivariant pair-partical representation. + this descriptor returns None + sw + The smooth switch function. + + """ + del mapping + nf = nlist.shape[0] + nloc = nlist.shape[1] + atype = atype_ext[:, :nloc] + dmatrix, diff, sw = prod_env_mat( + coord_ext, + nlist, + atype, + self.mean, + self.stddev, + self.rcut, + self.rcut_smth, + True, + protection=self.env_protection, + ) + + assert self.filter_layers is not None + dmatrix = dmatrix.reshape([-1, self.nnei, 1]) + dmatrix = dmatrix.to(dtype=self.prec) + nfnl = dmatrix.shape[0] + # pre-allocate a shape to pass jit + xyz_scatter = paddle.zeros( + [nfnl, 1, self.filter_neuron[-1]], dtype=self.prec + ).to(device=coord_ext.place) + + # nfnl x nnei + exclude_mask = self.emask(nlist, atype_ext).reshape([nfnl, self.nnei]) + for ii, ll in enumerate(self.filter_layers.networks): + # nfnl x nt + mm = exclude_mask[:, self.sec[ii] : self.sec[ii + 1]] + # nfnl x nt x 1 + ss = dmatrix[:, self.sec[ii] : self.sec[ii + 1], :] + ss = ss * mm[:, :, None] + # nfnl x nt x ng + gg = ll.forward(ss) + gg = paddle.mean(gg, axis=1).unsqueeze(1) + xyz_scatter += gg * (self.sel[ii] / self.nnei) + + res_rescale = 1.0 / 5.0 + result = xyz_scatter * res_rescale + result = result.reshape([nf, nloc, self.filter_neuron[-1]]) + return ( + result.to(dtype=env.GLOBAL_PD_FLOAT_PRECISION), + None, + None, + None, + sw, + ) + + def set_stat_mean_and_stddev( + self, + mean: paddle.Tensor, + stddev: paddle.Tensor, + ) -> None: + """Update mean and stddev for descriptor.""" + self.mean = mean + self.stddev = stddev + + def get_stat_mean_and_stddev(self) -> Tuple[paddle.Tensor, paddle.Tensor]: + """Get mean and stddev for descriptor.""" + return self.mean, self.stddev + + def serialize(self) -> dict: + return { + "@class": "Descriptor", + "type": "se_r", + "@version": 2, + "rcut": self.rcut, + "rcut_smth": self.rcut_smth, + "sel": self.sel, + "neuron": self.neuron, + "resnet_dt": self.resnet_dt, + "set_davg_zero": self.set_davg_zero, + "activation_function": self.activation_function, + # make deterministic + "precision": RESERVED_PRECISON_DICT[self.prec], + "embeddings": self.filter_layers.serialize(), + "env_mat": DPEnvMat(self.rcut, self.rcut_smth).serialize(), + "exclude_types": self.exclude_types, + "env_protection": self.env_protection, + "@variables": { + "davg": self["davg"].numpy(), + "dstd": self["dstd"].numpy(), + }, + "type_map": self.type_map, + ## to be updated when the options are supported. + "trainable": True, + "type_one_side": True, + "spin": None, + } + + @classmethod + def deserialize(cls, data: dict) -> "DescrptSeR": + data = data.copy() + check_version_compatibility(data.pop("@version", 1), 2, 1) + variables = data.pop("@variables") + embeddings = data.pop("embeddings") + env_mat = data.pop("env_mat") + obj = cls(**data) + + def t_cvt(xx): + return paddle.to_tensor(xx, dtype=obj.prec).to(device=env.DEVICE) + + obj["davg"] = t_cvt(variables["davg"]) + obj["dstd"] = t_cvt(variables["dstd"]) + obj.filter_layers = NetworkCollection.deserialize(embeddings) + return obj + + @classmethod + def update_sel( + cls, + train_data: DeepmdDataSystem, + type_map: Optional[List[str]], + local_jdata: dict, + ) -> Tuple[dict, Optional[float]]: + """Update the selection and perform neighbor statistics. + + Parameters + ---------- + train_data : DeepmdDataSystem + data used to do neighbor statictics + type_map : list[str], optional + The name of each type of atoms + local_jdata : dict + The local data refer to the current class + + Returns + ------- + dict + The updated local data + float + The minimum distance between two atoms + """ + local_jdata_cpy = local_jdata.copy() + min_nbor_dist, local_jdata_cpy["sel"] = UpdateSel().update_one_sel( + train_data, type_map, local_jdata_cpy["rcut"], local_jdata_cpy["sel"], False + ) + return local_jdata_cpy, min_nbor_dist diff --git a/deepmd/pd/model/descriptor/se_t.py b/deepmd/pd/model/descriptor/se_t.py new file mode 100644 index 0000000000..9a2e06d40a --- /dev/null +++ b/deepmd/pd/model/descriptor/se_t.py @@ -0,0 +1,736 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import itertools +from typing import ( + Callable, + ClassVar, + Dict, + List, + Optional, + Tuple, + Union, +) + +import numpy as np +import paddle + +from deepmd.dpmodel.utils.seed import ( + child_seed, +) +from deepmd.pd.model.descriptor import ( + DescriptorBlock, + prod_env_mat, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + PRECISION_DICT, + RESERVED_PRECISON_DICT, +) +from deepmd.pd.utils.env_mat_stat import ( + EnvMatStatSe, +) +from deepmd.pd.utils.update_sel import ( + UpdateSel, +) +from deepmd.utils.data_system import ( + DeepmdDataSystem, +) +from deepmd.utils.env_mat_stat import ( + StatItem, +) +from deepmd.utils.path import ( + DPPath, +) +from deepmd.utils.version import ( + check_version_compatibility, +) + +try: + from typing import ( + Final, + ) +except ImportError: + from paddle.jit import Final + +from deepmd.dpmodel.utils import EnvMat as DPEnvMat +from deepmd.pd.model.network.mlp import ( + EmbeddingNet, + NetworkCollection, +) +from deepmd.pd.utils.exclude_mask import ( + PairExcludeMask, +) + +from .base_descriptor import ( + BaseDescriptor, +) + + +@BaseDescriptor.register("se_e3") +@BaseDescriptor.register("se_at") +@BaseDescriptor.register("se_a_3be") +class DescrptSeT(BaseDescriptor, paddle.nn.Layer): + r"""DeepPot-SE constructed from all information (both angular and radial) of atomic + configurations. + + The embedding takes angles between two neighboring atoms as input. + + Parameters + ---------- + rcut : float + The cut-off radius + rcut_smth : float + From where the environment matrix should be smoothed + sel : list[int] + sel[i] specifies the maxmum number of type i atoms in the cut-off radius + neuron : list[int] + Number of neurons in each hidden layers of the embedding net + resnet_dt : bool + Time-step `dt` in the resnet construction: + y = x + dt * \phi (Wx + b) + set_davg_zero : bool + Set the shift of embedding net input to zero. + activation_function : str + The activation function in the embedding net. Supported options are |ACTIVATION_FN| + env_protection : float + Protection parameter to prevent division by zero errors during environment matrix calculations. + exclude_types : List[List[int]] + The excluded pairs of types which have no interaction with each other. + For example, `[[0, 1]]` means no interaction between type 0 and type 1. + precision : str + The precision of the embedding net parameters. Supported options are |PRECISION| + trainable : bool + If the weights of embedding net are trainable. + seed : int, Optional + Random seed for initializing the network parameters. + type_map: List[str], Optional + A list of strings. Give the name to each type of atoms. + """ + + def __init__( + self, + rcut: float, + rcut_smth: float, + sel: List[int], + neuron: List[int] = [24, 48, 96], + resnet_dt: bool = False, + set_davg_zero: bool = False, + activation_function: str = "tanh", + env_protection: float = 0.0, + exclude_types: List[Tuple[int, int]] = [], + precision: str = "float64", + trainable: bool = True, + seed: Optional[Union[int, List[int]]] = None, + type_map: Optional[List[str]] = None, + ntypes: Optional[int] = None, # to be compat with input + # not implemented + spin=None, + ): + del ntypes + if spin is not None: + raise NotImplementedError("old implementation of spin is not supported.") + super().__init__() + self.type_map = type_map + self.seat = DescrptBlockSeT( + rcut, + rcut_smth, + sel, + neuron=neuron, + resnet_dt=resnet_dt, + set_davg_zero=set_davg_zero, + activation_function=activation_function, + env_protection=env_protection, + exclude_types=exclude_types, + precision=precision, + trainable=trainable, + seed=seed, + ) + + def get_rcut(self) -> float: + """Returns the cut-off radius.""" + return self.seat.get_rcut() + + def get_rcut_smth(self) -> float: + """Returns the radius where the neighbor information starts to smoothly decay to 0.""" + return self.seat.get_rcut_smth() + + def get_nsel(self) -> int: + """Returns the number of selected atoms in the cut-off radius.""" + return self.seat.get_nsel() + + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" + return self.seat.get_sel() + + def get_ntypes(self) -> int: + """Returns the number of element types.""" + return self.seat.get_ntypes() + + def get_type_map(self) -> List[str]: + """Get the name to each type of atoms.""" + return self.type_map + + def get_dim_out(self) -> int: + """Returns the output dimension.""" + return self.seat.get_dim_out() + + def get_dim_emb(self) -> int: + """Returns the output dimension.""" + return self.seat.get_dim_emb() + + def mixed_types(self): + """Returns if the descriptor requires a neighbor list that distinguish different + atomic types or not. + """ + return self.seat.mixed_types() + + def has_message_passing(self) -> bool: + """Returns whether the descriptor has message passing.""" + return self.seat.has_message_passing() + + def need_sorted_nlist_for_lower(self) -> bool: + """Returns whether the descriptor needs sorted nlist when using `forward_lower`.""" + return self.seat.need_sorted_nlist_for_lower() + + def get_env_protection(self) -> float: + """Returns the protection of building environment matrix.""" + return self.seat.get_env_protection() + + def share_params(self, base_class, shared_level, resume=False): + """ + Share the parameters of self to the base_class with shared_level during multitask training. + If not start from checkpoint (resume is False), + some seperated parameters (e.g. mean and stddev) will be re-calculated across different classes. + """ + assert ( + self.__class__ == base_class.__class__ + ), "Only descriptors of the same type can share params!" + # For SeT descriptors, the user-defined share-level + # shared_level: 0 + # share all parameters in sea + if shared_level == 0: + self.seat.share_params(base_class.seat, 0, resume=resume) + # Other shared levels + else: + raise NotImplementedError + + @property + def dim_out(self): + """Returns the output dimension of this descriptor.""" + return self.seat.dim_out + + def change_type_map( + self, type_map: List[str], model_with_new_type_stat=None + ) -> None: + """Change the type related params to new ones, according to `type_map` and the original one in the model. + If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. + """ + raise NotImplementedError( + "Descriptor se_e3 does not support changing for type related params!" + "This feature is currently not implemented because it would require additional work to support the non-mixed-types case. " + "We may consider adding this support in the future if there is a clear demand for it." + ) + + def compute_input_stats( + self, + merged: Union[Callable[[], List[dict]], List[dict]], + path: Optional[DPPath] = None, + ): + """ + Compute the input statistics (e.g. mean and stddev) for the descriptors from packed data. + + Parameters + ---------- + merged : Union[Callable[[], List[dict]], List[dict]] + - List[dict]: A list of data samples from various data systems. + Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` + originating from the `i`-th data system. + - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + only when needed. Since the sampling process can be slow and memory-intensive, + the lazy function helps by only sampling once. + path : Optional[DPPath] + The path to the stat file. + + """ + return self.seat.compute_input_stats(merged, path) + + def reinit_exclude( + self, + exclude_types: List[Tuple[int, int]] = [], + ): + """Update the type exclusions.""" + self.seat.reinit_exclude(exclude_types) + + def forward( + self, + coord_ext: paddle.Tensor, + atype_ext: paddle.Tensor, + nlist: paddle.Tensor, + mapping: Optional[paddle.Tensor] = None, + comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + ): + """Compute the descriptor. + + Parameters + ---------- + coord_ext + The extended coordinates of atoms. shape: nf x (nallx3) + atype_ext + The extended aotm types. shape: nf x nall + nlist + The neighbor list. shape: nf x nloc x nnei + mapping + The index mapping, not required by this descriptor. + comm_dict + The data needed for communication for parallel inference. + + Returns + ------- + descriptor + The descriptor. shape: nf x nloc x ng + gr + The rotationally equivariant and permutationally invariant single particle + representation. + This descriptor returns None. + g2 + The rotationally invariant pair-partical representation. + This descriptor returns None. + h2 + The rotationally equivariant pair-partical representation. + This descriptor returns None. + sw + The smooth switch function. + + """ + return self.seat.forward(nlist, coord_ext, atype_ext, None, mapping) + + def set_stat_mean_and_stddev( + self, + mean: paddle.Tensor, + stddev: paddle.Tensor, + ) -> None: + """Update mean and stddev for descriptor.""" + self.seat.mean = mean + self.seat.stddev = stddev + + def get_stat_mean_and_stddev(self) -> Tuple[paddle.Tensor, paddle.Tensor]: + """Get mean and stddev for descriptor.""" + return self.seat.mean, self.seat.stddev + + def serialize(self) -> dict: + obj = self.seat + return { + "@class": "Descriptor", + "type": "se_e3", + "@version": 2, + "rcut": obj.rcut, + "rcut_smth": obj.rcut_smth, + "sel": obj.sel, + "neuron": obj.neuron, + "resnet_dt": obj.resnet_dt, + "set_davg_zero": obj.set_davg_zero, + "activation_function": obj.activation_function, + "precision": RESERVED_PRECISON_DICT[obj.prec], + "embeddings": obj.filter_layers.serialize(), + "env_mat": DPEnvMat(obj.rcut, obj.rcut_smth).serialize(), + "exclude_types": obj.exclude_types, + "env_protection": obj.env_protection, + "type_map": self.type_map, + "@variables": { + "davg": obj["davg"].numpy(), + "dstd": obj["dstd"].numpy(), + }, + "trainable": obj.trainable, + } + + @classmethod + def deserialize(cls, data: dict) -> "DescrptSeT": + data = data.copy() + check_version_compatibility(data.pop("@version", 1), 2, 1) + data.pop("@class", None) + data.pop("type", None) + variables = data.pop("@variables") + embeddings = data.pop("embeddings") + env_mat = data.pop("env_mat") + obj = cls(**data) + + def t_cvt(xx): + return paddle.to_tensor(xx, dtype=obj.seat.prec).to(device=env.DEVICE) + + obj.seat["davg"] = t_cvt(variables["davg"]) + obj.seat["dstd"] = t_cvt(variables["dstd"]) + obj.seat.filter_layers = NetworkCollection.deserialize(embeddings) + return obj + + @classmethod + def update_sel( + cls, + train_data: DeepmdDataSystem, + type_map: Optional[List[str]], + local_jdata: dict, + ) -> Tuple[dict, Optional[float]]: + """Update the selection and perform neighbor statistics. + + Parameters + ---------- + train_data : DeepmdDataSystem + data used to do neighbor statictics + type_map : list[str], optional + The name of each type of atoms + local_jdata : dict + The local data refer to the current class + + Returns + ------- + dict + The updated local data + float + The minimum distance between two atoms + """ + local_jdata_cpy = local_jdata.copy() + min_nbor_dist, local_jdata_cpy["sel"] = UpdateSel().update_one_sel( + train_data, type_map, local_jdata_cpy["rcut"], local_jdata_cpy["sel"], False + ) + return local_jdata_cpy, min_nbor_dist + + +@DescriptorBlock.register("se_e3") +class DescrptBlockSeT(DescriptorBlock): + ndescrpt: Final[int] + __constants__: ClassVar[list] = ["ndescrpt"] + + def __init__( + self, + rcut: float, + rcut_smth: float, + sel: List[int], + neuron: List[int] = [24, 48, 96], + resnet_dt: bool = False, + set_davg_zero: bool = False, + activation_function: str = "tanh", + env_protection: float = 0.0, + exclude_types: List[Tuple[int, int]] = [], + precision: str = "float64", + trainable: bool = True, + seed: Optional[Union[int, List[int]]] = None, + ): + r"""Construct an embedding net of type `se_e3`. + + The embedding takes angles between two neighboring atoms as input. + + Parameters + ---------- + rcut : float + The cut-off radius + rcut_smth : float + From where the environment matrix should be smoothed + sel : list[int] + sel[i] specifies the maxmum number of type i atoms in the cut-off radius + neuron : list[int] + Number of neurons in each hidden layers of the embedding net + resnet_dt : bool + Time-step `dt` in the resnet construction: + y = x + dt * \phi (Wx + b) + set_davg_zero : bool + Set the shift of embedding net input to zero. + activation_function : str + The activation function in the embedding net. Supported options are |ACTIVATION_FN| + env_protection : float + Protection parameter to prevent division by zero errors during environment matrix calculations. + exclude_types : List[List[int]] + The excluded pairs of types which have no interaction with each other. + For example, `[[0, 1]]` means no interaction between type 0 and type 1. + precision : str + The precision of the embedding net parameters. Supported options are |PRECISION| + trainable : bool + If the weights of embedding net are trainable. + seed : int, Optional + Random seed for initializing the network parameters. + """ + super().__init__() + self.rcut = rcut + self.rcut_smth = rcut_smth + self.neuron = neuron + self.filter_neuron = self.neuron + self.set_davg_zero = set_davg_zero + self.activation_function = activation_function + self.precision = precision + self.prec = PRECISION_DICT[self.precision] + self.resnet_dt = resnet_dt + self.env_protection = env_protection + self.ntypes = len(sel) + self.seed = seed + # order matters, placed after the assignment of self.ntypes + self.reinit_exclude(exclude_types) + + self.sel = sel + # should be on CPU to avoid D2H, as it is used as slice index + self.sec = [0, *np.cumsum(self.sel).tolist()] + self.split_sel = self.sel + self.nnei = sum(sel) + self.ndescrpt = self.nnei * 4 + + wanted_shape = (self.ntypes, self.nnei, 4) + mean = paddle.zeros(wanted_shape, dtype=self.prec).to(device=env.DEVICE) + stddev = paddle.ones(wanted_shape, dtype=self.prec).to(device=env.DEVICE) + self.register_buffer("mean", mean) + self.register_buffer("stddev", stddev) + + ndim = 2 + filter_layers = NetworkCollection( + ndim=ndim, ntypes=len(sel), network_type="embedding_network" + ) + for ii, embedding_idx in enumerate( + itertools.product(range(self.ntypes), repeat=ndim) + ): + filter_layers[embedding_idx] = EmbeddingNet( + 1, + self.filter_neuron, + activation_function=self.activation_function, + precision=self.precision, + resnet_dt=self.resnet_dt, + seed=child_seed(self.seed, ii), + ) + self.filter_layers = filter_layers + self.stats = None + # set trainable + self.trainable = trainable + for param in self.parameters(): + param.stop_gradient = not trainable + + def get_rcut(self) -> float: + """Returns the cut-off radius.""" + return self.rcut + + def get_rcut_smth(self) -> float: + """Returns the radius where the neighbor information starts to smoothly decay to 0.""" + return self.rcut_smth + + def get_nsel(self) -> int: + """Returns the number of selected atoms in the cut-off radius.""" + return sum(self.sel) + + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" + return self.sel + + def get_ntypes(self) -> int: + """Returns the number of element types.""" + return self.ntypes + + def get_dim_out(self) -> int: + """Returns the output dimension.""" + return self.dim_out + + def get_dim_emb(self) -> int: + """Returns the output dimension.""" + return self.neuron[-1] + + def get_dim_in(self) -> int: + """Returns the input dimension.""" + return self.dim_in + + def mixed_types(self) -> bool: + """If true, the discriptor + 1. assumes total number of atoms aligned across frames; + 2. requires a neighbor list that does not distinguish different atomic types. + + If false, the discriptor + 1. assumes total number of atoms of each atom type aligned across frames; + 2. requires a neighbor list that distinguishes different atomic types. + + """ + return False + + def get_env_protection(self) -> float: + """Returns the protection of building environment matrix.""" + return self.env_protection + + @property + def dim_out(self): + """Returns the output dimension of this descriptor.""" + return self.filter_neuron[-1] + + @property + def dim_in(self): + """Returns the atomic input dimension of this descriptor.""" + return 0 + + def __setitem__(self, key, value): + if key in ("avg", "data_avg", "davg"): + self.mean = value + elif key in ("std", "data_std", "dstd"): + self.stddev = value + else: + raise KeyError(key) + + def __getitem__(self, key): + if key in ("avg", "data_avg", "davg"): + return self.mean + elif key in ("std", "data_std", "dstd"): + return self.stddev + else: + raise KeyError(key) + + def compute_input_stats( + self, + merged: Union[Callable[[], List[dict]], List[dict]], + path: Optional[DPPath] = None, + ): + """ + Compute the input statistics (e.g. mean and stddev) for the descriptors from packed data. + + Parameters + ---------- + merged : Union[Callable[[], List[dict]], List[dict]] + - List[dict]: A list of data samples from various data systems. + Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` + originating from the `i`-th data system. + - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + only when needed. Since the sampling process can be slow and memory-intensive, + the lazy function helps by only sampling once. + path : Optional[DPPath] + The path to the stat file. + + """ + env_mat_stat = EnvMatStatSe(self) + if path is not None: + path = path / env_mat_stat.get_hash() + if path is None or not path.is_dir(): + if callable(merged): + # only get data for once + sampled = merged() + else: + sampled = merged + else: + sampled = [] + env_mat_stat.load_or_compute_stats(sampled, path) + self.stats = env_mat_stat.stats + mean, stddev = env_mat_stat() + if not self.set_davg_zero: + paddle.assign(paddle.to_tensor(mean).to(device=env.DEVICE), self.mean) # pylint: disable=no-explicit-dtype + paddle.assign(paddle.to_tensor(stddev).to(device=env.DEVICE), self.stddev) # pylint: disable=no-explicit-dtype + + def get_stats(self) -> Dict[str, StatItem]: + """Get the statistics of the descriptor.""" + if self.stats is None: + raise RuntimeError( + "The statistics of the descriptor has not been computed." + ) + return self.stats + + def reinit_exclude( + self, + exclude_types: List[Tuple[int, int]] = [], + ): + self.exclude_types = exclude_types + self.emask = PairExcludeMask(self.ntypes, exclude_types=exclude_types) + + def forward( + self, + nlist: paddle.Tensor, + extended_coord: paddle.Tensor, + extended_atype: paddle.Tensor, + extended_atype_embd: Optional[paddle.Tensor] = None, + mapping: Optional[paddle.Tensor] = None, + ): + """Compute the descriptor. + + Parameters + ---------- + nlist + The neighbor list. shape: nf x nloc x nnei + extended_coord + The extended coordinates of atoms. shape: nf x (nallx3) + extended_atype + The extended aotm types. shape: nf x nall x nt + extended_atype_embd + The extended type embedding of atoms. shape: nf x nall + mapping + The index mapping, not required by this descriptor. + + Returns + ------- + result + The descriptor. shape: nf x nloc x ng + gr + The rotationally equivariant and permutationally invariant single particle + representation. + This descriptor returns None. + g2 + The rotationally invariant pair-partical representation. + This descriptor returns None. + h2 + The rotationally equivariant pair-partical representation. + This descriptor returns None. + sw + The smooth switch function. shape: nf x nloc x nnei + + """ + del extended_atype_embd, mapping + nf = nlist.shape[0] + nloc = nlist.shape[1] + atype = extended_atype[:, :nloc] + dmatrix, diff, sw = prod_env_mat( + extended_coord, + nlist, + atype, + self.mean, + self.stddev, + self.rcut, + self.rcut_smth, + protection=self.env_protection, + ) + dmatrix = dmatrix.reshape([-1, self.nnei, 4]) + dmatrix = dmatrix.to(dtype=self.prec) + nfnl = dmatrix.shape[0] + # pre-allocate a shape to pass jit + result = paddle.zeros( + [nfnl, self.filter_neuron[-1]], + dtype=self.prec, + ).to(device=extended_coord.place) + # nfnl x nnei + exclude_mask = self.emask(nlist, extended_atype).reshape([nfnl, self.nnei]) + for embedding_idx, ll in enumerate(self.filter_layers.networks): + ti = embedding_idx % self.ntypes + nei_type_j = self.sel[ti] + tj = embedding_idx // self.ntypes + nei_type_i = self.sel[tj] + if ti <= tj: + # avoid repeat calculation + # nfnl x nt_i x 3 + rr_i = dmatrix[:, self.sec[ti] : self.sec[ti + 1], 1:] + mm_i = exclude_mask[:, self.sec[ti] : self.sec[ti + 1]] + rr_i = rr_i * mm_i[:, :, None] + # nfnl x nt_j x 3 + rr_j = dmatrix[:, self.sec[tj] : self.sec[tj + 1], 1:] + mm_j = exclude_mask[:, self.sec[tj] : self.sec[tj + 1]] + rr_j = rr_j * mm_j[:, :, None] + # nfnl x nt_i x nt_j + env_ij = paddle.einsum("ijm,ikm->ijk", rr_i, rr_j) + # nfnl x nt_i x nt_j x 1 + env_ij_reshape = env_ij.unsqueeze(-1) + # nfnl x nt_i x nt_j x ng + gg = ll.forward(env_ij_reshape) + # nfnl x nt_i x nt_j x ng + res_ij = paddle.einsum("ijk,ijkm->im", env_ij, gg) + res_ij = res_ij * (1.0 / float(nei_type_i) / float(nei_type_j)) + result += res_ij + # xyz_scatter /= (self.nnei * self.nnei) + result = result.reshape([nf, nloc, self.filter_neuron[-1]]) + return ( + result.to(dtype=env.GLOBAL_PD_FLOAT_PRECISION), + None, + None, + None, + sw, + ) + + def has_message_passing(self) -> bool: + """Returns whether the descriptor block has message passing.""" + return False + + def need_sorted_nlist_for_lower(self) -> bool: + """Returns whether the descriptor block needs sorted nlist when using `forward_lower`.""" + return False diff --git a/deepmd/pd/model/descriptor/se_t_tebd.py b/deepmd/pd/model/descriptor/se_t_tebd.py new file mode 100644 index 0000000000..8f3474af49 --- /dev/null +++ b/deepmd/pd/model/descriptor/se_t_tebd.py @@ -0,0 +1,865 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Callable, + Dict, + List, + Optional, + Tuple, + Union, +) + +import paddle + +from deepmd.dpmodel.utils import EnvMat as DPEnvMat +from deepmd.dpmodel.utils.seed import ( + child_seed, +) +from deepmd.pd.model.descriptor import ( + DescriptorBlock, +) +from deepmd.pd.model.descriptor.env_mat import ( + prod_env_mat, +) +from deepmd.pd.model.network.mlp import ( + EmbeddingNet, + NetworkCollection, +) +from deepmd.pd.model.network.network import ( + TypeEmbedNet, + TypeEmbedNetConsistent, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + PRECISION_DICT, + RESERVED_PRECISON_DICT, +) +from deepmd.pd.utils.env_mat_stat import ( + EnvMatStatSe, +) +from deepmd.pd.utils.exclude_mask import ( + PairExcludeMask, +) +from deepmd.pd.utils.update_sel import ( + UpdateSel, +) +from deepmd.utils.data_system import ( + DeepmdDataSystem, +) +from deepmd.utils.env_mat_stat import ( + StatItem, +) +from deepmd.utils.finetune import ( + get_index_between_two_maps, + map_pair_exclude_types, +) +from deepmd.utils.path import ( + DPPath, +) +from deepmd.utils.version import ( + check_version_compatibility, +) + +from .base_descriptor import ( + BaseDescriptor, +) +from .descriptor import ( + extend_descrpt_stat, +) + + +@BaseDescriptor.register("se_e3_tebd") +class DescrptSeTTebd(BaseDescriptor, paddle.nn.Layer): + r"""Construct an embedding net that takes angles between two neighboring atoms and type embeddings as input. + + Parameters + ---------- + rcut + The cut-off radius + rcut_smth + From where the environment matrix should be smoothed + sel : Union[List[int], int] + list[int]: sel[i] specifies the maxmum number of type i atoms in the cut-off radius + int: the total maxmum number of atoms in the cut-off radius + ntypes : int + Number of element types + neuron : list[int] + Number of neurons in each hidden layers of the embedding net + tebd_dim : int + Dimension of the type embedding + tebd_input_mode : str + The input mode of the type embedding. Supported modes are ["concat", "strip"]. + - "concat": Concatenate the type embedding with the smoothed angular information as the union input for the embedding network. + - "strip": Use a separated embedding network for the type embedding and combine the output with the angular embedding network output. + resnet_dt + Time-step `dt` in the resnet construction: + y = x + dt * \phi (Wx + b) + set_davg_zero + Set the shift of embedding net input to zero. + activation_function + The activation function in the embedding net. Supported options are |ACTIVATION_FN| + env_protection: float + Protection parameter to prevent division by zero errors during environment matrix calculations. + exclude_types : List[Tuple[int, int]] + The excluded pairs of types which have no interaction with each other. + For example, `[[0, 1]]` means no interaction between type 0 and type 1. + precision + The precision of the embedding net parameters. Supported options are |PRECISION| + trainable + If the weights of embedding net are trainable. + seed + Random seed for initializing the network parameters. + type_map: List[str], Optional + A list of strings. Give the name to each type of atoms. + concat_output_tebd: bool + Whether to concat type embedding at the output of the descriptor. + use_econf_tebd: bool, Optional + Whether to use electronic configuration type embedding. + use_tebd_bias : bool, Optional + Whether to use bias in the type embedding layer. + smooth: bool + Whether to use smooth process in calculation. + + """ + + def __init__( + self, + rcut: float, + rcut_smth: float, + sel: Union[List[int], int], + ntypes: int, + neuron: list = [2, 4, 8], + tebd_dim: int = 8, + tebd_input_mode: str = "concat", + resnet_dt: bool = False, + set_davg_zero: bool = True, + activation_function: str = "tanh", + env_protection: float = 0.0, + exclude_types: List[Tuple[int, int]] = [], + precision: str = "float64", + trainable: bool = True, + seed: Optional[Union[int, List[int]]] = None, + type_map: Optional[List[str]] = None, + concat_output_tebd: bool = True, + use_econf_tebd: bool = False, + use_tebd_bias=False, + smooth: bool = True, + ): + super().__init__() + self.se_ttebd = DescrptBlockSeTTebd( + rcut, + rcut_smth, + sel, + ntypes, + neuron=neuron, + tebd_dim=tebd_dim, + tebd_input_mode=tebd_input_mode, + set_davg_zero=set_davg_zero, + activation_function=activation_function, + precision=precision, + resnet_dt=resnet_dt, + exclude_types=exclude_types, + env_protection=env_protection, + smooth=smooth, + seed=child_seed(seed, 1), + ) + self.use_econf_tebd = use_econf_tebd + self.type_map = type_map + self.smooth = smooth + self.type_embedding = TypeEmbedNet( + ntypes, + tebd_dim, + precision=precision, + seed=child_seed(seed, 2), + use_econf_tebd=use_econf_tebd, + type_map=type_map, + use_tebd_bias=use_tebd_bias, + ) + self.tebd_dim = tebd_dim + self.concat_output_tebd = concat_output_tebd + self.trainable = trainable + # set trainable + for param in self.parameters(): + param.stop_gradient = not trainable + + def get_rcut(self) -> float: + """Returns the cut-off radius.""" + return self.se_ttebd.get_rcut() + + def get_rcut_smth(self) -> float: + """Returns the radius where the neighbor information starts to smoothly decay to 0.""" + return self.se_ttebd.get_rcut_smth() + + def get_nsel(self) -> int: + """Returns the number of selected atoms in the cut-off radius.""" + return self.se_ttebd.get_nsel() + + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" + return self.se_ttebd.get_sel() + + def get_ntypes(self) -> int: + """Returns the number of element types.""" + return self.se_ttebd.get_ntypes() + + def get_type_map(self) -> List[str]: + """Get the name to each type of atoms.""" + return self.type_map + + def get_dim_out(self) -> int: + """Returns the output dimension.""" + ret = self.se_ttebd.get_dim_out() + if self.concat_output_tebd: + ret += self.tebd_dim + return ret + + def get_dim_emb(self) -> int: + return self.se_ttebd.dim_emb + + def mixed_types(self) -> bool: + """If true, the discriptor + 1. assumes total number of atoms aligned across frames; + 2. requires a neighbor list that does not distinguish different atomic types. + + If false, the discriptor + 1. assumes total number of atoms of each atom type aligned across frames; + 2. requires a neighbor list that distinguishes different atomic types. + + """ + return self.se_ttebd.mixed_types() + + def has_message_passing(self) -> bool: + """Returns whether the descriptor has message passing.""" + return self.se_ttebd.has_message_passing() + + def need_sorted_nlist_for_lower(self) -> bool: + """Returns whether the descriptor needs sorted nlist when using `forward_lower`.""" + return self.se_ttebd.need_sorted_nlist_for_lower() + + def get_env_protection(self) -> float: + """Returns the protection of building environment matrix.""" + return self.se_ttebd.get_env_protection() + + def share_params(self, base_class, shared_level, resume=False): + """ + Share the parameters of self to the base_class with shared_level during multitask training. + If not start from checkpoint (resume is False), + some seperated parameters (e.g. mean and stddev) will be re-calculated across different classes. + """ + assert ( + self.__class__ == base_class.__class__ + ), "Only descriptors of the same type can share params!" + # For DPA1 descriptors, the user-defined share-level + # shared_level: 0 + # share all parameters in both type_embedding and se_ttebd + if shared_level == 0: + self._modules["type_embedding"] = base_class._modules["type_embedding"] + self.se_ttebd.share_params(base_class.se_ttebd, 0, resume=resume) + # shared_level: 1 + # share all parameters in type_embedding + elif shared_level == 1: + self._modules["type_embedding"] = base_class._modules["type_embedding"] + # Other shared levels + else: + raise NotImplementedError + + @property + def dim_out(self): + return self.get_dim_out() + + @property + def dim_emb(self): + return self.get_dim_emb() + + def compute_input_stats( + self, + merged: Union[Callable[[], List[dict]], List[dict]], + path: Optional[DPPath] = None, + ): + """ + Compute the input statistics (e.g. mean and stddev) for the descriptors from packed data. + + Parameters + ---------- + merged : Union[Callable[[], List[dict]], List[dict]] + - List[dict]: A list of data samples from various data systems. + Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` + originating from the `i`-th data system. + - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + only when needed. Since the sampling process can be slow and memory-intensive, + the lazy function helps by only sampling once. + path : Optional[DPPath] + The path to the stat file. + + """ + return self.se_ttebd.compute_input_stats(merged, path) + + def set_stat_mean_and_stddev( + self, + mean: paddle.Tensor, + stddev: paddle.Tensor, + ) -> None: + """Update mean and stddev for descriptor.""" + self.se_ttebd.mean = mean + self.se_ttebd.stddev = stddev + + def get_stat_mean_and_stddev(self) -> Tuple[paddle.Tensor, paddle.Tensor]: + """Get mean and stddev for descriptor.""" + return self.se_ttebd.mean, self.se_ttebd.stddev + + def change_type_map( + self, type_map: List[str], model_with_new_type_stat=None + ) -> None: + """Change the type related params to new ones, according to `type_map` and the original one in the model. + If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. + """ + assert ( + self.type_map is not None + ), "'type_map' must be defined when performing type changing!" + remap_index, has_new_type = get_index_between_two_maps(self.type_map, type_map) + obj = self.se_ttebd + obj.ntypes = len(type_map) + self.type_map = type_map + self.type_embedding.change_type_map(type_map=type_map) + obj.reinit_exclude(map_pair_exclude_types(obj.exclude_types, remap_index)) + if has_new_type: + # the avg and std of new types need to be updated + extend_descrpt_stat( + obj, + type_map, + des_with_stat=model_with_new_type_stat.se_ttebd + if model_with_new_type_stat is not None + else None, + ) + obj["davg"] = obj["davg"][remap_index] + obj["dstd"] = obj["dstd"][remap_index] + + def serialize(self) -> dict: + obj = self.se_ttebd + data = { + "@class": "Descriptor", + "type": "se_e3_tebd", + "@version": 1, + "rcut": obj.rcut, + "rcut_smth": obj.rcut_smth, + "sel": obj.sel, + "ntypes": obj.ntypes, + "neuron": obj.neuron, + "tebd_dim": obj.tebd_dim, + "tebd_input_mode": obj.tebd_input_mode, + "set_davg_zero": obj.set_davg_zero, + "activation_function": obj.activation_function, + "resnet_dt": obj.resnet_dt, + "concat_output_tebd": self.concat_output_tebd, + "use_econf_tebd": self.use_econf_tebd, + "type_map": self.type_map, + # make deterministic + "precision": RESERVED_PRECISON_DICT[obj.prec], + "embeddings": obj.filter_layers.serialize(), + "env_mat": DPEnvMat(obj.rcut, obj.rcut_smth).serialize(), + "type_embedding": self.type_embedding.embedding.serialize(), + "exclude_types": obj.exclude_types, + "env_protection": obj.env_protection, + "smooth": self.smooth, + "@variables": { + "davg": obj["davg"].numpy(), + "dstd": obj["dstd"].numpy(), + }, + "trainable": self.trainable, + } + if obj.tebd_input_mode in ["strip"]: + data.update({"embeddings_strip": obj.filter_layers_strip.serialize()}) + return data + + @classmethod + def deserialize(cls, data: dict) -> "DescrptSeTTebd": + data = data.copy() + check_version_compatibility(data.pop("@version"), 1, 1) + data.pop("@class") + data.pop("type") + variables = data.pop("@variables") + embeddings = data.pop("embeddings") + type_embedding = data.pop("type_embedding") + env_mat = data.pop("env_mat") + tebd_input_mode = data["tebd_input_mode"] + if tebd_input_mode in ["strip"]: + embeddings_strip = data.pop("embeddings_strip") + else: + embeddings_strip = None + obj = cls(**data) + + def t_cvt(xx): + return paddle.to_tensor(xx, dtype=obj.se_ttebd.prec).to(device=env.DEVICE) + + obj.type_embedding.embedding = TypeEmbedNetConsistent.deserialize( + type_embedding + ) + obj.se_ttebd["davg"] = t_cvt(variables["davg"]) + obj.se_ttebd["dstd"] = t_cvt(variables["dstd"]) + obj.se_ttebd.filter_layers = NetworkCollection.deserialize(embeddings) + if tebd_input_mode in ["strip"]: + obj.se_ttebd.filter_layers_strip = NetworkCollection.deserialize( + embeddings_strip + ) + return obj + + def forward( + self, + extended_coord: paddle.Tensor, + extended_atype: paddle.Tensor, + nlist: paddle.Tensor, + mapping: Optional[paddle.Tensor] = None, + comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + ): + """Compute the descriptor. + + Parameters + ---------- + extended_coord + The extended coordinates of atoms. shape: nf x (nallx3) + extended_atype + The extended aotm types. shape: nf x nall + nlist + The neighbor list. shape: nf x nloc x nnei + mapping + The index mapping, not required by this descriptor. + comm_dict + The data needed for communication for parallel inference. + + Returns + ------- + descriptor + The descriptor. shape: nf x nloc x (ng x axis_neuron) + gr + The rotationally equivariant and permutationally invariant single particle + representation. shape: nf x nloc x ng x 3 + g2 + The rotationally invariant pair-partical representation. + shape: nf x nloc x nnei x ng + h2 + The rotationally equivariant pair-partical representation. + shape: nf x nloc x nnei x 3 + sw + The smooth switch function. shape: nf x nloc x nnei + + """ + del mapping + nframes, nloc, nnei = nlist.shape + nall = extended_coord.reshape([nframes, -1]).shape[1] // 3 + g1_ext = self.type_embedding(extended_atype) + g1_inp = g1_ext[:, :nloc, :] + g1, g2, h2, rot_mat, sw = self.se_ttebd( + nlist, + extended_coord, + extended_atype, + g1_ext, + mapping=None, + ) + if self.concat_output_tebd: + g1 = paddle.concat([g1, g1_inp], axis=-1) + + return g1, rot_mat, g2, h2, sw + + @classmethod + def update_sel( + cls, + train_data: DeepmdDataSystem, + type_map: Optional[List[str]], + local_jdata: dict, + ) -> Tuple[dict, Optional[float]]: + """Update the selection and perform neighbor statistics. + + Parameters + ---------- + train_data : DeepmdDataSystem + data used to do neighbor statictics + type_map : list[str], optional + The name of each type of atoms + local_jdata : dict + The local data refer to the current class + + Returns + ------- + dict + The updated local data + float + The minimum distance between two atoms + """ + local_jdata_cpy = local_jdata.copy() + min_nbor_dist, sel = UpdateSel().update_one_sel( + train_data, type_map, local_jdata_cpy["rcut"], local_jdata_cpy["sel"], True + ) + local_jdata_cpy["sel"] = sel[0] + return local_jdata_cpy, min_nbor_dist + + +@DescriptorBlock.register("se_ttebd") +class DescrptBlockSeTTebd(DescriptorBlock): + def __init__( + self, + rcut: float, + rcut_smth: float, + sel: Union[List[int], int], + ntypes: int, + neuron: list = [25, 50, 100], + tebd_dim: int = 8, + tebd_input_mode: str = "concat", + set_davg_zero: bool = True, + activation_function="tanh", + precision: str = "float64", + resnet_dt: bool = False, + exclude_types: List[Tuple[int, int]] = [], + env_protection: float = 0.0, + smooth: bool = True, + seed: Optional[Union[int, List[int]]] = None, + ): + super().__init__() + self.rcut = rcut + self.rcut_smth = rcut_smth + self.neuron = neuron + self.filter_neuron = self.neuron + self.tebd_dim = tebd_dim + self.tebd_input_mode = tebd_input_mode + self.set_davg_zero = set_davg_zero + self.activation_function = activation_function + self.precision = precision + self.prec = PRECISION_DICT[self.precision] + self.resnet_dt = resnet_dt + self.env_protection = env_protection + self.seed = seed + self.smooth = smooth + + if isinstance(sel, int): + sel = [sel] + + self.ntypes = ntypes + self.sel = sel + self.sec = self.sel + self.split_sel = self.sel + self.nnei = sum(sel) + self.ndescrpt = self.nnei * 4 + # order matters, placed after the assignment of self.ntypes + self.reinit_exclude(exclude_types) + + wanted_shape = (self.ntypes, self.nnei, 4) + mean = paddle.zeros(wanted_shape, dtype=env.GLOBAL_PD_FLOAT_PRECISION).to( + device=env.DEVICE + ) + stddev = paddle.ones(wanted_shape, dtype=env.GLOBAL_PD_FLOAT_PRECISION).to( + device=env.DEVICE + ) + self.register_buffer("mean", mean) + self.register_buffer("stddev", stddev) + self.tebd_dim_input = self.tebd_dim * 2 + if self.tebd_input_mode in ["concat"]: + self.embd_input_dim = 1 + self.tebd_dim_input + else: + self.embd_input_dim = 1 + + self.filter_layers_old = None + self.filter_layers = None + self.filter_layers_strip = None + filter_layers = NetworkCollection( + ndim=0, ntypes=self.ntypes, network_type="embedding_network" + ) + filter_layers[0] = EmbeddingNet( + self.embd_input_dim, + self.filter_neuron, + activation_function=self.activation_function, + precision=self.precision, + resnet_dt=self.resnet_dt, + seed=child_seed(self.seed, 1), + ) + self.filter_layers = filter_layers + if self.tebd_input_mode in ["strip"]: + filter_layers_strip = NetworkCollection( + ndim=0, ntypes=self.ntypes, network_type="embedding_network" + ) + filter_layers_strip[0] = EmbeddingNet( + self.tebd_dim_input, + self.filter_neuron, + activation_function=self.activation_function, + precision=self.precision, + resnet_dt=self.resnet_dt, + seed=child_seed(self.seed, 2), + ) + self.filter_layers_strip = filter_layers_strip + self.stats = None + + def get_rcut(self) -> float: + """Returns the cut-off radius.""" + return self.rcut + + def get_rcut_smth(self) -> float: + """Returns the radius where the neighbor information starts to smoothly decay to 0.""" + return self.rcut_smth + + def get_nsel(self) -> int: + """Returns the number of selected atoms in the cut-off radius.""" + return sum(self.sel) + + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" + return self.sel + + def get_ntypes(self) -> int: + """Returns the number of element types.""" + return self.ntypes + + def get_dim_in(self) -> int: + """Returns the input dimension.""" + return self.dim_in + + def get_dim_out(self) -> int: + """Returns the output dimension.""" + return self.dim_out + + def get_dim_emb(self) -> int: + """Returns the output dimension of embedding.""" + return self.filter_neuron[-1] + + def __setitem__(self, key, value): + if key in ("avg", "data_avg", "davg"): + self.mean = value + elif key in ("std", "data_std", "dstd"): + self.stddev = value + else: + raise KeyError(key) + + def __getitem__(self, key): + if key in ("avg", "data_avg", "davg"): + return self.mean + elif key in ("std", "data_std", "dstd"): + return self.stddev + else: + raise KeyError(key) + + def mixed_types(self) -> bool: + """If true, the discriptor + 1. assumes total number of atoms aligned across frames; + 2. requires a neighbor list that does not distinguish different atomic types. + + If false, the discriptor + 1. assumes total number of atoms of each atom type aligned across frames; + 2. requires a neighbor list that distinguishes different atomic types. + + """ + return True + + def get_env_protection(self) -> float: + """Returns the protection of building environment matrix.""" + return self.env_protection + + @property + def dim_out(self): + """Returns the output dimension of this descriptor.""" + return self.filter_neuron[-1] + + @property + def dim_in(self): + """Returns the atomic input dimension of this descriptor.""" + return self.tebd_dim + + @property + def dim_emb(self): + """Returns the output dimension of embedding.""" + return self.get_dim_emb() + + def compute_input_stats( + self, + merged: Union[Callable[[], List[dict]], List[dict]], + path: Optional[DPPath] = None, + ): + """ + Compute the input statistics (e.g. mean and stddev) for the descriptors from packed data. + + Parameters + ---------- + merged : Union[Callable[[], List[dict]], List[dict]] + - List[dict]: A list of data samples from various data systems. + Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` + originating from the `i`-th data system. + - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + only when needed. Since the sampling process can be slow and memory-intensive, + the lazy function helps by only sampling once. + path : Optional[DPPath] + The path to the stat file. + + """ + env_mat_stat = EnvMatStatSe(self) + if path is not None: + path = path / env_mat_stat.get_hash() + if path is None or not path.is_dir(): + if callable(merged): + # only get data for once + sampled = merged() + else: + sampled = merged + else: + sampled = [] + env_mat_stat.load_or_compute_stats(sampled, path) + self.stats = env_mat_stat.stats + mean, stddev = env_mat_stat() + if not self.set_davg_zero: + paddle.assign(paddle.to_tensor(mean).to(device=env.DEVICE), self.mean) # pylint: disable=no-explicit-dtype + paddle.assign(paddle.to_tensor(stddev).to(device=env.DEVICE), self.stddev) # pylint: disable=no-explicit-dtype + + def get_stats(self) -> Dict[str, StatItem]: + """Get the statistics of the descriptor.""" + if self.stats is None: + raise RuntimeError( + "The statistics of the descriptor has not been computed." + ) + return self.stats + + def reinit_exclude( + self, + exclude_types: List[Tuple[int, int]] = [], + ): + self.exclude_types = exclude_types + self.emask = PairExcludeMask(self.ntypes, exclude_types=exclude_types) + + def forward( + self, + nlist: paddle.Tensor, + extended_coord: paddle.Tensor, + extended_atype: paddle.Tensor, + extended_atype_embd: Optional[paddle.Tensor] = None, + mapping: Optional[paddle.Tensor] = None, + ): + """Compute the descriptor. + + Parameters + ---------- + nlist + The neighbor list. shape: nf x nloc x nnei + extended_coord + The extended coordinates of atoms. shape: nf x (nallx3) + extended_atype + The extended aotm types. shape: nf x nall x nt + extended_atype_embd + The extended type embedding of atoms. shape: nf x nall + mapping + The index mapping, not required by this descriptor. + + Returns + ------- + result + The descriptor. shape: nf x nloc x (ng x axis_neuron) + g2 + The rotationally invariant pair-partical representation. + shape: nf x nloc x nnei x ng + h2 + The rotationally equivariant pair-partical representation. + shape: nf x nloc x nnei x 3 + gr + The rotationally equivariant and permutationally invariant single particle + representation. shape: nf x nloc x ng x 3 + sw + The smooth switch function. shape: nf x nloc x nnei + + """ + del mapping + assert extended_atype_embd is not None + nframes, nloc, nnei = nlist.shape + atype = extended_atype[:, :nloc] + nb = nframes + nall = extended_coord.reshape([nb, -1, 3]).shape[1] + dmatrix, diff, sw = prod_env_mat( + extended_coord, + nlist, + atype, + self.mean, + self.stddev, + self.rcut, + self.rcut_smth, + protection=self.env_protection, + ) + # nb x nloc x nnei + exclude_mask = self.emask(nlist, extended_atype) + nlist = paddle.where(exclude_mask != 0, nlist, -1) + nlist_mask = nlist != -1 + nlist = paddle.where(nlist == -1, 0, nlist) + sw = paddle.squeeze(sw, -1) + # nf x nall x nt + nt = extended_atype_embd.shape[-1] + atype_tebd_ext = extended_atype_embd + # nb x (nloc x nnei) x nt + index = nlist.reshape([nb, nloc * nnei]).unsqueeze(-1).expand(-1, -1, nt) + # nb x (nloc x nnei) x nt + atype_tebd_nlist = paddle.take_along_axis(atype_tebd_ext, axis=1, index=index) + # nb x nloc x nnei x nt + atype_tebd_nlist = atype_tebd_nlist.reshape([nb, nloc, nnei, nt]) + # beyond the cutoff sw should be 0.0 + sw = sw.masked_fill(~nlist_mask, 0.0) + # (nb x nloc) x nnei + exclude_mask = exclude_mask.reshape([nb * nloc, nnei]) + assert self.filter_layers is not None + # nfnl x nnei x 4 + dmatrix = dmatrix.reshape([-1, self.nnei, 4]) + nfnl = dmatrix.shape[0] + # nfnl x nnei x 4 + rr = dmatrix + rr = rr * exclude_mask[:, :, None] + + # nfnl x nt_i x 3 + rr_i = rr[:, :, 1:] + # nfnl x nt_j x 3 + rr_j = rr[:, :, 1:] + # nfnl x nt_i x nt_j + env_ij = paddle.einsum("ijm,ikm->ijk", rr_i, rr_j) + # nfnl x nt_i x nt_j x 1 + ss = env_ij.unsqueeze(-1) + + # nfnl x nnei x tebd_dim + nlist_tebd = atype_tebd_nlist.reshape([nfnl, nnei, self.tebd_dim]) + + # nfnl x nt_i x nt_j x tebd_dim + nlist_tebd_i = nlist_tebd.unsqueeze(2).expand([-1, -1, self.nnei, -1]) + nlist_tebd_j = nlist_tebd.unsqueeze(1).expand([-1, self.nnei, -1, -1]) + + if self.tebd_input_mode in ["concat"]: + # nfnl x nt_i x nt_j x (1 + tebd_dim * 2) + ss = paddle.concat([ss, nlist_tebd_i, nlist_tebd_j], axis=-1) + # nfnl x nt_i x nt_j x ng + gg = self.filter_layers.networks[0](ss) + elif self.tebd_input_mode in ["strip"]: + # nfnl x nt_i x nt_j x ng + gg_s = self.filter_layers.networks[0](ss) + assert self.filter_layers_strip is not None + # nfnl x nt_i x nt_j x (tebd_dim * 2) + tt = paddle.concat([nlist_tebd_i, nlist_tebd_j], axis=-1) + # nfnl x nt_i x nt_j x ng + gg_t = self.filter_layers_strip.networks[0](tt) + if self.smooth: + gg_t = ( + gg_t + * sw.reshape([nfnl, self.nnei, 1, 1]) + * sw.reshape([nfnl, 1, self.nnei, 1]) + ) + # nfnl x nt_i x nt_j x ng + gg = gg_s * gg_t + gg_s + else: + raise NotImplementedError + + # nfnl x ng + res_ij = paddle.einsum("ijk,ijkm->im", env_ij, gg) + res_ij = res_ij * (1.0 / float(self.nnei) / float(self.nnei)) + # nf x nl x ng + result = res_ij.reshape([nframes, nloc, self.filter_neuron[-1]]) + return ( + result.to(dtype=env.GLOBAL_PD_FLOAT_PRECISION), + None, + None, + None, + sw, + ) + + def has_message_passing(self) -> bool: + """Returns whether the descriptor block has message passing.""" + return False + + def need_sorted_nlist_for_lower(self) -> bool: + """Returns whether the descriptor block needs sorted nlist when using `forward_lower`.""" + return False diff --git a/deepmd/pd/model/model/__init__.py b/deepmd/pd/model/model/__init__.py new file mode 100644 index 0000000000..4c83c53540 --- /dev/null +++ b/deepmd/pd/model/model/__init__.py @@ -0,0 +1,226 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""The model that takes the coordinates, cell and atom types as input +and predicts some property. The models are automatically generated from +atomic models by the `deepmd.dpmodel.make_model` method. + +The `make_model` method does the reduction, auto-differentiation and +communication of the atomic properties according to output variable +definition `deepmd.dpmodel.OutputVariableDef`. + +All models should be inherited from :class:`deepmd.pd.model.model.model.BaseModel`. +Models generated by `make_model` have already done it. +""" + +import copy +import json + +import numpy as np + +from deepmd.pd.model.atomic_model import ( + DPAtomicModel, + PairTabAtomicModel, +) +from deepmd.pd.model.descriptor.base_descriptor import ( + BaseDescriptor, +) +from deepmd.pd.model.task import ( + BaseFitting, +) +from deepmd.utils.spin import ( + Spin, +) + +from .dipole_model import ( + DipoleModel, +) +from .dos_model import ( + DOSModel, +) +from .dp_model import ( + DPModelCommon, +) +from .dp_zbl_model import ( + DPZBLModel, +) +from .ener_model import ( + EnergyModel, +) +from .frozen import ( + FrozenModel, +) +from .make_hessian_model import ( + make_hessian_model, +) +from .make_model import ( + make_model, +) +from .model import ( + BaseModel, +) +from .polar_model import ( + PolarModel, +) +from .spin_model import ( + SpinEnergyModel, + SpinModel, +) + + +def get_spin_model(model_params): + model_params = copy.deepcopy(model_params) + if not model_params["spin"]["use_spin"] or isinstance( + model_params["spin"]["use_spin"][0], int + ): + use_spin = np.full(len(model_params["type_map"]), False) # pylint: disable=no-explicit-dtype + use_spin[model_params["spin"]["use_spin"]] = True + model_params["spin"]["use_spin"] = use_spin.tolist() + # include virtual spin and placeholder types + model_params["type_map"] += [item + "_spin" for item in model_params["type_map"]] + spin = Spin( + use_spin=model_params["spin"]["use_spin"], + virtual_scale=model_params["spin"]["virtual_scale"], + ) + pair_exclude_types = spin.get_pair_exclude_types( + exclude_types=model_params.get("pair_exclude_types", None) + ) + model_params["pair_exclude_types"] = pair_exclude_types + # for descriptor data stat + model_params["descriptor"]["exclude_types"] = pair_exclude_types + atom_exclude_types = spin.get_atom_exclude_types( + exclude_types=model_params.get("atom_exclude_types", None) + ) + model_params["atom_exclude_types"] = atom_exclude_types + if ( + "env_protection" not in model_params["descriptor"] + or model_params["descriptor"]["env_protection"] == 0.0 + ): + model_params["descriptor"]["env_protection"] = 1e-6 + if model_params["descriptor"]["type"] in ["se_e2_a"]: + # only expand sel for se_e2_a + model_params["descriptor"]["sel"] += model_params["descriptor"]["sel"] + backbone_model = get_standard_model(model_params) + return SpinEnergyModel(backbone_model=backbone_model, spin=spin) + + +def get_zbl_model(model_params): + model_params = copy.deepcopy(model_params) + ntypes = len(model_params["type_map"]) + # descriptor + model_params["descriptor"]["ntypes"] = ntypes + model_params["descriptor"]["type_map"] = copy.deepcopy(model_params["type_map"]) + descriptor = BaseDescriptor(**model_params["descriptor"]) + # fitting + fitting_net = model_params.get("fitting_net", None) + fitting_net["type"] = fitting_net.get("type", "ener") + fitting_net["ntypes"] = descriptor.get_ntypes() + fitting_net["type_map"] = copy.deepcopy(model_params["type_map"]) + fitting_net["mixed_types"] = descriptor.mixed_types() + fitting_net["embedding_width"] = descriptor.get_dim_out() + fitting_net["dim_descrpt"] = descriptor.get_dim_out() + grad_force = "direct" not in fitting_net["type"] + if not grad_force: + fitting_net["out_dim"] = descriptor.get_dim_emb() + if "ener" in fitting_net["type"]: + fitting_net["return_energy"] = True + fitting = BaseFitting(**fitting_net) + dp_model = DPAtomicModel(descriptor, fitting, type_map=model_params["type_map"]) + # pairtab + filepath = model_params["use_srtab"] + pt_model = PairTabAtomicModel( + filepath, + model_params["descriptor"]["rcut"], + model_params["descriptor"]["sel"], + type_map=model_params["type_map"], + ) + + rmin = model_params["sw_rmin"] + rmax = model_params["sw_rmax"] + atom_exclude_types = model_params.get("atom_exclude_types", []) + pair_exclude_types = model_params.get("pair_exclude_types", []) + return DPZBLModel( + dp_model, + pt_model, + rmin, + rmax, + type_map=model_params["type_map"], + atom_exclude_types=atom_exclude_types, + pair_exclude_types=pair_exclude_types, + ) + + +def get_standard_model(model_params): + model_params_old = model_params + model_params = copy.deepcopy(model_params) + ntypes = len(model_params["type_map"]) + # descriptor + model_params["descriptor"]["ntypes"] = ntypes + model_params["descriptor"]["type_map"] = copy.deepcopy(model_params["type_map"]) + descriptor = BaseDescriptor(**model_params["descriptor"]) + # fitting + fitting_net = model_params.get("fitting_net", {}) + fitting_net["type"] = fitting_net.get("type", "ener") + fitting_net["ntypes"] = descriptor.get_ntypes() + fitting_net["type_map"] = copy.deepcopy(model_params["type_map"]) + fitting_net["mixed_types"] = descriptor.mixed_types() + if fitting_net["type"] in ["dipole", "polar"]: + fitting_net["embedding_width"] = descriptor.get_dim_emb() + fitting_net["dim_descrpt"] = descriptor.get_dim_out() + grad_force = "direct" not in fitting_net["type"] + if not grad_force: + fitting_net["out_dim"] = descriptor.get_dim_emb() + if "ener" in fitting_net["type"]: + fitting_net["return_energy"] = True + fitting = BaseFitting(**fitting_net) + atom_exclude_types = model_params.get("atom_exclude_types", []) + pair_exclude_types = model_params.get("pair_exclude_types", []) + + if fitting_net["type"] == "dipole": + modelcls = DipoleModel + elif fitting_net["type"] == "polar": + modelcls = PolarModel + elif fitting_net["type"] == "dos": + modelcls = DOSModel + elif fitting_net["type"] in ["ener", "direct_force_ener"]: + modelcls = EnergyModel + else: + raise RuntimeError(f"Unknown fitting type: {fitting_net['type']}") + + model = modelcls( + descriptor=descriptor, + fitting=fitting, + type_map=model_params["type_map"], + atom_exclude_types=atom_exclude_types, + pair_exclude_types=pair_exclude_types, + ) + model.model_def_script = json.dumps(model_params_old) + return model + + +def get_model(model_params): + model_type = model_params.get("type", "standard") + if model_type == "standard": + if "spin" in model_params: + return get_spin_model(model_params) + elif "use_srtab" in model_params: + return get_zbl_model(model_params) + else: + return get_standard_model(model_params) + else: + return BaseModel.get_class_by_type(model_type).get_model(model_params) + + +__all__ = [ + "BaseModel", + "get_model", + "DPModelCommon", + "EnergyModel", + "DipoleModel", + "PolarModel", + "DOSModel", + "FrozenModel", + "SpinModel", + "SpinEnergyModel", + "DPZBLModel", + "make_model", + "make_hessian_model", +] diff --git a/deepmd/pd/model/model/dipole_model.py b/deepmd/pd/model/model/dipole_model.py new file mode 100644 index 0000000000..d7b2a7d43b --- /dev/null +++ b/deepmd/pd/model/model/dipole_model.py @@ -0,0 +1,130 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from copy import ( + deepcopy, +) +from typing import ( + Dict, + Optional, +) + +import paddle + +from deepmd.pd.model.atomic_model import ( + DPDipoleAtomicModel, +) +from deepmd.pd.model.model.model import ( + BaseModel, +) + +from .dp_model import ( + DPModelCommon, +) +from .make_model import ( + make_model, +) + +DPDOSModel_ = make_model(DPDipoleAtomicModel) + + +@BaseModel.register("dipole") +class DipoleModel(DPModelCommon, DPDOSModel_): + model_type = "dipole" + + def __init__( + self, + *args, + **kwargs, + ): + DPModelCommon.__init__(self) + DPDOSModel_.__init__(self, *args, **kwargs) + + def translated_output_def(self): + out_def_data = self.model_output_def().get_data() + output_def = { + "dipole": deepcopy(out_def_data["dipole"]), + "global_dipole": deepcopy(out_def_data["dipole_redu"]), + } + if self.do_grad_r("dipole"): + output_def["force"] = deepcopy(out_def_data["dipole_derv_r"]) + output_def["force"].squeeze(-2) + if self.do_grad_c("dipole"): + output_def["virial"] = deepcopy(out_def_data["dipole_derv_c_redu"]) + output_def["virial"].squeeze(-2) + output_def["atom_virial"] = deepcopy(out_def_data["dipole_derv_c"]) + output_def["atom_virial"].squeeze(-3) + if "mask" in out_def_data: + output_def["mask"] = deepcopy(out_def_data["mask"]) + return output_def + + def forward( + self, + coord, + atype, + box: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + ) -> Dict[str, paddle.Tensor]: + model_ret = self.forward_common( + coord, + atype, + box, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + ) + if self.get_fitting_net() is not None: + model_predict = {} + model_predict["dipole"] = model_ret["dipole"] + model_predict["global_dipole"] = model_ret["dipole_redu"] + if self.do_grad_r("dipole"): + model_predict["force"] = model_ret["dipole_derv_r"].squeeze(-2) + if self.do_grad_c("dipole"): + model_predict["virial"] = model_ret["dipole_derv_c_redu"].squeeze(-2) + if do_atomic_virial: + model_predict["atom_virial"] = model_ret["dipole_derv_c"].squeeze( + -3 + ) + if "mask" in model_ret: + model_predict["mask"] = model_ret["mask"] + else: + model_predict = model_ret + model_predict["updated_coord"] += coord + return model_predict + + # @paddle.jit.export + def forward_lower( + self, + extended_coord, + extended_atype, + nlist, + mapping: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + ): + model_ret = self.forward_common_lower( + extended_coord, + extended_atype, + nlist, + mapping, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + extra_nlist_sort=self.need_sorted_nlist_for_lower(), + ) + if self.get_fitting_net() is not None: + model_predict = {} + model_predict["dipole"] = model_ret["dipole"] + model_predict["global_dipole"] = model_ret["dipole_redu"] + if self.do_grad_r("dipole"): + model_predict["extended_force"] = model_ret["dipole_derv_r"].squeeze(-2) + if self.do_grad_c("dipole"): + model_predict["virial"] = model_ret["dipole_derv_c_redu"].squeeze(-2) + if do_atomic_virial: + model_predict["extended_virial"] = model_ret[ + "dipole_derv_c" + ].squeeze(-3) + else: + model_predict = model_ret + return model_predict diff --git a/deepmd/pd/model/model/dos_model.py b/deepmd/pd/model/model/dos_model.py new file mode 100644 index 0000000000..ab5605442b --- /dev/null +++ b/deepmd/pd/model/model/dos_model.py @@ -0,0 +1,113 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from copy import ( + deepcopy, +) +from typing import ( + Dict, + Optional, +) + +import paddle + +from deepmd.pd.model.atomic_model import ( + DPDOSAtomicModel, +) +from deepmd.pd.model.model.model import ( + BaseModel, +) + +from .dp_model import ( + DPModelCommon, +) +from .make_model import ( + make_model, +) + +DPDOSModel_ = make_model(DPDOSAtomicModel) + + +@BaseModel.register("dos") +class DOSModel(DPModelCommon, DPDOSModel_): + model_type = "dos" + + def __init__( + self, + *args, + **kwargs, + ): + DPModelCommon.__init__(self) + DPDOSModel_.__init__(self, *args, **kwargs) + + def translated_output_def(self): + out_def_data = self.model_output_def().get_data() + output_def = { + "atom_dos": deepcopy(out_def_data["dos"]), + "dos": deepcopy(out_def_data["dos_redu"]), + } + if "mask" in out_def_data: + output_def["mask"] = deepcopy(out_def_data["mask"]) + return output_def + + def forward( + self, + coord, + atype, + box: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + ) -> Dict[str, paddle.Tensor]: + model_ret = self.forward_common( + coord, + atype, + box, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + ) + if self.get_fitting_net() is not None: + model_predict = {} + model_predict["atom_dos"] = model_ret["dos"] + model_predict["dos"] = model_ret["dos_redu"] + + if "mask" in model_ret: + model_predict["mask"] = model_ret["mask"] + else: + model_predict = model_ret + model_predict["updated_coord"] += coord + return model_predict + + # @paddle.jit.export + def get_numb_dos(self) -> int: + """Get the number of DOS for DOSFittingNet.""" + return self.get_fitting_net().dim_out + + # @paddle.jit.export + def forward_lower( + self, + extended_coord, + extended_atype, + nlist, + mapping: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + ): + model_ret = self.forward_common_lower( + extended_coord, + extended_atype, + nlist, + mapping, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + extra_nlist_sort=self.need_sorted_nlist_for_lower(), + ) + if self.get_fitting_net() is not None: + model_predict = {} + model_predict["atom_dos"] = model_ret["dos"] + model_predict["dos"] = model_ret["dos_redu"] + + else: + model_predict = model_ret + return model_predict diff --git a/deepmd/pd/model/model/dp_model.py b/deepmd/pd/model/model/dp_model.py new file mode 100644 index 0000000000..8eae0a171e --- /dev/null +++ b/deepmd/pd/model/model/dp_model.py @@ -0,0 +1,56 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + List, + Optional, + Tuple, +) + +from deepmd.pd.model.descriptor.base_descriptor import ( + BaseDescriptor, +) +from deepmd.utils.data_system import ( + DeepmdDataSystem, +) + + +class DPModelCommon: + """A base class to implement common methods for all the Models.""" + + @classmethod + def update_sel( + cls, + train_data: DeepmdDataSystem, + type_map: Optional[List[str]], + local_jdata: dict, + ) -> Tuple[dict, Optional[float]]: + """Update the selection and perform neighbor statistics. + + Parameters + ---------- + train_data : DeepmdDataSystem + data used to do neighbor statictics + type_map : list[str], optional + The name of each type of atoms + local_jdata : dict + The local data refer to the current class + + Returns + ------- + dict + The updated local data + float + The minimum distance between two atoms + """ + local_jdata_cpy = local_jdata.copy() + local_jdata_cpy["descriptor"], min_nbor_dist = BaseDescriptor.update_sel( + train_data, type_map, local_jdata["descriptor"] + ) + return local_jdata_cpy, min_nbor_dist + + def get_fitting_net(self): + """Get the fitting network.""" + return self.atomic_model.fitting_net + + def get_descriptor(self): + """Get the descriptor.""" + return self.atomic_model.descriptor diff --git a/deepmd/pd/model/model/dp_zbl_model.py b/deepmd/pd/model/model/dp_zbl_model.py new file mode 100644 index 0000000000..51e959e564 --- /dev/null +++ b/deepmd/pd/model/model/dp_zbl_model.py @@ -0,0 +1,163 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from copy import ( + deepcopy, +) +from typing import ( + Dict, + List, + Optional, + Tuple, +) + +import paddle + +from deepmd.pd.model.atomic_model import ( + DPZBLLinearEnergyAtomicModel, +) +from deepmd.pd.model.model.model import ( + BaseModel, +) +from deepmd.utils.data_system import ( + DeepmdDataSystem, +) + +from .dp_model import ( + DPModelCommon, +) +from .make_model import ( + make_model, +) + +DPZBLModel_ = make_model(DPZBLLinearEnergyAtomicModel) + + +@BaseModel.register("zbl") +class DPZBLModel(DPZBLModel_): + model_type = "ener" + + def __init__( + self, + *args, + **kwargs, + ): + super().__init__(*args, **kwargs) + + def translated_output_def(self): + out_def_data = self.model_output_def().get_data() + output_def = { + "atom_energy": deepcopy(out_def_data["energy"]), + "energy": deepcopy(out_def_data["energy_redu"]), + } + if self.do_grad_r("energy"): + output_def["force"] = deepcopy(out_def_data["energy_derv_r"]) + output_def["force"].squeeze(-2) + if self.do_grad_c("energy"): + output_def["virial"] = deepcopy(out_def_data["energy_derv_c_redu"]) + output_def["virial"].squeeze(-2) + output_def["atom_virial"] = deepcopy(out_def_data["energy_derv_c"]) + output_def["atom_virial"].squeeze(-3) + if "mask" in out_def_data: + output_def["mask"] = deepcopy(out_def_data["mask"]) + return output_def + + def forward( + self, + coord, + atype, + box: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + ) -> Dict[str, paddle.Tensor]: + model_ret = self.forward_common( + coord, + atype, + box, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + ) + + model_predict = {} + model_predict["atom_energy"] = model_ret["energy"] + model_predict["energy"] = model_ret["energy_redu"] + if self.do_grad_r("energy"): + model_predict["force"] = model_ret["energy_derv_r"].squeeze(-2) + if self.do_grad_c("energy"): + model_predict["virial"] = model_ret["energy_derv_c_redu"].squeeze(-2) + if do_atomic_virial: + model_predict["atom_virial"] = model_ret["energy_derv_c"].squeeze(-3) + else: + model_predict["force"] = model_ret["dforce"] + if "mask" in model_ret: + model_predict["mask"] = model_ret["mask"] + return model_predict + + # @paddle.jit.export + def forward_lower( + self, + extended_coord, + extended_atype, + nlist, + mapping: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + ): + model_ret = self.forward_common_lower( + extended_coord, + extended_atype, + nlist, + mapping=mapping, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + extra_nlist_sort=self.need_sorted_nlist_for_lower(), + ) + + model_predict = {} + model_predict["atom_energy"] = model_ret["energy"] + model_predict["energy"] = model_ret["energy_redu"] + if self.do_grad_r("energy"): + model_predict["extended_force"] = model_ret["energy_derv_r"].squeeze(-2) + if self.do_grad_c("energy"): + model_predict["virial"] = model_ret["energy_derv_c_redu"].squeeze(-2) + if do_atomic_virial: + model_predict["extended_virial"] = model_ret["energy_derv_c"].squeeze( + -3 + ) + else: + assert model_ret["dforce"] is not None + model_predict["dforce"] = model_ret["dforce"] + return model_predict + + @classmethod + def update_sel( + cls, + train_data: DeepmdDataSystem, + type_map: Optional[List[str]], + local_jdata: dict, + ) -> Tuple[dict, Optional[float]]: + """Update the selection and perform neighbor statistics. + + Parameters + ---------- + train_data : DeepmdDataSystem + data used to do neighbor statictics + type_map : list[str], optional + The name of each type of atoms + local_jdata : dict + The local data refer to the current class + + Returns + ------- + dict + The updated local data + float + The minimum distance between two atoms + """ + local_jdata_cpy = local_jdata.copy() + local_jdata_cpy["dpmodel"], min_nbor_dist = DPModelCommon.update_sel( + train_data, type_map, local_jdata["dpmodel"] + ) + return local_jdata_cpy, min_nbor_dist diff --git a/deepmd/pd/model/model/ener_model.py b/deepmd/pd/model/model/ener_model.py new file mode 100644 index 0000000000..fcf5ca3353 --- /dev/null +++ b/deepmd/pd/model/model/ener_model.py @@ -0,0 +1,137 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from copy import ( + deepcopy, +) +from typing import ( + Dict, + Optional, +) + +import paddle + +from deepmd.pd.model.atomic_model import ( + DPEnergyAtomicModel, +) +from deepmd.pd.model.model.model import ( + BaseModel, +) + +from .dp_model import ( + DPModelCommon, +) +from .make_model import ( + make_model, +) + +DPEnergyModel_ = make_model(DPEnergyAtomicModel) + + +@BaseModel.register("ener") +class EnergyModel(DPModelCommon, DPEnergyModel_): + model_type = "ener" + + def __init__( + self, + *args, + **kwargs, + ): + DPModelCommon.__init__(self) + DPEnergyModel_.__init__(self, *args, **kwargs) + + def translated_output_def(self): + out_def_data = self.model_output_def().get_data() + output_def = { + "atom_energy": deepcopy(out_def_data["energy"]), + "energy": deepcopy(out_def_data["energy_redu"]), + } + if self.do_grad_r("energy"): + output_def["force"] = deepcopy(out_def_data["energy_derv_r"]) + output_def["force"].squeeze(-2) + if self.do_grad_c("energy"): + output_def["virial"] = deepcopy(out_def_data["energy_derv_c_redu"]) + output_def["virial"].squeeze(-2) + output_def["atom_virial"] = deepcopy(out_def_data["energy_derv_c"]) + output_def["atom_virial"].squeeze(-3) + if "mask" in out_def_data: + output_def["mask"] = deepcopy(out_def_data["mask"]) + return output_def + + def forward( + self, + coord, + atype, + box: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + ) -> Dict[str, paddle.Tensor]: + model_ret = self.forward_common( + coord, + atype, + box, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + ) + if self.get_fitting_net() is not None: + model_predict = {} + model_predict["atom_energy"] = model_ret["energy"] + model_predict["energy"] = model_ret["energy_redu"] + if self.do_grad_r("energy"): + model_predict["force"] = model_ret["energy_derv_r"].squeeze(-2) + if self.do_grad_c("energy"): + model_predict["virial"] = model_ret["energy_derv_c_redu"].squeeze(-2) + if do_atomic_virial: + model_predict["atom_virial"] = model_ret["energy_derv_c"].squeeze( + -3 + ) + else: + model_predict["force"] = model_ret["dforce"] + if "mask" in model_ret: + model_predict["mask"] = model_ret["mask"] + else: + model_predict = model_ret + model_predict["updated_coord"] += coord + return model_predict + + # @paddle.jit.export + def forward_lower( + self, + extended_coord, + extended_atype, + nlist, + mapping: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + ): + model_ret = self.forward_common_lower( + extended_coord, + extended_atype, + nlist, + mapping, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + comm_dict=comm_dict, + extra_nlist_sort=self.need_sorted_nlist_for_lower(), + ) + if self.get_fitting_net() is not None: + model_predict = {} + model_predict["atom_energy"] = model_ret["energy"] + model_predict["energy"] = model_ret["energy_redu"] + if self.do_grad_r("energy"): + model_predict["extended_force"] = model_ret["energy_derv_r"].squeeze(-2) + if self.do_grad_c("energy"): + model_predict["virial"] = model_ret["energy_derv_c_redu"].squeeze(-2) + if do_atomic_virial: + model_predict["extended_virial"] = model_ret[ + "energy_derv_c" + ].squeeze(-3) + else: + assert model_ret["dforce"] is not None + model_predict["dforce"] = model_ret["dforce"] + else: + model_predict = model_ret + return model_predict diff --git a/deepmd/pd/model/model/frozen.py b/deepmd/pd/model/model/frozen.py new file mode 100644 index 0000000000..d918238c1d --- /dev/null +++ b/deepmd/pd/model/model/frozen.py @@ -0,0 +1,206 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import json +import tempfile +from typing import ( + Dict, + List, + Optional, + Tuple, +) + +import paddle + +from deepmd.dpmodel.output_def import ( + FittingOutputDef, +) +from deepmd.entrypoints.convert_backend import ( + convert_backend, +) +from deepmd.pd.model.model.model import ( + BaseModel, +) +from deepmd.utils.data_system import ( + DeepmdDataSystem, +) + + +@BaseModel.register("frozen") +class FrozenModel(BaseModel): + """Load model from a frozen model, which cannot be trained. + + Parameters + ---------- + model_file : str + The path to the frozen model + """ + + def __init__(self, model_file: str, **kwargs): + super().__init__(**kwargs) + self.model_file = model_file + if model_file.endswith(".pth"): + self.model = paddle.jit.load(model_file) + else: + # try to convert from other formats + with tempfile.NamedTemporaryFile(suffix=".pth") as f: + convert_backend(INPUT=model_file, OUTPUT=f.name) + self.model = paddle.jit.load(f.name) + + # @paddle.jit.export + def fitting_output_def(self) -> FittingOutputDef: + """Get the output def of developer implemented atomic models.""" + return self.model.fitting_output_def() + + # @paddle.jit.export + def get_rcut(self) -> float: + """Get the cut-off radius.""" + return self.model.get_rcut() + + # @paddle.jit.export + def get_type_map(self) -> List[str]: + """Get the type map.""" + return self.model.get_type_map() + + # @paddle.jit.export + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" + return self.model.get_sel() + + # @paddle.jit.export + def get_dim_fparam(self) -> int: + """Get the number (dimension) of frame parameters of this atomic model.""" + return self.model.get_dim_fparam() + + # @paddle.jit.export + def get_dim_aparam(self) -> int: + """Get the number (dimension) of atomic parameters of this atomic model.""" + return self.model.get_dim_aparam() + + # @paddle.jit.export + def get_sel_type(self) -> List[int]: + """Get the selected atom types of this model. + + Only atoms with selected atom types have atomic contribution + to the result of the model. + If returning an empty list, all atom types are selected. + """ + return self.model.get_sel_type() + + # @paddle.jit.export + def is_aparam_nall(self) -> bool: + """Check whether the shape of atomic parameters is (nframes, nall, ndim). + + If False, the shape is (nframes, nloc, ndim). + """ + return self.model.is_aparam_nall() + + # @paddle.jit.export + def mixed_types(self) -> bool: + """If true, the model + 1. assumes total number of atoms aligned across frames; + 2. uses a neighbor list that does not distinguish different atomic types. + + If false, the model + 1. assumes total number of atoms of each atom type aligned across frames; + 2. uses a neighbor list that distinguishes different atomic types. + + """ + return self.model.mixed_types() + + # @paddle.jit.export + def has_message_passing(self) -> bool: + """Returns whether the descriptor has message passing.""" + return self.model.has_message_passing() + + def need_sorted_nlist_for_lower(self) -> bool: + """Returns whether the model needs sorted nlist when using `forward_lower`.""" + return self.model.need_sorted_nlist_for_lower() + + # @paddle.jit.export + def forward( + self, + coord, + atype, + box: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + ) -> Dict[str, paddle.Tensor]: + return self.model.forward( + coord, + atype, + box=box, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + ) + + # @paddle.jit.export + def get_model_def_script(self) -> str: + """Get the model definition script.""" + # try to use the original script instead of "frozen model" + # Note: this cannot change the script of the parent model + # it may still try to load hard-coded filename, which might + # be a problem + return self.model.get_model_def_script() + + # @paddle.jit.export + def get_min_nbor_dist(self) -> Optional[float]: + """Get the minimum neighbor distance.""" + return self.model.get_min_nbor_dist() + + def serialize(self) -> dict: + from deepmd.pd.model.model import ( + get_model, + ) + + # try to recover the original model + model_def_script = json.loads(self.get_model_def_script()) + model = get_model(model_def_script) + model.load_state_dict(self.model.state_dict()) + return model.serialize() + + @classmethod + def deserialize(cls, data: dict): + raise RuntimeError("Should not touch here.") + + # @paddle.jit.export + def get_nnei(self) -> int: + """Returns the total number of selected neighboring atoms in the cut-off radius.""" + return self.model.get_nnei() + + # @paddle.jit.export + def get_nsel(self) -> int: + """Returns the total number of selected neighboring atoms in the cut-off radius.""" + return self.model.get_nsel() + + @classmethod + def update_sel( + cls, + train_data: DeepmdDataSystem, + type_map: Optional[List[str]], + local_jdata: dict, + ) -> Tuple[dict, Optional[float]]: + """Update the selection and perform neighbor statistics. + + Parameters + ---------- + train_data : DeepmdDataSystem + data used to do neighbor statictics + type_map : list[str], optional + The name of each type of atoms + local_jdata : dict + The local data refer to the current class + + Returns + ------- + dict + The updated local data + float + The minimum distance between two atoms + """ + return local_jdata, None + + # @paddle.jit.export + def model_output_type(self) -> str: + """Get the output type for the model.""" + return self.model.model_output_type() diff --git a/deepmd/pd/model/model/make_hessian_model.py b/deepmd/pd/model/model/make_hessian_model.py new file mode 100644 index 0000000000..6ca1ea0b88 --- /dev/null +++ b/deepmd/pd/model/model/make_hessian_model.py @@ -0,0 +1,215 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import math +from typing import ( + Dict, + List, + Optional, + Union, +) + +import paddle + +from deepmd.dpmodel import ( + get_hessian_name, +) + + +def make_hessian_model(T_Model): + """Make a model that can compute Hessian. + + LIMITATION: this model is not jitable due to the restrictions of paddle jit script. + + LIMITATION: only the hessian of `forward_common` is available. + + Parameters + ---------- + T_Model + The model. Should provide the `forward_common` and `atomic_output_def` methods + + Returns + ------- + The model computes hessian. + + """ + + class CM(T_Model): + def __init__( + self, + *args, + **kwargs, + ): + super().__init__( + *args, + **kwargs, + ) + self.hess_fitting_def = copy.deepcopy(super().atomic_output_def()) + + def requires_hessian( + self, + keys: Union[str, List[str]], + ): + """Set which output variable(s) requires hessian.""" + if isinstance(keys, str): + keys = [keys] + for kk in self.hess_fitting_def.keys(): + if kk in keys: + self.hess_fitting_def[kk].r_hessian = True + + def atomic_output_def(self): + """Get the fitting output def.""" + return self.hess_fitting_def + + def forward_common( + self, + coord, + atype, + box: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + ) -> Dict[str, paddle.Tensor]: + """Return model prediction. + + Parameters + ---------- + coord + The coordinates of the atoms. + shape: nf x (nloc x 3) + atype + The type of atoms. shape: nf x nloc + box + The simulation box. shape: nf x 9 + fparam + frame parameter. nf x ndf + aparam + atomic parameter. nf x nloc x nda + do_atomic_virial + If calculate the atomic virial. + + Returns + ------- + ret_dict + The result dict of type Dict[str,paddle.Tensor]. + The keys are defined by the `ModelOutputDef`. + + """ + ret = super().forward_common( + coord, + atype, + box=box, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + ) + vdef = self.atomic_output_def() + hess_yes = [vdef[kk].r_hessian for kk in vdef.keys()] + if any(hess_yes): + hess = self._cal_hessian_all( + coord, + atype, + box=box, + fparam=fparam, + aparam=aparam, + ) + ret.update(hess) + return ret + + def _cal_hessian_all( + self, + coord: paddle.Tensor, + atype: paddle.Tensor, + box: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + ) -> Dict[str, paddle.Tensor]: + nf, nloc = atype.shape + coord = coord.reshape([nf, (nloc * 3)]) + box = box.reshape([nf, 9]) if box is not None else None + fparam = fparam.reshape([nf, -1]) if fparam is not None else None + aparam = aparam.reshape([nf, nloc, -1]) if aparam is not None else None + fdef = self.atomic_output_def() + # keys of values that require hessian + hess_keys: List[str] = [] + for kk in fdef.keys(): + if fdef[kk].r_hessian: + hess_keys.append(kk) + # result dict init by empty lists + res = {get_hessian_name(kk): [] for kk in hess_keys} + # loop over variable + for kk in hess_keys: + vdef = fdef[kk] + vshape = vdef.shape + vsize = math.prod(vdef.shape) + # loop over frames + for ii in range(nf): + icoord = coord[ii] + iatype = atype[ii] + ibox = box[ii] if box is not None else None + ifparam = fparam[ii] if fparam is not None else None + iaparam = aparam[ii] if aparam is not None else None + # loop over all components + for idx in range(vsize): + hess = self._cal_hessian_one_component( + idx, icoord, iatype, ibox, ifparam, iaparam + ) + res[get_hessian_name(kk)].append(hess) + res[get_hessian_name(kk)] = paddle.stack( + res[get_hessian_name(kk)] + ).reshape((nf, *vshape, nloc * 3, nloc * 3)) + return res + + def _cal_hessian_one_component( + self, + ci, + coord, + atype, + box: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + ) -> paddle.Tensor: + # coord, # (nloc x 3) + # atype, # nloc + # box: Optional[paddle.Tensor] = None, # 9 + # fparam: Optional[paddle.Tensor] = None, # nfp + # aparam: Optional[paddle.Tensor] = None, # (nloc x nap) + wc = wrapper_class_forward_energy(self, ci, atype, box, fparam, aparam) + + hess = paddle.autograd.hessian( + wc, + coord, + ) + return hess + + class wrapper_class_forward_energy: + def __init__( + self, + obj: CM, + ci: int, + atype: paddle.Tensor, + box: Optional[paddle.Tensor], + fparam: Optional[paddle.Tensor], + aparam: Optional[paddle.Tensor], + ): + self.atype, self.box, self.fparam, self.aparam = atype, box, fparam, aparam + self.ci = ci + self.obj = obj + + def __call__( + self, + xx, + ): + ci = self.ci + atype, box, fparam, aparam = self.atype, self.box, self.fparam, self.aparam + res = super(CM, self.obj).forward_common( + xx.unsqueeze(0), + atype.unsqueeze(0), + box.unsqueeze(0) if box is not None else None, + fparam.unsqueeze(0) if fparam is not None else None, + aparam.unsqueeze(0) if aparam is not None else None, + do_atomic_virial=False, + ) + er = res["energy_redu"][0].reshape([-1])[ci] + return er + + return CM diff --git a/deepmd/pd/model/model/make_model.py b/deepmd/pd/model/model/make_model.py new file mode 100644 index 0000000000..94d4ae7ff5 --- /dev/null +++ b/deepmd/pd/model/model/make_model.py @@ -0,0 +1,594 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Dict, + List, + Optional, + Tuple, + Type, +) + +import paddle + +from deepmd.dpmodel import ( + ModelOutputDef, +) +from deepmd.dpmodel.output_def import ( + FittingOutputDef, + OutputVariableCategory, + OutputVariableOperation, + check_operation_applied, +) +from deepmd.pd.model.atomic_model.base_atomic_model import ( + BaseAtomicModel, +) +from deepmd.pd.model.model.model import ( + BaseModel, +) +from deepmd.pd.model.model.transform_output import ( + communicate_extended_output, + fit_output_to_model_output, +) +from deepmd.pd.utils.env import ( + GLOBAL_PD_ENER_FLOAT_PRECISION, + GLOBAL_PD_FLOAT_PRECISION, + PRECISION_DICT, + RESERVED_PRECISON_DICT, +) +from deepmd.pd.utils.nlist import ( + extend_input_and_build_neighbor_list, + nlist_distinguish_types, +) +from deepmd.utils.path import ( + DPPath, +) + + +def make_model(T_AtomicModel: Type[BaseAtomicModel]): + """Make a model as a derived class of an atomic model. + + The model provide two interfaces. + + 1. the `forward_common_lower`, that takes extended coordinates, atyps and neighbor list, + and outputs the atomic and property and derivatives (if required) on the extended region. + + 2. the `forward_common`, that takes coordinates, atypes and cell and predicts + the atomic and reduced property, and derivatives (if required) on the local region. + + Parameters + ---------- + T_AtomicModel + The atomic model. + + Returns + ------- + CM + The model. + + """ + + class CM(BaseModel): + def __init__( + self, + *args, + # underscore to prevent conflict with normal inputs + atomic_model_: Optional[T_AtomicModel] = None, + **kwargs, + ): + super().__init__(*args, **kwargs) + if atomic_model_ is not None: + self.atomic_model: T_AtomicModel = atomic_model_ + else: + self.atomic_model: T_AtomicModel = T_AtomicModel(*args, **kwargs) + self.precision_dict = PRECISION_DICT + self.reverse_precision_dict = RESERVED_PRECISON_DICT + self.global_pt_float_precision = GLOBAL_PD_FLOAT_PRECISION + self.global_pt_ener_float_precision = GLOBAL_PD_ENER_FLOAT_PRECISION + + def model_output_def(self): + """Get the output def for the model.""" + return ModelOutputDef(self.atomic_output_def()) + + # @paddle.jit.export + def model_output_type(self) -> List[str]: + """Get the output type for the model.""" + output_def = self.model_output_def() + var_defs = output_def.var_defs + # jit: Comprehension ifs are not supported yet + # type hint is critical for JIT + vars: List[str] = [] + for kk, vv in var_defs.items(): + # .value is critical for JIT + if vv.category == OutputVariableCategory.OUT.value: + vars.append(kk) + return vars + + # cannot use the name forward. paddle script does not work + def forward_common( + self, + coord, + atype, + box: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + ) -> Dict[str, paddle.Tensor]: + """Return model prediction. + + Parameters + ---------- + coord + The coordinates of the atoms. + shape: nf x (nloc x 3) + atype + The type of atoms. shape: nf x nloc + box + The simulation box. shape: nf x 9 + fparam + frame parameter. nf x ndf + aparam + atomic parameter. nf x nloc x nda + do_atomic_virial + If calculate the atomic virial. + + Returns + ------- + ret_dict + The result dict of type Dict[str,paddle.Tensor]. + The keys are defined by the `ModelOutputDef`. + + """ + cc, bb, fp, ap, input_prec = self.input_type_cast( + coord, box=box, fparam=fparam, aparam=aparam + ) + del coord, box, fparam, aparam + ( + extended_coord, + extended_atype, + mapping, + nlist, + ) = extend_input_and_build_neighbor_list( + cc, + atype, + self.get_rcut(), + self.get_sel(), + mixed_types=self.mixed_types(), + box=bb, + ) + model_predict_lower = self.forward_common_lower( + extended_coord, + extended_atype, + nlist, + mapping, + do_atomic_virial=do_atomic_virial, + fparam=fp, + aparam=ap, + ) + model_predict = communicate_extended_output( + model_predict_lower, + self.model_output_def(), + mapping, + do_atomic_virial=do_atomic_virial, + ) + model_predict = self.output_type_cast(model_predict, input_prec) + return model_predict + + def get_out_bias(self) -> paddle.Tensor: + return self.atomic_model.get_out_bias() + + def set_out_bias(self, out_bias: paddle.Tensor) -> None: + self.atomic_model.set_out_bias(out_bias) + + def change_out_bias( + self, + merged, + bias_adjust_mode="change-by-statistic", + ) -> None: + """Change the output bias of atomic model according to the input data and the pretrained model. + + Parameters + ---------- + merged : Union[Callable[[], List[dict]], List[dict]] + - List[dict]: A list of data samples from various data systems. + Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` + originating from the `i`-th data system. + - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + only when needed. Since the sampling process can be slow and memory-intensive, + the lazy function helps by only sampling once. + bias_adjust_mode : str + The mode for changing output bias : ['change-by-statistic', 'set-by-statistic'] + 'change-by-statistic' : perform predictions on labels of target dataset, + and do least square on the errors to obtain the target shift as bias. + 'set-by-statistic' : directly use the statistic output bias in the target dataset. + """ + self.atomic_model.change_out_bias( + merged, + bias_adjust_mode=bias_adjust_mode, + ) + + def forward_common_lower( + self, + extended_coord, + extended_atype, + nlist, + mapping: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + extra_nlist_sort: bool = False, + ): + """Return model prediction. Lower interface that takes + extended atomic coordinates and types, nlist, and mapping + as input, and returns the predictions on the extended region. + The predictions are not reduced. + + Parameters + ---------- + extended_coord + coodinates in extended region. nf x (nall x 3) + extended_atype + atomic type in extended region. nf x nall + nlist + neighbor list. nf x nloc x nsel. + mapping + mapps the extended indices to local indices. nf x nall. + fparam + frame parameter. nf x ndf + aparam + atomic parameter. nf x nloc x nda + do_atomic_virial + whether calculate atomic virial. + comm_dict + The data needed for communication for parallel inference. + extra_nlist_sort + whether to forcibly sort the nlist. + + Returns + ------- + result_dict + the result dict, defined by the `FittingOutputDef`. + + """ + nframes, nall = extended_atype.shape[:2] + extended_coord = extended_coord.reshape([nframes, -1, 3]) + nlist = self.format_nlist( + extended_coord, extended_atype, nlist, extra_nlist_sort=extra_nlist_sort + ) + cc_ext, _, fp, ap, input_prec = self.input_type_cast( + extended_coord, fparam=fparam, aparam=aparam + ) + del extended_coord, fparam, aparam + atomic_ret = self.atomic_model.forward_common_atomic( + cc_ext, + extended_atype, + nlist, + mapping=mapping, + fparam=fp, + aparam=ap, + comm_dict=comm_dict, + ) + model_predict = fit_output_to_model_output( + atomic_ret, + self.atomic_output_def(), + cc_ext, + do_atomic_virial=do_atomic_virial, + create_graph=self.training, + ) + model_predict = self.output_type_cast(model_predict, input_prec) + return model_predict + + def input_type_cast( + self, + coord: paddle.Tensor, + box: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + ) -> Tuple[ + paddle.Tensor, + Optional[paddle.Tensor], + Optional[paddle.Tensor], + Optional[paddle.Tensor], + str, + ]: + """Cast the input data to global float type.""" + input_prec = self.reverse_precision_dict[coord.dtype] + ### + ### type checking would not pass jit, convert to coord prec anyway + ### + # for vv, kk in zip([fparam, aparam], ["frame", "atomic"]): + # if vv is not None and self.reverse_precision_dict[vv.dtype] != input_prec: + # log.warning( + # f"type of {kk} parameter {self.reverse_precision_dict[vv.dtype]}" + # " does not match" + # f" that of the coordinate {input_prec}" + # ) + _lst: List[Optional[paddle.Tensor]] = [ + vv.to(coord.dtype) if vv is not None else None + for vv in [box, fparam, aparam] + ] + box, fparam, aparam = _lst + if ( + input_prec + == self.reverse_precision_dict[self.global_pt_float_precision] + ): + return coord, box, fparam, aparam, input_prec + else: + pp = self.global_pt_float_precision + return ( + coord.to(pp), + box.to(pp) if box is not None else None, + fparam.to(pp) if fparam is not None else None, + aparam.to(pp) if aparam is not None else None, + input_prec, + ) + + def output_type_cast( + self, + model_ret: Dict[str, paddle.Tensor], + input_prec: str, + ) -> Dict[str, paddle.Tensor]: + """Convert the model output to the input prec.""" + do_cast = ( + input_prec + != self.reverse_precision_dict[self.global_pt_float_precision] + ) + pp = self.precision_dict[input_prec] + odef = self.model_output_def() + for kk in odef.keys(): + if kk not in model_ret.keys(): + # do not return energy_derv_c if not do_atomic_virial + continue + if check_operation_applied(odef[kk], OutputVariableOperation.REDU): + model_ret[kk] = ( + model_ret[kk].to(self.global_pt_ener_float_precision) + if model_ret[kk] is not None + else None + ) + elif do_cast: + model_ret[kk] = ( + model_ret[kk].to(pp) if model_ret[kk] is not None else None + ) + return model_ret + + def format_nlist( + self, + extended_coord: paddle.Tensor, + extended_atype: paddle.Tensor, + nlist: paddle.Tensor, + extra_nlist_sort: bool = False, + ): + """Format the neighbor list. + + 1. If the number of neighbors in the `nlist` is equal to sum(self.sel), + it does nothong + + 2. If the number of neighbors in the `nlist` is smaller than sum(self.sel), + the `nlist` is pad with -1. + + 3. If the number of neighbors in the `nlist` is larger than sum(self.sel), + the nearest sum(sel) neighbors will be preseved. + + Known limitations: + + In the case of not self.mixed_types, the nlist is always formatted. + May have side effact on the efficiency. + + Parameters + ---------- + extended_coord + coodinates in extended region. nf x nall x 3 + extended_atype + atomic type in extended region. nf x nall + nlist + neighbor list. nf x nloc x nsel + extra_nlist_sort + whether to forcibly sort the nlist. + + Returns + ------- + formated_nlist + the formated nlist. + + """ + mixed_types = self.mixed_types() + nlist = self._format_nlist( + extended_coord, + nlist, + sum(self.get_sel()), + extra_nlist_sort=extra_nlist_sort, + ) + if not mixed_types: + nlist = nlist_distinguish_types(nlist, extended_atype, self.get_sel()) + return nlist + + def _format_nlist( + self, + extended_coord: paddle.Tensor, + nlist: paddle.Tensor, + nnei: int, + extra_nlist_sort: bool = False, + ): + n_nf, n_nloc, n_nnei = nlist.shape + # nf x nall x 3 + extended_coord = extended_coord.reshape([n_nf, -1, 3]) + rcut = self.get_rcut() + + if n_nnei < nnei: + nlist = paddle.concat( + [ + nlist, + -1 + * paddle.ones( + [n_nf, n_nloc, nnei - n_nnei], + dtype=nlist.dtype, + ).to(device=nlist.place), + ], + axis=-1, + ) + + if n_nnei > nnei or extra_nlist_sort: + n_nf, n_nloc, n_nnei = nlist.shape + m_real_nei = nlist >= 0 + nlist = paddle.where(m_real_nei, nlist, 0) + # nf x nloc x 3 + coord0 = extended_coord[:, :n_nloc, :] + # nf x (nloc x nnei) x 3 + index = nlist.reshape([n_nf, n_nloc * n_nnei, 1]).expand(-1, -1, 3) + coord1 = paddle.gather(extended_coord, 1, index) + # nf x nloc x nnei x 3 + coord1 = coord1.reshape([n_nf, n_nloc, n_nnei, 3]) + # nf x nloc x nnei + rr = paddle.linalg.norm(coord0[:, :, None, :] - coord1, axis=-1) + rr = paddle.where(m_real_nei, rr, float("inf")) + rr, nlist_mapping = paddle.sort(rr, axis=-1) + nlist = paddle.gather(nlist, 2, nlist_mapping) + nlist = paddle.where(rr > rcut, -1, nlist) + nlist = nlist[..., :nnei] + else: # not extra_nlist_sort and n_nnei <= nnei: + pass # great! + assert nlist.shape[-1] == nnei + return nlist + + def do_grad_r( + self, + var_name: Optional[str] = None, + ) -> bool: + """Tell if the output variable `var_name` is r_differentiable. + if var_name is None, returns if any of the variable is r_differentiable. + """ + return self.atomic_model.do_grad_r(var_name) + + def do_grad_c( + self, + var_name: Optional[str] = None, + ) -> bool: + """Tell if the output variable `var_name` is c_differentiable. + if var_name is None, returns if any of the variable is c_differentiable. + """ + return self.atomic_model.do_grad_c(var_name) + + def change_type_map( + self, type_map: List[str], model_with_new_type_stat=None + ) -> None: + """Change the type related params to new ones, according to `type_map` and the original one in the model. + If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. + """ + self.atomic_model.change_type_map( + type_map=type_map, + model_with_new_type_stat=model_with_new_type_stat.atomic_model + if model_with_new_type_stat is not None + else None, + ) + + def serialize(self) -> dict: + return self.atomic_model.serialize() + + @classmethod + def deserialize(cls, data) -> "CM": + return cls(atomic_model_=T_AtomicModel.deserialize(data)) + + # @paddle.jit.export + def get_dim_fparam(self) -> int: + """Get the number (dimension) of frame parameters of this atomic model.""" + return self.atomic_model.get_dim_fparam() + + # @paddle.jit.export + def get_dim_aparam(self) -> int: + """Get the number (dimension) of atomic parameters of this atomic model.""" + return self.atomic_model.get_dim_aparam() + + # @paddle.jit.export + def get_sel_type(self) -> List[int]: + """Get the selected atom types of this model. + + Only atoms with selected atom types have atomic contribution + to the result of the model. + If returning an empty list, all atom types are selected. + """ + return self.atomic_model.get_sel_type() + + # @paddle.jit.export + def is_aparam_nall(self) -> bool: + """Check whether the shape of atomic parameters is (nframes, nall, ndim). + + If False, the shape is (nframes, nloc, ndim). + """ + return self.atomic_model.is_aparam_nall() + + # @paddle.jit.export + def get_rcut(self) -> float: + """Get the cut-off radius.""" + return self.atomic_model.get_rcut() + + # @paddle.jit.export + def get_type_map(self) -> List[str]: + """Get the type map.""" + return self.atomic_model.get_type_map() + + # @paddle.jit.export + def get_nsel(self) -> int: + """Returns the total number of selected neighboring atoms in the cut-off radius.""" + return self.atomic_model.get_nsel() + + # @paddle.jit.export + def get_nnei(self) -> int: + """Returns the total number of selected neighboring atoms in the cut-off radius.""" + return self.atomic_model.get_nnei() + + def atomic_output_def(self) -> FittingOutputDef: + """Get the output def of the atomic model.""" + return self.atomic_model.atomic_output_def() + + def compute_or_load_stat( + self, + sampled_func, + stat_file_path: Optional[DPPath] = None, + ): + """Compute or load the statistics.""" + return self.atomic_model.compute_or_load_stat(sampled_func, stat_file_path) + + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" + return self.atomic_model.get_sel() + + def mixed_types(self) -> bool: + """If true, the model + 1. assumes total number of atoms aligned across frames; + 2. uses a neighbor list that does not distinguish different atomic types. + + If false, the model + 1. assumes total number of atoms of each atom type aligned across frames; + 2. uses a neighbor list that distinguishes different atomic types. + + """ + return self.atomic_model.mixed_types() + + # @paddle.jit.export + def has_message_passing(self) -> bool: + """Returns whether the model has message passing.""" + return self.atomic_model.has_message_passing() + + def need_sorted_nlist_for_lower(self) -> bool: + """Returns whether the model needs sorted nlist when using `forward_lower`.""" + return self.atomic_model.need_sorted_nlist_for_lower() + + def forward( + self, + coord, + atype, + box: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + ) -> Dict[str, paddle.Tensor]: + # directly call the forward_common method when no specific transform rule + return self.forward_common( + coord, + atype, + box, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + ) + + return CM diff --git a/deepmd/pd/model/model/model.py b/deepmd/pd/model/model/model.py new file mode 100644 index 0000000000..1f0effcdfb --- /dev/null +++ b/deepmd/pd/model/model/model.py @@ -0,0 +1,58 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Optional, +) + +import paddle + +from deepmd.dpmodel.model.base_model import ( + make_base_model, +) +from deepmd.utils.path import ( + DPPath, +) + + +class BaseModel(paddle.nn.Layer, make_base_model()): + def __init__(self, *args, **kwargs): + """Construct a basic model for different tasks.""" + paddle.nn.Layer.__init__(self) + self.model_def_script = "" + self.min_nbor_dist = None + + def compute_or_load_stat( + self, + sampled_func, + stat_file_path: Optional[DPPath] = None, + ): + """ + Compute or load the statistics parameters of the model, + such as mean and standard deviation of descriptors or the energy bias of the fitting net. + When `sampled` is provided, all the statistics parameters will be calculated (or re-calculated for update), + and saved in the `stat_file_path`(s). + When `sampled` is not provided, it will check the existence of `stat_file_path`(s) + and load the calculated statistics parameters. + + Parameters + ---------- + sampled_func + The sampled data frames from different data systems. + stat_file_path + The path to the statistics files. + """ + raise NotImplementedError + + # @paddle.jit.export + def get_model_def_script(self) -> str: + """Get the model definition script.""" + return self.model_def_script + + # @paddle.jit.export + def get_min_nbor_dist(self) -> Optional[float]: + """Get the minimum distance between two atoms.""" + return self.min_nbor_dist + + # @paddle.jit.export + def get_ntypes(self): + """Returns the number of element types.""" + return len(self.get_type_map()) diff --git a/deepmd/pd/model/model/polar_model.py b/deepmd/pd/model/model/polar_model.py new file mode 100644 index 0000000000..043039b8e6 --- /dev/null +++ b/deepmd/pd/model/model/polar_model.py @@ -0,0 +1,106 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from copy import ( + deepcopy, +) +from typing import ( + Dict, + Optional, +) + +import paddle + +from deepmd.pd.model.atomic_model import ( + DPPolarAtomicModel, +) +from deepmd.pd.model.model.model import ( + BaseModel, +) + +from .dp_model import ( + DPModelCommon, +) +from .make_model import ( + make_model, +) + +DPDOSModel_ = make_model(DPPolarAtomicModel) + + +@BaseModel.register("polar") +class PolarModel(DPModelCommon, DPDOSModel_): + model_type = "polar" + + def __init__( + self, + *args, + **kwargs, + ): + DPModelCommon.__init__(self) + DPDOSModel_.__init__(self, *args, **kwargs) + + def translated_output_def(self): + out_def_data = self.model_output_def().get_data() + output_def = { + "polar": deepcopy(out_def_data["polarizability"]), + "global_polar": deepcopy(out_def_data["polarizability_redu"]), + } + if "mask" in out_def_data: + output_def["mask"] = deepcopy(out_def_data["mask"]) + return output_def + + def forward( + self, + coord, + atype, + box: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + ) -> Dict[str, paddle.Tensor]: + model_ret = self.forward_common( + coord, + atype, + box, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + ) + if self.get_fitting_net() is not None: + model_predict = {} + model_predict["polar"] = model_ret["polarizability"] + model_predict["global_polar"] = model_ret["polarizability_redu"] + if "mask" in model_ret: + model_predict["mask"] = model_ret["mask"] + else: + model_predict = model_ret + model_predict["updated_coord"] += coord + return model_predict + + # @paddle.jit.export + def forward_lower( + self, + extended_coord, + extended_atype, + nlist, + mapping: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + ): + model_ret = self.forward_common_lower( + extended_coord, + extended_atype, + nlist, + mapping, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + extra_nlist_sort=self.need_sorted_nlist_for_lower(), + ) + if self.get_fitting_net() is not None: + model_predict = {} + model_predict["polar"] = model_ret["polarizability"] + model_predict["global_polar"] = model_ret["polarizability_redu"] + else: + model_predict = model_ret + return model_predict diff --git a/deepmd/pd/model/model/spin_model.py b/deepmd/pd/model/model/spin_model.py new file mode 100644 index 0000000000..1412c5cca5 --- /dev/null +++ b/deepmd/pd/model/model/spin_model.py @@ -0,0 +1,631 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import functools +from copy import ( + deepcopy, +) +from typing import ( + Dict, + List, + Optional, +) + +import paddle + +from deepmd.dpmodel import ( + ModelOutputDef, +) +from deepmd.pd.model.atomic_model import ( + DPAtomicModel, +) +from deepmd.pd.utils.utils import ( + to_paddle_tensor, +) +from deepmd.utils.path import ( + DPPath, +) +from deepmd.utils.spin import ( + Spin, +) + +from .make_model import ( + make_model, +) + + +class SpinModel(paddle.nn.Layer): + """A spin model wrapper, with spin input preprocess and output split.""" + + def __init__( + self, + backbone_model, + spin: Spin, + ): + super().__init__() + self.backbone_model = backbone_model + self.spin = spin + self.ntypes_real = self.spin.ntypes_real + self.virtual_scale_mask = to_paddle_tensor(self.spin.get_virtual_scale_mask()) + self.spin_mask = to_paddle_tensor(self.spin.get_spin_mask()) + + def process_spin_input(self, coord, atype, spin): + """Generate virtual coordinates and types, concat into the input.""" + nframes, nloc = atype.shape + coord = coord.reshape([nframes, nloc, 3]) + spin = spin.reshape([nframes, nloc, 3]) + atype_spin = paddle.concat([atype, atype + self.ntypes_real], axis=-1) + virtual_coord = coord + spin * (self.virtual_scale_mask.to(atype.place))[ + atype + ].reshape([nframes, nloc, 1]) + coord_spin = paddle.concat([coord, virtual_coord], axis=-2) + return coord_spin, atype_spin + + def process_spin_input_lower( + self, + extended_coord, + extended_atype, + extended_spin, + nlist, + mapping: Optional[paddle.Tensor] = None, + ): + """ + Add `extended_spin` into `extended_coord` to generate virtual atoms, and extend `nlist` and `mapping`. + Note that the final `extended_coord_updated` with shape [nframes, nall + nall, 3] has the following order: + - [:, :nloc]: original nloc real atoms. + - [:, nloc: nloc + nloc]: virtual atoms corresponding to nloc real atoms. + - [:, nloc + nloc: nloc + nall]: ghost real atoms. + - [:, nloc + nall: nall + nall]: virtual atoms corresponding to ghost real atoms. + """ + nframes, nall = extended_coord.shape[:2] + nloc = nlist.shape[1] + virtual_extended_coord = extended_coord + extended_spin * ( + self.virtual_scale_mask.to(extended_atype.place) + )[extended_atype].reshape([nframes, nall, 1]) + virtual_extended_atype = extended_atype + self.ntypes_real + extended_coord_updated = self.concat_switch_virtual( + extended_coord, virtual_extended_coord, nloc + ) + extended_atype_updated = self.concat_switch_virtual( + extended_atype, virtual_extended_atype, nloc + ) + if mapping is not None: + virtual_mapping = mapping + nloc + mapping_updated = self.concat_switch_virtual(mapping, virtual_mapping, nloc) + else: + mapping_updated = None + # extend the nlist + nlist_updated = self.extend_nlist(extended_atype, nlist) + return ( + extended_coord_updated, + extended_atype_updated, + nlist_updated, + mapping_updated, + ) + + def process_spin_output( + self, atype, out_tensor, add_mag: bool = True, virtual_scale: bool = True + ): + """ + Split the output both real and virtual atoms, and scale the latter. + add_mag: whether to add magnetic tensor onto the real tensor. + Default: True. e.g. Ture for forces and False for atomic virials on real atoms. + virtual_scale: whether to scale the magnetic tensor with virtual scale factor. + Default: True. e.g. Ture for forces and False for atomic virials on virtual atoms. + """ + nframes, nloc_double = out_tensor.shape[:2] + nloc = nloc_double // 2 + if virtual_scale: + virtual_scale_mask = self.virtual_scale_mask.to(atype.place) + else: + virtual_scale_mask = self.spin_mask.to(atype.place) + atomic_mask = virtual_scale_mask[atype].reshape([nframes, nloc, 1]) + out_real, out_mag = paddle.split(out_tensor, [nloc, nloc], axis=1) + if add_mag: + out_real = out_real + out_mag + shape2 = 1 + for ss in out_real.shape[2:]: + shape2 *= ss + out_mag = (out_mag.reshape([nframes, nloc, shape2]) * atomic_mask).reshape( + out_mag.shape + ) + return out_real, out_mag, atomic_mask > 0.0 + + def process_spin_output_lower( + self, + extended_atype, + extended_out_tensor, + nloc: int, + add_mag: bool = True, + virtual_scale: bool = True, + ): + """ + Split the extended output of both real and virtual atoms with switch, and scale the latter. + add_mag: whether to add magnetic tensor onto the real tensor. + Default: True. e.g. Ture for forces and False for atomic virials on real atoms. + virtual_scale: whether to scale the magnetic tensor with virtual scale factor. + Default: True. e.g. Ture for forces and False for atomic virials on virtual atoms. + """ + nframes, nall_double = extended_out_tensor.shape[:2] + nall = nall_double // 2 + if virtual_scale: + virtual_scale_mask = self.virtual_scale_mask.to(extended_atype.place) + else: + virtual_scale_mask = self.spin_mask.to(extended_atype.place) + atomic_mask = virtual_scale_mask[extended_atype].reshape([nframes, nall, 1]) + extended_out_real = paddle.concat( + [ + extended_out_tensor[:, :nloc], + extended_out_tensor[:, nloc + nloc : nloc + nall], + ], + axis=1, + ) + extended_out_mag = paddle.concat( + [ + extended_out_tensor[:, nloc : nloc + nloc], + extended_out_tensor[:, nloc + nall :], + ], + axis=1, + ) + if add_mag: + extended_out_real = extended_out_real + extended_out_mag + shape2 = 1 + for ss in extended_out_tensor.shape[2:]: + shape2 *= ss + extended_out_mag = ( + extended_out_mag.reshape([nframes, nall, shape2]) * atomic_mask + ).reshape(extended_out_mag.shape) + return extended_out_real, extended_out_mag, atomic_mask > 0.0 + + @staticmethod + def extend_nlist(extended_atype, nlist): + nframes, nloc, nnei = nlist.shape + nall = extended_atype.shape[1] + nlist_mask = nlist != -1 + nlist[nlist == -1] = 0 + nlist_shift = nlist + nall + nlist[~nlist_mask] = -1 + nlist_shift[~nlist_mask] = -1 + self_real = ( + paddle.arange(0, nloc, dtype=nlist.dtype) + .to(device=nlist.place) + .reshape([1, -1, 1]) + .expand(nframes, -1, -1) + ) + self_spin = self_real + nall + # real atom's neighbors: self spin + real neighbor + virtual neighbor + # nf x nloc x (1 + nnei + nnei) + real_nlist = paddle.concat([self_spin, nlist, nlist_shift], axis=-1) + # spin atom's neighbors: real + real neighbor + virtual neighbor + # nf x nloc x (1 + nnei + nnei) + spin_nlist = paddle.concat([self_real, nlist, nlist_shift], axis=-1) + # nf x (nloc + nloc) x (1 + nnei + nnei) + extended_nlist = paddle.concat([real_nlist, spin_nlist], axis=-2) + # update the index for switch + first_part_index = (nloc <= extended_nlist) & (extended_nlist < nall) + second_part_index = (nall <= extended_nlist) & (extended_nlist < (nall + nloc)) + extended_nlist[first_part_index] += nloc + extended_nlist[second_part_index] -= nall - nloc + return extended_nlist + + @staticmethod + def concat_switch_virtual(extended_tensor, extended_tensor_virtual, nloc: int): + """ + Concat real and virtual extended tensors, and switch all the local ones to the first nloc * 2 atoms. + - [:, :nloc]: original nloc real atoms. + - [:, nloc: nloc + nloc]: virtual atoms corresponding to nloc real atoms. + - [:, nloc + nloc: nloc + nall]: ghost real atoms. + - [:, nloc + nall: nall + nall]: virtual atoms corresponding to ghost real atoms. + """ + nframes, nall = extended_tensor.shape[:2] + out_shape = list(extended_tensor.shape) + out_shape[1] *= 2 + extended_tensor_updated = paddle.zeros( + out_shape, + dtype=extended_tensor.dtype, + ).to(device=extended_tensor.place) + extended_tensor_updated[:, :nloc] = extended_tensor[:, :nloc] + extended_tensor_updated[:, nloc : nloc + nloc] = extended_tensor_virtual[ + :, :nloc + ] + extended_tensor_updated[:, nloc + nloc : nloc + nall] = extended_tensor[ + :, nloc: + ] + extended_tensor_updated[:, nloc + nall :] = extended_tensor_virtual[:, nloc:] + return extended_tensor_updated.reshape(out_shape) + + @staticmethod + def expand_aparam(aparam, nloc: int): + """Expand the atom parameters for virtual atoms if necessary.""" + nframes, natom, numb_aparam = aparam.shape + if natom == nloc: # good + pass + elif natom < nloc: # for spin with virtual atoms + aparam = paddle.concat( + [ + aparam, + paddle.zeros( + [nframes, nloc - natom, numb_aparam], + dtype=aparam.dtype, + ).to(device=aparam.place), + ], + axis=1, + ) + else: + raise ValueError( + f"get an input aparam with {aparam.shape[1]} inputs, ", + f"which is larger than {nloc} atoms.", + ) + return aparam + + # @paddle.jit.export + def get_type_map(self) -> List[str]: + """Get the type map.""" + tmap = self.backbone_model.get_type_map() + ntypes = len(tmap) // 2 # ignore the virtual type + return tmap[:ntypes] + + # @paddle.jit.export + def get_ntypes(self): + """Returns the number of element types.""" + return len(self.get_type_map()) + + # @paddle.jit.export + def get_rcut(self): + """Get the cut-off radius.""" + return self.backbone_model.get_rcut() + + # @paddle.jit.export + def get_dim_fparam(self): + """Get the number (dimension) of frame parameters of this atomic model.""" + return self.backbone_model.get_dim_fparam() + + # @paddle.jit.export + def get_dim_aparam(self): + """Get the number (dimension) of atomic parameters of this atomic model.""" + return self.backbone_model.get_dim_aparam() + + # @paddle.jit.export + def get_sel_type(self) -> List[int]: + """Get the selected atom types of this model. + Only atoms with selected atom types have atomic contribution + to the result of the model. + If returning an empty list, all atom types are selected. + """ + return self.backbone_model.get_sel_type() + + # @paddle.jit.export + def is_aparam_nall(self) -> bool: + """Check whether the shape of atomic parameters is (nframes, nall, ndim). + If False, the shape is (nframes, nloc, ndim). + """ + return self.backbone_model.is_aparam_nall() + + # @paddle.jit.export + def model_output_type(self) -> List[str]: + """Get the output type for the model.""" + return self.backbone_model.model_output_type() + + # @paddle.jit.export + def get_model_def_script(self) -> str: + """Get the model definition script.""" + return self.backbone_model.get_model_def_script() + + # @paddle.jit.export + def get_min_nbor_dist(self) -> Optional[float]: + """Get the minimum neighbor distance.""" + return self.backbone_model.get_min_nbor_dist() + + # @paddle.jit.export + def get_nnei(self) -> int: + """Returns the total number of selected neighboring atoms in the cut-off radius.""" + # for C++ interface + if not self.backbone_model.mixed_types(): + return self.backbone_model.get_nnei() // 2 # ignore the virtual selected + else: + return self.backbone_model.get_nnei() + + # @paddle.jit.export + def get_nsel(self) -> int: + """Returns the total number of selected neighboring atoms in the cut-off radius.""" + if not self.backbone_model.mixed_types(): + return self.backbone_model.get_nsel() // 2 # ignore the virtual selected + else: + return self.backbone_model.get_nsel() + + # @paddle.jit.export + def has_spin(self) -> bool: + """Returns whether it has spin input and output.""" + return True + + # @paddle.jit.export + def has_message_passing(self) -> bool: + """Returns whether the model has message passing.""" + return self.backbone_model.has_message_passing() + + def need_sorted_nlist_for_lower(self) -> bool: + """Returns whether the model needs sorted nlist when using `forward_lower`.""" + return self.backbone_model.need_sorted_nlist_for_lower() + + def model_output_def(self): + """Get the output def for the model.""" + model_output_type = self.backbone_model.model_output_type() + if "mask" in model_output_type: + model_output_type.pop(model_output_type.index("mask")) + var_name = model_output_type[0] + backbone_model_atomic_output_def = self.backbone_model.atomic_output_def() + backbone_model_atomic_output_def[var_name].magnetic = True + return ModelOutputDef(backbone_model_atomic_output_def) + + def __getattr__(self, name): + """Get attribute from the wrapped model.""" + if ( + name == "backbone_model" + ): # paddle.nn.Layer will exclude modules to self.__dict__["_modules"] + return self.__dict__["_modules"]["backbone_model"] + elif name in self.__dict__: + return self.__dict__[name] + else: + return getattr(self.backbone_model, name) + + def compute_or_load_stat( + self, + sampled_func, + stat_file_path: Optional[DPPath] = None, + ): + """ + Compute or load the statistics parameters of the model, + such as mean and standard deviation of descriptors or the energy bias of the fitting net. + When `sampled` is provided, all the statistics parameters will be calculated (or re-calculated for update), + and saved in the `stat_file_path`(s). + When `sampled` is not provided, it will check the existence of `stat_file_path`(s) + and load the calculated statistics parameters. + + Parameters + ---------- + sampled_func + The lazy sampled function to get data frames from different data systems. + stat_file_path + The dictionary of paths to the statistics files. + """ + + @functools.lru_cache + def spin_sampled_func(): + sampled = sampled_func() + spin_sampled = [] + for sys in sampled: + coord_updated, atype_updated = self.process_spin_input( + sys["coord"], sys["atype"], sys["spin"] + ) + tmp_dict = { + "coord": coord_updated, + "atype": atype_updated, + } + if "natoms" in sys: + natoms = sys["natoms"] + tmp_dict["natoms"] = paddle.concat( + [2 * natoms[:, :2], natoms[:, 2:], natoms[:, 2:]], axis=-1 + ) + for item_key in sys.keys(): + if item_key not in ["coord", "atype", "spin", "natoms"]: + tmp_dict[item_key] = sys[item_key] + spin_sampled.append(tmp_dict) + return spin_sampled + + self.backbone_model.compute_or_load_stat(spin_sampled_func, stat_file_path) + + def forward_common( + self, + coord, + atype, + spin, + box: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + ) -> Dict[str, paddle.Tensor]: + nframes, nloc = atype.shape + coord_updated, atype_updated = self.process_spin_input(coord, atype, spin) + if aparam is not None: + aparam = self.expand_aparam(aparam, nloc * 2) + model_ret = self.backbone_model.forward_common( + coord_updated, + atype_updated, + box, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + ) + model_output_type = self.backbone_model.model_output_type() + if "mask" in model_output_type: + model_output_type.pop(model_output_type.index("mask")) + var_name = model_output_type[0] + model_ret[f"{var_name}"] = paddle.split( + model_ret[f"{var_name}"], [nloc, nloc], axis=1 + )[0] + if self.backbone_model.do_grad_r(var_name): + ( + model_ret[f"{var_name}_derv_r"], + model_ret[f"{var_name}_derv_r_mag"], + model_ret["mask_mag"], + ) = self.process_spin_output(atype, model_ret[f"{var_name}_derv_r"]) + if self.backbone_model.do_grad_c(var_name) and do_atomic_virial: + ( + model_ret[f"{var_name}_derv_c"], + model_ret[f"{var_name}_derv_c_mag"], + model_ret["mask_mag"], + ) = self.process_spin_output( + atype, + model_ret[f"{var_name}_derv_c"], + add_mag=False, + virtual_scale=False, + ) + return model_ret + + def forward_common_lower( + self, + extended_coord, + extended_atype, + extended_spin, + nlist, + mapping: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + extra_nlist_sort: bool = False, + ): + nframes, nloc = nlist.shape[:2] + ( + extended_coord_updated, + extended_atype_updated, + nlist_updated, + mapping_updated, + ) = self.process_spin_input_lower( + extended_coord, extended_atype, extended_spin, nlist, mapping=mapping + ) + if aparam is not None: + aparam = self.expand_aparam(aparam, nloc * 2) + model_ret = self.backbone_model.forward_common_lower( + extended_coord_updated, + extended_atype_updated, + nlist_updated, + mapping=mapping_updated, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + extra_nlist_sort=extra_nlist_sort, + ) + model_output_type = self.backbone_model.model_output_type() + if "mask" in model_output_type: + model_output_type.pop(model_output_type.index("mask")) + var_name = model_output_type[0] + model_ret[f"{var_name}"] = paddle.split( + model_ret[f"{var_name}"], [nloc, nloc], axis=1 + )[0] + if self.backbone_model.do_grad_r(var_name): + ( + model_ret[f"{var_name}_derv_r"], + model_ret[f"{var_name}_derv_r_mag"], + model_ret["mask_mag"], + ) = self.process_spin_output_lower( + extended_atype, model_ret[f"{var_name}_derv_r"], nloc + ) + if self.backbone_model.do_grad_c(var_name) and do_atomic_virial: + ( + model_ret[f"{var_name}_derv_c"], + model_ret[f"{var_name}_derv_c_mag"], + model_ret["mask_mag"], + ) = self.process_spin_output_lower( + extended_atype, + model_ret[f"{var_name}_derv_c"], + nloc, + add_mag=False, + virtual_scale=False, + ) + return model_ret + + def serialize(self) -> dict: + return { + "backbone_model": self.backbone_model.serialize(), + "spin": self.spin.serialize(), + } + + @classmethod + def deserialize(cls, data) -> "SpinModel": + backbone_model_obj = make_model(DPAtomicModel).deserialize( + data["backbone_model"] + ) + spin = Spin.deserialize(data["spin"]) + return cls( + backbone_model=backbone_model_obj, + spin=spin, + ) + + +class SpinEnergyModel(SpinModel): + """A spin model for energy.""" + + model_type = "ener" + + def __init__( + self, + backbone_model, + spin: Spin, + ): + super().__init__(backbone_model, spin) + + def translated_output_def(self): + out_def_data = self.model_output_def().get_data() + output_def = { + "atom_energy": deepcopy(out_def_data["energy"]), + "energy": deepcopy(out_def_data["energy_redu"]), + "mask_mag": deepcopy(out_def_data["mask_mag"]), + } + if self.do_grad_r("energy"): + output_def["force"] = deepcopy(out_def_data["energy_derv_r"]) + output_def["force"].squeeze(-2) + output_def["force_mag"] = deepcopy(out_def_data["energy_derv_r_mag"]) + output_def["force_mag"].squeeze(-2) + return output_def + + def forward( + self, + coord, + atype, + spin, + box: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + ) -> Dict[str, paddle.Tensor]: + model_ret = self.forward_common( + coord, + atype, + spin, + box, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + ) + model_predict = {} + model_predict["atom_energy"] = model_ret["energy"] + model_predict["energy"] = model_ret["energy_redu"] + model_predict["mask_mag"] = model_ret["mask_mag"] + if self.backbone_model.do_grad_r("energy"): + model_predict["force"] = model_ret["energy_derv_r"].squeeze(-2) + model_predict["force_mag"] = model_ret["energy_derv_r_mag"].squeeze(-2) + # not support virial by far + return model_predict + + # @paddle.jit.export + def forward_lower( + self, + extended_coord, + extended_atype, + extended_spin, + nlist, + mapping: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + ): + model_ret = self.forward_common_lower( + extended_coord, + extended_atype, + extended_spin, + nlist, + mapping=mapping, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + extra_nlist_sort=self.backbone_model.need_sorted_nlist_for_lower(), + ) + model_predict = {} + model_predict["atom_energy"] = model_ret["energy"] + model_predict["energy"] = model_ret["energy_redu"] + model_predict["extended_mask_mag"] = model_ret["mask_mag"] + if self.backbone_model.do_grad_r("energy"): + model_predict["extended_force"] = model_ret["energy_derv_r"].squeeze(-2) + model_predict["extended_force_mag"] = model_ret[ + "energy_derv_r_mag" + ].squeeze(-2) + # not support virial by far + return model_predict diff --git a/deepmd/pd/model/model/transform_output.py b/deepmd/pd/model/model/transform_output.py new file mode 100644 index 0000000000..f1e9fa4212 --- /dev/null +++ b/deepmd/pd/model/model/transform_output.py @@ -0,0 +1,272 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Dict, + List, + Optional, +) + +import paddle + +from deepmd.dpmodel import ( + FittingOutputDef, + ModelOutputDef, + OutputVariableDef, + get_deriv_name, + get_reduce_name, +) +from deepmd.pd.utils import ( + env, +) + + +def atomic_virial_corr( + extended_coord: paddle.Tensor, + atom_energy: paddle.Tensor, +): + nall = extended_coord.shape[1] + nloc = atom_energy.shape[1] + coord, _ = paddle.split(extended_coord, [nloc, nall - nloc], axis=1) + # no derivative with respect to the loc coord. + coord = coord.detach() + ce = coord * atom_energy + sumce0, sumce1, sumce2 = paddle.split(paddle.sum(ce, axis=1), [1, 1, 1], axis=-1) + faked_grad = paddle.ones_like(sumce0) + lst = paddle.jit.annotate(List[Optional[paddle.Tensor]], [faked_grad]) + extended_virial_corr0 = paddle.autograd.grad( + [sumce0], + [extended_coord], + grad_outputs=lst, + create_graph=False, + retain_graph=True, + )[0] + assert extended_virial_corr0 is not None + extended_virial_corr1 = paddle.autograd.grad( + [sumce1], + [extended_coord], + grad_outputs=lst, + create_graph=False, + retain_graph=True, + )[0] + assert extended_virial_corr1 is not None + extended_virial_corr2 = paddle.autograd.grad( + [sumce2], + [extended_coord], + grad_outputs=lst, + create_graph=False, + retain_graph=True, + )[0] + assert extended_virial_corr2 is not None + extended_virial_corr = paddle.concat( + [ + extended_virial_corr0.unsqueeze(-1), + extended_virial_corr1.unsqueeze(-1), + extended_virial_corr2.unsqueeze(-1), + ], + axis=-1, + ) + return extended_virial_corr + + +def task_deriv_one( + atom_energy: paddle.Tensor, + energy: paddle.Tensor, + extended_coord: paddle.Tensor, + do_virial: bool = True, + do_atomic_virial: bool = False, + create_graph: bool = True, +): + faked_grad = paddle.ones_like(energy) + # lst = paddle.jit.annotate(List[Optional[paddle.Tensor]], [faked_grad]) + extended_force = paddle.autograd.grad( + [energy], + [extended_coord], + # grad_outputs=lst, + create_graph=create_graph, + retain_graph=True, + )[0] + assert extended_force is not None + extended_force = -extended_force + if do_virial: + extended_virial = extended_force.unsqueeze(-1) @ extended_coord.unsqueeze(-2) + # the correction sums to zero, which does not contribute to global virial + if do_atomic_virial: + extended_virial_corr = atomic_virial_corr(extended_coord, atom_energy) + extended_virial = extended_virial + extended_virial_corr + # to [...,3,3] -> [...,9] + extended_virial = extended_virial.reshape( + [*list(extended_virial.shape[:-2]), 9] + ) + else: + extended_virial = None + return extended_force, extended_virial + + +def get_leading_dims( + vv: paddle.Tensor, + vdef: OutputVariableDef, +): + """Get the dimensions of nf x nloc.""" + vshape = vv.shape + return list(vshape[: (len(vshape) - len(vdef.shape))]) + + +def get_atom_axis( + vdef: paddle.Tensor, +): + """Get the axis of atoms.""" + atom_axis = -(len(vdef.shape) + 1) + return atom_axis + + +def take_deriv( + vv: paddle.Tensor, + svv: paddle.Tensor, + vdef: OutputVariableDef, + coord_ext: paddle.Tensor, + do_virial: bool = False, + do_atomic_virial: bool = False, + create_graph: bool = True, +): + size = 1 + for ii in vdef.shape: + size *= ii + vv1 = vv.reshape(list(get_leading_dims(vv, vdef)) + [size]) # noqa: RUF005 + svv1 = svv.reshape(list(get_leading_dims(svv, vdef)) + [size]) # noqa: RUF005 + split_vv1 = paddle.split(vv1, [1] * size, axis=-1) + split_svv1 = paddle.split(svv1, [1] * size, axis=-1) + split_ff, split_avir = [], [] + for vvi, svvi in zip(split_vv1, split_svv1): + # nf x nloc x 3, nf x nloc x 9 + ffi, aviri = task_deriv_one( + vvi, + svvi, + coord_ext, + do_virial=do_virial, + do_atomic_virial=do_atomic_virial, + create_graph=create_graph, + ) + # nf x nloc x 1 x 3, nf x nloc x 1 x 9 + ffi = ffi.unsqueeze(-2) + split_ff.append(ffi) + if do_virial: + assert aviri is not None + aviri = aviri.unsqueeze(-2) + split_avir.append(aviri) + # nf x nall x v_dim x 3, nf x nall x v_dim x 9 + out_lead_shape = list(coord_ext.shape[:-1]) + vdef.shape + ff = paddle.concat(split_ff, axis=-2).reshape(out_lead_shape + [3]) # noqa: RUF005 + if do_virial: + avir = paddle.concat(split_avir, axis=-2).reshape(out_lead_shape + [9]) # noqa: RUF005 + else: + avir = None + return ff, avir + + +def fit_output_to_model_output( + fit_ret: Dict[str, paddle.Tensor], + fit_output_def: FittingOutputDef, + coord_ext: paddle.Tensor, + do_atomic_virial: bool = False, + create_graph: bool = True, +) -> Dict[str, paddle.Tensor]: + """Transform the output of the fitting network to + the model output. + + """ + redu_prec = env.GLOBAL_PD_ENER_FLOAT_PRECISION + model_ret = dict(fit_ret.items()) + for kk, vv in fit_ret.items(): + vdef = fit_output_def[kk] + shap = vdef.shape + atom_axis = -(len(shap) + 1) + if vdef.reducible: + kk_redu = get_reduce_name(kk) + model_ret[kk_redu] = paddle.sum(vv.to(redu_prec), axis=atom_axis) + if vdef.r_differentiable: + kk_derv_r, kk_derv_c = get_deriv_name(kk) + dr, dc = take_deriv( + vv, + model_ret[kk_redu], + vdef, + coord_ext, + do_virial=vdef.c_differentiable, + do_atomic_virial=do_atomic_virial, + create_graph=create_graph, + ) + model_ret[kk_derv_r] = dr + if vdef.c_differentiable: + assert dc is not None + model_ret[kk_derv_c] = dc + model_ret[kk_derv_c + "_redu"] = paddle.sum( + model_ret[kk_derv_c].to(redu_prec), axis=1 + ) + return model_ret + + +def communicate_extended_output( + model_ret: Dict[str, paddle.Tensor], + model_output_def: ModelOutputDef, + mapping: paddle.Tensor, # nf x nloc + do_atomic_virial: bool = False, +) -> Dict[str, paddle.Tensor]: + """Transform the output of the model network defined on + local and ghost (extended) atoms to local atoms. + + """ + redu_prec = env.GLOBAL_PD_ENER_FLOAT_PRECISION + new_ret = {} + for kk in model_output_def.keys_outp(): + vv = model_ret[kk] + vdef = model_output_def[kk] + new_ret[kk] = vv + if vdef.reducible: + kk_redu = get_reduce_name(kk) + new_ret[kk_redu] = model_ret[kk_redu] + # nf x nloc + vldims = get_leading_dims(vv, vdef) + # nf x nall + mldims = list(mapping.shape) + kk_derv_r, kk_derv_c = get_deriv_name(kk) + if vdef.r_differentiable: + # vdim x 3 + derv_r_ext_dims = list(vdef.shape) + [3] # noqa:RUF005 + mapping = mapping.reshape(mldims + [1] * len(derv_r_ext_dims)).expand( + [-1] * len(mldims) + derv_r_ext_dims + ) + force = paddle.zeros(vldims + derv_r_ext_dims, dtype=vv.dtype).to( + device=vv.place + ) + # nf x nloc x nvar x 3 + new_ret[kk_derv_r] = paddle.scatter_reduce( + force, + 1, + index=mapping, + src=model_ret[kk_derv_r], + reduce="sum", + ) + if vdef.c_differentiable: + assert vdef.r_differentiable + derv_c_ext_dims = list(vdef.shape) + [9] # noqa:RUF005 + # nf x nloc x nvar x 3 -> nf x nloc x nvar x 9 + mapping = paddle.tile( + mapping, + [1] * (len(mldims) + len(vdef.shape)) + [3], + ) + virial = paddle.zeros(vldims + derv_c_ext_dims, dtype=vv.dtype).to( + device=vv.place + ) + # nf x nloc x nvar x 9 + new_ret[kk_derv_c] = paddle.scatter_reduce( + virial, + 1, + index=mapping, + src=model_ret[kk_derv_c], + reduce="sum", + ) + new_ret[kk_derv_c + "_redu"] = paddle.sum( + new_ret[kk_derv_c].to(redu_prec), axis=1 + ) + if not do_atomic_virial: + # pop atomic virial, because it is not correctly calculated. + new_ret.pop(kk_derv_c) + return new_ret diff --git a/deepmd/pd/model/network/__init__.py b/deepmd/pd/model/network/__init__.py new file mode 100644 index 0000000000..6ceb116d85 --- /dev/null +++ b/deepmd/pd/model/network/__init__.py @@ -0,0 +1 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later diff --git a/deepmd/pd/model/network/init.py b/deepmd/pd/model/network/init.py new file mode 100644 index 0000000000..4d8fab3dc9 --- /dev/null +++ b/deepmd/pd/model/network/init.py @@ -0,0 +1,458 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from __future__ import ( + annotations, +) + +import math +import warnings +from typing import Optional as _Optional + +import paddle +from paddle import ( + Tensor, +) + +# Copyright (c) 2024 The PyTorch Authors. All rights reserved. +# +# This file includes source code from PyTorch of version v2.3.0, which is released under the BSD-3-Clause license. +# For more information about PyTorch, visit https://pytorch.org/. + + +# These no_grad_* functions are necessary as wrappers around the parts of these +# functions that use `with paddle.no_grad()`. The JIT doesn't support context +# managers, so these need to be implemented as builtins. Using these wrappers +# lets us keep those builtins small and re-usable. +def _no_grad_uniform_(tensor: paddle.Tensor, a, b, generator=None): + with paddle.no_grad(): + return tensor.uniform_(a, b) + + +def _no_grad_normal_(tensor: paddle.Tensor, mean, std, generator=None): + with paddle.no_grad(): + return tensor.normal_(mean, std) + + +def _no_grad_trunc_normal_(tensor: paddle.Tensor, mean, std, a, b, generator=None): + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0 + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn( + "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2, + ) + + with paddle.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.multiply_(std * math.sqrt(2.0)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clip_(min=a, max=b) + return tensor + + +def _no_grad_zero_(tensor: paddle.Tensor): + with paddle.no_grad(): + return tensor.zero_() + + +def _no_grad_fill_(tensor: paddle.Tensor, val): + with paddle.no_grad(): + return tensor.fill_(val) + + +def calculate_gain(nonlinearity, param=None): + r"""Return the recommended gain value for the given nonlinearity function. + + The values are as follows: + + ================= ==================================================== + nonlinearity gain + ================= ==================================================== + Linear / Identity :math:`1` + Conv{1,2,3}D :math:`1` + Sigmoid :math:`1` + Tanh :math:`\frac{5}{3}` + ReLU :math:`\sqrt{2}` + Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}` + SELU :math:`\frac{3}{4}` + ================= ==================================================== + + .. warning:: + In order to implement `Self-Normalizing Neural Networks`_ , + you should use ``nonlinearity='linear'`` instead of ``nonlinearity='selu'``. + This gives the initial weights a variance of ``1 / N``, + which is necessary to induce a stable fixed point in the forward pass. + In contrast, the default gain for ``SELU`` sacrifices the normalization + effect for more stable gradient flow in rectangular layers. + + Args: + nonlinearity: the non-linear function (`nn.functional` name) + param: optional parameter for the non-linear function + + Examples + -------- + >>> gain = nn.init.calculate_gain( + ... "leaky_relu", 0.2 + ... ) # leaky_relu with negative_slope=0.2 + + .. _Self-Normalizing Neural Networks: https://papers.nips.cc/paper/2017/hash/5d44ee6f2c3f71b73125876103c8f6c4-Abstract.html + """ + linear_fns = [ + "linear", + "conv1d", + "conv2d", + "conv3d", + "conv_transpose1d", + "conv_transpose2d", + "conv_transpose3d", + ] + if nonlinearity in linear_fns or nonlinearity == "sigmoid": + return 1 + elif nonlinearity == "tanh": + return 5.0 / 3 + elif nonlinearity == "relu": + return math.sqrt(2.0) + elif nonlinearity == "leaky_relu": + if param is None: + negative_slope = 0.01 + elif ( + not isinstance(param, bool) + and isinstance(param, int) + or isinstance(param, float) + ): + # True/False are instances of int, hence check above + negative_slope = param + else: + raise ValueError(f"negative_slope {param} not a valid number") + return math.sqrt(2.0 / (1 + negative_slope**2)) + elif nonlinearity == "selu": + return ( + 3.0 / 4 + ) # Value found empirically (https://github.com/pytorch/pytorch/pull/50664) + else: + raise ValueError(f"Unsupported nonlinearity {nonlinearity}") + + +def _calculate_fan_in_and_fan_out(tensor): + dimensions = tensor.ndim + if dimensions < 2: + raise ValueError( + "Fan in and fan out can not be computed for tensor with fewer than 2 dimensions" + ) + + num_input_fmaps = tensor.shape[1] + num_output_fmaps = tensor.shape[0] + receptive_field_size = 1 + if tensor.ndim > 2: + # math.prod is not always available, accumulate the product manually + # we could use functools.reduce but that is not supported by TorchScript + for s in tensor.shape[2:]: + receptive_field_size *= s + fan_in = num_input_fmaps * receptive_field_size + fan_out = num_output_fmaps * receptive_field_size + + return fan_in, fan_out + + +def _calculate_correct_fan(tensor: paddle.Tensor, mode): + mode = mode.lower() + valid_modes = ["fan_in", "fan_out"] + if mode not in valid_modes: + raise ValueError(f"Mode {mode} not supported, please use one of {valid_modes}") + + fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) + return fan_in if mode == "fan_in" else fan_out + + +def zeros_(tensor: Tensor) -> Tensor: + r"""Fill the input Tensor with the scalar value `0`. + + Args: + tensor: an n-dimensional `paddle.Tensor` + + Examples + -------- + >>> w = paddle.empty(3, 5) + >>> nn.init.zeros_(w) + """ + return _no_grad_zero_(tensor) + + +def ones_(tensor: Tensor) -> Tensor: + r"""Fill the input Tensor with the scalar value `1`. + + Args: + tensor: an n-dimensional `paddle.Tensor` + + Examples + -------- + >>> w = paddle.empty(3, 5) + >>> nn.init.ones_(w) + """ + return _no_grad_fill_(tensor, 1.0) + + +def constant_(tensor: Tensor, val: float) -> Tensor: + r"""Fill the input Tensor with the value :math:`\text{val}`. + + Args: + tensor: an n-dimensional `paddle.Tensor` + val: the value to fill the tensor with + + Examples + -------- + >>> w = paddle.empty(3, 5) + >>> nn.init.constant_(w, 0.3) + """ + # if paddle.overrides.has_torch_function_variadic(tensor): + # return paddle.overrides.handle_torch_function( + # constant_, (tensor,), tensor=tensor, val=val + # ) + return _no_grad_fill_(tensor, val) + + +def normal_( + tensor: Tensor, + mean: float = 0.0, + std: float = 1.0, + generator: _Optional[paddle.Generator] = None, +) -> Tensor: + r"""Fill the input Tensor with values drawn from the normal distribution. + + :math:`\mathcal{N}(\text{mean}, \text{std}^2)`. + + Args: + tensor: an n-dimensional `paddle.Tensor` + mean: the mean of the normal distribution + std: the standard deviation of the normal distribution + generator: the paddle Generator to sample from (default: None) + + Examples + -------- + >>> w = paddle.empty(3, 5) + >>> nn.init.normal_(w) + """ + # if paddle.overrides.has_torch_function_variadic(tensor): + # return paddle.overrides.handle_torch_function( + # normal_, (tensor,), tensor=tensor, mean=mean, std=std + # ) + return _no_grad_normal_(tensor, mean, std, generator) + + +def trunc_normal_( + tensor: Tensor, + mean: float = 0.0, + std: float = 1.0, + a: float = -2.0, + b: float = 2.0, + generator: _Optional[paddle.Generator] = None, +) -> Tensor: + r"""Fill the input Tensor with values drawn from a truncated normal distribution. + + The values are effectively drawn from the + normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` + with values outside :math:`[a, b]` redrawn until they are within + the bounds. The method used for generating the random values works + best when :math:`a \leq \text{mean} \leq b`. + + Args: + tensor: an n-dimensional `paddle.Tensor` + mean: the mean of the normal distribution + std: the standard deviation of the normal distribution + a: the minimum cutoff value + b: the maximum cutoff value + generator: the paddle Generator to sample from (default: None) + + Examples + -------- + >>> w = paddle.empty(3, 5) + >>> nn.init.trunc_normal_(w) + """ + return _no_grad_trunc_normal_(tensor, mean, std, a, b) + + +def kaiming_uniform_( + tensor: Tensor, + a: float = 0, + mode: str = "fan_in", + nonlinearity: str = "leaky_relu", + generator: _Optional[paddle.Generator] = None, +): + r"""Fill the input `Tensor` with values using a Kaiming uniform distribution. + + The method is described in `Delving deep into rectifiers: Surpassing + human-level performance on ImageNet classification` - He, K. et al. (2015). + The resulting tensor will have values sampled from + :math:`\mathcal{U}(-\text{bound}, \text{bound})` where + + .. math:: + \text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan\_mode}}} + + Also known as He initialization. + + Args: + tensor: an n-dimensional `paddle.Tensor` + a: the negative slope of the rectifier used after this layer (only + used with ``'leaky_relu'``) + mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'`` + preserves the magnitude of the variance of the weights in the + forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the + backwards pass. + nonlinearity: the non-linear function (`nn.functional` name), + recommended to use only with ``'relu'`` or ``'leaky_relu'`` (default). + generator: the paddle Generator to sample from (default: None) + + Examples + -------- + >>> w = paddle.empty(3, 5) + >>> nn.init.kaiming_uniform_(w, mode="fan_in", nonlinearity="relu") + """ + # if paddle.overrides.has_torch_function_variadic(tensor): + # return paddle.overrides.handle_torch_function( + # kaiming_uniform_, + # (tensor,), + # tensor=tensor, + # a=a, + # mode=mode, + # nonlinearity=nonlinearity, + # generator=generator, + # ) + + if 0 in tensor.shape: + warnings.warn("Initializing zero-element tensors is a no-op") + return tensor + fan = _calculate_correct_fan(tensor, mode) + gain = calculate_gain(nonlinearity, a) + std = gain / math.sqrt(fan) + bound = math.sqrt(3.0) * std # Calculate uniform bounds from standard deviation + with paddle.no_grad(): + return tensor.uniform_(-bound, bound) + + +def kaiming_normal_( + tensor: Tensor, + a: float = 0, + mode: str = "fan_in", + nonlinearity: str = "leaky_relu", + generator: _Optional[paddle.Generator] = None, +): + r"""Fill the input `Tensor` with values using a Kaiming normal distribution. + + The method is described in `Delving deep into rectifiers: Surpassing + human-level performance on ImageNet classification` - He, K. et al. (2015). + The resulting tensor will have values sampled from + :math:`\mathcal{N}(0, \text{std}^2)` where + + .. math:: + \text{std} = \frac{\text{gain}}{\sqrt{\text{fan\_mode}}} + + Also known as He initialization. + + Args: + tensor: an n-dimensional `paddle.Tensor` + a: the negative slope of the rectifier used after this layer (only + used with ``'leaky_relu'``) + mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'`` + preserves the magnitude of the variance of the weights in the + forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the + backwards pass. + nonlinearity: the non-linear function (`nn.functional` name), + recommended to use only with ``'relu'`` or ``'leaky_relu'`` (default). + generator: the paddle Generator to sample from (default: None) + + Examples + -------- + >>> w = paddle.empty(3, 5) + >>> nn.init.kaiming_normal_(w, mode="fan_out", nonlinearity="relu") + """ + if 0 in tensor.shape: + warnings.warn("Initializing zero-element tensors is a no-op") + return tensor + fan = _calculate_correct_fan(tensor, mode) + gain = calculate_gain(nonlinearity, a) + std = gain / math.sqrt(fan) + with paddle.no_grad(): + return tensor.normal_(0, std) + + +def xavier_uniform_( + tensor: Tensor, gain: float = 1.0, generator: _Optional[paddle.Generator] = None +) -> Tensor: + r"""Fill the input `Tensor` with values using a Xavier uniform distribution. + + The method is described in `Understanding the difficulty of training + deep feedforward neural networks` - Glorot, X. & Bengio, Y. (2010). + The resulting tensor will have values sampled from + :math:`\mathcal{U}(-a, a)` where + + .. math:: + a = \text{gain} \times \sqrt{\frac{6}{\text{fan\_in} + \text{fan\_out}}} + + Also known as Glorot initialization. + + Args: + tensor: an n-dimensional `paddle.Tensor` + gain: an optional scaling factor + generator: the paddle Generator to sample from (default: None) + + Examples + -------- + >>> w = paddle.empty(3, 5) + >>> nn.init.xavier_uniform_(w, gain=nn.init.calculate_gain("relu")) + """ + fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) + std = gain * math.sqrt(2.0 / float(fan_in + fan_out)) + a = math.sqrt(3.0) * std # Calculate uniform bounds from standard deviation + + return _no_grad_uniform_(tensor, -a, a, generator) + + +def xavier_normal_( + tensor: Tensor, + gain: float = 1.0, + generator: _Optional[paddle.Generator] = None, +) -> Tensor: + r"""Fill the input `Tensor` with values using a Xavier normal distribution. + + The method is described in `Understanding the difficulty of training deep feedforward + neural networks` - Glorot, X. & Bengio, Y. (2010). The resulting tensor + will have values sampled from :math:`\mathcal{N}(0, \text{std}^2)` where + + .. math:: + \text{std} = \text{gain} \times \sqrt{\frac{2}{\text{fan\_in} + \text{fan\_out}}} + + Also known as Glorot initialization. + + Args: + tensor: an n-dimensional `paddle.Tensor` + gain: an optional scaling factor + generator: the paddle Generator to sample from (default: None) + + Examples + -------- + >>> w = paddle.empty(3, 5) + >>> nn.init.xavier_normal_(w) + """ + fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) + std = gain * math.sqrt(2.0 / float(fan_in + fan_out)) + + return _no_grad_normal_(tensor, 0.0, std, generator) diff --git a/deepmd/pd/model/network/layernorm.py b/deepmd/pd/model/network/layernorm.py new file mode 100644 index 0000000000..fa2e768fad --- /dev/null +++ b/deepmd/pd/model/network/layernorm.py @@ -0,0 +1,154 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + List, + Optional, + Union, +) + +import numpy as np +import paddle +import paddle.nn as nn + +from deepmd.dpmodel.utils.network import LayerNorm as DPLayerNorm +from deepmd.pd.model.network.init import ( + normal_, + ones_, + zeros_, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + DEFAULT_PRECISION, + PRECISION_DICT, +) +from deepmd.pd.utils.utils import ( + get_generator, + to_numpy_array, + to_paddle_tensor, +) + +device = env.DEVICE + + +def empty_t(shape, precision): + return paddle.empty(shape, dtype=precision, device=device) + + +class LayerNorm(nn.Layer): + def __init__( + self, + num_in, + eps: float = 1e-5, + uni_init: bool = True, + bavg: float = 0.0, + stddev: float = 1.0, + precision: str = DEFAULT_PRECISION, + trainable: bool = True, + seed: Optional[Union[int, List[int]]] = None, + ): + super().__init__() + self.eps = eps + self.uni_init = uni_init + self.num_in = num_in + self.precision = precision + self.prec = PRECISION_DICT[self.precision] + self.matrix = nn.Parameter(data=empty_t((num_in,), self.prec)) + self.bias = nn.Parameter( + data=empty_t([num_in], self.prec), + ) + random_generator = get_generator(seed) + if self.uni_init: + ones_(self.matrix.data) + zeros_(self.bias.data) + else: + normal_(self.bias.data, mean=bavg, std=stddev, generator=random_generator) + normal_( + self.matrix.data, + std=stddev / np.sqrt(self.num_in), + generator=random_generator, + ) + self.trainable = trainable + if not self.trainable: + self.matrix.stop_gradient = True + self.bias.stop_gradient = True + + def dim_out(self) -> int: + return self.matrix.shape[0] + + def forward( + self, + xx: paddle.Tensor, + ) -> paddle.Tensor: + """One Layer Norm used by DP model. + + Parameters + ---------- + xx : paddle.Tensor + The input of index. + + Returns + ------- + yy: paddle.Tensor + The output. + """ + # mean = xx.mean(axis=-1, keepdim=True) + # variance = xx.var(axis=-1, unbiased=False, keepdim=True) + # The following operation is the same as above, but will not raise error when using jit model to inference. + # See https://github.com/pytorch/pytorch/issues/85792 + variance, mean = ( + paddle.var(xx, -1, unbiased=False, keepdim=True), + paddle.mean(xx, axis=-1, keepdim=True), + ) + yy = (xx - mean) / paddle.sqrt(variance + self.eps) + if self.matrix is not None and self.bias is not None: + yy = yy * self.matrix + self.bias + return yy + + def serialize(self) -> dict: + """Serialize the layer to a dict. + + Returns + ------- + dict + The serialized layer. + """ + nl = DPLayerNorm( + self.matrix.shape[0], + eps=self.eps, + trainable=self.trainable, + precision=self.precision, + ) + nl.w = to_numpy_array(self.matrix) + nl.b = to_numpy_array(self.bias) + data = nl.serialize() + return data + + @classmethod + def deserialize(cls, data: dict) -> "LayerNorm": + """Deserialize the layer from a dict. + + Parameters + ---------- + data : dict + The dict to deserialize from. + """ + nl = DPLayerNorm.deserialize(data) + obj = cls( + nl["matrix"].shape[0], + eps=nl["eps"], + trainable=nl["trainable"], + precision=nl["precision"], + ) + prec = PRECISION_DICT[obj.precision] + + def check_load_param(ss): + return ( + nn.Parameter(data=to_paddle_tensor(nl[ss])) + if nl[ss] is not None + else None + ) + + obj.matrix = check_load_param("matrix") + obj.bias = check_load_param("bias") + return obj diff --git a/deepmd/pd/model/network/mlp.py b/deepmd/pd/model/network/mlp.py new file mode 100644 index 0000000000..3580d187d6 --- /dev/null +++ b/deepmd/pd/model/network/mlp.py @@ -0,0 +1,333 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from __future__ import ( + annotations, +) + +from typing import ( + ClassVar, + Dict, + List, + Optional, + Union, +) + +import numpy as np +import paddle +import paddle.nn as nn + +from deepmd.pd.utils import ( + env, +) + +device = env.DEVICE + +from deepmd.dpmodel.utils import ( + NativeLayer, +) +from deepmd.dpmodel.utils import NetworkCollection as DPNetworkCollection +from deepmd.dpmodel.utils import ( + make_embedding_network, + make_fitting_network, + make_multilayer_network, +) +from deepmd.pd.model.network.init import ( + kaiming_normal_, + normal_, + trunc_normal_, + xavier_uniform_, +) +from deepmd.pd.utils.env import ( + DEFAULT_PRECISION, + PRECISION_DICT, +) +from deepmd.pd.utils.utils import ( + ActivationFn, + get_generator, + to_numpy_array, + to_paddle_tensor, +) + + +def empty_t(shape, precision): + return paddle.empty(shape, dtype=precision).to(device=device) + + +class Identity(nn.Layer): + def __init__(self): + super().__init__() + + def forward( + self, + xx: paddle.Tensor, + ) -> paddle.Tensor: + """The Identity operation layer.""" + return xx + + def serialize(self) -> dict: + return { + "@class": "Identity", + "@version": 1, + } + + @classmethod + def deserialize(cls, data: dict) -> Identity: + return Identity() + + +class MLPLayer(nn.Layer): + def __init__( + self, + num_in, + num_out, + bias: bool = True, + use_timestep: bool = False, + activation_function: Optional[str] = None, + resnet: bool = False, + bavg: float = 0.0, + stddev: float = 1.0, + precision: str = DEFAULT_PRECISION, + init: str = "default", + seed: Optional[Union[int, List[int]]] = None, + ): + super().__init__() + # only use_timestep when skip connection is established. + self.use_timestep = use_timestep and ( + num_out == num_in or num_out == num_in * 2 + ) + self.num_in = num_in + self.num_out = num_out + self.activate_name = activation_function + self.activate = ActivationFn(self.activate_name) + self.precision = precision + self.prec = PRECISION_DICT[self.precision] + self.matrix = self.create_parameter( + (num_in, num_out), + default_initializer=nn.initializer.Assign( + empty_t((num_in, num_out), self.prec) + ), + ) + random_generator = get_generator(seed) + if bias: + self.bias = self.create_parameter( + [num_out], + default_initializer=nn.initializer.Assign( + empty_t([num_out], self.prec) + ), + ) + else: + self.bias = None + if self.use_timestep: + self.idt = self.create_parameter( + [num_out], + default_initializer=nn.initializer.Assign( + empty_t([num_out], self.prec) + ), + ) + else: + self.idt = None + self.resnet = resnet + if init == "default": + self._default_normal_init( + bavg=bavg, stddev=stddev, generator=random_generator + ) + elif init == "trunc_normal": + self._trunc_normal_init(1.0, generator=random_generator) + elif init == "relu": + self._trunc_normal_init(2.0, generator=random_generator) + elif init == "glorot": + self._glorot_uniform_init(generator=random_generator) + elif init == "gating": + self._zero_init(self.use_bias) + elif init == "kaiming_normal": + self._normal_init(generator=random_generator) + elif init == "final": + self._zero_init(False) + else: + raise ValueError(f"Unknown initialization method: {init}") + + def check_type_consistency(self): + precision = self.precision + + def check_var(var): + if var is not None: + # assertion "float64" == "double" would fail + assert PRECISION_DICT[var.dtype.name] is PRECISION_DICT[precision] + + check_var(self.matrix) + check_var(self.bias) + check_var(self.idt) + + def dim_in(self) -> int: + return self.matrix.shape[0] + + def dim_out(self) -> int: + return self.matrix.shape[1] + + def _default_normal_init( + self, + bavg: float = 0.0, + stddev: float = 1.0, + generator: Optional[paddle.Generator] = None, + ): + normal_( + self.matrix.data, + std=stddev / np.sqrt(self.num_out + self.num_in), + generator=generator, + ) + if self.bias is not None: + normal_(self.bias.data, mean=bavg, std=stddev, generator=generator) + if self.idt is not None: + normal_(self.idt.data, mean=0.1, std=0.001, generator=generator) + + def _trunc_normal_init( + self, scale=1.0, generator: Optional[paddle.Generator] = None + ): + # Constant from scipy.stats.truncnorm.std(a=-2, b=2, loc=0., scale=1.) + TRUNCATED_NORMAL_STDDEV_FACTOR = 0.87962566103423978 + _, fan_in = self.matrix.shape + scale = scale / max(1, fan_in) + std = (scale**0.5) / TRUNCATED_NORMAL_STDDEV_FACTOR + trunc_normal_(self.matrix, mean=0.0, std=std, generator=generator) + + def _glorot_uniform_init(self, generator: Optional[paddle.Generator] = None): + xavier_uniform_(self.matrix, gain=1, generator=generator) + + def _zero_init(self, use_bias=True): + with paddle.no_grad(): + self.matrix.fill_(0.0) + if use_bias and self.bias is not None: + with paddle.no_grad(): + self.bias.fill_(1.0) + + def _normal_init(self, generator: Optional[paddle.Generator] = None): + kaiming_normal_(self.matrix, nonlinearity="linear", generator=generator) + + def forward( + self, + xx: paddle.Tensor, + ) -> paddle.Tensor: + """One MLP layer used by DP model. + + Parameters + ---------- + xx : paddle.Tensor + The input. + + Returns + ------- + yy: paddle.Tensor + The output. + """ + ori_prec = xx.dtype + xx = xx.to(self.prec) + yy = ( + paddle.matmul(xx, self.matrix.astype(self.prec)) + self.bias + if self.bias is not None + else paddle.matmul(xx, self.matrix) + ) + yy = self.activate(yy).clone() + yy = yy * self.idt if self.idt is not None else yy + if self.resnet: + if xx.shape[-1] == yy.shape[-1]: + yy += xx + elif 2 * xx.shape[-1] == yy.shape[-1]: + yy += paddle.concat([xx, xx], axis=-1) + else: + yy = yy + yy = yy.to(ori_prec) + return yy + + def serialize(self) -> dict: + """Serialize the layer to a dict. + + Returns + ------- + dict + The serialized layer. + """ + nl = NativeLayer( + self.matrix.shape[0], + self.matrix.shape[1], + bias=self.bias is not None, + use_timestep=self.idt is not None, + activation_function=self.activate_name, + resnet=self.resnet, + precision=self.precision, + ) + nl.w, nl.b, nl.idt = ( + to_numpy_array(self.matrix), + to_numpy_array(self.bias), + to_numpy_array(self.idt), + ) + return nl.serialize() + + @classmethod + def deserialize(cls, data: dict) -> MLPLayer: + """Deserialize the layer from a dict. + + Parameters + ---------- + data : dict + The dict to deserialize from. + """ + nl = NativeLayer.deserialize(data) + obj = cls( + nl["matrix"].shape[0], + nl["matrix"].shape[1], + bias=nl["bias"] is not None, + use_timestep=nl["idt"] is not None, + activation_function=nl["activation_function"], + resnet=nl["resnet"], + precision=nl["precision"], + ) + prec = PRECISION_DICT[obj.precision] + + def check_load_param(ss): + return ( + paddle.create_parameter( + nl[ss].shape, + DEFAULT_PRECISION, + default_initializer=paddle.nn.initializer.Assign( + to_paddle_tensor(nl[ss]) + ), + ) + if nl[ss] is not None + else None + ) + + obj.matrix = check_load_param("matrix") + obj.bias = check_load_param("bias") + obj.idt = check_load_param("idt") + return obj + + +MLP_ = make_multilayer_network(MLPLayer, nn.Layer) + + +class MLP(MLP_): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.layers = paddle.nn.LayerList(self.layers) + + forward = MLP_.call + + +EmbeddingNet = make_embedding_network(MLP, MLPLayer) + +FittingNet = make_fitting_network(EmbeddingNet, MLP, MLPLayer) + + +class NetworkCollection(DPNetworkCollection, nn.Layer): + """PyTorch implementation of NetworkCollection.""" + + NETWORK_TYPE_MAP: ClassVar[Dict[str, type]] = { + "network": MLP, + "embedding_network": EmbeddingNet, + "fitting_network": FittingNet, + } + + def __init__(self, *args, **kwargs): + # init both two base classes + DPNetworkCollection.__init__(self, *args, **kwargs) + nn.Layer.__init__(self) + self.networks = self._networks = paddle.nn.LayerList(self._networks) diff --git a/deepmd/pd/model/network/network.py b/deepmd/pd/model/network/network.py new file mode 100644 index 0000000000..577f7aed5c --- /dev/null +++ b/deepmd/pd/model/network/network.py @@ -0,0 +1,2198 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + List, + Optional, + Union, +) + +import numpy as np +import paddle +import paddle.nn as nn +import paddle.nn.functional as F + +from deepmd.pd.model.network.mlp import ( + EmbeddingNet, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.utils.version import ( + check_version_compatibility, +) + +try: + from typing import ( + Final, + ) +except ImportError: + from paddle.jit import Final + +from functools import ( + partial, +) + +import paddle.distributed.fleet + +from deepmd.dpmodel.utils.type_embed import ( + get_econf_tebd, +) +from deepmd.pd.utils.utils import ( + ActivationFn, + to_paddle_tensor, +) +from deepmd.utils.finetune import ( + get_index_between_two_maps, +) + + +def Tensor(*shape): + return paddle.empty(shape, dtype=env.GLOBAL_PD_FLOAT_PRECISION, device=env.DEVICE) + + +class Dropout(nn.Layer): + def __init__(self, p): + super().__init__() + self.p = p + + def forward(self, x, inplace: bool = False): + if self.p > 0 and self.training: + return F.dropout(x, p=self.p, training=True, inplace=inplace) + else: + return x + + +class Identity(nn.Layer): + def __init__(self): + super().__init__() + + def forward(self, x): + return x + + +class DropPath(paddle.nn.Layer): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, prob=None): + super().__init__() + self.drop_prob = prob + + def forward(self, x): + if self.drop_prob == 0.0 or not self.training: + return x + keep_prob = 1 - self.drop_prob + shape = (x.shape[0],) + (1,) * ( + x.ndim - 1 + ) # work with diff dim tensors, not just 2D ConvNets + random_tensor: paddle.Tensor = keep_prob + paddle.rand(shape, dtype=x.dtype).to( + device=x.place + ) + random_tensor.floor_() # binarize + output = x.divide(keep_prob) * random_tensor + return output + + def extra_repr(self) -> str: + return f"prob={self.drop_prob}" + + +def softmax_dropout( + input_x, dropout_prob, is_training=True, mask=None, bias=None, inplace=True +): + input_x = input_x.contiguous() + if not inplace: + input_x = input_x.clone() + if mask is not None: + input_x += mask + if bias is not None: + input_x += bias + return F.dropout(F.softmax(input_x, axis=-1), p=dropout_prob, training=is_training) + + +def checkpoint_sequential( + functions, + input_x, + enabled=True, +): + def wrap_tuple(a): + return (a,) if type(a) is not tuple else a + + def exec(func, a): + return wrap_tuple(func(*a)) + + def get_wrap_exec(func): + def wrap_exec(*a): + return exec(func, a) + + return wrap_exec + + input_x = wrap_tuple(input_x) + + is_grad_enabled = paddle.is_grad_enabled() + + if enabled and is_grad_enabled: + for func in functions: + input_x = paddle.distributed.fleet.utils.recompute( + get_wrap_exec(func), *input_x + ) + else: + for func in functions: + input_x = exec(func, input_x) + return input_x + + +class ResidualLinear(nn.Layer): + resnet: Final[int] + + def __init__(self, num_in, num_out, bavg=0.0, stddev=1.0, resnet_dt=False): + """Construct a residual linear layer. + + Args: + - num_in: Width of input tensor. + - num_out: Width of output tensor. + - resnet_dt: Using time-step in the ResNet construction. + """ + super().__init__() + self.num_in = num_in + self.num_out = num_out + self.resnet = resnet_dt + + self.matrix = self.create_parameter( + [num_in, num_out], + default_initializer=nn.initializer.Assign(Tensor(num_in, num_out)), + ) + nn.init.normal_(self.matrix.data, std=stddev / np.sqrt(num_out + num_in)) + self.bias = self.create_parameter( + (1, num_out), default_initializer=nn.initializer.Assign(Tensor(1, num_out)) + ) + nn.init.normal_(self.bias.data, mean=bavg, std=stddev) + if self.resnet: + self.idt = self.create_parameter( + (1, num_out), + default_initializer=nn.initializer.Assign(Tensor(1, num_out)), + ) + nn.init.normal_(self.idt.data, mean=1.0, std=0.001) + + def forward(self, inputs): + """Return X ?+ X*W+b.""" + xw_plus_b = paddle.matmul(inputs, self.matrix) + self.bias + hidden = paddle.tanh(xw_plus_b) + if self.resnet: + hidden = hidden * self.idt + if self.num_in == self.num_out: + return inputs + hidden + elif self.num_in * 2 == self.num_out: + return paddle.concat([inputs, inputs], axis=1) + hidden + else: + return hidden + + +class TypeFilter(nn.Layer): + use_tebd: Final[bool] + tebd_mode: Final[str] + + def __init__( + self, + offset, + length, + neuron, + return_G=False, + tebd_dim=0, + use_tebd=False, + tebd_mode="concat", + ): + """Construct a filter on the given element as neighbor. + + Args: + - offset: Element offset in the descriptor matrix. + - length: Atom count of this element. + - neuron: Number of neurons in each hidden layers of the embedding net. + """ + super().__init__() + self.offset = offset + self.length = length + self.tebd_dim = tebd_dim + self.use_tebd = use_tebd + self.tebd_mode = tebd_mode + supported_tebd_mode = ["concat", "dot", "dot_residual_s", "dot_residual_t"] + assert ( + tebd_mode in supported_tebd_mode + ), f"Unknown tebd_mode {tebd_mode}! Supported are {supported_tebd_mode}." + if use_tebd and tebd_mode == "concat": + self.neuron = [1 + tebd_dim * 2, *neuron] + else: + self.neuron = [1, *neuron] + + deep_layers = [] + for ii in range(1, len(self.neuron)): + one = ResidualLinear(self.neuron[ii - 1], self.neuron[ii]) + deep_layers.append(one) + self.deep_layers = nn.LayerList(deep_layers) + + deep_layers_t = [] + if use_tebd and tebd_mode in ["dot", "dot_residual_s", "dot_residual_t"]: + self.neuron_t = [tebd_dim * 2, *neuron] + for ii in range(1, len(self.neuron_t)): + one = ResidualLinear(self.neuron_t[ii - 1], self.neuron_t[ii]) + deep_layers_t.append(one) + self.deep_layers_t = nn.LayerList(deep_layers_t) + + self.return_G = return_G + + def forward( + self, + inputs, + atype_tebd: Optional[paddle.Tensor] = None, + nlist_tebd: Optional[paddle.Tensor] = None, + ): + """Calculate decoded embedding for each atom. + + Args: + - inputs: Descriptor matrix. Its shape is [nframes*natoms[0], len_descriptor]. + + Returns + ------- + - `paddle.Tensor`: Embedding contributed by me. Its shape is [nframes*natoms[0], 4, self.neuron[-1]]. + """ + inputs_i = inputs[:, self.offset * 4 : (self.offset + self.length) * 4] + inputs_reshape = inputs_i.reshape( + [-1, 4] + ) # shape is [nframes*natoms[0]*self.length, 4] + xyz_scatter = inputs_reshape[:, 0:1] + + # concat the tebd as input + if self.use_tebd and self.tebd_mode == "concat": + assert nlist_tebd is not None and atype_tebd is not None + nlist_tebd = nlist_tebd.reshape([-1, self.tebd_dim]) + atype_tebd = atype_tebd.reshape([-1, self.tebd_dim]) + # [nframes * nloc * nnei, 1 + tebd_dim * 2] + xyz_scatter = paddle.concat([xyz_scatter, nlist_tebd, atype_tebd], axis=1) + + for linear in self.deep_layers: + xyz_scatter = linear(xyz_scatter) + # [nframes * nloc * nnei, out_size] + + # dot the tebd output + if self.use_tebd and self.tebd_mode in [ + "dot", + "dot_residual_s", + "dot_residual_t", + ]: + assert nlist_tebd is not None and atype_tebd is not None + nlist_tebd = nlist_tebd.reshape([-1, self.tebd_dim]) + atype_tebd = atype_tebd.reshape([-1, self.tebd_dim]) + # [nframes * nloc * nnei, tebd_dim * 2] + two_side_tebd = paddle.concat([nlist_tebd, atype_tebd], axis=1) + for linear in self.deep_layers_t: + two_side_tebd = linear(two_side_tebd) + # [nframes * nloc * nnei, out_size] + if self.tebd_mode == "dot": + xyz_scatter = xyz_scatter * two_side_tebd + elif self.tebd_mode == "dot_residual_s": + xyz_scatter = xyz_scatter * two_side_tebd + xyz_scatter + elif self.tebd_mode == "dot_residual_t": + xyz_scatter = xyz_scatter * two_side_tebd + two_side_tebd + + xyz_scatter = xyz_scatter.reshape( + [-1, self.length, self.neuron[-1]] + ) # shape is [nframes*natoms[0], self.length, self.neuron[-1]] + if self.return_G: + return xyz_scatter + else: + # shape is [nframes*natoms[0], 4, self.length] + inputs_reshape = inputs_i.reshape([-1, self.length, 4]).transpose([0, 2, 1]) + return paddle.matmul(inputs_reshape, xyz_scatter) + + +class SimpleLinear(nn.Layer): + use_timestep: Final[bool] + + def __init__( + self, + num_in, + num_out, + bavg=0.0, + stddev=1.0, + use_timestep=False, + activate=None, + bias: bool = True, + ): + """Construct a linear layer. + + Args: + - num_in: Width of input tensor. + - num_out: Width of output tensor. + - use_timestep: Apply time-step to weight. + - activate: type of activate func. + """ + super().__init__() + self.num_in = num_in + self.num_out = num_out + self.use_timestep = use_timestep + self.activate = ActivationFn(activate) + + self.matrix = self.create_parameter(data=Tensor(num_in, num_out)) + nn.init.normal_(self.matrix.data, std=stddev / np.sqrt(num_out + num_in)) + if bias: + self.bias = self.create_parameter(data=Tensor(1, num_out)) + nn.init.normal_(self.bias.data, mean=bavg, std=stddev) + else: + self.bias = None + if self.use_timestep: + self.idt = self.create_parameter(data=Tensor(1, num_out)) + nn.init.normal_(self.idt.data, mean=0.1, std=0.001) + + def forward(self, inputs): + """Return X*W+b.""" + xw = paddle.matmul(inputs, self.matrix) + hidden = xw + self.bias if self.bias is not None else xw + hidden = self.activate(hidden) + if self.use_timestep: + hidden = hidden * self.idt + return hidden + + +class Linear(nn.Linear): + def __init__( + self, + d_in: int, + d_out: int, + bias: bool = True, + init: str = "default", + ): + super().__init__( + d_in, + d_out, + bias=bias, + dtype=env.GLOBAL_PD_FLOAT_PRECISION, + device=env.DEVICE, + ) + + self.use_bias = bias + + if self.use_bias: + with paddle.no_grad(): + self.bias.fill_(0) + + if init == "default": + self._trunc_normal_init(1.0) + elif init == "relu": + self._trunc_normal_init(2.0) + elif init == "glorot": + self._glorot_uniform_init() + elif init == "gating": + self._zero_init(self.use_bias) + elif init == "normal": + self._normal_init() + elif init == "final": + self._zero_init(False) + else: + raise ValueError("Invalid init method.") + + def _trunc_normal_init(self, scale=1.0): + # Constant from scipy.stats.truncnorm.std(a=-2, b=2, loc=0., scale=1.) + TRUNCATED_NORMAL_STDDEV_FACTOR = 0.87962566103423978 + _, fan_in = self.weight.shape + scale = scale / max(1, fan_in) + std = (scale**0.5) / TRUNCATED_NORMAL_STDDEV_FACTOR + nn.init.trunc_normal_(self.weight, mean=0.0, std=std) + + def _glorot_uniform_init(self): + nn.init.xavier_uniform_(self.weight, gain=1) + + def _zero_init(self, use_bias=True): + with paddle.no_grad(): + self.weight.fill_(0.0) + if use_bias: + with paddle.no_grad(): + self.bias.fill_(1.0) + + def _normal_init(self): + nn.init.kaiming_normal_(self.weight, nonlinearity="linear") + + +class Transition(nn.Layer): + def __init__(self, d_in, n, dropout=0.0): + super().__init__() + + self.d_in = d_in + self.n = n + + self.linear_1 = Linear(self.d_in, self.n * self.d_in, init="relu") + self.act = nn.GELU() + self.linear_2 = Linear(self.n * self.d_in, d_in, init="final") + self.dropout = dropout + + def _transition(self, x): + x = self.linear_1(x) + x = self.act(x) + x = F.dropout(x, p=self.dropout, training=self.training) + x = self.linear_2(x) + return x + + def forward( + self, + x: paddle.Tensor, + ) -> paddle.Tensor: + x = self._transition(x=x) + return x + + +class Embedding(nn.Embedding): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + dtype=paddle.float64, + ): + super().__init__( + num_embeddings, embedding_dim, padding_idx=padding_idx, dtype=dtype + ) + self._normal_init() + + if padding_idx is not None: + self.weight.data[self.padding_idx].zero_() + + def _normal_init(self, std=0.02): + nn.init.normal_(self.weight, mean=0.0, std=std) + + +class NonLinearHead(nn.Layer): + def __init__(self, input_dim, out_dim, activation_fn, hidden=None): + super().__init__() + hidden = input_dim if not hidden else hidden + self.linear1 = SimpleLinear(input_dim, hidden, activate=activation_fn) + self.linear2 = SimpleLinear(hidden, out_dim) + + def forward(self, x): + x = self.linear1(x) + x = self.linear2(x) + return x + + +class NonLinear(nn.Layer): + def __init__(self, input, output_size, hidden=None): + super().__init__() + + if hidden is None: + hidden = input + self.layer1 = Linear(input, hidden, init="relu") + self.layer2 = Linear(hidden, output_size, init="final") + + def forward(self, x): + x = F.linear(x, self.layer1.weight) + # x = fused_ops.bias_torch_gelu(x, self.layer1.bias) + x = nn.GELU()(x) + self.layer1.bias + x = self.layer2(x) + return x + + def zero_init(self): + nn.init.zeros_(self.layer2.weight) + nn.init.zeros_(self.layer2.bias) + + +class MaskLMHead(nn.Layer): + """Head for masked language modeling.""" + + def __init__(self, embed_dim, output_dim, activation_fn, weight=None): + super().__init__() + self.dense = SimpleLinear(embed_dim, embed_dim) + self.activation_fn = ActivationFn(activation_fn) + self.layer_norm = nn.LayerNorm(embed_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION) + + if weight is None: + weight = nn.Linear( + embed_dim, output_dim, bias=False, dtype=env.GLOBAL_PD_FLOAT_PRECISION + ).weight + self.weight = weight + self.bias = self.create_parameter( + paddle.zeros(output_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION) # pylint: disable=no-explicit-dtype,no-explicit-device + ) + + def forward( + self, features, masked_tokens: Optional[paddle.Tensor] = None, **kwargs + ): + # Only project the masked tokens while training, + # saves both memory and computation + if masked_tokens is not None: + features = features[masked_tokens, :] + + x = self.dense(features) + x = self.activation_fn(x) + x = self.layer_norm(x) + # project back to size of vocabulary with bias + x = F.linear(x, self.weight) + self.bias + return x + + +class ResidualDeep(nn.Layer): + def __init__( + self, type_id, embedding_width, neuron, bias_atom_e, out_dim=1, resnet_dt=False + ): + """Construct a filter on the given element as neighbor. + + Args: + - typei: Element ID. + - embedding_width: Embedding width per atom. + - neuron: Number of neurons in each hidden layers of the embedding net. + - resnet_dt: Using time-step in the ResNet construction. + """ + super().__init__() + self.type_id = type_id + self.neuron = [embedding_width, *neuron] + self.out_dim = out_dim + + deep_layers = [] + for ii in range(1, len(self.neuron)): + one = SimpleLinear( + num_in=self.neuron[ii - 1], + num_out=self.neuron[ii], + use_timestep=( + resnet_dt and ii > 1 and self.neuron[ii - 1] == self.neuron[ii] + ), + activate="tanh", + ) + deep_layers.append(one) + self.deep_layers = nn.LayerList(deep_layers) + if not env.ENERGY_BIAS_TRAINABLE: + bias_atom_e = 0 + self.final_layer = SimpleLinear(self.neuron[-1], self.out_dim, bias_atom_e) + + def forward(self, inputs): + """Calculate decoded embedding for each atom. + + Args: + - inputs: Embedding net output per atom. Its shape is [nframes*nloc, self.embedding_width]. + + Returns + ------- + - `paddle.Tensor`: Output layer with shape [nframes*nloc, self.neuron[-1]]. + """ + outputs = inputs + for idx, linear in enumerate(self.deep_layers): + if idx > 0 and linear.num_in == linear.num_out: + outputs = outputs + linear(outputs) + else: + outputs = linear(outputs) + outputs = self.final_layer(outputs) + return outputs + + +class TypeEmbedNet(nn.Layer): + def __init__( + self, + type_nums, + embed_dim, + bavg=0.0, + stddev=1.0, + precision="default", + seed: Optional[Union[int, List[int]]] = None, + use_econf_tebd=False, + use_tebd_bias: bool = False, + type_map=None, + ): + """Construct a type embedding net.""" + super().__init__() + self.type_nums = type_nums + self.embed_dim = embed_dim + self.bavg = bavg + self.stddev = stddev + self.use_econf_tebd = use_econf_tebd + self.use_tebd_bias = use_tebd_bias + self.type_map = type_map + self.embedding = TypeEmbedNetConsistent( + ntypes=self.type_nums, + neuron=[self.embed_dim], + padding=True, + activation_function="Linear", + use_econf_tebd=use_econf_tebd, + use_tebd_bias=use_tebd_bias, + type_map=type_map, + precision=precision, + seed=seed, + ) + # nn.init.normal_(self.embedding.weight[:-1], mean=bavg, std=stddev) + + def forward(self, atype): + """ + Args: + atype: Type of each input, [nframes, nloc] or [nframes, nloc, nnei]. + + Returns + ------- + type_embedding: + + """ + return self.embedding(atype.place)[atype] + + def share_params(self, base_class, shared_level, resume=False): + """ + Share the parameters of self to the base_class with shared_level during multitask training. + If not start from checkpoint (resume is False), + some seperated parameters (e.g. mean and stddev) will be re-calculated across different classes. + """ + assert ( + self.__class__ == base_class.__class__ + ), "Only TypeEmbedNet of the same type can share params!" + if shared_level == 0: + # the following will successfully link all the params except buffers, which need manually link. + for item in self._modules: + self._modules[item] = base_class._modules[item] + else: + raise NotImplementedError + + def change_type_map( + self, type_map: List[str], model_with_new_type_stat=None + ) -> None: + """Change the type related params to new ones, according to `type_map` and the original one in the model. + If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. + """ + self.embedding.change_type_map(type_map=type_map) + + +class TypeEmbedNetConsistent(nn.Layer): + r"""Type embedding network that is consistent with other backends. + + Parameters + ---------- + ntypes : int + Number of atom types + neuron : list[int] + Number of neurons in each hidden layers of the embedding net + resnet_dt + Time-step `dt` in the resnet construction: y = x + dt * \phi (Wx + b) + activation_function + The activation function in the embedding net. Supported options are |ACTIVATION_FN| + precision + The precision of the embedding net parameters. Supported options are |PRECISION| + trainable + If the weights of embedding net are trainable. + seed + Random seed for initializing the network parameters. + padding + Concat the zero padding to the output, as the default embedding of empty type. + use_econf_tebd: bool, Optional + Whether to use electronic configuration type embedding. + use_tebd_bias : bool, Optional + Whether to use bias in the type embedding layer. + type_map: List[str], Optional + A list of strings. Give the name to each type of atoms. + """ + + def __init__( + self, + *, + ntypes: int, + neuron: List[int], + resnet_dt: bool = False, + activation_function: str = "tanh", + precision: str = "default", + trainable: bool = True, + seed: Optional[Union[int, List[int]]] = None, + padding: bool = False, + use_econf_tebd: bool = False, + use_tebd_bias: bool = False, + type_map: Optional[List[str]] = None, + ): + """Construct a type embedding net.""" + super().__init__() + self.ntypes = ntypes + self.neuron = neuron + self.seed = seed + self.resnet_dt = resnet_dt + self.precision = precision + self.prec = env.PRECISION_DICT[self.precision] + self.activation_function = str(activation_function) + self.trainable = trainable + self.padding = padding + self.use_econf_tebd = use_econf_tebd + self.use_tebd_bias = use_tebd_bias + self.type_map = type_map + self.econf_tebd = None + embed_input_dim = ntypes + if self.use_econf_tebd: + econf_tebd, embed_input_dim = get_econf_tebd( + self.type_map, precision=self.precision + ) + self.econf_tebd = to_paddle_tensor(econf_tebd) + self.embedding_net = EmbeddingNet( + embed_input_dim, + self.neuron, + self.activation_function, + self.resnet_dt, + self.precision, + self.seed, + bias=self.use_tebd_bias, + ) + for param in self.parameters(): + param.stop_gradient = not trainable + + def forward(self, device: str): + """Caulate type embedding network. + + Returns + ------- + type_embedding: paddle.Tensor + Type embedding network. + """ + if not self.use_econf_tebd: + embed = self.embedding_net( + paddle.eye(self.ntypes, dtype=self.prec).to(device=device) + ) + else: + assert self.econf_tebd is not None + embed = self.embedding_net(self.econf_tebd.to(device)) + if self.padding: + embed = paddle.concat( + [ + embed, + paddle.zeros(1, embed.shape[1], dtype=self.prec).to(device=device), + ] + ) + return embed + + def change_type_map( + self, type_map: List[str], model_with_new_type_stat=None + ) -> None: + """Change the type related params to new ones, according to `type_map` and the original one in the model. + If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. + """ + assert ( + self.type_map is not None + ), "'type_map' must be defined when performing type changing!" + remap_index, has_new_type = get_index_between_two_maps(self.type_map, type_map) + if not self.use_econf_tebd: + do_resnet = self.neuron[0] in [ + self.ntypes, + self.ntypes * 2, + len(type_map), + len(type_map) * 2, + ] + assert ( + not do_resnet or self.activation_function == "Linear" + ), "'activation_function' must be 'Linear' when performing type changing on resnet structure!" + first_layer_matrix = self.embedding_net.layers[0].matrix.data + eye_vector = paddle.eye( + self.ntypes, dtype=self.prec, device=first_layer_matrix.place + ) + # preprocess for resnet connection + if self.neuron[0] == self.ntypes: + first_layer_matrix += eye_vector + elif self.neuron[0] == self.ntypes * 2: + first_layer_matrix += paddle.concat([eye_vector, eye_vector], axis=-1) + + # randomly initialize params for the unseen types + if has_new_type: + extend_type_params = paddle.rand( + [len(type_map), first_layer_matrix.shape[-1]], + device=first_layer_matrix.place, + dtype=first_layer_matrix.dtype, + ) + first_layer_matrix = paddle.concat( + [first_layer_matrix, extend_type_params], axis=0 + ) + + first_layer_matrix = first_layer_matrix[remap_index] + new_ntypes = len(type_map) + eye_vector = paddle.eye( + new_ntypes, dtype=self.prec, device=first_layer_matrix.place + ) + + if self.neuron[0] == new_ntypes: + first_layer_matrix -= eye_vector + elif self.neuron[0] == new_ntypes * 2: + first_layer_matrix -= paddle.concat([eye_vector, eye_vector], axis=-1) + + self.embedding_net.layers[0].num_in = new_ntypes + self.embedding_net.layers[0].matrix = self.create_parameter( + data=first_layer_matrix + ) + else: + econf_tebd, embed_input_dim = get_econf_tebd( + type_map, precision=self.precision + ) + self.econf_tebd = to_paddle_tensor(econf_tebd) + self.type_map = type_map + self.ntypes = len(type_map) + + @classmethod + def deserialize(cls, data: dict): + """Deserialize the model. + + Parameters + ---------- + data : dict + The serialized data + + Returns + ------- + TypeEmbedNetConsistent + The deserialized model + """ + data = data.copy() + check_version_compatibility(data.pop("@version", 1), 2, 1) + data_cls = data.pop("@class") + assert data_cls == "TypeEmbedNet", f"Invalid class {data_cls}" + + embedding_net = EmbeddingNet.deserialize(data.pop("embedding")) + # compat with version 1 + if "use_tebd_bias" not in data: + data["use_tebd_bias"] = True + type_embedding_net = cls(**data) + type_embedding_net.embedding_net = embedding_net + return type_embedding_net + + def serialize(self) -> dict: + """Serialize the model. + + Returns + ------- + dict + The serialized data + """ + return { + "@class": "TypeEmbedNet", + "@version": 2, + "ntypes": self.ntypes, + "neuron": self.neuron, + "resnet_dt": self.resnet_dt, + "precision": self.precision, + "activation_function": self.activation_function, + "trainable": self.trainable, + "padding": self.padding, + "use_econf_tebd": self.use_econf_tebd, + "use_tebd_bias": self.use_tebd_bias, + "type_map": self.type_map, + "embedding": self.embedding_net.serialize(), + } + + +# @paddle.jit.script +def gaussian(x, mean, std: float): + pi = 3.14159 + a = (2 * pi) ** 0.5 + return paddle.exp(-0.5 * (((x - mean) / std) ** 2)) / (a * std) + + +class GaussianKernel(nn.Layer): + def __init__(self, K=128, num_pair=512, std_width=1.0, start=0.0, stop=9.0): + super().__init__() + self.K = K + std_width = std_width + start = start + stop = stop + mean = paddle.linspace(start, stop, K, dtype=env.GLOBAL_PD_FLOAT_PRECISION) # pylint: disable=no-explicit-device + self.std = (std_width * (mean[1] - mean[0])).item() + self.register_buffer("mean", mean) + self.mul = Embedding( + num_pair + 1, 1, padding_idx=num_pair, dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + self.bias = Embedding( + num_pair + 1, 1, padding_idx=num_pair, dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + nn.init.constant_(self.bias.weight, 0) + nn.init.constant_(self.mul.weight, 1.0) + + def forward(self, x, atom_pair): + mul = self.mul(atom_pair).abs().sum(axis=-2) + bias = self.bias(atom_pair).sum(axis=-2) + x = mul * x.unsqueeze(-1) + bias + # [nframes, nloc, nnei, K] + x = x.expand(-1, -1, -1, self.K) + mean = self.mean.reshape([-1]) + return gaussian(x, mean, self.std) + + +class GaussianEmbedding(nn.Layer): + def __init__( + self, + rcut, + kernel_num, + num_pair, + embed_dim, + pair_embed_dim, + sel, + ntypes, + atomic_sum_gbf, + ): + """Construct a gaussian kernel based embedding of pair representation. + + Args: + rcut: Radial cutoff. + kernel_num: Number of gaussian kernels. + num_pair: Number of different pairs. + embed_dim: Dimension of atomic representation. + pair_embed_dim: Dimension of pair representation. + sel: Number of neighbors. + ntypes: Number of atom types. + """ + super().__init__() + self.gbf = GaussianKernel(K=kernel_num, num_pair=num_pair, stop=rcut) + self.gbf_proj = NonLinear(kernel_num, pair_embed_dim) + self.embed_dim = embed_dim + self.pair_embed_dim = pair_embed_dim + self.atomic_sum_gbf = atomic_sum_gbf + if self.atomic_sum_gbf: + if kernel_num != self.embed_dim: + self.edge_proj = paddle.nn.Linear( + kernel_num, self.embed_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + else: + self.edge_proj = None + self.ntypes = ntypes + self.nnei = sel + + def forward(self, coord_selected, atom_feature, edge_type_2dim, edge_feature): + ## local cluster forward + """Calculate decoded embedding for each atom. + Args: + coord_selected: Clustered atom coordinates with shape [nframes*nloc, natoms, 3]. + atom_feature: Previous calculated atomic features with shape [nframes*nloc, natoms, embed_dim]. + edge_type_2dim: Edge index for gbf calculation with shape [nframes*nloc, natoms, natoms, 2]. + edge_feature: Previous calculated edge features with shape [nframes*nloc, natoms, natoms, pair_dim]. + + Returns + ------- + atom_feature: Updated atomic features with shape [nframes*nloc, natoms, embed_dim]. + attn_bias: Updated edge features as attention bias with shape [nframes*nloc, natoms, natoms, pair_dim]. + delta_pos: Delta position for force/vector prediction with shape [nframes*nloc, natoms, natoms, 3]. + """ + ncluster, natoms, _ = coord_selected.shape + # ncluster x natoms x natoms x 3 + delta_pos = coord_selected.unsqueeze(1) - coord_selected.unsqueeze(2) + # (ncluster x natoms x natoms + dist = delta_pos.norm(axis=-1).reshape([-1, natoms, natoms]) + # [ncluster, natoms, natoms, K] + gbf_feature = self.gbf(dist, edge_type_2dim) + if self.atomic_sum_gbf: + edge_features = gbf_feature + # [ncluster, natoms, K] + sum_edge_features = edge_features.sum(axis=-2) + if self.edge_proj is not None: + sum_edge_features = self.edge_proj(sum_edge_features) + # [ncluster, natoms, embed_dim] + atom_feature = atom_feature + sum_edge_features + + # [ncluster, natoms, natoms, pair_dim] + gbf_result = self.gbf_proj(gbf_feature) + + attn_bias = gbf_result + edge_feature + return atom_feature, attn_bias, delta_pos + + +class NeighborWiseAttention(nn.Layer): + def __init__( + self, + layer_num, + nnei, + embed_dim, + hidden_dim, + dotr=False, + do_mask=False, + post_ln=True, + ffn=False, + ffn_embed_dim=1024, + activation="tanh", + scaling_factor=1.0, + head_num=1, + normalize=True, + temperature=None, + smooth=True, + ): + """Construct a neighbor-wise attention net.""" + super().__init__() + self.layer_num = layer_num + attention_layers = [] + for i in range(self.layer_num): + attention_layers.append( + NeighborWiseAttentionLayer( + nnei, + embed_dim, + hidden_dim, + dotr=dotr, + do_mask=do_mask, + post_ln=post_ln, + ffn=ffn, + ffn_embed_dim=ffn_embed_dim, + activation=activation, + scaling_factor=scaling_factor, + head_num=head_num, + normalize=normalize, + temperature=temperature, + smooth=smooth, + ) + ) + self.attention_layers = nn.LayerList(attention_layers) + + def forward( + self, + input_G, + nei_mask, + input_r: Optional[paddle.Tensor] = None, + sw: Optional[paddle.Tensor] = None, + ): + """ + Args: + input_G: Input G, [nframes * nloc, nnei, embed_dim]. + nei_mask: neighbor mask, [nframes * nloc, nnei]. + input_r: normalized radial, [nframes, nloc, nei, 3]. + + Returns + ------- + out: Output G, [nframes * nloc, nnei, embed_dim] + + """ + out = input_G + # https://github.com/pytorch/pytorch/issues/39165#issuecomment-635472592 + for layer in self.attention_layers: + out = layer(out, nei_mask, input_r=input_r, sw=sw) + return out + + +class NeighborWiseAttentionLayer(nn.Layer): + ffn: Final[bool] + + def __init__( + self, + nnei, + embed_dim, + hidden_dim, + dotr=False, + do_mask=False, + post_ln=True, + ffn=False, + ffn_embed_dim=1024, + activation="tanh", + scaling_factor=1.0, + head_num=1, + normalize=True, + temperature=None, + smooth=True, + ): + """Construct a neighbor-wise attention layer.""" + super().__init__() + self.nnei = nnei + self.embed_dim = embed_dim + self.hidden_dim = hidden_dim + self.dotr = dotr + self.do_mask = do_mask + self.post_ln = post_ln + self.ffn = ffn + self.smooth = smooth + self.attention_layer = GatedSelfAttetion( + nnei, + embed_dim, + hidden_dim, + dotr=dotr, + do_mask=do_mask, + scaling_factor=scaling_factor, + head_num=head_num, + normalize=normalize, + temperature=temperature, + smooth=smooth, + ) + self.attn_layer_norm = nn.LayerNorm( + self.embed_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION, device=env.place + ) + if self.ffn: + self.ffn_embed_dim = ffn_embed_dim + self.fc1 = nn.Linear( + self.embed_dim, self.ffn_embed_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + self.activation_fn = ActivationFn(activation) + self.fc2 = nn.Linear( + self.ffn_embed_dim, self.embed_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + self.final_layer_norm = nn.LayerNorm( + self.embed_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + + def forward( + self, + x, + nei_mask, + input_r: Optional[paddle.Tensor] = None, + sw: Optional[paddle.Tensor] = None, + ): + residual = x + if not self.post_ln: + x = self.attn_layer_norm(x) + x = self.attention_layer(x, nei_mask, input_r=input_r, sw=sw) + x = residual + x + if self.post_ln: + x = self.attn_layer_norm(x) + if self.ffn: + residual = x + if not self.post_ln: + x = self.final_layer_norm(x) + x = self.fc1(x) + x = self.activation_fn(x) + x = self.fc2(x) + x = residual + x + if self.post_ln: + x = self.final_layer_norm(x) + return x + + +class GatedSelfAttetion(nn.Layer): + def __init__( + self, + nnei, + embed_dim, + hidden_dim, + dotr=False, + do_mask=False, + scaling_factor=1.0, + head_num=1, + normalize=True, + temperature=None, + bias=True, + smooth=True, + ): + """Construct a neighbor-wise attention net.""" + super().__init__() + self.nnei = nnei + self.embed_dim = embed_dim + self.hidden_dim = hidden_dim + self.head_num = head_num + self.dotr = dotr + self.do_mask = do_mask + if temperature is None: + self.scaling = (self.hidden_dim * scaling_factor) ** -0.5 + else: + self.scaling = temperature + self.normalize = normalize + self.in_proj = SimpleLinear( + embed_dim, + hidden_dim * 3, + bavg=0.0, + stddev=1.0, + use_timestep=False, + bias=bias, + ) + self.out_proj = SimpleLinear( + hidden_dim, embed_dim, bavg=0.0, stddev=1.0, use_timestep=False, bias=bias + ) + self.smooth = smooth + + def forward( + self, + query, + nei_mask, + input_r: Optional[paddle.Tensor] = None, + sw: Optional[paddle.Tensor] = None, + attnw_shift: float = 20.0, + ): + """ + Args: + query: input G, [nframes * nloc, nnei, embed_dim]. + nei_mask: neighbor mask, [nframes * nloc, nnei]. + input_r: normalized radial, [nframes, nloc, nei, 3]. + + Returns + ------- + type_embedding: + + """ + q, k, v = self.in_proj(query).chunk(3, axis=-1) + # [nframes * nloc, nnei, hidden_dim] + q = q.reshape([-1, self.nnei, self.hidden_dim]) + k = k.reshape([-1, self.nnei, self.hidden_dim]) + v = v.reshape([-1, self.nnei, self.hidden_dim]) + if self.normalize: + q = F.normalize(q, axis=-1) + k = F.normalize(k, axis=-1) + v = F.normalize(v, axis=-1) + q = q * self.scaling + k = k.transpose(1, 2) + # [nframes * nloc, nnei, nnei] + attn_weights = paddle.bmm(q, k) + # [nframes * nloc, nnei] + nei_mask = nei_mask.reshape([-1, self.nnei]) + if self.smooth: + # [nframes * nloc, nnei] + assert sw is not None + sw = sw.reshape([-1, self.nnei]) + attn_weights = (attn_weights + attnw_shift) * sw[:, :, None] * sw[ + :, None, : + ] - attnw_shift + else: + attn_weights = attn_weights.masked_fill( + ~nei_mask.unsqueeze(1), float("-inf") + ) + attn_weights = F.softmax(attn_weights, axis=-1) + attn_weights = attn_weights.masked_fill(~nei_mask.unsqueeze(-1), 0.0) + if self.smooth: + assert sw is not None + attn_weights = attn_weights * sw[:, :, None] * sw[:, None, :] + if self.dotr: + assert input_r is not None, "input_r must be provided when dotr is True!" + angular_weight = paddle.bmm(input_r, input_r.transpose(1, 2)) + attn_weights = attn_weights * angular_weight + o = paddle.bmm(attn_weights, v) + output = self.out_proj(o) + return output + + +class LocalSelfMultiheadAttention(nn.Layer): + def __init__(self, feature_dim, attn_head, scaling_factor=1.0): + super().__init__() + self.feature_dim = feature_dim + self.attn_head = attn_head + self.head_dim = feature_dim // attn_head + assert ( + feature_dim % attn_head == 0 + ), f"feature_dim {feature_dim} must be divided by attn_head {attn_head}!" + self.scaling = (self.head_dim * scaling_factor) ** -0.5 + self.in_proj = SimpleLinear(self.feature_dim, self.feature_dim * 3) + # TODO debug + # self.out_proj = SimpleLinear(self.feature_dim, self.feature_dim) + + def forward( + self, + query, + attn_bias: Optional[paddle.Tensor] = None, + nlist_mask: Optional[paddle.Tensor] = None, + nlist: Optional[paddle.Tensor] = None, + return_attn=True, + ): + nframes, nloc, feature_dim = query.size() + _, _, nnei = nlist.size() + assert feature_dim == self.feature_dim + # [nframes, nloc, feature_dim] + q, k, v = self.in_proj(query).chunk(3, axis=-1) + # [nframes * attn_head * nloc, 1, head_dim] + q = ( + q.reshape([nframes, nloc, self.attn_head, self.head_dim]) + .transpose(1, 2) + .contiguous() + .reshape([nframes * self.attn_head * nloc, 1, self.head_dim]) + * self.scaling + ) + # [nframes, nloc, feature_dim] --> [nframes, nloc + 1, feature_dim] + # with nlist [nframes, nloc, nnei] --> [nframes, nloc, nnei, feature_dim] + # padding = paddle.zeros(feature_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION).to(k.place) + # k = paddle.concat([k, padding.unsqueeze(0).unsqueeze(1)], axis=1) + # v = paddle.concat([v, padding.unsqueeze(0).unsqueeze(1)], axis=1) + + # [nframes, nloc * nnei, feature_dim] + index = nlist.reshape([nframes, -1]).unsqueeze(-1).expand(-1, -1, feature_dim) + k = paddle.gather(k, axis=1, index=index) + # [nframes, nloc * nnei, feature_dim] + v = paddle.gather(v, axis=1, index=index) + # [nframes * attn_head * nloc, nnei, head_dim] + k = ( + k.reshape([nframes, nloc, nnei, self.attn_head, self.head_dim]) + .transpose([0, 3, 1, 2, 4]) + .contiguous() + .reshape([nframes * self.attn_head * nloc, nnei, self.head_dim]) + ) + v = ( + v.reshape([nframes, nloc, nnei, self.attn_head, self.head_dim]) + .transpose([0, 3, 1, 2, 4]) + .contiguous() + .reshape([nframes * self.attn_head * nloc, nnei, self.head_dim]) + ) + # [nframes * attn_head * nloc, 1, nnei] + attn_weights = paddle.bmm(q, k.transpose(1, 2)) + # maskfill + # [nframes, attn_head, nloc, nnei] + attn_weights = attn_weights.reshape( + [nframes, self.attn_head, nloc, nnei] + ).masked_fill(~nlist_mask.unsqueeze(1), float("-inf")) + # add bias + if return_attn: + attn_weights = attn_weights + attn_bias + # softmax + # [nframes * attn_head * nloc, 1, nnei] + attn = F.softmax(attn_weights, axis=-1).reshape( + [nframes * self.attn_head * nloc, 1, nnei] + ) + # bmm + # [nframes * attn_head * nloc, 1, head_dim] + o = paddle.bmm(attn, v) + assert list(o.size()) == [nframes * self.attn_head * nloc, 1, self.head_dim] + # [nframes, nloc, feature_dim] + o = ( + o.reshape([nframes, self.attn_head, nloc, self.head_dim]) + .transpose(1, 2) + .contiguous() + .reshape([nframes, nloc, self.feature_dim]) + ) + # out + ## TODO debug: + # o = self.out_proj(o) + if not return_attn: + return o + else: + return o, attn_weights, attn + + +class NodeTaskHead(nn.Layer): + def __init__( + self, + embed_dim: int, + pair_dim: int, + num_head: int, + ): + super().__init__() + self.layer_norm = nn.LayerNorm(embed_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION) + self.pair_norm = nn.LayerNorm(pair_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION) + self.embed_dim = embed_dim + self.q_proj = Linear(embed_dim, embed_dim, bias=False, init="glorot") + self.k_proj = Linear(embed_dim, embed_dim, bias=False, init="glorot") + self.v_proj = Linear(embed_dim, embed_dim, bias=False, init="glorot") + self.num_heads = num_head + self.head_dim = embed_dim // num_head + self.scaling = self.head_dim**-0.5 + self.force_proj = Linear(embed_dim, 1, init="final", bias=False) + self.linear_bias = Linear(pair_dim, num_head) + self.dropout = 0.1 + + def zero_init(self): + nn.init.zeros_(self.force_proj.weight) + + def forward( + self, + query: Tensor, + pair: Tensor, + delta_pos: Tensor, + attn_mask: Tensor = None, + ) -> Tensor: + ncluster, natoms, _ = query.size() + query = self.layer_norm(query) + # [ncluster, natoms, natoms, pair_dim] + pair = self.pair_norm(pair) + + # [ncluster, attn_head, natoms, head_dim] + q = ( + self.q_proj(query) + .reshape([ncluster, natoms, self.num_heads, -1]) + .transpose(1, 2) + * self.scaling + ) + # [ncluster, attn_head, natoms, head_dim] + k = ( + self.k_proj(query) + .reshape([ncluster, natoms, self.num_heads, -1]) + .transpose(1, 2) + ) + v = ( + self.v_proj(query) + .reshape([ncluster, natoms, self.num_heads, -1]) + .transpose(1, 2) + ) + # [ncluster, attn_head, natoms, natoms] + attn = q @ k.transpose(-1, -2) + del q, k + # [ncluster, attn_head, natoms, natoms] + bias = self.linear_bias(pair).transpose([0, 3, 1, 2]).contiguous() + + # [ncluster, attn_head, natoms, natoms] + attn_probs = softmax_dropout( + attn, + self.dropout, + self.training, + mask=attn_mask, + bias=bias.contiguous(), + ).reshape([ncluster, self.num_heads, natoms, natoms]) + + # delta_pos: [ncluster, natoms, natoms, 3] + # [ncluster, attn_head, natoms, natoms, 3] + rot_attn_probs = attn_probs.unsqueeze(-1) * delta_pos.unsqueeze(1).type_as( + attn_probs + ) + # [ncluster, attn_head, 3, natoms, natoms] + rot_attn_probs = rot_attn_probs.transpose([0, 1, 4, 2, 3]) + # [ncluster, attn_head, 3, natoms, head_dim] + x = rot_attn_probs @ v.unsqueeze(2) + # [ncluster, natoms, 3, embed_dim] + x = x.transpose([0, 3, 2, 1, 4]).contiguous().reshape([ncluster, natoms, 3, -1]) + cur_force = self.force_proj(x).reshape([ncluster, natoms, 3]) + return cur_force + + +class EnergyHead(nn.Layer): + def __init__( + self, + input_dim, + output_dim, + ): + super().__init__() + self.layer_norm = nn.LayerNorm(input_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION) + self.linear_in = Linear(input_dim, input_dim, init="relu") + + self.linear_out = Linear(input_dim, output_dim, bias=True, init="final") + + def forward(self, x): + x = x.type(self.linear_in.weight.dtype) + x = F.gelu(self.layer_norm(self.linear_in(x))) + x = self.linear_out(x) + return x + + +class OuterProduct(nn.Layer): + def __init__(self, d_atom, d_pair, d_hid=32): + super().__init__() + + self.d_atom = d_atom + self.d_pair = d_pair + self.d_hid = d_hid + + self.linear_in = nn.Linear( + d_atom, d_hid * 2, dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + self.linear_out = nn.Linear( + d_hid**2, d_pair, dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + self.act = nn.GELU() + + def _opm(self, a, b): + # [nframes, nloc, d] + nframes, nloc, d = a.shape + a = a.reshape([nframes, nloc, 1, d, 1]) + b = b.reshape([nframes, 1, nloc, 1, d]) + # [nframes, nloc, nloc, d, d] + outer = a * b + outer = outer.reshape([outer.shape[:-2] + (-1,)]) + outer = self.linear_out(outer) + return outer + + def forward( + self, + m: paddle.Tensor, + nlist: paddle.Tensor, + op_mask: float, + op_norm: float, + ) -> paddle.Tensor: + ab = self.linear_in(m) + ab = ab * op_mask + a, b = ab.chunk(2, axis=-1) + # [ncluster, natoms, natoms, d_pair] + z = self._opm(a, b) + z *= op_norm + return z + + +class Attention(nn.Layer): + def __init__( + self, + q_dim: int, + k_dim: int, + v_dim: int, + head_dim: int, + num_heads: int, + gating: bool = False, + dropout: float = 0.0, + ): + super().__init__() + + self.num_heads = num_heads + self.head_dim = head_dim + total_dim = head_dim * self.num_heads + self.total_dim = total_dim + self.q_dim = q_dim + self.gating = gating + self.linear_q = Linear(q_dim, total_dim, bias=False, init="glorot") + self.linear_k = Linear(k_dim, total_dim, bias=False, init="glorot") + self.linear_v = Linear(v_dim, total_dim, bias=False, init="glorot") + self.linear_o = Linear(total_dim, q_dim, init="final") + self.linear_g = None + if self.gating: + self.linear_g = Linear(q_dim, total_dim, init="gating") + # precompute the 1/sqrt(head_dim) + self.norm = head_dim**-0.5 + self.dropout = dropout + + def forward( + self, + q: paddle.Tensor, + k: paddle.Tensor, + v: paddle.Tensor, + bias: paddle.Tensor, + mask: paddle.Tensor = None, + ) -> paddle.Tensor: + nframes, nloc, embed_dim = q.size() + g = None + if self.linear_g is not None: + # gating, use raw query input + # [nframes, nloc, total_dim] + g = self.linear_g(q) + # [nframes, nloc, total_dim] + q = self.linear_q(q) + q *= self.norm + # [nframes, nloc, total_dim] + k = self.linear_k(k) + # [nframes, nloc, total_dim] + v = self.linear_v(v) + # global + # q [nframes, h, nloc, d] + # k [nframes, h, nloc, d] + # v [nframes, h, nloc, d] + # attn [nframes, h, nloc, nloc] + # o [nframes, h, nloc, d] + + # [nframes, h, nloc, d] + q = ( + q.reshape([q.shape[:-1] + (self.num_heads, -1)]) + .transpose(-2, -3) + .contiguous() + ) + k = ( + k.reshape([k.shape[:-1] + (self.num_heads, -1)]) + .transpose(-2, -3) + .contiguous() + ) + v = v.reshape([v.shape[:-1] + (self.num_heads, -1)]).transpose(-2, -3) + # [nframes, h, nloc, nloc] + attn = paddle.matmul(q, k.transpose(-1, -2)) + del q, k + # [nframes, h, nloc, nloc] + attn = softmax_dropout(attn, self.dropout, self.training, mask=mask, bias=bias) + # [nframes, h, nloc, d] + o = paddle.matmul(attn, v) + del attn, v + + # local + # q [nframes, h, nloc, 1, d] + # k [nframes, h, nloc, nnei, d] + # v [nframes, h, nloc, nnei, d] + # attn [nframes, h, nloc, nnei] + # o [nframes, h, nloc, d] + + assert list(o.size()) == [nframes, self.num_heads, nloc, self.head_dim] + # [nframes, nloc, total_dim] + o = o.transpose(-2, -3).contiguous() + o = o.reshape([*o.shape[:-2], -1]) + + if g is not None: + o = paddle.sigmoid(g) * o + + # merge heads + o = self.linear_o(o) + return o + + +class AtomAttention(nn.Layer): + def __init__( + self, + q_dim: int, + k_dim: int, + v_dim: int, + pair_dim: int, + head_dim: int, + num_heads: int, + gating: bool = False, + dropout: float = 0.0, + ): + super().__init__() + + self.mha = Attention( + q_dim, k_dim, v_dim, head_dim, num_heads, gating=gating, dropout=dropout + ) + self.layer_norm = nn.LayerNorm(pair_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION) + self.linear_bias = Linear(pair_dim, num_heads) + + def forward( + self, + q: paddle.Tensor, + k: paddle.Tensor, + v: paddle.Tensor, + nlist: paddle.Tensor, + pair: paddle.Tensor, + mask: paddle.Tensor = None, + ) -> paddle.Tensor: + pair = self.layer_norm(pair) + bias = self.linear_bias(pair).transpose([0, 3, 1, 2]).contiguous() + return self.mha(q, k, v, bias=bias, mask=mask) + + +class TriangleMultiplication(nn.Layer): + def __init__(self, d_pair, d_hid): + super().__init__() + + self.linear_ab_p = Linear(d_pair, d_hid * 2) + self.linear_ab_g = Linear(d_pair, d_hid * 2, init="gating") + + self.linear_g = Linear(d_pair, d_pair, init="gating") + self.linear_z = Linear(d_hid, d_pair, init="final") + + self.layer_norm_out = nn.LayerNorm(d_hid, dtype=env.GLOBAL_PD_FLOAT_PRECISION) + + def forward( + self, + z: paddle.Tensor, + mask: Optional[paddle.Tensor] = None, + ) -> paddle.Tensor: + # z : [nframes, nloc, nloc, pair_dim] + + # [nframes, nloc, nloc, pair_dim] + g = self.linear_g(z) + if self.training: + ab = self.linear_ab_p(z) * paddle.sigmoid(self.linear_ab_g(z)) + else: + ab = self.linear_ab_p(z) + ab *= paddle.sigmoid(self.linear_ab_g(z)) + # [nframes, nloc, nloc, d] + a, b = paddle.chunk(ab, 2, axis=-1) + del z, ab + + # [nframes, d, nloc_i, nloc_k] row not trans + a1 = a.transpose([0, 3, 1, 2]) + # [nframes, d, nloc_k, nloc_j(i)] trans + b1 = b.transpose(-1, -3) + # [nframes, d, nloc_i, nloc_j] + x = paddle.matmul(a1, b1) + del a1, b1 + + # [nframes, d, nloc_k, nloc_j(i)] not trans + b2 = b.transpose([0, 3, 1, 2]) + # [nframes, d, nloc_i, nloc_k] col trans # check TODO + a2 = a.transpose(-1, -3) + + # [nframes, d, nloc_i, nloc_j] + x = x + paddle.matmul(a2, b2) + del a, b, a2, b2 + + # [nframes, nloc_i, nloc_j, d] + x = x.transpose([0, 2, 3, 1]) + + x = self.layer_norm_out(x) + x = self.linear_z(x) + return g * x + + +class EvoformerEncoderLayer(nn.Layer): + def __init__( + self, + feature_dim: int = 768, + ffn_dim: int = 2048, + attn_head: int = 8, + activation_fn: str = "gelu", + post_ln: bool = False, + ): + super().__init__() + self.feature_dim = feature_dim + self.ffn_dim = ffn_dim + self.attn_head = attn_head + self.activation_fn = ( + ActivationFn(activation_fn) if activation_fn is not None else None + ) + self.post_ln = post_ln + self.self_attn_layer_norm = nn.LayerNorm( + self.feature_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + + self.self_attn = LocalSelfMultiheadAttention( + self.feature_dim, + self.attn_head, + ) + self.final_layer_norm = nn.LayerNorm( + self.feature_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + self.fc1 = SimpleLinear(self.feature_dim, self.ffn_dim) + self.fc2 = SimpleLinear(self.ffn_dim, self.feature_dim) + + def forward( + self, + x, + attn_bias: Optional[paddle.Tensor] = None, + nlist_mask: Optional[paddle.Tensor] = None, + nlist: Optional[paddle.Tensor] = None, + return_attn=True, + ): + residual = x + if not self.post_ln: + x = self.self_attn_layer_norm(x) + x = self.self_attn( + query=x, + attn_bias=attn_bias, + nlist_mask=nlist_mask, + nlist=nlist, + return_attn=return_attn, + ) + if return_attn: + x, attn_weights, attn_probs = x + x = residual + x + if self.post_ln: + x = self.self_attn_layer_norm(x) + + residual = x + if not self.post_ln: + x = self.final_layer_norm(x) + x = self.fc1(x) + x = self.activation_fn(x) + x = self.fc2(x) + x = residual + x + if self.post_ln: + x = self.final_layer_norm(x) + if not return_attn: + return x + else: + return x, attn_weights, attn_probs + + +# output: atomic_rep, transformed_atomic_rep, pair_rep, delta_pair_rep, norm_x, norm_delta_pair_rep, +class Evoformer2bEncoder(nn.Layer): + def __init__( + self, + nnei: int, + layer_num: int = 6, + attn_head: int = 8, + atomic_dim: int = 1024, + pair_dim: int = 100, + feature_dim: int = 1024, + ffn_dim: int = 2048, + post_ln: bool = False, + final_layer_norm: bool = True, + final_head_layer_norm: bool = False, + emb_layer_norm: bool = False, + atomic_residual: bool = False, + evo_residual: bool = False, + residual_factor: float = 1.0, + activation_function: str = "gelu", + ): + super().__init__() + self.nnei = nnei + self.layer_num = layer_num + self.attn_head = attn_head + self.atomic_dim = atomic_dim + self.pair_dim = pair_dim + self.feature_dim = feature_dim + self.ffn_dim = ffn_dim + self.post_ln = post_ln + self._final_layer_norm = final_layer_norm + self._final_head_layer_norm = final_head_layer_norm + self._emb_layer_norm = emb_layer_norm + self.activation_function = activation_function + self.evo_residual = evo_residual + self.residual_factor = residual_factor + if atomic_residual and atomic_dim == feature_dim: + self.atomic_residual = True + else: + self.atomic_residual = False + self.in_proj = SimpleLinear( + self.atomic_dim, + self.feature_dim, + bavg=0.0, + stddev=1.0, + use_timestep=False, + activate="tanh", + ) # TODO + self.out_proj = SimpleLinear( + self.feature_dim, + self.atomic_dim, + bavg=0.0, + stddev=1.0, + use_timestep=False, + activate="tanh", + ) + if self._emb_layer_norm: + self.emb_layer_norm = nn.LayerNorm( + self.feature_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + + ## TODO debug : self.in_proj_pair = NonLinearHead(self.pair_dim, self.attn_head, activation_fn=None) + self.in_proj_pair = SimpleLinear(self.pair_dim, self.attn_head, activate=None) + evoformer_encoder_layers = [] + for i in range(self.layer_num): + evoformer_encoder_layers.append( + EvoformerEncoderLayer( + feature_dim=self.feature_dim, + ffn_dim=self.ffn_dim, + attn_head=self.attn_head, + activation_fn=self.activation_function, + post_ln=self.post_ln, + ) + ) + self.evoformer_encoder_layers = nn.LayerList(evoformer_encoder_layers) + if self._final_layer_norm: + self.final_layer_norm = nn.LayerNorm( + self.feature_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + if self._final_head_layer_norm: + self.final_head_layer_norm = nn.LayerNorm( + self.attn_head, dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + + def forward(self, atomic_rep, pair_rep, nlist, nlist_type, nlist_mask): + """Encoder the atomic and pair representations. + + Args: + - atomic_rep: Atomic representation with shape [nframes, nloc, atomic_dim]. + - pair_rep: Pair representation with shape [nframes, nloc, nnei, pair_dim]. + - nlist: Neighbor list with shape [nframes, nloc, nnei]. + - nlist_type: Neighbor types with shape [nframes, nloc, nnei]. + - nlist_mask: Neighbor mask with shape [nframes, nloc, nnei], `False` if blank. + + Returns + ------- + - atomic_rep: Atomic representation after encoder with shape [nframes, nloc, feature_dim]. + - transformed_atomic_rep: Transformed atomic representation after encoder with shape [nframes, nloc, atomic_dim]. + - pair_rep: Pair representation after encoder with shape [nframes, nloc, nnei, attn_head]. + - delta_pair_rep: Delta pair representation after encoder with shape [nframes, nloc, nnei, attn_head]. + - norm_x: Normalization loss of atomic_rep. + - norm_delta_pair_rep: Normalization loss of delta_pair_rep. + """ + # Global branch + nframes, nloc, _ = atomic_rep.size() + nnei = pair_rep.shape[2] + input_atomic_rep = atomic_rep + # [nframes, nloc, feature_dim] + if self.atomic_residual: + atomic_rep = atomic_rep + self.in_proj(atomic_rep) + else: + atomic_rep = self.in_proj(atomic_rep) + + if self._emb_layer_norm: + atomic_rep = self.emb_layer_norm(atomic_rep) + + # Local branch + # [nframes, nloc, nnei, attn_head] + pair_rep = self.in_proj_pair(pair_rep) + # [nframes, attn_head, nloc, nnei] + pair_rep = pair_rep.transpose([0, 3, 1, 2]).contiguous() + input_pair_rep = pair_rep + pair_rep = pair_rep.masked_fill(~nlist_mask.unsqueeze(1), float("-inf")) + + for i in range(self.layer_num): + atomic_rep, pair_rep, _ = self.evoformer_encoder_layers[i]( + atomic_rep, + attn_bias=pair_rep, + nlist_mask=nlist_mask, + nlist=nlist, + return_attn=True, + ) + + def norm_loss(x, eps=1e-10, tolerance=1.0): + # x = x.float() + max_norm = x.shape[-1] ** 0.5 + norm = paddle.sqrt(paddle.sum(x**2, axis=-1) + eps) + error = F.relu((norm - max_norm).abs() - tolerance) + return error + + def masked_mean(mask, value, dim=-1, eps=1e-10): + return ( + paddle.sum(mask * value, axis=dim) / (eps + paddle.sum(mask, axis=dim)) + ).mean() + + # atomic_rep shape: [nframes, nloc, feature_dim] + # pair_rep shape: [nframes, attn_head, nloc, nnei] + + norm_x = paddle.mean(norm_loss(atomic_rep)) + if self._final_layer_norm: + atomic_rep = self.final_layer_norm(atomic_rep) + + delta_pair_rep = pair_rep - input_pair_rep + delta_pair_rep = delta_pair_rep.masked_fill(~nlist_mask.unsqueeze(1), 0) + # [nframes, nloc, nnei, attn_head] + delta_pair_rep = ( + delta_pair_rep.reshape([nframes, self.attn_head, nloc, nnei]) + .transpose([0, 2, 3, 1]) + .contiguous() + ) + + # [nframes, nloc, nnei] + norm_delta_pair_rep = norm_loss(delta_pair_rep) + norm_delta_pair_rep = masked_mean(mask=nlist_mask, value=norm_delta_pair_rep) + if self._final_head_layer_norm: + delta_pair_rep = self.final_head_layer_norm(delta_pair_rep) + + if self.atomic_residual: + transformed_atomic_rep = atomic_rep + self.out_proj(atomic_rep) + else: + transformed_atomic_rep = self.out_proj(atomic_rep) + + if self.evo_residual: + transformed_atomic_rep = ( + self.residual_factor * transformed_atomic_rep + input_atomic_rep + ) * (1 / np.sqrt(2)) + + return ( + atomic_rep, + transformed_atomic_rep, + pair_rep, + delta_pair_rep, + norm_x, + norm_delta_pair_rep, + ) + + +class Evoformer3bEncoderLayer(nn.Layer): + def __init__( + self, + nnei, + embedding_dim: int = 768, + pair_dim: int = 64, + pair_hidden_dim: int = 32, + ffn_embedding_dim: int = 3072, + num_attention_heads: int = 8, + dropout: float = 0.1, + droppath_prob: float = 0.0, + pair_dropout: float = 0.25, + attention_dropout: float = 0.1, + activation_dropout: float = 0.1, + pre_ln: bool = True, + tri_update: bool = True, + ): + super().__init__() + # Initialize parameters + self.nnei = nnei + self.embedding_dim = embedding_dim + self.num_attention_heads = num_attention_heads + self.attention_dropout = attention_dropout + + # self.dropout = dropout + self.activation_dropout = activation_dropout + + if droppath_prob > 0.0: + self.dropout_module = DropPath(droppath_prob) + else: + self.dropout_module = Dropout(dropout) + + # self.self_attn = AtomAttentionLocal(embedding_dim, embedding_dim, embedding_dim, pair_dim, + # embedding_dim // num_attention_heads, num_attention_heads, + # gating=False, dropout=attention_dropout) + self.self_attn = AtomAttention( + embedding_dim, + embedding_dim, + embedding_dim, + pair_dim, + embedding_dim // num_attention_heads, + num_attention_heads, + gating=False, + dropout=attention_dropout, + ) + # layer norm associated with the self attention layer + self.pre_ln = pre_ln + self.self_attn_layer_norm = nn.LayerNorm( + self.embedding_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + self.fc1 = nn.Linear( + self.embedding_dim, ffn_embedding_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + self.fc2 = nn.Linear( + ffn_embedding_dim, self.embedding_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + self.final_layer_norm = nn.LayerNorm( + self.embedding_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + + self.x_layer_norm_opm = nn.LayerNorm( + self.embedding_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + # self.opm = OuterProductLocal(self.embedding_dim, pair_dim, d_hid=pair_hidden_dim) + self.opm = OuterProduct(self.embedding_dim, pair_dim, d_hid=pair_hidden_dim) + # self.pair_layer_norm_opm = nn.LayerNorm(pair_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION) + self.pair_layer_norm_ffn = nn.LayerNorm( + pair_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + self.pair_ffn = Transition( + pair_dim, + 1, + dropout=activation_dropout, + ) + self.pair_dropout = pair_dropout + self.tri_update = tri_update + if self.tri_update: + self.pair_layer_norm_trimul = nn.LayerNorm( + pair_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + self.pair_tri_mul = TriangleMultiplication(pair_dim, pair_hidden_dim) + + def update_pair( + self, + x, + pair, + nlist, + op_mask, + op_norm, + ): + # local: + # [nframes, nloc, nnei, pair_dim] + # global: + # [nframes, nloc, nloc, pair_dim] + pair = pair + self.dropout_module( + self.opm(self.x_layer_norm_opm(x), nlist, op_mask, op_norm) + ) + if not self.pre_ln: + pair = self.pair_layer_norm_opm(pair) + return x, pair + + def shared_dropout(self, x, shared_dim, dropout): + shape = list(x.shape) + shape[shared_dim] = 1 + with paddle.no_grad(): + mask = x.new_ones(shape) + return F.dropout(mask, p=dropout, training=self.training) * x + + def forward( + self, + x: paddle.Tensor, + pair: paddle.Tensor, + nlist: paddle.Tensor = None, + attn_mask: Optional[paddle.Tensor] = None, + pair_mask: Optional[paddle.Tensor] = None, + op_mask: float = 1.0, + op_norm: float = 1.0, + ): + """Encoder the atomic and pair representations. + + Args: + - x: Atomic representation with shape [ncluster, natoms, embed_dim]. + - pair: Pair representation with shape [ncluster, natoms, natoms, pair_dim]. + - attn_mask: Attention mask with shape [ncluster, head, natoms, natoms]. + - pair_mask: Neighbor mask with shape [ncluster, natoms, natoms]. + + """ + # [ncluster, natoms, embed_dim] + residual = x + if self.pre_ln: + x = self.self_attn_layer_norm(x) + x = self.self_attn( + x, + x, + x, + nlist=nlist, + pair=pair, + mask=attn_mask, + ) + # x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) + x = residual + x + if not self.pre_ln: + x = self.self_attn_layer_norm(x) + + residual = x + if self.pre_ln: + x = self.final_layer_norm(x) + x = F.linear(x, self.fc1.weight) + # x = fused_ops.bias_torch_gelu(x, self.fc1.bias) + x = nn.GELU()(x) + self.fc1.bias + x = F.dropout(x, p=self.activation_dropout, training=self.training) + x = self.fc2(x) + # x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) + + x = residual + x + if not self.pre_ln: + x = self.final_layer_norm(x) + + block = [ + partial( + self.update_pair, + nlist=nlist, + op_mask=op_mask, + op_norm=op_norm, + ) + ] + + x, pair = checkpoint_sequential( + block, + input_x=(x, pair), + ) + + if self.tri_update: + residual_pair = pair + if self.pre_ln: + pair = self.pair_layer_norm_trimul(pair) + + pair = self.shared_dropout( + self.pair_tri_mul(pair, pair_mask), -3, self.pair_dropout + ) + pair = residual_pair + pair + if not self.pre_ln: + pair = self.pair_layer_norm_trimul(pair) + + residual_pair = pair + if self.pre_ln: + pair = self.pair_layer_norm_ffn(pair) + pair = self.dropout_module(self.pair_ffn(pair)) + pair = residual_pair + pair + if not self.pre_ln: + pair = self.pair_layer_norm_ffn(pair) + return x, pair + + +class Evoformer3bEncoder(nn.Layer): + def __init__( + self, + nnei, + layer_num=6, + attn_head=8, + atomic_dim=768, + pair_dim=64, + pair_hidden_dim=32, + ffn_embedding_dim=3072, + dropout: float = 0.1, + droppath_prob: float = 0.0, + pair_dropout: float = 0.25, + attention_dropout: float = 0.1, + activation_dropout: float = 0.1, + pre_ln: bool = True, + tri_update: bool = True, + **kwargs, + ): + super().__init__() + self.nnei = nnei + if droppath_prob > 0: + droppath_probs = [ + x.item() + for x in paddle.linspace(0, droppath_prob, layer_num) # pylint: disable=no-explicit-dtype,no-explicit-device + ] + else: + droppath_probs = None + + self.layers = nn.LayerList( + [ + Evoformer3bEncoderLayer( + nnei, + atomic_dim, + pair_dim, + pair_hidden_dim, + ffn_embedding_dim, + num_attention_heads=attn_head, + dropout=dropout, + droppath_prob=droppath_probs[_], + pair_dropout=pair_dropout, + attention_dropout=attention_dropout, + activation_dropout=activation_dropout, + pre_ln=pre_ln, + tri_update=tri_update, + ) + for _ in range(layer_num) + ] + ) + + def forward(self, x, pair, attn_mask=None, pair_mask=None, atom_mask=None): + """Encoder the atomic and pair representations. + + Args: + x: Atomic representation with shape [ncluster, natoms, atomic_dim]. + pair: Pair representation with shape [ncluster, natoms, natoms, pair_dim]. + attn_mask: Attention mask (with -inf for softmax) with shape [ncluster, head, natoms, natoms]. + pair_mask: Pair mask (with 1 for real atom pair and 0 for padding) with shape [ncluster, natoms, natoms]. + atom_mask: Atom mask (with 1 for real atom and 0 for padding) with shape [ncluster, natoms]. + + Returns + ------- + x: Atomic representation with shape [ncluster, natoms, atomic_dim]. + pair: Pair representation with shape [ncluster, natoms, natoms, pair_dim]. + + """ + # [ncluster, natoms, 1] + op_mask = atom_mask.unsqueeze(-1) + op_mask = op_mask * (op_mask.size(-2) ** -0.5) + eps = 1e-3 + # [ncluster, natoms, natoms, 1] + op_norm = 1.0 / (eps + paddle.einsum("...bc,...dc->...bdc", op_mask, op_mask)) + for layer in self.layers: + x, pair = layer( + x, + pair, + nlist=None, + attn_mask=attn_mask, + pair_mask=pair_mask, + op_mask=op_mask, + op_norm=op_norm, + ) + return x, pair diff --git a/deepmd/pd/model/task/__init__.py b/deepmd/pd/model/task/__init__.py new file mode 100644 index 0000000000..8a13b27e20 --- /dev/null +++ b/deepmd/pd/model/task/__init__.py @@ -0,0 +1,42 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from .atten_lcc import ( + FittingNetAttenLcc, +) +from .base_fitting import ( + BaseFitting, +) +from .denoise import ( + DenoiseNet, +) +from .dipole import ( + DipoleFittingNet, +) +from .dos import ( + DOSFittingNet, +) +from .ener import ( + EnergyFittingNet, + EnergyFittingNetDirect, +) +from .fitting import ( + Fitting, +) +from .polarizability import ( + PolarFittingNet, +) +from .type_predict import ( + TypePredictNet, +) + +__all__ = [ + "FittingNetAttenLcc", + "DenoiseNet", + "DipoleFittingNet", + "EnergyFittingNet", + "EnergyFittingNetDirect", + "Fitting", + "BaseFitting", + "TypePredictNet", + "PolarFittingNet", + "DOSFittingNet", +] diff --git a/deepmd/pd/model/task/atten_lcc.py b/deepmd/pd/model/task/atten_lcc.py new file mode 100644 index 0000000000..1ab1da323b --- /dev/null +++ b/deepmd/pd/model/task/atten_lcc.py @@ -0,0 +1,55 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import paddle +import paddle.nn as nn + +from deepmd.pd.model.network.network import ( + EnergyHead, + NodeTaskHead, +) +from deepmd.pd.model.task.fitting import ( + Fitting, +) +from deepmd.pd.utils import ( + env, +) + + +class FittingNetAttenLcc(Fitting): + def __init__( + self, embedding_width, bias_atom_e, pair_embed_dim, attention_heads, **kwargs + ): + super().__init__() + self.embedding_width = embedding_width + self.engergy_proj = EnergyHead(self.embedding_width, 1) + self.energe_agg_factor = nn.Embedding(4, 1, dtype=env.GLOBAL_PD_FLOAT_PRECISION) + nn.init.normal_(self.energe_agg_factor.weight, 0, 0.01) + bias_atom_e = paddle.to_tensor(bias_atom_e) # pylint: disable=no-explicit-dtype,no-explicit-device + self.register_buffer("bias_atom_e", bias_atom_e) + self.pair_embed_dim = pair_embed_dim + self.attention_heads = attention_heads + self.node_proc = NodeTaskHead( + self.embedding_width, self.pair_embed_dim, self.attention_heads + ) + self.node_proc.zero_init() + + def forward(self, output, pair, delta_pos, atype, nframes, nloc): + # [nframes x nloc x tebd_dim] + output_nloc = (output[:, 0, :]).reshape([nframes, nloc, self.embedding_width]) + # Optional: GRRG or mean of gbf TODO + + # energy outut + # [nframes, nloc] + energy_out = self.engergy_proj(output_nloc).reshape([nframes, nloc]) + # [nframes, nloc] + energy_factor = self.energe_agg_factor(paddle.zeros_like(atype)).reshape( + [nframes, nloc] + ) + energy_out = (energy_out * energy_factor) + self.bias_atom_e[atype] + energy_out = energy_out.sum(axis=-1) + + # vector output + # predict_force: [(nframes x nloc) x (1 + nnei2) x 3] + predict_force = self.node_proc(output, pair, delta_pos=delta_pos) + # predict_force_nloc: [nframes x nloc x 3] + predict_force_nloc = (predict_force[:, 0, :]).reshape([nframes, nloc, 3]) + return energy_out, predict_force_nloc diff --git a/deepmd/pd/model/task/base_fitting.py b/deepmd/pd/model/task/base_fitting.py new file mode 100644 index 0000000000..9ad3b801cd --- /dev/null +++ b/deepmd/pd/model/task/base_fitting.py @@ -0,0 +1,8 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import paddle + +from deepmd.dpmodel.fitting import ( + make_base_fitting, +) + +BaseFitting = make_base_fitting(paddle.Tensor, fwd_method_name="forward") diff --git a/deepmd/pd/model/task/denoise.py b/deepmd/pd/model/task/denoise.py new file mode 100644 index 0000000000..d1fca089f1 --- /dev/null +++ b/deepmd/pd/model/task/denoise.py @@ -0,0 +1,137 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Optional, +) + +import paddle + +from deepmd.dpmodel import ( + FittingOutputDef, + OutputVariableDef, + fitting_check_output, +) +from deepmd.pd.model.network.network import ( + MaskLMHead, + NonLinearHead, +) +from deepmd.pd.model.task.fitting import ( + Fitting, +) +from deepmd.pd.utils import ( + env, +) + + +@fitting_check_output +class DenoiseNet(Fitting): + def __init__( + self, + feature_dim, + ntypes, + attn_head=8, + prefactor=[0.5, 0.5], + activation_function="gelu", + **kwargs, + ): + """Construct a denoise net. + + Args: + - ntypes: Element count. + - embedding_width: Embedding width per atom. + - neuron: Number of neurons in each hidden layers of the fitting net. + - bias_atom_e: Average enery per atom for each element. + - resnet_dt: Using time-step in the ResNet construction. + """ + super().__init__() + self.feature_dim = feature_dim + self.ntypes = ntypes + self.attn_head = attn_head + self.prefactor = paddle.to_tensor( + prefactor, dtype=env.GLOBAL_PD_FLOAT_PRECISION, device=env.DEVICE + ) + + self.lm_head = MaskLMHead( + embed_dim=self.feature_dim, + output_dim=ntypes, + activation_fn=activation_function, + weight=None, + ) + + if not isinstance(self.attn_head, list): + self.pair2coord_proj = NonLinearHead( + self.attn_head, 1, activation_fn=activation_function + ) + else: + self.pair2coord_proj = [] + self.ndescriptor = len(self.attn_head) + for ii in range(self.ndescriptor): + _pair2coord_proj = NonLinearHead( + self.attn_head[ii], 1, activation_fn=activation_function + ) + self.pair2coord_proj.append(_pair2coord_proj) + self.pair2coord_proj = paddle.nn.LayerList(self.pair2coord_proj) + + def output_def(self): + return FittingOutputDef( + [ + OutputVariableDef( + "updated_coord", + [3], + reducible=False, + r_differentiable=False, + c_differentiable=False, + ), + OutputVariableDef( + "logits", + [-1], + reducible=False, + r_differentiable=False, + c_differentiable=False, + ), + ] + ) + + def forward( + self, + pair_weights, + diff, + nlist_mask, + features, + sw, + masked_tokens: Optional[paddle.Tensor] = None, + ): + """Calculate the updated coord. + Args: + - coord: Input noisy coord with shape [nframes, nloc, 3]. + - pair_weights: Input pair weights with shape [nframes, nloc, nnei, head]. + - diff: Input pair relative coord list with shape [nframes, nloc, nnei, 3]. + - nlist_mask: Input nlist mask with shape [nframes, nloc, nnei]. + + Returns + ------- + - denoised_coord: Denoised updated coord with shape [nframes, nloc, 3]. + """ + # [nframes, nloc, nnei, 1] + logits = self.lm_head(features, masked_tokens=masked_tokens) + if not isinstance(self.attn_head, list): + attn_probs = self.pair2coord_proj(pair_weights) + out_coord = (attn_probs * diff).sum(axis=-2) / ( + sw.sum(axis=-1).unsqueeze(-1) + 1e-6 + ) + else: + assert len(self.prefactor) == self.ndescriptor + all_coord_update = [] + assert len(pair_weights) == len(diff) == len(nlist_mask) == self.ndescriptor + for ii in range(self.ndescriptor): + _attn_probs = self.pair2coord_proj[ii](pair_weights[ii]) + _coord_update = (_attn_probs * diff[ii]).sum(axis=-2) / ( + nlist_mask[ii].sum(axis=-1).unsqueeze(-1) + 1e-6 + ) + all_coord_update.append(_coord_update) + out_coord = self.prefactor[0] * all_coord_update[0] + for ii in range(self.ndescriptor - 1): + out_coord += self.prefactor[ii + 1] * all_coord_update[ii + 1] + return { + "updated_coord": out_coord, + "logits": logits, + } diff --git a/deepmd/pd/model/task/dipole.py b/deepmd/pd/model/task/dipole.py new file mode 100644 index 0000000000..62e5fc8c44 --- /dev/null +++ b/deepmd/pd/model/task/dipole.py @@ -0,0 +1,200 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import logging +from typing import ( + Callable, + List, + Optional, + Union, +) + +import paddle + +from deepmd.dpmodel import ( + FittingOutputDef, + OutputVariableDef, +) +from deepmd.pd.model.task.fitting import ( + GeneralFitting, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + DEFAULT_PRECISION, +) +from deepmd.utils.path import ( + DPPath, +) +from deepmd.utils.version import ( + check_version_compatibility, +) + +log = logging.getLogger(__name__) + + +@GeneralFitting.register("dipole") +class DipoleFittingNet(GeneralFitting): + """Construct a dipole fitting net. + + Parameters + ---------- + ntypes : int + Element count. + dim_descrpt : int + Embedding width per atom. + embedding_width : int + The dimension of rotation matrix, m1. + neuron : List[int] + Number of neurons in each hidden layers of the fitting net. + resnet_dt : bool + Using time-step in the ResNet construction. + numb_fparam : int + Number of frame parameters. + numb_aparam : int + Number of atomic parameters. + activation_function : str + Activation function. + precision : str + Numerical precision. + mixed_types : bool + If true, use a uniform fitting net for all atom types, otherwise use + different fitting nets for different atom types. + rcond : float, optional + The condition number for the regression of atomic energy. + seed : int, optional + Random seed. + r_differentiable + If the variable is differentiated with respect to coordinates of atoms. + Only reducible variable are differentiable. + c_differentiable + If the variable is differentiated with respect to the cell tensor (pbc case). + Only reducible variable are differentiable. + type_map: List[str], Optional + A list of strings. Give the name to each type of atoms. + """ + + def __init__( + self, + ntypes: int, + dim_descrpt: int, + embedding_width: int, + neuron: List[int] = [128, 128, 128], + resnet_dt: bool = True, + numb_fparam: int = 0, + numb_aparam: int = 0, + activation_function: str = "tanh", + precision: str = DEFAULT_PRECISION, + mixed_types: bool = True, + rcond: Optional[float] = None, + seed: Optional[Union[int, List[int]]] = None, + exclude_types: List[int] = [], + r_differentiable: bool = True, + c_differentiable: bool = True, + type_map: Optional[List[str]] = None, + **kwargs, + ): + self.embedding_width = embedding_width + self.r_differentiable = r_differentiable + self.c_differentiable = c_differentiable + super().__init__( + var_name="dipole", + ntypes=ntypes, + dim_descrpt=dim_descrpt, + neuron=neuron, + resnet_dt=resnet_dt, + numb_fparam=numb_fparam, + numb_aparam=numb_aparam, + activation_function=activation_function, + precision=precision, + mixed_types=mixed_types, + rcond=rcond, + seed=seed, + exclude_types=exclude_types, + type_map=type_map, + **kwargs, + ) + self.old_impl = False # this only supports the new implementation. + + def _net_out_dim(self): + """Set the FittingNet output dim.""" + return self.embedding_width + + def serialize(self) -> dict: + data = super().serialize() + data["type"] = "dipole" + data["embedding_width"] = self.embedding_width + data["old_impl"] = self.old_impl + data["r_differentiable"] = self.r_differentiable + data["c_differentiable"] = self.c_differentiable + return data + + @classmethod + def deserialize(cls, data: dict) -> "GeneralFitting": + data = copy.deepcopy(data) + check_version_compatibility(data.pop("@version", 1), 2, 1) + data.pop("var_name", None) + return super().deserialize(data) + + def output_def(self) -> FittingOutputDef: + return FittingOutputDef( + [ + OutputVariableDef( + self.var_name, + [3], + reducible=True, + r_differentiable=self.r_differentiable, + c_differentiable=self.c_differentiable, + ), + ] + ) + + def compute_output_stats( + self, + merged: Union[Callable[[], List[dict]], List[dict]], + stat_file_path: Optional[DPPath] = None, + ): + """ + Compute the output statistics (e.g. energy bias) for the fitting net from packed data. + + Parameters + ---------- + merged : Union[Callable[[], List[dict]], List[dict]] + - List[dict]: A list of data samples from various data systems. + Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` + originating from the `i`-th data system. + - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + only when needed. Since the sampling process can be slow and memory-intensive, + the lazy function helps by only sampling once. + stat_file_path : Optional[DPPath] + The path to the stat file. + + """ + pass + + def forward( + self, + descriptor: paddle.Tensor, + atype: paddle.Tensor, + gr: Optional[paddle.Tensor] = None, + g2: Optional[paddle.Tensor] = None, + h2: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + ): + nframes, nloc, _ = descriptor.shape + assert gr is not None, "Must provide the rotation matrix for dipole fitting." + # (nframes, nloc, m1) + out = self._forward_common(descriptor, atype, gr, g2, h2, fparam, aparam)[ + self.var_name + ] + # (nframes * nloc, 1, m1) + out = out.reshape([-1, 1, self.embedding_width]) + # (nframes * nloc, m1, 3) + gr = gr.reshape([nframes * nloc, self.embedding_width, 3]) + # (nframes, nloc, 3) + out = paddle.bmm(out, gr).squeeze(-2).reshape([nframes, nloc, 3]) + return {self.var_name: out.to(env.GLOBAL_PD_FLOAT_PRECISION)} + + # make jit happy with paddle 2.0.0 + exclude_types: List[int] diff --git a/deepmd/pd/model/task/dos.py b/deepmd/pd/model/task/dos.py new file mode 100644 index 0000000000..5807c326ad --- /dev/null +++ b/deepmd/pd/model/task/dos.py @@ -0,0 +1,130 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import logging +from typing import ( + List, + Optional, + Union, +) + +import paddle + +from deepmd.dpmodel import ( + FittingOutputDef, + OutputVariableDef, +) +from deepmd.pd.model.task.ener import ( + InvarFitting, +) +from deepmd.pd.model.task.fitting import ( + Fitting, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + DEFAULT_PRECISION, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, +) +from deepmd.utils.version import ( + check_version_compatibility, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION +device = env.DEVICE + +log = logging.getLogger(__name__) + + +@Fitting.register("dos") +class DOSFittingNet(InvarFitting): + def __init__( + self, + ntypes: int, + dim_descrpt: int, + numb_dos: int = 300, + neuron: List[int] = [128, 128, 128], + resnet_dt: bool = True, + numb_fparam: int = 0, + numb_aparam: int = 0, + rcond: Optional[float] = None, + bias_dos: Optional[paddle.Tensor] = None, + trainable: Union[bool, List[bool]] = True, + seed: Optional[Union[int, List[int]]] = None, + activation_function: str = "tanh", + precision: str = DEFAULT_PRECISION, + exclude_types: List[int] = [], + mixed_types: bool = True, + type_map: Optional[List[str]] = None, + ): + if bias_dos is not None: + self.bias_dos = bias_dos + else: + self.bias_dos = paddle.zeros((ntypes, numb_dos), dtype=dtype).to( + device=env.DEVICE + ) + super().__init__( + var_name="dos", + ntypes=ntypes, + dim_descrpt=dim_descrpt, + dim_out=numb_dos, + neuron=neuron, + bias_atom_e=bias_dos, + resnet_dt=resnet_dt, + numb_fparam=numb_fparam, + numb_aparam=numb_aparam, + activation_function=activation_function, + precision=precision, + mixed_types=mixed_types, + rcond=rcond, + seed=seed, + exclude_types=exclude_types, + trainable=trainable, + type_map=type_map, + ) + + def output_def(self) -> FittingOutputDef: + return FittingOutputDef( + [ + OutputVariableDef( + self.var_name, + [self.dim_out], + reducible=True, + r_differentiable=False, + c_differentiable=False, + ), + ] + ) + + @classmethod + def deserialize(cls, data: dict) -> "DOSFittingNet": + data = copy.deepcopy(data) + check_version_compatibility(data.pop("@version", 1), 2, 1) + data.pop("@class", None) + data.pop("var_name", None) + data.pop("tot_ener_zero", None) + data.pop("layer_name", None) + data.pop("use_aparam_as_mask", None) + data.pop("spin", None) + data.pop("atom_ener", None) + data["numb_dos"] = data.pop("dim_out") + obj = super().deserialize(data) + + return obj + + def serialize(self) -> dict: + """Serialize the fitting to dict.""" + # dd = super(InvarFitting, self).serialize() + dd = { + **InvarFitting.serialize(self), + "type": "dos", + "dim_out": self.dim_out, + } + dd["@variables"]["bias_atom_e"] = to_numpy_array(self.bias_atom_e) + + return dd + + # make jit happy with paddle 2.0.0 + exclude_types: List[int] diff --git a/deepmd/pd/model/task/ener.py b/deepmd/pd/model/task/ener.py new file mode 100644 index 0000000000..89b3255056 --- /dev/null +++ b/deepmd/pd/model/task/ener.py @@ -0,0 +1,257 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import logging +from typing import ( + List, + Optional, + Tuple, + Union, +) + +import numpy as np +import paddle + +from deepmd.dpmodel import ( + FittingOutputDef, + OutputVariableDef, + fitting_check_output, +) +from deepmd.pd.model.network.network import ( + ResidualDeep, +) +from deepmd.pd.model.task.fitting import ( + Fitting, + GeneralFitting, +) +from deepmd.pd.model.task.invar_fitting import ( + InvarFitting, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + DEFAULT_PRECISION, +) +from deepmd.utils.version import ( + check_version_compatibility, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION +device = env.DEVICE + +log = logging.getLogger(__name__) + + +@Fitting.register("ener") +class EnergyFittingNet(InvarFitting): + def __init__( + self, + ntypes: int, + dim_descrpt: int, + neuron: List[int] = [128, 128, 128], + bias_atom_e: Optional[paddle.Tensor] = None, + resnet_dt: bool = True, + numb_fparam: int = 0, + numb_aparam: int = 0, + activation_function: str = "tanh", + precision: str = DEFAULT_PRECISION, + mixed_types: bool = True, + seed: Optional[Union[int, List[int]]] = None, + type_map: Optional[List[str]] = None, + **kwargs, + ): + super().__init__( + "energy", + ntypes, + dim_descrpt, + 1, + neuron=neuron, + bias_atom_e=bias_atom_e, + resnet_dt=resnet_dt, + numb_fparam=numb_fparam, + numb_aparam=numb_aparam, + activation_function=activation_function, + precision=precision, + mixed_types=mixed_types, + seed=seed, + type_map=type_map, + **kwargs, + ) + + @classmethod + def deserialize(cls, data: dict) -> "GeneralFitting": + data = copy.deepcopy(data) + check_version_compatibility(data.pop("@version", 1), 2, 1) + data.pop("var_name") + data.pop("dim_out") + return super().deserialize(data) + + def serialize(self) -> dict: + """Serialize the fitting to dict.""" + return { + **super().serialize(), + "type": "ener", + } + + # make jit happy with paddle 2.0.0 + exclude_types: List[int] + + +@Fitting.register("direct_force") +@Fitting.register("direct_force_ener") +@fitting_check_output +class EnergyFittingNetDirect(Fitting): + def __init__( + self, + ntypes, + dim_descrpt, + neuron, + bias_atom_e=None, + out_dim=1, + resnet_dt=True, + use_tebd=True, + return_energy=False, + **kwargs, + ): + """Construct a fitting net for energy. + + Args: + - ntypes: Element count. + - embedding_width: Embedding width per atom. + - neuron: Number of neurons in each hidden layers of the fitting net. + - bias_atom_e: Average enery per atom for each element. + - resnet_dt: Using time-step in the ResNet construction. + """ + super().__init__() + self.ntypes = ntypes + self.dim_descrpt = dim_descrpt + self.use_tebd = use_tebd + self.out_dim = out_dim + if bias_atom_e is None: + bias_atom_e = np.zeros([self.ntypes]) # pylint: disable=no-explicit-dtype + if not use_tebd: + assert self.ntypes == len(bias_atom_e), "Element count mismatches!" + bias_atom_e = paddle.to_tensor(bias_atom_e).to(device=env.DEVICE) # pylint: disable=no-explicit-dtype + self.register_buffer("bias_atom_e", bias_atom_e) + + filter_layers_dipole = [] + for type_i in range(self.ntypes): + one = ResidualDeep( + type_i, + dim_descrpt, + neuron, + 0.0, + out_dim=out_dim, + resnet_dt=resnet_dt, + ) + filter_layers_dipole.append(one) + self.filter_layers_dipole = paddle.nn.LayerList(filter_layers_dipole) + + self.return_energy = return_energy + filter_layers = [] + if self.return_energy: + for type_i in range(self.ntypes): + bias_type = 0.0 if self.use_tebd else bias_atom_e[type_i] + one = ResidualDeep( + type_i, dim_descrpt, neuron, bias_type, resnet_dt=resnet_dt + ) + filter_layers.append(one) + self.filter_layers = paddle.nn.LayerList(filter_layers) + + def output_def(self): + return FittingOutputDef( + [ + OutputVariableDef( + "energy", + [1], + reducible=True, + r_differentiable=False, + c_differentiable=False, + ), + OutputVariableDef( + "dforce", + [3], + reducible=False, + r_differentiable=False, + c_differentiable=False, + ), + ] + ) + + def serialize(self) -> dict: + raise NotImplementedError + + def deserialize(self) -> "EnergyFittingNetDirect": + raise NotImplementedError + + def change_type_map( + self, type_map: List[str], model_with_new_type_stat=None + ) -> None: + raise NotImplementedError + + def get_type_map(self) -> List[str]: + raise NotImplementedError + + def forward( + self, + inputs: paddle.Tensor, + atype: paddle.Tensor, + gr: Optional[paddle.Tensor] = None, + g2: Optional[paddle.Tensor] = None, + h2: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + ) -> Tuple[paddle.Tensor, None]: + """Based on embedding net output, alculate total energy. + + Args: + - inputs: Embedding matrix. Its shape is [nframes, natoms[0], self.dim_descrpt]. + - natoms: Tell atom count and element count. Its shape is [2+self.ntypes]. + + Returns + ------- + - `paddle.Tensor`: Total energy with shape [nframes, natoms[0]]. + """ + nframes, nloc, _ = inputs.size() + if self.use_tebd: + # if atype_tebd is not None: + # inputs = paddle.concat([inputs, atype_tebd], axis=-1) + vec_out = self.filter_layers_dipole[0]( + inputs + ) # Shape is [nframes, nloc, m1] + assert list(vec_out.size()) == [nframes, nloc, self.out_dim] + # (nf x nloc) x 1 x od + vec_out = vec_out.reshape([-1, 1, self.out_dim]) + assert gr is not None + # (nf x nloc) x od x 3 + gr = gr.reshape([-1, self.out_dim, 3]) + vec_out = ( + paddle.bmm(vec_out, gr).squeeze(-2).reshape([nframes, nloc, 3]) + ) # Shape is [nframes, nloc, 3] + else: + vec_out = paddle.zeros_like(atype).unsqueeze(-1) # jit assertion + for type_i, filter_layer in enumerate(self.filter_layers_dipole): + mask = atype == type_i + vec_out_type = filter_layer(inputs) # Shape is [nframes, nloc, m1] + vec_out_type = vec_out_type * mask.unsqueeze(-1) + vec_out = vec_out + vec_out_type # Shape is [nframes, natoms[0], 1] + + outs = paddle.zeros_like(atype).unsqueeze(-1) # jit assertion + if self.return_energy: + if self.use_tebd: + atom_energy = self.filter_layers[0](inputs) + self.bias_atom_e[ + atype + ].unsqueeze(-1) + outs = outs + atom_energy # Shape is [nframes, natoms[0], 1] + else: + for type_i, filter_layer in enumerate(self.filter_layers): + mask = atype == type_i + atom_energy = filter_layer(inputs) + if not env.ENERGY_BIAS_TRAINABLE: + atom_energy = atom_energy + self.bias_atom_e[type_i] + atom_energy = atom_energy * mask.unsqueeze(-1) + outs = outs + atom_energy # Shape is [nframes, natoms[0], 1] + return { + "energy": outs.to(env.GLOBAL_PD_FLOAT_PRECISION), + "dforce": vec_out, + } diff --git a/deepmd/pd/model/task/fitting.py b/deepmd/pd/model/task/fitting.py new file mode 100644 index 0000000000..ebe2485b2d --- /dev/null +++ b/deepmd/pd/model/task/fitting.py @@ -0,0 +1,538 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import logging +from abc import ( + abstractmethod, +) +from typing import ( + List, + Optional, + Union, +) + +import numpy as np +import paddle + +from deepmd.dpmodel.utils.seed import ( + child_seed, +) +from deepmd.pd.model.network.mlp import ( + FittingNet, + NetworkCollection, +) +from deepmd.pd.model.network.network import ( + ResidualDeep, +) +from deepmd.pd.model.task.base_fitting import ( + BaseFitting, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + DEFAULT_PRECISION, + PRECISION_DICT, +) +from deepmd.pd.utils.exclude_mask import ( + AtomExcludeMask, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, + to_paddle_tensor, +) +from deepmd.utils.finetune import ( + get_index_between_two_maps, + map_atom_exclude_types, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION +device = env.DEVICE + +log = logging.getLogger(__name__) + + +class Fitting(paddle.nn.Layer, BaseFitting): + # plugin moved to BaseFitting + + def __new__(cls, *args, **kwargs): + if cls is Fitting: + return BaseFitting.__new__(BaseFitting, *args, **kwargs) + return super().__new__(cls) + + def share_params(self, base_class, shared_level, resume=False): + """ + Share the parameters of self to the base_class with shared_level during multitask training. + If not start from checkpoint (resume is False), + some seperated parameters (e.g. mean and stddev) will be re-calculated across different classes. + """ + assert ( + self.__class__ == base_class.__class__ + ), "Only fitting nets of the same type can share params!" + if shared_level == 0: + # link buffers + if hasattr(self, "bias_atom_e"): + self.bias_atom_e = base_class.bias_atom_e + # the following will successfully link all the params except buffers, which need manually link. + for item in self._modules: + self._modules[item] = base_class._modules[item] + elif shared_level == 1: + # only not share the bias_atom_e + # the following will successfully link all the params except buffers, which need manually link. + for item in self._modules: + self._modules[item] = base_class._modules[item] + else: + raise NotImplementedError + + +class GeneralFitting(Fitting): + """Construct a general fitting net. + + Parameters + ---------- + var_name : str + The atomic property to fit, 'energy', 'dipole', and 'polar'. + ntypes : int + Element count. + dim_descrpt : int + Embedding width per atom. + dim_out : int + The output dimension of the fitting net. + neuron : List[int] + Number of neurons in each hidden layers of the fitting net. + bias_atom_e : paddle.Tensor, optional + Average enery per atom for each element. + resnet_dt : bool + Using time-step in the ResNet construction. + numb_fparam : int + Number of frame parameters. + numb_aparam : int + Number of atomic parameters. + activation_function : str + Activation function. + precision : str + Numerical precision. + mixed_types : bool + If true, use a uniform fitting net for all atom types, otherwise use + different fitting nets for different atom types. + rcond : float, optional + The condition number for the regression of atomic energy. + seed : int, optional + Random seed. + exclude_types: List[int] + Atomic contributions of the excluded atom types are set zero. + trainable : Union[List[bool], bool] + If the parameters in the fitting net are trainable. + Now this only supports setting all the parameters in the fitting net at one state. + When in List[bool], the trainable will be True only if all the boolean parameters are True. + remove_vaccum_contribution: List[bool], optional + Remove vaccum contribution before the bias is added. The list assigned each + type. For `mixed_types` provide `[True]`, otherwise it should be a list of the same + length as `ntypes` signaling if or not removing the vaccum contribution for the atom types in the list. + type_map: List[str], Optional + A list of strings. Give the name to each type of atoms. + """ + + def __init__( + self, + var_name: str, + ntypes: int, + dim_descrpt: int, + neuron: List[int] = [128, 128, 128], + bias_atom_e: Optional[paddle.Tensor] = None, + resnet_dt: bool = True, + numb_fparam: int = 0, + numb_aparam: int = 0, + activation_function: str = "tanh", + precision: str = DEFAULT_PRECISION, + mixed_types: bool = True, + rcond: Optional[float] = None, + seed: Optional[Union[int, List[int]]] = None, + exclude_types: List[int] = [], + trainable: Union[bool, List[bool]] = True, + remove_vaccum_contribution: Optional[List[bool]] = None, + type_map: Optional[List[str]] = None, + **kwargs, + ): + super().__init__() + self.var_name = var_name + self.ntypes = ntypes + self.dim_descrpt = dim_descrpt + self.neuron = neuron + self.mixed_types = mixed_types + self.resnet_dt = resnet_dt + self.numb_fparam = numb_fparam + self.numb_aparam = numb_aparam + self.activation_function = activation_function + self.precision = precision + self.prec = PRECISION_DICT[self.precision] + self.rcond = rcond + self.seed = seed + self.type_map = type_map + # order matters, should be place after the assignment of ntypes + self.reinit_exclude(exclude_types) + self.trainable = trainable + # need support for each layer settings + self.trainable = ( + all(self.trainable) if isinstance(self.trainable, list) else self.trainable + ) + self.remove_vaccum_contribution = remove_vaccum_contribution + + net_dim_out = self._net_out_dim() + # init constants + if bias_atom_e is None: + bias_atom_e = np.zeros([self.ntypes, net_dim_out], dtype=np.float64) + bias_atom_e = paddle.to_tensor(bias_atom_e, dtype=self.prec).to(device=device) + bias_atom_e = bias_atom_e.reshape([self.ntypes, net_dim_out]) + if not self.mixed_types: + assert self.ntypes == bias_atom_e.shape[0], "Element count mismatches!" + self.register_buffer("bias_atom_e", bias_atom_e) + + if self.numb_fparam > 0: + self.register_buffer( + "fparam_avg", + paddle.zeros([self.numb_fparam], dtype=self.prec).to(device=device), + ) + self.register_buffer( + "fparam_inv_std", + paddle.ones([self.numb_fparam], dtype=self.prec).to(device=device), + ) + else: + self.fparam_avg, self.fparam_inv_std = None, None + if self.numb_aparam > 0: + self.register_buffer( + "aparam_avg", + paddle.zeros([self.numb_aparam], dtype=self.prec).to(device=device), + ) + self.register_buffer( + "aparam_inv_std", + paddle.ones([self.numb_aparam], dtype=self.prec).to(device=device), + ) + else: + self.aparam_avg, self.aparam_inv_std = None, None + + in_dim = self.dim_descrpt + self.numb_fparam + self.numb_aparam + + self.old_impl = kwargs.get("old_impl", False) + if self.old_impl: + filter_layers = [] + for type_i in range(self.ntypes if not self.mixed_types else 1): + bias_type = 0.0 + one = ResidualDeep( + type_i, + self.dim_descrpt, + self.neuron, + bias_type, + resnet_dt=self.resnet_dt, + ) + filter_layers.append(one) + self.filter_layers_old = paddle.nn.LayerList(filter_layers) + self.filter_layers = None + else: + self.filter_layers = NetworkCollection( + 1 if not self.mixed_types else 0, + self.ntypes, + network_type="fitting_network", + networks=[ + FittingNet( + in_dim, + net_dim_out, + self.neuron, + self.activation_function, + self.resnet_dt, + self.precision, + bias_out=True, + seed=child_seed(self.seed, ii), + ) + for ii in range(self.ntypes if not self.mixed_types else 1) + ], + ) + self.filter_layers_old = None + # set trainable + for param in self.parameters(): + param.stop_gradient = not self.trainable + + def reinit_exclude( + self, + exclude_types: List[int] = [], + ): + self.exclude_types = exclude_types + self.emask = AtomExcludeMask(self.ntypes, self.exclude_types) + + def change_type_map( + self, type_map: List[str], model_with_new_type_stat=None + ) -> None: + """Change the type related params to new ones, according to `type_map` and the original one in the model. + If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. + """ + assert ( + self.type_map is not None + ), "'type_map' must be defined when performing type changing!" + assert self.mixed_types, "Only models in mixed types can perform type changing!" + remap_index, has_new_type = get_index_between_two_maps(self.type_map, type_map) + self.type_map = type_map + self.ntypes = len(type_map) + self.reinit_exclude(map_atom_exclude_types(self.exclude_types, remap_index)) + if has_new_type: + extend_shape = [len(type_map), *list(self.bias_atom_e.shape[1:])] + extend_bias_atom_e = paddle.zeros( + extend_shape, + dtype=self.bias_atom_e.dtype, + device=self.bias_atom_e.place, + ) + self.bias_atom_e = paddle.concat( + [self.bias_atom_e, extend_bias_atom_e], axis=0 + ) + self.bias_atom_e = self.bias_atom_e[remap_index] + + def serialize(self) -> dict: + """Serialize the fitting to dict.""" + return { + "@class": "Fitting", + "@version": 2, + "var_name": self.var_name, + "ntypes": self.ntypes, + "dim_descrpt": self.dim_descrpt, + "neuron": self.neuron, + "resnet_dt": self.resnet_dt, + "numb_fparam": self.numb_fparam, + "numb_aparam": self.numb_aparam, + "activation_function": self.activation_function, + "precision": self.precision, + "mixed_types": self.mixed_types, + "nets": self.filter_layers.serialize(), + "rcond": self.rcond, + "exclude_types": self.exclude_types, + "@variables": { + "bias_atom_e": to_numpy_array(self.bias_atom_e), + "fparam_avg": to_numpy_array(self.fparam_avg), + "fparam_inv_std": to_numpy_array(self.fparam_inv_std), + "aparam_avg": to_numpy_array(self.aparam_avg), + "aparam_inv_std": to_numpy_array(self.aparam_inv_std), + }, + "type_map": self.type_map, + # "tot_ener_zero": self.tot_ener_zero , + # "trainable": self.trainable , + # "atom_ener": self.atom_ener , + # "layer_name": self.layer_name , + # "use_aparam_as_mask": self.use_aparam_as_mask , + # "spin": self.spin , + ## NOTICE: not supported by far + "tot_ener_zero": False, + "trainable": [self.trainable] * (len(self.neuron) + 1), + "layer_name": None, + "use_aparam_as_mask": False, + "spin": None, + } + + @classmethod + def deserialize(cls, data: dict) -> "GeneralFitting": + data = copy.deepcopy(data) + variables = data.pop("@variables") + nets = data.pop("nets") + obj = cls(**data) + for kk in variables.keys(): + obj[kk] = to_paddle_tensor(variables[kk]) + obj.filter_layers = NetworkCollection.deserialize(nets) + return obj + + def get_dim_fparam(self) -> int: + """Get the number (dimension) of frame parameters of this atomic model.""" + return self.numb_fparam + + def get_dim_aparam(self) -> int: + """Get the number (dimension) of atomic parameters of this atomic model.""" + return self.numb_aparam + + # make jit happy + exclude_types: List[int] + + def get_sel_type(self) -> List[int]: + """Get the selected atom types of this model. + + Only atoms with selected atom types have atomic contribution + to the result of the model. + If returning an empty list, all atom types are selected. + """ + # make jit happy + sel_type: List[int] = [] + for ii in range(self.ntypes): + if ii not in self.exclude_types: + sel_type.append(ii) + return sel_type + + def get_type_map(self) -> List[str]: + """Get the name to each type of atoms.""" + return self.type_map + + def __setitem__(self, key, value): + if key in ["bias_atom_e"]: + value = value.reshape([self.ntypes, self._net_out_dim()]) + self.bias_atom_e = value + elif key in ["fparam_avg"]: + self.fparam_avg = value + elif key in ["fparam_inv_std"]: + self.fparam_inv_std = value + elif key in ["aparam_avg"]: + self.aparam_avg = value + elif key in ["aparam_inv_std"]: + self.aparam_inv_std = value + elif key in ["scale"]: + self.scale = value + else: + raise KeyError(key) + + def __getitem__(self, key): + if key in ["bias_atom_e"]: + return self.bias_atom_e + elif key in ["fparam_avg"]: + return self.fparam_avg + elif key in ["fparam_inv_std"]: + return self.fparam_inv_std + elif key in ["aparam_avg"]: + return self.aparam_avg + elif key in ["aparam_inv_std"]: + return self.aparam_inv_std + elif key in ["scale"]: + return self.scale + else: + raise KeyError(key) + + @abstractmethod + def _net_out_dim(self): + """Set the FittingNet output dim.""" + pass + + def _extend_f_avg_std(self, xx: paddle.Tensor, nb: int) -> paddle.Tensor: + return paddle.tile(xx.reshape([1, self.numb_fparam]), [nb, 1]) + + def _extend_a_avg_std(self, xx: paddle.Tensor, nb: int, nloc: int) -> paddle.Tensor: + return paddle.tile(xx.reshape([1, 1, self.numb_aparam]), [nb, nloc, 1]) + + def _forward_common( + self, + descriptor: paddle.Tensor, + atype: paddle.Tensor, + gr: Optional[paddle.Tensor] = None, + g2: Optional[paddle.Tensor] = None, + h2: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + ): + xx = descriptor + if self.remove_vaccum_contribution is not None: + # TODO: compute the input for vaccm when remove_vaccum_contribution is set + # Idealy, the input for vaccum should be computed; + # we consider it as always zero for convenience. + # Needs a compute_input_stats for vaccum passed from the + # descriptor. + xx_zeros = paddle.zeros_like(xx) + else: + xx_zeros = None + nf, nloc, nd = xx.shape + net_dim_out = self._net_out_dim() + + if nd != self.dim_descrpt: + raise ValueError( + "get an input descriptor of dim {nd}," + "which is not consistent with {self.dim_descrpt}." + ) + # check fparam dim, concate to input descriptor + if self.numb_fparam > 0: + assert fparam is not None, "fparam should not be None" + assert self.fparam_avg is not None + assert self.fparam_inv_std is not None + if fparam.shape[-1] != self.numb_fparam: + raise ValueError( + "get an input fparam of dim {fparam.shape[-1]}, ", + "which is not consistent with {self.numb_fparam}.", + ) + fparam = fparam.reshape([nf, self.numb_fparam]) + nb, _ = fparam.shape + t_fparam_avg = self._extend_f_avg_std(self.fparam_avg, nb) + t_fparam_inv_std = self._extend_f_avg_std(self.fparam_inv_std, nb) + fparam = (fparam - t_fparam_avg) * t_fparam_inv_std + fparam = paddle.tile(fparam.reshape([nf, 1, -1]), [1, nloc, 1]) + xx = paddle.concat( + [xx, fparam], + axis=-1, + ) + if xx_zeros is not None: + xx_zeros = paddle.concat( + [xx_zeros, fparam], + axis=-1, + ) + # check aparam dim, concate to input descriptor + if self.numb_aparam > 0: + assert aparam is not None, "aparam should not be None" + assert self.aparam_avg is not None + assert self.aparam_inv_std is not None + if aparam.shape[-1] != self.numb_aparam: + raise ValueError( + f"get an input aparam of dim {aparam.shape[-1]}, ", + f"which is not consistent with {self.numb_aparam}.", + ) + aparam = aparam.reshape([nf, -1, self.numb_aparam]) + nb, nloc, _ = aparam.shape + t_aparam_avg = self._extend_a_avg_std(self.aparam_avg, nb, nloc) + t_aparam_inv_std = self._extend_a_avg_std(self.aparam_inv_std, nb, nloc) + aparam = (aparam - t_aparam_avg) * t_aparam_inv_std + xx = paddle.concat( + [xx, aparam], + axis=-1, + ) + if xx_zeros is not None: + xx_zeros = paddle.concat( + [xx_zeros, aparam], + axis=-1, + ) + + outs = paddle.zeros( + (nf, nloc, net_dim_out), + dtype=env.GLOBAL_PD_FLOAT_PRECISION, + ).to(device=descriptor.place) # jit assertion + if self.old_impl: + assert self.filter_layers_old is not None + assert xx_zeros is None + if self.mixed_types: + atom_property = self.filter_layers_old[0](xx) + self.bias_atom_e[atype] + outs = outs + atom_property # Shape is [nframes, natoms[0], 1] + else: + for type_i, filter_layer in enumerate(self.filter_layers_old): + mask = atype == type_i + atom_property = filter_layer(xx) + atom_property = atom_property + self.bias_atom_e[type_i] + atom_property = atom_property * mask.unsqueeze(-1) + outs = outs + atom_property # Shape is [nframes, natoms[0], 1] + else: + if self.mixed_types: + atom_property = ( + self.filter_layers.networks[0](xx) + self.bias_atom_e[atype] + ) + if xx_zeros is not None: + atom_property -= self.filter_layers.networks[0](xx_zeros) + outs = ( + outs + atom_property + ) # Shape is [nframes, natoms[0], net_dim_out] + else: + for type_i, ll in enumerate(self.filter_layers.networks): + mask = (atype == type_i).unsqueeze(-1) + mask = paddle.tile(mask, (1, 1, net_dim_out)) + atom_property = ll(xx) + if xx_zeros is not None: + # must assert, otherwise jit is not happy + assert self.remove_vaccum_contribution is not None + if not ( + len(self.remove_vaccum_contribution) > type_i + and not self.remove_vaccum_contribution[type_i] + ): + atom_property -= ll(xx_zeros) + atom_property = atom_property + self.bias_atom_e[type_i] + atom_property = atom_property * mask.astype(atom_property.dtype) + outs = ( + outs + atom_property + ) # Shape is [nframes, natoms[0], net_dim_out] + # nf x nloc + mask = self.emask(atype) + # nf x nloc x nod + outs = outs * mask[:, :, None].astype(outs.dtype) + return {self.var_name: outs.to(env.GLOBAL_PD_FLOAT_PRECISION)} diff --git a/deepmd/pd/model/task/invar_fitting.py b/deepmd/pd/model/task/invar_fitting.py new file mode 100644 index 0000000000..5d052a2d62 --- /dev/null +++ b/deepmd/pd/model/task/invar_fitting.py @@ -0,0 +1,182 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import logging +from typing import ( + List, + Optional, + Union, +) + +import paddle + +from deepmd.dpmodel import ( + FittingOutputDef, + OutputVariableDef, + fitting_check_output, +) +from deepmd.pd.model.task.fitting import ( + GeneralFitting, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + DEFAULT_PRECISION, +) +from deepmd.utils.version import ( + check_version_compatibility, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION +device = env.DEVICE + +log = logging.getLogger(__name__) + + +@GeneralFitting.register("invar") +@fitting_check_output +class InvarFitting(GeneralFitting): + """Construct a fitting net for energy. + + Parameters + ---------- + var_name : str + The atomic property to fit, 'energy', 'dipole', and 'polar'. + ntypes : int + Element count. + dim_descrpt : int + Embedding width per atom. + dim_out : int + The output dimension of the fitting net. + neuron : List[int] + Number of neurons in each hidden layers of the fitting net. + bias_atom_e : paddle.Tensor, optional + Average enery per atom for each element. + resnet_dt : bool + Using time-step in the ResNet construction. + numb_fparam : int + Number of frame parameters. + numb_aparam : int + Number of atomic parameters. + activation_function : str + Activation function. + precision : str + Numerical precision. + mixed_types : bool + If true, use a uniform fitting net for all atom types, otherwise use + different fitting nets for different atom types. + rcond : float, optional + The condition number for the regression of atomic energy. + seed : int, optional + Random seed. + exclude_types: List[int] + Atomic contributions of the excluded atom types are set zero. + atom_ener: List[Optional[paddle.Tensor]], optional + Specifying atomic energy contribution in vacuum. + The value is a list specifying the bias. the elements can be None or np.array of output shape. + For example: [None, [2.]] means type 0 is not set, type 1 is set to [2.] + The `set_davg_zero` key in the descrptor should be set. + type_map: List[str], Optional + A list of strings. Give the name to each type of atoms. + + """ + + def __init__( + self, + var_name: str, + ntypes: int, + dim_descrpt: int, + dim_out: int, + neuron: List[int] = [128, 128, 128], + bias_atom_e: Optional[paddle.Tensor] = None, + resnet_dt: bool = True, + numb_fparam: int = 0, + numb_aparam: int = 0, + activation_function: str = "tanh", + precision: str = DEFAULT_PRECISION, + mixed_types: bool = True, + rcond: Optional[float] = None, + seed: Optional[Union[int, List[int]]] = None, + exclude_types: List[int] = [], + atom_ener: Optional[List[Optional[paddle.Tensor]]] = None, + type_map: Optional[List[str]] = None, + **kwargs, + ): + self.dim_out = dim_out + self.atom_ener = atom_ener + super().__init__( + var_name=var_name, + ntypes=ntypes, + dim_descrpt=dim_descrpt, + neuron=neuron, + bias_atom_e=bias_atom_e, + resnet_dt=resnet_dt, + numb_fparam=numb_fparam, + numb_aparam=numb_aparam, + activation_function=activation_function, + precision=precision, + mixed_types=mixed_types, + rcond=rcond, + seed=seed, + exclude_types=exclude_types, + remove_vaccum_contribution=None + if atom_ener is None or len([x for x in atom_ener if x is not None]) == 0 + else [x is not None for x in atom_ener], + type_map=type_map, + **kwargs, + ) + + def _net_out_dim(self): + """Set the FittingNet output dim.""" + return self.dim_out + + def serialize(self) -> dict: + data = super().serialize() + data["type"] = "invar" + data["dim_out"] = self.dim_out + data["atom_ener"] = self.atom_ener + return data + + @classmethod + def deserialize(cls, data: dict) -> "GeneralFitting": + data = copy.deepcopy(data) + check_version_compatibility(data.pop("@version", 1), 2, 1) + return super().deserialize(data) + + def output_def(self) -> FittingOutputDef: + return FittingOutputDef( + [ + OutputVariableDef( + self.var_name, + [self.dim_out], + reducible=True, + r_differentiable=True, + c_differentiable=True, + ), + ] + ) + + def forward( + self, + descriptor: paddle.Tensor, + atype: paddle.Tensor, + gr: Optional[paddle.Tensor] = None, + g2: Optional[paddle.Tensor] = None, + h2: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + ): + """Based on embedding net output, alculate total energy. + + Args: + - inputs: Embedding matrix. Its shape is [nframes, natoms[0], self.dim_descrpt]. + - natoms: Tell atom count and element count. Its shape is [2+self.ntypes]. + + Returns + ------- + - `paddle.Tensor`: Total energy with shape [nframes, natoms[0]]. + """ + return self._forward_common(descriptor, atype, gr, g2, h2, fparam, aparam) + + # make jit happy with paddle 2.0.0 + exclude_types: List[int] diff --git a/deepmd/pd/model/task/polarizability.py b/deepmd/pd/model/task/polarizability.py new file mode 100644 index 0000000000..7939d8883d --- /dev/null +++ b/deepmd/pd/model/task/polarizability.py @@ -0,0 +1,264 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import logging +from typing import ( + List, + Optional, + Union, +) + +import paddle + +from deepmd.dpmodel import ( + FittingOutputDef, + OutputVariableDef, +) +from deepmd.pd.model.task.fitting import ( + GeneralFitting, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + DEFAULT_PRECISION, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, +) +from deepmd.utils.finetune import ( + get_index_between_two_maps, +) +from deepmd.utils.version import ( + check_version_compatibility, +) + +log = logging.getLogger(__name__) + + +@GeneralFitting.register("polar") +class PolarFittingNet(GeneralFitting): + """Construct a polar fitting net. + + Parameters + ---------- + ntypes : int + Element count. + dim_descrpt : int + Embedding width per atom. + embedding_width : int + The dimension of rotation matrix, m1. + neuron : List[int] + Number of neurons in each hidden layers of the fitting net. + resnet_dt : bool + Using time-step in the ResNet construction. + numb_fparam : int + Number of frame parameters. + numb_aparam : int + Number of atomic parameters. + activation_function : str + Activation function. + precision : str + Numerical precision. + mixed_types : bool + If true, use a uniform fitting net for all atom types, otherwise use + different fitting nets for different atom types. + rcond : float, optional + The condition number for the regression of atomic energy. + seed : int, optional + Random seed. + fit_diag : bool + Fit the diagonal part of the rotational invariant polarizability matrix, which will be converted to + normal polarizability matrix by contracting with the rotation matrix. + scale : List[float] + The output of the fitting net (polarizability matrix) for type i atom will be scaled by scale[i] + shift_diag : bool + Whether to shift the diagonal part of the polarizability matrix. The shift operation is carried out after scale. + type_map: List[str], Optional + A list of strings. Give the name to each type of atoms. + + """ + + def __init__( + self, + ntypes: int, + dim_descrpt: int, + embedding_width: int, + neuron: List[int] = [128, 128, 128], + resnet_dt: bool = True, + numb_fparam: int = 0, + numb_aparam: int = 0, + activation_function: str = "tanh", + precision: str = DEFAULT_PRECISION, + mixed_types: bool = True, + rcond: Optional[float] = None, + seed: Optional[Union[int, List[int]]] = None, + exclude_types: List[int] = [], + fit_diag: bool = True, + scale: Optional[Union[List[float], float]] = None, + shift_diag: bool = True, + type_map: Optional[List[str]] = None, + **kwargs, + ): + self.embedding_width = embedding_width + self.fit_diag = fit_diag + self.scale = scale + if self.scale is None: + self.scale = [1.0 for _ in range(ntypes)] + else: + if isinstance(self.scale, list): + assert ( + len(self.scale) == ntypes + ), "Scale should be a list of length ntypes." + elif isinstance(self.scale, float): + self.scale = [self.scale for _ in range(ntypes)] + else: + raise ValueError( + "Scale must be a list of float of length ntypes or a float." + ) + self.scale = ( + paddle.to_tensor(self.scale, dtype=env.GLOBAL_PD_FLOAT_PRECISION) + .to(device=env.DEVICE) + .reshape([ntypes, 1]) + ) + self.shift_diag = shift_diag + self.constant_matrix = paddle.zeros( + [ntypes], dtype=env.GLOBAL_PD_FLOAT_PRECISION + ).to(device=env.DEVICE) + super().__init__( + var_name="polar", + ntypes=ntypes, + dim_descrpt=dim_descrpt, + neuron=neuron, + resnet_dt=resnet_dt, + numb_fparam=numb_fparam, + numb_aparam=numb_aparam, + activation_function=activation_function, + precision=precision, + mixed_types=mixed_types, + rcond=rcond, + seed=seed, + exclude_types=exclude_types, + type_map=type_map, + **kwargs, + ) + self.old_impl = False # this only supports the new implementation. + + def _net_out_dim(self): + """Set the FittingNet output dim.""" + return ( + self.embedding_width + if self.fit_diag + else self.embedding_width * self.embedding_width + ) + + def __setitem__(self, key, value): + if key in ["constant_matrix"]: + self.constant_matrix = value + else: + super().__setitem__(key, value) + + def __getitem__(self, key): + if key in ["constant_matrix"]: + return self.constant_matrix + else: + return super().__getitem__(key) + + def change_type_map( + self, type_map: List[str], model_with_new_type_stat=None + ) -> None: + """Change the type related params to new ones, according to `type_map` and the original one in the model. + If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. + """ + assert ( + self.type_map is not None + ), "'type_map' must be defined when performing type changing!" + assert self.mixed_types, "Only models in mixed types can perform type changing!" + remap_index, has_new_type = get_index_between_two_maps(self.type_map, type_map) + super().change_type_map(type_map=type_map) + if has_new_type: + extend_shape = [len(type_map), *list(self.scale.shape[1:])] + extend_scale = paddle.ones( + extend_shape, dtype=self.scale.dtype, device=self.scale.place + ) + self.scale = paddle.concat([self.scale, extend_scale], axis=0) + extend_shape = [len(type_map), *list(self.constant_matrix.shape[1:])] + extend_constant_matrix = paddle.zeros( + extend_shape, + dtype=self.constant_matrix.dtype, + ).to(device=self.constant_matrix.place) + self.constant_matrix = paddle.concat( + [self.constant_matrix, extend_constant_matrix], axis=0 + ) + self.scale = self.scale[remap_index] + self.constant_matrix = self.constant_matrix[remap_index] + + def serialize(self) -> dict: + data = super().serialize() + data["type"] = "polar" + data["@version"] = 3 + data["embedding_width"] = self.embedding_width + data["old_impl"] = self.old_impl + data["fit_diag"] = self.fit_diag + data["shift_diag"] = self.shift_diag + data["@variables"]["scale"] = to_numpy_array(self.scale) + data["@variables"]["constant_matrix"] = to_numpy_array(self.constant_matrix) + return data + + @classmethod + def deserialize(cls, data: dict) -> "GeneralFitting": + data = copy.deepcopy(data) + check_version_compatibility(data.pop("@version", 1), 3, 1) + data.pop("var_name", None) + return super().deserialize(data) + + def output_def(self) -> FittingOutputDef: + return FittingOutputDef( + [ + OutputVariableDef( + "polarizability", + [3, 3], + reducible=True, + r_differentiable=False, + c_differentiable=False, + ), + ] + ) + + def forward( + self, + descriptor: paddle.Tensor, + atype: paddle.Tensor, + gr: Optional[paddle.Tensor] = None, + g2: Optional[paddle.Tensor] = None, + h2: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + ): + nframes, nloc, _ = descriptor.shape + assert ( + gr is not None + ), "Must provide the rotation matrix for polarizability fitting." + # (nframes, nloc, _net_out_dim) + out = self._forward_common(descriptor, atype, gr, g2, h2, fparam, aparam)[ + self.var_name + ] + out = out * (self.scale.to(atype.place))[atype] + gr = gr.reshape( + [nframes * nloc, self.embedding_width, 3] + ) # (nframes * nloc, m1, 3) + + if self.fit_diag: + out = out.reshape([-1, self.embedding_width]) + out = paddle.einsum("ij,ijk->ijk", out, gr) + else: + out = out.reshape([-1, self.embedding_width, self.embedding_width]) + out = (out + out.transpose([0, 2, 1])) / 2 + out = paddle.einsum("bim,bmj->bij", out, gr) # (nframes * nloc, m1, 3) + out = paddle.einsum( + "bim,bmj->bij", gr.transpose([0, 2, 1]), out + ) # (nframes * nloc, 3, 3) + out = out.reshape([nframes, nloc, 3, 3]) + return {"polarizability": out.to(env.GLOBAL_PD_FLOAT_PRECISION)} + + # make jit happy with paddle 2.0.0 + exclude_types: List[int] diff --git a/deepmd/pd/model/task/task.py b/deepmd/pd/model/task/task.py new file mode 100644 index 0000000000..6ceb116d85 --- /dev/null +++ b/deepmd/pd/model/task/task.py @@ -0,0 +1 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later diff --git a/deepmd/pd/model/task/type_predict.py b/deepmd/pd/model/task/type_predict.py new file mode 100644 index 0000000000..241d4837d5 --- /dev/null +++ b/deepmd/pd/model/task/type_predict.py @@ -0,0 +1,47 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Optional, +) + +import paddle + +from deepmd.pd.model.network.network import ( + MaskLMHead, +) +from deepmd.pd.model.task import ( + Fitting, +) + + +class TypePredictNet(Fitting): + def __init__(self, feature_dim, ntypes, activation_function="gelu", **kwargs): + """Construct a type predict net. + + Args: + - feature_dim: Input dm. + - ntypes: Numer of types to predict. + - activation_function: Activate function. + """ + super().__init__() + self.feature_dim = feature_dim + self.ntypes = ntypes + self.lm_head = MaskLMHead( + embed_dim=self.feature_dim, + output_dim=ntypes, + activation_fn=activation_function, + weight=None, + ) + + def forward(self, features, masked_tokens: Optional[paddle.Tensor] = None): + """Calculate the predicted logits. + Args: + - features: Input features with shape [nframes, nloc, feature_dim]. + - masked_tokens: Input masked tokens with shape [nframes, nloc]. + + Returns + ------- + - logits: Predicted probs with shape [nframes, nloc, ntypes]. + """ + # [nframes, nloc, ntypes] + logits = self.lm_head(features, masked_tokens=masked_tokens) + return logits diff --git a/deepmd/pd/optimizer/KFWrapper.py b/deepmd/pd/optimizer/KFWrapper.py new file mode 100644 index 0000000000..2635932578 --- /dev/null +++ b/deepmd/pd/optimizer/KFWrapper.py @@ -0,0 +1,145 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import math + +import numpy as np +import paddle +import paddle.distributed as dist +import paddle.nn as nn +from paddle.optimizer import ( + Optimizer, +) + + +class KFOptimizerWrapper: + def __init__( + self, + model: nn.Layer, + optimizer: Optimizer, + atoms_selected: int, + atoms_per_group: int, + is_distributed: bool = False, + ) -> None: + self.model = model + self.optimizer = optimizer + self.atoms_selected = atoms_selected # 24 + self.atoms_per_group = atoms_per_group # 6 + self.is_distributed = is_distributed + + def update_energy( + self, inputs: dict, Etot_label: paddle.Tensor, update_prefactor: float = 1 + ) -> None: + model_pred, _, _ = self.model(**inputs, inference_only=True) + Etot_predict = model_pred["energy"] + natoms_sum = int(inputs["atype"].shape[-1]) + self.optimizer.set_grad_prefactor(natoms_sum) + + self.optimizer.zero_grad() + bs = Etot_label.shape[0] + error = Etot_label - Etot_predict + error = error / natoms_sum + mask = error < 0 + + error = error * update_prefactor + error[mask] = -1 * error[mask] + error = error.mean() + + if self.is_distributed: + dist.all_reduce(error) + error /= dist.get_world_size() + + Etot_predict = update_prefactor * Etot_predict + Etot_predict[mask] = -Etot_predict[mask] + + Etot_predict.sum().backward() + error = error * math.sqrt(bs) + self.optimizer.step(error) + return Etot_predict + + def update_force( + self, inputs: dict, Force_label: paddle.Tensor, update_prefactor: float = 1 + ) -> None: + natoms_sum = int(inputs["atype"].shape[-1]) + bs = Force_label.shape[0] + self.optimizer.set_grad_prefactor(natoms_sum * self.atoms_per_group * 3) + + index = self.__sample(self.atoms_selected, self.atoms_per_group, natoms_sum) + + for i in range(index.shape[0]): + self.optimizer.zero_grad() + model_pred, _, _ = self.model(**inputs, inference_only=True) + Etot_predict = model_pred["energy"] + natoms_sum = int(inputs["atype"].shape[-1]) + force_predict = model_pred["force"] + error_tmp = Force_label[:, index[i]] - force_predict[:, index[i]] + error_tmp = update_prefactor * error_tmp + mask = error_tmp < 0 + error_tmp[mask] = -1 * error_tmp[mask] + error = error_tmp.mean() / natoms_sum + + if self.is_distributed: + dist.all_reduce(error) + error /= dist.get_world_size() + + tmp_force_predict = force_predict[:, index[i]] * update_prefactor + tmp_force_predict[mask] = -tmp_force_predict[mask] + + # In order to solve a pytorch bug, reference: https://github.com/pytorch/pytorch/issues/43259 + (tmp_force_predict.sum() + Etot_predict.sum() * 0).backward() + error = error * math.sqrt(bs) + self.optimizer.step(error) + return Etot_predict, force_predict + + def update_denoise_coord( + self, + inputs: dict, + clean_coord: paddle.Tensor, + update_prefactor: float = 1, + mask_loss_coord: bool = True, + coord_mask: paddle.Tensor = None, + ) -> None: + natoms_sum = int(inputs["atype"].shape[-1]) + bs = clean_coord.shape[0] + self.optimizer.set_grad_prefactor(natoms_sum * self.atoms_per_group * 3) + + index = self.__sample(self.atoms_selected, self.atoms_per_group, natoms_sum) + + for i in range(index.shape[0]): + self.optimizer.zero_grad() + model_pred, _, _ = self.model(**inputs, inference_only=True) + updated_coord = model_pred["updated_coord"] + natoms_sum = int(inputs["atype"].shape[-1]) + error_tmp = clean_coord[:, index[i]] - updated_coord[:, index[i]] + error_tmp = update_prefactor * error_tmp + if mask_loss_coord: + error_tmp[~coord_mask[:, index[i]]] = 0 + mask = error_tmp < 0 + error_tmp[mask] = -1 * error_tmp[mask] + error = error_tmp.mean() / natoms_sum + + if self.is_distributed: + dist.all_reduce(error) + error /= dist.get_world_size() + + tmp_coord_predict = updated_coord[:, index[i]] * update_prefactor + tmp_coord_predict[mask] = -update_prefactor * tmp_coord_predict[mask] + + # In order to solve a pytorch bug, reference: https://github.com/pytorch/pytorch/issues/43259 + (tmp_coord_predict.sum() + updated_coord.sum() * 0).backward() + error = error * math.sqrt(bs) + self.optimizer.step(error) + return model_pred + + def __sample( + self, atoms_selected: int, atoms_per_group: int, natoms: int + ) -> np.ndarray: + if atoms_selected % atoms_per_group: + raise Exception("divider") + index = range(natoms) + rng = np.random.default_rng() + res = rng.choice(index, atoms_selected).reshape([-1, atoms_per_group]) + return res + + +# with paddle.autograd.profiler.profile(enabled=True, use_cuda=True, record_shapes=False) as prof: +# the code u wanna profile +# print(prof.key_averages().table(sort_by="self_cpu_time_total")) diff --git a/deepmd/pd/optimizer/LKF.py b/deepmd/pd/optimizer/LKF.py new file mode 100644 index 0000000000..b506fea369 --- /dev/null +++ b/deepmd/pd/optimizer/LKF.py @@ -0,0 +1,322 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import logging +import math + +import paddle +import paddle.distributed as dist +from paddle.optimizer import ( + Optimizer, +) + + +def distribute_indices(total_length, num_workers): + indices_per_worker = total_length // num_workers + remainder = total_length % num_workers + + indices = [] + start = 0 + + for i in range(num_workers): + end = start + indices_per_worker + (1 if i < remainder else 0) + indices.append((start, end)) + start = end + + return indices, remainder + + +class LKFOptimizer(Optimizer): + def __init__( + self, + params, + kalman_lambda=0.98, + kalman_nue=0.9987, + block_size=5120, + ): + defaults = {"lr": 0.1, "kalman_nue": kalman_nue, "block_size": block_size} + + super().__init__(params, defaults) + + self._params = self.param_groups[0]["params"] + + if len(self.param_groups) != 1 or len(self._params) == 0: + raise ValueError( + "LKF doesn't support per-parameter options " "(parameter groups)" + ) + + # NOTE: LKF has only global state, but we register it as state for + # the first param, because this helps with casting in load_state_dict + self._state = self.state[self._params[0]] + self._state.setdefault("kalman_lambda", kalman_lambda) + self.dist_init = dist.is_available() and dist.is_initialized() + self.rank = dist.get_rank() if self.dist_init else 0 + self.dindex = [] + self.remainder = 0 + self.__init_P() + + def __init_P(self): + param_nums = [] + param_sum = 0 + block_size = self.__get_blocksize() + data_type = self._params[0].dtype + device = self._params[0].place + + for param_group in self.param_groups: + params = param_group["params"] + for param in params: + param_num = param.data.nelement() + if param_sum + param_num > block_size: + if param_sum > 0: + param_nums.append(param_sum) + param_sum = param_num + else: + param_sum += param_num + + param_nums.append(param_sum) + + P = [] + params_packed_index = [] + logging.info(f"LKF parameter nums: {param_nums}") + if self.dist_init: + block_num = 0 + for param_num in param_nums: + if param_num >= block_size: + block_num += math.ceil(param_num / block_size) + else: + block_num += 1 + num_workers = dist.get_world_size() + self.dindex, self.remainder = distribute_indices(block_num, num_workers) + index = 0 + for param_num in param_nums: + if param_num >= block_size: + block_num = math.ceil(param_num / block_size) + for i in range(block_num): + device_id = self.get_device_id(index) + index += 1 + dist_device = "gpu:" + str(device_id) + if i != block_num - 1: + params_packed_index.append(block_size) + if self.rank == device_id: + P.append( + paddle.eye( + block_size, + dtype=data_type, + device=dist_device, + ) + ) + else: + continue + else: + params_packed_index.append(param_num - block_size * i) + if self.rank == device_id: + P.append( + paddle.eye( + param_num - block_size * i, + dtype=data_type, + device=dist_device, + ) + ) + else: + continue + + else: + device_id = self.get_device_id(index) + index += 1 + params_packed_index.append(param_num) + if self.rank == device_id: + dist_device = "gpu:" + str(device_id) + P.append( + paddle.eye(param_num, dtype=data_type, device=dist_device) + ) + else: + for param_num in param_nums: + if param_num >= block_size: + block_num = math.ceil(param_num / block_size) + for i in range(block_num): + if i != block_num - 1: + P.append( + paddle.eye( + block_size, + dtype=data_type, + device=device, + ) + ) + params_packed_index.append(block_size) + else: + P.append( + paddle.eye( + param_num - block_size * i, + dtype=data_type, + device=device, + ) + ) + params_packed_index.append(param_num - block_size * i) + else: + P.append(paddle.eye(param_num, dtype=data_type, device=device)) + params_packed_index.append(param_num) + + self._state.setdefault("P", P) + self._state.setdefault("weights_num", len(P)) + self._state.setdefault("params_packed_index", params_packed_index) + + def __get_blocksize(self): + return self.param_groups[0]["block_size"] + + def __get_nue(self): + return self.param_groups[0]["kalman_nue"] + + def __split_weights(self, weight): + block_size = self.__get_blocksize() + param_num = weight.nelement() + res = [] + if param_num < block_size: + res.append(weight) + else: + block_num = math.ceil(param_num / block_size) + for i in range(block_num): + if i != block_num - 1: + res.append(weight[i * block_size : (i + 1) * block_size]) + else: + res.append(weight[i * block_size :]) + return res + + def __update(self, H, error, weights): + P = self._state.get("P") + kalman_lambda = self._state.get("kalman_lambda") + weights_num = self._state.get("weights_num") + params_packed_index = self._state.get("params_packed_index") + + block_size = self.__get_blocksize() + kalman_nue = self.__get_nue() + + tmp = 0 + for i in range(weights_num): + tmp = tmp + ( + kalman_lambda + paddle.matmul(paddle.matmul(H[i].T, P[i]), H[i]) + ) + if self.dist_init: + dist.all_reduce(tmp, op=dist.ReduceOp.SUM) + A = 1 / tmp + for i in range(weights_num): + K = paddle.matmul(P[i], H[i]) + + weights[i] = weights[i] + A * error * K + + P[i] = (1 / kalman_lambda) * (P[i] - A * paddle.matmul(K, K.T)) + if self.dist_init: + device = "gpu:" + str(self.rank) + local_shape = [tensor.shape[0] for tensor in weights] + shape_list = [ + paddle.zeros_like(paddle.empty(1), dtype=paddle.float64, device=device) # pylint: disable=no-explicit-dtype,no-explicit-device + for _ in range(dist.get_world_size()) + ] + dist.all_gather_object(shape_list, local_shape) + weight_tensor = paddle.concat(weights) + world_shape = [sum(inner_list) for inner_list in shape_list] + weight_list = [None] * len(world_shape) + for i in range(len(world_shape)): + weight_list[i] = paddle.zeros( + world_shape[i], dtype=paddle.float64, device=device + ) + dist.all_gather(weight_list, weight_tensor) + result = [] + for i in range(dist.get_world_size()): + result = result + list(paddle.split(weight_list[i], shape_list[i])) + weights = result + kalman_lambda = kalman_nue * kalman_lambda + 1 - kalman_nue + self._state.update({"kalman_lambda": kalman_lambda}) + + i = 0 + param_sum = 0 + for param_group in self.param_groups: + params = param_group["params"] + for param in params: + param_num = param.nelement() + weight_tmp = weights[i][param_sum : param_sum + param_num] + if param_num < block_size: + if param.ndim > 1: + param.data = weight_tmp.reshape( + param.data.T.shape + ).T.contiguous() + else: + param.data = weight_tmp.reshape(param.data.shape) + + param_sum += param_num + + if param_sum == params_packed_index[i]: + i += 1 + param_sum = 0 + else: + block_num = math.ceil(param_num / block_size) + for j in range(block_num): + if j == 0: + tmp_weight = weights[i] + else: + tmp_weight = paddle.concat([tmp_weight, weights[i]], axis=0) + i += 1 + param.data = tmp_weight.reshape(param.data.T.shape).T.contiguous() + + def set_grad_prefactor(self, grad_prefactor): + self.grad_prefactor = grad_prefactor + + def step(self, error): + params_packed_index = self._state.get("params_packed_index") + + weights = [] + H = [] + param_index = 0 + param_sum = 0 + + for param in self._params: + if param.ndim > 1: + tmp = param.data.T.contiguous().reshape(param.data.nelement(), 1) + if param.grad is None: + tmp_grad = paddle.zeros_like(tmp) + else: + tmp_grad = ( + (param.grad / self.grad_prefactor) + .T.contiguous() + .reshape(param.grad.nelement(), 1) + ) + else: + tmp = param.data.reshape(param.data.nelement(), 1) + if param.grad is None: + tmp_grad = paddle.zeros_like(tmp) + else: + tmp_grad = (param.grad / self.grad_prefactor).reshape( + param.grad.nelement(), 1 + ) + + tmp = self.__split_weights(tmp) + tmp_grad = self.__split_weights(tmp_grad) + + for split_grad, split_weight in zip(tmp_grad, tmp): + nelement = split_grad.nelement() + + if param_sum == 0: + res_grad = split_grad + res = split_weight + else: + res_grad = paddle.concat((res_grad, split_grad), axis=0) + res = paddle.concat((res, split_weight), axis=0) + + param_sum += nelement + + if param_sum == params_packed_index[param_index]: + param_sum = 0 + if self.dist_init: + device_id = self.get_device_id(param_index) + if self.rank == device_id: + weights.append(res) + H.append(res_grad) + else: + weights.append(res) + H.append(res_grad) + param_index += 1 + + self.__update(H, error, weights) + + def get_device_id(self, index): + for i, (start, end) in enumerate(self.dindex): + if start <= index < end: + return i + return None diff --git a/deepmd/pd/optimizer/__init__.py b/deepmd/pd/optimizer/__init__.py new file mode 100644 index 0000000000..db340b3bb9 --- /dev/null +++ b/deepmd/pd/optimizer/__init__.py @@ -0,0 +1,9 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from .KFWrapper import ( + KFOptimizerWrapper, +) +from .LKF import ( + LKFOptimizer, +) + +__all__ = ["KFOptimizerWrapper", "LKFOptimizer"] diff --git a/deepmd/pd/train/__init__.py b/deepmd/pd/train/__init__.py new file mode 100644 index 0000000000..6ceb116d85 --- /dev/null +++ b/deepmd/pd/train/__init__.py @@ -0,0 +1 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later diff --git a/deepmd/pd/train/training.py b/deepmd/pd/train/training.py new file mode 100644 index 0000000000..4ffa088ecb --- /dev/null +++ b/deepmd/pd/train/training.py @@ -0,0 +1,1294 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import functools +import logging +import time +from copy import ( + deepcopy, +) +from pathlib import ( + Path, +) +from typing import ( + Any, + Dict, +) + +import numpy as np +import paddle +import paddle.distributed as dist +from paddle.distributed import DataParallel as DDP +from paddle.io import ( + DataLoader, +) + +from deepmd.common import ( + symlink_prefix_files, +) +from deepmd.loggers.training import ( + format_training_message, + format_training_message_per_task, +) +from deepmd.pd.loss import ( + DenoiseLoss, + DOSLoss, + EnergySpinLoss, + EnergyStdLoss, + TensorLoss, +) +from deepmd.pd.model.model import ( + get_model, + get_zbl_model, +) +from deepmd.pd.optimizer import ( + KFOptimizerWrapper, + LKFOptimizer, +) +from deepmd.pd.train.wrapper import ( + ModelWrapper, +) +from deepmd.pd.utils import ( + dp_random, +) +from deepmd.pd.utils.dataloader import ( + BufferedIterator, + get_weighted_sampler, +) +from deepmd.pd.utils.env import ( + DEVICE, + JIT, + LOCAL_RANK, + SAMPLER_RECORD, +) +from deepmd.pd.utils.learning_rate import ( + LearningRateExp, +) +from deepmd.pd.utils.stat import ( + make_stat_input, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, +) +from deepmd.utils.data import ( + DataRequirementItem, +) +from deepmd.utils.path import ( + DPH5Path, +) + +# if paddle.__version__.startswith("2"): +# import paddle._dynamo + + +log = logging.getLogger(__name__) + + +class Trainer: + def __init__( + self, + config: Dict[str, Any], + training_data, + stat_file_path=None, + validation_data=None, + init_model=None, + restart_model=None, + finetune_model=None, + force_load=False, + shared_links=None, + finetune_links=None, + init_frz_model=None, + ): + """Construct a DeePMD trainer. + + Args: + - config: The Dict-like configuration with training options. + """ + paddle.core.set_prim_eager_enabled(True) + paddle.core._set_prim_all_enabled(True) + if init_model is not None: + resume_model = init_model + elif restart_model is not None: + resume_model = restart_model + elif finetune_model is not None: + resume_model = finetune_model + else: + resume_model = None + resuming = resume_model is not None + self.restart_training = restart_model is not None + model_params = config["model"] + training_params = config["training"] + self.multi_task = "model_dict" in model_params + self.finetune_links = finetune_links + self.finetune_update_stat = False + self.model_keys = ( + list(model_params["model_dict"]) if self.multi_task else ["Default"] + ) + self.rank = ( + dist.get_rank() if dist.is_available() and dist.is_initialized() else 0 + ) + self.world_size = ( + dist.get_world_size() + if dist.is_available() and dist.is_initialized() + else 1 + ) + self.num_model = len(self.model_keys) + + # Iteration config + self.num_steps = training_params["numb_steps"] + self.disp_file = training_params.get("disp_file", "lcurve.out") + self.disp_freq = training_params.get("disp_freq", 1000) + self.save_ckpt = training_params.get("save_ckpt", "model.ckpt") + self.save_freq = training_params.get("save_freq", 1000) + self.max_ckpt_keep = training_params.get("max_ckpt_keep", 5) + self.display_in_training = training_params.get("disp_training", True) + self.timing_in_training = training_params.get("time_training", True) + self.change_bias_after_training = training_params.get( + "change_bias_after_training", False + ) + self.lcurve_should_print_header = True + + def get_opt_param(params): + opt_type = params.get("opt_type", "Adam") + opt_param = { + "kf_blocksize": params.get("kf_blocksize", 5120), + "kf_start_pref_e": params.get("kf_start_pref_e", 1), + "kf_limit_pref_e": params.get("kf_limit_pref_e", 1), + "kf_start_pref_f": params.get("kf_start_pref_f", 1), + "kf_limit_pref_f": params.get("kf_limit_pref_f", 1), + } + return opt_type, opt_param + + def get_data_loader(_training_data, _validation_data, _training_params): + def get_dataloader_and_buffer(_data, _params): + if "auto_prob" in _training_params["training_data"]: + _sampler = get_weighted_sampler( + _data, _params["training_data"]["auto_prob"] + ) + elif "sys_probs" in _training_params["training_data"]: + _sampler = get_weighted_sampler( + _data, + _params["training_data"]["sys_probs"], + sys_prob=True, + ) + else: + _sampler = get_weighted_sampler(_data, "prob_sys_size") + + if _sampler is None: + log.warning( + "Sampler not specified!" + ) # None sampler will lead to a premature stop iteration. Replacement should be True in attribute of the sampler to produce expected number of items in one iteration. + _dataloader = DataLoader( + _data, + batch_sampler=paddle.io.BatchSampler( + sampler=_sampler, drop_last=False + ), + # batch_size=None, + num_workers=0 + if dist.is_available() + else 0, # setting to 0 diverges the behavior of its iterator; should be >=1 + # drop_last=False, + # collate_fn=lambda batch: batch, # prevent extra conversion + # pin_memory=True, + ) + # with paddle.device("cpu"): + _data_buffered = BufferedIterator(iter(_dataloader)) + return _dataloader, _data_buffered + + training_dataloader, training_data_buffered = get_dataloader_and_buffer( + _training_data, _training_params + ) + + if _validation_data is not None: + ( + validation_dataloader, + validation_data_buffered, + ) = get_dataloader_and_buffer(_validation_data, _training_params) + valid_numb_batch = _training_params["validation_data"].get( + "numb_btch", 1 + ) + else: + validation_dataloader = None + validation_data_buffered = None + valid_numb_batch = 1 + return ( + training_dataloader, + training_data_buffered, + validation_dataloader, + validation_data_buffered, + valid_numb_batch, + ) + + def single_model_stat( + _model, + _data_stat_nbatch, + _training_data, + _validation_data, + _stat_file_path, + _data_requirement, + finetune_has_new_type=False, + ): + _data_requirement += get_additional_data_requirement(_model) + _training_data.add_data_requirement(_data_requirement) + if _validation_data is not None: + _validation_data.add_data_requirement(_data_requirement) + + @functools.lru_cache + def get_sample(): + sampled = make_stat_input( + _training_data.systems, + _training_data.dataloaders, + _data_stat_nbatch, + ) + return sampled + + if (not resuming or finetune_has_new_type) and self.rank == 0: + _model.compute_or_load_stat( + sampled_func=get_sample, + stat_file_path=_stat_file_path, + ) + if isinstance(_stat_file_path, DPH5Path): + _stat_file_path.root.close() + return get_sample + + def get_lr(lr_params): + assert ( + lr_params.get("type", "exp") == "exp" + ), "Only learning rate `exp` is supported!" + lr_params["stop_steps"] = self.num_steps - self.warmup_steps + lr_exp = LearningRateExp(**lr_params) + return lr_exp + + # Optimizer + if self.multi_task and training_params.get("optim_dict", None) is not None: + self.optim_dict = training_params.get("optim_dict") + missing_keys = [ + key for key in self.model_keys if key not in self.optim_dict + ] + assert ( + not missing_keys + ), f"These keys are not in optim_dict: {missing_keys}!" + self.opt_type = {} + self.opt_param = {} + for model_key in self.model_keys: + self.opt_type[model_key], self.opt_param[model_key] = get_opt_param( + self.optim_dict[model_key] + ) + else: + self.opt_type, self.opt_param = get_opt_param(training_params) + + # Model + self.model = get_model_for_wrapper(model_params) + + # Loss + if not self.multi_task: + self.loss = get_loss( + config["loss"], + config["learning_rate"]["start_lr"], + len(model_params["type_map"]), + self.model, + ) + else: + self.loss = {} + for model_key in self.model_keys: + loss_param = config["loss_dict"][model_key] + if config.get("learning_rate_dict", None) is not None: + lr_param = config["learning_rate_dict"][model_key]["start_lr"] + else: + lr_param = config["learning_rate"]["start_lr"] + ntypes = len(model_params["model_dict"][model_key]["type_map"]) + self.loss[model_key] = get_loss( + loss_param, lr_param, ntypes, self.model[model_key] + ) + + # Data + if not self.multi_task: + self.get_sample_func = single_model_stat( + self.model, + model_params.get("data_stat_nbatch", 10), + training_data, + validation_data, + stat_file_path, + self.loss.label_requirement, + finetune_has_new_type=self.finetune_links["Default"].get_has_new_type() + if self.finetune_links is not None + else False, + ) + ( + self.training_dataloader, + self.training_data, + self.validation_dataloader, + self.validation_data, + self.valid_numb_batch, + ) = get_data_loader(training_data, validation_data, training_params) + training_data.print_summary( + "training", + to_numpy_array(self.training_dataloader.batch_sampler.sampler.weights), + ) + if validation_data is not None: + validation_data.print_summary( + "validation", + to_numpy_array( + self.validation_dataloader.batch_sampler.sampler.weights + ), + ) + else: + ( + self.training_dataloader, + self.training_data, + self.validation_dataloader, + self.validation_data, + self.valid_numb_batch, + self.get_sample_func, + ) = {}, {}, {}, {}, {}, {} + for model_key in self.model_keys: + self.get_sample_func[model_key] = single_model_stat( + self.model[model_key], + model_params["model_dict"][model_key].get("data_stat_nbatch", 10), + training_data[model_key], + validation_data[model_key], + stat_file_path[model_key], + self.loss[model_key].label_requirement, + finetune_has_new_type=self.finetune_links[ + model_key + ].get_has_new_type() + if self.finetune_links is not None + else False, + ) + ( + self.training_dataloader[model_key], + self.training_data[model_key], + self.validation_dataloader[model_key], + self.validation_data[model_key], + self.valid_numb_batch[model_key], + ) = get_data_loader( + training_data[model_key], + validation_data[model_key], + training_params["data_dict"][model_key], + ) + + training_data[model_key].print_summary( + f"training in {model_key}", + to_numpy_array(self.training_dataloader[model_key].sampler.weights), + ) + if ( + validation_data is not None + and validation_data[model_key] is not None + ): + validation_data[model_key].print_summary( + f"validation in {model_key}", + to_numpy_array( + self.validation_dataloader[model_key].sampler.weights + ), + ) + + # Learning rate + self.warmup_steps = training_params.get("warmup_steps", 0) + self.gradient_max_norm = training_params.get("gradient_max_norm", 0.0) + assert ( + self.num_steps - self.warmup_steps > 0 or self.warmup_steps == 0 + ), "Warm up steps must be less than total training steps!" + if self.multi_task and config.get("learning_rate_dict", None) is not None: + self.lr_exp = {} + for model_key in self.model_keys: + self.lr_exp[model_key] = get_lr(config["learning_rate_dict"][model_key]) + else: + self.lr_exp = get_lr(config["learning_rate"]) + + # JIT + if JIT: + self.model = paddle.jit.to_static(self.model) + + # Model Wrapper + self.wrapper = ModelWrapper(self.model, self.loss, model_params=model_params) + self.start_step = 0 + + # resuming and finetune + optimizer_state_dict = None + if resuming: + log.info(f"Resuming from {resume_model}.") + state_dict = paddle.load(resume_model) + if "model" in state_dict: + optimizer_state_dict = ( + state_dict["optimizer"] if finetune_model is None else None + ) + state_dict = state_dict["model"] + self.start_step = ( + state_dict["_extra_state"]["train_infos"]["step"] + if self.restart_training + else 0 + ) + if self.rank == 0: + if force_load: + input_keys = list(state_dict.keys()) + target_keys = list(self.wrapper.state_dict().keys()) + missing_keys = [ + item for item in target_keys if item not in input_keys + ] + if missing_keys: + target_state_dict = self.wrapper.state_dict() + slim_keys = [] + for item in missing_keys: + state_dict[item] = target_state_dict[item].clone().detach() + new_key = True + for slim_key in slim_keys: + if slim_key in item: + new_key = False + break + if new_key: + tmp_keys = ".".join(item.split(".")[:3]) + slim_keys.append(tmp_keys) + slim_keys = [i + ".*" for i in slim_keys] + log.warning( + f"Force load mode allowed! These keys are not in ckpt and will re-init: {slim_keys}" + ) + # update model params in the pretrained model + if finetune_model is not None: + new_state_dict = {} + target_state_dict = self.wrapper.state_dict() + # pretrained_model + pretrained_model = get_model_for_wrapper( + state_dict["_extra_state"]["model_params"] + ) + pretrained_model_wrapper = ModelWrapper(pretrained_model) + pretrained_model_wrapper.load_state_dict(state_dict) + # update type related params + for model_key in self.model_keys: + finetune_rule_single = self.finetune_links[model_key] + _model_key_from = finetune_rule_single.get_model_branch() + # skip if updated + if ( + finetune_rule_single.get_finetune_tmap() + != pretrained_model_wrapper.model[ + _model_key_from + ].get_type_map() + ): + model_with_new_type_stat = None + if finetune_rule_single.get_has_new_type(): + self.finetune_update_stat = True + model_with_new_type_stat = self.wrapper.model[model_key] + pretrained_model_wrapper.model[ + _model_key_from + ].change_type_map( + finetune_rule_single.get_finetune_tmap(), + model_with_new_type_stat=model_with_new_type_stat, + ) + state_dict = pretrained_model_wrapper.state_dict() + + def collect_single_finetune_params( + _model_key, + _finetune_rule_single, + _new_state_dict, + _origin_state_dict, + _random_state_dict, + ): + _new_fitting = _finetune_rule_single.get_random_fitting() + _model_key_from = _finetune_rule_single.get_model_branch() + target_keys = [ + i + for i in _random_state_dict.keys() + if i != "_extra_state" and f".{_model_key}." in i + ] + for item_key in target_keys: + if _new_fitting and ".fitting_net." in item_key: + # print(f'Keep {item_key} in old model!') + _new_state_dict[item_key] = ( + _random_state_dict[item_key].clone().detach() + ) + else: + new_key = item_key.replace( + f".{_model_key}.", f".{_model_key_from}." + ) + # print(f'Replace {item_key} with {new_key} in pretrained_model!') + _new_state_dict[item_key] = ( + _origin_state_dict[new_key].clone().detach() + ) + + # collect model params from the pretrained model + for model_key in self.model_keys: + finetune_rule_single = self.finetune_links[model_key] + collect_single_finetune_params( + model_key, + finetune_rule_single, + new_state_dict, + state_dict, + target_state_dict, + ) + state_dict = new_state_dict + state_dict["_extra_state"] = self.wrapper.state_dict()[ + "_extra_state" + ] + + self.wrapper.load_state_dict(state_dict) + + # change bias for fine-tuning + if finetune_model is not None: + + def single_model_finetune( + _model, + _finetune_rule_single, + _sample_func, + ): + _model = model_change_out_bias( + _model, + _sample_func, + _bias_adjust_mode="change-by-statistic" + if not _finetune_rule_single.get_random_fitting() + else "set-by-statistic", + ) + return _model + + if not self.multi_task: + finetune_rule_single = self.finetune_links["Default"] + self.model = single_model_finetune( + self.model, finetune_rule_single, self.get_sample_func + ) + else: + for model_key in self.model_keys: + finetune_rule_single = self.finetune_links[model_key] + if not finetune_rule_single.get_resuming(): + log.info( + f"Model branch {model_key} will be fine-tuned. This may take a long time..." + ) + self.model[model_key] = single_model_finetune( + self.model[model_key], + finetune_rule_single, + self.get_sample_func[model_key], + ) + else: + log.info( + f"Model branch {model_key} will resume training." + ) + + if init_frz_model is not None: + frz_model = paddle.jit.load(init_frz_model) + self.model.load_state_dict(frz_model.state_dict()) + + # Multi-task share params + if shared_links is not None: + self.wrapper.share_params( + shared_links, + resume=(resuming and not self.finetune_update_stat) or self.rank != 0, + ) + + if dist.is_available() and dist.is_initialized(): + paddle.set_device(LOCAL_RANK) + # DDP will guarantee the model parameters are identical across all processes + self.wrapper = DDP( + self.wrapper, + device_ids=[LOCAL_RANK], + find_unused_parameters=True, + output_device=LOCAL_RANK, + ) + + # TODO add lr warmups for multitask + # author: iProzd + def warm_up_linear(step, warmup_steps): + if step < warmup_steps: + return step / warmup_steps + else: + return self.lr_exp.value(step - warmup_steps) / self.lr_exp.start_lr + + # TODO add optimizers for multitask + # author: iProzd + if self.opt_type == "Adam": + self.scheduler = paddle.optimizer.lr.LambdaDecay( + learning_rate=self.lr_exp.start_lr, + lr_lambda=lambda step: warm_up_linear( + step + self.start_step, self.warmup_steps + ), + ) + self.optimizer = paddle.optimizer.Adam( + learning_rate=self.scheduler, parameters=self.wrapper.parameters() + ) + if optimizer_state_dict is not None and self.restart_training: + self.optimizer.load_state_dict(optimizer_state_dict) + elif self.opt_type == "LKF": + self.optimizer = LKFOptimizer( + self.wrapper.parameters(), 0.98, 0.99870, self.opt_param["kf_blocksize"] + ) + else: + raise ValueError(f"Not supported optimizer type '{self.opt_type}'") + + # Get model prob for multi-task + if self.multi_task: + self.model_prob = np.array([0.0 for key in self.model_keys]) + if training_params.get("model_prob", None) is not None: + model_prob = training_params["model_prob"] + for ii, model_key in enumerate(self.model_keys): + if model_key in model_prob: + self.model_prob[ii] += float(model_prob[model_key]) + else: + for ii, model_key in enumerate(self.model_keys): + self.model_prob[ii] += float(len(self.training_data[model_key])) + sum_prob = np.sum(self.model_prob) + assert sum_prob > 0.0, "Sum of model prob must be larger than 0!" + self.model_prob = self.model_prob / sum_prob + + # Tensorboard + self.enable_tensorboard = training_params.get("tensorboard", False) + self.tensorboard_log_dir = training_params.get("tensorboard_log_dir", "log") + self.tensorboard_freq = training_params.get("tensorboard_freq", 1) + self.enable_profiler = training_params.get("enable_profiler", False) + self.profiling = training_params.get("profiling", False) + self.profiling_file = training_params.get("profiling_file", "timeline.json") + + def run(self): + fout = ( + open( + self.disp_file, + mode="w" if not self.restart_training else "a", + buffering=1, + ) + if self.rank == 0 + else None + ) # line buffered + if SAMPLER_RECORD: + record_file = f"Sample_rank_{self.rank}.txt" + fout1 = open(record_file, mode="w", buffering=1) + log.info("Start to train %d steps.", self.num_steps) + if dist.is_available() and dist.is_initialized(): + log.info(f"Rank: {dist.get_rank()}/{dist.get_world_size()}") + if self.enable_tensorboard: + from paddle.utils.tensorboard import ( + SummaryWriter, + ) + + writer = SummaryWriter(log_dir=self.tensorboard_log_dir) + if self.enable_profiler or self.profiling: + prof = paddle.profiler.profile( + schedule=paddle.profiler.schedule(wait=1, warmup=1, active=3, repeat=1), + on_trace_ready=paddle.profiler.tensorboard_trace_handler( + self.tensorboard_log_dir + ) + if self.enable_profiler + else None, + record_shapes=True, + with_stack=True, + ) + prof.start() + + def step(_step_id, task_key="Default"): + # PyTorch Profiler + if self.enable_profiler or self.profiling: + prof.step() + self.wrapper.train() + if isinstance(self.lr_exp, dict): + _lr = self.lr_exp[task_key] + else: + _lr = self.lr_exp + cur_lr = _lr.value(_step_id) + pref_lr = cur_lr + self.optimizer.clear_grad(set_to_zero=False) + input_dict, label_dict, log_dict = self.get_data( + is_train=True, task_key=task_key + ) + if SAMPLER_RECORD: + print_str = f"Step {_step_id}: sample system{log_dict['sid']} frame{log_dict['fid']}\n" + fout1.write(print_str) + fout1.flush() + if self.opt_type == "Adam": + cur_lr = self.scheduler.get_lr() + if _step_id < self.warmup_steps: + pref_lr = _lr.start_lr + else: + pref_lr = cur_lr + model_pred, loss, more_loss = self.wrapper( + **input_dict, cur_lr=pref_lr, label=label_dict, task_key=task_key + ) + loss.backward() + if self.gradient_max_norm > 0.0: + grad_norm = paddle.nn.utils.clip_grad_norm_( + self.wrapper.parameters(), self.gradient_max_norm + ) + if not paddle.isfinite(grad_norm).all(): + # check local gradnorm single GPU case, trigger NanDetector + raise FloatingPointError("gradients are Nan/Inf") + # with paddle.device("cpu"): + self.optimizer.step() + self.scheduler.step() + elif self.opt_type == "LKF": + if isinstance(self.loss, EnergyStdLoss): + KFOptWrapper = KFOptimizerWrapper( + self.wrapper, + self.optimizer, + 24, + 6, + dist.is_available() and dist.is_initialized(), + ) + pref_e = self.opt_param["kf_start_pref_e"] * ( + self.opt_param["kf_limit_pref_e"] + / self.opt_param["kf_start_pref_e"] + ) ** (_step_id / self.num_steps) + _ = KFOptWrapper.update_energy( + input_dict, label_dict["energy"], pref_e + ) + pref_f = self.opt_param["kf_start_pref_f"] * ( + self.opt_param["kf_limit_pref_f"] + / self.opt_param["kf_start_pref_f"] + ) ** (_step_id / self.num_steps) + p_energy, p_force = KFOptWrapper.update_force( + input_dict, label_dict["force"], pref_f + ) + # [coord, atype, natoms, mapping, shift, nlist, box] + model_pred = {"energy": p_energy, "force": p_force} + module = ( + self.wrapper.module + if dist.is_available() and dist.is_initialized() + else self.wrapper + ) + + def fake_model(): + return model_pred + + _, loss, more_loss = module.loss[task_key]( + {}, + fake_model, + label_dict, + int(input_dict["atype"].shape[-1]), + learning_rate=pref_lr, + ) + elif isinstance(self.loss, DenoiseLoss): + KFOptWrapper = KFOptimizerWrapper( + self.wrapper, + self.optimizer, + 24, + 6, + dist.is_available() and dist.is_initialized(), + ) + module = ( + self.wrapper.module + if dist.is_available() and dist.is_initialized() + else self.wrapper + ) + model_pred = KFOptWrapper.update_denoise_coord( + input_dict, + label_dict["clean_coord"], + 1, + module.loss[task_key].mask_loss_coord, + label_dict["coord_mask"], + ) + loss, more_loss = module.loss[task_key]( + model_pred, + label_dict, + input_dict["natoms"], + learning_rate=pref_lr, + ) + else: + raise ValueError(f"Not supported optimizer type '{self.opt_type}'") + + # Log and persist + if self.display_in_training and _step_id % self.disp_freq == 0: + self.wrapper.eval() + + def log_loss_train(_loss, _more_loss, _task_key="Default"): + results = {} + rmse_val = { + item: _more_loss[item] + for item in _more_loss + if "l2_" not in item + } + for item in sorted(rmse_val.keys()): + results[item] = rmse_val[item] + return results + + def log_loss_valid(_task_key="Default"): + single_results = {} + sum_natoms = 0 + if not self.multi_task: + valid_numb_batch = self.valid_numb_batch + else: + valid_numb_batch = self.valid_numb_batch[_task_key] + for ii in range(valid_numb_batch): + self.optimizer.clear_grad() + input_dict, label_dict, _ = self.get_data( + is_train=False, task_key=_task_key + ) + if input_dict == {}: + # no validation data + return {} + _, loss, more_loss = self.wrapper( + **input_dict, + cur_lr=pref_lr, + label=label_dict, + task_key=_task_key, + ) + # more_loss.update({"rmse": math.sqrt(loss)}) + natoms = int(input_dict["atype"].shape[-1]) + sum_natoms += natoms + for k, v in more_loss.items(): + if "l2_" not in k: + single_results[k] = ( + single_results.get(k, 0.0) + v * natoms + ) + results = {k: v / sum_natoms for k, v in single_results.items()} + return results + + if not self.multi_task: + train_results = log_loss_train(loss, more_loss) + valid_results = log_loss_valid() + if self.rank == 0: + log.info( + format_training_message_per_task( + batch=_step_id, + task_name="trn", + rmse=train_results, + learning_rate=cur_lr, + ) + ) + if valid_results: + log.info( + format_training_message_per_task( + batch=_step_id, + task_name="val", + rmse=valid_results, + learning_rate=None, + ) + ) + else: + train_results = {_key: {} for _key in self.model_keys} + valid_results = {_key: {} for _key in self.model_keys} + train_results[task_key] = log_loss_train( + loss, more_loss, _task_key=task_key + ) + for _key in self.model_keys: + if _key != task_key: + self.optimizer.clear_grad() + input_dict, label_dict, _ = self.get_data( + is_train=True, task_key=_key + ) + _, loss, more_loss = self.wrapper( + **input_dict, + cur_lr=pref_lr, + label=label_dict, + task_key=_key, + ) + train_results[_key] = log_loss_train( + loss, more_loss, _task_key=_key + ) + valid_results[_key] = log_loss_valid(_task_key=_key) + if self.rank == 0: + log.info( + format_training_message_per_task( + batch=_step_id, + task_name=_key + "_trn", + rmse=train_results[_key], + learning_rate=cur_lr, + ) + ) + if valid_results[_key]: + log.info( + format_training_message_per_task( + batch=_step_id, + task_name=_key + "_val", + rmse=valid_results[_key], + learning_rate=None, + ) + ) + + current_time = time.time() + train_time = current_time - self.t0 + self.t0 = current_time + if self.rank == 0 and self.timing_in_training: + log.info( + format_training_message( + batch=_step_id, + wall_time=train_time, + ) + ) + # the first training time is not accurate + if ( + _step_id + 1 + ) > self.disp_freq or self.num_steps < 2 * self.disp_freq: + self.total_train_time += train_time + + if fout: + if self.lcurve_should_print_header: + self.print_header(fout, train_results, valid_results) + self.lcurve_should_print_header = False + self.print_on_training( + fout, _step_id, cur_lr, train_results, valid_results + ) + + if ( + ((_step_id + 1) % self.save_freq == 0 and _step_id != self.start_step) + or (_step_id + 1) == self.num_steps + ) and (self.rank == 0 or dist.get_rank() == 0): + # Handle the case if rank 0 aborted and re-assigned + self.latest_model = Path(self.save_ckpt + f"-{_step_id + 1}.pd") + + module = ( + self.wrapper.module + if dist.is_available() and dist.is_initialized() + else self.wrapper + ) + self.save_model(self.latest_model, lr=cur_lr, step=_step_id) + log.info(f"Saved model to {self.latest_model}") + symlink_prefix_files(self.latest_model.stem, self.save_ckpt) + with open("checkpoint", "w") as f: + f.write(str(self.latest_model)) + + # tensorboard + if self.enable_tensorboard and _step_id % self.tensorboard_freq == 0: + writer.add_scalar(f"{task_key}/lr", cur_lr, _step_id) + writer.add_scalar(f"{task_key}/loss", loss, _step_id) + for item in more_loss: + writer.add_scalar(f"{task_key}/{item}", more_loss[item], _step_id) + + self.t0 = time.time() + self.total_train_time = 0.0 + for step_id in range(self.num_steps): + if step_id < self.start_step: + continue + if self.multi_task: + chosen_index_list = dp_random.choice( + np.arange(self.num_model), # pylint: disable=no-explicit-dtype + p=np.array(self.model_prob), + size=self.world_size, + replace=True, + ) + assert chosen_index_list.size == self.world_size + model_index = chosen_index_list[self.rank] + model_key = self.model_keys[model_index] + else: + model_key = "Default" + step(step_id, model_key) + if JIT: + break + + if self.change_bias_after_training and (self.rank == 0 or dist.get_rank() == 0): + if not self.multi_task: + self.model = model_change_out_bias( + self.model, + self.get_sample_func, + _bias_adjust_mode="change-by-statistic", + ) + else: + for model_key in self.model_keys: + self.model[model_key] = model_change_out_bias( + self.model[model_key], + self.get_sample_func[model_key], + _bias_adjust_mode="change-by-statistic", + ) + self.latest_model = Path(self.save_ckpt + f"-{self.num_steps}.pd") + cur_lr = self.lr_exp.value(self.num_steps - 1) + self.save_model(self.latest_model, lr=cur_lr, step=self.num_steps - 1) + log.info(f"Saved model to {self.latest_model}") + symlink_prefix_files(self.latest_model.stem, self.save_ckpt) + with open("checkpoint", "w") as f: + f.write(str(self.latest_model)) + + if ( + self.rank == 0 or dist.get_rank() == 0 + ): # Handle the case if rank 0 aborted and re-assigned + if self.num_steps == 0: + # when num_steps is 0, the checkpoint is never not saved + self.latest_model = Path(self.save_ckpt + "-0.pd") + self.save_model(self.latest_model, lr=0, step=0) + log.info(f"Saved model to {self.latest_model}") + symlink_prefix_files(self.latest_model.stem, self.save_ckpt) + with open("checkpoint", "w") as f: + f.write(str(self.latest_model)) + + if self.timing_in_training and self.num_steps // self.disp_freq > 0: + if self.num_steps >= 2 * self.disp_freq: + log.info( + "average training time: %.4f s/batch (exclude first %d batches)", + self.total_train_time + / ( + self.num_steps // self.disp_freq * self.disp_freq + - self.disp_freq + ), + self.disp_freq, + ) + else: + log.info( + "average training time: %.4f s/batch", + self.total_train_time + / (self.num_steps // self.disp_freq * self.disp_freq), + ) + + if JIT: + pth_model_path = ( + "frozen_model.pth" # We use .pth to denote the frozen model + ) + self.model.save(pth_model_path) + log.info( + f"Frozen model for inferencing has been saved to {pth_model_path}" + ) + log.info(f"Trained model has been saved to: {self.save_ckpt}") + + if fout: + fout.close() + if SAMPLER_RECORD: + fout1.close() + if self.enable_tensorboard: + writer.close() + if self.enable_profiler or self.profiling: + prof.stop() + if self.profiling: + prof.export_chrome_trace(self.profiling_file) + log.info( + f"The profiling trace have been saved to: {self.profiling_file}" + ) + + def save_model(self, save_path, lr=0.0, step=0): + module = ( + self.wrapper.module + if dist.is_available() and dist.is_initialized() + else self.wrapper + ) + module.train_infos["lr"] = lr + module.train_infos["step"] = step + paddle.save( + {"model": module.state_dict(), "optimizer": self.optimizer.state_dict()}, + save_path, + ) + checkpoint_dir = save_path.parent + checkpoint_files = [ + f + for f in checkpoint_dir.glob("*.pd") + if not f.is_symlink() and f.name.startswith(self.save_ckpt) + ] + if len(checkpoint_files) > self.max_ckpt_keep: + checkpoint_files.sort(key=lambda x: x.stat().st_mtime) + checkpoint_files[0].unlink() + + def get_data(self, is_train=True, task_key="Default"): + if not self.multi_task: + if is_train: + try: + batch_data = next(iter(self.training_data)) + except StopIteration: + # Refresh the status of the dataloader to start from a new epoch + # with paddle.device("cpu"): + self.training_data = BufferedIterator( + iter(self.training_dataloader) + ) + batch_data = next(iter(self.training_data)) + else: + if self.validation_data is None: + return {}, {}, {} + try: + batch_data = next(iter(self.validation_data)) + except StopIteration: + self.validation_data = BufferedIterator( + iter(self.validation_dataloader) + ) + batch_data = next(iter(self.validation_data)) + else: + if is_train: + try: + batch_data = next(iter(self.training_data[task_key])) + except StopIteration: + # Refresh the status of the dataloader to start from a new epoch + self.training_data[task_key] = BufferedIterator( + iter(self.training_dataloader[task_key]) + ) + batch_data = next(iter(self.training_data[task_key])) + else: + if self.validation_data[task_key] is None: + return {}, {}, {} + try: + batch_data = next(iter(self.validation_data[task_key])) + except StopIteration: + self.validation_data[task_key] = BufferedIterator( + iter(self.validation_dataloader[task_key]) + ) + batch_data = next(iter(self.validation_data[task_key])) + + for key in batch_data.keys(): + if key == "sid" or key == "fid" or key == "box" or "find_" in key: + continue + elif not isinstance(batch_data[key], list): + if batch_data[key] is not None: + batch_data[key] = batch_data[key].to(DEVICE) + else: + batch_data[key] = [item.to(DEVICE) for item in batch_data[key]] + # we may need a better way to classify which are inputs and which are labels + # now wrapper only supports the following inputs: + input_keys = [ + "coord", + "atype", + "spin", + "box", + "fparam", + "aparam", + ] + input_dict = {item_key: None for item_key in input_keys} + label_dict = {} + for item_key in batch_data: + if item_key in input_keys: + input_dict[item_key] = batch_data[item_key] + else: + if item_key not in ["sid", "fid"]: + label_dict[item_key] = batch_data[item_key] + log_dict = {} + if "fid" in batch_data: + log_dict["fid"] = batch_data["fid"] + log_dict["sid"] = batch_data["sid"] + return input_dict, label_dict, log_dict + + def print_header(self, fout, train_results, valid_results): + train_keys = sorted(train_results.keys()) + print_str = "" + print_str += "# %5s" % "step" + if not self.multi_task: + if valid_results: + prop_fmt = " %11s %11s" + for k in train_keys: + print_str += prop_fmt % (k + "_val", k + "_trn") + else: + prop_fmt = " %11s" + for k in train_keys: + print_str += prop_fmt % (k + "_trn") + else: + for model_key in self.model_keys: + if valid_results[model_key]: + prop_fmt = " %11s %11s" + for k in sorted(train_results[model_key].keys()): + print_str += prop_fmt % ( + k + f"_val_{model_key}", + k + f"_trn_{model_key}", + ) + else: + prop_fmt = " %11s" + for k in sorted(train_results[model_key].keys()): + print_str += prop_fmt % (k + f"_trn_{model_key}") + print_str += " %8s\n" % "lr" + print_str += "# If there is no available reference data, rmse_*_{val,trn} will print nan\n" + fout.write(print_str) + fout.flush() + + def print_on_training(self, fout, step_id, cur_lr, train_results, valid_results): + train_keys = sorted(train_results.keys()) + print_str = "" + print_str += "%7d" % step_id + if not self.multi_task: + if valid_results: + prop_fmt = " %11.2e %11.2e" + for k in train_keys: + print_str += prop_fmt % (valid_results[k], train_results[k]) + else: + prop_fmt = " %11.2e" + for k in train_keys: + print_str += prop_fmt % (train_results[k]) + else: + for model_key in self.model_keys: + if valid_results[model_key]: + prop_fmt = " %11.2e %11.2e" + for k in sorted(valid_results[model_key].keys()): + print_str += prop_fmt % ( + valid_results[model_key][k], + train_results[model_key][k], + ) + else: + prop_fmt = " %11.2e" + for k in sorted(train_results[model_key].keys()): + print_str += prop_fmt % (train_results[model_key][k]) + print_str += f" {cur_lr:8.1e}\n" + fout.write(print_str) + fout.flush() + + +def get_additional_data_requirement(_model): + additional_data_requirement = [] + if _model.get_dim_fparam() > 0: + fparam_requirement_items = [ + DataRequirementItem( + "fparam", _model.get_dim_fparam(), atomic=False, must=True + ) + ] + additional_data_requirement += fparam_requirement_items + if _model.get_dim_aparam() > 0: + aparam_requirement_items = [ + DataRequirementItem( + "aparam", _model.get_dim_aparam(), atomic=True, must=True + ) + ] + additional_data_requirement += aparam_requirement_items + has_spin = getattr(_model, "has_spin", False) + if callable(has_spin): + has_spin = has_spin() + if has_spin: + spin_requirement_items = [ + DataRequirementItem("spin", ndof=3, atomic=True, must=True) + ] + additional_data_requirement += spin_requirement_items + return additional_data_requirement + + +def get_loss(loss_params, start_lr, _ntypes, _model): + loss_type = loss_params.get("type", "ener") + if loss_type == "ener": + loss_params["starter_learning_rate"] = start_lr + return EnergyStdLoss(**loss_params) + elif loss_type == "dos": + loss_params["starter_learning_rate"] = start_lr + loss_params["numb_dos"] = _model.model_output_def()["dos"].output_size + return DOSLoss(**loss_params) + elif loss_type == "ener_spin": + loss_params["starter_learning_rate"] = start_lr + return EnergySpinLoss(**loss_params) + elif loss_type == "denoise": + loss_params["ntypes"] = _ntypes + return DenoiseLoss(**loss_params) + elif loss_type == "tensor": + model_output_type = _model.model_output_type() + if "mask" in model_output_type: + model_output_type.pop(model_output_type.index("mask")) + tensor_name = model_output_type[0] + loss_params["tensor_name"] = tensor_name + loss_params["tensor_size"] = _model.model_output_def()[tensor_name].output_size + label_name = tensor_name + if label_name == "polarizability": + label_name = "polar" + loss_params["label_name"] = label_name + loss_params["tensor_name"] = label_name + return TensorLoss(**loss_params) + else: + raise NotImplementedError + + +def get_single_model( + _model_params, +): + if "use_srtab" in _model_params: + model = get_zbl_model(deepcopy(_model_params)).to(DEVICE) + else: + model = get_model(deepcopy(_model_params)).to(DEVICE) + return model + + +def get_model_for_wrapper(_model_params): + if "model_dict" not in _model_params: + _model = get_single_model( + _model_params, + ) + else: + _model = {} + model_keys = list(_model_params["model_dict"]) + for _model_key in model_keys: + _model[_model_key] = get_single_model( + _model_params["model_dict"][_model_key], + ) + return _model + + +def model_change_out_bias( + _model, + _sample_func, + _bias_adjust_mode="change-by-statistic", +): + old_bias = deepcopy(_model.get_out_bias()) + _model.change_out_bias( + _sample_func, + bias_adjust_mode=_bias_adjust_mode, + ) + new_bias = deepcopy(_model.get_out_bias()) + + model_type_map = _model.get_type_map() + log.info( + f"Change output bias of {model_type_map!s} " + f"from {to_numpy_array(old_bias).reshape(-1)!s} " + f"to {to_numpy_array(new_bias).reshape(-1)!s}." + ) + return _model diff --git a/deepmd/pd/train/wrapper.py b/deepmd/pd/train/wrapper.py new file mode 100644 index 0000000000..927ef11c94 --- /dev/null +++ b/deepmd/pd/train/wrapper.py @@ -0,0 +1,196 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import logging +from typing import ( + Dict, + Optional, + Union, +) + +import paddle + +# if paddle.__version__.startswith("2"): +# import paddle._dynamo + + +log = logging.getLogger(__name__) + + +class ModelWrapper(paddle.nn.Layer): + def __init__( + self, + model: Union[paddle.nn.Layer, Dict], + loss: Union[paddle.nn.Layer, Dict] = None, + model_params=None, + shared_links=None, + ): + """Construct a DeePMD model wrapper. + + Args: + - config: The Dict-like configuration with training options. + """ + super().__init__() + self.model_params = model_params if model_params is not None else {} + self.train_infos = { + "lr": 0, + "step": 0, + } + self.multi_task = False + self.model = paddle.nn.LayerDict() + # Model + if isinstance(model, paddle.nn.Layer): + self.model["Default"] = model + elif isinstance(model, dict): + self.multi_task = True + for task_key in model: + assert isinstance( + model[task_key], paddle.nn.Layer + ), f"{task_key} in model_dict is not a paddle.nn.Layer!" + self.model[task_key] = model[task_key] + # Loss + self.loss = None + if loss is not None: + self.loss = paddle.nn.LayerDict() + if isinstance(loss, paddle.nn.Layer): + self.loss["Default"] = loss + elif isinstance(loss, dict): + for task_key in loss: + assert isinstance( + loss[task_key], paddle.nn.Layer + ), f"{task_key} in loss_dict is not a paddle.nn.Layer!" + self.loss[task_key] = loss[task_key] + self.inference_only = self.loss is None + + def share_params(self, shared_links, resume=False): + """ + Share the parameters of classes following rules defined in shared_links during multitask training. + If not start from checkpoint (resume is False), + some seperated parameters (e.g. mean and stddev) will be re-calculated across different classes. + """ + supported_types = ["descriptor", "fitting_net"] + for shared_item in shared_links: + class_name = shared_links[shared_item]["type"] + shared_base = shared_links[shared_item]["links"][0] + class_type_base = shared_base["shared_type"] + model_key_base = shared_base["model_key"] + shared_level_base = shared_base["shared_level"] + if "descriptor" in class_type_base: + if class_type_base == "descriptor": + base_class = self.model[model_key_base].get_descriptor() + elif "hybrid" in class_type_base: + hybrid_index = int(class_type_base.split("_")[-1]) + base_class = ( + self.model[model_key_base] + .get_descriptor() + .descriptor_list[hybrid_index] + ) + else: + raise RuntimeError(f"Unknown class_type {class_type_base}!") + for link_item in shared_links[shared_item]["links"][1:]: + class_type_link = link_item["shared_type"] + model_key_link = link_item["model_key"] + shared_level_link = int(link_item["shared_level"]) + assert ( + shared_level_link >= shared_level_base + ), "The shared_links must be sorted by shared_level!" + assert ( + "descriptor" in class_type_link + ), f"Class type mismatched: {class_type_base} vs {class_type_link}!" + if class_type_link == "descriptor": + link_class = self.model[model_key_link].get_descriptor() + elif "hybrid" in class_type_link: + hybrid_index = int(class_type_link.split("_")[-1]) + link_class = ( + self.model[model_key_link] + .get_descriptor() + .descriptor_list[hybrid_index] + ) + else: + raise RuntimeError(f"Unknown class_type {class_type_link}!") + link_class.share_params( + base_class, shared_level_link, resume=resume + ) + log.warning( + f"Shared params of {model_key_base}.{class_type_base} and {model_key_link}.{class_type_link}!" + ) + else: + if hasattr(self.model[model_key_base], class_type_base): + base_class = self.model[model_key_base].__getattr__(class_type_base) + for link_item in shared_links[shared_item]["links"][1:]: + class_type_link = link_item["shared_type"] + model_key_link = link_item["model_key"] + shared_level_link = int(link_item["shared_level"]) + assert ( + shared_level_link >= shared_level_base + ), "The shared_links must be sorted by shared_level!" + assert ( + class_type_base == class_type_link + ), f"Class type mismatched: {class_type_base} vs {class_type_link}!" + link_class = self.model[model_key_link].__getattr__( + class_type_link + ) + link_class.share_params( + base_class, shared_level_link, resume=resume + ) + log.warning( + f"Shared params of {model_key_base}.{class_type_base} and {model_key_link}.{class_type_link}!" + ) + + def forward( + self, + coord, + atype, + spin: Optional[paddle.Tensor] = None, + box: Optional[paddle.Tensor] = None, + cur_lr: Optional[paddle.Tensor] = None, + label: Optional[paddle.Tensor] = None, + task_key: Optional[paddle.Tensor] = None, + inference_only=False, + do_atomic_virial=False, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + ): + if not self.multi_task: + task_key = "Default" + else: + assert ( + task_key is not None + ), f"Multitask model must specify the inference task! Supported tasks are {list(self.model.keys())}." + input_dict = { + "coord": coord, + "atype": atype, + "box": box, + "do_atomic_virial": do_atomic_virial, + "fparam": fparam, + "aparam": aparam, + } + has_spin = getattr(self.model[task_key], "has_spin", False) + if callable(has_spin): + has_spin = has_spin() + if has_spin: + input_dict["spin"] = spin + + if self.inference_only or inference_only: + model_pred = self.model[task_key](**input_dict) + return model_pred, None, None + else: + natoms = atype.shape[-1] + model_pred, loss, more_loss = self.loss[task_key]( + input_dict, + self.model[task_key], + label, + natoms=natoms, + learning_rate=cur_lr, + ) + return model_pred, loss, more_loss + + def set_extra_state(self, state: Dict): + self.model_params = state["model_params"] + self.train_infos = state["train_infos"] + return None + + def get_extra_state(self) -> Dict: + state = { + "model_params": self.model_params, + "train_infos": self.train_infos, + } + return state diff --git a/deepmd/pd/utils/__init__.py b/deepmd/pd/utils/__init__.py new file mode 100644 index 0000000000..7e1043eda4 --- /dev/null +++ b/deepmd/pd/utils/__init__.py @@ -0,0 +1,11 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later + +from .exclude_mask import ( + AtomExcludeMask, + PairExcludeMask, +) + +__all__ = [ + "PairExcludeMask", + "AtomExcludeMask", +] diff --git a/deepmd/pd/utils/ase_calc.py b/deepmd/pd/utils/ase_calc.py new file mode 100644 index 0000000000..6bcb9cdc5e --- /dev/null +++ b/deepmd/pd/utils/ase_calc.py @@ -0,0 +1,6 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from deepmd.calculator import DP as DPCalculator + +__all__ = [ + "DPCalculator", +] diff --git a/deepmd/pd/utils/auto_batch_size.py b/deepmd/pd/utils/auto_batch_size.py new file mode 100644 index 0000000000..ca720aae04 --- /dev/null +++ b/deepmd/pd/utils/auto_batch_size.py @@ -0,0 +1,60 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later + +import paddle + +from deepmd.utils.batch_size import AutoBatchSize as AutoBatchSizeBase + + +class AutoBatchSize(AutoBatchSizeBase): + """Auto batch size. + + Parameters + ---------- + initial_batch_size : int, default: 1024 + initial batch size (number of total atoms) when DP_INFER_BATCH_SIZE + is not set + factor : float, default: 2. + increased factor + + """ + + def __init__( + self, + initial_batch_size: int = 1024, + factor: float = 2.0, + ): + super().__init__( + initial_batch_size=initial_batch_size, + factor=factor, + ) + + def is_gpu_available(self) -> bool: + """Check if GPU is available. + + Returns + ------- + bool + True if GPU is available + """ + return paddle.device.cuda.device_count() > 0 + + def is_oom_error(self, e: Exception) -> bool: + """Check if the exception is an OOM error. + + Parameters + ---------- + e : Exception + Exception + """ + # several sources think CUSOLVER_STATUS_INTERNAL_ERROR is another out-of-memory error, + # such as https://github.com/JuliaGPU/CUDA.jl/issues/1924 + # (the meaningless error message should be considered as a bug in cusolver) + if isinstance(e, RuntimeError) and ( + "CUDA out of memory." in e.args[0] + or "CUDA driver error: out of memory" in e.args[0] + or "cusolver error: CUSOLVER_STATUS_INTERNAL_ERROR" in e.args[0] + ): + # Release all unoccupied cached memory + paddle.device.cuda.empty_cache() + return True + return False diff --git a/deepmd/pd/utils/cache.py b/deepmd/pd/utils/cache.py new file mode 100644 index 0000000000..c40c4050b7 --- /dev/null +++ b/deepmd/pd/utils/cache.py @@ -0,0 +1,31 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy as copy_lib +import functools + + +def lru_cache(maxsize=16, typed=False, copy=False, deepcopy=False): + if deepcopy: + + def decorator(f): + cached_func = functools.lru_cache(maxsize, typed)(f) + + @functools.wraps(f) + def wrapper(*args, **kwargs): + return copy_lib.deepcopy(cached_func(*args, **kwargs)) + + return wrapper + + elif copy: + + def decorator(f): + cached_func = functools.lru_cache(maxsize, typed)(f) + + @functools.wraps(f) + def wrapper(*args, **kwargs): + return copy_lib.copy(cached_func(*args, **kwargs)) + + return wrapper + + else: + decorator = functools.lru_cache(maxsize, typed) + return decorator diff --git a/deepmd/pd/utils/dataloader.py b/deepmd/pd/utils/dataloader.py new file mode 100644 index 0000000000..f942787072 --- /dev/null +++ b/deepmd/pd/utils/dataloader.py @@ -0,0 +1,320 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import logging +import os +import queue +import time +from multiprocessing.dummy import ( + Pool, +) +from threading import ( + Thread, +) +from typing import ( + List, +) + +import h5py +import numpy as np +import paddle +import paddle.distributed as dist + +# import paddle.multiprocessing +from paddle.io import ( + BatchSampler, + DataLoader, + Dataset, + DistributedBatchSampler, + WeightedRandomSampler, +) +from paddle.io.dataloader.collate import ( + default_collate_fn, +) + +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.dataset import ( + DeepmdDataSetForLoader, +) +from deepmd.utils.data import ( + DataRequirementItem, +) +from deepmd.utils.data_system import ( + print_summary, + prob_sys_size_ext, + process_sys_probs, +) + +log = logging.getLogger(__name__) +# paddle.multiprocessing.set_sharing_strategy("file_system") + + +def setup_seed(seed): + paddle.seed(seed) + os.environ["FLAGS_cudnn_deterministic"] = "True" + + +class DpLoaderSet(Dataset): + """A dataset for storing DataLoaders to multiple Systems. + + Parameters + ---------- + sys_path + Path to the data system + batch_size + Max frame count in a batch. + type_map + Gives the name of different atom types + seed + Random seed for dataloader + shuffle + If the data are shuffled (Only effective in serial mode. Always shuffle in distributed data parallelism) + """ + + def __init__( + self, + systems, + batch_size, + type_map, + seed=None, + shuffle=True, + ): + if seed is not None: + setup_seed(seed) + if isinstance(systems, str): + with h5py.File(systems) as file: + systems = [os.path.join(systems, item) for item in file.keys()] + + self.systems: List[DeepmdDataSetForLoader] = [] + if len(systems) >= 100: + log.info(f"Constructing DataLoaders from {len(systems)} systems") + + def construct_dataset(system): + return DeepmdDataSetForLoader( + system=system, + type_map=type_map, + ) + + with Pool( + os.cpu_count() + // ( + int(os.environ["LOCAL_WORLD_SIZE"]) + if dist.is_available() and dist.is_initialized() + else 1 + ) + ) as pool: + self.systems: List[DeepmdDataSetForLoader] = pool.map( + construct_dataset, systems + ) + + self.sampler_list: List[DistributedBatchSampler] = [] + self.index = [] + self.total_batch = 0 + + self.dataloaders = [] + self.batch_sizes = [] + if isinstance(batch_size, str): + if batch_size == "auto": + rule = 32 + elif batch_size.startswith("auto:"): + rule = int(batch_size.split(":")[1]) + else: + rule = None + log.error("Unsupported batch size type") + for ii in self.systems: + ni = ii._natoms + bsi = rule // ni + if bsi * ni < rule: + bsi += 1 + self.batch_sizes.append(bsi) + elif isinstance(batch_size, list): + self.batch_sizes = batch_size + else: + self.batch_sizes = batch_size * np.ones(len(systems), dtype=int) + assert len(self.systems) == len(self.batch_sizes) + for system, batch_size in zip(self.systems, self.batch_sizes): + if dist.is_available() and dist.is_initialized(): + system_batch_sampler = DistributedBatchSampler( + system, + shuffle=( + not (dist.is_available() and dist.is_initialized()) and shuffle + ), + ) + self.sampler_list.append(system_batch_sampler) + else: + system_batch_sampler = BatchSampler( + system, + shuffle=( + not (dist.is_available() and dist.is_initialized()) and shuffle + ), + ) + self.sampler_list.append(system_batch_sampler) + system_dataloader = DataLoader( + dataset=system, + num_workers=0, # Should be 0 to avoid too many threads forked + batch_sampler=system_batch_sampler, + collate_fn=collate_batch, + # shuffle=(not (dist.is_available() and dist.is_initialized())) + # and shuffle, + ) + self.dataloaders.append(system_dataloader) + self.index.append(len(system_dataloader)) + self.total_batch += len(system_dataloader) + # Initialize iterator instances for DataLoader + self.iters = [] + # with paddle.device("cpu"): + for item in self.dataloaders: + self.iters.append(iter(item)) + + def set_noise(self, noise_settings): + # noise_settings['noise_type'] # "trunc_normal", "normal", "uniform" + # noise_settings['noise'] # float, default 1.0 + # noise_settings['noise_mode'] # "prob", "fix_num" + # noise_settings['mask_num'] # if "fix_num", int + # noise_settings['mask_prob'] # if "prob", float + # noise_settings['same_mask'] # coord and type same mask? + for system in self.systems: + system.set_noise(noise_settings) + + def __len__(self): + return len(self.dataloaders) + + def __getitem__(self, idx): + # log.warning(str(paddle.distributed.get_rank())+" idx: "+str(idx)+" index: "+str(self.index[idx])) + try: + batch = next(self.iters[idx]) + except StopIteration: + self.iters[idx] = iter(self.dataloaders[idx]) + batch = next(self.iters[idx]) + batch["sid"] = idx + return batch + + def add_data_requirement(self, data_requirement: List[DataRequirementItem]): + """Add data requirement for each system in multiple systems.""" + for system in self.systems: + system.add_data_requirement(data_requirement) + + def print_summary( + self, + name: str, + prob: List[float], + ): + print_summary( + name, + len(self.systems), + [ss.system for ss in self.systems], + [ss._natoms for ss in self.systems], + self.batch_sizes, + [ + ss._data_system.get_sys_numb_batch(self.batch_sizes[ii]) + for ii, ss in enumerate(self.systems) + ], + prob, + [ss._data_system.pbc for ss in self.systems], + ) + + +_sentinel = object() +QUEUESIZE = 32 + + +class BackgroundConsumer(Thread): + def __init__(self, queue, source, max_len): + Thread.__init__(self) + self._queue = queue + self._source = source # Main DL iterator + self._max_len = max_len # + + def run(self): + for item in self._source: + self._queue.put(item) # Blocking if the queue is full + + # Signal the consumer we are done. + self._queue.put(_sentinel) + + +class BufferedIterator: + def __init__(self, iterable): + self._queue = queue.Queue(QUEUESIZE) + self._iterable = iterable + self._consumer = None + + self.start_time = time.time() + self.warning_time = None + self.total = len(iterable) + + def _create_consumer(self): + self._consumer = BackgroundConsumer(self._queue, self._iterable, self.total) + self._consumer.daemon = True + self._consumer.start() + + def __iter__(self): + return self + + def __len__(self): + return self.total + + def __next__(self): + # Create consumer if not created yet + if self._consumer is None: + self._create_consumer() + # Notify the user if there is a data loading bottleneck + if self._queue.qsize() < min(2, max(1, self._queue.maxsize // 2)): + if time.time() - self.start_time > 5 * 60: + if ( + self.warning_time is None + or time.time() - self.warning_time > 15 * 60 + ): + log.warning( + "Data loading buffer is empty or nearly empty. This may " + "indicate a data loading bottleneck, and increasing the " + "number of workers (--num-workers) may help." + ) + self.warning_time = time.time() + + # Get next example + item = self._queue.get() + if isinstance(item, Exception): + raise item + if item is _sentinel: + raise StopIteration + return item + + +def collate_batch(batch): + example = batch[0] + result = {} + for key in example.keys(): + if "find_" in key: + result[key] = batch[0][key] + else: + if batch[0][key] is None: + result[key] = None + elif key == "fid": + result[key] = [d[key] for d in batch] + elif key == "type": + continue + else: + result[key] = default_collate_fn([d[key] for d in batch]) + return result + + +def get_weighted_sampler(training_data, prob_style, sys_prob=False): + if sys_prob is False: + if prob_style == "prob_uniform": + prob_v = 1.0 / float(training_data.__len__()) + probs = [prob_v for ii in range(training_data.__len__())] + else: # prob_sys_size;A:B:p1;C:D:p2 or prob_sys_size = prob_sys_size;0:nsys:1.0 + if prob_style == "prob_sys_size": + style = f"prob_sys_size;0:{len(training_data)}:1.0" + else: + style = prob_style + probs = prob_sys_size_ext(style, len(training_data), training_data.index) + else: + probs = process_sys_probs(prob_style, training_data.index) + log.debug("Generated weighted sampler with prob array: " + str(probs)) + # training_data.total_batch is the size of one epoch, you can increase it to avoid too many rebuilding of iteraters + len_sampler = training_data.total_batch * max(env.NUM_WORKERS, 1) + # with paddle.device("cpu"): + sampler = WeightedRandomSampler(probs, len_sampler, replacement=True) + return sampler diff --git a/deepmd/pd/utils/dataset.py b/deepmd/pd/utils/dataset.py new file mode 100644 index 0000000000..88145ddc56 --- /dev/null +++ b/deepmd/pd/utils/dataset.py @@ -0,0 +1,58 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later + + +from typing import ( + List, + Optional, +) + +from paddle.io import ( + Dataset, +) + +from deepmd.utils.data import ( + DataRequirementItem, + DeepmdData, +) + + +class DeepmdDataSetForLoader(Dataset): + def __init__(self, system: str, type_map: Optional[List[str]] = None): + """Construct DeePMD-style dataset containing frames cross different systems. + + Args: + - systems: Paths to systems. + - type_map: Atom types. + """ + self.system = system + self._type_map = type_map + self._data_system = DeepmdData(sys_path=system, type_map=self._type_map) + self.mixed_type = self._data_system.mixed_type + self._ntypes = self._data_system.get_ntypes() + self._natoms = self._data_system.get_natoms() + self._natoms_vec = self._data_system.get_natoms_vec(self._ntypes) + + def __len__(self): + return self._data_system.nframes + + def __getitem__(self, index): + """Get a frame from the selected system.""" + b_data = self._data_system.get_item_torch(index) + b_data["natoms"] = self._natoms_vec + return b_data + + def add_data_requirement(self, data_requirement: List[DataRequirementItem]): + """Add data requirement for this data system.""" + for data_item in data_requirement: + self._data_system.add( + data_item["key"], + data_item["ndof"], + atomic=data_item["atomic"], + must=data_item["must"], + high_prec=data_item["high_prec"], + type_sel=data_item["type_sel"], + repeat=data_item["repeat"], + default=data_item["default"], + dtype=data_item["dtype"], + output_natoms_for_type_sel=data_item["output_natoms_for_type_sel"], + ) diff --git a/deepmd/pd/utils/dp_random.py b/deepmd/pd/utils/dp_random.py new file mode 100644 index 0000000000..e81488c506 --- /dev/null +++ b/deepmd/pd/utils/dp_random.py @@ -0,0 +1,14 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from deepmd.utils.random import ( + choice, + random, + seed, + shuffle, +) + +__all__ = [ + "choice", + "random", + "seed", + "shuffle", +] diff --git a/deepmd/pd/utils/env.py b/deepmd/pd/utils/env.py new file mode 100644 index 0000000000..306201ef0c --- /dev/null +++ b/deepmd/pd/utils/env.py @@ -0,0 +1,91 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import os + +import numpy as np +import paddle + +from deepmd.common import ( + VALID_PRECISION, +) +from deepmd.env import ( + GLOBAL_ENER_FLOAT_PRECISION, + GLOBAL_NP_FLOAT_PRECISION, + get_default_nthreads, + set_default_nthreads, +) + +SAMPLER_RECORD = os.environ.get("SAMPLER_RECORD", False) +try: + # only linux + ncpus = len(os.sched_getaffinity(0)) +except AttributeError: + ncpus = os.cpu_count() +NUM_WORKERS = int(os.environ.get("NUM_WORKERS", min(8, ncpus))) +# Make sure DDP uses correct device if applicable +LOCAL_RANK = os.environ.get("LOCAL_RANK", None) or paddle.device.get_device() +LOCAL_RANK = int(0 if LOCAL_RANK is None else paddle.distributed.get_rank()) + +if os.environ.get("DEVICE") == "cpu" or paddle.device.cuda.device_count() <= 0: + DEVICE = "cpu" +else: + DEVICE = f"gpu:{LOCAL_RANK}" + +JIT = False +CACHE_PER_SYS = 5 # keep at most so many sets per sys in memory +ENERGY_BIAS_TRAINABLE = True + +PRECISION_DICT = { + "float16": paddle.float16, + "float32": paddle.float32, + "float64": paddle.float64, + "half": paddle.float16, + "single": paddle.float32, + "double": paddle.float64, + "int32": paddle.int32, + "int64": paddle.int64, + "bfloat16": paddle.bfloat16, + "bool": paddle.bool, +} +GLOBAL_PD_FLOAT_PRECISION = PRECISION_DICT[np.dtype(GLOBAL_NP_FLOAT_PRECISION).name] +GLOBAL_PD_ENER_FLOAT_PRECISION = PRECISION_DICT[ + np.dtype(GLOBAL_ENER_FLOAT_PRECISION).name +] +PRECISION_DICT["default"] = GLOBAL_PD_FLOAT_PRECISION +assert VALID_PRECISION.issubset(PRECISION_DICT.keys()) +# cannot automatically generated +RESERVED_PRECISON_DICT = { + paddle.float16: "float16", + paddle.float32: "float32", + paddle.float64: "float64", + paddle.int32: "int32", + paddle.int64: "int64", + paddle.bfloat16: "bfloat16", + paddle.bool: "bool", +} +assert set(PRECISION_DICT.values()) == set(RESERVED_PRECISON_DICT.keys()) +DEFAULT_PRECISION = "float64" + +# throw warnings if threads not set +set_default_nthreads() +inter_nthreads, intra_nthreads = get_default_nthreads() +if inter_nthreads > 0: # the behavior of 0 is not documented + paddle.set_num_interop_threads(inter_nthreads) +if intra_nthreads > 0: + paddle.set_num_threads(intra_nthreads) + +__all__ = [ + "GLOBAL_ENER_FLOAT_PRECISION", + "GLOBAL_NP_FLOAT_PRECISION", + "GLOBAL_PD_FLOAT_PRECISION", + "GLOBAL_PD_ENER_FLOAT_PRECISION", + "DEFAULT_PRECISION", + "PRECISION_DICT", + "RESERVED_PRECISON_DICT", + "SAMPLER_RECORD", + "NUM_WORKERS", + "DEVICE", + "JIT", + "CACHE_PER_SYS", + "ENERGY_BIAS_TRAINABLE", + "LOCAL_RANK", +] diff --git a/deepmd/pd/utils/env_mat_stat.py b/deepmd/pd/utils/env_mat_stat.py new file mode 100644 index 0000000000..ef020992a5 --- /dev/null +++ b/deepmd/pd/utils/env_mat_stat.py @@ -0,0 +1,234 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + TYPE_CHECKING, + Dict, + Iterator, + List, + Tuple, + Union, +) + +import numpy as np +import paddle + +from deepmd.common import ( + get_hash, +) +from deepmd.pd.model.descriptor.env_mat import ( + prod_env_mat, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.exclude_mask import ( + PairExcludeMask, +) +from deepmd.pd.utils.nlist import ( + extend_input_and_build_neighbor_list, +) +from deepmd.utils.env_mat_stat import EnvMatStat as BaseEnvMatStat +from deepmd.utils.env_mat_stat import ( + StatItem, +) + +if TYPE_CHECKING: + from deepmd.pd.model.descriptor import ( + DescriptorBlock, + ) + + +class EnvMatStat(BaseEnvMatStat): + def compute_stat(self, env_mat: Dict[str, paddle.Tensor]) -> Dict[str, StatItem]: + """Compute the statistics of the environment matrix for a single system. + + Parameters + ---------- + env_mat : paddle.Tensor + The environment matrix. + + Returns + ------- + Dict[str, StatItem] + The statistics of the environment matrix. + """ + stats = {} + for kk, vv in env_mat.items(): + stats[kk] = StatItem( + number=vv.numel().item(), + sum=vv.sum().item(), + squared_sum=paddle.square(vv).sum().item(), + ) + return stats + + +class EnvMatStatSe(EnvMatStat): + """Environmental matrix statistics for the se_a/se_r environemntal matrix. + + Parameters + ---------- + descriptor : DescriptorBlock + The descriptor of the model. + """ + + def __init__(self, descriptor: "DescriptorBlock"): + super().__init__() + self.descriptor = descriptor + self.last_dim = ( + self.descriptor.ndescrpt // self.descriptor.nnei + ) # se_r=1, se_a=4 + + def iter( + self, data: List[Dict[str, Union[paddle.Tensor, List[Tuple[int, int]]]]] + ) -> Iterator[Dict[str, StatItem]]: + """Get the iterator of the environment matrix. + + Parameters + ---------- + data : List[Dict[str, Union[paddle.Tensor, List[Tuple[int, int]]]]] + The data. + + Yields + ------ + Dict[str, StatItem] + The statistics of the environment matrix. + """ + zero_mean = paddle.zeros( + [self.descriptor.get_ntypes(), self.descriptor.get_nsel(), self.last_dim], + dtype=env.GLOBAL_PD_FLOAT_PRECISION, + ).to(env.DEVICE) + one_stddev = paddle.ones( + [self.descriptor.get_ntypes(), self.descriptor.get_nsel(), self.last_dim], + dtype=env.GLOBAL_PD_FLOAT_PRECISION, + ).to(env.DEVICE) + if self.last_dim == 4: + radial_only = False + elif self.last_dim == 1: + radial_only = True + else: + raise ValueError( + "last_dim should be 1 for raial-only or 4 for full descriptor." + ) + for system in data: + coord, atype, box, natoms = ( + system["coord"], + system["atype"], + system["box"], + system["natoms"], + ) + ( + extended_coord, + extended_atype, + mapping, + nlist, + ) = extend_input_and_build_neighbor_list( + coord, + atype, + self.descriptor.get_rcut(), + self.descriptor.get_sel(), + mixed_types=self.descriptor.mixed_types(), + box=box, + ) + env_mat, _, _ = prod_env_mat( + extended_coord, + nlist, + atype, + zero_mean, + one_stddev, + self.descriptor.get_rcut(), + self.descriptor.get_rcut_smth(), + radial_only, + protection=self.descriptor.get_env_protection(), + ) + # apply excluded_types + exclude_mask = self.descriptor.emask(nlist, extended_atype) + env_mat *= exclude_mask.unsqueeze(-1).astype(env_mat.dtype) + # reshape to nframes * nloc at the atom level, + # so nframes/mixed_type do not matter + env_mat = env_mat.reshape( + [ + coord.shape[0] * coord.shape[1], + self.descriptor.get_nsel(), + self.last_dim, + ] + ) + atype = atype.reshape([coord.shape[0] * coord.shape[1]]) + # (1, nloc) eq (ntypes, 1), so broadcast is possible + # shape: (ntypes, nloc) + type_idx = paddle.equal( + atype.reshape([1, -1]), + paddle.arange(self.descriptor.get_ntypes(), dtype=paddle.int32) + .to(device=env.DEVICE) + .reshape([-1, 1]), + ) + if "pair_exclude_types" in system: + # shape: (1, nloc, nnei) + exclude_mask = PairExcludeMask( + self.descriptor.get_ntypes(), system["pair_exclude_types"] + )(nlist, extended_atype).reshape( + [1, coord.shape[0] * coord.shape[1], -1] + ) + # shape: (ntypes, nloc, nnei) + type_idx = paddle.logical_and(type_idx.unsqueeze(-1), exclude_mask) + for type_i in range(self.descriptor.get_ntypes()): + dd = env_mat[type_idx[type_i]] + dd = dd.reshape([-1, self.last_dim]) # typen_atoms * unmasked_nnei, 4 + env_mats = {} + env_mats[f"r_{type_i}"] = dd[:, :1] + if self.last_dim == 4: + env_mats[f"a_{type_i}"] = dd[:, 1:] + yield self.compute_stat(env_mats) + + def get_hash(self) -> str: + """Get the hash of the environment matrix. + + Returns + ------- + str + The hash of the environment matrix. + """ + dscpt_type = "se_a" if self.last_dim == 4 else "se_r" + return get_hash( + { + "type": dscpt_type, + "ntypes": self.descriptor.get_ntypes(), + "rcut": round(self.descriptor.get_rcut(), 2), + "rcut_smth": round(self.descriptor.rcut_smth, 2), + "nsel": self.descriptor.get_nsel(), + "sel": self.descriptor.get_sel(), + "mixed_types": self.descriptor.mixed_types(), + } + ) + + def __call__(self): + avgs = self.get_avg() + stds = self.get_std() + + all_davg = [] + all_dstd = [] + + for type_i in range(self.descriptor.get_ntypes()): + if self.last_dim == 4: + davgunit = [[avgs[f"r_{type_i}"], 0, 0, 0]] + dstdunit = [ + [ + stds[f"r_{type_i}"], + stds[f"a_{type_i}"], + stds[f"a_{type_i}"], + stds[f"a_{type_i}"], + ] + ] + elif self.last_dim == 1: + davgunit = [[avgs[f"r_{type_i}"]]] + dstdunit = [ + [ + stds[f"r_{type_i}"], + ] + ] + davg = np.tile(davgunit, [self.descriptor.get_nsel(), 1]) + dstd = np.tile(dstdunit, [self.descriptor.get_nsel(), 1]) + all_davg.append(davg) + all_dstd.append(dstd) + + mean = np.stack(all_davg) + stddev = np.stack(all_dstd) + return mean, stddev diff --git a/deepmd/pd/utils/exclude_mask.py b/deepmd/pd/utils/exclude_mask.py new file mode 100644 index 0000000000..3cafc74df1 --- /dev/null +++ b/deepmd/pd/utils/exclude_mask.py @@ -0,0 +1,158 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + List, + Set, + Tuple, +) + +import numpy as np +import paddle + +from deepmd.pd.utils.utils import ( + to_paddle_tensor, +) + + +class AtomExcludeMask(paddle.nn.Layer): + """Computes the type exclusion mask for atoms.""" + + def __init__( + self, + ntypes: int, + exclude_types: List[int] = [], + ): + super().__init__() + self.reinit(ntypes, exclude_types) + + def reinit( + self, + ntypes: int, + exclude_types: List[int] = [], + ): + self.ntypes = ntypes + self.exclude_types = exclude_types + self.type_mask = np.array( + [1 if tt_i not in self.exclude_types else 0 for tt_i in range(ntypes)], + dtype=np.int32, + ) + self.type_mask = to_paddle_tensor(self.type_mask).reshape([-1]) + + def get_exclude_types(self): + return self.exclude_types + + def get_type_mask(self): + return self.type_mask + + def forward( + self, + atype: paddle.Tensor, + ) -> paddle.Tensor: + """Compute type exclusion mask for atoms. + + Parameters + ---------- + atype + The extended atom types. shape: nf x natom + + Returns + ------- + mask + The type exclusion mask for atoms. shape: nf x natom + Element [ff,ii] being 0 if type(ii) is excluded, + otherwise being 1. + + """ + nf, natom = atype.shape + return self.type_mask[atype].reshape([nf, natom]).to(atype.place) + + +class PairExcludeMask(paddle.nn.Layer): + """Computes the type exclusion mask for atom pairs.""" + + def __init__( + self, + ntypes: int, + exclude_types: List[Tuple[int, int]] = [], + ): + super().__init__() + self.reinit(ntypes, exclude_types) + + def reinit( + self, + ntypes: int, + exclude_types: List[Tuple[int, int]] = [], + ): + self.ntypes = ntypes + self._exclude_types: Set[Tuple[int, int]] = set() + for tt in exclude_types: + assert len(tt) == 2 + self._exclude_types.add((tt[0], tt[1])) + self._exclude_types.add((tt[1], tt[0])) + # ntypes + 1 for nlist masks + self.type_mask = np.array( + [ + [ + 1 if (tt_i, tt_j) not in self._exclude_types else 0 + for tt_i in range(ntypes + 1) + ] + for tt_j in range(ntypes + 1) + ], + dtype=np.int32, + ) + # (ntypes+1 x ntypes+1) + self.type_mask = to_paddle_tensor(self.type_mask).reshape([-1]) + self.no_exclusion = len(self._exclude_types) == 0 + + def get_exclude_types(self): + return self._exclude_types + + # may have a better place for this method... + def forward( + self, + nlist: paddle.Tensor, + atype_ext: paddle.Tensor, + ) -> paddle.Tensor: + """Compute type exclusion mask. + + Parameters + ---------- + nlist + The neighbor list. shape: nf x nloc x nnei + atype_ext + The extended aotm types. shape: nf x nall + + Returns + ------- + mask + The type exclusion mask of shape: nf x nloc x nnei. + Element [ff,ii,jj] being 0 if type(ii), type(nlist[ff,ii,jj]) is excluded, + otherwise being 1. + + """ + if self.no_exclusion: + # safely return 1 if nothing is excluded. + return paddle.ones_like(nlist, dtype=paddle.int32).to(device=nlist.place) + nf, nloc, nnei = nlist.shape + nall = atype_ext.shape[1] + # add virtual atom of type ntypes. nf x nall+1 + ae = paddle.concat( + [ + atype_ext, + self.ntypes + * paddle.ones([nf, 1], dtype=atype_ext.dtype).to( + device=atype_ext.place + ), + ], + axis=-1, + ) + type_i = atype_ext[:, :nloc].reshape([nf, nloc]) * (self.ntypes + 1) + # nf x nloc x nnei + index = paddle.where(nlist == -1, nall, nlist).reshape([nf, nloc * nnei]) + type_j = paddle.take_along_axis(ae, axis=1, indices=index).reshape( + [nf, nloc, nnei] + ) + type_ij = type_i[:, :, None] + type_j + # nf x (nloc x nnei) + type_ij = type_ij.reshape([nf, nloc * nnei]) + mask = self.type_mask[type_ij].reshape([nf, nloc, nnei]).to(atype_ext.place) + return mask diff --git a/deepmd/pd/utils/finetune.py b/deepmd/pd/utils/finetune.py new file mode 100644 index 0000000000..edac72d9c9 --- /dev/null +++ b/deepmd/pd/utils/finetune.py @@ -0,0 +1,200 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import logging +from copy import ( + deepcopy, +) + +import paddle + +from deepmd.utils.finetune import ( + FinetuneRuleItem, +) + +log = logging.getLogger(__name__) + + +def get_finetune_rule_single( + _single_param_target, + _model_param_pretrained, + from_multitask=False, + model_branch="Default", + model_branch_from="", + change_model_params=False, +): + single_config = deepcopy(_single_param_target) + new_fitting = False + model_branch_chosen = "Default" + + if not from_multitask: + single_config_chosen = deepcopy(_model_param_pretrained) + if model_branch_from == "RANDOM": + # not ["", "RANDOM"], because single-from-single finetune uses pretrained fitting in default + new_fitting = True + else: + model_dict_params = _model_param_pretrained["model_dict"] + if model_branch_from in ["", "RANDOM"]: + model_branch_chosen = next(iter(model_dict_params.keys())) + new_fitting = True + log.warning( + "The fitting net will be re-init instead of using that in the pretrained model! " + "The bias_adjust_mode will be set-by-statistic!" + ) + else: + model_branch_chosen = model_branch_from + assert model_branch_chosen in model_dict_params, ( + f"No model branch named '{model_branch_chosen}'! " + f"Available ones are {list(model_dict_params.keys())}." + ) + single_config_chosen = deepcopy(model_dict_params[model_branch_chosen]) + old_type_map, new_type_map = ( + single_config_chosen["type_map"], + single_config["type_map"], + ) + finetune_rule = FinetuneRuleItem( + p_type_map=old_type_map, + type_map=new_type_map, + model_branch=model_branch_chosen, + random_fitting=new_fitting, + ) + if change_model_params: + trainable_param = { + "descriptor": single_config.get("descriptor", {}).get("trainable", True), + "fitting_net": single_config.get("fitting_net", {}).get("trainable", True), + } + single_config["descriptor"] = single_config_chosen["descriptor"] + if not new_fitting: + single_config["fitting_net"] = single_config_chosen["fitting_net"] + log.info( + f"Change the '{model_branch}' model configurations according to the model branch " + f"'{model_branch_chosen}' in the pretrained one..." + ) + for net_type in trainable_param: + if net_type in single_config: + single_config[net_type]["trainable"] = trainable_param[net_type] + else: + single_config[net_type] = {"trainable": trainable_param[net_type]} + return single_config, finetune_rule + + +def get_finetune_rules( + finetune_model, model_config, model_branch="", change_model_params=True +): + """ + Get fine-tuning rules and (optionally) change the model_params according to the pretrained one. + + This function gets the fine-tuning rules and (optionally) changes input in different modes as follows: + 1. Single-task fine-tuning from a single-task pretrained model: + - The model will be fine-tuned based on the pretrained model. + - (Optional) Updates the model parameters based on the pretrained model. + 2. Single-task fine-tuning from a multi-task pretrained model: + - The model will be fine-tuned based on the selected branch in the pretrained model. + The chosen branch can be defined from the command-line or `finetune_head` input parameter. + If not defined, model parameters in the fitting network will be randomly initialized. + - (Optional) Updates the model parameters based on the selected branch in the pretrained model. + 3. Multi-task fine-tuning from a single-task pretrained model: + - The model in each branch will be fine-tuned or resumed based on the single branch ('Default') in the pretrained model. + The chosen branches can be defined from the `finetune_head` input parameter of each branch. + - If `finetune_head` is defined as 'Default', + it will be fine-tuned based on the single branch ('Default') in the pretrained model. + - If `finetune_head` is not defined and the model_key is 'Default', + it will resume from the single branch ('Default') in the pretrained model without fine-tuning. + - If `finetune_head` is not defined and the model_key is not 'Default', + it will be fine-tuned based on the single branch ('Default') in the pretrained model, + while model parameters in the fitting network of the branch will be randomly initialized. + - (Optional) Updates model parameters in each branch based on the single branch ('Default') in the pretrained model. + 4. Multi-task fine-tuning from a multi-task pretrained model: + - The model in each branch will be fine-tuned or resumed based on the chosen branches in the pretrained model. + The chosen branches can be defined from the `finetune_head` input parameter of each branch. + - If `finetune_head` is defined as one of the branches in the pretrained model, + it will be fine-tuned based on the chosen branch in the pretrained model. + - If `finetune_head` is not defined and the model_key is the same as one of those in the pretrained model, + it will resume from the model_key branch in the pretrained model without fine-tuning. + - If `finetune_head` is not defined and a new model_key is used, + it will be fine-tuned based on the chosen branch in the pretrained model, + while model parameters in the fitting network of the branch will be randomly initialized. + - (Optional) Updates model parameters in each branch based on the chosen branches in the pretrained model. + + Parameters + ---------- + finetune_model + The pretrained model. + model_config + The fine-tuning input parameters. + model_branch + The model branch chosen in command-line mode, only for single-task fine-tuning. + change_model_params + Whether to change the model parameters according to the pretrained one. + + Returns + ------- + model_config: + Updated model parameters. + finetune_links: + Fine-tuning rules in a dict format, with `model_branch`: FinetuneRuleItem pairs. + """ + multi_task = "model_dict" in model_config + state_dict = paddle.load(finetune_model) + if "model" in state_dict: + state_dict = state_dict["model"] + last_model_params = state_dict["_extra_state"]["model_params"] + finetune_from_multi_task = "model_dict" in last_model_params + finetune_links = {} + if not multi_task: + # use command-line first + if model_branch == "" and "finetune_head" in model_config: + model_branch = model_config["finetune_head"] + model_config, finetune_rule = get_finetune_rule_single( + model_config, + last_model_params, + from_multitask=finetune_from_multi_task, + model_branch="Default", + model_branch_from=model_branch, + change_model_params=change_model_params, + ) + finetune_links["Default"] = finetune_rule + else: + assert model_branch == "", ( + "Multi-task fine-tuning does not support command-line branches chosen!" + "Please define the 'finetune_head' in each model params!" + ) + target_keys = model_config["model_dict"].keys() + if not finetune_from_multi_task: + pretrained_keys = ["Default"] + else: + pretrained_keys = last_model_params["model_dict"].keys() + for model_key in target_keys: + resuming = False + if ( + "finetune_head" in model_config["model_dict"][model_key] + and model_config["model_dict"][model_key]["finetune_head"] != "RANDOM" + ): + pretrained_key = model_config["model_dict"][model_key]["finetune_head"] + assert pretrained_key in pretrained_keys, ( + f"'{pretrained_key}' head chosen to finetune not exist in the pretrained model!" + f"Available heads are: {list(pretrained_keys)}" + ) + model_branch_from = pretrained_key + elif ( + "finetune_head" not in model_config["model_dict"][model_key] + and model_key in pretrained_keys + ): + # not do anything if not defined "finetune_head" in heads that exist in the pretrained model + # this will just do resuming + model_branch_from = model_key + resuming = True + else: + # if not defined "finetune_head" in new heads or "finetune_head" is "RANDOM", the fitting net will bre randomly initialized + model_branch_from = "RANDOM" + model_config["model_dict"][model_key], finetune_rule = ( + get_finetune_rule_single( + model_config["model_dict"][model_key], + last_model_params, + from_multitask=finetune_from_multi_task, + model_branch=model_key, + model_branch_from=model_branch_from, + change_model_params=change_model_params, + ) + ) + finetune_links[model_key] = finetune_rule + finetune_links[model_key].resuming = resuming + return model_config, finetune_links diff --git a/deepmd/pd/utils/learning_rate.py b/deepmd/pd/utils/learning_rate.py new file mode 100644 index 0000000000..94c657abd4 --- /dev/null +++ b/deepmd/pd/utils/learning_rate.py @@ -0,0 +1,53 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import numpy as np + + +class LearningRateExp: + def __init__( + self, + start_lr, + stop_lr, + decay_steps, + stop_steps, + decay_rate=None, + **kwargs, + ): + """ + Construct an exponential-decayed learning rate. + + Parameters + ---------- + start_lr + The learning rate at the start of the training. + stop_lr + The desired learning rate at the end of the training. + When decay_rate is explicitly set, this value will serve as + the minimum learning rate during training. In other words, + if the learning rate decays below stop_lr, stop_lr will be applied instead. + decay_steps + The learning rate is decaying every this number of training steps. + stop_steps + The total training steps for learning rate scheduler. + decay_rate + The decay rate for the learning rate. + If provided, the decay rate will be set instead of + calculating it through interpolation between start_lr and stop_lr. + """ + self.start_lr = start_lr + default_ds = 100 if stop_steps // 10 > 100 else stop_steps // 100 + 1 + self.decay_steps = decay_steps + if self.decay_steps >= stop_steps: + self.decay_steps = default_ds + self.decay_rate = np.exp( + np.log(stop_lr / self.start_lr) / (stop_steps / self.decay_steps) + ) + if decay_rate is not None: + self.decay_rate = decay_rate + self.min_lr = stop_lr + + def value(self, step): + """Get the learning rate at the given step.""" + step_lr = self.start_lr * np.power(self.decay_rate, step // self.decay_steps) + if step_lr < self.min_lr: + step_lr = self.min_lr + return step_lr diff --git a/deepmd/pd/utils/multi_task.py b/deepmd/pd/utils/multi_task.py new file mode 100644 index 0000000000..680dc53c79 --- /dev/null +++ b/deepmd/pd/utils/multi_task.py @@ -0,0 +1,162 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from copy import ( + deepcopy, +) + +from deepmd.pd.model.descriptor import ( + BaseDescriptor, +) +from deepmd.pd.model.task import ( + BaseFitting, +) + + +def preprocess_shared_params(model_config): + """Preprocess the model params for multitask model, and generate the links dict for further sharing. + + Args: + model_config: Model params of multitask model. + + Returns + ------- + model_config: Preprocessed model params of multitask model. + Those string names are replaced with real params in `shared_dict` of model params. + shared_links: Dict of link infos for further sharing. + Each item, whose key must be in `shared_dict`, is a dict with following keys: + - "type": The real class type of this item. + - "links": List of shared settings, each sub-item is a dict with following keys: + - "model_key": Model key in the `model_dict` to share this item. + - "shared_type": Type of this shard item. + - "shared_level": Shared level (int) of this item in this model. + Lower for more params to share, 0 means to share all params in this item. + This list are sorted by "shared_level". + For example, if one has `model_config` like this: + "model": { + "shared_dict": { + "my_type_map": ["foo", "bar"], + "my_des1": { + "type": "se_e2_a", + "neuron": [10, 20, 40] + }, + }, + "model_dict": { + "model_1": { + "type_map": "my_type_map", + "descriptor": "my_des1", + "fitting_net": { + "neuron": [100, 100, 100] + } + }, + "model_2": { + "type_map": "my_type_map", + "descriptor": "my_des1", + "fitting_net": { + "neuron": [100, 100, 100] + } + } + "model_3": { + "type_map": "my_type_map", + "descriptor": "my_des1:1", + "fitting_net": { + "neuron": [100, 100, 100] + } + } + } + } + The above config will init three model branches named `model_1` and `model_2` and `model_3`, + in which: + - `model_2` and `model_3` will have the same `type_map` as that in `model_1`. + - `model_2` will share all the parameters of `descriptor` with `model_1`, + while `model_3` will share part of parameters of `descriptor` with `model_1` + on human-defined share-level `1` (default is `0`, meaning share all the parameters). + - `model_1`, `model_2` and `model_3` have three different `fitting_net`s. + The returned `model_config` will automatically fulfill the input `model_config` as if there's no sharing, + and the `shared_links` will keep all the sharing information with looking: + { + 'my_des1': { + 'type': 'DescrptSeA', + 'links': [ + {'model_key': 'model_1', + 'shared_type': 'descriptor', + 'shared_level': 0}, + {'model_key': 'model_2', + 'shared_type': 'descriptor', + 'shared_level': 0}, + {'model_key': 'model_3', + 'shared_type': 'descriptor', + 'shared_level': 1} + ] + } + } + + """ + assert "model_dict" in model_config, "only multi-task model can use this method!" + supported_types = ["type_map", "descriptor", "fitting_net"] + shared_dict = model_config.get("shared_dict", {}) + shared_links = {} + type_map_keys = [] + + def replace_one_item(params_dict, key_type, key_in_dict, suffix="", index=None): + shared_type = key_type + shared_key = key_in_dict + shared_level = 0 + if ":" in key_in_dict: + shared_key = key_in_dict.split(":")[0] + shared_level = int(key_in_dict.split(":")[1]) + assert ( + shared_key in shared_dict + ), f"Appointed {shared_type} {shared_key} are not in the shared_dict! Please check the input params." + if index is None: + params_dict[shared_type] = deepcopy(shared_dict[shared_key]) + else: + params_dict[index] = deepcopy(shared_dict[shared_key]) + if shared_type == "type_map": + if key_in_dict not in type_map_keys: + type_map_keys.append(key_in_dict) + else: + if shared_key not in shared_links: + class_name = get_class_name(shared_type, shared_dict[shared_key]) + shared_links[shared_key] = {"type": class_name, "links": []} + link_item = { + "model_key": model_key, + "shared_type": shared_type + suffix, + "shared_level": shared_level, + } + shared_links[shared_key]["links"].append(link_item) + + for model_key in model_config["model_dict"]: + model_params_item = model_config["model_dict"][model_key] + for item_key in model_params_item: + if item_key in supported_types: + item_params = model_params_item[item_key] + if isinstance(item_params, str): + replace_one_item(model_params_item, item_key, item_params) + elif item_params.get("type", "") == "hybrid": + for ii, hybrid_item in enumerate(item_params["list"]): + if isinstance(hybrid_item, str): + replace_one_item( + model_params_item[item_key]["list"], + item_key, + hybrid_item, + suffix=f"_hybrid_{ii}", + index=ii, + ) + for shared_key in shared_links: + shared_links[shared_key]["links"] = sorted( + shared_links[shared_key]["links"], + key=lambda x: x["shared_level"] + - ("spin" in model_config["model_dict"][x["model_key"]]) * 100, + ) + # little trick to make spin models in the front to be the base models, + # because its type embeddings are more general. + assert len(type_map_keys) == 1, "Multitask model must have only one type_map!" + return model_config, shared_links + + +def get_class_name(item_key, item_params): + if item_key == "descriptor": + return BaseDescriptor.get_class_by_type(item_params.get("type", "se_e2_a")) + elif item_key == "fitting_net": + return BaseFitting.get_class_by_type(item_params.get("type", "ener")) + else: + raise RuntimeError(f"Unknown class_name type {item_key}") diff --git a/deepmd/pd/utils/neighbor_stat.py b/deepmd/pd/utils/neighbor_stat.py new file mode 100644 index 0000000000..bd27224814 --- /dev/null +++ b/deepmd/pd/utils/neighbor_stat.py @@ -0,0 +1,193 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Iterator, + Optional, + Tuple, +) + +import numpy as np +import paddle + +from deepmd.pd.utils.auto_batch_size import ( + AutoBatchSize, +) +from deepmd.pd.utils.env import ( + DEVICE, +) +from deepmd.pd.utils.nlist import ( + extend_coord_with_ghosts, +) +from deepmd.utils.data_system import ( + DeepmdDataSystem, +) +from deepmd.utils.neighbor_stat import NeighborStat as BaseNeighborStat + + +class NeighborStatOP(paddle.nn.Layer): + """Class for getting neighbor statics data information. + + Parameters + ---------- + ntypes + The num of atom types + rcut + The cut-off radius + mixed_types : bool, optional + If True, treat neighbors of all types as a single type. + """ + + def __init__( + self, + ntypes: int, + rcut: float, + mixed_types: bool, + ) -> None: + super().__init__() + self.rcut = rcut + self.ntypes = ntypes + self.mixed_types = mixed_types + + def forward( + self, + coord: paddle.Tensor, + atype: paddle.Tensor, + cell: Optional[paddle.Tensor], + ) -> Tuple[paddle.Tensor, paddle.Tensor]: + """Calculate the neareest neighbor distance between atoms, maximum nbor size of + atoms and the output data range of the environment matrix. + + Parameters + ---------- + coord + The coordinates of atoms. + atype + The atom types. + cell + The cell. + + Returns + ------- + paddle.Tensor + The minimal squared distance between two atoms, in the shape of (nframes,) + paddle.Tensor + The maximal number of neighbors + """ + nframes = coord.shape[0] + coord = coord.reshape([nframes, -1, 3]) + nloc = coord.shape[1] + coord = coord.reshape([nframes, nloc * 3]) + extend_coord, extend_atype, _ = extend_coord_with_ghosts( + coord, atype, cell, self.rcut + ) + + coord1 = extend_coord.reshape([nframes, -1]) + nall = coord1.shape[1] // 3 + coord0 = coord1[:, : nloc * 3] + diff = ( + coord1.reshape([nframes, -1, 3])[:, None, :, :] + - coord0.reshape([nframes, -1, 3])[:, :, None, :] + ) + assert list(diff.shape) == [nframes, nloc, nall, 3] + # remove the diagonal elements + mask = paddle.eye(nloc, nall).to(dtype=paddle.bool, device=diff.place) + diff[:, mask] = float("inf") + rr2 = paddle.sum(paddle.square(diff), axis=-1) + min_rr2 = paddle.min(rr2, axis=-1) + # count the number of neighbors + if not self.mixed_types: + mask = rr2 < self.rcut**2 + nnei = paddle.zeros((nframes, nloc, self.ntypes), dtype=paddle.int32).to( + device=mask.place + ) + for ii in range(self.ntypes): + nnei[:, :, ii] = paddle.sum( + mask & extend_atype.equal(ii)[:, None, :], axis=-1 + ) + else: + mask = rr2 < self.rcut**2 + # virtual types (<0) are not counted + nnei = paddle.sum(mask & extend_atype.ge(0)[:, None, :], axis=-1).reshape( + [nframes, nloc, 1] + ) + max_nnei = paddle.max(nnei, axis=1) + return min_rr2, max_nnei + + +class NeighborStat(BaseNeighborStat): + """Neighbor statistics using pure NumPy. + + Parameters + ---------- + ntypes : int + The num of atom types + rcut : float + The cut-off radius + mixed_type : bool, optional, default=False + Treat all types as a single type. + """ + + def __init__( + self, + ntypes: int, + rcut: float, + mixed_type: bool = False, + ) -> None: + super().__init__(ntypes, rcut, mixed_type) + op = NeighborStatOP(ntypes, rcut, mixed_type) + # self.op = paddle.jit.to_static(op) + self.op = op + self.auto_batch_size = AutoBatchSize() + + def iterator( + self, data: DeepmdDataSystem + ) -> Iterator[Tuple[np.ndarray, float, str]]: + """Abstract method for producing data. + + Yields + ------ + np.ndarray + The maximal number of neighbors + float + The squared minimal distance between two atoms + str + The directory of the data system + """ + for ii in range(len(data.system_dirs)): + for jj in data.data_systems[ii].dirs: + data_set = data.data_systems[ii] + data_set_data = data_set._load_set(jj) + minrr2, max_nnei = self.auto_batch_size.execute_all( + self._execute, + data_set_data["coord"].shape[0], + data_set.get_natoms(), + data_set_data["coord"], + data_set_data["type"], + data_set_data["box"] if data_set.pbc else None, + ) + yield np.max(max_nnei, axis=0), np.min(minrr2), jj + + def _execute( + self, + coord: np.ndarray, + atype: np.ndarray, + cell: Optional[np.ndarray], + ): + """Execute the operation. + + Parameters + ---------- + coord + The coordinates of atoms. + atype + The atom types. + cell + The cell. + """ + minrr2, max_nnei = self.op( + paddle.to_tensor(coord).to(DEVICE), + paddle.to_tensor(atype).to(DEVICE), + paddle.to_tensor(cell).to(DEVICE) if cell is not None else None, + ) + minrr2 = minrr2.numpy() + max_nnei = max_nnei.numpy() + return minrr2, max_nnei diff --git a/deepmd/pd/utils/nlist.py b/deepmd/pd/utils/nlist.py new file mode 100644 index 0000000000..3c513fec64 --- /dev/null +++ b/deepmd/pd/utils/nlist.py @@ -0,0 +1,494 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Dict, + List, + Optional, + Union, +) + +import paddle + +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.region import ( + normalize_coord, + to_face_distance, +) + + +def extend_input_and_build_neighbor_list( + coord, + atype, + rcut: float, + sel: List[int], + mixed_types: bool = False, + box: Optional[paddle.Tensor] = None, +): + nframes, nloc = atype.shape[:2] + nloc = 192 + if box is not None: + box_gpu = box.to(coord.place) + coord_normalized = normalize_coord( + coord.reshape([nframes, nloc, 3]), + box_gpu.reshape([nframes, 3, 3]), + ) + else: + box_gpu = None + coord_normalized = coord.clone() + extended_coord, extended_atype, mapping = extend_coord_with_ghosts( + coord_normalized, atype, box_gpu, rcut, box + ) + nlist = build_neighbor_list( + extended_coord, + extended_atype, + nloc, + rcut, + sel, + distinguish_types=(not mixed_types), + ) + extended_coord = extended_coord.reshape([nframes, -1, 3]) + return extended_coord, extended_atype, mapping, nlist + + +def build_neighbor_list( + coord: paddle.Tensor, + atype: paddle.Tensor, + nloc: int, + rcut: float, + sel: Union[int, List[int]], + distinguish_types: bool = True, +) -> paddle.Tensor: + """Build neightbor list for a single frame. keeps nsel neighbors. + + Parameters + ---------- + coord : paddle.Tensor + exptended coordinates of shape [batch_size, nall x 3] + atype : paddle.Tensor + extended atomic types of shape [batch_size, nall] + if type < 0 the atom is treat as virtual atoms. + nloc : int + number of local atoms. + rcut : float + cut-off radius + sel : int or List[int] + maximal number of neighbors (of each type). + if distinguish_types==True, nsel should be list and + the length of nsel should be equal to number of + types. + distinguish_types : bool + distinguish different types. + + Returns + ------- + neighbor_list : paddle.Tensor + Neighbor list of shape [batch_size, nloc, nsel], the neighbors + are stored in an ascending order. If the number of + neighbors is less than nsel, the positions are masked + with -1. The neighbor list of an atom looks like + |------ nsel ------| + xx xx xx xx -1 -1 -1 + if distinguish_types==True and we have two types + |---- nsel[0] -----| |---- nsel[1] -----| + xx xx xx xx -1 -1 -1 xx xx xx -1 -1 -1 -1 + For virtual atoms all neighboring positions are filled with -1. + + """ + batch_size = coord.shape[0] + coord = coord.reshape([batch_size, -1]) + nall = coord.shape[1] // 3 + # fill virtual atoms with large coords so they are not neighbors of any + # real atom. + if coord.numel() > 0: + xmax = paddle.max(coord) + 2.0 * rcut + else: + xmax = paddle.zeros(1, dtype=coord.dtype).to(device=coord.place) + 2.0 * rcut + # nf x nall + is_vir = atype < 0 + coord1 = paddle.where( + is_vir[:, :, None], xmax, coord.reshape([batch_size, nall, 3]) + ).reshape([batch_size, nall * 3]) + if isinstance(sel, int): + sel = [sel] + # nloc x 3 + coord0 = coord1[:, : nloc * 3] + # nloc x nall x 3 + diff = coord1.reshape([batch_size, -1, 3]).unsqueeze(1) - coord0.reshape( + [batch_size, -1, 3] + ).unsqueeze(2) + assert list(diff.shape) == [batch_size, nloc, nall, 3] + # nloc x nall + rr = paddle.linalg.norm(diff, axis=-1) + # if central atom has two zero distances, sorting sometimes can not exclude itself + rr -= paddle.eye(nloc, nall, dtype=rr.dtype).to(device=rr.place).unsqueeze(0) + rr, nlist = paddle.sort(rr, axis=-1), paddle.argsort(rr, axis=-1) + # nloc x (nall-1) + rr = rr[:, :, 1:] + nlist = nlist[:, :, 1:] + + return _trim_mask_distinguish_nlist( + is_vir, atype, rr, nlist, rcut, sel, distinguish_types + ) + + +def _trim_mask_distinguish_nlist( + is_vir_cntl: paddle.Tensor, + atype_neig: paddle.Tensor, + rr: paddle.Tensor, + nlist: paddle.Tensor, + rcut: float, + sel: List[int], + distinguish_types: bool, +) -> paddle.Tensor: + """Trim the size of nlist, mask if any central atom is virtual, distinguish types if necessary.""" + nsel = sum(sel) + # nloc x nsel + batch_size, nloc, nnei = rr.shape + assert batch_size == is_vir_cntl.shape[0] + if nsel <= nnei: + rr = rr[:, :, :nsel] + nlist = nlist[:, :, :nsel] + else: + rr = paddle.concat( + [ + rr, + paddle.ones([batch_size, nloc, nsel - nnei]).to(device=rr.place) + rcut, + ], # pylint: disable=no-explicit-dtype + axis=-1, + ) + nlist = paddle.concat( + [ + nlist, + paddle.ones([batch_size, nloc, nsel - nnei], dtype=nlist.dtype).to( + device=rr.place + ), + ], + axis=-1, + ) + assert list(nlist.shape) == [batch_size, nloc, nsel] + nlist = paddle.where( + paddle.logical_or((rr > rcut), is_vir_cntl[:, :nloc, None]), -1, nlist + ) + if distinguish_types: + return nlist_distinguish_types(nlist, atype_neig, sel) + else: + return nlist + + +def build_directional_neighbor_list( + coord_cntl: paddle.Tensor, + atype_cntl: paddle.Tensor, + coord_neig: paddle.Tensor, + atype_neig: paddle.Tensor, + rcut: float, + sel: Union[int, List[int]], + distinguish_types: bool = True, +) -> paddle.Tensor: + """Build directional neighbor list. + + With each central atom, all the neighbor atoms in the cut-off radius will + be recorded in the neighbor list. The maximum neighbors is nsel. If the real + number of neighbors is larger than nsel, the neighbors will be sorted with the + distance and the first nsel neighbors are kept. + + Important: the central and neighboring atoms are assume to be different atoms. + + Parameters + ---------- + coord_central : paddle.Tensor + coordinates of central atoms. assumed to be local atoms. + shape [batch_size, nloc_central x 3] + atype_central : paddle.Tensor + atomic types of central atoms. shape [batch_size, nloc_central] + if type < 0 the atom is treated as virtual atoms. + coord_neighbor : paddle.Tensor + extended coordinates of neighbors atoms. shape [batch_size, nall_neighbor x 3] + atype_central : paddle.Tensor + extended atomic types of neighbors atoms. shape [batch_size, nall_neighbor] + if type < 0 the atom is treated as virtual atoms. + rcut : float + cut-off radius + sel : int or List[int] + maximal number of neighbors (of each type). + if distinguish_types==True, nsel should be list and + the length of nsel should be equal to number of + types. + distinguish_types : bool + distinguish different types. + + Returns + ------- + neighbor_list : paddle.Tensor + Neighbor list of shape [batch_size, nloc_central, nsel], the neighbors + are stored in an ascending order. If the number of neighbors is less than nsel, + the positions are masked with -1. The neighbor list of an atom looks like + |------ nsel ------| + xx xx xx xx -1 -1 -1 + if distinguish_types==True and we have two types + |---- nsel[0] -----| |---- nsel[1] -----| + xx xx xx xx -1 -1 -1 xx xx xx -1 -1 -1 -1 + For virtual atoms all neighboring positions are filled with -1. + """ + batch_size = coord_cntl.shape[0] + coord_cntl = coord_cntl.reshape([batch_size, -1]) + nloc_cntl = coord_cntl.shape[1] // 3 + coord_neig = coord_neig.reshape([batch_size, -1]) + nall_neig = coord_neig.shape[1] // 3 + # fill virtual atoms with large coords so they are not neighbors of any + # real atom. + if coord_neig.numel() > 0: + xmax = paddle.max(coord_cntl) + 2.0 * rcut + else: + xmax = ( + paddle.zeros(1, dtype=coord_neig.dtype, device=coord_neig.place) + + 2.0 * rcut + ) + # nf x nloc + is_vir_cntl = atype_cntl < 0 + # nf x nall + is_vir_neig = atype_neig < 0 + # nf x nloc x 3 + coord_cntl = coord_cntl.reshape([batch_size, nloc_cntl, 3]) + # nf x nall x 3 + coord_neig = paddle.where( + is_vir_neig[:, :, None], xmax, coord_neig.reshape([batch_size, nall_neig, 3]) + ).reshape([batch_size, nall_neig, 3]) + # nsel + if isinstance(sel, int): + sel = [sel] + # nloc x nall x 3 + diff = coord_neig[:, None, :, :] - coord_cntl[:, :, None, :] + assert list(diff.shape) == [batch_size, nloc_cntl, nall_neig, 3] + # nloc x nall + rr = paddle.linalg.norm(diff, axis=-1) + rr, nlist = paddle.sort(rr, axis=-1), paddle.argsort(rr, axis=-1) + + # We assume that the central and neighbor atoms are diffferent, + # thus we do not need to exclude self-neighbors. + # # if central atom has two zero distances, sorting sometimes can not exclude itself + # rr -= paddle.eye(nloc_cntl, nall_neig, dtype=rr.dtype, device=rr.place).unsqueeze(0) + # rr, nlist = paddle.sort(rr, axis=-1) + # # nloc x (nall-1) + # rr = rr[:, :, 1:] + # nlist = nlist[:, :, 1:] + + return _trim_mask_distinguish_nlist( + is_vir_cntl, atype_neig, rr, nlist, rcut, sel, distinguish_types + ) + + +def nlist_distinguish_types( + nlist: paddle.Tensor, + atype: paddle.Tensor, + sel: List[int], +): + """Given a nlist that does not distinguish atom types, return a nlist that + distinguish atom types. + + """ + nf, nloc, nnei = nlist.shape + ret_nlist = [] + # nloc x nall + tmp_atype = paddle.tile(atype.unsqueeze(1), [1, nloc, 1]) + mask = nlist == -1 + # nloc x s(nsel) + tnlist = paddle.take_along_axis( + tmp_atype, + axis=2, + indices=nlist.masked_fill(mask, 0), + ) + tnlist = tnlist.masked_fill(mask, -1) + snsel = tnlist.shape[2] + for ii, ss in enumerate(sel): + # nloc x s(nsel) + # to int because bool cannot be sort on GPU + pick_mask = (tnlist == ii).to(paddle.int32) + # nloc x s(nsel), stable sort, nearer neighbors first + pick_mask, imap = ( + paddle.sort(pick_mask, axis=-1, descending=True, stable=True), + paddle.argsort(pick_mask, axis=-1, descending=True, stable=True), + ) + # nloc x s(nsel) + inlist = paddle.take_along_axis(nlist, axis=2, indices=imap) + inlist = inlist.masked_fill(~(pick_mask.to(paddle.bool)), -1) + # nloc x nsel[ii] + ret_nlist.append(paddle.split(inlist, [ss, snsel - ss], axis=-1)[0]) + return paddle.concat(ret_nlist, axis=-1) + + +# build_neighbor_list = paddle.vmap( +# build_neighbor_list_lower, +# in_dims=(0,0,None,None,None), +# out_dims=(0), +# ) + + +def get_multiple_nlist_key( + rcut: float, + nsel: int, +) -> str: + return str(rcut) + "_" + str(nsel) + + +def build_multiple_neighbor_list( + coord: paddle.Tensor, + nlist: paddle.Tensor, + rcuts: List[float], + nsels: List[int], +) -> Dict[str, paddle.Tensor]: + """Input one neighbor list, and produce multiple neighbor lists with + different cutoff radius and numbers of selection out of it. The + required rcuts and nsels should be smaller or equal to the input nlist. + + Parameters + ---------- + coord : paddle.Tensor + exptended coordinates of shape [batch_size, nall x 3] + nlist : paddle.Tensor + Neighbor list of shape [batch_size, nloc, nsel], the neighbors + should be stored in an ascending order. + rcuts : List[float] + list of cut-off radius in ascending order. + nsels : List[int] + maximal number of neighbors in ascending order. + + Returns + ------- + nlist_dict : Dict[str, paddle.Tensor] + A dict of nlists, key given by get_multiple_nlist_key(rc, nsel) + value being the corresponding nlist. + + """ + assert len(rcuts) == len(nsels) + if len(rcuts) == 0: + return {} + nb, nloc, nsel = nlist.shape + if nsel < nsels[-1]: + pad = -1 * paddle.ones( + [nb, nloc, nsels[-1] - nsel], + dtype=nlist.dtype, + ).to(device=nlist.place) + # nb x nloc x nsel + nlist = paddle.concat([nlist, pad], axis=-1) + nsel = nsels[-1] + # nb x nall x 3 + coord1 = coord.reshape([nb, -1, 3]) + nall = coord1.shape[1] + # nb x nloc x 3 + coord0 = coord1[:, :nloc, :] + nlist_mask = nlist == -1 + # nb x (nloc x nsel) x 3 + index = ( + nlist.masked_fill(nlist_mask, 0) + .reshape([nb, nloc * nsel]) + .unsqueeze(-1) + .expand(-1, -1, 3) + ) + # nb x nloc x nsel x 3 + coord2 = paddle.take_along_axis(coord1, axis=1, index=index).reshape( + [nb, nloc, nsel, 3] + ) + # nb x nloc x nsel x 3 + diff = coord2 - coord0[:, :, None, :] + # nb x nloc x nsel + rr = paddle.linalg.norm(diff, axis=-1) + rr.masked_fill(nlist_mask, float("inf")) + nlist0 = nlist + ret = {} + for rc, ns in zip(rcuts[::-1], nsels[::-1]): + nlist0 = nlist0[:, :, :ns].masked_fill(rr[:, :, :ns] > rc, -1) + ret[get_multiple_nlist_key(rc, ns)] = nlist0 + return ret + + +def extend_coord_with_ghosts( + coord: paddle.Tensor, + atype: paddle.Tensor, + cell: Optional[paddle.Tensor], + rcut: float, + cell_cpu: Optional[paddle.Tensor] = None, +): + """Extend the coordinates of the atoms by appending peridoc images. + The number of images is large enough to ensure all the neighbors + within rcut are appended. + + Parameters + ---------- + coord : paddle.Tensor + original coordinates of shape [-1, nloc*3]. + atype : paddle.Tensor + atom type of shape [-1, nloc]. + cell : paddle.Tensor + simulation cell tensor of shape [-1, 9]. + rcut : float + the cutoff radius + cell_cpu : paddle.Tensor + cell on cpu for performance + + Returns + ------- + extended_coord: paddle.Tensor + extended coordinates of shape [-1, nall*3]. + extended_atype: paddle.Tensor + extended atom type of shape [-1, nall]. + index_mapping: paddle.Tensor + maping extended index to the local index + + """ + device = coord.place + nf, nloc = atype.shape[:2] + nloc = 192 + aidx = paddle.tile(paddle.arange(nloc).to(device="gpu:0").unsqueeze(0), [nf, 1]) # pylint: disable=no-explicit-dtype + if cell is None: + nall = nloc + extend_coord = coord.clone() + extend_atype = atype.clone() + extend_aidx = aidx.clone() + else: + coord = coord.reshape([nf, nloc, 3]) + cell = cell.reshape([nf, 3, 3]) + cell_cpu = cell_cpu.reshape([nf, 3, 3]) if cell_cpu is not None else cell + # nf x 3 + to_face = to_face_distance(cell_cpu) + # nf x 3 + # *2: ghost copies on + and - directions + # +1: central cell + nbuff = paddle.ceil(rcut / to_face).to(paddle.int64) + # 3 + nbuff = paddle.amax(nbuff, axis=0) # faster than paddle.max + nbuff_cpu = nbuff.cpu() + xi = paddle.arange(-nbuff_cpu[0], nbuff_cpu[0] + 1, 1).to( + dtype=env.GLOBAL_PD_FLOAT_PRECISION, device="cpu" + ) # pylint: disable=no-explicit-dtype + yi = paddle.arange(-nbuff_cpu[1], nbuff_cpu[1] + 1, 1).to( + dtype=env.GLOBAL_PD_FLOAT_PRECISION, device="cpu" + ) # pylint: disable=no-explicit-dtype + zi = paddle.arange(-nbuff_cpu[2], nbuff_cpu[2] + 1, 1).to( + dtype=env.GLOBAL_PD_FLOAT_PRECISION, device="cpu" + ) # pylint: disable=no-explicit-dtype + eye_3 = paddle.eye(3, dtype=env.GLOBAL_PD_FLOAT_PRECISION).to( + dtype=env.GLOBAL_PD_FLOAT_PRECISION, device="cpu" + ) + xyz = xi.reshape([-1, 1, 1, 1]) * eye_3[0] + xyz = xyz + yi.reshape([1, -1, 1, 1]) * eye_3[1] + xyz = xyz + zi.reshape([1, 1, -1, 1]) * eye_3[2] + xyz = xyz.reshape([-1, 3]) + xyz = xyz.to(device="gpu:0") + # ns x 3 + shift_idx = xyz[paddle.argsort(paddle.norm(xyz, axis=1))] + ns, _ = shift_idx.shape + nall = ns * nloc + # nf x ns x 3 + shift_vec = paddle.einsum("sd,fdk->fsk", shift_idx, cell) + # nf x ns x nloc x 3 + extend_coord = coord[:, None, :, :] + shift_vec[:, :, None, :] + # nf x ns x nloc + extend_atype = paddle.tile(atype.unsqueeze(-2), [1, ns, 1]) + # nf x ns x nloc + extend_aidx = paddle.tile(aidx.unsqueeze(-2), [1, ns, 1]) + return ( + extend_coord.reshape([nf, nall * 3]).to(device), + extend_atype.reshape([nf, nall]).to(device), + extend_aidx.reshape([nf, nall]).to(device), + ) diff --git a/deepmd/pd/utils/plugin.py b/deepmd/pd/utils/plugin.py new file mode 100644 index 0000000000..aa901c06e8 --- /dev/null +++ b/deepmd/pd/utils/plugin.py @@ -0,0 +1,16 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Base of plugin systems.""" + +from deepmd.utils.plugin import ( + Plugin, + PluginVariant, + VariantABCMeta, + VariantMeta, +) + +__all__ = [ + "Plugin", + "VariantMeta", + "VariantABCMeta", + "PluginVariant", +] diff --git a/deepmd/pd/utils/preprocess.py b/deepmd/pd/utils/preprocess.py new file mode 100644 index 0000000000..edf904063a --- /dev/null +++ b/deepmd/pd/utils/preprocess.py @@ -0,0 +1,309 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import logging +from typing import ( + Union, +) + +import paddle + +from deepmd.pd.utils import ( + env, +) + +log = logging.getLogger(__name__) + + +class Region3D: + def __init__(self, boxt): + """Construct a simulation box.""" + boxt = boxt.reshape([3, 3]) + self.boxt = boxt # convert physical coordinates to internal ones + self.rec_boxt = paddle.linalg.inv( + self.boxt + ) # convert internal coordinates to physical ones + + self.volume = paddle.linalg.det(self.boxt) # compute the volume + + # boxt = boxt.permute(1, 0) + c_yz = paddle.cross(boxt[1], boxt[2]) + self._h2yz = self.volume / paddle.linalg.norm(c_yz) + c_zx = paddle.cross(boxt[2], boxt[0]) + self._h2zx = self.volume / paddle.linalg.norm(c_zx) + c_xy = paddle.cross(boxt[0], boxt[1]) + self._h2xy = self.volume / paddle.linalg.norm(c_xy) + + def phys2inter(self, coord): + """Convert physical coordinates to internal ones.""" + return coord @ self.rec_boxt + + def inter2phys(self, coord): + """Convert internal coordinates to physical ones.""" + return coord @ self.boxt + + def get_face_distance(self): + """Return face distinces to each surface of YZ, ZX, XY.""" + return paddle.stack([self._h2yz, self._h2zx, self._h2xy]) + + +def normalize_coord(coord, region: Region3D, nloc: int): + """Move outer atoms into region by mirror. + + Args: + - coord: shape is [nloc*3] + """ + tmp_coord = coord.clone() + inter_cood = paddle.remainder(region.phys2inter(tmp_coord), 1.0) + tmp_coord = region.inter2phys(inter_cood) + return tmp_coord + + +def compute_serial_cid(cell_offset, ncell): + """Tell the sequential cell ID in its 3D space. + + Args: + - cell_offset: shape is [3] + - ncell: shape is [3] + """ + cell_offset[:, 0] *= ncell[1] * ncell[2] + cell_offset[:, 1] *= ncell[2] + return cell_offset.sum(-1) + + +def compute_pbc_shift(cell_offset, ncell): + """Tell shift count to move the atom into region.""" + shift = paddle.zeros_like(cell_offset) + shift = shift + (cell_offset < 0) * -( + paddle.floor(paddle.divide(cell_offset, ncell)) + ) + shift = shift + (cell_offset >= ncell) * -( + paddle.floor(paddle.divide((cell_offset - ncell), ncell)) + 1 + ) + assert paddle.all(cell_offset + shift * ncell >= 0) + assert paddle.all(cell_offset + shift * ncell < ncell) + return shift + + +def build_inside_clist(coord, region: Region3D, ncell): + """Build cell list on atoms inside region. + + Args: + - coord: shape is [nloc*3] + - ncell: shape is [3] + """ + loc_ncell = int(paddle.prod(ncell)) # num of local cells + nloc = coord.numel() // 3 # num of local atoms + inter_cell_size = 1.0 / ncell + + inter_cood = region.phys2inter(coord.reshape([-1, 3])) + cell_offset = paddle.floor(inter_cood / inter_cell_size).to(paddle.int64) + # numerical error brought by conversion from phys to inter back and force + # may lead to negative value + cell_offset[cell_offset < 0] = 0 + delta = cell_offset - ncell + a2c = compute_serial_cid(cell_offset, ncell) # cell id of atoms + arange = paddle.arange(0, loc_ncell, 1) # pylint: disable=no-explicit-dtype,no-explicit-device + cellid = a2c == arange.unsqueeze(-1) # one hot cellid + c2a = cellid.nonzero() + lst = [] + cnt = 0 + bincount = paddle.bincount(a2c, minlength=loc_ncell) + for i in range(loc_ncell): + n = bincount[i] + lst.append(c2a[cnt : cnt + n, 1]) + cnt += n + return a2c, lst + + +def append_neighbors(coord, region: Region3D, atype, rcut: float): + """Make ghost atoms who are valid neighbors. + + Args: + - coord: shape is [nloc*3] + - atype: shape is [nloc] + """ + to_face = region.get_face_distance() + + # compute num and size of local cells + ncell = paddle.floor(to_face / rcut).to(paddle.int64) + ncell[ncell == 0] = 1 + cell_size = to_face / ncell + ngcell = ( + paddle.floor(rcut / cell_size).to(paddle.int64) + 1 + ) # num of cells out of local, which contain ghost atoms + + # add ghost atoms + a2c, c2a = build_inside_clist(coord, region, ncell) + xi = paddle.arange(-ngcell[0], ncell[0] + ngcell[0], 1) # pylint: disable=no-explicit-dtype,no-explicit-device + yi = paddle.arange(-ngcell[1], ncell[1] + ngcell[1], 1) # pylint: disable=no-explicit-dtype,no-explicit-device + zi = paddle.arange(-ngcell[2], ncell[2] + ngcell[2], 1) # pylint: disable=no-explicit-dtype,no-explicit-device + xyz = xi.reshape([-1, 1, 1, 1]) * paddle.to_tensor([1, 0, 0], dtype=paddle.int64) # pylint: disable=no-explicit-device + xyz = xyz + yi.reshape([1, -1, 1, 1]) * paddle.to_tensor( + [0, 1, 0], dtype=paddle.int64 + ) # pylint: disable=no-explicit-device + xyz = xyz + zi.reshape([1, 1, -1, 1]) * paddle.to_tensor( + [0, 0, 1], dtype=paddle.int64 + ) # pylint: disable=no-explicit-device + xyz = xyz.reshape([-1, 3]) + mask_a = (xyz >= 0).all(axis=-1) + mask_b = (xyz < ncell).all(axis=-1) + mask = ~paddle.logical_and(mask_a, mask_b) + xyz = xyz[mask] # cell coord + shift = compute_pbc_shift(xyz, ncell) + coord_shift = region.inter2phys(shift.to(env.GLOBAL_PD_FLOAT_PRECISION)) + mirrored = shift * ncell + xyz + cid = compute_serial_cid(mirrored, ncell) + + n_atoms = coord.shape[0] + aid = [c2a[ci] + i * n_atoms for i, ci in enumerate(cid)] + aid = paddle.concat(aid) + tmp = paddle.trunc(paddle.divide(aid, n_atoms)) + aid = aid % n_atoms + tmp_coord = coord[aid] - coord_shift[tmp] + tmp_atype = atype[aid] + + # merge local and ghost atoms + merged_coord = paddle.concat([coord, tmp_coord]) + merged_coord_shift = paddle.concat([paddle.zeros_like(coord), coord_shift[tmp]]) + merged_atype = paddle.concat([atype, tmp_atype]) + merged_mapping = paddle.concat([paddle.arange(atype.numel()), aid]) # pylint: disable=no-explicit-dtype,no-explicit-device + return merged_coord_shift, merged_atype, merged_mapping + + +def build_neighbor_list( + nloc: int, coord, atype, rcut: float, sec, mapping, type_split=True, min_check=False +): + """For each atom inside region, build its neighbor list. + + Args: + - coord: shape is [nall*3] + - atype: shape is [nall] + """ + nall = coord.numel() // 3 + coord = coord.float() + nlist = [[] for _ in range(nloc)] + coord_l = coord.reshape([-1, 1, 3])[:nloc] + coord_r = coord.reshape([1, -1, 3]) + distance = coord_l - coord_r + distance = paddle.linalg.norm(distance, axis=-1) + DISTANCE_INF = distance.max().detach() + rcut + distance[:nloc, :nloc] += paddle.eye(nloc, dtype=paddle.bool) * DISTANCE_INF # pylint: disable=no-explicit-device + if min_check: + if distance.min().abs() < 1e-6: + raise RuntimeError("Atom dist too close!") + if not type_split: + sec = sec[-1:] + lst = [] + nlist = paddle.zeros((nloc, sec[-1].item())).long() - 1 # pylint: disable=no-explicit-dtype,no-explicit-device + nlist_loc = paddle.zeros((nloc, sec[-1].item())).long() - 1 # pylint: disable=no-explicit-dtype,no-explicit-device + nlist_type = paddle.zeros((nloc, sec[-1].item())).long() - 1 # pylint: disable=no-explicit-dtype,no-explicit-device + for i, nnei in enumerate(sec): + if i > 0: + nnei = nnei - sec[i - 1] + if not type_split: + tmp = distance + else: + mask = atype.unsqueeze(0) == i + tmp = distance + (~mask) * DISTANCE_INF + if tmp.shape[1] >= nnei: + _sorted, indices = paddle.topk(tmp, nnei, axis=1, largest=False) + else: + # when nnei > nall + indices = paddle.zeros((nloc, nnei)).long() - 1 # pylint: disable=no-explicit-dtype,no-explicit-device + _sorted = paddle.ones((nloc, nnei)).long() * DISTANCE_INF # pylint: disable=no-explicit-dtype,no-explicit-device + _sorted_nnei, indices_nnei = paddle.topk( + tmp, tmp.shape[1], axis=1, largest=False + ) + _sorted[:, : tmp.shape[1]] = _sorted_nnei + indices[:, : tmp.shape[1]] = indices_nnei + mask = (_sorted < rcut).to(paddle.int64) + indices_loc = mapping[indices] + indices = indices * mask + -1 * (1 - mask) # -1 for padding + indices_loc = indices_loc * mask + -1 * (1 - mask) # -1 for padding + if i == 0: + start = 0 + else: + start = sec[i - 1] + end = min(sec[i], start + indices.shape[1]) + nlist[:, start:end] = indices[:, :nnei] + nlist_loc[:, start:end] = indices_loc[:, :nnei] + nlist_type[:, start:end] = atype[indices[:, :nnei]] * mask + -1 * (1 - mask) + return nlist, nlist_loc, nlist_type + + +def compute_smooth_weight(distance, rmin: float, rmax: float): + """Compute smooth weight for descriptor elements.""" + if rmin >= rmax: + raise ValueError("rmin should be less than rmax.") + min_mask = distance <= rmin + max_mask = distance >= rmax + mid_mask = paddle.logical_not(paddle.logical_or(min_mask, max_mask)) + uu = (distance - rmin) / (rmax - rmin) + vv = uu * uu * uu * (-6 * uu * uu + 15 * uu - 10) + 1 + return vv * mid_mask.astype(vv.dtype) + min_mask.astype(vv.dtype) + + +def make_env_mat( + coord, + atype, + region, + rcut: Union[float, list], + sec, + pbc=True, + type_split=True, + min_check=False, +): + """Based on atom coordinates, return environment matrix. + + Returns + ------- + nlist: nlist, [nloc, nnei] + merged_coord_shift: shift on nall atoms, [nall, 3] + merged_mapping: mapping from nall index to nloc index, [nall] + """ + # move outer atoms into cell + hybrid = isinstance(rcut, list) + _rcut = rcut + if hybrid: + _rcut = max(rcut) + if pbc: + merged_coord_shift, merged_atype, merged_mapping = append_neighbors( + coord, region, atype, _rcut + ) + merged_coord = coord[merged_mapping] - merged_coord_shift + if merged_coord.shape[0] <= coord.shape[0]: + log.warning("No ghost atom is added for system ") + else: + merged_coord_shift = paddle.zeros_like(coord) + merged_atype = atype.clone() + merged_mapping = paddle.arange(atype.numel()) # pylint: disable=no-explicit-dtype,no-explicit-device + merged_coord = coord.clone() + + # build nlist + if not hybrid: + nlist, nlist_loc, nlist_type = build_neighbor_list( + coord.shape[0], + merged_coord, + merged_atype, + rcut, + sec, + merged_mapping, + type_split=type_split, + min_check=min_check, + ) + else: + nlist, nlist_loc, nlist_type = [], [], [] + for ii, single_rcut in enumerate(rcut): + nlist_tmp, nlist_loc_tmp, nlist_type_tmp = build_neighbor_list( + coord.shape[0], + merged_coord, + merged_atype, + single_rcut, + sec[ii], + merged_mapping, + type_split=type_split, + min_check=min_check, + ) + nlist.append(nlist_tmp) + nlist_loc.append(nlist_loc_tmp) + nlist_type.append(nlist_type_tmp) + return nlist, nlist_loc, nlist_type, merged_coord_shift, merged_mapping diff --git a/deepmd/pd/utils/region.py b/deepmd/pd/utils/region.py new file mode 100644 index 0000000000..0d3940049f --- /dev/null +++ b/deepmd/pd/utils/region.py @@ -0,0 +1,116 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import paddle + + +def phys2inter( + coord: paddle.Tensor, + cell: paddle.Tensor, +) -> paddle.Tensor: + """Convert physical coordinates to internal(direct) coordinates. + + Parameters + ---------- + coord : paddle.Tensor + physical coordinates of shape [*, na, 3]. + cell : paddle.Tensor + simulation cell tensor of shape [*, 3, 3]. + + Returns + ------- + inter_coord: paddle.Tensor + the internal coordinates + + """ + rec_cell = paddle.linalg.inv(cell) + return paddle.matmul(coord, rec_cell) + + +def inter2phys( + coord: paddle.Tensor, + cell: paddle.Tensor, +) -> paddle.Tensor: + """Convert internal(direct) coordinates to physical coordinates. + + Parameters + ---------- + coord : paddle.Tensor + internal coordinates of shape [*, na, 3]. + cell : paddle.Tensor + simulation cell tensor of shape [*, 3, 3]. + + Returns + ------- + phys_coord: paddle.Tensor + the physical coordinates + + """ + return paddle.matmul(coord, cell) + + +def to_face_distance( + cell: paddle.Tensor, +) -> paddle.Tensor: + """Compute the to-face-distance of the simulation cell. + + Parameters + ---------- + cell : paddle.Tensor + simulation cell tensor of shape [*, 3, 3]. + + Returns + ------- + dist: paddle.Tensor + the to face distances of shape [*, 3] + + """ + cshape = cell.shape + dist = b_to_face_distance(cell.reshape([-1, 3, 3])) + return dist.reshape(list(cshape[:-2]) + [3]) # noqa:RUF005 + + +def _to_face_distance(cell): + volume = paddle.linalg.det(cell) + c_yz = paddle.cross(cell[1], cell[2]) + _h2yz = volume / paddle.linalg.norm(c_yz) + c_zx = paddle.cross(cell[2], cell[0]) + _h2zx = volume / paddle.linalg.norm(c_zx) + c_xy = paddle.cross(cell[0], cell[1]) + _h2xy = volume / paddle.linalg.norm(c_xy) + return paddle.stack([_h2yz, _h2zx, _h2xy]) + + +def b_to_face_distance(cell): + volume = paddle.linalg.det(cell) + c_yz = paddle.cross(cell[:, 1], cell[:, 2], axis=-1) + _h2yz = volume / paddle.linalg.norm(c_yz, axis=-1) + c_zx = paddle.cross(cell[:, 2], cell[:, 0], axis=-1) + _h2zx = volume / paddle.linalg.norm(c_zx, axis=-1) + c_xy = paddle.cross(cell[:, 0], cell[:, 1], axis=-1) + _h2xy = volume / paddle.linalg.norm(c_xy, axis=-1) + return paddle.stack([_h2yz, _h2zx, _h2xy], axis=1) + + +# b_to_face_distance = paddle.vmap( +# _to_face_distance, in_dims=(0), out_dims=(0)) + + +def normalize_coord( + coord: paddle.Tensor, + cell: paddle.Tensor, +) -> paddle.Tensor: + """Apply PBC according to the atomic coordinates. + + Parameters + ---------- + coord : paddle.Tensor + orignal coordinates of shape [*, na, 3]. + + Returns + ------- + wrapped_coord: paddle.Tensor + wrapped coordinates of shape [*, na, 3]. + + """ + icoord = phys2inter(coord, cell) + icoord = paddle.remainder(icoord, paddle.to_tensor(1.0)) + return inter2phys(icoord, cell) diff --git a/deepmd/pd/utils/serialization.py b/deepmd/pd/utils/serialization.py new file mode 100644 index 0000000000..7cf1de56bd --- /dev/null +++ b/deepmd/pd/utils/serialization.py @@ -0,0 +1,78 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import json + +import paddle + +from deepmd.pd.model.model import ( + get_model, +) +from deepmd.pd.model.model.model import ( + BaseModel, +) +from deepmd.pd.train.wrapper import ( + ModelWrapper, +) + + +def serialize_from_file(model_file: str) -> dict: + """Serialize the model file to a dictionary. + + Parameters + ---------- + model_file : str + The model file to be serialized. + + Returns + ------- + dict + The serialized model data. + """ + if model_file.endswith(".pth"): + saved_model = paddle.jit.load(model_file) + model_def_script = json.loads(saved_model.model_def_script) + model = get_model(model_def_script) + model.load_state_dict(saved_model.state_dict()) + elif model_file.endswith(".pd"): + state_dict = paddle.load(model_file) + if "model" in state_dict: + state_dict = state_dict["model"] + model_def_script = state_dict["_extra_state"]["model_params"] + model = get_model(model_def_script) + modelwrapper = ModelWrapper(model) + modelwrapper.load_state_dict(state_dict) + model = modelwrapper.model["Default"] + else: + raise ValueError("Pypaddle backend only supports converting .pth or .pd file") + + model_dict = model.serialize() + data = { + "backend": "Pypaddle", + "pt_version": paddle.__version__, + "model": model_dict, + "model_def_script": model_def_script, + "@variables": {}, + } + if model.get_min_nbor_dist() is not None: + data["@variables"]["min_nbor_dist"] = model.get_min_nbor_dist() + return data + + +def deserialize_to_file(model_file: str, data: dict) -> None: + """Deserialize the dictionary to a model file. + + Parameters + ---------- + model_file : str + The model file to be saved. + data : dict + The dictionary to be deserialized. + """ + if not model_file.endswith(".pth"): + raise ValueError("Pypaddle backend only supports converting .pth file") + model = BaseModel.deserialize(data["model"]) + # JIT will happy in this way... + model.model_def_script = json.dumps(data["model_def_script"]) + if "min_nbor_dist" in data.get("@variables", {}): + model.min_nbor_dist = float(data["@variables"]["min_nbor_dist"]) + model = paddle.jit.to_static(model) + paddle.jit.save(model, model_file) diff --git a/deepmd/pd/utils/stat.py b/deepmd/pd/utils/stat.py new file mode 100644 index 0000000000..12acf1b37a --- /dev/null +++ b/deepmd/pd/utils/stat.py @@ -0,0 +1,589 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import logging +from collections import ( + defaultdict, +) +from typing import ( + Callable, + Dict, + List, + Optional, + Union, +) + +import numpy as np +import paddle + +from deepmd.pd.utils import ( + AtomExcludeMask, +) +from deepmd.pd.utils.auto_batch_size import ( + AutoBatchSize, +) +from deepmd.pd.utils.utils import ( + dict_to_device, + to_numpy_array, + to_paddle_tensor, +) +from deepmd.utils.out_stat import ( + compute_stats_from_atomic, + compute_stats_from_redu, +) +from deepmd.utils.path import ( + DPPath, +) + +log = logging.getLogger(__name__) + + +def make_stat_input(datasets, dataloaders, nbatches): + """Pack data for statistics. + + Args: + - dataset: A list of dataset to analyze. + - nbatches: Batch count for collecting stats. + + Returns + ------- + - a list of dicts, each of which contains data from a system + """ + lst = [] + log.info(f"Packing data for statistics from {len(datasets)} systems") + for i in range(len(datasets)): + sys_stat = {} + + device = paddle.get_device() + paddle.set_device("cpu") + # with paddle.device("cpu"): + iterator = iter(dataloaders[i]) + numb_batches = min(nbatches, len(dataloaders[i])) + for _ in range(numb_batches): + try: + stat_data = next(iterator) + except StopIteration: + iterator = iter(dataloaders[i]) + stat_data = next(iterator) + for dd in stat_data: + if stat_data[dd] is None: + sys_stat[dd] = None + elif isinstance(stat_data[dd], paddle.Tensor): + if dd not in sys_stat: + sys_stat[dd] = [] + sys_stat[dd].append(stat_data[dd]) + elif isinstance(stat_data[dd], np.float32): + sys_stat[dd] = stat_data[dd] + else: + pass + paddle.set_device(device) + + for key in sys_stat: + if isinstance(sys_stat[key], np.float32): + pass + elif sys_stat[key] is None or sys_stat[key][0] is None: + sys_stat[key] = None + elif isinstance(stat_data[dd], paddle.Tensor): + sys_stat[key] = paddle.concat(sys_stat[key], axis=0) + dict_to_device(sys_stat) + lst.append(sys_stat) + return lst + + +def _restore_from_file( + stat_file_path: DPPath, + keys: List[str] = ["energy"], +) -> Optional[dict]: + if stat_file_path is None: + return None, None + stat_files = [stat_file_path / f"bias_atom_{kk}" for kk in keys] + if all(not (ii.is_file()) for ii in stat_files): + return None, None + stat_files = [stat_file_path / f"std_atom_{kk}" for kk in keys] + if all(not (ii.is_file()) for ii in stat_files): + return None, None + + ret_bias = {} + ret_std = {} + for kk in keys: + fp = stat_file_path / f"bias_atom_{kk}" + # only read the key that exists + if fp.is_file(): + ret_bias[kk] = fp.load_numpy() + for kk in keys: + fp = stat_file_path / f"std_atom_{kk}" + # only read the key that exists + if fp.is_file(): + ret_std[kk] = fp.load_numpy() + return ret_bias, ret_std + + +def _save_to_file( + stat_file_path: DPPath, + bias_out: dict, + std_out: dict, +): + assert stat_file_path is not None + stat_file_path.mkdir(exist_ok=True, parents=True) + for kk, vv in bias_out.items(): + fp = stat_file_path / f"bias_atom_{kk}" + fp.save_numpy(vv) + for kk, vv in std_out.items(): + fp = stat_file_path / f"std_atom_{kk}" + fp.save_numpy(vv) + + +def _post_process_stat( + out_bias, + out_std, +): + """Post process the statistics. + + For global statistics, we do not have the std for each type of atoms, + thus fake the output std by ones for all the types. + + """ + new_std = {} + for kk, vv in out_bias.items(): + new_std[kk] = np.ones_like(vv) + return out_bias, new_std + + +def _compute_model_predict( + sampled: Union[Callable[[], List[dict]], List[dict]], + keys: List[str], + model_forward: Callable[..., paddle.Tensor], +): + auto_batch_size = AutoBatchSize() + model_predict = {kk: [] for kk in keys} + for system in sampled: + nframes = system["coord"].shape[0] + coord, atype, box, natoms = ( + system["coord"], + system["atype"], + system["box"], + system["natoms"], + ) + fparam = system.get("fparam", None) + aparam = system.get("aparam", None) + + def model_forward_auto_batch_size(*args, **kwargs): + return auto_batch_size.execute_all( + model_forward, + nframes, + system["atype"].shape[-1], + *args, + **kwargs, + ) + + sample_predict = model_forward_auto_batch_size( + coord, atype, box, fparam=fparam, aparam=aparam + ) + for kk in keys: + model_predict[kk].append( + to_numpy_array( + sample_predict[kk] # nf x nloc x odims + ) + ) + return model_predict + + +def _make_preset_out_bias( + ntypes: int, + ibias: List[Optional[np.array]], +) -> Optional[np.array]: + """Make preset out bias. + + output: + a np array of shape [ntypes, *(odim0, odim1, ...)] is any item is not None + None if all items are None. + """ + if len(ibias) != ntypes: + raise ValueError("the length of preset bias list should be ntypes") + if all(ii is None for ii in ibias): + return None + for refb in ibias: + if refb is not None: + break + refb = np.array(refb) + nbias = [ + np.full_like(refb, np.nan, dtype=np.float64) if ii is None else ii + for ii in ibias + ] + return np.array(nbias) + + +def _fill_stat_with_global( + atomic_stat: Union[np.ndarray, None], + global_stat: np.ndarray, +): + """This function is used to fill atomic stat with global stat. + + Parameters + ---------- + atomic_stat : Union[np.ndarray, None] + The atomic stat. + global_stat : np.ndarray + The global stat. + if the atomic stat is None, use global stat. + if the atomic stat is not None, but has nan values (missing atypes), fill with global stat. + """ + if atomic_stat is None: + return global_stat + else: + atomic_stat = atomic_stat.reshape(*global_stat.shape) + return np.nan_to_num( + np.where( + np.isnan(atomic_stat) & ~np.isnan(global_stat), global_stat, atomic_stat + ) + ) + + +def compute_output_stats( + merged: Union[Callable[[], List[dict]], List[dict]], + ntypes: int, + keys: Union[str, List[str]] = ["energy"], + stat_file_path: Optional[DPPath] = None, + rcond: Optional[float] = None, + preset_bias: Optional[Dict[str, List[Optional[paddle.Tensor]]]] = None, + model_forward: Optional[Callable[..., paddle.Tensor]] = None, +): + """ + Compute the output statistics (e.g. energy bias) for the fitting net from packed data. + + Parameters + ---------- + merged : Union[Callable[[], List[dict]], List[dict]] + - List[dict]: A list of data samples from various data systems. + Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` + originating from the `i`-th data system. + - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + only when needed. Since the sampling process can be slow and memory-intensive, + the lazy function helps by only sampling once. + ntypes : int + The number of atom types. + stat_file_path : DPPath, optional + The path to the stat file. + rcond : float, optional + The condition number for the regression of atomic energy. + preset_bias : Dict[str, List[Optional[paddle.Tensor]]], optional + Specifying atomic energy contribution in vacuum. Given by key:value pairs. + The value is a list specifying the bias. the elements can be None or np.array of output shape. + For example: [None, [2.]] means type 0 is not set, type 1 is set to [2.] + The `set_davg_zero` key in the descrptor should be set. + model_forward : Callable[..., paddle.Tensor], optional + The wrapped forward function of atomic model. + If not None, the model will be utilized to generate the original energy prediction, + which will be subtracted from the energy label of the data. + The difference will then be used to calculate the delta complement energy bias for each type. + """ + # try to restore the bias from stat file + bias_atom_e, std_atom_e = _restore_from_file(stat_file_path, keys) + + # failed to restore the bias from stat file. compute + if bias_atom_e is None: + # only get data once, sampled is a list of dict[str, paddle.Tensor] + sampled = merged() if callable(merged) else merged + if model_forward is not None: + model_pred = _compute_model_predict(sampled, keys, model_forward) + else: + model_pred = None + + # remove the keys that are not in the sample + keys = [keys] if isinstance(keys, str) else keys + assert isinstance(keys, list) + new_keys = [ + ii + for ii in keys + if (ii in sampled[0].keys()) or ("atom_" + ii in sampled[0].keys()) + ] + del keys + keys = new_keys + # split system based on label + atomic_sampled_idx = defaultdict(list) + global_sampled_idx = defaultdict(list) + + for kk in keys: + for idx, system in enumerate(sampled): + if (("find_atom_" + kk) in system) and ( + system["find_atom_" + kk] > 0.0 + ): + atomic_sampled_idx[kk].append(idx) + elif (("find_" + kk) in system) and (system["find_" + kk] > 0.0): + global_sampled_idx[kk].append(idx) + + else: + continue + + # use index to gather model predictions for the corresponding systems. + + model_pred_g = ( + { + kk: [ + np.sum(vv[idx], axis=1) for idx in global_sampled_idx[kk] + ] # sum atomic dim + for kk, vv in model_pred.items() + } + if model_pred + else None + ) + model_pred_a = ( + { + kk: [vv[idx] for idx in atomic_sampled_idx[kk]] + for kk, vv in model_pred.items() + } + if model_pred + else None + ) + + # concat all frames within those systems + model_pred_g = ( + { + kk: np.concatenate(model_pred_g[kk]) + for kk in model_pred_g.keys() + if len(model_pred_g[kk]) > 0 + } + if model_pred + else None + ) + model_pred_a = ( + { + kk: np.concatenate(model_pred_a[kk]) + for kk in model_pred_a.keys() + if len(model_pred_a[kk]) > 0 + } + if model_pred + else None + ) + + # compute stat + bias_atom_g, std_atom_g = compute_output_stats_global( + sampled, + ntypes, + keys, + rcond, + preset_bias, + model_pred_g, + ) + bias_atom_a, std_atom_a = compute_output_stats_atomic( + sampled, + ntypes, + keys, + model_pred_a, + ) + + # merge global/atomic bias + bias_atom_e, std_atom_e = {}, {} + for kk in keys: + # use atomic bias whenever available + if kk in bias_atom_a: + bias_atom_e[kk] = bias_atom_a[kk] + std_atom_e[kk] = std_atom_a[kk] + else: + bias_atom_e[kk] = None + std_atom_e[kk] = None + # use global bias to fill missing atomic bias + if kk in bias_atom_g: + bias_atom_e[kk] = _fill_stat_with_global( + bias_atom_e[kk], bias_atom_g[kk] + ) + std_atom_e[kk] = _fill_stat_with_global(std_atom_e[kk], std_atom_g[kk]) + if (bias_atom_e[kk] is None) or (std_atom_e[kk] is None): + raise RuntimeError("Fail to compute stat.") + + if stat_file_path is not None: + _save_to_file(stat_file_path, bias_atom_e, std_atom_e) + + bias_atom_e = {kk: to_paddle_tensor(vv) for kk, vv in bias_atom_e.items()} + std_atom_e = {kk: to_paddle_tensor(vv) for kk, vv in std_atom_e.items()} + return bias_atom_e, std_atom_e + + +def compute_output_stats_global( + sampled: List[dict], + ntypes: int, + keys: List[str], + rcond: Optional[float] = None, + preset_bias: Optional[Dict[str, List[Optional[paddle.Tensor]]]] = None, + model_pred: Optional[Dict[str, np.ndarray]] = None, +): + """This function only handle stat computation from reduced global labels.""" + # return directly if model predict is empty for global + if model_pred == {}: + return {}, {} + + # get label dict from sample; for each key, only picking the system with global labels. + outputs = { + kk: [ + system[kk] + for system in sampled + if kk in system and system.get(f"find_{kk}", 0) > 0 + ] + for kk in keys + } + + data_mixed_type = "real_natoms_vec" in sampled[0] + natoms_key = "natoms" if not data_mixed_type else "real_natoms_vec" + for system in sampled: + if "atom_exclude_types" in system: + type_mask = AtomExcludeMask( + ntypes, system["atom_exclude_types"] + ).get_type_mask() + system[natoms_key][:, 2:] *= type_mask.unsqueeze(0) + + input_natoms = { + kk: [ + item[natoms_key] + for item in sampled + if kk in item and item.get(f"find_{kk}", 0) > 0 + ] + for kk in keys + } + # shape: (nframes, ndim) + merged_output = { + kk: to_numpy_array(paddle.concat(outputs[kk])) + for kk in keys + if len(outputs[kk]) > 0 + } + # shape: (nframes, ntypes) + + merged_natoms = { + kk: to_numpy_array(paddle.concat(input_natoms[kk])[:, 2:]) + for kk in keys + if len(input_natoms[kk]) > 0 + } + nf = {kk: merged_natoms[kk].shape[0] for kk in keys if kk in merged_natoms} + if preset_bias is not None: + assigned_atom_ener = { + kk: _make_preset_out_bias(ntypes, preset_bias[kk]) + if kk in preset_bias.keys() + else None + for kk in keys + } + else: + assigned_atom_ener = {kk: None for kk in keys} + + if model_pred is None: + stats_input = merged_output + else: + # subtract the model bias and output the delta bias + + stats_input = { + kk: merged_output[kk] - model_pred[kk] for kk in keys if kk in merged_output + } + + bias_atom_e = {} + std_atom_e = {} + for kk in keys: + if kk in stats_input: + bias_atom_e[kk], std_atom_e[kk] = compute_stats_from_redu( + stats_input[kk], + merged_natoms[kk], + assigned_bias=assigned_atom_ener[kk], + rcond=rcond, + ) + else: + # this key does not have global labels, skip it. + continue + bias_atom_e, std_atom_e = _post_process_stat(bias_atom_e, std_atom_e) + + # unbias_e is only used for print rmse + + if model_pred is None: + unbias_e = { + kk: merged_natoms[kk] @ bias_atom_e[kk].reshape([ntypes, -1]) + for kk in bias_atom_e.keys() + } + else: + unbias_e = { + kk: model_pred[kk].reshape([nf[kk], -1]) + + merged_natoms[kk] @ bias_atom_e[kk].reshape([ntypes, -1]) + for kk in bias_atom_e.keys() + } + atom_numbs = {kk: merged_natoms[kk].sum(-1) for kk in bias_atom_e.keys()} + + def rmse(x): + return paddle.sqrt(paddle.mean(paddle.square(x))) + + for kk in bias_atom_e.keys(): + rmse_ae = rmse( + ( + unbias_e[kk].reshape([nf[kk], -1]).astype(merged_output[kk].dtype) + - merged_output[kk].reshape([nf[kk], -1]) + ) + / atom_numbs[kk][:, None].astype(merged_output[kk].dtype) + ) + log.info( + f"RMSE of {kk} per atom after linear regression is: {rmse_ae} in the unit of {kk}." + ) + return bias_atom_e, std_atom_e + + +def compute_output_stats_atomic( + sampled: List[dict], + ntypes: int, + keys: List[str], + model_pred: Optional[Dict[str, np.ndarray]] = None, +): + # get label dict from sample; for each key, only picking the system with atomic labels. + outputs = { + kk: [ + system["atom_" + kk] + for system in sampled + if ("atom_" + kk) in system and system.get(f"find_atom_{kk}", 0) > 0 + ] + for kk in keys + } + natoms = { + kk: [ + system["atype"] + for system in sampled + if ("atom_" + kk) in system and system.get(f"find_atom_{kk}", 0) > 0 + ] + for kk in keys + } + # shape: (nframes, nloc, ndim) + merged_output = { + kk: to_numpy_array(paddle.concat(outputs[kk])) + for kk in keys + if len(outputs[kk]) > 0 + } + merged_natoms = { + kk: to_numpy_array(paddle.concat(natoms[kk])) + for kk in keys + if len(natoms[kk]) > 0 + } + # reshape merged data to [nf, nloc, ndim] + merged_output = { + kk: merged_output[kk].reshape((*merged_natoms[kk].shape, -1)) + for kk in merged_output + } + + if model_pred is None: + stats_input = merged_output + else: + # subtract the model bias and output the delta bias + stats_input = { + kk: merged_output[kk] - model_pred[kk].reshape(merged_output[kk].shape) + for kk in keys + if kk in merged_output + } + + bias_atom_e = {} + std_atom_e = {} + + for kk in keys: + if kk in stats_input: + bias_atom_e[kk], std_atom_e[kk] = compute_stats_from_atomic( + stats_input[kk], + merged_natoms[kk], + ) + # correction for missing types + missing_types = ntypes - merged_natoms[kk].max() - 1 + if missing_types > 0: + nan_padding = np.empty((missing_types, bias_atom_e[kk].shape[1])) # pylint: disable=no-explicit-dtype + nan_padding.fill(np.nan) + bias_atom_e[kk] = np.concatenate([bias_atom_e[kk], nan_padding], axis=0) + std_atom_e[kk] = np.concatenate([std_atom_e[kk], nan_padding], axis=0) + else: + # this key does not have atomic labels, skip it. + continue + return bias_atom_e, std_atom_e diff --git a/deepmd/pd/utils/update_sel.py b/deepmd/pd/utils/update_sel.py new file mode 100644 index 0000000000..26898ec76c --- /dev/null +++ b/deepmd/pd/utils/update_sel.py @@ -0,0 +1,17 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Type, +) + +from deepmd.pd.utils.neighbor_stat import ( + NeighborStat, +) +from deepmd.utils.update_sel import ( + BaseUpdateSel, +) + + +class UpdateSel(BaseUpdateSel): + @property + def neighbor_stat(self) -> Type[NeighborStat]: + return NeighborStat diff --git a/deepmd/pd/utils/utils.py b/deepmd/pd/utils/utils.py new file mode 100644 index 0000000000..aa6069b6be --- /dev/null +++ b/deepmd/pd/utils/utils.py @@ -0,0 +1,168 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from __future__ import ( + annotations, +) + +from typing import ( + List, + Optional, + Union, + overload, +) + +import ml_dtypes +import numpy as np +import paddle +import paddle.nn.functional as F + +from deepmd.dpmodel.common import PRECISION_DICT as NP_PRECISION_DICT + +from .env import ( + DEVICE, +) +from .env import PRECISION_DICT as PD_PRECISION_DICT + + +class ActivationFn(paddle.nn.Layer): + def __init__(self, activation: Optional[str]): + super().__init__() + self.activation: str = activation if activation is not None else "linear" + + def forward(self, x: paddle.Tensor) -> paddle.Tensor: + """Returns the tensor after applying activation function corresponding to `activation`.""" + # See jit supported types: https://pypaddle.org/docs/stable/jit_language_reference.html#supported-type + + if self.activation.lower() == "relu": + return F.relu(x) + elif self.activation.lower() == "gelu" or self.activation.lower() == "gelu_tf": + return F.gelu(x, approximate="tanh") + elif self.activation.lower() == "tanh": + return paddle.tanh(x) + elif self.activation.lower() == "relu6": + return F.relu6(x) + elif self.activation.lower() == "softplus": + return F.softplus(x) + elif self.activation.lower() == "sigmoid": + return paddle.sigmoid(x) + elif self.activation.lower() == "linear" or self.activation.lower() == "none": + return x + else: + raise RuntimeError(f"activation function {self.activation} not supported") + + +@overload +def to_numpy_array(xx: paddle.Tensor) -> np.ndarray: ... + + +@overload +def to_numpy_array(xx: None) -> None: ... + + +def to_numpy_array( + xx, +): + if xx is None: + return None + assert xx is not None + # Create a reverse mapping of PD_PRECISION_DICT + reverse_precision_dict = {v: k for k, v in PD_PRECISION_DICT.items()} + # Use the reverse mapping to find keys with the desired value + prec = reverse_precision_dict.get(xx.dtype, None) + prec = NP_PRECISION_DICT.get(prec, np.float64) + if prec is None: + raise ValueError(f"unknown precision {xx.dtype}") + if xx.dtype == paddle.bfloat16: + xx = xx.astype(paddle.get_default_dtype()) + return xx.astype(prec) + + +@overload +def to_paddle_tensor(xx: np.ndarray) -> paddle.Tensor: ... + + +@overload +def to_paddle_tensor(xx: None) -> None: ... + + +def to_paddle_tensor( + xx, +): + if xx is None: + return None + assert xx is not None + if not isinstance(xx, np.ndarray): + return xx + # Create a reverse mapping of NP_PRECISION_DICT + reverse_precision_dict = {v: k for k, v in NP_PRECISION_DICT.items()} + # Use the reverse mapping to find keys with the desired value + prec = reverse_precision_dict.get(xx.dtype.type, None) + prec = PD_PRECISION_DICT.get(prec, None) + if prec is None: + raise ValueError(f"unknown precision {xx.dtype}") + if xx.dtype == ml_dtypes.bfloat16: + # https://github.com/pypaddle/pypaddle/issues/109873 + xx = xx.astype(np.float32) + return paddle.to_tensor(xx, dtype=prec).to(device=DEVICE) + + +def dict_to_device(sample_dict): + for key in sample_dict: + if isinstance(sample_dict[key], list): + sample_dict[key] = [item.to(DEVICE) for item in sample_dict[key]] + if isinstance(sample_dict[key], np.float32): + sample_dict[key] = ( + paddle.ones(1, dtype=paddle.float32).to(device=DEVICE) + * sample_dict[key] + ) + else: + if sample_dict[key] is not None: + sample_dict[key] = sample_dict[key].to(DEVICE) + + +# https://github.com/numpy/numpy/blob/a4cddb60489f821a1a4dffc16cd5c69755d43bdb/numpy/random/bit_generator.pyx#L58-L63 +INIT_A = 0x43B0D7E5 +MULT_A = 0x931E8875 +MIX_MULT_L = 0xCA01F9DD +MIX_MULT_R = 0x4973F715 +XSHIFT = 16 + + +def hashmix(value: int, hash_const: List[int]): + value ^= INIT_A + hash_const[0] *= MULT_A + value *= INIT_A + # prevent overflow + hash_const[0] &= 0xFFFF_FFFF_FFFF_FFFF + value &= 0xFFFF_FFFF_FFFF_FFFF + value ^= value >> XSHIFT + return value + + +def mix(x: int, y: int): + result = MIX_MULT_L * x - MIX_MULT_R * y + # prevent overflow + result &= 0xFFFF_FFFF_FFFF_FFFF + result ^= result >> XSHIFT + return result + + +def mix_entropy(entropy_array: List[int]) -> int: + # https://github.com/numpy/numpy/blob/a4cddb60489f821a1a4dffc16cd5c69755d43bdb/numpy/random/bit_generator.pyx#L341-L374 + hash_const = [INIT_A] + mixer = hashmix(entropy_array[0], hash_const) + for i_src in range(1, len(entropy_array)): + mixer = mix(mixer, hashmix(entropy_array[i_src], hash_const)) + return mixer + + +def get_generator( + seed: Optional[Union[int, List[int]]] = None, +) -> Optional[paddle.Generator]: + if False: + if isinstance(seed, list): + seed = mix_entropy(seed) + generator = paddle.Generator(device=DEVICE) + generator.manual_seed(seed) + return generator + else: + return None diff --git a/deepmd/utils/batch_size.py b/deepmd/utils/batch_size.py index 8fe67ad6fc..03b562e99b 100644 --- a/deepmd/utils/batch_size.py +++ b/deepmd/utils/batch_size.py @@ -177,7 +177,7 @@ def execute_with_batch_size( ) -> Tuple[int, Tuple[np.ndarray]]: end_index = start_index + batch_size end_index = min(end_index, total_size) - return (end_index - start_index), callable( + result = callable( *[ ( vv[start_index:end_index, ...] @@ -195,6 +195,7 @@ def execute_with_batch_size( for kk, vv in kwargs.items() }, ) + return (end_index - start_index), result index = 0 results = None diff --git a/doc/install/install-from-source.md b/doc/install/install-from-source.md index 90b165ffb5..6f17a272c6 100644 --- a/doc/install/install-from-source.md +++ b/doc/install/install-from-source.md @@ -167,7 +167,7 @@ The path to the ROCM toolkit directory. :::{envvar} DP_ENABLE_PYTORCH -**Choices**: `0`, `1`; **Default**: `1` +**Choices**: `0`, `1`; **Default**: `0` {{ pytorch_icon }} Enable customized C++ OPs for the PyTorch backend. PyTorch can still run without customized C++ OPs, but features will be limited. ::: From 7f32618be9e1a7945d348e59666a549c2a5345ef Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Wed, 4 Sep 2024 14:48:07 +0800 Subject: [PATCH 02/93] update runnable code with water/se_e2_a --- deepmd/loggers/training.py | 2 +- deepmd/pd/cxx_op.py | 18 ++++---- deepmd/pd/entrypoints/main.py | 26 +++++------ deepmd/pd/infer/deep_eval.py | 6 +-- deepmd/pd/loss/ener.py | 4 +- .../atomic_model/pairtab_atomic_model.py | 12 +++-- deepmd/pd/model/descriptor/descriptor.py | 2 +- deepmd/pd/model/descriptor/env_mat.py | 9 +++- .../descriptor/repformer_layer_old_impl.py | 8 +++- deepmd/pd/model/descriptor/repformers.py | 8 ++-- deepmd/pd/model/descriptor/se_atten.py | 4 +- deepmd/pd/model/descriptor/se_r.py | 2 +- deepmd/pd/model/descriptor/se_t_tebd.py | 4 +- deepmd/pd/model/model/frozen.py | 6 +-- deepmd/pd/model/model/make_model.py | 6 ++- deepmd/pd/model/model/transform_output.py | 5 ++- deepmd/pd/model/network/init.py | 37 ++++++++++----- deepmd/pd/model/network/layernorm.py | 16 +++++-- deepmd/pd/model/network/mlp.py | 20 ++++----- deepmd/pd/model/network/network.py | 45 +++++++++++-------- deepmd/pd/model/task/atten_lcc.py | 5 ++- deepmd/pd/optimizer/LKF.py | 2 +- deepmd/pd/train/training.py | 23 +++++----- deepmd/pd/utils/auto_batch_size.py | 2 +- deepmd/pd/utils/dataloader.py | 8 +--- deepmd/pd/utils/dataset.py | 2 +- deepmd/pd/utils/exclude_mask.py | 8 +++- deepmd/pd/utils/neighbor_stat.py | 23 +++++----- deepmd/pd/utils/nlist.py | 33 +++++++++----- deepmd/pd/utils/preprocess.py | 10 +++-- deepmd/pd/utils/region.py | 22 ++++++--- deepmd/pd/utils/serialization.py | 16 ++++--- deepmd/utils/data.py | 34 ++++++++++++++ pyproject.toml | 2 + 34 files changed, 280 insertions(+), 150 deletions(-) diff --git a/deepmd/loggers/training.py b/deepmd/loggers/training.py index 954473e309..a4ae8fe608 100644 --- a/deepmd/loggers/training.py +++ b/deepmd/loggers/training.py @@ -29,6 +29,6 @@ def format_training_message_per_task( rmse = dict(sorted(rmse.items())) return ( f"batch {batch:7d}: {task_name}" - f"{', '.join([f'{kk} = {vv:8.2e}' for kk, vv in rmse.items()])}" + f"{', '.join([f'{kk} = {float(vv):8.2e}' for kk, vv in rmse.items()])}" f"{lr}" ) diff --git a/deepmd/pd/cxx_op.py b/deepmd/pd/cxx_op.py index 8f17da28a7..8239e94c7c 100644 --- a/deepmd/pd/cxx_op.py +++ b/deepmd/pd/cxx_op.py @@ -51,10 +51,10 @@ def load_library(module_name: str) -> bool: if PT_CXX11_ABI_FLAG != pt_cxx11_abi_flag: raise RuntimeError( "This deepmd-kit package was compiled with " - "CXX11_ABI_FLAG=%d, but PyTorch runtime was compiled " + "CXX11_ABI_FLAG=%d, but Paddle runtime was compiled " "with CXX11_ABI_FLAG=%d. These two library ABIs are " "incompatible and thus an error is raised when loading %s. " - "You need to rebuild deepmd-kit against this PyTorch " + "You need to rebuild deepmd-kit against this Paddle " "runtime." % ( PT_CXX11_ABI_FLAG, @@ -66,20 +66,20 @@ def load_library(module_name: str) -> bool: # different versions may cause incompatibility, see TF if PT_VERSION != pt_py_version: raise RuntimeError( - "The version of PyTorch used to compile this " - f"deepmd-kit package is {PT_VERSION}, but the version of PyTorch " + "The version of Paddle used to compile this " + f"deepmd-kit package is {PT_VERSION}, but the version of Paddle " f"runtime you are using is {pt_py_version}. These two versions are " f"incompatible and thus an error is raised when loading {module_name}. " - f"You need to install PyTorch {PT_VERSION}, or rebuild deepmd-kit " - f"against PyTorch {pt_py_version}.\nIf you are using a wheel from " + f"You need to install Paddle {PT_VERSION}, or rebuild deepmd-kit " + f"against Paddle {pt_py_version}.\nIf you are using a wheel from " "PyPI, you may consider to install deepmd-kit execuating " - "`DP_ENABLE_PYTORCH=1 pip install deepmd-kit --no-binary deepmd-kit` " + "`DP_ENABLE_Paddle=1 pip install deepmd-kit --no-binary deepmd-kit` " "instead." ) from e error_message = ( - "This deepmd-kit package is inconsitent with PyTorch " + "This deepmd-kit package is inconsitent with Paddle " f"Runtime, thus an error is raised when loading {module_name}. " - "You need to rebuild deepmd-kit against this PyTorch " + "You need to rebuild deepmd-kit against this Paddle " "runtime." ) if PT_CXX11_ABI_FLAG == 1: diff --git a/deepmd/pd/entrypoints/main.py b/deepmd/pd/entrypoints/main.py index 6d876dde2b..9704679522 100644 --- a/deepmd/pd/entrypoints/main.py +++ b/deepmd/pd/entrypoints/main.py @@ -364,8 +364,8 @@ def show(FLAGS): model_params = json.loads(model_params_string) else: raise RuntimeError( - "The model provided must be a checkpoint file with a .pd extension " - "or a frozen model with a .pth extension" + "The model provided must be a checkpoint file with a .pdparams extension " + "or a frozen model with a .pdmodel extension" ) model_is_multi_task = "model_dict" in model_params log.info("This is a multitask model") if model_is_multi_task else log.info( @@ -417,12 +417,12 @@ def show(FLAGS): def change_bias(FLAGS): - if FLAGS.INPUT.endswith(".pd"): + if FLAGS.INPUT.endswith(".pdparams"): old_state_dict = paddle.load(FLAGS.INPUT) model_state_dict = copy.deepcopy(old_state_dict.get("model", old_state_dict)) model_params = model_state_dict["_extra_state"]["model_params"] - elif FLAGS.INPUT.endswith(".pth"): - old_model = paddle.jit.load(FLAGS.INPUT) + elif FLAGS.INPUT.endswith(".pdmodel"): + old_model = paddle.jit.load(FLAGS.INPUT[: -len(".pdmodel")]) model_params_string = old_model.get_model_def_script() model_params = json.loads(model_params_string) old_state_dict = old_model.state_dict() @@ -430,7 +430,7 @@ def change_bias(FLAGS): else: raise RuntimeError( "The model provided must be a checkpoint file with a .pd extension " - "or a frozen model with a .pth extension" + "or a frozen model with a .pdparams extension" ) multi_task = "model_dict" in model_params model_branch = FLAGS.model_branch @@ -455,10 +455,10 @@ def change_bias(FLAGS): model_to_change = model if not multi_task else model[model_branch] if FLAGS.INPUT.endswith(".pd"): wrapper = ModelWrapper(model) - wrapper.load_state_dict(old_state_dict["model"]) + wrapper.set_state_dict(old_state_dict["model"]) else: - # for .pth - model.load_state_dict(old_state_dict) + # for .pdparams + model.set_state_dict(old_state_dict) if FLAGS.bias_value is not None: # use user-defined bias @@ -528,11 +528,11 @@ def change_bias(FLAGS): old_state_dict["_extra_state"] = model_state_dict["_extra_state"] paddle.save(old_state_dict, output_path) else: - # for .pth + # for .pdparams output_path = ( FLAGS.output if FLAGS.output is not None - else FLAGS.INPUT.replace(".pth", "_updated.pth") + else FLAGS.INPUT.replace(".pdparams", "_updated.pdparams") ) model = paddle.jit.script(model) paddle.jit.save( @@ -550,7 +550,7 @@ def main(args: Optional[Union[List[str], argparse.Namespace]] = None): else: FLAGS = args - set_log_handles(FLAGS.log_level, FLAGS.log_path, mpi_log=None) + set_log_handles(FLAGS.log_level, Path(FLAGS.log_path), mpi_log=None) log.debug("Log handles were successfully set") log.info("DeePMD version: %s", __version__) @@ -563,7 +563,7 @@ def main(args: Optional[Union[List[str], argparse.Namespace]] = None): FLAGS.model = str(checkpoint_path.joinpath(latest_ckpt_file)) else: FLAGS.model = FLAGS.checkpoint_folder - FLAGS.output = str(Path(FLAGS.output).with_suffix(".pth")) + FLAGS.output = str(Path(FLAGS.output).with_suffix(".pdparams")) freeze(FLAGS) elif FLAGS.command == "show": show(FLAGS) diff --git a/deepmd/pd/infer/deep_eval.py b/deepmd/pd/infer/deep_eval.py index 50241c7b41..361a6605dc 100644 --- a/deepmd/pd/infer/deep_eval.py +++ b/deepmd/pd/infer/deep_eval.py @@ -61,7 +61,7 @@ class DeepEval(DeepEvalBackend): - """PyTorch backend implementaion of DeepEval. + """Paddle backend implementaion of DeepEval. Parameters ---------- @@ -118,8 +118,8 @@ def __init__( model = get_model(self.input_param).to(DEVICE) model = paddle.jit.to_static(model) self.dp = ModelWrapper(model) - self.dp.load_state_dict(state_dict) - elif str(self.model_path).endswith(".pth"): + self.dp.set_state_dict(state_dict) + elif str(self.model_path).endswith(".pdparams"): model = paddle.jit.load(model_file) self.dp = ModelWrapper(model) else: diff --git a/deepmd/pd/loss/ener.py b/deepmd/pd/loss/ener.py index 8b2dee5879..a97f813a88 100644 --- a/deepmd/pd/loss/ener.py +++ b/deepmd/pd/loss/ener.py @@ -11,6 +11,7 @@ TaskLoss, ) from deepmd.pd.utils import ( + aux, env, ) from deepmd.pd.utils.env import ( @@ -224,7 +225,8 @@ def forward(self, input_dict, model, label, natoms, learning_rate, mae=False): if self.relative_f is not None: force_label_3 = force_label.reshape([-1, 3]) - norm_f = force_label_3.norm(axis=1, keepdim=True) + self.relative_f + # norm_f = force_label_3.norm(axis=1, keepdim=True) + self.relative_f + norm_f = aux.norm(force_label_3, axis=1, keepdim=True) + self.relative_f diff_f_3 = diff_f.reshape([-1, 3]) diff_f_3 = diff_f_3 / norm_f diff_f = diff_f_3.reshape([-1]) diff --git a/deepmd/pd/model/atomic_model/pairtab_atomic_model.py b/deepmd/pd/model/atomic_model/pairtab_atomic_model.py index b58bc12564..da3dee872c 100644 --- a/deepmd/pd/model/atomic_model/pairtab_atomic_model.py +++ b/deepmd/pd/model/atomic_model/pairtab_atomic_model.py @@ -15,6 +15,7 @@ OutputVariableDef, ) from deepmd.pd.utils import ( + aux, env, ) from deepmd.utils.pair_tab import ( @@ -383,10 +384,12 @@ def _get_pairwise_dist( nframes, nloc, nnei = nlist.shape coord_l = coords[:, :nloc].reshape([nframes, -1, 1, 3]) index = nlist.reshape([nframes, -1]).unsqueeze(-1).expand(-1, -1, 3) - coord_r = paddle.take_along_axis(coords, axis=1, indices=index) + # coord_r = paddle.take_along_axis(coords, axis=1, indices=index) + coord_r = aux.take_along_axis(coords, axis=1, indices=index) coord_r = coord_r.reshape([nframes, nloc, nnei, 3]) diff = coord_r - coord_l - pairwise_rr = paddle.linalg.norm(diff, axis=-1, keepdim=True).squeeze(-1) + # pairwise_rr = paddle.linalg.norm(diff, axis=-1, keepdim=True).squeeze(-1) + pairwise_rr = aux.norm(diff, axis=-1, keepdim=True).squeeze(-1) return pairwise_rr @staticmethod @@ -437,7 +440,10 @@ def _extract_spline_coefficient( # tab_data_idx: (nframes * nloc * nnei, 4) tab_data_idx = tab_data_idx.reshape([nframes * nloc * nnei, 1]).expand(-1, 4) # (nframes, nloc, nnei, 4) - final_coef = paddle.take_along_axis( + # final_coef = paddle.take_along_axis( + # tab_data, axis=0, indices=tab_data_idx + # ).reshape([nframes, nloc, nnei, 4]) + final_coef = aux.take_along_axis( tab_data, axis=0, indices=tab_data_idx ).reshape([nframes, nloc, nnei, 4]) diff --git a/deepmd/pd/model/descriptor/descriptor.py b/deepmd/pd/model/descriptor/descriptor.py index 96c1b276f7..63607ec946 100644 --- a/deepmd/pd/model/descriptor/descriptor.py +++ b/deepmd/pd/model/descriptor/descriptor.py @@ -158,7 +158,7 @@ def share_params(self, base_class, shared_level, resume=False): # must share, even if not do stat self.mean = base_class.mean self.stddev = base_class.stddev - # self.load_state_dict(base_class.state_dict()) # this does not work, because it only inits the model + # self.set_state_dict(base_class.state_dict()) # this does not work, because it only inits the model # the following will successfully link all the params except buffers for item in self._modules: self._modules[item] = base_class._modules[item] diff --git a/deepmd/pd/model/descriptor/env_mat.py b/deepmd/pd/model/descriptor/env_mat.py index 7e072a4d74..3bb4d177fb 100644 --- a/deepmd/pd/model/descriptor/env_mat.py +++ b/deepmd/pd/model/descriptor/env_mat.py @@ -2,6 +2,9 @@ import paddle +from deepmd.pd.utils import ( + aux, +) from deepmd.pd.utils.preprocess import ( compute_smooth_weight, ) @@ -24,10 +27,12 @@ def _make_env_mat( nlist = paddle.where(mask, nlist, nall - 1) coord_l = coord[:, :natoms].reshape([bsz, -1, 1, 3]) index = nlist.reshape([bsz, -1]).unsqueeze(-1).expand([-1, -1, 3]) - coord_r = paddle.take_along_axis(coord, axis=1, indices=index) + # coord_r = paddle.take_along_axis(coord, axis=1, indices=index) + coord_r = aux.take_along_axis(coord, axis=1, indices=index) coord_r = coord_r.reshape([bsz, natoms, nnei, 3]) diff = coord_r - coord_l - length = paddle.linalg.norm(diff, axis=-1, keepdim=True) + # length = paddle.linalg.norm(diff, axis=-1, keepdim=True) + length = aux.norm(diff, axis=-1, keepdim=True) # for index 0 nloc atom length = length + (~mask.unsqueeze(-1)).astype(length.dtype) t0 = 1 / (length + protection) diff --git a/deepmd/pd/model/descriptor/repformer_layer_old_impl.py b/deepmd/pd/model/descriptor/repformer_layer_old_impl.py index 5ad7624288..52c0f0bf68 100644 --- a/deepmd/pd/model/descriptor/repformer_layer_old_impl.py +++ b/deepmd/pd/model/descriptor/repformer_layer_old_impl.py @@ -10,6 +10,7 @@ SimpleLinear, ) from deepmd.pd.utils import ( + aux, env, ) from deepmd.pd.utils.utils import ( @@ -28,7 +29,9 @@ def _make_nei_g1( # index: nb x (nloc x nnei) x ng1 index = nlist.reshape([nb, nloc * nnei]).unsqueeze(-1).expand(-1, -1, ng1) # gg1 : nb x (nloc x nnei) x ng1 - gg1 = paddle.take_along_axis(g1_ext, axis=1, index=index) + # print(g1_ext.shape, index.shape) + # gg1 = paddle.take_along_axis(g1_ext, axis=1, index=index) + gg1 = aux.take_along_axis(g1_ext, axis=1, index=index) # gg1 : nb x nloc x nnei x ng1 gg1 = gg1.reshape([nb, nloc, nnei, ng1]) return gg1 @@ -57,7 +60,8 @@ def _apply_h_norm( """ nf, nl, nnei, _ = hh.shape # nf x nloc x nnei - normh = paddle.linalg.norm(hh, axis=-1) + # normh = paddle.linalg.norm(hh, axis=-1) + normh = aux.norm(hh, axis=-1) # nf x nloc std = paddle.std(normh, axis=-1) # nf x nloc x nnei x 3 diff --git a/deepmd/pd/model/descriptor/repformers.py b/deepmd/pd/model/descriptor/repformers.py index b4fa0eaec1..70e1b0864b 100644 --- a/deepmd/pd/model/descriptor/repformers.py +++ b/deepmd/pd/model/descriptor/repformers.py @@ -23,6 +23,7 @@ MLPLayer, ) from deepmd.pd.utils import ( + aux, env, ) from deepmd.pd.utils.env_mat_stat import ( @@ -60,7 +61,7 @@ # argument8, # ) -> paddle.Tensor: # raise NotImplementedError( -# "border_op is not available since customized PyTorch OP library is not built when freezing the model." +# "border_op is not available since customized Paddle OP library is not built when freezing the model." # ) # # Note: this hack cannot actually save a model that can be runned using LAMMPS. @@ -306,7 +307,7 @@ def __init__( device=env.DEVICE ) stddev = paddle.ones(wanted_shape, dtype=env.GLOBAL_PD_FLOAT_PRECISION).to( - device=env.DEVICE + device=aux.DEVICE ) self.register_buffer("mean", mean) self.register_buffer("stddev", stddev) @@ -445,7 +446,8 @@ def forward( if not self.direct_dist: g2, h2 = paddle.split(dmatrix, [1, 3], axis=-1) else: - g2, h2 = paddle.linalg.norm(diff, axis=-1, keepdim=True), diff + # g2, h2 = paddle.linalg.norm(diff, axis=-1, keepdim=True), diff + g2, h2 = aux.norm(diff, axis=-1, keepdim=True), diff g2 = g2 / self.rcut h2 = h2 / self.rcut # nb x nloc x nnei x ng2 diff --git a/deepmd/pd/model/descriptor/se_atten.py b/deepmd/pd/model/descriptor/se_atten.py index 1d6c2c8e87..964f81ab50 100644 --- a/deepmd/pd/model/descriptor/se_atten.py +++ b/deepmd/pd/model/descriptor/se_atten.py @@ -34,6 +34,7 @@ TypeFilter, ) from deepmd.pd.utils import ( + aux, env, ) from deepmd.pd.utils.env import ( @@ -496,7 +497,8 @@ def forward( # nb x (nloc x nnei) x nt index = nlist.reshape([nb, nloc * nnei]).unsqueeze(-1).expand(-1, -1, nt) # nb x (nloc x nnei) x nt - atype_tebd_nlist = paddle.take_along_axis(atype_tebd_ext, axis=1, index=index) + # atype_tebd_nlist = paddle.take_along_axis(atype_tebd_ext, axis=1, index=index) + atype_tebd_nlist = aux.take_along_axis(atype_tebd_ext, axis=1, index=index) # nb x nloc x nnei x nt atype_tebd_nlist = atype_tebd_nlist.reshape([nb, nloc, nnei, nt]) # beyond the cutoff sw should be 0.0 diff --git a/deepmd/pd/model/descriptor/se_r.py b/deepmd/pd/model/descriptor/se_r.py index a1e49b09a8..42e3130518 100644 --- a/deepmd/pd/model/descriptor/se_r.py +++ b/deepmd/pd/model/descriptor/se_r.py @@ -220,7 +220,7 @@ def share_params(self, base_class, shared_level, resume=False): ) # pylint: disable=no-explicit-dtype self.mean = base_class.mean self.stddev = base_class.stddev - # self.load_state_dict(base_class.state_dict()) # this does not work, because it only inits the model + # self.set_state_dict(base_class.state_dict()) # this does not work, because it only inits the model # the following will successfully link all the params except buffers for item in self._modules: self._modules[item] = base_class._modules[item] diff --git a/deepmd/pd/model/descriptor/se_t_tebd.py b/deepmd/pd/model/descriptor/se_t_tebd.py index 8f3474af49..0a329a806b 100644 --- a/deepmd/pd/model/descriptor/se_t_tebd.py +++ b/deepmd/pd/model/descriptor/se_t_tebd.py @@ -29,6 +29,7 @@ TypeEmbedNetConsistent, ) from deepmd.pd.utils import ( + aux, env, ) from deepmd.pd.utils.env import ( @@ -788,7 +789,8 @@ def forward( # nb x (nloc x nnei) x nt index = nlist.reshape([nb, nloc * nnei]).unsqueeze(-1).expand(-1, -1, nt) # nb x (nloc x nnei) x nt - atype_tebd_nlist = paddle.take_along_axis(atype_tebd_ext, axis=1, index=index) + # atype_tebd_nlist = paddle.take_along_axis(atype_tebd_ext, axis=1, index=index) + atype_tebd_nlist = aux.take_along_axis(atype_tebd_ext, axis=1, index=index) # nb x nloc x nnei x nt atype_tebd_nlist = atype_tebd_nlist.reshape([nb, nloc, nnei, nt]) # beyond the cutoff sw should be 0.0 diff --git a/deepmd/pd/model/model/frozen.py b/deepmd/pd/model/model/frozen.py index d918238c1d..a2886b7e22 100644 --- a/deepmd/pd/model/model/frozen.py +++ b/deepmd/pd/model/model/frozen.py @@ -37,11 +37,11 @@ class FrozenModel(BaseModel): def __init__(self, model_file: str, **kwargs): super().__init__(**kwargs) self.model_file = model_file - if model_file.endswith(".pth"): + if model_file.endswith(".pdparams"): self.model = paddle.jit.load(model_file) else: # try to convert from other formats - with tempfile.NamedTemporaryFile(suffix=".pth") as f: + with tempfile.NamedTemporaryFile(suffix=".pdparams") as f: convert_backend(INPUT=model_file, OUTPUT=f.name) self.model = paddle.jit.load(f.name) @@ -156,7 +156,7 @@ def serialize(self) -> dict: # try to recover the original model model_def_script = json.loads(self.get_model_def_script()) model = get_model(model_def_script) - model.load_state_dict(self.model.state_dict()) + model.set_state_dict(self.model.state_dict()) return model.serialize() @classmethod diff --git a/deepmd/pd/model/model/make_model.py b/deepmd/pd/model/model/make_model.py index 94d4ae7ff5..87f4f5b0b3 100644 --- a/deepmd/pd/model/model/make_model.py +++ b/deepmd/pd/model/model/make_model.py @@ -28,6 +28,9 @@ communicate_extended_output, fit_output_to_model_output, ) +from deepmd.pd.utils import ( + aux, +) from deepmd.pd.utils.env import ( GLOBAL_PD_ENER_FLOAT_PRECISION, GLOBAL_PD_FLOAT_PRECISION, @@ -438,7 +441,8 @@ def _format_nlist( # nf x nloc x nnei x 3 coord1 = coord1.reshape([n_nf, n_nloc, n_nnei, 3]) # nf x nloc x nnei - rr = paddle.linalg.norm(coord0[:, :, None, :] - coord1, axis=-1) + # rr = paddle.linalg.norm(coord0[:, :, None, :] - coord1, axis=-1) + rr = aux.norm(coord0[:, :, None, :] - coord1, axis=-1) rr = paddle.where(m_real_nei, rr, float("inf")) rr, nlist_mapping = paddle.sort(rr, axis=-1) nlist = paddle.gather(nlist, 2, nlist_mapping) diff --git a/deepmd/pd/model/model/transform_output.py b/deepmd/pd/model/model/transform_output.py index f1e9fa4212..8bdb3661fb 100644 --- a/deepmd/pd/model/model/transform_output.py +++ b/deepmd/pd/model/model/transform_output.py @@ -15,6 +15,7 @@ get_reduce_name, ) from deepmd.pd.utils import ( + aux, env, ) @@ -237,7 +238,7 @@ def communicate_extended_output( device=vv.place ) # nf x nloc x nvar x 3 - new_ret[kk_derv_r] = paddle.scatter_reduce( + new_ret[kk_derv_r] = aux.scatter_reduce( force, 1, index=mapping, @@ -256,7 +257,7 @@ def communicate_extended_output( device=vv.place ) # nf x nloc x nvar x 9 - new_ret[kk_derv_c] = paddle.scatter_reduce( + new_ret[kk_derv_c] = aux.scatter_reduce( virial, 1, index=mapping, diff --git a/deepmd/pd/model/network/init.py b/deepmd/pd/model/network/init.py index 4d8fab3dc9..21cdea5161 100644 --- a/deepmd/pd/model/network/init.py +++ b/deepmd/pd/model/network/init.py @@ -152,15 +152,18 @@ def calculate_gain(nonlinearity, param=None): raise ValueError(f"Unsupported nonlinearity {nonlinearity}") -def _calculate_fan_in_and_fan_out(tensor): +def _calculate_fan_in_and_fan_out(tensor, reverse=False): dimensions = tensor.ndim if dimensions < 2: raise ValueError( "Fan in and fan out can not be computed for tensor with fewer than 2 dimensions" ) - num_input_fmaps = tensor.shape[1] - num_output_fmaps = tensor.shape[0] + if reverse: + num_input_fmaps, num_output_fmaps = tensor.shape[0], tensor.shape[1] + else: + num_input_fmaps, num_output_fmaps = tensor.shape[1], tensor.shape[0] + receptive_field_size = 1 if tensor.ndim > 2: # math.prod is not always available, accumulate the product manually @@ -173,13 +176,13 @@ def _calculate_fan_in_and_fan_out(tensor): return fan_in, fan_out -def _calculate_correct_fan(tensor: paddle.Tensor, mode): +def _calculate_correct_fan(tensor, mode, reverse=False): mode = mode.lower() valid_modes = ["fan_in", "fan_out"] if mode not in valid_modes: raise ValueError(f"Mode {mode} not supported, please use one of {valid_modes}") - fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) + fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor, reverse) return fan_in if mode == "fan_in" else fan_out @@ -296,6 +299,7 @@ def kaiming_uniform_( mode: str = "fan_in", nonlinearity: str = "leaky_relu", generator: _Optional[paddle.Generator] = None, + reverse: bool = False, ): r"""Fill the input `Tensor` with values using a Kaiming uniform distribution. @@ -320,6 +324,8 @@ def kaiming_uniform_( nonlinearity: the non-linear function (`nn.functional` name), recommended to use only with ``'relu'`` or ``'leaky_relu'`` (default). generator: the paddle Generator to sample from (default: None) + reverse (bool, optional): Tensor data format order, False by default as + [fout, fin, ...].. Defaults to False. Examples -------- @@ -340,7 +346,7 @@ def kaiming_uniform_( if 0 in tensor.shape: warnings.warn("Initializing zero-element tensors is a no-op") return tensor - fan = _calculate_correct_fan(tensor, mode) + fan = _calculate_correct_fan(tensor, mode, reverse) gain = calculate_gain(nonlinearity, a) std = gain / math.sqrt(fan) bound = math.sqrt(3.0) * std # Calculate uniform bounds from standard deviation @@ -354,6 +360,7 @@ def kaiming_normal_( mode: str = "fan_in", nonlinearity: str = "leaky_relu", generator: _Optional[paddle.Generator] = None, + reverse: bool = False, ): r"""Fill the input `Tensor` with values using a Kaiming normal distribution. @@ -378,6 +385,8 @@ def kaiming_normal_( nonlinearity: the non-linear function (`nn.functional` name), recommended to use only with ``'relu'`` or ``'leaky_relu'`` (default). generator: the paddle Generator to sample from (default: None) + reverse (bool, optional): Tensor data format order, False by default as + [fout, fin, ...].. Defaults to False. Examples -------- @@ -387,7 +396,7 @@ def kaiming_normal_( if 0 in tensor.shape: warnings.warn("Initializing zero-element tensors is a no-op") return tensor - fan = _calculate_correct_fan(tensor, mode) + fan = _calculate_correct_fan(tensor, mode, reverse) gain = calculate_gain(nonlinearity, a) std = gain / math.sqrt(fan) with paddle.no_grad(): @@ -395,7 +404,10 @@ def kaiming_normal_( def xavier_uniform_( - tensor: Tensor, gain: float = 1.0, generator: _Optional[paddle.Generator] = None + tensor: Tensor, + gain: float = 1.0, + generator: _Optional[paddle.Generator] = None, + reverse: bool = False, ) -> Tensor: r"""Fill the input `Tensor` with values using a Xavier uniform distribution. @@ -413,13 +425,15 @@ def xavier_uniform_( tensor: an n-dimensional `paddle.Tensor` gain: an optional scaling factor generator: the paddle Generator to sample from (default: None) + reverse (bool, optional): Tensor data format order, False by default as + [fout, fin, ...].. Defaults to False. Examples -------- >>> w = paddle.empty(3, 5) >>> nn.init.xavier_uniform_(w, gain=nn.init.calculate_gain("relu")) """ - fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) + fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor, reverse=reverse) std = gain * math.sqrt(2.0 / float(fan_in + fan_out)) a = math.sqrt(3.0) * std # Calculate uniform bounds from standard deviation @@ -430,6 +444,7 @@ def xavier_normal_( tensor: Tensor, gain: float = 1.0, generator: _Optional[paddle.Generator] = None, + reverse: bool = False, ) -> Tensor: r"""Fill the input `Tensor` with values using a Xavier normal distribution. @@ -446,13 +461,15 @@ def xavier_normal_( tensor: an n-dimensional `paddle.Tensor` gain: an optional scaling factor generator: the paddle Generator to sample from (default: None) + reverse (bool, optional): Tensor data format order, False by + default as [fout, fin, ...]. Defaults to False. Examples -------- >>> w = paddle.empty(3, 5) >>> nn.init.xavier_normal_(w) """ - fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) + fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor, reverse=reverse) std = gain * math.sqrt(2.0 / float(fan_in + fan_out)) return _no_grad_normal_(tensor, 0.0, std, generator) diff --git a/deepmd/pd/model/network/layernorm.py b/deepmd/pd/model/network/layernorm.py index fa2e768fad..64d039b0bc 100644 --- a/deepmd/pd/model/network/layernorm.py +++ b/deepmd/pd/model/network/layernorm.py @@ -32,7 +32,7 @@ def empty_t(shape, precision): - return paddle.empty(shape, dtype=precision, device=device) + return paddle.empty(shape, dtype=precision).to(device=device) class LayerNorm(nn.Layer): @@ -53,9 +53,17 @@ def __init__( self.num_in = num_in self.precision = precision self.prec = PRECISION_DICT[self.precision] - self.matrix = nn.Parameter(data=empty_t((num_in,), self.prec)) - self.bias = nn.Parameter( - data=empty_t([num_in], self.prec), + self.matrix = self.create_parameter( + shape=[num_in], + dtype=self.prec, + default_initializer=nn.initializer.Assign( + empty_t((num_in,), self.prec), + ), + ) + self.bias = self.create_parameter( + shape=[num_in], + dtype=self.prec, + default_initializer=nn.initializer.Assign(empty_t([num_in], self.prec)), ) random_generator = get_generator(seed) if self.uni_init: diff --git a/deepmd/pd/model/network/mlp.py b/deepmd/pd/model/network/mlp.py index 3580d187d6..d7ace02b13 100644 --- a/deepmd/pd/model/network/mlp.py +++ b/deepmd/pd/model/network/mlp.py @@ -102,6 +102,7 @@ def __init__( self.prec = PRECISION_DICT[self.precision] self.matrix = self.create_parameter( (num_in, num_out), + dtype=self.prec, default_initializer=nn.initializer.Assign( empty_t((num_in, num_out), self.prec) ), @@ -110,6 +111,7 @@ def __init__( if bias: self.bias = self.create_parameter( [num_out], + dtype=self.prec, default_initializer=nn.initializer.Assign( empty_t([num_out], self.prec) ), @@ -119,6 +121,7 @@ def __init__( if self.use_timestep: self.idt = self.create_parameter( [num_out], + dtype=self.prec, default_initializer=nn.initializer.Assign( empty_t([num_out], self.prec) ), @@ -283,17 +286,14 @@ def deserialize(cls, data: dict) -> MLPLayer: prec = PRECISION_DICT[obj.precision] def check_load_param(ss): - return ( - paddle.create_parameter( - nl[ss].shape, - DEFAULT_PRECISION, - default_initializer=paddle.nn.initializer.Assign( - to_paddle_tensor(nl[ss]) - ), + if nl[ss] is not None: + tensor = to_paddle_tensor(nl[ss]) + return paddle.create_parameter( + tensor.shape, + dtype=tensor.dtype, + default_initializer=paddle.nn.initializer.Assign(tensor), ) - if nl[ss] is not None - else None - ) + return None obj.matrix = check_load_param("matrix") obj.bias = check_load_param("bias") diff --git a/deepmd/pd/model/network/network.py b/deepmd/pd/model/network/network.py index 577f7aed5c..6b565031a5 100644 --- a/deepmd/pd/model/network/network.py +++ b/deepmd/pd/model/network/network.py @@ -10,10 +10,14 @@ import paddle.nn as nn import paddle.nn.functional as F +from deepmd.pd.model.network import ( + init, +) from deepmd.pd.model.network.mlp import ( EmbeddingNet, ) from deepmd.pd.utils import ( + aux, env, ) from deepmd.utils.version import ( @@ -157,19 +161,23 @@ def __init__(self, num_in, num_out, bavg=0.0, stddev=1.0, resnet_dt=False): self.matrix = self.create_parameter( [num_in, num_out], + dtype=env.GLOBAL_PD_FLOAT_PRECISION, default_initializer=nn.initializer.Assign(Tensor(num_in, num_out)), ) - nn.init.normal_(self.matrix.data, std=stddev / np.sqrt(num_out + num_in)) + init.normal_(self.matrix.data, std=stddev / np.sqrt(num_out + num_in)) self.bias = self.create_parameter( - (1, num_out), default_initializer=nn.initializer.Assign(Tensor(1, num_out)) + (1, num_out), + dtype=env.GLOBAL_PD_FLOAT_PRECISION, + default_initializer=nn.initializer.Assign(Tensor(1, num_out)), ) - nn.init.normal_(self.bias.data, mean=bavg, std=stddev) + init.normal_(self.bias.data, mean=bavg, std=stddev) if self.resnet: self.idt = self.create_parameter( (1, num_out), + dtype=env.GLOBAL_PD_FLOAT_PRECISION, default_initializer=nn.initializer.Assign(Tensor(1, num_out)), ) - nn.init.normal_(self.idt.data, mean=1.0, std=0.001) + init.normal_(self.idt.data, mean=1.0, std=0.001) def forward(self, inputs): """Return X ?+ X*W+b.""" @@ -330,15 +338,15 @@ def __init__( self.activate = ActivationFn(activate) self.matrix = self.create_parameter(data=Tensor(num_in, num_out)) - nn.init.normal_(self.matrix.data, std=stddev / np.sqrt(num_out + num_in)) + init.normal_(self.matrix.data, std=stddev / np.sqrt(num_out + num_in)) if bias: self.bias = self.create_parameter(data=Tensor(1, num_out)) - nn.init.normal_(self.bias.data, mean=bavg, std=stddev) + init.normal_(self.bias.data, mean=bavg, std=stddev) else: self.bias = None if self.use_timestep: self.idt = self.create_parameter(data=Tensor(1, num_out)) - nn.init.normal_(self.idt.data, mean=0.1, std=0.001) + init.normal_(self.idt.data, mean=0.1, std=0.001) def forward(self, inputs): """Return X*W+b.""" @@ -393,10 +401,10 @@ def _trunc_normal_init(self, scale=1.0): _, fan_in = self.weight.shape scale = scale / max(1, fan_in) std = (scale**0.5) / TRUNCATED_NORMAL_STDDEV_FACTOR - nn.init.trunc_normal_(self.weight, mean=0.0, std=std) + init.trunc_normal_(self.weight, mean=0.0, std=std) def _glorot_uniform_init(self): - nn.init.xavier_uniform_(self.weight, gain=1) + init.xavier_uniform_(self.weight, gain=1) def _zero_init(self, use_bias=True): with paddle.no_grad(): @@ -406,7 +414,7 @@ def _zero_init(self, use_bias=True): self.bias.fill_(1.0) def _normal_init(self): - nn.init.kaiming_normal_(self.weight, nonlinearity="linear") + init.kaiming_normal_(self.weight, nonlinearity="linear") class Transition(nn.Layer): @@ -453,7 +461,7 @@ def __init__( self.weight.data[self.padding_idx].zero_() def _normal_init(self, std=0.02): - nn.init.normal_(self.weight, mean=0.0, std=std) + init.normal_(self.weight, mean=0.0, std=std) class NonLinearHead(nn.Layer): @@ -486,8 +494,8 @@ def forward(self, x): return x def zero_init(self): - nn.init.zeros_(self.layer2.weight) - nn.init.zeros_(self.layer2.bias) + init.zeros_(self.layer2.weight) + init.zeros_(self.layer2.bias) class MaskLMHead(nn.Layer): @@ -610,7 +618,7 @@ def __init__( precision=precision, seed=seed, ) - # nn.init.normal_(self.embedding.weight[:-1], mean=bavg, std=stddev) + # init.normal_(self.embedding.weight[:-1], mean=bavg, std=stddev) def forward(self, atype): """ @@ -889,8 +897,8 @@ def __init__(self, K=128, num_pair=512, std_width=1.0, start=0.0, stop=9.0): self.bias = Embedding( num_pair + 1, 1, padding_idx=num_pair, dtype=env.GLOBAL_PD_FLOAT_PRECISION ) - nn.init.constant_(self.bias.weight, 0) - nn.init.constant_(self.mul.weight, 1.0) + init.constant_(self.bias.weight, 0) + init.constant_(self.mul.weight, 1.0) def forward(self, x, atom_pair): mul = self.mul(atom_pair).abs().sum(axis=-2) @@ -960,7 +968,8 @@ def forward(self, coord_selected, atom_feature, edge_type_2dim, edge_feature): # ncluster x natoms x natoms x 3 delta_pos = coord_selected.unsqueeze(1) - coord_selected.unsqueeze(2) # (ncluster x natoms x natoms - dist = delta_pos.norm(axis=-1).reshape([-1, natoms, natoms]) + # dist = delta_pos.norm(axis=-1).reshape([-1, natoms, natoms]) + dist = aux.norm(delta_pos, axis=-1).reshape([-1, natoms, natoms]) # [ncluster, natoms, natoms, K] gbf_feature = self.gbf(dist, edge_type_2dim) if self.atomic_sum_gbf: @@ -1349,7 +1358,7 @@ def __init__( self.dropout = 0.1 def zero_init(self): - nn.init.zeros_(self.force_proj.weight) + init.zeros_(self.force_proj.weight) def forward( self, diff --git a/deepmd/pd/model/task/atten_lcc.py b/deepmd/pd/model/task/atten_lcc.py index 1ab1da323b..7b6d2f5828 100644 --- a/deepmd/pd/model/task/atten_lcc.py +++ b/deepmd/pd/model/task/atten_lcc.py @@ -2,6 +2,9 @@ import paddle import paddle.nn as nn +from deepmd.pd.model.network import ( + init, +) from deepmd.pd.model.network.network import ( EnergyHead, NodeTaskHead, @@ -22,7 +25,7 @@ def __init__( self.embedding_width = embedding_width self.engergy_proj = EnergyHead(self.embedding_width, 1) self.energe_agg_factor = nn.Embedding(4, 1, dtype=env.GLOBAL_PD_FLOAT_PRECISION) - nn.init.normal_(self.energe_agg_factor.weight, 0, 0.01) + init.normal_(self.energe_agg_factor.weight, 0, 0.01) bias_atom_e = paddle.to_tensor(bias_atom_e) # pylint: disable=no-explicit-dtype,no-explicit-device self.register_buffer("bias_atom_e", bias_atom_e) self.pair_embed_dim = pair_embed_dim diff --git a/deepmd/pd/optimizer/LKF.py b/deepmd/pd/optimizer/LKF.py index b506fea369..20275fc0a2 100644 --- a/deepmd/pd/optimizer/LKF.py +++ b/deepmd/pd/optimizer/LKF.py @@ -44,7 +44,7 @@ def __init__( ) # NOTE: LKF has only global state, but we register it as state for - # the first param, because this helps with casting in load_state_dict + # the first param, because this helps with casting in set_state_dict self._state = self.state[self._params[0]] self._state.setdefault("kalman_lambda", kalman_lambda) self.dist_init = dist.is_available() and dist.is_initialized() diff --git a/deepmd/pd/train/training.py b/deepmd/pd/train/training.py index 4ffa088ecb..a344327f5a 100644 --- a/deepmd/pd/train/training.py +++ b/deepmd/pd/train/training.py @@ -186,7 +186,7 @@ def get_dataloader_and_buffer(_data, _params): if dist.is_available() else 0, # setting to 0 diverges the behavior of its iterator; should be >=1 # drop_last=False, - # collate_fn=lambda batch: batch, # prevent extra conversion + collate_fn=lambda batch: batch[0], # prevent extra conversion # pin_memory=True, ) # with paddle.device("cpu"): @@ -449,7 +449,7 @@ def get_lr(lr_params): state_dict["_extra_state"]["model_params"] ) pretrained_model_wrapper = ModelWrapper(pretrained_model) - pretrained_model_wrapper.load_state_dict(state_dict) + pretrained_model_wrapper.set_state_dict(state_dict) # update type related params for model_key in self.model_keys: finetune_rule_single = self.finetune_links[model_key] @@ -517,7 +517,7 @@ def collect_single_finetune_params( "_extra_state" ] - self.wrapper.load_state_dict(state_dict) + self.wrapper.set_state_dict(state_dict) # change bias for fine-tuning if finetune_model is not None: @@ -560,7 +560,7 @@ def single_model_finetune( if init_frz_model is not None: frz_model = paddle.jit.load(init_frz_model) - self.model.load_state_dict(frz_model.state_dict()) + self.model.set_state_dict(frz_model.state_dict()) # Multi-task share params if shared_links is not None: @@ -600,7 +600,7 @@ def warm_up_linear(step, warmup_steps): learning_rate=self.scheduler, parameters=self.wrapper.parameters() ) if optimizer_state_dict is not None and self.restart_training: - self.optimizer.load_state_dict(optimizer_state_dict) + self.optimizer.set_state_dict(optimizer_state_dict) elif self.opt_type == "LKF": self.optimizer = LKFOptimizer( self.wrapper.parameters(), 0.98, 0.99870, self.opt_param["kf_blocksize"] @@ -648,7 +648,7 @@ def run(self): if dist.is_available() and dist.is_initialized(): log.info(f"Rank: {dist.get_rank()}/{dist.get_world_size()}") if self.enable_tensorboard: - from paddle.utils.tensorboard import ( + from tensorboardX import ( SummaryWriter, ) @@ -667,7 +667,7 @@ def run(self): prof.start() def step(_step_id, task_key="Default"): - # PyTorch Profiler + # Paddle Profiler if self.enable_profiler or self.profiling: prof.step() self.wrapper.train() @@ -1007,12 +1007,10 @@ def log_loss_valid(_task_key="Default"): ) if JIT: - pth_model_path = ( - "frozen_model.pth" # We use .pth to denote the frozen model - ) - self.model.save(pth_model_path) + pdparams_model_path = "frozen_model.pdparams" # We use .pdparams to denote the frozen model + self.model.save(pdparams_model_path) log.info( - f"Frozen model for inferencing has been saved to {pth_model_path}" + f"Frozen model for inferencing has been saved to {pdparams_model_path}" ) log.info(f"Trained model has been saved to: {self.save_ckpt}") @@ -1023,7 +1021,6 @@ def log_loss_valid(_task_key="Default"): if self.enable_tensorboard: writer.close() if self.enable_profiler or self.profiling: - prof.stop() if self.profiling: prof.export_chrome_trace(self.profiling_file) log.info( diff --git a/deepmd/pd/utils/auto_batch_size.py b/deepmd/pd/utils/auto_batch_size.py index ca720aae04..8cdb5ddea2 100644 --- a/deepmd/pd/utils/auto_batch_size.py +++ b/deepmd/pd/utils/auto_batch_size.py @@ -55,6 +55,6 @@ def is_oom_error(self, e: Exception) -> bool: or "cusolver error: CUSOLVER_STATUS_INTERNAL_ERROR" in e.args[0] ): # Release all unoccupied cached memory - paddle.device.cuda.empty_cache() + # paddle.device.cuda.empty_cache() return True return False diff --git a/deepmd/pd/utils/dataloader.py b/deepmd/pd/utils/dataloader.py index f942787072..2d1896424c 100644 --- a/deepmd/pd/utils/dataloader.py +++ b/deepmd/pd/utils/dataloader.py @@ -136,17 +136,13 @@ def construct_dataset(system): if dist.is_available() and dist.is_initialized(): system_batch_sampler = DistributedBatchSampler( system, - shuffle=( - not (dist.is_available() and dist.is_initialized()) and shuffle - ), + shuffle=False, ) self.sampler_list.append(system_batch_sampler) else: system_batch_sampler = BatchSampler( system, - shuffle=( - not (dist.is_available() and dist.is_initialized()) and shuffle - ), + shuffle=shuffle, ) self.sampler_list.append(system_batch_sampler) system_dataloader = DataLoader( diff --git a/deepmd/pd/utils/dataset.py b/deepmd/pd/utils/dataset.py index 88145ddc56..bf7197a182 100644 --- a/deepmd/pd/utils/dataset.py +++ b/deepmd/pd/utils/dataset.py @@ -37,7 +37,7 @@ def __len__(self): def __getitem__(self, index): """Get a frame from the selected system.""" - b_data = self._data_system.get_item_torch(index) + b_data = self._data_system.get_item_paddle(index) b_data["natoms"] = self._natoms_vec return b_data diff --git a/deepmd/pd/utils/exclude_mask.py b/deepmd/pd/utils/exclude_mask.py index 3cafc74df1..75ee964da7 100644 --- a/deepmd/pd/utils/exclude_mask.py +++ b/deepmd/pd/utils/exclude_mask.py @@ -8,6 +8,9 @@ import numpy as np import paddle +from deepmd.pd.utils import ( + aux, +) from deepmd.pd.utils.utils import ( to_paddle_tensor, ) @@ -148,7 +151,10 @@ def forward( type_i = atype_ext[:, :nloc].reshape([nf, nloc]) * (self.ntypes + 1) # nf x nloc x nnei index = paddle.where(nlist == -1, nall, nlist).reshape([nf, nloc * nnei]) - type_j = paddle.take_along_axis(ae, axis=1, indices=index).reshape( + # type_j = paddle.take_along_axis(ae, axis=1, indices=index).reshape( + # [nf, nloc, nnei] + # ) + type_j = aux.take_along_axis(ae, axis=1, indices=index).reshape( [nf, nloc, nnei] ) type_ij = type_i[:, :, None] + type_j diff --git a/deepmd/pd/utils/neighbor_stat.py b/deepmd/pd/utils/neighbor_stat.py index bd27224814..1b9f55b2dd 100644 --- a/deepmd/pd/utils/neighbor_stat.py +++ b/deepmd/pd/utils/neighbor_stat.py @@ -83,22 +83,23 @@ def forward( coord1 = extend_coord.reshape([nframes, -1]) nall = coord1.shape[1] // 3 coord0 = coord1[:, : nloc * 3] - diff = ( - coord1.reshape([nframes, -1, 3])[:, None, :, :] - - coord0.reshape([nframes, -1, 3])[:, :, None, :] - ) + diff: paddle.Tensor = coord1.reshape([nframes, -1, 3]).unsqueeze( + 1 + ) - coord0.reshape([nframes, -1, 3]).unsqueeze(2) assert list(diff.shape) == [nframes, nloc, nall, 3] # remove the diagonal elements mask = paddle.eye(nloc, nall).to(dtype=paddle.bool, device=diff.place) - diff[:, mask] = float("inf") + # diff[:, mask] = float("inf") + diff.masked_fill_( + paddle.broadcast_to(mask.unsqueeze([0, -1]), diff.shape), + paddle.to_tensor(float("inf")), + ) rr2 = paddle.sum(paddle.square(diff), axis=-1) min_rr2 = paddle.min(rr2, axis=-1) # count the number of neighbors if not self.mixed_types: mask = rr2 < self.rcut**2 - nnei = paddle.zeros((nframes, nloc, self.ntypes), dtype=paddle.int32).to( - device=mask.place - ) + nnei = paddle.zeros((nframes, nloc, self.ntypes), dtype=paddle.int64) for ii in range(self.ntypes): nnei[:, :, ii] = paddle.sum( mask & extend_atype.equal(ii)[:, None, :], axis=-1 @@ -184,9 +185,9 @@ def _execute( The cell. """ minrr2, max_nnei = self.op( - paddle.to_tensor(coord).to(DEVICE), - paddle.to_tensor(atype).to(DEVICE), - paddle.to_tensor(cell).to(DEVICE) if cell is not None else None, + paddle.to_tensor(coord, place=DEVICE), + paddle.to_tensor(atype, place=DEVICE), + paddle.to_tensor(cell, place=DEVICE) if cell is not None else None, ) minrr2 = minrr2.numpy() max_nnei = max_nnei.numpy() diff --git a/deepmd/pd/utils/nlist.py b/deepmd/pd/utils/nlist.py index 3c513fec64..a6b58e3269 100644 --- a/deepmd/pd/utils/nlist.py +++ b/deepmd/pd/utils/nlist.py @@ -9,6 +9,7 @@ import paddle from deepmd.pd.utils import ( + aux, env, ) from deepmd.pd.utils.region import ( @@ -26,7 +27,6 @@ def extend_input_and_build_neighbor_list( box: Optional[paddle.Tensor] = None, ): nframes, nloc = atype.shape[:2] - nloc = 192 if box is not None: box_gpu = box.to(coord.place) coord_normalized = normalize_coord( @@ -119,7 +119,8 @@ def build_neighbor_list( ).unsqueeze(2) assert list(diff.shape) == [batch_size, nloc, nall, 3] # nloc x nall - rr = paddle.linalg.norm(diff, axis=-1) + # rr = paddle.linalg.norm(diff, axis=-1) + rr = aux.norm(diff, axis=-1) # if central atom has two zero distances, sorting sometimes can not exclude itself rr -= paddle.eye(nloc, nall, dtype=rr.dtype).to(device=rr.place).unsqueeze(0) rr, nlist = paddle.sort(rr, axis=-1), paddle.argsort(rr, axis=-1) @@ -261,7 +262,8 @@ def build_directional_neighbor_list( diff = coord_neig[:, None, :, :] - coord_cntl[:, :, None, :] assert list(diff.shape) == [batch_size, nloc_cntl, nall_neig, 3] # nloc x nall - rr = paddle.linalg.norm(diff, axis=-1) + # rr = paddle.linalg.norm(diff, axis=-1) + rr = aux.norm(diff, axis=-1) rr, nlist = paddle.sort(rr, axis=-1), paddle.argsort(rr, axis=-1) # We assume that the central and neighbor atoms are diffferent, @@ -293,7 +295,12 @@ def nlist_distinguish_types( tmp_atype = paddle.tile(atype.unsqueeze(1), [1, nloc, 1]) mask = nlist == -1 # nloc x s(nsel) - tnlist = paddle.take_along_axis( + # tnlist = paddle.take_along_axis( + # tmp_atype, + # axis=2, + # indices=nlist.masked_fill(mask, 0), + # ) + tnlist = aux.take_along_axis( tmp_atype, axis=2, indices=nlist.masked_fill(mask, 0), @@ -310,7 +317,8 @@ def nlist_distinguish_types( paddle.argsort(pick_mask, axis=-1, descending=True, stable=True), ) # nloc x s(nsel) - inlist = paddle.take_along_axis(nlist, axis=2, indices=imap) + # inlist = paddle.take_along_axis(nlist, axis=2, indices=imap) + inlist = aux.take_along_axis(nlist, axis=2, indices=imap) inlist = inlist.masked_fill(~(pick_mask.to(paddle.bool)), -1) # nloc x nsel[ii] ret_nlist.append(paddle.split(inlist, [ss, snsel - ss], axis=-1)[0]) @@ -386,13 +394,17 @@ def build_multiple_neighbor_list( .expand(-1, -1, 3) ) # nb x nloc x nsel x 3 - coord2 = paddle.take_along_axis(coord1, axis=1, index=index).reshape( + # coord2 = paddle.take_along_axis(coord1, axis=1, index=index).reshape( + # [nb, nloc, nsel, 3] + # ) + coord2 = aux.take_along_axis(coord1, axis=1, index=index).reshape( [nb, nloc, nsel, 3] ) # nb x nloc x nsel x 3 diff = coord2 - coord0[:, :, None, :] # nb x nloc x nsel - rr = paddle.linalg.norm(diff, axis=-1) + # rr = paddle.linalg.norm(diff, axis=-1) + rr = aux.norm(diff, axis=-1) rr.masked_fill(nlist_mask, float("inf")) nlist0 = nlist ret = {} @@ -439,7 +451,7 @@ def extend_coord_with_ghosts( device = coord.place nf, nloc = atype.shape[:2] nloc = 192 - aidx = paddle.tile(paddle.arange(nloc).to(device="gpu:0").unsqueeze(0), [nf, 1]) # pylint: disable=no-explicit-dtype + aidx = paddle.tile(paddle.arange(nloc).to(device=device).unsqueeze(0), [nf, 1]) # pylint: disable=no-explicit-dtype if cell is None: nall = nloc extend_coord = coord.clone() @@ -474,9 +486,10 @@ def extend_coord_with_ghosts( xyz = xyz + yi.reshape([1, -1, 1, 1]) * eye_3[1] xyz = xyz + zi.reshape([1, 1, -1, 1]) * eye_3[2] xyz = xyz.reshape([-1, 3]) - xyz = xyz.to(device="gpu:0") + xyz = xyz.to(device=device) # ns x 3 - shift_idx = xyz[paddle.argsort(paddle.norm(xyz, axis=1))] + # shift_idx = xyz[paddle.argsort(paddle.norm(xyz, axis=1))] + shift_idx = xyz[paddle.argsort(aux.norm(xyz, axis=1))] ns, _ = shift_idx.shape nall = ns * nloc # nf x ns x 3 diff --git a/deepmd/pd/utils/preprocess.py b/deepmd/pd/utils/preprocess.py index edf904063a..abe5116242 100644 --- a/deepmd/pd/utils/preprocess.py +++ b/deepmd/pd/utils/preprocess.py @@ -7,6 +7,7 @@ import paddle from deepmd.pd.utils import ( + aux, env, ) @@ -26,11 +27,14 @@ def __init__(self, boxt): # boxt = boxt.permute(1, 0) c_yz = paddle.cross(boxt[1], boxt[2]) - self._h2yz = self.volume / paddle.linalg.norm(c_yz) + # self._h2yz = self.volume / paddle.linalg.norm(c_yz) + self._h2yz = self.volume / aux.norm(c_yz) c_zx = paddle.cross(boxt[2], boxt[0]) - self._h2zx = self.volume / paddle.linalg.norm(c_zx) + # self._h2zx = self.volume / paddle.linalg.norm(c_zx) + self._h2zx = self.volume / aux.norm(c_zx) c_xy = paddle.cross(boxt[0], boxt[1]) - self._h2xy = self.volume / paddle.linalg.norm(c_xy) + # self._h2xy = self.volume / paddle.linalg.norm(c_xy) + self._h2xy = self.volume / aux.norm(c_xy) def phys2inter(self, coord): """Convert physical coordinates to internal ones.""" diff --git a/deepmd/pd/utils/region.py b/deepmd/pd/utils/region.py index 0d3940049f..6619d94092 100644 --- a/deepmd/pd/utils/region.py +++ b/deepmd/pd/utils/region.py @@ -1,6 +1,10 @@ # SPDX-License-Identifier: LGPL-3.0-or-later import paddle +from deepmd.pd.utils import ( + aux, +) + def phys2inter( coord: paddle.Tensor, @@ -71,22 +75,28 @@ def to_face_distance( def _to_face_distance(cell): volume = paddle.linalg.det(cell) c_yz = paddle.cross(cell[1], cell[2]) - _h2yz = volume / paddle.linalg.norm(c_yz) + # _h2yz = volume / paddle.linalg.norm(c_yz) + _h2yz = volume / aux.norm(c_yz) c_zx = paddle.cross(cell[2], cell[0]) - _h2zx = volume / paddle.linalg.norm(c_zx) + # _h2zx = volume / paddle.linalg.norm(c_zx) + _h2zx = volume / aux.norm(c_zx) c_xy = paddle.cross(cell[0], cell[1]) - _h2xy = volume / paddle.linalg.norm(c_xy) + # _h2xy = volume / paddle.linalg.norm(c_xy) + _h2xy = volume / aux.norm(c_xy) return paddle.stack([_h2yz, _h2zx, _h2xy]) def b_to_face_distance(cell): volume = paddle.linalg.det(cell) c_yz = paddle.cross(cell[:, 1], cell[:, 2], axis=-1) - _h2yz = volume / paddle.linalg.norm(c_yz, axis=-1) + # _h2yz = volume / paddle.linalg.norm(c_yz, axis=-1) + _h2yz = volume / aux.norm(c_yz, axis=-1) c_zx = paddle.cross(cell[:, 2], cell[:, 0], axis=-1) - _h2zx = volume / paddle.linalg.norm(c_zx, axis=-1) + # _h2zx = volume / paddle.linalg.norm(c_zx, axis=-1) + _h2zx = volume / aux.norm(c_zx, axis=-1) c_xy = paddle.cross(cell[:, 0], cell[:, 1], axis=-1) - _h2xy = volume / paddle.linalg.norm(c_xy, axis=-1) + # _h2xy = volume / paddle.linalg.norm(c_xy, axis=-1) + _h2xy = volume / aux.norm(c_xy, axis=-1) return paddle.stack([_h2yz, _h2zx, _h2xy], axis=1) diff --git a/deepmd/pd/utils/serialization.py b/deepmd/pd/utils/serialization.py index 7cf1de56bd..f8fb45940a 100644 --- a/deepmd/pd/utils/serialization.py +++ b/deepmd/pd/utils/serialization.py @@ -27,22 +27,24 @@ def serialize_from_file(model_file: str) -> dict: dict The serialized model data. """ - if model_file.endswith(".pth"): + if model_file.endswith(".pdparams"): saved_model = paddle.jit.load(model_file) model_def_script = json.loads(saved_model.model_def_script) model = get_model(model_def_script) - model.load_state_dict(saved_model.state_dict()) - elif model_file.endswith(".pd"): + model.set_state_dict(saved_model.state_dict()) + elif model_file.endswith(".pdmodel"): state_dict = paddle.load(model_file) if "model" in state_dict: state_dict = state_dict["model"] model_def_script = state_dict["_extra_state"]["model_params"] model = get_model(model_def_script) modelwrapper = ModelWrapper(model) - modelwrapper.load_state_dict(state_dict) + modelwrapper.set_state_dict(state_dict) model = modelwrapper.model["Default"] else: - raise ValueError("Pypaddle backend only supports converting .pth or .pd file") + raise ValueError( + "Pypaddle backend only supports converting .pdparams or .pd file" + ) model_dict = model.serialize() data = { @@ -67,8 +69,8 @@ def deserialize_to_file(model_file: str, data: dict) -> None: data : dict The dictionary to be deserialized. """ - if not model_file.endswith(".pth"): - raise ValueError("Pypaddle backend only supports converting .pth file") + if not model_file.endswith(".pdparams"): + raise ValueError("Pypaddle backend only supports converting .pdparams file") model = BaseModel.deserialize(data["model"]) # JIT will happy in this way... model.model_def_script = json.dumps(data["model_def_script"]) diff --git a/deepmd/utils/data.py b/deepmd/utils/data.py index 5d324afb95..4ce2a7d3b3 100644 --- a/deepmd/utils/data.py +++ b/deepmd/utils/data.py @@ -248,6 +248,21 @@ def get_item_torch(self, index: int) -> dict: frame["fid"] = index return frame + def get_item_paddle(self, index: int) -> dict: + """Get a single frame data . The frame is picked from the data system by index. The index is coded across all the sets. + + Parameters + ---------- + index + index of the frame + """ + i = bisect.bisect_right(self.prefix_sum, index) + frames = self._load_set(self.dirs[i]) + frame = self._get_subdata(frames, index - self.prefix_sum[i]) + frame = self.reformat_data_torch(frame) + frame["fid"] = index + return frame + def get_batch(self, batch_size: int) -> dict: """Get a batch of data with `batch_size` frames. The frames are randomly picked from the data system. @@ -485,6 +500,25 @@ def reformat_data_torch(self, data): data["box"] = None return data + def reformat_data_paddle(self, data): + """Modify the data format for the requirements of Torch backend. + + Parameters + ---------- + data + original data + """ + for kk in self.data_dict.keys(): + if "find_" in kk: + pass + else: + if kk in data and self.data_dict[kk]["atomic"]: + data[kk] = data[kk].reshape([-1, self.data_dict[kk]["ndof"]]) + data["atype"] = data["type"] + if not self.pbc: + data["box"] = None + return data + def _load_set(self, set_name: DPPath): # get nframes if not isinstance(set_name, DPPath): diff --git a/pyproject.toml b/pyproject.toml index d60b5ee3aa..99508e969e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -370,6 +370,8 @@ ignore = [ "D205", # 1 blank line required between summary line and description "D401", # TODO: first line should be in imperative mood "D404", # TODO: first word of the docstring should not be This + "UP007", + "UP006", ] ignore-init-module-imports = true From 217bf363b1422ecd2e546a82b61de09dede1d1f6 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Wed, 4 Sep 2024 20:33:19 +0800 Subject: [PATCH 03/93] update correct water/se_e2_a code --- deepmd/pd/entrypoints/main.py | 8 +++++--- deepmd/pd/infer/deep_eval.py | 10 ++++++++-- deepmd/pd/train/training.py | 4 ++-- 3 files changed, 15 insertions(+), 7 deletions(-) diff --git a/deepmd/pd/entrypoints/main.py b/deepmd/pd/entrypoints/main.py index 9704679522..ed5523afd1 100644 --- a/deepmd/pd/entrypoints/main.py +++ b/deepmd/pd/entrypoints/main.py @@ -354,13 +354,15 @@ def freeze(FLAGS): def show(FLAGS): - if FLAGS.INPUT.split(".")[-1] == "pt": + if FLAGS.INPUT.split(".")[-1] == "pd": state_dict = paddle.load(FLAGS.INPUT) if "model" in state_dict: state_dict = state_dict["model"] model_params = state_dict["_extra_state"]["model_params"] - elif FLAGS.INPUT.split(".")[-1] == "pth": - model_params_string = paddle.jit.load(FLAGS.INPUT).model_def_script + elif FLAGS.INPUT.split(".")[-1] == "pdmodel": + model_params_string = paddle.jit.load( + FLAGS.INPUT[: -len(".pdmodel")] + ).model_def_script model_params = json.loads(model_params_string) else: raise RuntimeError( diff --git a/deepmd/pd/infer/deep_eval.py b/deepmd/pd/infer/deep_eval.py index 361a6605dc..ad9b5a8881 100644 --- a/deepmd/pd/infer/deep_eval.py +++ b/deepmd/pd/infer/deep_eval.py @@ -91,12 +91,18 @@ def __init__( head: Optional[str] = None, **kwargs: Any, ): + paddle.core.set_prim_eager_enabled(True) + paddle.core._set_prim_all_enabled(True) self.output_def = output_def self.model_path = model_file if str(self.model_path).endswith(".pd"): state_dict = paddle.load(model_file) if "model" in state_dict: state_dict = state_dict["model"] + # TODO: fix there + state_dict["_extra_state"] = eval( + "{'model_params': {'type_map': ['O', 'H'], 'descriptor': {'type': 'se_e2_a', 'sel': [46, 92], 'rcut_smth': 0.5, 'rcut': 6.0, 'neuron': [25, 50, 100], 'resnet_dt': False, 'axis_neuron': 16, 'type_one_side': True, 'seed': 1, 'activation_function': 'tanh', 'precision': 'default', 'trainable': True, 'exclude_types': [], 'env_protection': 0.0, 'set_davg_zero': False}, 'fitting_net': {'neuron': [240, 240, 240], 'resnet_dt': True, 'seed': 1, 'type': 'ener', 'numb_fparam': 0, 'numb_aparam': 0, 'activation_function': 'tanh', 'precision': 'default', 'trainable': True, 'rcond': None, 'atom_ener': [], 'use_aparam_as_mask': False}, 'data_stat_nbatch': 20, 'data_stat_protect': 0.01, 'data_bias_nsample': 10, 'pair_exclude_types': [], 'atom_exclude_types': [], 'srtab_add_bias': True, 'type': 'standard'}, 'train_infos': {'lr': 5.861945287651712e-08, 'step': 99999}}" + ) self.input_param = state_dict["_extra_state"]["model_params"] self.multi_task = "model_dict" in self.input_param if self.multi_task: @@ -116,10 +122,10 @@ def __init__( ] = state_dict[item].clone() state_dict = state_dict_head model = get_model(self.input_param).to(DEVICE) - model = paddle.jit.to_static(model) + # model = paddle.jit.to_static(model) self.dp = ModelWrapper(model) self.dp.set_state_dict(state_dict) - elif str(self.model_path).endswith(".pdparams"): + elif str(self.model_path).endswith(".pdmodel"): model = paddle.jit.load(model_file) self.dp = ModelWrapper(model) else: diff --git a/deepmd/pd/train/training.py b/deepmd/pd/train/training.py index a344327f5a..4512eb32ba 100644 --- a/deepmd/pd/train/training.py +++ b/deepmd/pd/train/training.py @@ -1027,7 +1027,7 @@ def log_loss_valid(_task_key="Default"): f"The profiling trace have been saved to: {self.profiling_file}" ) - def save_model(self, save_path, lr=0.0, step=0): + def save_model(self, save_path: Path, lr=0.0, step=0): module = ( self.wrapper.module if dist.is_available() and dist.is_initialized() @@ -1037,7 +1037,7 @@ def save_model(self, save_path, lr=0.0, step=0): module.train_infos["step"] = step paddle.save( {"model": module.state_dict(), "optimizer": self.optimizer.state_dict()}, - save_path, + str(save_path), ) checkpoint_dir = save_path.parent checkpoint_files = [ From 0ad720df3a6d9cacbec8fcd8f41097f8fb5dbec9 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Thu, 5 Sep 2024 12:03:03 +0800 Subject: [PATCH 04/93] fix extra state --- deepmd/pd/infer/deep_eval.py | 8 ++------ deepmd/pd/train/training.py | 4 ++-- deepmd/pd/train/wrapper.py | 36 +++++++++++++++++++++++++++++++----- deepmd/pd/utils/env.py | 22 +++++++++++++++++++--- 4 files changed, 54 insertions(+), 16 deletions(-) diff --git a/deepmd/pd/infer/deep_eval.py b/deepmd/pd/infer/deep_eval.py index ad9b5a8881..287ab596e9 100644 --- a/deepmd/pd/infer/deep_eval.py +++ b/deepmd/pd/infer/deep_eval.py @@ -51,6 +51,7 @@ from deepmd.pd.utils.env import ( DEVICE, GLOBAL_PD_FLOAT_PRECISION, + enable_prim, ) from deepmd.pd.utils.utils import ( to_paddle_tensor, @@ -91,18 +92,13 @@ def __init__( head: Optional[str] = None, **kwargs: Any, ): - paddle.core.set_prim_eager_enabled(True) - paddle.core._set_prim_all_enabled(True) + enable_prim(True) self.output_def = output_def self.model_path = model_file if str(self.model_path).endswith(".pd"): state_dict = paddle.load(model_file) if "model" in state_dict: state_dict = state_dict["model"] - # TODO: fix there - state_dict["_extra_state"] = eval( - "{'model_params': {'type_map': ['O', 'H'], 'descriptor': {'type': 'se_e2_a', 'sel': [46, 92], 'rcut_smth': 0.5, 'rcut': 6.0, 'neuron': [25, 50, 100], 'resnet_dt': False, 'axis_neuron': 16, 'type_one_side': True, 'seed': 1, 'activation_function': 'tanh', 'precision': 'default', 'trainable': True, 'exclude_types': [], 'env_protection': 0.0, 'set_davg_zero': False}, 'fitting_net': {'neuron': [240, 240, 240], 'resnet_dt': True, 'seed': 1, 'type': 'ener', 'numb_fparam': 0, 'numb_aparam': 0, 'activation_function': 'tanh', 'precision': 'default', 'trainable': True, 'rcond': None, 'atom_ener': [], 'use_aparam_as_mask': False}, 'data_stat_nbatch': 20, 'data_stat_protect': 0.01, 'data_bias_nsample': 10, 'pair_exclude_types': [], 'atom_exclude_types': [], 'srtab_add_bias': True, 'type': 'standard'}, 'train_infos': {'lr': 5.861945287651712e-08, 'step': 99999}}" - ) self.input_param = state_dict["_extra_state"]["model_params"] self.multi_task = "model_dict" in self.input_param if self.multi_task: diff --git a/deepmd/pd/train/training.py b/deepmd/pd/train/training.py index 4512eb32ba..7b7cf9ace7 100644 --- a/deepmd/pd/train/training.py +++ b/deepmd/pd/train/training.py @@ -58,6 +58,7 @@ JIT, LOCAL_RANK, SAMPLER_RECORD, + enable_prim, ) from deepmd.pd.utils.learning_rate import ( LearningRateExp, @@ -102,8 +103,7 @@ def __init__( Args: - config: The Dict-like configuration with training options. """ - paddle.core.set_prim_eager_enabled(True) - paddle.core._set_prim_all_enabled(True) + enable_prim(True) if init_model is not None: resume_model = init_model elif restart_model is not None: diff --git a/deepmd/pd/train/wrapper.py b/deepmd/pd/train/wrapper.py index 927ef11c94..9e5cfab2a1 100644 --- a/deepmd/pd/train/wrapper.py +++ b/deepmd/pd/train/wrapper.py @@ -1,13 +1,20 @@ # SPDX-License-Identifier: LGPL-3.0-or-later +from __future__ import ( + annotations, +) + import logging from typing import ( Dict, Optional, + OrderedDict, Union, ) import paddle +_StateDict = Union[Dict[str, paddle.Tensor], OrderedDict[str, paddle.Tensor]] + # if paddle.__version__.startswith("2"): # import paddle._dynamo @@ -183,14 +190,33 @@ def forward( ) return model_pred, loss, more_loss - def set_extra_state(self, state: Dict): - self.model_params = state["model_params"] - self.train_infos = state["train_infos"] + def load_state_dict( + self, + state_dict: _StateDict, + ) -> tuple[list[str], list[str]]: + self.set_extra_state(state_dict["_extra_state"]) + return super().set_state_dict(state_dict) + + def set_state_dict( + self, + state_dict: _StateDict, + ) -> tuple[list[str], list[str]]: + return self.load_state_dict(state_dict) + + def state_dict(self): + state_dict = super().state_dict() + extra_state = self.get_extra_state() + state_dict.update({"_extra_state": extra_state}) + return state_dict + + def set_extra_state(self, extra_state: Dict): + self.model_params = extra_state["model_params"] + self.train_infos = extra_state["train_infos"] return None def get_extra_state(self) -> Dict: - state = { + extra_state = { "model_params": self.model_params, "train_infos": self.train_infos, } - return state + return extra_state diff --git a/deepmd/pd/utils/env.py b/deepmd/pd/utils/env.py index 306201ef0c..4082acee82 100644 --- a/deepmd/pd/utils/env.py +++ b/deepmd/pd/utils/env.py @@ -1,4 +1,5 @@ # SPDX-License-Identifier: LGPL-3.0-or-later +import logging import os import numpy as np @@ -14,6 +15,8 @@ set_default_nthreads, ) +log = logging.getLogger(__name__) + SAMPLER_RECORD = os.environ.get("SAMPLER_RECORD", False) try: # only linux @@ -68,10 +71,23 @@ # throw warnings if threads not set set_default_nthreads() inter_nthreads, intra_nthreads = get_default_nthreads() -if inter_nthreads > 0: # the behavior of 0 is not documented - paddle.set_num_interop_threads(inter_nthreads) +# if inter_nthreads > 0: # the behavior of 0 is not documented +# paddle.set_num_interop_threads(inter_nthreads) if intra_nthreads > 0: - paddle.set_num_threads(intra_nthreads) + paddle.framework.core.set_num_threads(intra_nthreads) + + +def enable_prim(enable: bool = True): + """Enable running program in primitive C++ API in eager/static mode.""" + if enable: + from paddle.framework import ( + core, + ) + + core.set_prim_eager_enabled(True) + core._set_prim_all_enabled(True) + log.info("Enable prim in eager and static mode.") + __all__ = [ "GLOBAL_ENER_FLOAT_PRECISION", From 1d7b0d120825890eba6fcc847223074cf38b66f7 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Fri, 6 Sep 2024 11:37:37 +0800 Subject: [PATCH 05/93] Fix typo and bugs --- deepmd/main.py | 3 +- deepmd/pd/entrypoints/main.py | 10 ++- deepmd/pd/infer/inference.py | 2 +- deepmd/pd/loss/denoise.py | 4 +- .../atomic_model/pairtab_atomic_model.py | 8 +-- deepmd/pd/model/descriptor/gaussian_lcc.py | 21 +++--- deepmd/pd/model/descriptor/repformer_layer.py | 2 +- .../descriptor/repformer_layer_old_impl.py | 4 +- deepmd/pd/model/descriptor/repformers.py | 2 +- deepmd/pd/model/descriptor/se_atten.py | 10 +-- deepmd/pd/model/descriptor/se_r.py | 2 +- deepmd/pd/model/descriptor/se_t.py | 4 +- deepmd/pd/model/descriptor/se_t_tebd.py | 6 +- deepmd/pd/model/model/make_model.py | 4 +- deepmd/pd/model/model/polar_model.py | 2 +- deepmd/pd/model/model/spin_model.py | 2 +- deepmd/pd/model/network/network.py | 12 ++-- deepmd/pd/model/task/fitting.py | 3 +- deepmd/pd/optimizer/LKF.py | 68 +++++++++++-------- deepmd/pd/utils/env_mat_stat.py | 6 +- deepmd/pd/utils/nlist.py | 8 +-- deepmd/pd/utils/stat.py | 2 +- deepmd/pd/utils/utils.py | 4 +- 23 files changed, 105 insertions(+), 84 deletions(-) diff --git a/deepmd/main.py b/deepmd/main.py index 777bfd3aa3..4b2d88f598 100644 --- a/deepmd/main.py +++ b/deepmd/main.py @@ -102,9 +102,10 @@ def main_parser() -> argparse.ArgumentParser: formatter_class=RawTextArgumentDefaultsHelpFormatter, epilog=textwrap.dedent( """\ - Use --tf or --pt to choose the backend: + Use --tf, --pt or --pd to choose the backend: dp --tf train input.json dp --pt train input.json + dp --pd train input.json """ ), ) diff --git a/deepmd/pd/entrypoints/main.py b/deepmd/pd/entrypoints/main.py index ed5523afd1..4f5db306f3 100644 --- a/deepmd/pd/entrypoints/main.py +++ b/deepmd/pd/entrypoints/main.py @@ -344,7 +344,7 @@ def train(FLAGS): def freeze(FLAGS): model = inference.Tester(FLAGS.model, head=FLAGS.head).model model.eval() - model = paddle.jit.script(model) + model = paddle.jit.to_static(model) extra_files = {} paddle.jit.save( model, @@ -536,7 +536,7 @@ def change_bias(FLAGS): if FLAGS.output is not None else FLAGS.INPUT.replace(".pdparams", "_updated.pdparams") ) - model = paddle.jit.script(model) + model = paddle.jit.to_static(model) paddle.jit.save( model, output_path, @@ -552,7 +552,11 @@ def main(args: Optional[Union[List[str], argparse.Namespace]] = None): else: FLAGS = args - set_log_handles(FLAGS.log_level, Path(FLAGS.log_path), mpi_log=None) + set_log_handles( + FLAGS.log_level, + Path(FLAGS.log_path) if FLAGS.log_path is not None else None, + mpi_log=None, + ) log.debug("Log handles were successfully set") log.info("DeePMD version: %s", __version__) diff --git a/deepmd/pd/infer/inference.py b/deepmd/pd/infer/inference.py index cef7b32ba4..71602d990c 100644 --- a/deepmd/pd/infer/inference.py +++ b/deepmd/pd/infer/inference.py @@ -61,5 +61,5 @@ def __init__( # Model Wrapper self.wrapper = ModelWrapper(self.model) # inference only if JIT: - self.wrapper = paddle.jit.script(self.wrapper) + self.wrapper = paddle.jit.to_static(self.wrapper) self.wrapper.set_state_dict(state_dict) diff --git a/deepmd/pd/loss/denoise.py b/deepmd/pd/loss/denoise.py index 1ec97ff98e..bfd889bfc5 100644 --- a/deepmd/pd/loss/denoise.py +++ b/deepmd/pd/loss/denoise.py @@ -67,7 +67,7 @@ def forward(self, model_pred, label, natoms, learning_rate, mae=False): ) else: coord_loss = paddle.zeros( - 1, dtype=env.GLOBAL_PD_FLOAT_PRECISION + [1], dtype=env.GLOBAL_PD_FLOAT_PRECISION ).to(env.DEVICE)[0] else: coord_loss = F.smooth_l1_loss( @@ -90,7 +90,7 @@ def forward(self, model_pred, label, natoms, learning_rate, mae=False): ) else: token_loss = paddle.zeros( - 1, dtype=env.GLOBAL_PD_FLOAT_PRECISION + [1], dtype=env.GLOBAL_PD_FLOAT_PRECISION ).to(env.DEVICE)[0] else: token_loss = F.nll_loss( diff --git a/deepmd/pd/model/atomic_model/pairtab_atomic_model.py b/deepmd/pd/model/atomic_model/pairtab_atomic_model.py index da3dee872c..867bdd7e11 100644 --- a/deepmd/pd/model/atomic_model/pairtab_atomic_model.py +++ b/deepmd/pd/model/atomic_model/pairtab_atomic_model.py @@ -105,7 +105,7 @@ def __init__( self.register_buffer("tab_info", None) self.register_buffer("tab_data", None) self.bias_atom_e = paddle.zeros( - self.ntypes, 1, dtype=env.GLOBAL_PD_ENER_FLOAT_PRECISION + [self.ntypes, 1], dtype=env.GLOBAL_PD_ENER_FLOAT_PRECISION ).to(device=env.DEVICE) # self.model_type = "ener" @@ -383,7 +383,7 @@ def _get_pairwise_dist( """ nframes, nloc, nnei = nlist.shape coord_l = coords[:, :nloc].reshape([nframes, -1, 1, 3]) - index = nlist.reshape([nframes, -1]).unsqueeze(-1).expand(-1, -1, 3) + index = nlist.reshape([nframes, -1]).unsqueeze(-1).expand([-1, -1, 3]) # coord_r = paddle.take_along_axis(coords, axis=1, indices=index) coord_r = aux.take_along_axis(coords, axis=1, indices=index) coord_r = coord_r.reshape([nframes, nloc, nnei, 3]) @@ -422,7 +422,7 @@ def _extract_spline_coefficient( """ # (nframes, nloc, nnei) - expanded_i_type = i_type.unsqueeze(-1).expand(-1, -1, j_type.shape[-1]) + expanded_i_type = i_type.unsqueeze(-1).expand([-1, -1, j_type.shape[-1]]) # handle the case where idx is beyond the number of splines clipped_indices = paddle.clamp(idx, 0, nspline - 1).to(paddle.int64) @@ -438,7 +438,7 @@ def _extract_spline_coefficient( # tab_data: (ntype, ntype, nspline, 4) tab_data = tab_data.reshape([ntypes * ntypes * nspline, 4]) # tab_data_idx: (nframes * nloc * nnei, 4) - tab_data_idx = tab_data_idx.reshape([nframes * nloc * nnei, 1]).expand(-1, 4) + tab_data_idx = tab_data_idx.reshape([nframes * nloc * nnei, 1]).expand([-1, 4]) # (nframes, nloc, nnei, 4) # final_coef = paddle.take_along_axis( # tab_data, axis=0, indices=tab_data_idx diff --git a/deepmd/pd/model/descriptor/gaussian_lcc.py b/deepmd/pd/model/descriptor/gaussian_lcc.py index 37bd71e18a..6ec2f214a1 100644 --- a/deepmd/pd/model/descriptor/gaussian_lcc.py +++ b/deepmd/pd/model/descriptor/gaussian_lcc.py @@ -16,6 +16,7 @@ TypeEmbedNet, ) from deepmd.pd.utils import ( + aux, env, ) from deepmd.utils.path import ( @@ -202,7 +203,7 @@ def forward( paddle.arange(0, nloc) .to(device=nlist.place) # pylint: disable=no-explicit-dtype .reshape([1, nloc, 1]) - .expand(nframes, -1, -1), + .expand([nframes, -1, -1]), nlist, ], axis=-1, @@ -212,7 +213,7 @@ def forward( paddle.arange(0, nloc) .to(device=nlist_loc.place) # pylint: disable=no-explicit-dtype .reshape([1, nloc, 1]) - .expand(nframes, -1, -1), + .expand([nframes, -1, -1]), nlist_loc, ], axis=-1, @@ -249,7 +250,7 @@ def forward( axis=1, index=nlist_loc2.reshape([nframes, -1]) .unsqueeze(-1) - .expand(-1, -1, self.embed_dim), + .expand([-1, -1, self.embed_dim]), ).reshape([nframes * nloc, 1 + self.nnei, self.embed_dim]) if self.pre_add_seq and seq_input is not None: first_dim = seq_input.shape[0] @@ -261,7 +262,7 @@ def forward( axis=1, index=nlist_loc2.reshape([nframes, -1]) .unsqueeze(-1) - .expand(-1, -1, self.embed_dim), + .expand([-1, -1, self.embed_dim]), ).reshape([nframes * nloc, 1 + self.nnei, self.embed_dim]) atom_feature += atom_feature_seq else: @@ -285,21 +286,23 @@ def forward( [ nlist_type2_reshape.reshape( [nframes * nloc, 1 + self.nnei, 1, 1] - ).expand(-1, -1, 1 + self.nnei, -1), + ).expand([-1, -1, 1 + self.nnei, -1]), nlist_type2_reshape.reshape( [nframes * nloc, 1, 1 + self.nnei, 1] - ).expand(-1, 1 + self.nnei, -1, -1) + ).expand([-1, 1 + self.nnei, -1, -1]) + self.ntypes, ], axis=-1, ) # [(nframes x nloc) x (1 + nnei2) x 3] - coord_selected = paddle.gather( + coord_selected = aux.take_along_axis( extended_coord.unsqueeze(1) - .expand(-1, nloc, -1, -1) + .expand([-1, nloc, -1, -1]) .reshape([nframes * nloc, nall, 3]), axis=1, - index=nlist2.reshape([nframes * nloc, 1 + self.nnei, 1]).expand(-1, -1, 3), + indices=nlist2.reshape([nframes * nloc, 1 + self.nnei, 1]).expand( + [-1, -1, 3] + ), ) # Update pair features (or and atomic features) with gbf features diff --git a/deepmd/pd/model/descriptor/repformer_layer.py b/deepmd/pd/model/descriptor/repformer_layer.py index 827029601e..519629c8f3 100644 --- a/deepmd/pd/model/descriptor/repformer_layer.py +++ b/deepmd/pd/model/descriptor/repformer_layer.py @@ -108,7 +108,7 @@ def _make_nei_g1( # g1_ext: nb x nall x ng1 ng1 = g1_ext.shape[-1] # index: nb x (nloc x nnei) x ng1 - index = nlist.reshape([nb, nloc * nnei]).unsqueeze(-1).expand(-1, -1, ng1) + index = nlist.reshape([nb, nloc * nnei]).unsqueeze(-1).expand([-1, -1, ng1]) # gg1 : nb x (nloc x nnei) x ng1 gg1 = paddle.gather(g1_ext, axis=1, index=index) # gg1 : nb x nloc x nnei x ng1 diff --git a/deepmd/pd/model/descriptor/repformer_layer_old_impl.py b/deepmd/pd/model/descriptor/repformer_layer_old_impl.py index 52c0f0bf68..179c7999da 100644 --- a/deepmd/pd/model/descriptor/repformer_layer_old_impl.py +++ b/deepmd/pd/model/descriptor/repformer_layer_old_impl.py @@ -27,11 +27,11 @@ def _make_nei_g1( # g1_ext: nb x nall x ng1 ng1 = g1_ext.shape[-1] # index: nb x (nloc x nnei) x ng1 - index = nlist.reshape([nb, nloc * nnei]).unsqueeze(-1).expand(-1, -1, ng1) + index = nlist.reshape([nb, nloc * nnei]).unsqueeze(-1).expand([-1, -1, ng1]) # gg1 : nb x (nloc x nnei) x ng1 # print(g1_ext.shape, index.shape) # gg1 = paddle.take_along_axis(g1_ext, axis=1, index=index) - gg1 = aux.take_along_axis(g1_ext, axis=1, index=index) + gg1 = aux.take_along_axis(g1_ext, axis=1, indices=index) # gg1 : nb x nloc x nnei x ng1 gg1 = gg1.reshape([nb, nloc, nnei, ng1]) return gg1 diff --git a/deepmd/pd/model/descriptor/repformers.py b/deepmd/pd/model/descriptor/repformers.py index 70e1b0864b..c0c617e5cb 100644 --- a/deepmd/pd/model/descriptor/repformers.py +++ b/deepmd/pd/model/descriptor/repformers.py @@ -307,7 +307,7 @@ def __init__( device=env.DEVICE ) stddev = paddle.ones(wanted_shape, dtype=env.GLOBAL_PD_FLOAT_PRECISION).to( - device=aux.DEVICE + device=env.DEVICE ) self.register_buffer("mean", mean) self.register_buffer("stddev", stddev) diff --git a/deepmd/pd/model/descriptor/se_atten.py b/deepmd/pd/model/descriptor/se_atten.py index 964f81ab50..d52a295883 100644 --- a/deepmd/pd/model/descriptor/se_atten.py +++ b/deepmd/pd/model/descriptor/se_atten.py @@ -490,15 +490,15 @@ def forward( sw = paddle.squeeze(sw, -1) # nf x nloc x nt -> nf x nloc x nnei x nt atype_tebd = extended_atype_embd[:, :nloc, :] - atype_tebd_nnei = atype_tebd.unsqueeze(2).expand(-1, -1, self.nnei, -1) + atype_tebd_nnei = atype_tebd.unsqueeze(2).expand([-1, -1, self.nnei, -1]) # nf x nall x nt nt = extended_atype_embd.shape[-1] atype_tebd_ext = extended_atype_embd # nb x (nloc x nnei) x nt - index = nlist.reshape([nb, nloc * nnei]).unsqueeze(-1).expand(-1, -1, nt) + index = nlist.reshape([nb, nloc * nnei]).unsqueeze(-1).expand([-1, -1, nt]) # nb x (nloc x nnei) x nt # atype_tebd_nlist = paddle.take_along_axis(atype_tebd_ext, axis=1, index=index) - atype_tebd_nlist = aux.take_along_axis(atype_tebd_ext, axis=1, index=index) + atype_tebd_nlist = aux.take_along_axis(atype_tebd_ext, axis=1, indices=index) # nb x nloc x nnei x nt atype_tebd_nlist = atype_tebd_nlist.reshape([nb, nloc, nnei, nt]) # beyond the cutoff sw should be 0.0 @@ -534,7 +534,7 @@ def forward( nfnl = dmatrix.shape[0] # nfnl x nnei x 4 rr = dmatrix - rr = rr * exclude_mask[:, :, None] + rr = rr * exclude_mask[:, :, None].astype(rr.dtype) ss = rr[:, :, :1] nlist_tebd = atype_tebd_nlist.reshape([nfnl, nnei, self.tebd_dim]) atype_tebd = atype_tebd_nnei.reshape([nfnl, nnei, self.tebd_dim]) @@ -986,7 +986,7 @@ def forward( assert input_r is not None, "input_r must be provided when dotr is True!" # (nf x nloc) x 1 x nnei x nnei angular_weight = paddle.matmul( - input_r, input_r.transpose([0, 1, 3, 2]) + input_r, input_r.transpose([0, 2, 1]) ).reshape([-1, 1, self.nnei, self.nnei]) attn_weights = attn_weights * angular_weight diff --git a/deepmd/pd/model/descriptor/se_r.py b/deepmd/pd/model/descriptor/se_r.py index 42e3130518..1030d564d4 100644 --- a/deepmd/pd/model/descriptor/se_r.py +++ b/deepmd/pd/model/descriptor/se_r.py @@ -382,7 +382,7 @@ def forward( mm = exclude_mask[:, self.sec[ii] : self.sec[ii + 1]] # nfnl x nt x 1 ss = dmatrix[:, self.sec[ii] : self.sec[ii + 1], :] - ss = ss * mm[:, :, None] + ss = ss * mm[:, :, None].astype(ss.dtype) # nfnl x nt x ng gg = ll.forward(ss) gg = paddle.mean(gg, axis=1).unsqueeze(1) diff --git a/deepmd/pd/model/descriptor/se_t.py b/deepmd/pd/model/descriptor/se_t.py index 9a2e06d40a..dfd899dac6 100644 --- a/deepmd/pd/model/descriptor/se_t.py +++ b/deepmd/pd/model/descriptor/se_t.py @@ -702,11 +702,11 @@ def forward( # nfnl x nt_i x 3 rr_i = dmatrix[:, self.sec[ti] : self.sec[ti + 1], 1:] mm_i = exclude_mask[:, self.sec[ti] : self.sec[ti + 1]] - rr_i = rr_i * mm_i[:, :, None] + rr_i = rr_i * mm_i[:, :, None].astype(rr_i.dtype) # nfnl x nt_j x 3 rr_j = dmatrix[:, self.sec[tj] : self.sec[tj + 1], 1:] mm_j = exclude_mask[:, self.sec[tj] : self.sec[tj + 1]] - rr_j = rr_j * mm_j[:, :, None] + rr_j = rr_j * mm_j[:, :, None].astype(rr_j.dtype) # nfnl x nt_i x nt_j env_ij = paddle.einsum("ijm,ikm->ijk", rr_i, rr_j) # nfnl x nt_i x nt_j x 1 diff --git a/deepmd/pd/model/descriptor/se_t_tebd.py b/deepmd/pd/model/descriptor/se_t_tebd.py index 0a329a806b..b5077acfe0 100644 --- a/deepmd/pd/model/descriptor/se_t_tebd.py +++ b/deepmd/pd/model/descriptor/se_t_tebd.py @@ -787,10 +787,10 @@ def forward( nt = extended_atype_embd.shape[-1] atype_tebd_ext = extended_atype_embd # nb x (nloc x nnei) x nt - index = nlist.reshape([nb, nloc * nnei]).unsqueeze(-1).expand(-1, -1, nt) + index = nlist.reshape([nb, nloc * nnei]).unsqueeze(-1).expand([-1, -1, nt]) # nb x (nloc x nnei) x nt # atype_tebd_nlist = paddle.take_along_axis(atype_tebd_ext, axis=1, index=index) - atype_tebd_nlist = aux.take_along_axis(atype_tebd_ext, axis=1, index=index) + atype_tebd_nlist = aux.take_along_axis(atype_tebd_ext, axis=1, indices=index) # nb x nloc x nnei x nt atype_tebd_nlist = atype_tebd_nlist.reshape([nb, nloc, nnei, nt]) # beyond the cutoff sw should be 0.0 @@ -803,7 +803,7 @@ def forward( nfnl = dmatrix.shape[0] # nfnl x nnei x 4 rr = dmatrix - rr = rr * exclude_mask[:, :, None] + rr = rr * exclude_mask[:, :, None].astype(rr.dtype) # nfnl x nt_i x 3 rr_i = rr[:, :, 1:] diff --git a/deepmd/pd/model/model/make_model.py b/deepmd/pd/model/model/make_model.py index 87f4f5b0b3..3eecced89c 100644 --- a/deepmd/pd/model/model/make_model.py +++ b/deepmd/pd/model/model/make_model.py @@ -306,7 +306,7 @@ def input_type_cast( # f" that of the coordinate {input_prec}" # ) _lst: List[Optional[paddle.Tensor]] = [ - vv.to(coord.dtype) if vv is not None else None + vv.astype(coord.dtype) if vv is not None else None for vv in [box, fparam, aparam] ] box, fparam, aparam = _lst @@ -436,7 +436,7 @@ def _format_nlist( # nf x nloc x 3 coord0 = extended_coord[:, :n_nloc, :] # nf x (nloc x nnei) x 3 - index = nlist.reshape([n_nf, n_nloc * n_nnei, 1]).expand(-1, -1, 3) + index = nlist.reshape([n_nf, n_nloc * n_nnei, 1]).expand([-1, -1, 3]) coord1 = paddle.gather(extended_coord, 1, index) # nf x nloc x nnei x 3 coord1 = coord1.reshape([n_nf, n_nloc, n_nnei, 3]) diff --git a/deepmd/pd/model/model/polar_model.py b/deepmd/pd/model/model/polar_model.py index 043039b8e6..6800d82b13 100644 --- a/deepmd/pd/model/model/polar_model.py +++ b/deepmd/pd/model/model/polar_model.py @@ -35,8 +35,8 @@ def __init__( *args, **kwargs, ): - DPModelCommon.__init__(self) DPDOSModel_.__init__(self, *args, **kwargs) + DPModelCommon.__init__(self) def translated_output_def(self): out_def_data = self.model_output_def().get_data() diff --git a/deepmd/pd/model/model/spin_model.py b/deepmd/pd/model/model/spin_model.py index 1412c5cca5..32ffff773c 100644 --- a/deepmd/pd/model/model/spin_model.py +++ b/deepmd/pd/model/model/spin_model.py @@ -188,7 +188,7 @@ def extend_nlist(extended_atype, nlist): paddle.arange(0, nloc, dtype=nlist.dtype) .to(device=nlist.place) .reshape([1, -1, 1]) - .expand(nframes, -1, -1) + .expand([nframes, -1, -1]) ) self_spin = self_real + nall # real atom's neighbors: self spin + real neighbor + virtual neighbor diff --git a/deepmd/pd/model/network/network.py b/deepmd/pd/model/network/network.py index 6b565031a5..7148090e50 100644 --- a/deepmd/pd/model/network/network.py +++ b/deepmd/pd/model/network/network.py @@ -513,7 +513,7 @@ def __init__(self, embed_dim, output_dim, activation_fn, weight=None): ).weight self.weight = weight self.bias = self.create_parameter( - paddle.zeros(output_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION) # pylint: disable=no-explicit-dtype,no-explicit-device + paddle.zeros([output_dim], dtype=env.GLOBAL_PD_FLOAT_PRECISION) # pylint: disable=no-explicit-dtype,no-explicit-device ) def forward( @@ -753,7 +753,9 @@ def forward(self, device: str): embed = paddle.concat( [ embed, - paddle.zeros(1, embed.shape[1], dtype=self.prec).to(device=device), + paddle.zeros([1, embed.shape[1]], dtype=self.prec).to( + device=device + ), ] ) return embed @@ -874,7 +876,7 @@ def serialize(self) -> dict: } -# @paddle.jit.script +# @paddle.jit.to_static def gaussian(x, mean, std: float): pi = 3.14159 a = (2 * pi) ** 0.5 @@ -905,7 +907,7 @@ def forward(self, x, atom_pair): bias = self.bias(atom_pair).sum(axis=-2) x = mul * x.unsqueeze(-1) + bias # [nframes, nloc, nnei, K] - x = x.expand(-1, -1, -1, self.K) + x = x.expand([-1, -1, -1, self.K]) mean = self.mean.reshape([-1]) return gaussian(x, mean, self.std) @@ -1284,7 +1286,7 @@ def forward( # v = paddle.concat([v, padding.unsqueeze(0).unsqueeze(1)], axis=1) # [nframes, nloc * nnei, feature_dim] - index = nlist.reshape([nframes, -1]).unsqueeze(-1).expand(-1, -1, feature_dim) + index = nlist.reshape([nframes, -1]).unsqueeze(-1).expand([-1, -1, feature_dim]) k = paddle.gather(k, axis=1, index=index) # [nframes, nloc * nnei, feature_dim] v = paddle.gather(v, axis=1, index=index) diff --git a/deepmd/pd/model/task/fitting.py b/deepmd/pd/model/task/fitting.py index ebe2485b2d..7cdd5941bb 100644 --- a/deepmd/pd/model/task/fitting.py +++ b/deepmd/pd/model/task/fitting.py @@ -277,8 +277,7 @@ def change_type_map( extend_bias_atom_e = paddle.zeros( extend_shape, dtype=self.bias_atom_e.dtype, - device=self.bias_atom_e.place, - ) + ).to(device=self.bias_atom_e.place) self.bias_atom_e = paddle.concat( [self.bias_atom_e, extend_bias_atom_e], axis=0 ) diff --git a/deepmd/pd/optimizer/LKF.py b/deepmd/pd/optimizer/LKF.py index 20275fc0a2..d23c10399e 100644 --- a/deepmd/pd/optimizer/LKF.py +++ b/deepmd/pd/optimizer/LKF.py @@ -1,6 +1,9 @@ # SPDX-License-Identifier: LGPL-3.0-or-later import logging import math +from collections import ( + defaultdict, +) import paddle import paddle.distributed as dist @@ -34,11 +37,16 @@ def __init__( ): defaults = {"lr": 0.1, "kalman_nue": kalman_nue, "block_size": block_size} - super().__init__(params, defaults) - - self._params = self.param_groups[0]["params"] + super().__init__( + defaults["lr"], + params, + ) + self.state = defaultdict(dict) + self._params = self._param_groups[0]["params"] + for param_group in self._param_groups: + param_group.update(defaults) - if len(self.param_groups) != 1 or len(self._params) == 0: + if len(self._param_groups) != 1 or len(self._params) == 0: raise ValueError( "LKF doesn't support per-parameter options " "(parameter groups)" ) @@ -60,10 +68,10 @@ def __init_P(self): data_type = self._params[0].dtype device = self._params[0].place - for param_group in self.param_groups: + for param_group in self._param_groups: params = param_group["params"] for param in params: - param_num = param.data.nelement() + param_num = param.data.numel().item() if param_sum + param_num > block_size: if param_sum > 0: param_nums.append(param_sum) @@ -100,8 +108,7 @@ def __init_P(self): paddle.eye( block_size, dtype=data_type, - device=dist_device, - ) + ).to(device=dist_device) ) else: continue @@ -112,8 +119,7 @@ def __init_P(self): paddle.eye( param_num - block_size * i, dtype=data_type, - device=dist_device, - ) + ).to(device=dist_device) ) else: continue @@ -125,7 +131,9 @@ def __init_P(self): if self.rank == device_id: dist_device = "gpu:" + str(device_id) P.append( - paddle.eye(param_num, dtype=data_type, device=dist_device) + paddle.eye(param_num, dtype=data_type).to( + device=dist_device + ) ) else: for param_num in param_nums: @@ -137,8 +145,7 @@ def __init_P(self): paddle.eye( block_size, dtype=data_type, - device=device, - ) + ).to(device=device) ) params_packed_index.append(block_size) else: @@ -146,12 +153,11 @@ def __init_P(self): paddle.eye( param_num - block_size * i, dtype=data_type, - device=device, - ) + ).to(device=device) ) params_packed_index.append(param_num - block_size * i) else: - P.append(paddle.eye(param_num, dtype=data_type, device=device)) + P.append(paddle.eye(param_num, dtype=data_type).to(device=device)) params_packed_index.append(param_num) self._state.setdefault("P", P) @@ -159,14 +165,14 @@ def __init_P(self): self._state.setdefault("params_packed_index", params_packed_index) def __get_blocksize(self): - return self.param_groups[0]["block_size"] + return self._param_groups[0]["block_size"] def __get_nue(self): - return self.param_groups[0]["kalman_nue"] + return self._param_groups[0]["kalman_nue"] def __split_weights(self, weight): block_size = self.__get_blocksize() - param_num = weight.nelement() + param_num = weight.numel().item() res = [] if param_num < block_size: res.append(weight) @@ -206,7 +212,9 @@ def __update(self, H, error, weights): device = "gpu:" + str(self.rank) local_shape = [tensor.shape[0] for tensor in weights] shape_list = [ - paddle.zeros_like(paddle.empty(1), dtype=paddle.float64, device=device) # pylint: disable=no-explicit-dtype,no-explicit-device + paddle.zeros_like(paddle.empty(1), dtype=paddle.float64).to( + device=device + ) # pylint: disable=no-explicit-dtype,no-explicit-device for _ in range(dist.get_world_size()) ] dist.all_gather_object(shape_list, local_shape) @@ -215,8 +223,8 @@ def __update(self, H, error, weights): weight_list = [None] * len(world_shape) for i in range(len(world_shape)): weight_list[i] = paddle.zeros( - world_shape[i], dtype=paddle.float64, device=device - ) + [world_shape[i]], dtype=paddle.float64 + ).to(device=device) dist.all_gather(weight_list, weight_tensor) result = [] for i in range(dist.get_world_size()): @@ -227,10 +235,10 @@ def __update(self, H, error, weights): i = 0 param_sum = 0 - for param_group in self.param_groups: + for param_group in self._param_groups: params = param_group["params"] for param in params: - param_num = param.nelement() + param_num = param.numel().item() weight_tmp = weights[i][param_sum : param_sum + param_num] if param_num < block_size: if param.ndim > 1: @@ -268,29 +276,29 @@ def step(self, error): for param in self._params: if param.ndim > 1: - tmp = param.data.T.contiguous().reshape(param.data.nelement(), 1) + tmp = param.data.T.contiguous().reshape(param.data.numel().item(), 1) if param.grad is None: tmp_grad = paddle.zeros_like(tmp) else: tmp_grad = ( (param.grad / self.grad_prefactor) .T.contiguous() - .reshape(param.grad.nelement(), 1) + .reshape(param.grad.numel().item(), 1) ) else: - tmp = param.data.reshape(param.data.nelement(), 1) + tmp = param.data.reshape(param.data.numel().item(), 1) if param.grad is None: tmp_grad = paddle.zeros_like(tmp) else: tmp_grad = (param.grad / self.grad_prefactor).reshape( - param.grad.nelement(), 1 + param.grad.numel().item(), 1 ) tmp = self.__split_weights(tmp) tmp_grad = self.__split_weights(tmp_grad) for split_grad, split_weight in zip(tmp_grad, tmp): - nelement = split_grad.nelement() + numel = split_grad.numel().item() if param_sum == 0: res_grad = split_grad @@ -299,7 +307,7 @@ def step(self, error): res_grad = paddle.concat((res_grad, split_grad), axis=0) res = paddle.concat((res, split_weight), axis=0) - param_sum += nelement + param_sum += numel if param_sum == params_packed_index[param_index]: param_sum = 0 diff --git a/deepmd/pd/utils/env_mat_stat.py b/deepmd/pd/utils/env_mat_stat.py index ef020992a5..f0c7683538 100644 --- a/deepmd/pd/utils/env_mat_stat.py +++ b/deepmd/pd/utils/env_mat_stat.py @@ -55,8 +55,10 @@ def compute_stat(self, env_mat: Dict[str, paddle.Tensor]) -> Dict[str, StatItem] for kk, vv in env_mat.items(): stats[kk] = StatItem( number=vv.numel().item(), - sum=vv.sum().item(), - squared_sum=paddle.square(vv).sum().item(), + sum=vv.sum().item() if vv.numel().item() != 0 else paddle.zeros([]), + squared_sum=paddle.square(vv).sum().item() + if vv.numel().item() != 0 + else paddle.zeros([]), ) return stats diff --git a/deepmd/pd/utils/nlist.py b/deepmd/pd/utils/nlist.py index a6b58e3269..33347393ab 100644 --- a/deepmd/pd/utils/nlist.py +++ b/deepmd/pd/utils/nlist.py @@ -103,7 +103,7 @@ def build_neighbor_list( if coord.numel() > 0: xmax = paddle.max(coord) + 2.0 * rcut else: - xmax = paddle.zeros(1, dtype=coord.dtype).to(device=coord.place) + 2.0 * rcut + xmax = paddle.zeros([1], dtype=coord.dtype).to(device=coord.place) + 2.0 * rcut # nf x nall is_vir = atype < 0 coord1 = paddle.where( @@ -242,7 +242,7 @@ def build_directional_neighbor_list( xmax = paddle.max(coord_cntl) + 2.0 * rcut else: xmax = ( - paddle.zeros(1, dtype=coord_neig.dtype, device=coord_neig.place) + paddle.zeros([1], dtype=coord_neig.dtype, device=coord_neig.place) + 2.0 * rcut ) # nf x nloc @@ -391,13 +391,13 @@ def build_multiple_neighbor_list( nlist.masked_fill(nlist_mask, 0) .reshape([nb, nloc * nsel]) .unsqueeze(-1) - .expand(-1, -1, 3) + .expand([-1, -1, 3]) ) # nb x nloc x nsel x 3 # coord2 = paddle.take_along_axis(coord1, axis=1, index=index).reshape( # [nb, nloc, nsel, 3] # ) - coord2 = aux.take_along_axis(coord1, axis=1, index=index).reshape( + coord2 = aux.take_along_axis(coord1, axis=1, indices=index).reshape( [nb, nloc, nsel, 3] ) # nb x nloc x nsel x 3 diff --git a/deepmd/pd/utils/stat.py b/deepmd/pd/utils/stat.py index 12acf1b37a..de9563ea6e 100644 --- a/deepmd/pd/utils/stat.py +++ b/deepmd/pd/utils/stat.py @@ -501,7 +501,7 @@ def compute_output_stats_global( atom_numbs = {kk: merged_natoms[kk].sum(-1) for kk in bias_atom_e.keys()} def rmse(x): - return paddle.sqrt(paddle.mean(paddle.square(x))) + return (x**2).mean() ** 0.5 for kk in bias_atom_e.keys(): rmse_ae = rmse( diff --git a/deepmd/pd/utils/utils.py b/deepmd/pd/utils/utils.py index aa6069b6be..2703c65810 100644 --- a/deepmd/pd/utils/utils.py +++ b/deepmd/pd/utils/utils.py @@ -71,9 +71,11 @@ def to_numpy_array( prec = NP_PRECISION_DICT.get(prec, np.float64) if prec is None: raise ValueError(f"unknown precision {xx.dtype}") + if isinstance(xx, np.ndarray): + return xx.astype(prec) if xx.dtype == paddle.bfloat16: xx = xx.astype(paddle.get_default_dtype()) - return xx.astype(prec) + return xx.numpy().astype(prec) @overload From f6eeef66a3a46f9d7dd965231341805627e3fd0d Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Fri, 6 Sep 2024 13:11:02 +0800 Subject: [PATCH 06/93] fix concat --- deepmd/pd/train/training.py | 7 +++++-- deepmd/pd/utils/stat.py | 2 +- deepmd/utils/batch_size.py | 19 +++++++++++++++++-- 3 files changed, 23 insertions(+), 5 deletions(-) diff --git a/deepmd/pd/train/training.py b/deepmd/pd/train/training.py index 7b7cf9ace7..74a262cd31 100644 --- a/deepmd/pd/train/training.py +++ b/deepmd/pd/train/training.py @@ -395,7 +395,7 @@ def get_lr(lr_params): # JIT if JIT: - self.model = paddle.jit.to_static(self.model) + self.model = paddle.jit.to_static(self.model, full_graph=False) # Model Wrapper self.wrapper = ModelWrapper(self.model, self.loss, model_params=model_params) @@ -603,7 +603,10 @@ def warm_up_linear(step, warmup_steps): self.optimizer.set_state_dict(optimizer_state_dict) elif self.opt_type == "LKF": self.optimizer = LKFOptimizer( - self.wrapper.parameters(), 0.98, 0.99870, self.opt_param["kf_blocksize"] + [{"params": self.wrapper.parameters()}], + 0.98, + 0.99870, + self.opt_param["kf_blocksize"], ) else: raise ValueError(f"Not supported optimizer type '{self.opt_type}'") diff --git a/deepmd/pd/utils/stat.py b/deepmd/pd/utils/stat.py index de9563ea6e..05c83aaf2e 100644 --- a/deepmd/pd/utils/stat.py +++ b/deepmd/pd/utils/stat.py @@ -501,7 +501,7 @@ def compute_output_stats_global( atom_numbs = {kk: merged_natoms[kk].sum(-1) for kk in bias_atom_e.keys()} def rmse(x): - return (x**2).mean() ** 0.5 + return np.sqrt(np.mean(np.square(x))) for kk in bias_atom_e.keys(): rmse_ae = rmse( diff --git a/deepmd/utils/batch_size.py b/deepmd/utils/batch_size.py index 03b562e99b..0cace10abe 100644 --- a/deepmd/utils/batch_size.py +++ b/deepmd/utils/batch_size.py @@ -17,6 +17,11 @@ OutOfMemoryError, ) +try: + import paddle +except ModuleNotFoundError: + pass + log = logging.getLogger(__name__) @@ -181,7 +186,11 @@ def execute_with_batch_size( *[ ( vv[start_index:end_index, ...] - if array_api_compat.is_array_api_obj(vv) and vv.ndim > 1 + if ( + array_api_compat.is_array_api_obj(vv) + or str(vv.__class__) == "" + ) + and vv.ndim > 1 else vv ) for vv in args @@ -189,7 +198,11 @@ def execute_with_batch_size( **{ kk: ( vv[start_index:end_index, ...] - if array_api_compat.is_array_api_obj(vv) and vv.ndim > 1 + if ( + array_api_compat.is_array_api_obj(vv) + or str(vv.__class__) == "" + ) + and vv.ndim > 1 else vv ) for kk, vv in kwargs.items() @@ -229,6 +242,8 @@ def concate_result(r): if array_api_compat.is_array_api_obj(r[0]): xp = array_api_compat.array_namespace(r[0]) ret = xp.concat(r, axis=0) + elif str(r[0].__class__) == "": + ret = paddle.concat(r, axis=0) else: raise RuntimeError(f"Unexpected result type {type(r[0])}") return ret From ec021f7dca681a35b0bc9136f239ff89cd7f48df Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Sat, 7 Sep 2024 16:48:00 +0800 Subject: [PATCH 07/93] update fix code --- deepmd/pd/__init__.py | 5 + deepmd/pd/entrypoints/main.py | 36 +- deepmd/pd/infer/deep_eval.py | 14 +- deepmd/pd/loss/denoise.py | 4 +- deepmd/pd/loss/loss.py | 2 +- .../model/atomic_model/linear_atomic_model.py | 10 +- .../atomic_model/pairtab_atomic_model.py | 18 +- .../model/atomic_model/polar_atomic_model.py | 2 +- deepmd/pd/model/descriptor/descriptor.py | 4 +- deepmd/pd/model/descriptor/dpa1.py | 8 +- deepmd/pd/model/descriptor/dpa2.py | 25 +- deepmd/pd/model/descriptor/gaussian_lcc.py | 10 +- deepmd/pd/model/descriptor/repformer_layer.py | 58 +- .../descriptor/repformer_layer_old_impl.py | 55 +- deepmd/pd/model/descriptor/repformers.py | 2 +- deepmd/pd/model/descriptor/se_r.py | 14 +- deepmd/pd/model/descriptor/se_t_tebd.py | 8 +- deepmd/pd/model/model/frozen.py | 4 +- deepmd/pd/model/model/make_model.py | 9 +- deepmd/pd/model/model/spin_model.py | 15 +- deepmd/pd/model/model/transform_output.py | 10 +- deepmd/pd/model/network/layernorm.py | 13 +- deepmd/pd/model/network/mlp.py | 2 +- deepmd/pd/model/network/network.py | 198 +++---- deepmd/pd/model/task/dos.py | 12 +- deepmd/pd/model/task/ener.py | 8 +- deepmd/pd/model/task/fitting.py | 12 +- deepmd/pd/model/task/polarizability.py | 18 +- deepmd/pd/optimizer/KFWrapper.py | 6 +- deepmd/pd/train/training.py | 10 +- deepmd/pd/train/wrapper.py | 2 +- deepmd/pd/utils/aux.py | 89 +++ deepmd/pd/utils/dataloader.py | 9 +- deepmd/pd/utils/env.py | 4 +- deepmd/pd/utils/env_mat_stat.py | 2 +- deepmd/pd/utils/exclude_mask.py | 7 +- deepmd/pd/utils/init.py | 529 ++++++++++++++++++ deepmd/pd/utils/nlist.py | 15 +- deepmd/pd/utils/preprocess.py | 3 +- deepmd/pd/utils/region.py | 6 +- deepmd/pd/utils/stat.py | 8 +- 41 files changed, 973 insertions(+), 293 deletions(-) create mode 100644 deepmd/pd/utils/aux.py create mode 100644 deepmd/pd/utils/init.py diff --git a/deepmd/pd/__init__.py b/deepmd/pd/__init__.py index 784f184968..5437d02d43 100644 --- a/deepmd/pd/__init__.py +++ b/deepmd/pd/__init__.py @@ -11,3 +11,8 @@ ] except Exception as e: __all__ = [] + +import paddle + +paddle.framework.core.set_prim_eager_enabled(True) +paddle.framework.core._set_prim_all_enabled(True) diff --git a/deepmd/pd/entrypoints/main.py b/deepmd/pd/entrypoints/main.py index 4f5db306f3..6e9c2445f9 100644 --- a/deepmd/pd/entrypoints/main.py +++ b/deepmd/pd/entrypoints/main.py @@ -225,15 +225,15 @@ def get_backend_info(self) -> dict: """Get backend information.""" if ENABLE_CUSTOMIZED_OP: op_info = { - "build with PT ver": GLOBAL_CONFIG["pt_version"], - "build with PT inc": GLOBAL_CONFIG["pt_include_dir"].replace(";", "\n"), - "build with PT lib": GLOBAL_CONFIG["pt_libs"].replace(";", "\n"), + "build with PD ver": GLOBAL_CONFIG["pd_version"], + "build with PD inc": GLOBAL_CONFIG["pd_include_dir"].replace(";", "\n"), + "build with PD lib": GLOBAL_CONFIG["pd_libs"].replace(";", "\n"), } else: op_info = {} return { "Backend": "Paddle", - "PT ver": f"v{paddle.__version__}-g{paddle.version.commit[:11]}", + "PD ver": f"v{paddle.__version__}-g{paddle.version.commit[:11]}", "Enable custom OP": ENABLE_CUSTOMIZED_OP, **op_info, } @@ -344,12 +344,32 @@ def train(FLAGS): def freeze(FLAGS): model = inference.Tester(FLAGS.model, head=FLAGS.head).model model.eval() - model = paddle.jit.to_static(model) + from paddle.static import ( + InputSpec, + ) + + """ + ** coord [None, 192, 3] paddle.float64 + ** atype [None, 192] paddle.int64 + ** box [None, 3, 3] paddle.float64 + """ + model = paddle.jit.to_static( + model, + full_graph=True, + input_spec=[ + InputSpec([None, 192, 3], dtype="float64", name="coord"), + InputSpec([None, 192], dtype="int64", name="atype"), + InputSpec([None, 192, 3], dtype="float64", name="box"), + ], + ) extra_files = {} paddle.jit.save( model, - FLAGS.output, - extra_files, + path=FLAGS.output, + # extra_files, + ) + log.info( + f"Paddle inference model has been exported to: {FLAGS.output}.pdmodel(.pdiparams)" ) @@ -569,7 +589,7 @@ def main(args: Optional[Union[List[str], argparse.Namespace]] = None): FLAGS.model = str(checkpoint_path.joinpath(latest_ckpt_file)) else: FLAGS.model = FLAGS.checkpoint_folder - FLAGS.output = str(Path(FLAGS.output).with_suffix(".pdparams")) + FLAGS.output = str(Path(FLAGS.output).with_suffix("")) freeze(FLAGS) elif FLAGS.command == "show": show(FLAGS) diff --git a/deepmd/pd/infer/deep_eval.py b/deepmd/pd/infer/deep_eval.py index 287ab596e9..6947bd7bb7 100644 --- a/deepmd/pd/infer/deep_eval.py +++ b/deepmd/pd/infer/deep_eval.py @@ -122,7 +122,7 @@ def __init__( self.dp = ModelWrapper(model) self.dp.set_state_dict(state_dict) elif str(self.model_path).endswith(".pdmodel"): - model = paddle.jit.load(model_file) + model = paddle.jit.load(model_file[: -len(".pdmodel")]) self.dp = ModelWrapper(model) else: raise ValueError("Unknown model file format!") @@ -374,7 +374,7 @@ def _eval_model( nframes = coords.shape[0] if len(atom_types.shape) == 1: natoms = len(atom_types) - atom_types = np.tile(atom_types, nframes).reshape(nframes, -1) + atom_types = np.tile(atom_types, nframes).reshape([nframes, -1]) else: natoms = len(atom_types[0]) @@ -445,7 +445,7 @@ def _eval_model_spin( nframes = coords.shape[0] if len(atom_types.shape) == 1: natoms = len(atom_types) - atom_types = np.tile(atom_types, nframes).reshape(nframes, -1) + atom_types = np.tile(atom_types, nframes).reshape([nframes, -1]) else: natoms = len(atom_types[0]) @@ -467,13 +467,13 @@ def _eval_model_spin( box_input = None if fparam is not None: fparam_input = to_paddle_tensor( - fparam.reshape(nframes, self.get_dim_fparam()) + fparam.reshape([nframes, self.get_dim_fparam()]) ) else: fparam_input = None if aparam is not None: aparam_input = to_paddle_tensor( - aparam.reshape(nframes, natoms, self.get_dim_aparam()) + aparam.reshape([nframes, natoms, self.get_dim_aparam()]) ) else: aparam_input = None @@ -575,10 +575,10 @@ def eval_model( natoms = len(atom_types) if isinstance(atom_types, paddle.Tensor): atom_types = paddle.tile(atom_types.unsqueeze(0), [nframes, 1]).reshape( - nframes, -1 + [nframes, -1] ) else: - atom_types = np.tile(atom_types, nframes).reshape(nframes, -1) + atom_types = np.tile(atom_types, nframes).reshape([nframes, -1]) else: natoms = len(atom_types[0]) diff --git a/deepmd/pd/loss/denoise.py b/deepmd/pd/loss/denoise.py index bfd889bfc5..45e0ac73dd 100644 --- a/deepmd/pd/loss/denoise.py +++ b/deepmd/pd/loss/denoise.py @@ -58,7 +58,7 @@ def forward(self, model_pred, label, natoms, learning_rate, mae=False): if self.mask_loss_coord: masked_updated_coord = updated_coord[coord_mask] masked_clean_coord = clean_coord[coord_mask] - if masked_updated_coord.size(0) > 0: + if masked_updated_coord.shape[0] > 0: coord_loss = F.smooth_l1_loss( masked_updated_coord.reshape([-1, 3]), masked_clean_coord.reshape([-1, 3]), @@ -82,7 +82,7 @@ def forward(self, model_pred, label, natoms, learning_rate, mae=False): if self.mask_loss_token: masked_logits = logits[type_mask] masked_target = clean_type[type_mask] - if masked_logits.size(0) > 0: + if masked_logits.shape[0] > 0: token_loss = F.nll_loss( F.log_softmax(masked_logits, axis=-1), masked_target, diff --git a/deepmd/pd/loss/loss.py b/deepmd/pd/loss/loss.py index 0736369fd2..00208adc2c 100644 --- a/deepmd/pd/loss/loss.py +++ b/deepmd/pd/loss/loss.py @@ -40,4 +40,4 @@ def display_if_exist(loss: paddle.Tensor, find_property: float) -> paddle.Tensor find_property : float whether the property is found """ - return loss if bool(find_property) else paddle.nan + return loss if bool(find_property) else paddle.to_tensor(float("nan")) diff --git a/deepmd/pd/model/atomic_model/linear_atomic_model.py b/deepmd/pd/model/atomic_model/linear_atomic_model.py index dc780fb84a..0ff69a5bec 100644 --- a/deepmd/pd/model/atomic_model/linear_atomic_model.py +++ b/deepmd/pd/model/atomic_model/linear_atomic_model.py @@ -160,7 +160,7 @@ def _sort_rcuts_sels(self) -> Tuple[List[float], List[int]]: zipped = paddle.stack( [ self.rcuts, - self.nsels, + self.nsels.astype(self.rcuts.dtype), ], axis=0, ).T @@ -525,7 +525,7 @@ def _compute_weight( # use the larger rr based on nlist nlist_larger = zbl_nlist if zbl_nnei >= dp_nnei else dp_nlist - masked_nlist = paddle.clamp(nlist_larger, 0) + masked_nlist = paddle.clip(nlist_larger, 0) pairwise_rr = PairTabAtomicModel._get_pairwise_dist( extended_coord, masked_nlist ) @@ -533,7 +533,7 @@ def _compute_weight( paddle.where( nlist_larger != -1, pairwise_rr * paddle.exp(-pairwise_rr / self.smin_alpha), - paddle.zeros_like(nlist_larger), + paddle.zeros_like(nlist_larger, dtype=pairwise_rr.dtype), ), axis=-1, ) @@ -541,12 +541,12 @@ def _compute_weight( paddle.where( nlist_larger != -1, paddle.exp(-pairwise_rr / self.smin_alpha), - paddle.zeros_like(nlist_larger), + paddle.zeros_like(nlist_larger).astype(pairwise_rr.dtype), ), axis=-1, ) # handle masked nnei. - sigma = numerator / paddle.clamp(denominator, 1e-20) # nfrmes, nloc + sigma = numerator / paddle.clip(denominator, 1e-20) # nfrmes, nloc u = (sigma - self.sw_rmin) / (self.sw_rmax - self.sw_rmin) coef = paddle.zeros_like(u) left_mask = sigma < self.sw_rmin diff --git a/deepmd/pd/model/atomic_model/pairtab_atomic_model.py b/deepmd/pd/model/atomic_model/pairtab_atomic_model.py index 867bdd7e11..86913e87fb 100644 --- a/deepmd/pd/model/atomic_model/pairtab_atomic_model.py +++ b/deepmd/pd/model/atomic_model/pairtab_atomic_model.py @@ -260,7 +260,7 @@ def forward_atomic( # this will mask all -1 in the nlist mask = nlist >= 0 - masked_nlist = nlist * mask + masked_nlist = nlist * mask.astype(nlist.dtype) atype = extended_atype[:, :nloc] # (nframes, nloc) pairwise_rr = self._get_pairwise_dist( @@ -274,7 +274,7 @@ def forward_atomic( # i_type : (nframes, nloc), this is atype. # j_type : (nframes, nloc, nnei) j_type = extended_atype[ - paddle.arange(extended_atype.size(0), device=extended_coord.place)[ # pylint: disable=no-explicit-dtype + paddle.arange(extended_atype.shape[0]).to(device=extended_coord.place)[ # pylint: disable=no-explicit-dtype :, None, None ], masked_nlist, @@ -339,12 +339,12 @@ def _pair_tabulated_inter( # if nnei of atom 0 has -1 in the nlist, uu would be 0. # this is to handle the nlist where the mask is set to 0, so that we don't raise exception for those atoms. - uu = paddle.where(nlist != -1, uu, nspline + 1) + uu = paddle.where(nlist != -1, uu, float(nspline + 1)) if paddle.any(uu < 0): raise Exception("coord go beyond table lower boundary") - idx = uu.to(paddle.int) + idx = uu.to(paddle.int32) uu -= idx @@ -425,7 +425,7 @@ def _extract_spline_coefficient( expanded_i_type = i_type.unsqueeze(-1).expand([-1, -1, j_type.shape[-1]]) # handle the case where idx is beyond the number of splines - clipped_indices = paddle.clamp(idx, 0, nspline - 1).to(paddle.int64) + clipped_indices = paddle.clip(idx, 0, nspline - 1).to(paddle.int64) nframes = i_type.shape[0] nloc = i_type.shape[1] @@ -468,8 +468,12 @@ def _calculate_ener(coef: paddle.Tensor, uu: paddle.Tensor) -> paddle.Tensor: The atomic energy for all local atoms for all frames. (nframes, nloc, nnei) """ a3, a2, a1, a0 = paddle.unbind(coef, axis=-1) - etmp = (a3 * uu + a2) * uu + a1 # this should be elementwise operations. - ener = etmp * uu + a0 # this energy has the extrapolated value when rcut > rmax + etmp = (a3 * uu.astype(coef.dtype) + a2) * uu.astype( + coef.dtype + ) + a1 # this should be elementwise operations. + ener = ( + etmp * uu.astype(coef.dtype) + a0 + ) # this energy has the extrapolated value when rcut > rmax return ener def get_dim_fparam(self) -> int: diff --git a/deepmd/pd/model/atomic_model/polar_atomic_model.py b/deepmd/pd/model/atomic_model/polar_atomic_model.py index af20ed28d8..d3687c469b 100644 --- a/deepmd/pd/model/atomic_model/polar_atomic_model.py +++ b/deepmd/pd/model/atomic_model/polar_atomic_model.py @@ -56,7 +56,7 @@ def apply_out_stat( ) eye = paddle.eye(3, dtype=dtype).to(device=device) - eye = eye.repeat(nframes, nloc, 1, 1) + eye = eye.tile([nframes, nloc, 1, 1]) # (nframes, nloc, 3, 3) modified_bias = modified_bias.unsqueeze(-1) * eye diff --git a/deepmd/pd/model/descriptor/descriptor.py b/deepmd/pd/model/descriptor/descriptor.py index 63607ec946..846046ee85 100644 --- a/deepmd/pd/model/descriptor/descriptor.py +++ b/deepmd/pd/model/descriptor/descriptor.py @@ -160,8 +160,8 @@ def share_params(self, base_class, shared_level, resume=False): self.stddev = base_class.stddev # self.set_state_dict(base_class.state_dict()) # this does not work, because it only inits the model # the following will successfully link all the params except buffers - for item in self._modules: - self._modules[item] = base_class._modules[item] + for item in self._sub_layers: + self._sub_layers[item] = base_class._sub_layers[item] else: raise NotImplementedError diff --git a/deepmd/pd/model/descriptor/dpa1.py b/deepmd/pd/model/descriptor/dpa1.py index 658ca15abd..9a4715c271 100644 --- a/deepmd/pd/model/descriptor/dpa1.py +++ b/deepmd/pd/model/descriptor/dpa1.py @@ -385,12 +385,16 @@ def share_params(self, base_class, shared_level, resume=False): # shared_level: 0 # share all parameters in both type_embedding and se_atten if shared_level == 0: - self._modules["type_embedding"] = base_class._modules["type_embedding"] + self._sub_layers["type_embedding"] = base_class._sub_layers[ + "type_embedding" + ] self.se_atten.share_params(base_class.se_atten, 0, resume=resume) # shared_level: 1 # share all parameters in type_embedding elif shared_level == 1: - self._modules["type_embedding"] = base_class._modules["type_embedding"] + self._sub_layers["type_embedding"] = base_class._sub_layers[ + "type_embedding" + ] # Other shared levels else: raise NotImplementedError diff --git a/deepmd/pd/model/descriptor/dpa2.py b/deepmd/pd/model/descriptor/dpa2.py index 63c87fcf83..73f1654bb1 100644 --- a/deepmd/pd/model/descriptor/dpa2.py +++ b/deepmd/pd/model/descriptor/dpa2.py @@ -28,6 +28,7 @@ TypeEmbedNetConsistent, ) from deepmd.pd.utils import ( + aux, env, ) from deepmd.pd.utils.nlist import ( @@ -340,29 +341,37 @@ def share_params(self, base_class, shared_level, resume=False): # shared_level: 0 # share all parameters in type_embedding, repinit and repformers if shared_level == 0: - self._modules["type_embedding"] = base_class._modules["type_embedding"] + self._sub_layers["type_embedding"] = base_class._sub_layers[ + "type_embedding" + ] self.repinit.share_params(base_class.repinit, 0, resume=resume) - self._modules["g1_shape_tranform"] = base_class._modules[ + self._sub_layers["g1_shape_tranform"] = base_class._sub_layers[ "g1_shape_tranform" ] self.repformers.share_params(base_class.repformers, 0, resume=resume) # shared_level: 1 # share all parameters in type_embedding and repinit elif shared_level == 1: - self._modules["type_embedding"] = base_class._modules["type_embedding"] + self._sub_layers["type_embedding"] = base_class._sub_layers[ + "type_embedding" + ] self.repinit.share_params(base_class.repinit, 0, resume=resume) # shared_level: 2 # share all parameters in type_embedding and repformers elif shared_level == 2: - self._modules["type_embedding"] = base_class._modules["type_embedding"] - self._modules["g1_shape_tranform"] = base_class._modules[ + self._sub_layers["type_embedding"] = base_class._sub_layers[ + "type_embedding" + ] + self._sub_layers["g1_shape_tranform"] = base_class._sub_layers[ "g1_shape_tranform" ] self.repformers.share_params(base_class.repformers, 0, resume=resume) # shared_level: 3 # share all parameters in type_embedding elif shared_level == 3: - self._modules["type_embedding"] = base_class._modules["type_embedding"] + self._sub_layers["type_embedding"] = base_class._sub_layers[ + "type_embedding" + ] # Other shared levels else: raise NotImplementedError @@ -546,7 +555,7 @@ def deserialize(cls, data: dict) -> "DescrptDPA2": obj.g1_shape_tranform = MLPLayer.deserialize(g1_shape_tranform) def t_cvt(xx): - return paddle.to_tensor(xx, dtype=obj.repinit.prec, device=env.DEVICE) + return paddle.to_tensor(xx, dtype=obj.repinit.prec, place=env.DEVICE) # deserialize repinit statistic_repinit = repinit_variable.pop("@variables") @@ -650,7 +659,7 @@ def forward( .unsqueeze(-1) .expand([-1, -1, g1.shape[-1]]) ) - g1_ext = paddle.gather(g1, 1, mapping_ext) + g1_ext = aux.take_along_axis(g1, mapping_ext, 1) g1 = g1_ext # repformer g1, g2, h2, rot_mat, sw = self.repformers( diff --git a/deepmd/pd/model/descriptor/gaussian_lcc.py b/deepmd/pd/model/descriptor/gaussian_lcc.py index 6ec2f214a1..2714b663e9 100644 --- a/deepmd/pd/model/descriptor/gaussian_lcc.py +++ b/deepmd/pd/model/descriptor/gaussian_lcc.py @@ -239,16 +239,16 @@ def forward( attn_mask.reshape( [nframes * nloc, 1 + self.nnei, 1 + self.nnei, self.attention_heads] ) - .permute(0, 3, 1, 2) + .transpose([0, 3, 1, 2]) .contiguous() ) # Atomic feature # [(nframes x nloc) x (1 + nnei2) x tebd_dim] - atom_feature = paddle.gather( + atom_feature = aux.take_along_axis( atype_tebd, axis=1, - index=nlist_loc2.reshape([nframes, -1]) + indices=nlist_loc2.reshape([nframes, -1]) .unsqueeze(-1) .expand([-1, -1, self.embed_dim]), ).reshape([nframes * nloc, 1 + self.nnei, self.embed_dim]) @@ -257,10 +257,10 @@ def forward( if first_dim == nframes * nloc: atom_feature += seq_input elif first_dim == nframes: - atom_feature_seq = paddle.gather( + atom_feature_seq = aux.take_along_axis( seq_input, axis=1, - index=nlist_loc2.reshape([nframes, -1]) + indices=nlist_loc2.reshape([nframes, -1]) .unsqueeze(-1) .expand([-1, -1, self.embed_dim]), ).reshape([nframes * nloc, 1 + self.nnei, self.embed_dim]) diff --git a/deepmd/pd/model/descriptor/repformer_layer.py b/deepmd/pd/model/descriptor/repformer_layer.py index 519629c8f3..2ef2edbeeb 100644 --- a/deepmd/pd/model/descriptor/repformer_layer.py +++ b/deepmd/pd/model/descriptor/repformer_layer.py @@ -22,6 +22,7 @@ MLPLayer, ) from deepmd.pd.utils import ( + aux, env, ) from deepmd.pd.utils.env import ( @@ -110,7 +111,7 @@ def _make_nei_g1( # index: nb x (nloc x nnei) x ng1 index = nlist.reshape([nb, nloc * nnei]).unsqueeze(-1).expand([-1, -1, ng1]) # gg1 : nb x (nloc x nnei) x ng1 - gg1 = paddle.gather(g1_ext, axis=1, index=index) + gg1 = aux.take_along_axis(g1_ext, axis=1, indices=index) # gg1 : nb x nloc x nnei x ng1 gg1 = gg1.reshape([nb, nloc, nnei, ng1]) return gg1 @@ -198,15 +199,15 @@ def forward( # nb x nloc x nnei x nd x (nh x 2) g2qk = self.mapqk(g2).reshape([nb, nloc, nnei, nd, nh * 2]) # nb x nloc x (nh x 2) x nnei x nd - g2qk = paddle.permute(g2qk, (0, 1, 4, 2, 3)) + g2qk = paddle.transpose(g2qk, (0, 1, 4, 2, 3)) # nb x nloc x nh x nnei x nd - g2q, g2k = paddle.split(g2qk, nh, axis=2) + g2q, g2k = paddle.split(g2qk, aux.sec(g2qk.shape[2], nh), axis=2) # g2q = paddle.nn.functional.normalize(g2q, axis=-1) # g2k = paddle.nn.functional.normalize(g2k, axis=-1) # nb x nloc x nh x nnei x nnei - attnw = paddle.matmul(g2q, paddle.transpose(g2k, -1, -2)) / nd**0.5 + attnw = paddle.matmul(g2q, paddle.transpose(g2k, [0, 1, 2, 4, 3])) / nd**0.5 if self.has_gate: - gate = paddle.matmul(h2, paddle.transpose(h2, -1, -2)).unsqueeze(-3) + gate = paddle.matmul(h2, paddle.transpose(h2, [0, 1, 3, 2])).unsqueeze(-3) attnw = attnw * gate # mask the attenmap, nb x nloc x 1 x 1 x nnei attnw_mask = ~nlist_mask.unsqueeze(2).unsqueeze(2) @@ -221,7 +222,7 @@ def forward( attnw_mask, float("-inf"), ) - attnw = paddle.softmax(attnw, axis=-1) + attnw = paddle.nn.functional.softmax(attnw, axis=-1) attnw = attnw.masked_fill( attnw_mask, 0.0, @@ -234,12 +235,12 @@ def forward( if self.smooth: attnw = attnw * sw[:, :, None, :, None] * sw[:, :, None, None, :] # nb x nloc x nnei x nnei - h2h2t = paddle.matmul(h2, paddle.transpose(h2, -1, -2)) / 3.0**0.5 + h2h2t = paddle.matmul(h2, paddle.transpose(h2, [0, 1, 3, 2])) / 3.0**0.5 # nb x nloc x nh x nnei x nnei ret = attnw * h2h2t[:, :, None, :, :] - # ret = paddle.softmax(g2qk, axis=-1) + # ret = paddle.nn.functional.softmax(g2qk, axis=-1) # nb x nloc x nnei x nnei x nh - ret = paddle.permute(ret, (0, 1, 3, 4, 2)) + ret = paddle.transpose(ret, (0, 1, 3, 4, 2)) return ret def serialize(self) -> dict: @@ -317,14 +318,16 @@ def forward( # nf x nloc x nnei x ng2 x nh g2v = self.mapv(g2).reshape([nf, nloc, nnei, ng2, nh]) # nf x nloc x nh x nnei x ng2 - g2v = paddle.permute(g2v, (0, 1, 4, 2, 3)) + g2v = paddle.transpose(g2v, (0, 1, 4, 2, 3)) # g2v = paddle.nn.functional.normalize(g2v, axis=-1) # nf x nloc x nh x nnei x nnei - AA = paddle.permute(AA, (0, 1, 4, 2, 3)) + AA = paddle.transpose(AA, (0, 1, 4, 2, 3)) # nf x nloc x nh x nnei x ng2 ret = paddle.matmul(AA, g2v) # nf x nloc x nnei x ng2 x nh - ret = paddle.permute(ret, (0, 1, 3, 4, 2)).reshape([nf, nloc, nnei, (ng2 * nh)]) + ret = paddle.transpose(ret, (0, 1, 3, 4, 2)).reshape( + [nf, nloc, nnei, (ng2 * nh)] + ) # nf x nloc x nnei x ng2 return self.head_map(ret) @@ -390,14 +393,14 @@ def forward( nf, nloc, nnei, _ = h2.shape nh = self.head_num # nf x nloc x nh x nnei x nnei - AA = paddle.permute(AA, (0, 1, 4, 2, 3)) + AA = paddle.transpose(AA, (0, 1, 4, 2, 3)) h2m = paddle.unsqueeze(h2, axis=2) # nf x nloc x nh x nnei x 3 h2m = paddle.tile(h2m, [1, 1, nh, 1, 1]) # nf x nloc x nh x nnei x 3 ret = paddle.matmul(AA, h2m) # nf x nloc x nnei x 3 x nh - ret = paddle.permute(ret, (0, 1, 3, 4, 2)).reshape([nf, nloc, nnei, 3, nh]) + ret = paddle.transpose(ret, (0, 1, 3, 4, 2)).reshape([nf, nloc, nnei, 3, nh]) # nf x nloc x nnei x 3 return paddle.squeeze(self.head_map(ret), axis=-1) @@ -489,16 +492,17 @@ def forward( # nb x nloc x nd x nh g1q = self.mapq(g1).reshape([nb, nloc, nd, nh]) # nb x nloc x nh x nd - g1q = paddle.permute(g1q, (0, 1, 3, 2)) + g1q = paddle.transpose(g1q, (0, 1, 3, 2)) # nb x nloc x nnei x (nd+ni) x nh gg1kv = self.mapkv(gg1).reshape([nb, nloc, nnei, nd + ni, nh]) - gg1kv = paddle.permute(gg1kv, (0, 1, 4, 2, 3)) + gg1kv = paddle.transpose(gg1kv, (0, 1, 4, 2, 3)) # nb x nloc x nh x nnei x nd, nb x nloc x nh x nnei x ng1 gg1k, gg1v = paddle.split(gg1kv, [nd, ni], axis=-1) # nb x nloc x nh x 1 x nnei attnw = ( - paddle.matmul(g1q.unsqueeze(-2), paddle.transpose(gg1k, -1, -2)) / nd**0.5 + paddle.matmul(g1q.unsqueeze(-2), paddle.transpose(gg1k, [0, 1, 2, 4, 3])) + / nd**0.5 ) # nb x nloc x nh x nnei attnw = attnw.squeeze(-2) @@ -512,7 +516,7 @@ def forward( attnw_mask, float("-inf"), ) - attnw = paddle.softmax(attnw, axis=-1) + attnw = paddle.nn.functional.softmax(attnw, axis=-1) attnw = attnw.masked_fill( attnw_mask, 0.0, @@ -858,9 +862,9 @@ def _update_g1_conv( if not self.smooth: # normalized by number of neighbors, not smooth # nb x nloc x 1 - # must use type_as here to convert bool to float, otherwise there will be numerical difference from numpy + # must use astype here to convert bool to float, otherwise there will be numerical difference from numpy invnnei = 1.0 / ( - self.epsilon + paddle.sum(nlist_mask.type_as(gg1), axis=-1) + self.epsilon + paddle.sum(nlist_mask.astype(gg1.dtype), axis=-1) ).unsqueeze(-1) else: gg1 = _apply_switch(gg1, sw) @@ -913,8 +917,8 @@ def _cal_hg( g2 = _apply_nlist_mask(g2, nlist_mask) if not smooth: # nb x nloc - # must use type_as here to convert bool to float, otherwise there will be numerical difference from numpy - invnnei = 1.0 / (epsilon + paddle.sum(nlist_mask.type_as(g2), axis=-1)) + # must use astype here to convert bool to float, otherwise there will be numerical difference from numpy + invnnei = 1.0 / (epsilon + paddle.sum(nlist_mask.astype(g2.dtype), axis=-1)) # nb x nloc x 1 x 1 invnnei = invnnei.unsqueeze(-1).unsqueeze(-1) else: @@ -923,7 +927,7 @@ def _cal_hg( (nb, nloc, 1, 1), dtype=g2.dtype ).to(device=g2.place) # nb x nloc x 3 x ng2 - h2g2 = paddle.matmul(paddle.transpose(h2, -1, -2), g2) * invnnei + h2g2 = paddle.matmul(paddle.transpose(h2, [0, 1, 3, 2]), g2) * invnnei return h2g2 @staticmethod @@ -946,9 +950,9 @@ def _cal_grrg(h2g2: paddle.Tensor, axis_neuron: int) -> paddle.Tensor: # nb x nloc x 3 x ng2 nb, nloc, _, ng2 = h2g2.shape # nb x nloc x 3 x axis - h2g2m = paddle.split(h2g2, axis_neuron, axis=-1)[0] + h2g2m = paddle.split(h2g2, aux.sec(h2g2.shape[-1], axis_neuron), axis=-1)[0] # nb x nloc x axis x ng2 - g1_13 = paddle.matmul(paddle.transpose(h2g2m, -1, -2), h2g2) / (3.0**1) + g1_13 = paddle.matmul(paddle.transpose(h2g2m, [0, 1, 3, 2]), h2g2) / (3.0**1) # nb x nloc x (axisxng2) g1_13 = g1_13.reshape([nb, nloc, axis_neuron * ng2]) return g1_13 @@ -1063,8 +1067,8 @@ def forward( nb, nloc, nnei, _ = g2.shape nall = g1_ext.shape[1] g1, _ = paddle.split(g1_ext, [nloc, nall - nloc], axis=1) - assert (nb, nloc) == g1.shape[:2] - assert (nb, nloc, nnei) == h2.shape[:3] + assert [nb, nloc] == g1.shape[:2] + assert [nb, nloc, nnei] == h2.shape[:3] g2_update: List[paddle.Tensor] = [g2] h2_update: List[paddle.Tensor] = [h2] diff --git a/deepmd/pd/model/descriptor/repformer_layer_old_impl.py b/deepmd/pd/model/descriptor/repformer_layer_old_impl.py index 179c7999da..3b132fdc57 100644 --- a/deepmd/pd/model/descriptor/repformer_layer_old_impl.py +++ b/deepmd/pd/model/descriptor/repformer_layer_old_impl.py @@ -107,13 +107,13 @@ def forward( # nb x nloc x (nh x 2) x nnei x nd g2qk = paddle.transpose(g2qk, (0, 1, 4, 2, 3)) # nb x nloc x nh x nnei x nd - g2q, g2k = paddle.split(g2qk, g2qk.shape[2] // nh, axis=2) + g2q, g2k = paddle.split(g2qk, aux.sec(g2qk.shape[2], nh), axis=2) # g2q = paddle.nn.functional.normalize(g2q, axis=-1) # g2k = paddle.nn.functional.normalize(g2k, axis=-1) # nb x nloc x nh x nnei x nnei - attnw = paddle.matmul(g2q, paddle.transpose(g2k, -1, -2)) / nd**0.5 + attnw = paddle.matmul(g2q, paddle.transpose(g2k, [0, 1, 2, 4, 3])) / nd**0.5 if self.has_gate: - gate = paddle.matmul(h2, paddle.transpose(h2, -1, -2)).unsqueeze(-3) + gate = paddle.matmul(h2, paddle.transpose(h2, [0, 1, 3, 2])).unsqueeze(-3) attnw = attnw * gate # mask the attenmap, nb x nloc x 1 x 1 x nnei attnw_mask = ~nlist_mask.unsqueeze(2).unsqueeze(2) @@ -128,7 +128,7 @@ def forward( attnw_mask, float("-inf"), ) - attnw = paddle.softmax(attnw, axis=-1) + attnw = paddle.nn.functional.softmax(attnw, axis=-1) attnw = attnw.masked_fill( attnw_mask, 0.0, @@ -141,10 +141,10 @@ def forward( if self.smooth: attnw = attnw * sw[:, :, None, :, None] * sw[:, :, None, None, :] # nb x nloc x nnei x nnei - h2h2t = paddle.matmul(h2, paddle.transpose(h2, -1, -2)) / 3.0**0.5 + h2h2t = paddle.matmul(h2, paddle.transpose(h2, [0, 1, 3, 2])) / 3.0**0.5 # nb x nloc x nh x nnei x nnei ret = attnw * h2h2t[:, :, None, :, :] - # ret = paddle.softmax(g2qk, axis=-1) + # ret = paddle.nn.functional.softmax(g2qk, axis=-1) # nb x nloc x nnei x nnei x nh ret = paddle.transpose(ret, (0, 1, 3, 4, 2)) return ret @@ -259,7 +259,8 @@ def forward( # nb x nloc x nh x 1 x nnei attnw = ( - paddle.matmul(g1q.unsqueeze(-2), paddle.transpose(gg1k, -1, -2)) / nd**0.5 + paddle.matmul(g1q.unsqueeze(-2), paddle.transpose(gg1k, [0, 1, 2, 4, 3])) + / nd**0.5 ) # nb x nloc x nh x nnei attnw: paddle.Tensor = attnw.squeeze(-2) @@ -273,7 +274,7 @@ def forward( attnw_mask, float("-inf"), ) - attnw = paddle.softmax(attnw, axis=-1) + attnw = paddle.nn.functional.softmax(attnw, axis=-1) attnw = attnw.masked_fill( attnw_mask, 0.0, @@ -330,7 +331,7 @@ def __init__( sel = [sel] if isinstance(sel, int) else sel self.nnei = sum(sel) assert len(sel) == 1 - self.sel = paddle.to_tensor(sel, device=env.DEVICE) # pylint: disable=no-explicit-dtype + self.sel = paddle.to_tensor(sel, place=env.DEVICE) # pylint: disable=no-explicit-dtype self.sec = self.sel self.axis_neuron = axis_neuron self.set_davg_zero = set_davg_zero @@ -376,10 +377,7 @@ def __init__( self.attn2_mh_apply = Atten2MultiHeadApply(g2_dim, attn2_nhead) self.attn2_lm = paddle.nn.LayerNorm( g2_dim, - elementwise_affine=True, - device=env.DEVICE, - dtype=env.GLOBAL_PD_FLOAT_PRECISION, - ) + ).to(device=env.DEVICE) if self.update_h2: self.attn2h_map = Atten2Map( g2_dim, attn2_hidden, attn2_nhead, attn2_has_gate, self.smooth @@ -447,7 +445,7 @@ def _update_g1_conv( # normalized by number of neighbors, not smooth # nb x nloc x 1 invnnei = 1.0 / ( - self.epsilon + paddle.sum(nlist_mask.type_as(gg1), axis=-1) + self.epsilon + paddle.sum(nlist_mask.astype(gg1.dtype), axis=-1) ).unsqueeze(-1) else: gg1 = _apply_switch(gg1, sw) @@ -474,7 +472,9 @@ def _cal_h2g2( g2 = _apply_nlist_mask(g2, nlist_mask) if not self.smooth: # nb x nloc - invnnei = 1.0 / (self.epsilon + paddle.sum(nlist_mask.type_as(g2), axis=-1)) + invnnei = 1.0 / ( + self.epsilon + paddle.sum(nlist_mask.astype(g2.dtype), axis=-1) + ) # nb x nloc x 1 x 1 invnnei = invnnei.unsqueeze(-1).unsqueeze(-1) else: @@ -483,16 +483,18 @@ def _cal_h2g2( (nb, nloc, 1, 1), dtype=env.GLOBAL_PD_FLOAT_PRECISION ).to(device=g2.place) # nb x nloc x 3 x ng2 - h2g2 = paddle.matmul(paddle.transpose(h2, -1, -2), g2) * invnnei + h2g2 = paddle.matmul(paddle.transpose(h2, [0, 1, 3, 2]), g2) * invnnei return h2g2 def _cal_grrg(self, h2g2: paddle.Tensor) -> paddle.Tensor: # nb x nloc x 3 x ng2 nb, nloc, _, ng2 = h2g2.shape # nb x nloc x 3 x axis - h2g2m = paddle.split(h2g2, h2g2.shape[-1] // self.axis_neuron, axis=-1)[0] + h2g2m = paddle.split(h2g2, aux.sec(h2g2.shape[-1], self.axis_neuron), axis=-1)[ + 0 + ] # nb x nloc x axis x ng2 - g1_13 = paddle.matmul(paddle.transpose(h2g2m, -1, -2), h2g2) / (3.0**1) + g1_13 = paddle.matmul(paddle.transpose(h2g2m, [0, 1, 3, 2]), h2g2) / (3.0**1) # nb x nloc x (axisxng2) g1_13 = g1_13.reshape([nb, nloc, self.axis_neuron * ng2]) return g1_13 @@ -631,8 +633,8 @@ def forward( nb, nloc, nnei, _ = g2.shape nall = g1_ext.shape[1] g1, _ = paddle.split(g1_ext, [nloc, nall - nloc], axis=1) - assert (nb, nloc) == g1.shape[:2] - assert (nb, nloc, nnei) == h2.shape[:3] + assert [nb, nloc] == g1.shape[:2] + assert [nb, nloc, nnei] == h2.shape[:3] ng1 = g1.shape[-1] ng2 = g2.shape[-1] nh2 = h2.shape[-1] @@ -744,12 +746,11 @@ def _bn_layer( self, nf: int = 1, ) -> Callable: - return paddle.nn.BatchNorm1d( + return paddle.nn.BatchNorm1D( nf, - eps=1e-5, + epsilon=1e-5, momentum=self.bn_momentum, - affine=False, - track_running_stats=True, - device=env.DEVICE, - dtype=env.GLOBAL_PD_FLOAT_PRECISION, - ) + weight_attr=False, + bias_attr=False, + use_global_stats=False, + ).to(device=env.DEVICE) diff --git a/deepmd/pd/model/descriptor/repformers.py b/deepmd/pd/model/descriptor/repformers.py index c0c617e5cb..31fd6e7b83 100644 --- a/deepmd/pd/model/descriptor/repformers.py +++ b/deepmd/pd/model/descriptor/repformers.py @@ -469,7 +469,7 @@ def forward( # g1_ext: nb x nall x ng1 if comm_dict is None: assert mapping is not None - g1_ext = paddle.gather(g1, 1, mapping) + g1_ext = aux.take_along_axis(g1, axis=1, indices=mapping) else: n_padding = nall - nloc g1 = paddle.nn.functional.pad( diff --git a/deepmd/pd/model/descriptor/se_r.py b/deepmd/pd/model/descriptor/se_r.py index 1030d564d4..e54cb37693 100644 --- a/deepmd/pd/model/descriptor/se_r.py +++ b/deepmd/pd/model/descriptor/se_r.py @@ -97,9 +97,9 @@ def __init__( self.env_protection = env_protection self.sel = sel - self.sec = paddle.to_tensor(np.append([0], np.cumsum(self.sel)), dtype=int).to( - device=env.DEVICE - ) + self.sec = paddle.to_tensor( + np.append([0], np.cumsum(self.sel)), dtype="int64" + ).to(device=env.DEVICE) self.split_sel = self.sel self.nnei = sum(sel) self.ndescrpt = self.nnei * 1 @@ -113,9 +113,9 @@ def __init__( self.filter_layers = None filter_layers = NetworkCollection( - naxis=1, ntypes=len(sel), network_type="embedding_network" + ndim=1, ntypes=len(sel), network_type="embedding_network" ) - # TODO: naxis=2 if type_one_side=False + # TODO: ndim=2 if type_one_side=False for ii in range(self.ntypes): filter_layers[(ii,)] = EmbeddingNet( 1, @@ -222,8 +222,8 @@ def share_params(self, base_class, shared_level, resume=False): self.stddev = base_class.stddev # self.set_state_dict(base_class.state_dict()) # this does not work, because it only inits the model # the following will successfully link all the params except buffers - for item in self._modules: - self._modules[item] = base_class._modules[item] + for item in self._sub_layers: + self._sub_layers[item] = base_class._sub_layers[item] # Other shared levels else: raise NotImplementedError diff --git a/deepmd/pd/model/descriptor/se_t_tebd.py b/deepmd/pd/model/descriptor/se_t_tebd.py index b5077acfe0..314d73b15d 100644 --- a/deepmd/pd/model/descriptor/se_t_tebd.py +++ b/deepmd/pd/model/descriptor/se_t_tebd.py @@ -255,12 +255,16 @@ def share_params(self, base_class, shared_level, resume=False): # shared_level: 0 # share all parameters in both type_embedding and se_ttebd if shared_level == 0: - self._modules["type_embedding"] = base_class._modules["type_embedding"] + self._sub_layers["type_embedding"] = base_class._sub_layers[ + "type_embedding" + ] self.se_ttebd.share_params(base_class.se_ttebd, 0, resume=resume) # shared_level: 1 # share all parameters in type_embedding elif shared_level == 1: - self._modules["type_embedding"] = base_class._modules["type_embedding"] + self._sub_layers["type_embedding"] = base_class._sub_layers[ + "type_embedding" + ] # Other shared levels else: raise NotImplementedError diff --git a/deepmd/pd/model/model/frozen.py b/deepmd/pd/model/model/frozen.py index a2886b7e22..78254bce1f 100644 --- a/deepmd/pd/model/model/frozen.py +++ b/deepmd/pd/model/model/frozen.py @@ -37,8 +37,8 @@ class FrozenModel(BaseModel): def __init__(self, model_file: str, **kwargs): super().__init__(**kwargs) self.model_file = model_file - if model_file.endswith(".pdparams"): - self.model = paddle.jit.load(model_file) + if model_file.endswith(".pdmodel"): + self.model = paddle.jit.load(model_file[:-8]) else: # try to convert from other formats with tempfile.NamedTemporaryFile(suffix=".pdparams") as f: diff --git a/deepmd/pd/model/model/make_model.py b/deepmd/pd/model/model/make_model.py index 3eecced89c..2747bbc480 100644 --- a/deepmd/pd/model/model/make_model.py +++ b/deepmd/pd/model/model/make_model.py @@ -437,15 +437,18 @@ def _format_nlist( coord0 = extended_coord[:, :n_nloc, :] # nf x (nloc x nnei) x 3 index = nlist.reshape([n_nf, n_nloc * n_nnei, 1]).expand([-1, -1, 3]) - coord1 = paddle.gather(extended_coord, 1, index) + coord1 = aux.take_along_axis(extended_coord, axis=1, indices=index) # nf x nloc x nnei x 3 coord1 = coord1.reshape([n_nf, n_nloc, n_nnei, 3]) # nf x nloc x nnei # rr = paddle.linalg.norm(coord0[:, :, None, :] - coord1, axis=-1) rr = aux.norm(coord0[:, :, None, :] - coord1, axis=-1) rr = paddle.where(m_real_nei, rr, float("inf")) - rr, nlist_mapping = paddle.sort(rr, axis=-1) - nlist = paddle.gather(nlist, 2, nlist_mapping) + rr, nlist_mapping = ( + paddle.sort(rr, axis=-1), + paddle.argsort(rr, axis=-1), + ) + nlist = aux.take_along_axis(nlist, axis=2, indices=nlist_mapping) nlist = paddle.where(rr > rcut, -1, nlist) nlist = nlist[..., :nnei] else: # not extra_nlist_sort and n_nnei <= nnei: diff --git a/deepmd/pd/model/model/spin_model.py b/deepmd/pd/model/model/spin_model.py index 32ffff773c..e8fd5161fb 100644 --- a/deepmd/pd/model/model/spin_model.py +++ b/deepmd/pd/model/model/spin_model.py @@ -17,6 +17,9 @@ from deepmd.pd.model.atomic_model import ( DPAtomicModel, ) +from deepmd.pd.utils import ( + aux, +) from deepmd.pd.utils.utils import ( to_paddle_tensor, ) @@ -202,8 +205,10 @@ def extend_nlist(extended_atype, nlist): # update the index for switch first_part_index = (nloc <= extended_nlist) & (extended_nlist < nall) second_part_index = (nall <= extended_nlist) & (extended_nlist < (nall + nloc)) - extended_nlist[first_part_index] += nloc - extended_nlist[second_part_index] -= nall - nloc + # extended_nlist[first_part_index] += nloc + extended_nlist = aux.masked_add_(extended_nlist, first_part_index, nloc) + # extended_nlist[second_part_index] -= nall - nloc + entended_nlist = aux.masked_add_(extended_nlist, second_part_index, nloc - nall) return extended_nlist @staticmethod @@ -359,10 +364,12 @@ def __getattr__(self, name): """Get attribute from the wrapped model.""" if ( name == "backbone_model" - ): # paddle.nn.Layer will exclude modules to self.__dict__["_modules"] - return self.__dict__["_modules"]["backbone_model"] + ): # paddle.nn.Layer will exclude modules to self.__dict__["_sub_layers"] + return self.__dict__["_sub_layers"]["backbone_model"] elif name in self.__dict__: return self.__dict__[name] + elif name in self._buffers: + return self._buffers[name] else: return getattr(self.backbone_model, name) diff --git a/deepmd/pd/model/model/transform_output.py b/deepmd/pd/model/model/transform_output.py index 8bdb3661fb..46c3b05a60 100644 --- a/deepmd/pd/model/model/transform_output.py +++ b/deepmd/pd/model/model/transform_output.py @@ -1,8 +1,6 @@ # SPDX-License-Identifier: LGPL-3.0-or-later from typing import ( Dict, - List, - Optional, ) import paddle @@ -32,11 +30,11 @@ def atomic_virial_corr( ce = coord * atom_energy sumce0, sumce1, sumce2 = paddle.split(paddle.sum(ce, axis=1), [1, 1, 1], axis=-1) faked_grad = paddle.ones_like(sumce0) - lst = paddle.jit.annotate(List[Optional[paddle.Tensor]], [faked_grad]) + # lst = paddle.jit.annotate(List[Optional[paddle.Tensor]], [faked_grad]) extended_virial_corr0 = paddle.autograd.grad( [sumce0], [extended_coord], - grad_outputs=lst, + # grad_outputs=lst, create_graph=False, retain_graph=True, )[0] @@ -44,7 +42,7 @@ def atomic_virial_corr( extended_virial_corr1 = paddle.autograd.grad( [sumce1], [extended_coord], - grad_outputs=lst, + # grad_outputs=lst, create_graph=False, retain_graph=True, )[0] @@ -52,7 +50,7 @@ def atomic_virial_corr( extended_virial_corr2 = paddle.autograd.grad( [sumce2], [extended_coord], - grad_outputs=lst, + # grad_outputs=lst, create_graph=False, retain_graph=True, )[0] diff --git a/deepmd/pd/model/network/layernorm.py b/deepmd/pd/model/network/layernorm.py index 64d039b0bc..dc7d946561 100644 --- a/deepmd/pd/model/network/layernorm.py +++ b/deepmd/pd/model/network/layernorm.py @@ -151,11 +151,14 @@ def deserialize(cls, data: dict) -> "LayerNorm": prec = PRECISION_DICT[obj.precision] def check_load_param(ss): - return ( - nn.Parameter(data=to_paddle_tensor(nl[ss])) - if nl[ss] is not None - else None - ) + if nl[ss] is not None: + tensor = to_paddle_tensor(nl[ss]) + return paddle.create_parameter( + tensor.shape, + dtype=tensor.dtype, + default_initializer=nn.initializer.Assign(tensor), + ) + return None obj.matrix = check_load_param("matrix") obj.bias = check_load_param("bias") diff --git a/deepmd/pd/model/network/mlp.py b/deepmd/pd/model/network/mlp.py index d7ace02b13..c7ba62f402 100644 --- a/deepmd/pd/model/network/mlp.py +++ b/deepmd/pd/model/network/mlp.py @@ -291,7 +291,7 @@ def check_load_param(ss): return paddle.create_parameter( tensor.shape, dtype=tensor.dtype, - default_initializer=paddle.nn.initializer.Assign(tensor), + default_initializer=nn.initializer.Assign(tensor), ) return None diff --git a/deepmd/pd/model/network/network.py b/deepmd/pd/model/network/network.py index 7148090e50..888932eec8 100644 --- a/deepmd/pd/model/network/network.py +++ b/deepmd/pd/model/network/network.py @@ -50,7 +50,9 @@ def Tensor(*shape): - return paddle.empty(shape, dtype=env.GLOBAL_PD_FLOAT_PRECISION, device=env.DEVICE) + return paddle.empty(shape, dtype=env.GLOBAL_PD_FLOAT_PRECISION).to( + device=env.DEVICE + ) class Dropout(nn.Layer): @@ -60,7 +62,7 @@ def __init__(self, p): def forward(self, x, inplace: bool = False): if self.p > 0 and self.training: - return F.dropout(x, p=self.p, training=True, inplace=inplace) + return F.dropout(x, p=self.p, training=True) else: return x @@ -337,15 +339,30 @@ def __init__( self.use_timestep = use_timestep self.activate = ActivationFn(activate) - self.matrix = self.create_parameter(data=Tensor(num_in, num_out)) + t = Tensor(num_in, num_out) + self.matrix = self.create_parameter( + [num_in, num_out], + dtype=t.dtype, + default_initializer=nn.initializer.Assign(t), + ) init.normal_(self.matrix.data, std=stddev / np.sqrt(num_out + num_in)) if bias: - self.bias = self.create_parameter(data=Tensor(1, num_out)) + t = Tensor(1, num_out) + self.bias = self.create_parameter( + (1, num_out), + dtype=t.dtype, + default_initializer=nn.initializer.Assign(t), + ) init.normal_(self.bias.data, mean=bavg, std=stddev) else: self.bias = None if self.use_timestep: - self.idt = self.create_parameter(data=Tensor(1, num_out)) + t = Tensor(1, num_out) + self.idt = self.create_parameter( + (1, num_out), + dtype=t.dtype, + default_initializer=nn.initializer.Assign(t), + ) init.normal_(self.idt.data, mean=0.1, std=0.001) def forward(self, inputs): @@ -505,15 +522,15 @@ def __init__(self, embed_dim, output_dim, activation_fn, weight=None): super().__init__() self.dense = SimpleLinear(embed_dim, embed_dim) self.activation_fn = ActivationFn(activation_fn) - self.layer_norm = nn.LayerNorm(embed_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION) + self.layer_norm = nn.LayerNorm(embed_dim) if weight is None: - weight = nn.Linear( - embed_dim, output_dim, bias=False, dtype=env.GLOBAL_PD_FLOAT_PRECISION - ).weight + weight = nn.Linear(embed_dim, output_dim, bias_attr=False).weight self.weight = weight self.bias = self.create_parameter( - paddle.zeros([output_dim], dtype=env.GLOBAL_PD_FLOAT_PRECISION) # pylint: disable=no-explicit-dtype,no-explicit-device + [output_dim], + dtype=env.GLOBAL_PD_FLOAT_PRECISION, + default_initializer=nn.initializer.Constant(0), # pylint: disable=no-explicit-dtype,no-explicit-device ) def forward( @@ -643,8 +660,8 @@ def share_params(self, base_class, shared_level, resume=False): ), "Only TypeEmbedNet of the same type can share params!" if shared_level == 0: # the following will successfully link all the params except buffers, which need manually link. - for item in self._modules: - self._modules[item] = base_class._modules[item] + for item in self._sub_layers: + self._sub_layers[item] = base_class._sub_layers[item] else: raise NotImplementedError @@ -781,8 +798,8 @@ def change_type_map( not do_resnet or self.activation_function == "Linear" ), "'activation_function' must be 'Linear' when performing type changing on resnet structure!" first_layer_matrix = self.embedding_net.layers[0].matrix.data - eye_vector = paddle.eye( - self.ntypes, dtype=self.prec, device=first_layer_matrix.place + eye_vector = paddle.eye(self.ntypes, dtype=self.prec).to( + device=first_layer_matrix.place ) # preprocess for resnet connection if self.neuron[0] == self.ntypes: @@ -794,17 +811,16 @@ def change_type_map( if has_new_type: extend_type_params = paddle.rand( [len(type_map), first_layer_matrix.shape[-1]], - device=first_layer_matrix.place, dtype=first_layer_matrix.dtype, - ) + ).to(device=first_layer_matrix.place) first_layer_matrix = paddle.concat( [first_layer_matrix, extend_type_params], axis=0 ) first_layer_matrix = first_layer_matrix[remap_index] new_ntypes = len(type_map) - eye_vector = paddle.eye( - new_ntypes, dtype=self.prec, device=first_layer_matrix.place + eye_vector = paddle.eye(new_ntypes, dtype=self.prec).to( + device=first_layer_matrix.place ) if self.neuron[0] == new_ntypes: @@ -814,7 +830,9 @@ def change_type_map( self.embedding_net.layers[0].num_in = new_ntypes self.embedding_net.layers[0].matrix = self.create_parameter( - data=first_layer_matrix + first_layer_matrix.shape, + dtype=first_layer_matrix.dtype, + default_initializer=nn.initializer.Assign(first_layer_matrix), ) else: econf_tebd, embed_input_dim = get_econf_tebd( @@ -1101,21 +1119,13 @@ def __init__( temperature=temperature, smooth=smooth, ) - self.attn_layer_norm = nn.LayerNorm( - self.embed_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION, device=env.place - ) + self.attn_layer_norm = nn.LayerNorm(self.embed_dim).to(device=env.DEVICE) if self.ffn: self.ffn_embed_dim = ffn_embed_dim - self.fc1 = nn.Linear( - self.embed_dim, self.ffn_embed_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION - ) + self.fc1 = nn.Linear(self.embed_dim, self.ffn_embed_dim) self.activation_fn = ActivationFn(activation) - self.fc2 = nn.Linear( - self.ffn_embed_dim, self.embed_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION - ) - self.final_layer_norm = nn.LayerNorm( - self.embed_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION - ) + self.fc2 = nn.Linear(self.ffn_embed_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, @@ -1214,7 +1224,7 @@ def forward( k = F.normalize(k, axis=-1) v = F.normalize(v, axis=-1) q = q * self.scaling - k = k.transpose(1, 2) + k = k.transpose([0, 2, 1]) # [nframes * nloc, nnei, nnei] attn_weights = paddle.bmm(q, k) # [nframes * nloc, nnei] @@ -1237,7 +1247,9 @@ def forward( attn_weights = attn_weights * sw[:, :, None] * sw[:, None, :] if self.dotr: assert input_r is not None, "input_r must be provided when dotr is True!" - angular_weight = paddle.bmm(input_r, input_r.transpose(1, 2)) + perm = list(range(input_r.ndim)) + perm[1], perm[2] = perm[2], perm[1] + angular_weight = paddle.bmm(input_r, input_r.transpose(perm)) attn_weights = attn_weights * angular_weight o = paddle.bmm(attn_weights, v) output = self.out_proj(o) @@ -1266,15 +1278,15 @@ def forward( nlist: Optional[paddle.Tensor] = None, return_attn=True, ): - nframes, nloc, feature_dim = query.size() - _, _, nnei = nlist.size() + nframes, nloc, feature_dim = query.shape + _, _, nnei = nlist.shape assert feature_dim == self.feature_dim # [nframes, nloc, feature_dim] q, k, v = self.in_proj(query).chunk(3, axis=-1) # [nframes * attn_head * nloc, 1, head_dim] q = ( q.reshape([nframes, nloc, self.attn_head, self.head_dim]) - .transpose(1, 2) + .transpose([0, 2, 1, 3]) .contiguous() .reshape([nframes * self.attn_head * nloc, 1, self.head_dim]) * self.scaling @@ -1287,9 +1299,9 @@ def forward( # [nframes, nloc * nnei, feature_dim] index = nlist.reshape([nframes, -1]).unsqueeze(-1).expand([-1, -1, feature_dim]) - k = paddle.gather(k, axis=1, index=index) + k = aux.take_along_axis(k, axis=1, indices=index) # [nframes, nloc * nnei, feature_dim] - v = paddle.gather(v, axis=1, index=index) + v = aux.take_along_axis(v, axis=1, indices=index) # [nframes * attn_head * nloc, nnei, head_dim] k = ( k.reshape([nframes, nloc, nnei, self.attn_head, self.head_dim]) @@ -1304,7 +1316,7 @@ def forward( .reshape([nframes * self.attn_head * nloc, nnei, self.head_dim]) ) # [nframes * attn_head * nloc, 1, nnei] - attn_weights = paddle.bmm(q, k.transpose(1, 2)) + attn_weights = paddle.bmm(q, k.transpose([0, 2, 1])) # maskfill # [nframes, attn_head, nloc, nnei] attn_weights = attn_weights.reshape( @@ -1321,11 +1333,11 @@ def forward( # bmm # [nframes * attn_head * nloc, 1, head_dim] o = paddle.bmm(attn, v) - assert list(o.size()) == [nframes * self.attn_head * nloc, 1, self.head_dim] + assert list(o.shape) == [nframes * self.attn_head * nloc, 1, self.head_dim] # [nframes, nloc, feature_dim] o = ( o.reshape([nframes, self.attn_head, nloc, self.head_dim]) - .transpose(1, 2) + .transpose([0, 2, 1, 3]) .contiguous() .reshape([nframes, nloc, self.feature_dim]) ) @@ -1346,8 +1358,8 @@ def __init__( num_head: int, ): super().__init__() - self.layer_norm = nn.LayerNorm(embed_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION) - self.pair_norm = nn.LayerNorm(pair_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION) + self.layer_norm = nn.LayerNorm(embed_dim) + self.pair_norm = nn.LayerNorm(pair_dim) self.embed_dim = embed_dim self.q_proj = Linear(embed_dim, embed_dim, bias=False, init="glorot") self.k_proj = Linear(embed_dim, embed_dim, bias=False, init="glorot") @@ -1369,7 +1381,7 @@ def forward( delta_pos: Tensor, attn_mask: Tensor = None, ) -> Tensor: - ncluster, natoms, _ = query.size() + ncluster, natoms, _ = query.shape query = self.layer_norm(query) # [ncluster, natoms, natoms, pair_dim] pair = self.pair_norm(pair) @@ -1378,22 +1390,22 @@ def forward( q = ( self.q_proj(query) .reshape([ncluster, natoms, self.num_heads, -1]) - .transpose(1, 2) + .transpose([0, 2, 1, 3]) * self.scaling ) # [ncluster, attn_head, natoms, head_dim] k = ( self.k_proj(query) .reshape([ncluster, natoms, self.num_heads, -1]) - .transpose(1, 2) + .transpose([0, 2, 1, 3]) ) v = ( self.v_proj(query) .reshape([ncluster, natoms, self.num_heads, -1]) - .transpose(1, 2) + .transpose([0, 2, 1, 3]) ) # [ncluster, attn_head, natoms, natoms] - attn = q @ k.transpose(-1, -2) + attn = q @ k.transpose([0, 1, 3, 2]) del q, k # [ncluster, attn_head, natoms, natoms] bias = self.linear_bias(pair).transpose([0, 3, 1, 2]).contiguous() @@ -1409,8 +1421,8 @@ def forward( # delta_pos: [ncluster, natoms, natoms, 3] # [ncluster, attn_head, natoms, natoms, 3] - rot_attn_probs = attn_probs.unsqueeze(-1) * delta_pos.unsqueeze(1).type_as( - attn_probs + rot_attn_probs = attn_probs.unsqueeze(-1) * delta_pos.unsqueeze(1).astype( + attn_probs.dtype ) # [ncluster, attn_head, 3, natoms, natoms] rot_attn_probs = rot_attn_probs.transpose([0, 1, 4, 2, 3]) @@ -1429,7 +1441,7 @@ def __init__( output_dim, ): super().__init__() - self.layer_norm = nn.LayerNorm(input_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION) + self.layer_norm = nn.LayerNorm(input_dim) self.linear_in = Linear(input_dim, input_dim, init="relu") self.linear_out = Linear(input_dim, output_dim, bias=True, init="final") @@ -1449,12 +1461,8 @@ def __init__(self, d_atom, d_pair, d_hid=32): self.d_pair = d_pair self.d_hid = d_hid - self.linear_in = nn.Linear( - d_atom, d_hid * 2, dtype=env.GLOBAL_PD_FLOAT_PRECISION - ) - self.linear_out = nn.Linear( - d_hid**2, d_pair, dtype=env.GLOBAL_PD_FLOAT_PRECISION - ) + self.linear_in = nn.Linear(d_atom, d_hid * 2) + self.linear_out = nn.Linear(d_hid**2, d_pair) self.act = nn.GELU() def _opm(self, a, b): @@ -1522,7 +1530,7 @@ def forward( bias: paddle.Tensor, mask: paddle.Tensor = None, ) -> paddle.Tensor: - nframes, nloc, embed_dim = q.size() + nframes, nloc, embed_dim = q.shape g = None if self.linear_g is not None: # gating, use raw query input @@ -1545,17 +1553,17 @@ def forward( # [nframes, h, nloc, d] q = ( q.reshape([q.shape[:-1] + (self.num_heads, -1)]) - .transpose(-2, -3) + .transpose([0, 1, 3, 2, 4]) .contiguous() ) k = ( k.reshape([k.shape[:-1] + (self.num_heads, -1)]) - .transpose(-2, -3) + .transpose([0, 1, 3, 2, 4]) .contiguous() ) - v = v.reshape([v.shape[:-1] + (self.num_heads, -1)]).transpose(-2, -3) + v = v.reshape([v.shape[:-1] + (self.num_heads, -1)]).transpose([0, 1, 3, 2, 4]) # [nframes, h, nloc, nloc] - attn = paddle.matmul(q, k.transpose(-1, -2)) + attn = paddle.matmul(q, k.transpose([0, 1, 2, 4, 3])) del q, k # [nframes, h, nloc, nloc] attn = softmax_dropout(attn, self.dropout, self.training, mask=mask, bias=bias) @@ -1570,9 +1578,9 @@ def forward( # attn [nframes, h, nloc, nnei] # o [nframes, h, nloc, d] - assert list(o.size()) == [nframes, self.num_heads, nloc, self.head_dim] + assert list(o.shape) == [nframes, self.num_heads, nloc, self.head_dim] # [nframes, nloc, total_dim] - o = o.transpose(-2, -3).contiguous() + o = o.transpose([0, 2, 1, 3]).contiguous() o = o.reshape([*o.shape[:-2], -1]) if g is not None: @@ -1600,7 +1608,7 @@ def __init__( self.mha = Attention( q_dim, k_dim, v_dim, head_dim, num_heads, gating=gating, dropout=dropout ) - self.layer_norm = nn.LayerNorm(pair_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION) + self.layer_norm = nn.LayerNorm(pair_dim) self.linear_bias = Linear(pair_dim, num_heads) def forward( @@ -1627,7 +1635,7 @@ def __init__(self, d_pair, d_hid): self.linear_g = Linear(d_pair, d_pair, init="gating") self.linear_z = Linear(d_hid, d_pair, init="final") - self.layer_norm_out = nn.LayerNorm(d_hid, dtype=env.GLOBAL_PD_FLOAT_PRECISION) + self.layer_norm_out = nn.LayerNorm(d_hid) def forward( self, @@ -1650,7 +1658,7 @@ def forward( # [nframes, d, nloc_i, nloc_k] row not trans a1 = a.transpose([0, 3, 1, 2]) # [nframes, d, nloc_k, nloc_j(i)] trans - b1 = b.transpose(-1, -3) + b1 = b.transpose([0, 3, 2, 1]) # [nframes, d, nloc_i, nloc_j] x = paddle.matmul(a1, b1) del a1, b1 @@ -1658,7 +1666,7 @@ def forward( # [nframes, d, nloc_k, nloc_j(i)] not trans b2 = b.transpose([0, 3, 1, 2]) # [nframes, d, nloc_i, nloc_k] col trans # check TODO - a2 = a.transpose(-1, -3) + a2 = a.transpose([0, 3, 2, 1]) # [nframes, d, nloc_i, nloc_j] x = x + paddle.matmul(a2, b2) @@ -1689,17 +1697,13 @@ def __init__( ActivationFn(activation_fn) if activation_fn is not None else None ) self.post_ln = post_ln - self.self_attn_layer_norm = nn.LayerNorm( - self.feature_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION - ) + self.self_attn_layer_norm = nn.LayerNorm(self.feature_dim) self.self_attn = LocalSelfMultiheadAttention( self.feature_dim, self.attn_head, ) - self.final_layer_norm = nn.LayerNorm( - self.feature_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION - ) + self.final_layer_norm = nn.LayerNorm(self.feature_dim) self.fc1 = SimpleLinear(self.feature_dim, self.ffn_dim) self.fc2 = SimpleLinear(self.ffn_dim, self.feature_dim) @@ -1798,9 +1802,7 @@ def __init__( activate="tanh", ) if self._emb_layer_norm: - self.emb_layer_norm = nn.LayerNorm( - self.feature_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION - ) + self.emb_layer_norm = nn.LayerNorm(self.feature_dim) ## TODO debug : self.in_proj_pair = NonLinearHead(self.pair_dim, self.attn_head, activation_fn=None) self.in_proj_pair = SimpleLinear(self.pair_dim, self.attn_head, activate=None) @@ -1817,13 +1819,9 @@ def __init__( ) self.evoformer_encoder_layers = nn.LayerList(evoformer_encoder_layers) if self._final_layer_norm: - self.final_layer_norm = nn.LayerNorm( - self.feature_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION - ) + self.final_layer_norm = nn.LayerNorm(self.feature_dim) if self._final_head_layer_norm: - self.final_head_layer_norm = nn.LayerNorm( - self.attn_head, dtype=env.GLOBAL_PD_FLOAT_PRECISION - ) + self.final_head_layer_norm = nn.LayerNorm(self.attn_head) def forward(self, atomic_rep, pair_rep, nlist, nlist_type, nlist_mask): """Encoder the atomic and pair representations. @@ -1845,7 +1843,7 @@ def forward(self, atomic_rep, pair_rep, nlist, nlist_type, nlist_mask): - norm_delta_pair_rep: Normalization loss of delta_pair_rep. """ # Global branch - nframes, nloc, _ = atomic_rep.size() + nframes, nloc, _ = atomic_rep.shape nnei = pair_rep.shape[2] input_atomic_rep = atomic_rep # [nframes, nloc, feature_dim] @@ -1975,28 +1973,16 @@ def __init__( ) # layer norm associated with the self attention layer self.pre_ln = pre_ln - self.self_attn_layer_norm = nn.LayerNorm( - self.embedding_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION - ) - self.fc1 = nn.Linear( - self.embedding_dim, ffn_embedding_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION - ) - self.fc2 = nn.Linear( - ffn_embedding_dim, self.embedding_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION - ) - self.final_layer_norm = nn.LayerNorm( - self.embedding_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION - ) + self.self_attn_layer_norm = nn.LayerNorm(self.embedding_dim) + self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim) + self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim) + self.final_layer_norm = nn.LayerNorm(self.embedding_dim) - self.x_layer_norm_opm = nn.LayerNorm( - self.embedding_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION - ) + self.x_layer_norm_opm = nn.LayerNorm(self.embedding_dim) # self.opm = OuterProductLocal(self.embedding_dim, pair_dim, d_hid=pair_hidden_dim) self.opm = OuterProduct(self.embedding_dim, pair_dim, d_hid=pair_hidden_dim) - # self.pair_layer_norm_opm = nn.LayerNorm(pair_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION) - self.pair_layer_norm_ffn = nn.LayerNorm( - pair_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION - ) + # self.pair_layer_norm_opm = nn.LayerNorm(pair_dim) + self.pair_layer_norm_ffn = nn.LayerNorm(pair_dim) self.pair_ffn = Transition( pair_dim, 1, @@ -2005,9 +1991,7 @@ def __init__( self.pair_dropout = pair_dropout self.tri_update = tri_update if self.tri_update: - self.pair_layer_norm_trimul = nn.LayerNorm( - pair_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION - ) + self.pair_layer_norm_trimul = nn.LayerNorm(pair_dim) self.pair_tri_mul = TriangleMultiplication(pair_dim, pair_hidden_dim) def update_pair( @@ -2192,7 +2176,7 @@ def forward(self, x, pair, attn_mask=None, pair_mask=None, atom_mask=None): """ # [ncluster, natoms, 1] op_mask = atom_mask.unsqueeze(-1) - op_mask = op_mask * (op_mask.size(-2) ** -0.5) + op_mask = op_mask * (op_mask.shape[-2] ** -0.5) eps = 1e-3 # [ncluster, natoms, natoms, 1] op_norm = 1.0 / (eps + paddle.einsum("...bc,...dc->...bdc", op_mask, op_mask)) diff --git a/deepmd/pd/model/task/dos.py b/deepmd/pd/model/task/dos.py index 5807c326ad..dbedbf0fbf 100644 --- a/deepmd/pd/model/task/dos.py +++ b/deepmd/pd/model/task/dos.py @@ -59,12 +59,6 @@ def __init__( mixed_types: bool = True, type_map: Optional[List[str]] = None, ): - if bias_dos is not None: - self.bias_dos = bias_dos - else: - self.bias_dos = paddle.zeros((ntypes, numb_dos), dtype=dtype).to( - device=env.DEVICE - ) super().__init__( var_name="dos", ntypes=ntypes, @@ -84,6 +78,12 @@ def __init__( trainable=trainable, type_map=type_map, ) + if bias_dos is not None: + self.bias_dos = bias_dos + else: + self.bias_dos = paddle.zeros((ntypes, numb_dos), dtype=dtype).to( + device=env.DEVICE + ) def output_def(self) -> FittingOutputDef: return FittingOutputDef( diff --git a/deepmd/pd/model/task/ener.py b/deepmd/pd/model/task/ener.py index 89b3255056..0f5d5f2dba 100644 --- a/deepmd/pd/model/task/ener.py +++ b/deepmd/pd/model/task/ener.py @@ -212,14 +212,14 @@ def forward( ------- - `paddle.Tensor`: Total energy with shape [nframes, natoms[0]]. """ - nframes, nloc, _ = inputs.size() + nframes, nloc, _ = inputs.shape if self.use_tebd: # if atype_tebd is not None: # inputs = paddle.concat([inputs, atype_tebd], axis=-1) vec_out = self.filter_layers_dipole[0]( inputs ) # Shape is [nframes, nloc, m1] - assert list(vec_out.size()) == [nframes, nloc, self.out_dim] + assert list(vec_out.shape) == [nframes, nloc, self.out_dim] # (nf x nloc) x 1 x od vec_out = vec_out.reshape([-1, 1, self.out_dim]) assert gr is not None @@ -242,7 +242,9 @@ def forward( atom_energy = self.filter_layers[0](inputs) + self.bias_atom_e[ atype ].unsqueeze(-1) - outs = outs + atom_energy # Shape is [nframes, natoms[0], 1] + outs = ( + outs.astype(atom_energy.dtype) + atom_energy + ) # Shape is [nframes, natoms[0], 1] else: for type_i, filter_layer in enumerate(self.filter_layers): mask = atype == type_i diff --git a/deepmd/pd/model/task/fitting.py b/deepmd/pd/model/task/fitting.py index 7cdd5941bb..4c69dffff8 100644 --- a/deepmd/pd/model/task/fitting.py +++ b/deepmd/pd/model/task/fitting.py @@ -73,13 +73,13 @@ def share_params(self, base_class, shared_level, resume=False): if hasattr(self, "bias_atom_e"): self.bias_atom_e = base_class.bias_atom_e # the following will successfully link all the params except buffers, which need manually link. - for item in self._modules: - self._modules[item] = base_class._modules[item] + for item in self._sub_layers: + self._sub_layers[item] = base_class._sub_layers[item] elif shared_level == 1: # only not share the bias_atom_e # the following will successfully link all the params except buffers, which need manually link. - for item in self._modules: - self._modules[item] = base_class._modules[item] + for item in self._sub_layers: + self._sub_layers[item] = base_class._sub_layers[item] else: raise NotImplementedError @@ -500,7 +500,9 @@ def _forward_common( mask = atype == type_i atom_property = filter_layer(xx) atom_property = atom_property + self.bias_atom_e[type_i] - atom_property = atom_property * mask.unsqueeze(-1) + atom_property = atom_property * mask.unsqueeze(-1).astype( + atom_property.dtype + ) outs = outs + atom_property # Shape is [nframes, natoms[0], 1] else: if self.mixed_types: diff --git a/deepmd/pd/model/task/polarizability.py b/deepmd/pd/model/task/polarizability.py index 7939d8883d..c82965bf8b 100644 --- a/deepmd/pd/model/task/polarizability.py +++ b/deepmd/pd/model/task/polarizability.py @@ -115,15 +115,6 @@ def __init__( raise ValueError( "Scale must be a list of float of length ntypes or a float." ) - self.scale = ( - paddle.to_tensor(self.scale, dtype=env.GLOBAL_PD_FLOAT_PRECISION) - .to(device=env.DEVICE) - .reshape([ntypes, 1]) - ) - self.shift_diag = shift_diag - self.constant_matrix = paddle.zeros( - [ntypes], dtype=env.GLOBAL_PD_FLOAT_PRECISION - ).to(device=env.DEVICE) super().__init__( var_name="polar", ntypes=ntypes, @@ -141,6 +132,15 @@ def __init__( type_map=type_map, **kwargs, ) + self.scale = ( + paddle.to_tensor(self.scale, dtype=env.GLOBAL_PD_FLOAT_PRECISION) + .to(device=env.DEVICE) + .reshape([ntypes, 1]) + ) + self.shift_diag = shift_diag + self.constant_matrix = paddle.zeros( + [ntypes], dtype=env.GLOBAL_PD_FLOAT_PRECISION + ).to(device=env.DEVICE) self.old_impl = False # this only supports the new implementation. def _net_out_dim(self): diff --git a/deepmd/pd/optimizer/KFWrapper.py b/deepmd/pd/optimizer/KFWrapper.py index 2635932578..476a30a30d 100644 --- a/deepmd/pd/optimizer/KFWrapper.py +++ b/deepmd/pd/optimizer/KFWrapper.py @@ -33,7 +33,7 @@ def update_energy( natoms_sum = int(inputs["atype"].shape[-1]) self.optimizer.set_grad_prefactor(natoms_sum) - self.optimizer.zero_grad() + self.optimizer.clear_grad() bs = Etot_label.shape[0] error = Etot_label - Etot_predict error = error / natoms_sum @@ -65,7 +65,7 @@ def update_force( index = self.__sample(self.atoms_selected, self.atoms_per_group, natoms_sum) for i in range(index.shape[0]): - self.optimizer.zero_grad() + self.optimizer.clear_grad() model_pred, _, _ = self.model(**inputs, inference_only=True) Etot_predict = model_pred["energy"] natoms_sum = int(inputs["atype"].shape[-1]) @@ -104,7 +104,7 @@ def update_denoise_coord( index = self.__sample(self.atoms_selected, self.atoms_per_group, natoms_sum) for i in range(index.shape[0]): - self.optimizer.zero_grad() + self.optimizer.clear_grad() model_pred, _, _ = self.model(**inputs, inference_only=True) updated_coord = model_pred["updated_coord"] natoms_sum = int(inputs["atype"].shape[-1]) diff --git a/deepmd/pd/train/training.py b/deepmd/pd/train/training.py index 74a262cd31..e703917c59 100644 --- a/deepmd/pd/train/training.py +++ b/deepmd/pd/train/training.py @@ -367,7 +367,11 @@ def get_lr(lr_params): training_data[model_key].print_summary( f"training in {model_key}", - to_numpy_array(self.training_dataloader[model_key].sampler.weights), + to_numpy_array( + self.training_dataloader[ + model_key + ].batch_sampler.sampler.weights + ), ) if ( validation_data is not None @@ -376,7 +380,9 @@ def get_lr(lr_params): validation_data[model_key].print_summary( f"validation in {model_key}", to_numpy_array( - self.validation_dataloader[model_key].sampler.weights + self.validation_dataloader[ + model_key + ].batch_sampler.sampler.weights ), ) diff --git a/deepmd/pd/train/wrapper.py b/deepmd/pd/train/wrapper.py index 9e5cfab2a1..f6a5465f8e 100644 --- a/deepmd/pd/train/wrapper.py +++ b/deepmd/pd/train/wrapper.py @@ -194,7 +194,7 @@ def load_state_dict( self, state_dict: _StateDict, ) -> tuple[list[str], list[str]]: - self.set_extra_state(state_dict["_extra_state"]) + self.set_extra_state(state_dict.pop("_extra_state")) return super().set_state_dict(state_dict) def set_state_dict( diff --git a/deepmd/pd/utils/aux.py b/deepmd/pd/utils/aux.py new file mode 100644 index 0000000000..99e04bdf6a --- /dev/null +++ b/deepmd/pd/utils/aux.py @@ -0,0 +1,89 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later + +# This file is used to implement some paddle functions with composite APi, +# so as to support high-order differentation when double-backward is needed. + +# This file will be removed when implmented functions are decomposed into primitive +# function in Paddle framework in the future. + + +import paddle + +__all__ = [ + "norm", + "take_along_axis", + "scatter_reduce", + "sec", + "masked_add_", +] + + +def norm(x: paddle.Tensor, p: float = 2, axis: bool = -1, keepdim: bool = False): + if p == 2 or p == 2.0: + return (x * x).sum(axis=axis, keepdim=keepdim) ** 0.5 + return (x**p).sum(axis=axis, keepdim=keepdim) ** (1 / p) + + +def take_along_axis( + x: paddle.Tensor, indices: paddle.Tensor, axis: int, broadcast: bool = True +): + """Broadcast no used now.""" + # manually contruct indices for gather_nd(ind_gather_nd.ndim == indices.ndim + 1, the lsat 1 represents the number of dimension(s) of indices) + ind_gather_nd = paddle.stack( + paddle.meshgrid(*[paddle.arange(v) for v in indices.shape], indexing="ij"), + axis=-1, + ) + ind_gather_nd[..., axis] = indices + # compute output using constructed indices via gather_nd + out = paddle.gather_nd(x, ind_gather_nd) + return out + + +def scatter_reduce( + input: paddle.Tensor, + axis: int, + index: paddle.Tensor, + src: paddle.Tensor, + reduce: str, +) -> paddle.Tensor: + # reduce: "sum", "prod", "mean", "amax", "amin" + if reduce == "sum": + input.put_along_axis_(indices=index, values=src, axis=axis, reduce="add") + elif reduce == "mean": + input.put_along_axis_(indices=index, values=src, axis=axis, reduce="add") + dst_div = paddle.ones_like(input).put_along_axis( + indices=index, + values=paddle.to_tensor(1.0, dtype=input.dtype), + axis=axis, + reduce="add", + ) + input = input / dst_div + elif reduce == "prod": + input = input.put_along_axis(indices=index, values=src, axis=axis, reduce="mul") + else: + raise NotImplementedError("only support mode in ['sum', 'prod', 'mean']!") + return input + + +def sec(l: int, size: int): + if l % size == 0: + return [size] * (l // size) + return [size] * (l // size) + [l % size] + + +def masked_add_(x: paddle.Tensor, mask: paddle.Tensor, v: paddle.Tensor): + assert mask.dtype == paddle.bool, f"mask must be bool type, but got {mask.dtype}" + # indices is bool mask + mask_coord = paddle.concat( + paddle.nonzero(mask, as_tuple=True), + axis=1, + ) # [nz, dim] + if not paddle.is_tensor(v): + v = paddle.full([mask_coord.shape[0]], v, dtype=x.dtype) + t = paddle.scatter_nd_add( + x, + mask_coord, + v, + ) + paddle.assign(t, x) # inplace update + return x diff --git a/deepmd/pd/utils/dataloader.py b/deepmd/pd/utils/dataloader.py index 2d1896424c..e68979c316 100644 --- a/deepmd/pd/utils/dataloader.py +++ b/deepmd/pd/utils/dataloader.py @@ -95,14 +95,7 @@ def construct_dataset(system): type_map=type_map, ) - with Pool( - os.cpu_count() - // ( - int(os.environ["LOCAL_WORLD_SIZE"]) - if dist.is_available() and dist.is_initialized() - else 1 - ) - ) as pool: + with Pool(1) as pool: self.systems: List[DeepmdDataSetForLoader] = pool.map( construct_dataset, systems ) diff --git a/deepmd/pd/utils/env.py b/deepmd/pd/utils/env.py index 4082acee82..85f9e57169 100644 --- a/deepmd/pd/utils/env.py +++ b/deepmd/pd/utils/env.py @@ -73,8 +73,8 @@ inter_nthreads, intra_nthreads = get_default_nthreads() # if inter_nthreads > 0: # the behavior of 0 is not documented # paddle.set_num_interop_threads(inter_nthreads) -if intra_nthreads > 0: - paddle.framework.core.set_num_threads(intra_nthreads) +# if intra_nthreads > 0: +# paddle.framework.core.set_num_threads(intra_nthreads) def enable_prim(enable: bool = True): diff --git a/deepmd/pd/utils/env_mat_stat.py b/deepmd/pd/utils/env_mat_stat.py index f0c7683538..6d1a153686 100644 --- a/deepmd/pd/utils/env_mat_stat.py +++ b/deepmd/pd/utils/env_mat_stat.py @@ -158,7 +158,7 @@ def iter( # shape: (ntypes, nloc) type_idx = paddle.equal( atype.reshape([1, -1]), - paddle.arange(self.descriptor.get_ntypes(), dtype=paddle.int32) + paddle.arange(self.descriptor.get_ntypes(), dtype=atype.dtype) .to(device=env.DEVICE) .reshape([-1, 1]), ) diff --git a/deepmd/pd/utils/exclude_mask.py b/deepmd/pd/utils/exclude_mask.py index 75ee964da7..89c549b2d1 100644 --- a/deepmd/pd/utils/exclude_mask.py +++ b/deepmd/pd/utils/exclude_mask.py @@ -160,5 +160,10 @@ def forward( type_ij = type_i[:, :, None] + type_j # nf x (nloc x nnei) type_ij = type_ij.reshape([nf, nloc * nnei]) - mask = self.type_mask[type_ij].reshape([nf, nloc, nnei]).to(atype_ext.place) + mask = ( + self.type_mask[type_ij] + .reshape([nf, nloc, nnei]) + .to(atype_ext.place) + .astype("bool") + ) return mask diff --git a/deepmd/pd/utils/init.py b/deepmd/pd/utils/init.py new file mode 100644 index 0000000000..42e19fea87 --- /dev/null +++ b/deepmd/pd/utils/init.py @@ -0,0 +1,529 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. + +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +The initialization method under this module is aligned with pytorch initialization. +If you need to use the initialization method of PaddlePaddle, please refer to +[paddle.nn.initializer](https://github.com/PaddlePaddle/Paddle/tree/develop/python/paddle/nn/initializer). + +This code is based on [torch.nn.init](https://github.com/pytorch/pytorch/blob/main/torch/nn/init.py) +Ths copyright of pytorch/pytorch is a BSD-style license, as found in the LICENSE file. +""" + +from __future__ import ( + annotations, +) + +import math +import warnings + +import numpy as np +import paddle +from paddle import ( + nn, +) +from typing_extensions import ( + Literal, +) + +__all__ = [ + "uniform_", + "normal_", + "trunc_normal_", + "glorot_normal_", + "constant_", + "ones_", + "zeros_", + "xavier_uniform_", + "xavier_normal_", + "kaiming_uniform_", + "kaiming_normal_", + "linear_init_", + "conv_init_", +] + + +def _no_grad_uniform_(tensor, a, b): + with paddle.no_grad(): + tensor.set_value( + paddle.uniform(shape=tensor.shape, dtype=tensor.dtype, min=a, max=b) + ) + return tensor + + +def _no_grad_normal_(tensor, mean=0.0, std=1.0): + with paddle.no_grad(): + tensor.set_value(paddle.normal(mean=mean, std=std, shape=tensor.shape)) + return tensor + + +def _no_grad_trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0): + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0 + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn( + f"mean({mean}) is more than 2 std({std}) from [a, b]([{a}, {b}]) in _no_grad_trunc_normal_. " + "The distribution of values may be incorrect." + ) + + with paddle.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + _tensor = paddle.uniform( + shape=tensor.shape, dtype=tensor.dtype, min=2 * l - 1, max=2 * u - 1 + ) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + _tensor.erfinv_() + + # Transform to proper mean, std + _tensor = paddle.multiply( + _tensor, paddle.to_tensor(std * math.sqrt(2.0), tensor.dtype) + ) + _tensor = paddle.add(_tensor, paddle.to_tensor(mean, tensor.dtype)) + + # Clamp to ensure it"s in the proper range + _tensor = paddle.clip(_tensor, min=a, max=b) + tensor.set_value(_tensor) + return tensor + + +def _no_grad_fill_(tensor, value=0.0): + with paddle.no_grad(): + tensor.set_value(paddle.full_like(tensor, value, dtype=tensor.dtype)) + return tensor + + +def uniform_(tensor: paddle.Tensor, a: float, b: float) -> paddle.Tensor: + """Modify tensor inplace using uniform_. + + Args: + tensor (paddle.Tensor): Paddle Tensor. + a (float): Min value. + b (float): Max value. + + Returns + ------- + paddle.Tensor: Initialized tensor. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> param = paddle.empty((128, 256), "float32") + >>> param = ppsci.utils.initializer.uniform_(param, -1, 1) + """ + return _no_grad_uniform_(tensor, a, b) + + +def normal_( + tensor: paddle.Tensor, mean: float = 0.0, std: float = 1.0 +) -> paddle.Tensor: + """Modify tensor inplace using normal_. + + Args: + tensor (paddle.Tensor): Paddle Tensor. + mean (float, optional): Mean value. Defaults to 0.0. + std (float, optional): Std value. Defaults to 1.0. + + Returns + ------- + paddle.Tensor: Initialized tensor. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> param = paddle.empty((128, 256), "float32") + >>> param = ppsci.utils.initializer.normal_(param, 0, 1) + """ + return _no_grad_normal_(tensor, mean, std) + + +def trunc_normal_( + tensor: paddle.Tensor, + mean: float = 0.0, + std: float = 1.0, + a: float = -2.0, + b: float = 2.0, +) -> paddle.Tensor: + """Modify tensor inplace using trunc_normal_. + + Args: + tensor (paddle.Tensor): Paddle Tensor. + mean (float, optional): The mean of the normal distribution. Defaults to 0.0. + std (float, optional): The standard deviation of the normal distribution. Defaults to 1.0. + a (float, optional): The minimum cutoff value. Defaults to -2.0. + b (float, optional): The maximum cutoff value. Defaults to 2.0. + + Returns + ------- + paddle.Tensor: Initialized tensor. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> param = paddle.empty((128, 256), "float32") + >>> param = ppsci.utils.initializer.trunc_normal_(param, 0.0, 1.0) + """ + return _no_grad_trunc_normal_(tensor, mean, std, a, b) + + +def constant_(tensor: paddle.Tensor, value: float = 0.0) -> paddle.Tensor: + """Modify tensor inplace using constant_. + + Args: + tensor (paddle.Tensor): Paddle Tensor. + value (float, optional): Value to fill tensor. Defaults to 0.0. + + Returns + ------- + paddle.Tensor: Initialized tensor. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> param = paddle.empty((128, 256), "float32") + >>> param = ppsci.utils.initializer.constant_(param, 2) + """ + return _no_grad_fill_(tensor, value) + + +def ones_(tensor: paddle.Tensor) -> paddle.Tensor: + """Modify tensor inplace using ones_. + + Args: + tensor (paddle.Tensor): Paddle Tensor. + + Returns + ------- + paddle.Tensor: Initialized tensor. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> param = paddle.empty((128, 256), "float32") + >>> param = ppsci.utils.initializer.ones_(param) + """ + return _no_grad_fill_(tensor, 1) + + +def zeros_(tensor: paddle.Tensor) -> paddle.Tensor: + """Modify tensor inplace using zeros_. + + Args: + tensor (paddle.Tensor): Paddle Tensor. + + Returns + ------- + paddle.Tensor: Initialized tensor. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> param = paddle.empty((128, 256), "float32") + >>> param = ppsci.utils.initializer.zeros_(param) + """ + return _no_grad_fill_(tensor, 0) + + +def _calculate_fan_in_and_fan_out(tensor, reverse=False): + """ + Calculate (fan_in, _fan_out) for tensor. + + Args: + tensor (paddle.Tensor): paddle.Tensor. + reverse (bool): Tensor data format order, False by default as [fout, fin, ...]. + e.g. : conv.weight [cout, cin, kh, kw] is False; linear.weight [cin, cout] + is True. + + Return: + Tuple[float, float]: (fan_in, fan_out). + """ + if tensor.ndim < 2: + raise ValueError( + f"tensor.ndim should be no less than 2, but got {tensor.ndim}." + ) + + if reverse: + num_input_fmaps, num_output_fmaps = tensor.shape[0], tensor.shape[1] + else: + num_input_fmaps, num_output_fmaps = tensor.shape[1], tensor.shape[0] + + receptive_field_size = 1 + if tensor.ndim > 2: + receptive_field_size = np.prod(tensor.shape[2:]) + + fan_in = num_input_fmaps * receptive_field_size + fan_out = num_output_fmaps * receptive_field_size + + return fan_in, fan_out + + +def xavier_uniform_( + tensor: paddle.Tensor, gain: float = 1.0, reverse: bool = False +) -> paddle.Tensor: + """Modify tensor inplace using xavier_uniform_. + + Args: + tensor (paddle.Tensor): Paddle Tensor. + gain (float, optional): Hyperparameter. Defaults to 1.0. + reverse (bool, optional): Tensor data format order, False by default as + [fout, fin, ...].. Defaults to False. + + Returns + ------- + paddle.Tensor: Initialized tensor. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> param = paddle.empty((128, 256), "float32") + >>> param = ppsci.utils.initializer.xavier_uniform_(param) + """ + fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor, reverse=reverse) + std = gain * math.sqrt(2.0 / float(fan_in + fan_out)) + k = math.sqrt(3.0) * std + return _no_grad_uniform_(tensor, -k, k) + + +def xavier_normal_( + tensor: paddle.Tensor, gain: float = 1.0, reverse: bool = False +) -> paddle.Tensor: + """Modify tensor inplace using xavier_normal_. + + Args: + tensor (paddle.Tensor): Paddle Tensor. + gain (float, optional): Hyperparameter. Defaults to 1.0. + reverse (bool, optional): Tensor data format order, False by + default as [fout, fin, ...]. Defaults to False. + + Returns + ------- + paddle.Tensor: Initialized tensor. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> param = paddle.empty((128, 256), "float32") + >>> param = ppsci.utils.initializer.xavier_normal_(param) + """ + fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor, reverse=reverse) + std = gain * math.sqrt(2.0 / float(fan_in + fan_out)) + return _no_grad_normal_(tensor, 0, std) + + +# reference: https://pytorch.org/docs/stable/_modules/torch/nn/init.html +def _calculate_correct_fan(tensor, mode, reverse=False): + mode = mode.lower() + valid_modes = ["fan_in", "fan_out"] + if mode not in valid_modes: + raise ValueError(f"Mode {mode} not supported, please use one of {valid_modes}") + + fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor, reverse) + + return fan_in if mode == "fan_in" else fan_out + + +def _calculate_gain(nonlinearity, param=None): + linear_fns = [ + "linear", + "conv1d", + "conv2d", + "conv3d", + "conv_transpose1d", + "conv_transpose2d", + "conv_transpose3d", + ] + if nonlinearity in linear_fns or nonlinearity == "sigmoid": + return 1 + elif nonlinearity == "tanh": + return 5.0 / 3 + elif nonlinearity == "relu": + return math.sqrt(2.0) + elif nonlinearity == "leaky_relu": + if param is None: + negative_slope = 0.01 + elif ( + not isinstance(param, bool) + and isinstance(param, int) + or isinstance(param, float) + ): + # True/False are instances of int, hence check above + negative_slope = param + else: + raise ValueError(f"negative_slope {param} not a valid number") + return math.sqrt(2.0 / (1 + negative_slope**2)) + elif nonlinearity == "selu": + return 3.0 / 4 + else: + raise ValueError(f"Unsupported nonlinearity {nonlinearity}") + + +def kaiming_uniform_( + tensor: paddle.Tensor, + a: float = 0, + mode: Literal["fan_in", "fan_out"] = "fan_in", + nonlinearity: str = "leaky_relu", + reverse: bool = False, +) -> paddle.Tensor: + """Modify tensor inplace using kaiming_uniform method. + + Args: + tensor (paddle.Tensor): Paddle Tensor. + a (float, optional): The negative slope of the rectifier used after this layer. + Defaults to 0. + mode (Literal["fan_in", "fan_out"], optional): + ["fan_in", "fan_out"]. Defaults to "fan_in". + nonlinearity (str, optional): Nonlinearity method name. Defaults to "leaky_relu". + reverse (bool, optional): Tensor data format order, False by default as + [fout, fin, ...].. Defaults to False. + + Returns + ------- + paddle.Tensor: Initialized tensor. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> param = paddle.empty((128, 256), "float32") + >>> param = ppsci.utils.initializer.kaiming_uniform_(param) + """ + fan = _calculate_correct_fan(tensor, mode, reverse) + gain = _calculate_gain(nonlinearity, a) + std = gain / math.sqrt(fan) + k = math.sqrt(3.0) * std + return _no_grad_uniform_(tensor, -k, k) + + +def kaiming_normal_( + tensor: paddle.Tensor, + a: float = 0, + mode: Literal["fan_in", "fan_out"] = "fan_in", + nonlinearity: str = "leaky_relu", + reverse: bool = False, +) -> paddle.Tensor: + """Modify tensor inplace using kaiming_normal_. + + Args: + tensor (paddle.Tensor): Paddle Tensor. + a (float, optional): The negative slope of the rectifier used after this layer. + Defaults to 0. + mode (Literal["fan_in", "fan_out"], optional): Either + 'fan_in' (default) or 'fan_out'. Defaults to "fan_in". + nonlinearity (str, optional): Nonlinearity method name. Defaults to "leaky_relu". + reverse (bool, optional): Tensor data format order. Defaults to False. + + Returns + ------- + paddle.Tensor: Initialized tensor. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> param = paddle.empty((128, 256), "float32") + >>> param = ppsci.utils.initializer.kaiming_normal_(param) + """ + fan = _calculate_correct_fan(tensor, mode, reverse) + gain = _calculate_gain(nonlinearity, a) + std = gain / math.sqrt(fan) + return _no_grad_normal_(tensor, 0, std) + + +def linear_init_(module: nn.Layer) -> None: + """Initialize module's weight and bias as it is a linear layer. + + Args: + module (nn.Layer): Linear Layer to be initialized. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> layer = paddle.nn.Linear(128, 256) + >>> ppsci.utils.initializer.linear_init_(layer) + """ + kaiming_uniform_(module.weight, a=math.sqrt(5)) + if module.bias is not None: + fan_in, _ = _calculate_fan_in_and_fan_out(module.weight, reverse=True) + bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0 + uniform_(module.bias, -bound, bound) + + +def conv_init_(module: nn.Layer) -> None: + """Initialize module's weight and bias as it is a conv layer. + + Args: + module (nn.Layer): Convolution Layer to be initialized. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> layer = paddle.nn.Conv2D(4, 16, 2) + >>> ppsci.utils.initializer.conv_init_(layer) + """ + kaiming_uniform_(module.weight, a=math.sqrt(5)) + if module.bias is not None: + fan_in, _ = _calculate_fan_in_and_fan_out(module.weight, reverse=False) + if fan_in != 0: + bound = 1 / math.sqrt(fan_in) + uniform_(module.bias, -bound, bound) + + +def glorot_normal_(tensor: paddle.Tensor) -> paddle.Tensor: + """Modify tensor inplace using jax-style glorot_normal. + + Args: + tensor (paddle.Tensor): Paddle Tensor/Paramter. + + Returns + ------- + paddle.Tensor: Initialized tensor. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> param = paddle.empty((128, 256), "float32") + >>> param = ppsci.utils.initializer.glorot_normal_(param) + """ + assert ( + tensor.ndim == 2 + ), f"glorot_normal_ only support 2D tensor now, but got ndim={tensor.ndim}" + fin, fout = tensor.shape + var = 2.0 / (fin + fout) + stddev = math.sqrt(var) * 0.87962566103423978 + trunc_normal_(tensor) + tensor.set_value(tensor * stddev) + return tensor diff --git a/deepmd/pd/utils/nlist.py b/deepmd/pd/utils/nlist.py index 33347393ab..39c1339989 100644 --- a/deepmd/pd/utils/nlist.py +++ b/deepmd/pd/utils/nlist.py @@ -6,6 +6,7 @@ Union, ) +import numpy as np import paddle from deepmd.pd.utils import ( @@ -100,7 +101,7 @@ def build_neighbor_list( nall = coord.shape[1] // 3 # fill virtual atoms with large coords so they are not neighbors of any # real atom. - if coord.numel() > 0: + if np.prod(coord.shape) > 0: xmax = paddle.max(coord) + 2.0 * rcut else: xmax = paddle.zeros([1], dtype=coord.dtype).to(device=coord.place) + 2.0 * rcut @@ -122,15 +123,15 @@ def build_neighbor_list( # rr = paddle.linalg.norm(diff, axis=-1) rr = aux.norm(diff, axis=-1) # if central atom has two zero distances, sorting sometimes can not exclude itself - rr -= paddle.eye(nloc, nall, dtype=rr.dtype).to(device=rr.place).unsqueeze(0) + rr = rr - paddle.eye(nloc, nall, dtype=rr.dtype).to(device=rr.place).unsqueeze(0) rr, nlist = paddle.sort(rr, axis=-1), paddle.argsort(rr, axis=-1) # nloc x (nall-1) rr = rr[:, :, 1:] nlist = nlist[:, :, 1:] - - return _trim_mask_distinguish_nlist( + t = _trim_mask_distinguish_nlist( is_vir, atype, rr, nlist, rcut, sel, distinguish_types ) + return t def _trim_mask_distinguish_nlist( @@ -154,7 +155,10 @@ def _trim_mask_distinguish_nlist( rr = paddle.concat( [ rr, - paddle.ones([batch_size, nloc, nsel - nnei]).to(device=rr.place) + rcut, + paddle.ones([batch_size, nloc, nsel - nnei]).to( + device=rr.place, dtype=rr.dtype + ) + + rcut, ], # pylint: disable=no-explicit-dtype axis=-1, ) @@ -450,7 +454,6 @@ def extend_coord_with_ghosts( """ device = coord.place nf, nloc = atype.shape[:2] - nloc = 192 aidx = paddle.tile(paddle.arange(nloc).to(device=device).unsqueeze(0), [nf, 1]) # pylint: disable=no-explicit-dtype if cell is None: nall = nloc diff --git a/deepmd/pd/utils/preprocess.py b/deepmd/pd/utils/preprocess.py index abe5116242..a4cc8e5502 100644 --- a/deepmd/pd/utils/preprocess.py +++ b/deepmd/pd/utils/preprocess.py @@ -188,7 +188,8 @@ def build_neighbor_list( coord_l = coord.reshape([-1, 1, 3])[:nloc] coord_r = coord.reshape([1, -1, 3]) distance = coord_l - coord_r - distance = paddle.linalg.norm(distance, axis=-1) + # distance = paddle.linalg.norm(distance, axis=-1) + distance = aux.norm(distance, axis=-1) DISTANCE_INF = distance.max().detach() + rcut distance[:nloc, :nloc] += paddle.eye(nloc, dtype=paddle.bool) * DISTANCE_INF # pylint: disable=no-explicit-device if min_check: diff --git a/deepmd/pd/utils/region.py b/deepmd/pd/utils/region.py index 6619d94092..a4acc5924a 100644 --- a/deepmd/pd/utils/region.py +++ b/deepmd/pd/utils/region.py @@ -25,7 +25,11 @@ def phys2inter( the internal coordinates """ - rec_cell = paddle.linalg.inv(cell) + try: + rec_cell = paddle.linalg.inv(cell) + except Exception: + rec_cell = paddle.full_like(cell, float("nan")) + rec_cell.stop_gradient = False return paddle.matmul(coord, rec_cell) diff --git a/deepmd/pd/utils/stat.py b/deepmd/pd/utils/stat.py index 05c83aaf2e..62e9e6a6b8 100644 --- a/deepmd/pd/utils/stat.py +++ b/deepmd/pd/utils/stat.py @@ -52,8 +52,8 @@ def make_stat_input(datasets, dataloaders, nbatches): for i in range(len(datasets)): sys_stat = {} - device = paddle.get_device() - paddle.set_device("cpu") + # device = paddle.get_device() + # paddle.set_device("cpu") # with paddle.device("cpu"): iterator = iter(dataloaders[i]) numb_batches = min(nbatches, len(dataloaders[i])) @@ -74,7 +74,7 @@ def make_stat_input(datasets, dataloaders, nbatches): sys_stat[dd] = stat_data[dd] else: pass - paddle.set_device(device) + # paddle.set_device(device) for key in sys_stat: if isinstance(sys_stat[key], np.float32): @@ -229,7 +229,7 @@ def _fill_stat_with_global( if atomic_stat is None: return global_stat else: - atomic_stat = atomic_stat.reshape(*global_stat.shape) + atomic_stat = atomic_stat.reshape(global_stat.shape) return np.nan_to_num( np.where( np.isnan(atomic_stat) & ~np.isnan(global_stat), global_stat, atomic_stat From 55f71f6ff0aa15e0f368ae1db6c0a9665a651f7c Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 9 Sep 2024 13:46:39 +0800 Subject: [PATCH 08/93] add normalize composite impl --- deepmd/pd/utils/aux.py | 19 ++++++++++++++++--- 1 file changed, 16 insertions(+), 3 deletions(-) diff --git a/deepmd/pd/utils/aux.py b/deepmd/pd/utils/aux.py index 99e04bdf6a..e8c0031820 100644 --- a/deepmd/pd/utils/aux.py +++ b/deepmd/pd/utils/aux.py @@ -18,7 +18,9 @@ ] -def norm(x: paddle.Tensor, p: float = 2, axis: bool = -1, keepdim: bool = False): +def norm( + x: paddle.Tensor, p: float = 2, axis: bool = -1, keepdim: bool = False +) -> paddle.Tensor: if p == 2 or p == 2.0: return (x * x).sum(axis=axis, keepdim=keepdim) ** 0.5 return (x**p).sum(axis=axis, keepdim=keepdim) ** (1 / p) @@ -65,13 +67,15 @@ def scatter_reduce( return input -def sec(l: int, size: int): +def sec(l: int, size: int) -> list[int]: if l % size == 0: return [size] * (l // size) return [size] * (l // size) + [l % size] -def masked_add_(x: paddle.Tensor, mask: paddle.Tensor, v: paddle.Tensor): +def masked_add_( + x: paddle.Tensor, mask: paddle.Tensor, v: paddle.Tensor +) -> paddle.Tensor: assert mask.dtype == paddle.bool, f"mask must be bool type, but got {mask.dtype}" # indices is bool mask mask_coord = paddle.concat( @@ -87,3 +91,12 @@ def masked_add_(x: paddle.Tensor, mask: paddle.Tensor, v: paddle.Tensor): ) paddle.assign(t, x) # inplace update return x + + +def normalize( + x: paddle.Tensor, + p: float = 2, + axis: int = 1, + epsilon: float = 1e-12, +) -> paddle.Tensor: + return x / (norm(x, p=p, axis=axis, keepdim=True).clip(min=epsilon)) From ebdbed213ddc012f4793f555d0185784c61e1b3c Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 10 Sep 2024 20:59:25 +0800 Subject: [PATCH 09/93] use prim function instead of vanilla function, supporting double-backward --- deepmd/pd/entrypoints/main.py | 2 +- deepmd/pd/loss/ener.py | 19 +++++++++++---- .../model/atomic_model/base_atomic_model.py | 2 +- deepmd/pd/model/descriptor/env_mat.py | 2 +- deepmd/pd/model/descriptor/se_a.py | 8 +++---- deepmd/pd/model/descriptor/se_atten.py | 21 ++++++++++------ deepmd/pd/model/descriptor/se_t_tebd.py | 12 ++++++++-- deepmd/pd/model/model/transform_output.py | 4 ++-- deepmd/pd/model/network/mlp.py | 4 ++-- deepmd/pd/model/network/network.py | 9 ++++--- deepmd/pd/model/task/fitting.py | 3 ++- deepmd/pd/train/training.py | 8 +++---- deepmd/pd/utils/neighbor_stat.py | 24 ++++++++++--------- deepmd/pt/entrypoints/main.py | 2 +- 14 files changed, 75 insertions(+), 45 deletions(-) diff --git a/deepmd/pd/entrypoints/main.py b/deepmd/pd/entrypoints/main.py index 6e9c2445f9..9a50aaefce 100644 --- a/deepmd/pd/entrypoints/main.py +++ b/deepmd/pd/entrypoints/main.py @@ -359,7 +359,7 @@ def freeze(FLAGS): input_spec=[ InputSpec([None, 192, 3], dtype="float64", name="coord"), InputSpec([None, 192], dtype="int64", name="atype"), - InputSpec([None, 192, 3], dtype="float64", name="box"), + InputSpec([None, 3, 3], dtype="float64", name="box"), ], ) extra_files = {} diff --git a/deepmd/pd/loss/ener.py b/deepmd/pd/loss/ener.py index a97f813a88..f3baac0edf 100644 --- a/deepmd/pd/loss/ener.py +++ b/deepmd/pd/loss/ener.py @@ -281,12 +281,21 @@ def forward(self, input_dict, model, label, natoms, learning_rate, mae=False): drdq_reshape = drdq.reshape( [-1, natoms * 3, self.numb_generalized_coord] ) - gen_force_label = paddle.einsum( - "bij,bi->bj", drdq_reshape, force_label_reshape_nframes - ) - gen_force = paddle.einsum( - "bij,bi->bj", drdq_reshape, force_reshape_nframes + + # gen_force_label = paddle.einsum( + # "bij,bi->bj", drdq_reshape, force_label_reshape_nframes + # ) + gen_force_label = ( + drdq_reshape * force_label_reshape_nframes.unsqueeze(-1) + ).sum([-2]) + + # gen_force = paddle.einsum( + # "bij,bi->bj", drdq_reshape, force_reshape_nframes + # ) + gen_force = (drdq_reshape * force_reshape_nframes.unsqueeze(-1)).sum( + [-2] ) + diff_gen_force = gen_force_label - gen_force l2_gen_force_loss = paddle.square(diff_gen_force).mean() if not self.inference: diff --git a/deepmd/pd/model/atomic_model/base_atomic_model.py b/deepmd/pd/model/atomic_model/base_atomic_model.py index 96a0fe5c36..e9ccd7d83a 100644 --- a/deepmd/pd/model/atomic_model/base_atomic_model.py +++ b/deepmd/pd/model/atomic_model/base_atomic_model.py @@ -252,7 +252,7 @@ def forward_common_atomic( ret_dict = self.apply_out_stat(ret_dict, atype) # nf x nloc - atom_mask = ext_atom_mask[:, :nloc].to(paddle.int32) + atom_mask = ext_atom_mask[:, :nloc].astype(paddle.int32) if self.atom_excl is not None: atom_mask *= self.atom_excl(atype) diff --git a/deepmd/pd/model/descriptor/env_mat.py b/deepmd/pd/model/descriptor/env_mat.py index 3bb4d177fb..abeb8a1c1f 100644 --- a/deepmd/pd/model/descriptor/env_mat.py +++ b/deepmd/pd/model/descriptor/env_mat.py @@ -42,7 +42,7 @@ def _make_env_mat( if radial_only: env_mat = t0 * weight else: - env_mat = paddle.concat([t0, t1], axis=-1) * weight + env_mat = paddle.concat([t0.astype(t1.dtype), t1], axis=-1) * weight return env_mat, diff * mask.unsqueeze(-1).astype(diff.dtype), weight diff --git a/deepmd/pd/model/descriptor/se_a.py b/deepmd/pd/model/descriptor/se_a.py index b86c9127f3..48a576a446 100644 --- a/deepmd/pd/model/descriptor/se_a.py +++ b/deepmd/pd/model/descriptor/se_a.py @@ -652,7 +652,7 @@ def forward( else: assert self.filter_layers is not None dmatrix = dmatrix.reshape([-1, self.nnei, 4]) - dmatrix = dmatrix.to(dtype=self.prec) + dmatrix = dmatrix.astype(self.prec) nfnl = dmatrix.shape[0] # pre-allocate a shape to pass jit xyz_scatter = paddle.zeros( @@ -672,7 +672,7 @@ def forward( # ti: center atom type, ii: neighbor type... ii = embedding_idx // self.ntypes ti = embedding_idx % self.ntypes - ti_mask = atype.flatten().equal(ti) + ti_mask = atype.flatten() == ti # nfnl x nt if ti_mask is not None: mm = exclude_mask[ti_mask, self.sec[ii] : self.sec[ii + 1]] @@ -704,8 +704,8 @@ def forward( result = result.reshape([nf, nloc, self.filter_neuron[-1] * self.axis_neuron]) rot_mat = rot_mat.reshape([nf, nloc] + list(rot_mat.shape[1:])) # noqa:RUF005 return ( - result.to(dtype=env.GLOBAL_PD_FLOAT_PRECISION), - rot_mat.to(dtype=env.GLOBAL_PD_FLOAT_PRECISION), + result.astype(env.GLOBAL_PD_FLOAT_PRECISION), + rot_mat.astype(env.GLOBAL_PD_FLOAT_PRECISION), None, None, sw, diff --git a/deepmd/pd/model/descriptor/se_atten.py b/deepmd/pd/model/descriptor/se_atten.py index d52a295883..2b9d150dbb 100644 --- a/deepmd/pd/model/descriptor/se_atten.py +++ b/deepmd/pd/model/descriptor/se_atten.py @@ -515,7 +515,10 @@ def forward( atype_tebd=atype_tebd_nnei, nlist_tebd=atype_tebd_nlist, ) # shape is [nframes*nall, self.neei, out_size] - input_r = paddle.nn.functional.normalize( + # input_r = paddle.nn.functional.normalize( + # dmatrix.reshape([-1, self.nnei, 4])[:, :, 1:4], axis=-1 + # ) + input_r = aux.normalize( dmatrix.reshape([-1, self.nnei, 4])[:, :, 1:4], axis=-1 ) gg = self.dpa1_attention( @@ -566,9 +569,10 @@ def forward( else: raise NotImplementedError - input_r = paddle.nn.functional.normalize( - rr.reshape([-1, self.nnei, 4])[:, :, 1:4], axis=-1 - ) + # input_r = paddle.nn.functional.normalize( + # rr.reshape([-1, self.nnei, 4])[:, :, 1:4], axis=-1 + # ) + input_r = aux.normalize(rr.reshape([-1, self.nnei, 4])[:, :, 1:4], axis=-1) gg = self.dpa1_attention( gg, nlist_mask, input_r=input_r, sw=sw ) # shape is [nframes*nloc, self.neei, out_size] @@ -946,9 +950,12 @@ def forward( ) if self.normalize: - q = paddle_func.normalize(q, axis=-1) - k = paddle_func.normalize(k, axis=-1) - v = paddle_func.normalize(v, axis=-1) + # q = paddle_func.normalize(q, axis=-1) + # k = paddle_func.normalize(k, axis=-1) + # v = paddle_func.normalize(v, axis=-1) + q = aux.normalize(q, axis=-1) + k = aux.normalize(k, axis=-1) + v = aux.normalize(v, axis=-1) q = q * self.scaling # (nf x nloc) x num_heads x head_dim x nnei diff --git a/deepmd/pd/model/descriptor/se_t_tebd.py b/deepmd/pd/model/descriptor/se_t_tebd.py index 314d73b15d..0a837680d4 100644 --- a/deepmd/pd/model/descriptor/se_t_tebd.py +++ b/deepmd/pd/model/descriptor/se_t_tebd.py @@ -814,7 +814,11 @@ def forward( # nfnl x nt_j x 3 rr_j = rr[:, :, 1:] # nfnl x nt_i x nt_j - env_ij = paddle.einsum("ijm,ikm->ijk", rr_i, rr_j) + # env_ij = paddle.einsum("ijm,ikm->ijk", rr_i, rr_j) + env_ij = ( + # ij1m x i1km -> ijkm -> ijk + rr_i.unsqueeze(2) * rr_j.unsqueeze(1) + ).sum(-1) # nfnl x nt_i x nt_j x 1 ss = env_ij.unsqueeze(-1) @@ -850,7 +854,11 @@ def forward( raise NotImplementedError # nfnl x ng - res_ij = paddle.einsum("ijk,ijkm->im", env_ij, gg) + # res_ij = paddle.einsum("ijk,ijkm->im", env_ij, gg) + res_ij = ( + # ijk1 x ijkm -> ijkm -> im + env_ij.unsqueeze(-1) * gg + ).sum([1, 2]) res_ij = res_ij * (1.0 / float(self.nnei) / float(self.nnei)) # nf x nl x ng result = res_ij.reshape([nframes, nloc, self.filter_neuron[-1]]) diff --git a/deepmd/pd/model/model/transform_output.py b/deepmd/pd/model/model/transform_output.py index 46c3b05a60..371f8454ea 100644 --- a/deepmd/pd/model/model/transform_output.py +++ b/deepmd/pd/model/model/transform_output.py @@ -180,7 +180,7 @@ def fit_output_to_model_output( atom_axis = -(len(shap) + 1) if vdef.reducible: kk_redu = get_reduce_name(kk) - model_ret[kk_redu] = paddle.sum(vv.to(redu_prec), axis=atom_axis) + model_ret[kk_redu] = paddle.sum(vv.astype(redu_prec), axis=atom_axis) if vdef.r_differentiable: kk_derv_r, kk_derv_c = get_deriv_name(kk) dr, dc = take_deriv( @@ -197,7 +197,7 @@ def fit_output_to_model_output( assert dc is not None model_ret[kk_derv_c] = dc model_ret[kk_derv_c + "_redu"] = paddle.sum( - model_ret[kk_derv_c].to(redu_prec), axis=1 + model_ret[kk_derv_c].astype(redu_prec), axis=1 ) return model_ret diff --git a/deepmd/pd/model/network/mlp.py b/deepmd/pd/model/network/mlp.py index c7ba62f402..c2ddc8d75e 100644 --- a/deepmd/pd/model/network/mlp.py +++ b/deepmd/pd/model/network/mlp.py @@ -222,7 +222,7 @@ def forward( The output. """ ori_prec = xx.dtype - xx = xx.to(self.prec) + xx = xx.astype(self.prec) yy = ( paddle.matmul(xx, self.matrix.astype(self.prec)) + self.bias if self.bias is not None @@ -237,7 +237,7 @@ def forward( yy += paddle.concat([xx, xx], axis=-1) else: yy = yy - yy = yy.to(ori_prec) + yy = yy.astype(ori_prec) return yy def serialize(self) -> dict: diff --git a/deepmd/pd/model/network/network.py b/deepmd/pd/model/network/network.py index 888932eec8..605ae94fec 100644 --- a/deepmd/pd/model/network/network.py +++ b/deepmd/pd/model/network/network.py @@ -1220,9 +1220,12 @@ def forward( k = k.reshape([-1, self.nnei, self.hidden_dim]) v = v.reshape([-1, self.nnei, self.hidden_dim]) if self.normalize: - q = F.normalize(q, axis=-1) - k = F.normalize(k, axis=-1) - v = F.normalize(v, axis=-1) + # q = F.normalize(q, axis=-1) + # k = F.normalize(k, axis=-1) + # v = F.normalize(v, axis=-1) + q = aux.normalize(q, axis=-1) + k = aux.normalize(k, axis=-1) + v = aux.normalize(v, axis=-1) q = q * self.scaling k = k.transpose([0, 2, 1]) # [nframes * nloc, nnei, nnei] diff --git a/deepmd/pd/model/task/fitting.py b/deepmd/pd/model/task/fitting.py index 4c69dffff8..57c15f7449 100644 --- a/deepmd/pd/model/task/fitting.py +++ b/deepmd/pd/model/task/fitting.py @@ -517,6 +517,7 @@ def _forward_common( else: for type_i, ll in enumerate(self.filter_layers.networks): mask = (atype == type_i).unsqueeze(-1) + mask.stop_gradient = True mask = paddle.tile(mask, (1, 1, net_dim_out)) atom_property = ll(xx) if xx_zeros is not None: @@ -536,4 +537,4 @@ def _forward_common( mask = self.emask(atype) # nf x nloc x nod outs = outs * mask[:, :, None].astype(outs.dtype) - return {self.var_name: outs.to(env.GLOBAL_PD_FLOAT_PRECISION)} + return {self.var_name: outs.astype(env.GLOBAL_PD_FLOAT_PRECISION)} diff --git a/deepmd/pd/train/training.py b/deepmd/pd/train/training.py index e703917c59..36cbd46caf 100644 --- a/deepmd/pd/train/training.py +++ b/deepmd/pd/train/training.py @@ -57,6 +57,7 @@ DEVICE, JIT, LOCAL_RANK, + NUM_WORKERS, SAMPLER_RECORD, enable_prim, ) @@ -179,13 +180,12 @@ def get_dataloader_and_buffer(_data, _params): _dataloader = DataLoader( _data, batch_sampler=paddle.io.BatchSampler( - sampler=_sampler, drop_last=False + sampler=_sampler, + drop_last=False, ), - # batch_size=None, - num_workers=0 + num_workers=NUM_WORKERS if dist.is_available() else 0, # setting to 0 diverges the behavior of its iterator; should be >=1 - # drop_last=False, collate_fn=lambda batch: batch[0], # prevent extra conversion # pin_memory=True, ) diff --git a/deepmd/pd/utils/neighbor_stat.py b/deepmd/pd/utils/neighbor_stat.py index 1b9f55b2dd..990d3d4f08 100644 --- a/deepmd/pd/utils/neighbor_stat.py +++ b/deepmd/pd/utils/neighbor_stat.py @@ -90,10 +90,11 @@ def forward( # remove the diagonal elements mask = paddle.eye(nloc, nall).to(dtype=paddle.bool, device=diff.place) # diff[:, mask] = float("inf") - diff.masked_fill_( - paddle.broadcast_to(mask.unsqueeze([0, -1]), diff.shape), - paddle.to_tensor(float("inf")), - ) + # diff.masked_fill_( + # paddle.broadcast_to(mask.unsqueeze([0, -1]), diff.shape), + # paddle.to_tensor(float("inf")), + # ) + diff[paddle.broadcast_to(mask.unsqueeze([0, -1]), diff.shape)] = float("inf") rr2 = paddle.sum(paddle.square(diff), axis=-1) min_rr2 = paddle.min(rr2, axis=-1) # count the number of neighbors @@ -102,12 +103,12 @@ def forward( nnei = paddle.zeros((nframes, nloc, self.ntypes), dtype=paddle.int64) for ii in range(self.ntypes): nnei[:, :, ii] = paddle.sum( - mask & extend_atype.equal(ii)[:, None, :], axis=-1 + mask & ((extend_atype == ii)[:, None, :]), axis=-1 ) else: mask = rr2 < self.rcut**2 # virtual types (<0) are not counted - nnei = paddle.sum(mask & extend_atype.ge(0)[:, None, :], axis=-1).reshape( + nnei = paddle.sum(mask & ((extend_atype > 0)[:, None, :]), axis=-1).reshape( [nframes, nloc, 1] ) max_nnei = paddle.max(nnei, axis=1) @@ -184,11 +185,12 @@ def _execute( cell The cell. """ - minrr2, max_nnei = self.op( - paddle.to_tensor(coord, place=DEVICE), - paddle.to_tensor(atype, place=DEVICE), - paddle.to_tensor(cell, place=DEVICE) if cell is not None else None, - ) + with paddle.no_grad(): + minrr2, max_nnei = self.op( + paddle.to_tensor(coord, place=DEVICE), + paddle.to_tensor(atype, place=DEVICE), + paddle.to_tensor(cell, place=DEVICE) if cell is not None else None, + ) minrr2 = minrr2.numpy() max_nnei = max_nnei.numpy() return minrr2, max_nnei diff --git a/deepmd/pt/entrypoints/main.py b/deepmd/pt/entrypoints/main.py index 7ba5f0b63a..274a682f17 100644 --- a/deepmd/pt/entrypoints/main.py +++ b/deepmd/pt/entrypoints/main.py @@ -555,7 +555,7 @@ def main(args: Optional[Union[List[str], argparse.Namespace]] = None): else: FLAGS = args - set_log_handles(FLAGS.log_level, FLAGS.log_path, mpi_log=None) + set_log_handles(FLAGS.log_level, Path(FLAGS.log_path), mpi_log=None) log.debug("Log handles were successfully set") log.info("DeePMD version: %s", __version__) From 2e79d685caed8b30efd094e7133037c71a7bd0aa Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Sat, 14 Sep 2024 16:50:22 +0800 Subject: [PATCH 10/93] update inference code(WIP) --- deepmd/pd/entrypoints/main.py | 5 +- deepmd/pd/model/model/make_model.py | 3 +- deepmd/pd/utils/nlist.py | 45 +- examples/water/lmp/in.lammps | 8 +- source/CMakeLists.txt | 319 ++++---- source/api_c/include/deepmd.hpp | 1 + source/api_cc/include/DeepPotPD.h | 386 ++++++++++ source/api_cc/src/DeepPot.cc | 16 +- source/api_cc/src/DeepPotPD.cc | 1105 +++++++++++++++++++++++++++ source/api_cc/src/common.cc | 3 + source/install/build_cc.sh | 3 +- source/install/test_cc_local.sh | 2 +- source/lmp/pair_deepmd.cpp | 16 +- source/op/pt/comm.cc | 3 +- 14 files changed, 1730 insertions(+), 185 deletions(-) create mode 100644 source/api_cc/include/DeepPotPD.h create mode 100644 source/api_cc/src/DeepPotPD.cc diff --git a/deepmd/pd/entrypoints/main.py b/deepmd/pd/entrypoints/main.py index 9a50aaefce..f05543d239 100644 --- a/deepmd/pd/entrypoints/main.py +++ b/deepmd/pd/entrypoints/main.py @@ -366,10 +366,13 @@ def freeze(FLAGS): paddle.jit.save( model, path=FLAGS.output, + skip_prune_program=True, # extra_files, ) + pir_flag = os.getenv("FLAGS_enable_pir_api", "false") + suffix = "json" if pir_flag.lower() in ["true", "1"] else "pdmodel" log.info( - f"Paddle inference model has been exported to: {FLAGS.output}.pdmodel(.pdiparams)" + f"Paddle inference model has been exported to: {FLAGS.output}.{suffix}(.pdiparams)" ) diff --git a/deepmd/pd/model/model/make_model.py b/deepmd/pd/model/model/make_model.py index 2747bbc480..597171d596 100644 --- a/deepmd/pd/model/model/make_model.py +++ b/deepmd/pd/model/model/make_model.py @@ -429,7 +429,8 @@ def _format_nlist( axis=-1, ) - if n_nnei > nnei or extra_nlist_sort: + # if n_nnei > nnei or extra_nlist_sort: + if False: n_nf, n_nloc, n_nnei = nlist.shape m_real_nei = nlist >= 0 nlist = paddle.where(m_real_nei, nlist, 0) diff --git a/deepmd/pd/utils/nlist.py b/deepmd/pd/utils/nlist.py index 39c1339989..ef27be31eb 100644 --- a/deepmd/pd/utils/nlist.py +++ b/deepmd/pd/utils/nlist.py @@ -6,7 +6,6 @@ Union, ) -import numpy as np import paddle from deepmd.pd.utils import ( @@ -101,10 +100,11 @@ def build_neighbor_list( nall = coord.shape[1] // 3 # fill virtual atoms with large coords so they are not neighbors of any # real atom. - if np.prod(coord.shape) > 0: + # if coord.numel().item() > 0: + if True > 0: xmax = paddle.max(coord) + 2.0 * rcut else: - xmax = paddle.zeros([1], dtype=coord.dtype).to(device=coord.place) + 2.0 * rcut + xmax = paddle.zeros([], dtype=coord.dtype).to(device=coord.place) + 2.0 * rcut # nf x nall is_vir = atype < 0 coord1 = paddle.where( @@ -118,7 +118,8 @@ def build_neighbor_list( diff = coord1.reshape([batch_size, -1, 3]).unsqueeze(1) - coord0.reshape( [batch_size, -1, 3] ).unsqueeze(2) - assert list(diff.shape) == [batch_size, nloc, nall, 3] + if paddle.in_dynamic_mode(): + assert list(diff.shape) == [batch_size, nloc, nall, 3] # nloc x nall # rr = paddle.linalg.norm(diff, axis=-1) rr = aux.norm(diff, axis=-1) @@ -147,7 +148,8 @@ def _trim_mask_distinguish_nlist( nsel = sum(sel) # nloc x nsel batch_size, nloc, nnei = rr.shape - assert batch_size == is_vir_cntl.shape[0] + if paddle.in_dynamic_mode(): + assert batch_size == is_vir_cntl.shape[0] if nsel <= nnei: rr = rr[:, :, :nsel] nlist = nlist[:, :, :nsel] @@ -171,7 +173,8 @@ def _trim_mask_distinguish_nlist( ], axis=-1, ) - assert list(nlist.shape) == [batch_size, nloc, nsel] + if paddle.in_dynamic_mode(): + assert list(nlist.shape) == [batch_size, nloc, nsel] nlist = paddle.where( paddle.logical_or((rr > rcut), is_vir_cntl[:, :nloc, None]), -1, nlist ) @@ -264,7 +267,8 @@ def build_directional_neighbor_list( sel = [sel] # nloc x nall x 3 diff = coord_neig[:, None, :, :] - coord_cntl[:, :, None, :] - assert list(diff.shape) == [batch_size, nloc_cntl, nall_neig, 3] + if paddle.in_dynamic_mode(): + assert list(diff.shape) == [batch_size, nloc_cntl, nall_neig, 3] # nloc x nall # rr = paddle.linalg.norm(diff, axis=-1) rr = aux.norm(diff, axis=-1) @@ -372,7 +376,8 @@ def build_multiple_neighbor_list( value being the corresponding nlist. """ - assert len(rcuts) == len(nsels) + if paddle.in_dynamic_mode(): + assert len(rcuts) == len(nsels) if len(rcuts) == 0: return {} nb, nloc, nsel = nlist.shape @@ -473,17 +478,25 @@ def extend_coord_with_ghosts( # 3 nbuff = paddle.amax(nbuff, axis=0) # faster than paddle.max nbuff_cpu = nbuff.cpu() - xi = paddle.arange(-nbuff_cpu[0], nbuff_cpu[0] + 1, 1).to( - dtype=env.GLOBAL_PD_FLOAT_PRECISION, device="cpu" + xi = ( + paddle.arange(-nbuff_cpu[0], nbuff_cpu[0] + 1, 1) + .to(dtype=env.GLOBAL_PD_FLOAT_PRECISION) + .cpu() ) # pylint: disable=no-explicit-dtype - yi = paddle.arange(-nbuff_cpu[1], nbuff_cpu[1] + 1, 1).to( - dtype=env.GLOBAL_PD_FLOAT_PRECISION, device="cpu" + yi = ( + paddle.arange(-nbuff_cpu[1], nbuff_cpu[1] + 1, 1) + .to(dtype=env.GLOBAL_PD_FLOAT_PRECISION) + .cpu() ) # pylint: disable=no-explicit-dtype - zi = paddle.arange(-nbuff_cpu[2], nbuff_cpu[2] + 1, 1).to( - dtype=env.GLOBAL_PD_FLOAT_PRECISION, device="cpu" + zi = ( + paddle.arange(-nbuff_cpu[2], nbuff_cpu[2] + 1, 1) + .to(dtype=env.GLOBAL_PD_FLOAT_PRECISION) + .cpu() ) # pylint: disable=no-explicit-dtype - eye_3 = paddle.eye(3, dtype=env.GLOBAL_PD_FLOAT_PRECISION).to( - dtype=env.GLOBAL_PD_FLOAT_PRECISION, device="cpu" + eye_3 = ( + paddle.eye(3, dtype=env.GLOBAL_PD_FLOAT_PRECISION) + .to(dtype=env.GLOBAL_PD_FLOAT_PRECISION) + .cpu() ) xyz = xi.reshape([-1, 1, 1, 1]) * eye_3[0] xyz = xyz + yi.reshape([1, -1, 1, 1]) * eye_3[1] diff --git a/examples/water/lmp/in.lammps b/examples/water/lmp/in.lammps index ea3b5d52cd..805ef8bed0 100644 --- a/examples/water/lmp/in.lammps +++ b/examples/water/lmp/in.lammps @@ -12,7 +12,7 @@ mass 1 16 mass 2 2 # See https://deepmd.rtfd.io/lammps/ for usage -pair_style deepmd frozen_model.pb +pair_style deepmd /workspace/hesensen/deepmd_backend/deepmd_paddle_new/examples/water/se_e2_a/torch_infer.pth # If atom names (O H in this example) are not set in the pair_coeff command, the type_map defined by the training parameter will be used by default. pair_coeff * * O H @@ -21,7 +21,7 @@ velocity all create 330.0 23456789 fix 1 all nvt temp 330.0 330.0 0.5 timestep 0.0005 thermo_style custom step pe ke etotal temp press vol -thermo 100 -dump 1 all custom 100 water.dump id type x y z +thermo 1 +dump 1 all custom 1 water.dump id type x y z -run 1000 +run 10 diff --git a/source/CMakeLists.txt b/source/CMakeLists.txt index 71b3dca1ea..1bbab7e398 100644 --- a/source/CMakeLists.txt +++ b/source/CMakeLists.txt @@ -2,25 +2,138 @@ cmake_minimum_required(VERSION 3.16) project(DeePMD) -option(ENABLE_TENSORFLOW "Enable TensorFlow interface" OFF) -option(ENABLE_PYTORCH "Enable PyTorch interface" OFF) +macro(safe_set_static_flag) + foreach(flag_var + CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE + CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO) + if(${flag_var} MATCHES "/MD") + string(REGEX REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}") + endif(${flag_var} MATCHES "/MD") + endforeach(flag_var) +endmacro() + +if(NOT DEFINED PADDLE_LIB) + message( + WARNING + "Optional arg: 'PADDLE_LIB' is not set, skip all compilation of paddle code now. " + "And do not forget to set 'PADDLE_LIB' with '-DPADDLE_LIB=/path/paddle/lib' before " + "using paddle custom operators") +endif() + +if(DEFINED PADDLE_LIB) + set(PADDLE_LIB + ${PADDLE_LIB} + CACHE PATH "/path/paddle/lib") + + include_directories("${PADDLE_LIB}/") + set(PADDLE_LIB_THIRD_PARTY_PATH "${PADDLE_LIB}/third_party/install/") + + include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}protobuf/include") + include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}glog/include") + include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}gflags/include") + include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}xxhash/include") + + link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}protobuf/lib") + link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}glog/lib") + link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}gflags/lib") + link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}xxhash/lib") + link_directories("${PADDLE_LIB}/paddle/lib") +endif() + +# add custom operators +option(USE_TENSORRT "Compile demo with TensorRT." OFF) + +if(WITH_GPU) + if(NOT WIN32) + set(CUDA_LIB + "/usr/local/cuda/lib64/" + CACHE STRING "CUDA Library") + else() + if(CUDA_LIB STREQUAL "") + set(CUDA_LIB + "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\lib\\x64" + ) + endif() + endif(NOT WIN32) +endif() + +if(NOT WIN32) + if(USE_TENSORRT AND WITH_GPU) + include_directories("${TENSORRT_INCLUDE_DIR}") + link_directories("${TENSORRT_LIB_DIR}") + endif() +endif(NOT WIN32) + +if(DEFINED PADDLE_LIB) + if(WITH_STATIC_LIB) + set(DEPS + ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_STATIC_LIBRARY_SUFFIX} + ) + else() + if(WIN32) + set(DEPS + ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_STATIC_LIBRARY_SUFFIX} + ) + else() + set(DEPS + ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_SHARED_LIBRARY_SUFFIX} + ) + endif() + endif() +endif() + +if(NOT WIN32) + set(EXTERNAL_LIB "-lrt -ldl -lpthread") + set(DEPS + ${DEPS} + ${MATH_LIB} + ${MKLDNN_LIB} + glog + gflags + protobuf + xxhash + ${EXTERNAL_LIB}) +else() + set(DEPS + ${DEPS} + ${MATH_LIB} + ${MKLDNN_LIB} + glog + gflags_static + libprotobuf + xxhash + ${EXTERNAL_LIB}) + set(DEPS ${DEPS} shlwapi.lib) +endif(NOT WIN32) + +if(WITH_GPU) + if(NOT WIN32) + if(USE_TENSORRT) + set(DEPS ${DEPS} + ${TENSORRT_LIB_DIR}/libnvinfer${CMAKE_SHARED_LIBRARY_SUFFIX}) + set(DEPS + ${DEPS} + ${TENSORRT_LIB_DIR}/libnvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX}) + endif() + set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX}) + else() + if(USE_TENSORRT) + set(DEPS ${DEPS} + ${TENSORRT_LIB_DIR}/nvinfer${CMAKE_STATIC_LIBRARY_SUFFIX}) + set(DEPS ${DEPS} + ${TENSORRT_LIB_DIR}/nvinfer_plugin${CMAKE_STATIC_LIBRARY_SUFFIX}) + endif() + set(DEPS ${DEPS} ${CUDA_LIB}/cudart${CMAKE_STATIC_LIBRARY_SUFFIX}) + set(DEPS ${DEPS} ${CUDA_LIB}/cublas${CMAKE_STATIC_LIBRARY_SUFFIX}) + set(DEPS ${DEPS} ${CUDA_LIB}/cudnn${CMAKE_STATIC_LIBRARY_SUFFIX}) + endif() +endif() + option(BUILD_TESTING "Build test and enable converage" OFF) set(DEEPMD_C_ROOT "" CACHE PATH "Path to imported DeePMD-kit C library") -set(CMAKE_CXX_STANDARD 11) -macro(set_if_higher VARIABLE VALUE) - # ${VARIABLE} is a variable name, not a string - if(${VARIABLE} LESS "${VALUE}") - set(${VARIABLE} ${VALUE}) - endif() -endmacro() -if(NOT DEEPMD_C_ROOT) - # we can still allow C++ 11 for programs linked to the C library - set_if_higher(CMAKE_CXX_STANDARD 14) -endif() - if(BUILD_TESTING) enable_testing() add_subdirectory(${CMAKE_SOURCE_DIR}/cmake/coverage_config coverage_config) @@ -39,6 +152,10 @@ if((NOT BUILD_PY_IF) AND (NOT BUILD_CPP_IF)) endif() if(BUILD_CPP_IF AND BUILD_TESTING) + if(NOT INSTALL_TENSORFLOW) + # some errors in conda packages... + find_package(GTest) + endif() if(NOT GTEST_LIBRARIES) configure_file(${CMAKE_CURRENT_SOURCE_DIR}/cmake/googletest.cmake.in googletest-download/CMakeLists.txt @ONLY) @@ -107,16 +224,12 @@ set(DP_VARIANT "cpu") # define USE_CUDA_TOOLKIT if(USE_CUDA_TOOLKIT) - cmake_minimum_required(VERSION 3.23) - find_package(CUDAToolkit REQUIRED) - if(NOT DEFINED CMAKE_CUDA_COMPILER) - set(CMAKE_CUDA_COMPILER ${CUDAToolkit_NVCC_EXECUTABLE}) - endif() - if(NOT DEFINED CMAKE_CUDA_HOST_COMPILER) - set(CMAKE_CUDA_HOST_COMPILER ${CMAKE_CXX_COMPILER}) - endif() + set(CUDA_USE_STATIC_CUDA_RUNTIME + OFF + CACHE INTERNAL "") + find_package(CUDA REQUIRED) add_definitions("-DGOOGLE_CUDA") - message(STATUS "Found CUDA in ${CUDAToolkit_BIN_DIR}, build nv GPU support") + message(STATUS "Found CUDA in ${CUDA_TOOLKIT_ROOT_DIR}, build nv GPU support") set(DP_VARIANT "cuda") else() message(STATUS "Will not build nv GPU support") @@ -124,15 +237,10 @@ endif(USE_CUDA_TOOLKIT) # define USE_ROCM_TOOLKIT if(USE_ROCM_TOOLKIT) - cmake_minimum_required(VERSION 3.21) - include(CMakeDetermineHIPCompiler) - list(APPEND CMAKE_PREFIX_PATH ${CMAKE_HIP_COMPILER_ROCM_ROOT}) - find_package(hip REQUIRED) - find_package(hipcub REQUIRED) + find_package(ROCM REQUIRED) add_definitions("-DTENSORFLOW_USE_ROCM") - message( - STATUS - "Found ROCM in ${CMAKE_HIP_COMPILER_ROCM_ROOT}, build AMD GPU support") + add_compile_definitions(__HIP_PLATFORM_HCC__) + message(STATUS "Found ROCM in ${ROCM_ROOT}, build AMD GPU support") set(DP_VARIANT "rocm") else() message(STATUS "Will not build AMD GPU support") @@ -141,11 +249,7 @@ endif(USE_ROCM_TOOLKIT) set(DEEPMD_SOURCE_DIR ${PROJECT_SOURCE_DIR}/..) # setup tensorflow libraries by python -if(INSTALL_TENSORFLOW) - set(USE_TF_PYTHON_LIBS TRUE) -endif(INSTALL_TENSORFLOW) if(USE_TF_PYTHON_LIBS) - set(ENABLE_TENSORFLOW TRUE) if(NOT "$ENV{CIBUILDWHEEL}" STREQUAL "1") find_package( Python @@ -156,110 +260,10 @@ if(USE_TF_PYTHON_LIBS) set(PYTHON_INCLUDE_DIRS ${PYTHON_INCLUDE_DIR}) endif() endif(USE_TF_PYTHON_LIBS) -if(TENSORFLOW_ROOT) - set(ENABLE_TENSORFLOW TRUE) -endif() # find tensorflow, I need tf abi info -if(ENABLE_TENSORFLOW AND NOT DEEPMD_C_ROOT) - find_package(tensorflow REQUIRED) - list(APPEND BACKEND_LIBRARY_PATH ${TensorFlow_LIBRARY_PATH}) - list(APPEND BACKEND_INCLUDE_DIRS ${TENSORFLOW_INCLUDE_DIRS}) -endif() -if(BUILD_CPP_IF - AND USE_PT_PYTHON_LIBS - AND NOT CMAKE_CROSSCOMPILING - AND NOT SKBUILD) - find_package( - Python - COMPONENTS Interpreter - REQUIRED) - execute_process( - COMMAND ${Python_EXECUTABLE} -c - "import torch;print(torch.utils.cmake_prefix_path)" - WORKING_DIRECTORY ${PROJECT_SOURCE_DIR} - OUTPUT_VARIABLE PYTORCH_CMAKE_PREFIX_PATH - RESULT_VARIABLE PYTORCH_CMAKE_PREFIX_PATH_RESULT_VAR - ERROR_VARIABLE PYTORCH_CMAKE_PREFIX_PATH_ERROR_VAR - OUTPUT_STRIP_TRAILING_WHITESPACE) - if(NOT ${PYTORCH_CMAKE_PREFIX_PATH_RESULT_VAR} EQUAL 0) - message( - FATAL_ERROR - "Cannot determine PyTorch CMake prefix path, error code: $PYTORCH_CMAKE_PREFIX_PATH_RESULT_VAR}, error message: ${PYTORCH_CMAKE_PREFIX_PATH_ERROR_VAR}" - ) - endif() - list(APPEND CMAKE_PREFIX_PATH ${PYTORCH_CMAKE_PREFIX_PATH}) -endif() -if(ENABLE_PYTORCH AND NOT DEEPMD_C_ROOT) - find_package(Torch REQUIRED) - if(NOT Torch_VERSION VERSION_LESS "2.1.0") - set_if_higher(CMAKE_CXX_STANDARD 17) - elseif(NOT Torch_VERSION VERSION_LESS "1.5.0") - set_if_higher(CMAKE_CXX_STANDARD 14) - endif() - string(REGEX MATCH "_GLIBCXX_USE_CXX11_ABI=([0-9]+)" CXXABI_PT_MATCH - "${TORCH_CXX_FLAGS}") - if(CXXABI_PT_MATCH) - set(OP_CXX_ABI_PT ${CMAKE_MATCH_1}) - message(STATUS "PyTorch CXX11 ABI: ${CMAKE_MATCH_1}") - if(DEFINED OP_CXX_ABI) - if(NOT ${CMAKE_MATCH_1} EQUAL ${OP_CXX_ABI}) - if(NOT BUILD_PY_IF) - message( - FATAL_ERROR - "PyTorch CXX11 ABI mismatch TensorFlow: ${CMAKE_MATCH_1} != ${OP_CXX_ABI}" - ) - else() - if(NOT BUILD_CPP_IF) - message( - STATUS - "PyTorch CXX11 ABI mismatch TensorFlow: ${CMAKE_MATCH_1} != ${OP_CXX_ABI}. " - "Try to build libraries with both ABIs.") - else() - message( - WARNING - "PyTorch CXX11 ABI mismatch TensorFlow: ${CMAKE_MATCH_1} != ${OP_CXX_ABI}. " - "PyTorch C++ OP will be built but PyTorch support for C++ libraries will be disabled. " - "Note that we don't officially support building C++ libraries in the Python package, " - "except for the wheels we officially release.") - endif() - set(DEEPMD_BUILD_COMPAT_CXXABI ON) - set(OP_CXX_ABI_COMPAT ${OP_CXX_ABI_PT}) - endif() - else() - set(DEEPMD_BUILD_COMPAT_CXXABI OFF) - endif() - else() - set(OP_CXX_ABI ${CMAKE_MATCH_1}) - add_definitions(-D_GLIBCXX_USE_CXX11_ABI=${OP_CXX_ABI}) - endif() - else() - # Maybe in macos/windows - if(NOT DEFINED OP_CXX_ABI) - set(OP_CXX_ABI 0) - endif() - set(OP_CXX_ABI_PT "${OP_CXX_ABI}") - endif() - # get torch directory get the directory of the target "torch" - get_target_property(_TORCH_LOCATION torch LOCATION) - get_filename_component(PyTorch_LIBRARY_PATH ${_TORCH_LOCATION} DIRECTORY) - list(APPEND BACKEND_LIBRARY_PATH ${PyTorch_LIBRARY_PATH}) - list(APPEND BACKEND_INCLUDE_DIRS ${TORCH_INCLUDE_DIRS}) -endif() -# log enabled backends if(NOT DEEPMD_C_ROOT) - message(STATUS "Enabled backends:") - if(ENABLE_TENSORFLOW) - message(STATUS "- TensorFlow") - endif() - if(ENABLE_PYTORCH) - message(STATUS "- PyTorch") - endif() - if(NOT ENABLE_TENSORFLOW - AND NOT ENABLE_PYTORCH - AND NOT BUILD_PY_IF) - message(FATAL_ERROR "No backend is enabled.") - endif() + find_package(tensorflow REQUIRED) endif() # find threads @@ -298,6 +302,7 @@ if(BUILD_CPP_IF) set(LIB_DEEPMD_CC "deepmd_cc") set(LIB_DEEPMD_C "deepmd_c") if(USE_CUDA_TOOLKIT) + set(LIB_DEEPMD_OP_DEVICE "deepmd_paddle_op_cuda") set(LIB_DEEPMD_OP_DEVICE "deepmd_op_cuda") elseif(USE_ROCM_TOOLKIT) set(LIB_DEEPMD_OP_DEVICE "deepmd_op_rocm") @@ -306,8 +311,7 @@ if(BUILD_CPP_IF) endif() if(CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 4.8) set(LIB_DEEPMD_NATIVE "deepmd_native_md") - set(LIB_DEEPMD_IPI "deepmd_ipi") - set(LIB_DEEPMD_GROMACS "deepmd_gromacs") + # set(LIB_DEEPMD_IPI "deepmd_ipi") set(LIB_DEEPMD_GROMACS "deepmd_gromacs") else() message( STATUS @@ -346,21 +350,12 @@ if(DEEPMD_C_ROOT) IMPORTED_LOCATION "${deepmd_c}" INTERFACE_INCLUDE_DIRECTORIES "${DEEPMD_INCLUDE_C_DIR}/deepmd") # use variable for TF path to set deepmd_c path - set(TENSORFLOW_ROOT "${DEEPMD_C_ROOT}") set(TensorFlow_LIBRARY_PATH "${DEEPMD_C_ROOT}/lib") - set(BACKEND_LIBRARY_PATH "${DEEPMD_C_ROOT}/lib") set(TENSORFLOW_INCLUDE_DIRS "${DEEPMD_C_ROOT}/include") - set(BACKEND_INCLUDE_DIRS "${DEEPMD_C_ROOT}/include") - set(TORCH_LIBRARIES "${DEEPMD_C_ROOT}/lib/libtorch.so") endif() if(NOT DEEPMD_C_ROOT) - if(ENABLE_TENSORFLOW) - add_subdirectory(op/tf/) - endif() - if(ENABLE_PYTORCH) - add_subdirectory(op/pt/) - endif() + # add_subdirectory(op/) add_subdirectory(lib/) endif() if(BUILD_PY_IF) @@ -376,19 +371,33 @@ if(BUILD_CPP_IF) add_subdirectory(lmp/) endif() if(CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 4.8) - # add_subdirectory (md/) - if(ENABLE_IPI OR NOT BUILD_PY_IF) - add_subdirectory(ipi/) - endif() - if(NOT BUILD_PY_IF) - add_subdirectory(gmx/) - endif() + # add_subdirectory (md/) if(ENABLE_IPI OR NOT BUILD_PY_IF AND NOT + # DEEPMD_C_ROOT) # ipi has a dependency on libdeepmd add_subdirectory(ipi/) + # endif() if(NOT BUILD_PY_IF) add_subdirectory(gmx/) endif() endif() if(BUILD_NODEJS_IF) add_subdirectory(nodejs/) endif() endif(BUILD_CPP_IF) +# if(WIN32) if(USE_TENSORRT) add_custom_command(TARGET ${DEMO_NAME} POST_BUILD +# COMMAND ${CMAKE_COMMAND} -E copy +# ${TENSORRT_LIB_DIR}/nvinfer${CMAKE_SHARED_LIBRARY_SUFFIX} +# ${CMAKE_BINARY_DIR}/${CMAKE_BUILD_TYPE} COMMAND ${CMAKE_COMMAND} -E copy +# ${TENSORRT_LIB_DIR}/nvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX} +# ${CMAKE_BINARY_DIR}/${CMAKE_BUILD_TYPE} ) endif() if(WITH_MKL) +# add_custom_command(TARGET ${DEMO_NAME} POST_BUILD COMMAND ${CMAKE_COMMAND} -E +# copy ${MATH_LIB_PATH}/lib/mklml.dll ${CMAKE_BINARY_DIR}/Release COMMAND +# ${CMAKE_COMMAND} -E copy ${MATH_LIB_PATH}/lib/libiomp5md.dll +# ${CMAKE_BINARY_DIR}/Release COMMAND ${CMAKE_COMMAND} -E copy +# ${MKLDNN_PATH}/lib/mkldnn.dll ${CMAKE_BINARY_DIR}/Release ) else() +# add_custom_command(TARGET ${DEMO_NAME} POST_BUILD COMMAND ${CMAKE_COMMAND} -E +# copy ${OPENBLAS_LIB_PATH}/lib/openblas.dll ${CMAKE_BINARY_DIR}/Release ) +# endif() if(NOT WITH_STATIC_LIB) add_custom_command(TARGET ${DEMO_NAME} +# POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy +# "${PADDLE_LIB}/paddle/lib/paddle_fluid.dll" +# ${CMAKE_BINARY_DIR}/${CMAKE_BUILD_TYPE} ) endif() endif() + # uninstall target configure_file( "${CMAKE_CURRENT_SOURCE_DIR}/cmake/cmake_uninstall.cmake.in" diff --git a/source/api_c/include/deepmd.hpp b/source/api_c/include/deepmd.hpp index 9d0310d99a..1c23612293 100644 --- a/source/api_c/include/deepmd.hpp +++ b/source/api_c/include/deepmd.hpp @@ -685,6 +685,7 @@ class DeepPot { << std::endl; return; } + std::cout << "** [deepmd.hpp] DeepPot.init" << std::endl; dp = DP_NewDeepPotWithParam2(model.c_str(), gpu_rank, file_content.c_str(), file_content.size()); DP_CHECK_OK(DP_DeepPotCheckOK, dp); diff --git a/source/api_cc/include/DeepPotPD.h b/source/api_cc/include/DeepPotPD.h new file mode 100644 index 0000000000..410873b39e --- /dev/null +++ b/source/api_cc/include/DeepPotPD.h @@ -0,0 +1,386 @@ +// SPDX-License-Identifier: LGPL-3.0-or-later +#pragma once + +#include "paddle/include/paddle_inference_api.h" +// #include "paddle/extension.h" +// #include "paddle/phi/backends/all_context.h" + +#include "DeepPot.h" +#include "common.h" +#include "neighbor_list.h" + +namespace deepmd { +/** + * @brief Paddle implementation for Deep Potential. + **/ +class DeepPotPD : public DeepPotBase { + public: + /** + * @brief DP constructor without initialization. + **/ + DeepPotPD(); + ~DeepPotPD(); + /** + * @brief DP constructor with initialization. + * @param[in] model The name of the frozen model file. + * @param[in] gpu_rank The GPU rank. Default is 0. + * @param[in] file_content The content of the model file. If it is not empty, + *DP will read from the string instead of the file. + **/ + DeepPotPD(const std::string& model, + const int& gpu_rank = 0, + const std::string& file_content = ""); + /** + * @brief Initialize the DP. + * @param[in] model The name of the frozen model file. + * @param[in] gpu_rank The GPU rank. Default is 0. + * @param[in] file_content The content of the model file. If it is not empty, + *DP will read from the string instead of the file. + **/ + void init(const std::string& model, + const int& gpu_rank = 0, + const std::string& file_content = ""); + + private: + /** + * @brief Evaluate the energy, force, virial, atomic energy, and atomic virial + *by using this DP. + * @param[out] ener The system energy. + * @param[out] force The force on each atom. + * @param[out] virial The virial. + * @param[out] atom_energy The atomic energy. + * @param[out] atom_virial The atomic virial. + * @param[in] coord The coordinates of atoms. The array should be of size + *nframes x natoms x 3. + * @param[in] atype The atom types. The list should contain natoms ints. + * @param[in] box The cell of the region. The array should be of size nframes + *x 9. + * @param[in] fparam The frame parameter. The array can be of size : + * nframes x dim_fparam. + * dim_fparam. Then all frames are assumed to be provided with the same + *fparam. + * @param[in] aparam The atomic parameter The array can be of size : + * nframes x natoms x dim_aparam. + * natoms x dim_aparam. Then all frames are assumed to be provided with the + *same aparam. + * @param[in] atomic Whether to compute the atomic energy and virial. + **/ + template + void compute(ENERGYVTYPE& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + /** + * @brief Evaluate the energy, force, virial, atomic energy, and atomic virial + *by using this DP. + * @param[out] ener The system energy. + * @param[out] force The force on each atom. + * @param[out] virial The virial. + * @param[out] atom_energy The atomic energy. + * @param[out] atom_virial The atomic virial. + * @param[in] coord The coordinates of atoms. The array should be of size + *nframes x natoms x 3. + * @param[in] atype The atom types. The list should contain natoms ints. + * @param[in] box The cell of the region. The array should be of size nframes + *x 9. + * @param[in] nghost The number of ghost atoms. + * @param[in] lmp_list The input neighbour list. + * @param[in] ago Update the internal neighbour list if ago is 0. + * @param[in] fparam The frame parameter. The array can be of size : + * nframes x dim_fparam. + * dim_fparam. Then all frames are assumed to be provided with the same + *fparam. + * @param[in] aparam The atomic parameter The array can be of size : + * nframes x natoms x dim_aparam. + * natoms x dim_aparam. Then all frames are assumed to be provided with the + *same aparam. + * @param[in] atomic Whether to compute the atomic energy and virial. + **/ + template + void compute(ENERGYVTYPE& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const int nghost, + const InputNlist& lmp_list, + const int& ago, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + /** + * @brief Evaluate the energy, force, and virial with the mixed type + *by using this DP. + * @param[out] ener The system energy. + * @param[out] force The force on each atom. + * @param[out] virial The virial. + * @param[in] nframes The number of frames. + * @param[in] coord The coordinates of atoms. The array should be of size + *nframes x natoms x 3. + * @param[in] atype The atom types. The array should be of size nframes x + *natoms. + * @param[in] box The cell of the region. The array should be of size nframes + *x 9. + * @param[in] fparam The frame parameter. The array can be of size : + * nframes x dim_fparam. + * dim_fparam. Then all frames are assumed to be provided with the same + *fparam. + * @param[in] aparam The atomic parameter The array can be of size : + * nframes x natoms x dim_aparam. + * natoms x dim_aparam. Then all frames are assumed to be provided with the + *same aparam. + * @param[in] atomic Whether to compute the atomic energy and virial. + **/ + // template + // void compute_mixed_type(ENERGYVTYPE& ener, + // std::vector& force, + // std::vector& virial, + // const int& nframes, + // const std::vector& coord, + // const std::vector& atype, + // const std::vector& box, + // const std::vector& fparam, + // const std::vector& aparam, + // const bool atomic); + /** + * @brief Evaluate the energy, force, and virial with the mixed type + *by using this DP. + * @param[out] ener The system energy. + * @param[out] force The force on each atom. + * @param[out] virial The virial. + * @param[out] atom_energy The atomic energy. + * @param[out] atom_virial The atomic virial. + * @param[in] nframes The number of frames. + * @param[in] coord The coordinates of atoms. The array should be of size + *nframes x natoms x 3. + * @param[in] atype The atom types. The array should be of size nframes x + *natoms. + * @param[in] box The cell of the region. The array should be of size nframes + *x 9. + * @param[in] fparam The frame parameter. The array can be of size : + * nframes x dim_fparam. + * dim_fparam. Then all frames are assumed to be provided with the same + *fparam. + * @param[in] aparam The atomic parameter The array can be of size : + * nframes x natoms x dim_aparam. + * natoms x dim_aparam. Then all frames are assumed to be provided with the + *same aparam. + * @param[in] atomic Whether to compute the atomic energy and virial. + **/ + template + void compute_mixed_type(ENERGYVTYPE& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const int& nframes, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + + public: + /** + * @brief Get the cutoff radius. + * @return The cutoff radius. + **/ + double cutoff() const { + assert(inited); + return rcut; + }; + /** + * @brief Get the number of types. + * @return The number of types. + **/ + int numb_types() const { + assert(inited); + return ntypes; + }; + /** + * @brief Get the number of types with spin. + * @return The number of types with spin. + **/ + int numb_types_spin() const { + assert(inited); + return ntypes_spin; + }; + /** + * @brief Get the dimension of the frame parameter. + * @return The dimension of the frame parameter. + **/ + int dim_fparam() const { + assert(inited); + return dfparam; + }; + /** + * @brief Get the dimension of the atomic parameter. + * @return The dimension of the atomic parameter. + **/ + int dim_aparam() const { + assert(inited); + return daparam; + }; + /** + * @brief Get the type map (element name of the atom types) of this model. + * @param[out] type_map The type map of this model. + **/ + void get_type_map(std::string& type_map); + + /** + * @brief Get whether the atom dimension of aparam is nall instead of fparam. + * @param[out] aparam_nall whether the atom dimension of aparam is nall + *instead of fparam. + **/ + bool is_aparam_nall() const { + assert(inited); + return aparam_nall; + }; + + // forward to template class + void computew(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + void computew(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + void computew(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const int nghost, + const InputNlist& inlist, + const int& ago, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + void computew(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const int nghost, + const InputNlist& inlist, + const int& ago, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + void computew_mixed_type(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const int& nframes, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + void computew_mixed_type(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const int& nframes, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + + private: + int num_intra_nthreads, num_inter_nthreads; + bool inited; + int ntypes; + int ntypes_spin; + int dfparam; + int daparam; + bool aparam_nall; + // copy neighbor list info from host + std::shared_ptr predictor = nullptr; + std::shared_ptr config = nullptr; + double rcut; + NeighborListData nlist_data; + int max_num_neighbors; + InputNlist nlist; + AtomMap atommap; + int gpu_id = 0; + int do_message_passing = 0; // 1:dpa2 model 0:others + bool gpu_enabled = true; + int dtype = paddle_infer::DataType::FLOAT32; + // paddle::Tensor firstneigh_tensor; + // std::unordered_map comm_dict; + /** + * @brief Translate Paddle exceptions to the DeePMD-kit exception. + * @param[in] f The function to run. + * @example translate_error([&](){...}); + */ + // void translate_error(std::function f); + /** + * @brief Validate the size of frame and atomic parameters. + * @param[in] nframes The number of frames. + * @param[in] nloc The number of local atoms. + * @param[in] fparam The frame parameter. + * @param[in] aparam The atomic parameter. + * @tparam VALUETYPE The type of the parameters, double or float. + */ + template + void validate_fparam_aparam(const int nframes, + const int& nloc, + const std::vector& fparam, + const std::vector& aparam) const; + /** + * @brief Tile the frame or atomic parameters if there is only + * a single frame of frame or atomic parameters. + * @param[out] out_param The tiled frame or atomic parameters. + * @param[in] nframes The number of frames. + * @param[in] dparam The dimension of the frame or atomic parameters in a + * frame. + * @param[in] param The frame or atomic parameters. + * @tparam VALUETYPE The type of the parameters, double or float. + */ + template + void tile_fparam_aparam(std::vector& out_param, + const int& nframes, + const int& dparam, + const std::vector& param) const; + +}; + +} // namespace deepmd diff --git a/source/api_cc/src/DeepPot.cc b/source/api_cc/src/DeepPot.cc index c184446288..18ddb6ab6c 100644 --- a/source/api_cc/src/DeepPot.cc +++ b/source/api_cc/src/DeepPot.cc @@ -12,6 +12,10 @@ #ifdef BUILD_PYTORCH #include "DeepPotPT.h" #endif +// #define BUILD_PADDLE +// #ifdef BUILD_PADDLE +#include "DeepPotPD.h" +// #endif #include "device.h" using namespace deepmd; @@ -30,6 +34,7 @@ DeepPot::~DeepPot() {} void DeepPot::init(const std::string& model, const int& gpu_rank, const std::string& file_content) { + std::cout << "** access here" << std::endl; if (inited) { std::cerr << "WARNING: deepmd-kit should not be initialized twice, do " "nothing at the second call of initializer" @@ -41,9 +46,12 @@ void DeepPot::init(const std::string& model, backend = deepmd::DPBackend::PyTorch; } else if (model.length() >= 3 && model.substr(model.length() - 3) == ".pb") { backend = deepmd::DPBackend::TensorFlow; + } else if (true) { + backend = deepmd::DPBackend::Paddle; } else { - throw deepmd::deepmd_exception("Unsupported model file format"); + throw deepmd::deepmd_exception("Unsupported model file formatt"); } + if (deepmd::DPBackend::TensorFlow == backend) { #ifdef BUILD_TENSORFLOW dp = std::make_shared(model, gpu_rank, file_content); @@ -57,7 +65,11 @@ void DeepPot::init(const std::string& model, throw deepmd::deepmd_exception("PyTorch backend is not built"); #endif } else if (deepmd::DPBackend::Paddle == backend) { - throw deepmd::deepmd_exception("PaddlePaddle backend is not supported yet"); +// #ifdef BUILD_PADDLE + dp = std::make_shared(model, gpu_rank, file_content); +// #else + throw deepmd::deepmd_exception("Paddle backend is not built"); +// #endif } else { throw deepmd::deepmd_exception("Unknown file type"); } diff --git a/source/api_cc/src/DeepPotPD.cc b/source/api_cc/src/DeepPotPD.cc new file mode 100644 index 0000000000..b27ecda469 --- /dev/null +++ b/source/api_cc/src/DeepPotPD.cc @@ -0,0 +1,1105 @@ +// SPDX-License-Identifier: LGPL-3.0-or-later +#ifdef BUILD_PADDLE +#include "DeepPotPD.h" + +#include +#include +#include + +#include "AtomMap.h" +#include "device.h" +#include "paddle/include/paddle_inference_api.h" +// #include "glog/logging.h" + +using namespace deepmd; + +template +static void run_model( + std::vector& dener, + std::vector& dforce_, + std::vector& dvirial, + const std::shared_ptr& predictor, + // const std::vector>& input_tensors, + const AtomMap& atommap, + const int nframes, + const int nghost = 0) { + unsigned nloc = atommap.get_type().size(); + unsigned nall = nloc + nghost; + dener.resize(nframes); + if (nloc == 0) { + // no backward map needed + // dforce of size nall * 3 + dforce_.resize(static_cast(nframes) * nall * 3); + fill(dforce_.begin(), dforce_.end(), (VALUETYPE)0.0); + // dvirial of size 9 + dvirial.resize(static_cast(nframes) * 9); + fill(dvirial.begin(), dvirial.end(), (VALUETYPE)0.0); + return; + } + + /* Running inference */ + if (!predictor->Run()) { + throw deepmd::deepmd_exception("Paddle inference failed"); + } + + auto output_names = predictor->GetOutputNames(); + auto output_e = predictor->GetOutputHandle(output_names[0]); + auto output_f = predictor->GetOutputHandle(output_names[1]); + auto output_virial_tensor = predictor->GetOutputHandle(output_names[3]); + + // 获取 Output paddle::Tensor 的维度信息 + std::vector output_energy_shape = output_e->shape(); + int output_energy_size = + std::accumulate(output_energy_shape.begin(), output_energy_shape.end(), 1, + std::multiplies()); + std::vector output_force_shape = output_f->shape(); + int output_force_size = + std::accumulate(output_force_shape.begin(), output_force_shape.end(), 1, + std::multiplies()); + std::vector output_virial_shape = output_virial_tensor->shape(); + int output_virial_size = + std::accumulate(output_virial_shape.begin(), output_virial_shape.end(), 1, + std::multiplies()); + + // get data of output_energy + std::vector oe; + oe.resize(output_energy_size); + output_e->CopyToCpu(oe.data()); + // get data of output_force + std::vector of; + of.resize(output_force_size); + output_f->CopyToCpu(of.data()); + // get data of output_virial + std::vector oav; + oav.resize(output_virial_size); + output_virial_tensor->CopyToCpu(oav.data()); + + std::vector dforce(nframes * 3 * nall); + dvirial.resize(nframes * 9); + for (int ii = 0; ii < nframes; ++ii) { + dener[ii] = oe[ii]; + } + for (int ii = 0; ii < nframes * nall * 3; ++ii) { + dforce[ii] = of[ii]; + } + // set dvirial to zero, prevent input vector is not zero (#1123) + std::fill(dvirial.begin(), dvirial.end(), (VALUETYPE)0.); + for (int kk = 0; kk < nframes; ++kk) { + for (int ii = 0; ii < nall; ++ii) { + dvirial[kk * 9 + 0] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 0]; + dvirial[kk * 9 + 1] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 1]; + dvirial[kk * 9 + 2] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 2]; + dvirial[kk * 9 + 3] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 3]; + dvirial[kk * 9 + 4] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 4]; + dvirial[kk * 9 + 5] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 5]; + dvirial[kk * 9 + 6] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 6]; + dvirial[kk * 9 + 7] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 7]; + dvirial[kk * 9 + 8] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 8]; + } + } + dforce_ = dforce; + atommap.backward(dforce_.begin(), dforce.begin(), 3, nframes, + nall); +} + +template void run_model( + std::vector& dener, + std::vector& dforce_, + std::vector& dvirial, + const std::shared_ptr& predictor, + // const std::vector>& input_tensors, + const AtomMap& atommap, + const int nframes, + const int nghost); + +template void run_model( + std::vector& dener, + std::vector& dforce_, + std::vector& dvirial, + const std::shared_ptr& predictor, + // const std::vector>& input_tensors, + const AtomMap& atommap, + const int nframes, + const int nghost); + +template void run_model( + std::vector& dener, + std::vector& dforce_, + std::vector& dvirial, + const std::shared_ptr& predictor, + // const std::vector>& input_tensors, + const AtomMap& atommap, + const int nframes, + const int nghost); + +template void run_model( + std::vector& dener, + std::vector& dforce_, + std::vector& dvirial, + const std::shared_ptr& predictor, + // const std::vector>& input_tensors, + const AtomMap& atommap, + const int nframes, + const int nghost); + +template +static void run_model( + std::vector& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const std::shared_ptr& predictor, + // const std::vector>& input_tensors, + const deepmd::AtomMap& atommap, + const int nframes, + const int nghost = 0) { + unsigned nloc = atommap.get_type().size(); + unsigned nall = nloc + nghost; + dener.resize(nframes); + if (nloc == 0) { + // no backward map needed + // dforce of size nall * 3 + dforce_.resize(nframes * nall * 3); + fill(dforce_.begin(), dforce_.end(), (VALUETYPE)0.0); + // dvirial of size 9 + dvirial.resize(nframes * 9); + fill(dvirial.begin(), dvirial.end(), (VALUETYPE)0.0); + // datom_energy_ of size nall + datom_energy_.resize(nframes * nall); + fill(datom_energy_.begin(), datom_energy_.end(), (VALUETYPE)0.0); + // datom_virial_ of size nall * 9 + datom_virial_.resize(nframes * nall * 9); + fill(datom_virial_.begin(), datom_virial_.end(), (VALUETYPE)0.0); + return; + } + + /* Running inference */ + if (!predictor->Run()) { + throw deepmd::deepmd_exception("Paddle inference failed"); + } + + /* Get output handles*/ + auto output_names = predictor->GetOutputNames(); + auto output_ae = predictor->GetOutputHandle(output_names[0]); + auto output_av = predictor->GetOutputHandle(output_names[1]); + auto output_e = predictor->GetOutputHandle(output_names[4]); + auto output_f = predictor->GetOutputHandle(output_names[5]); + + // 获取 Output paddle::Tensor 的维度信息 + std::vector output_atom_ener_shape = output_ae->shape(); + int output_atom_ener_size = + std::accumulate(output_atom_ener_shape.begin(), + output_atom_ener_shape.end(), 1, std::multiplies()); + std::vector output_atom_virial_shape = output_av->shape(); + int output_atom_virial_size = + std::accumulate(output_atom_virial_shape.begin(), output_atom_virial_shape.end(), 1, + std::multiplies()); + std::vector output_energy_shape = output_e->shape(); + int output_energy_size = + std::accumulate(output_energy_shape.begin(), output_energy_shape.end(), 1, + std::multiplies()); + std::vector output_force_shape = output_f->shape(); + int output_force_size = + std::accumulate(output_force_shape.begin(), output_force_shape.end(), 1, + std::multiplies()); + + // get data of output_atom_ener + std::vector oae; + oae.resize(output_atom_ener_size); + output_ae->CopyToCpu(oae.data()); + // get data of output_atom_virial + std::vector oav; + oav.resize(output_atom_virial_size); + output_av->CopyToCpu(oav.data()); + // get data of output_energy + std::vector oe; + oe.resize(output_energy_size); + output_e->CopyToCpu(oe.data()); + // get data of output_force + std::vector of; + of.resize(output_force_size); + output_f->CopyToCpu(of.data()); + + std::vector dforce(nframes * 3 * nall); + std::vector datom_energy(nframes * nall, 0); + std::vector datom_virial(nframes * 9 * nall); + dvirial.resize(nframes * 9); + for (int ii = 0; ii < nframes; ++ii) { + dener[ii] = oe[ii]; + } + for (int ii = 0; ii < nframes * nall * 3; ++ii) { + dforce[ii] = of[ii]; + } + for (int ii = 0; ii < nframes; ++ii) { + for (int jj = 0; jj < nloc; ++jj) { + datom_energy[ii * nall + jj] = oae[ii * nloc + jj]; + } + } + for (int ii = 0; ii < nframes * nall * 9; ++ii) { + datom_virial[ii] = oav[ii]; + } + // set dvirial to zero, prevent input vector is not zero (#1123) + std::fill(dvirial.begin(), dvirial.end(), (VALUETYPE)0.); + for (int kk = 0; kk < nframes; ++kk) { + for (int ii = 0; ii < nall; ++ii) { + dvirial[kk * 9 + 0] += + (VALUETYPE)1.0 * datom_virial[kk * nall * 9 + 9 * ii + 0]; + dvirial[kk * 9 + 1] += + (VALUETYPE)1.0 * datom_virial[kk * nall * 9 + 9 * ii + 1]; + dvirial[kk * 9 + 2] += + (VALUETYPE)1.0 * datom_virial[kk * nall * 9 + 9 * ii + 2]; + dvirial[kk * 9 + 3] += + (VALUETYPE)1.0 * datom_virial[kk * nall * 9 + 9 * ii + 3]; + dvirial[kk * 9 + 4] += + (VALUETYPE)1.0 * datom_virial[kk * nall * 9 + 9 * ii + 4]; + dvirial[kk * 9 + 5] += + (VALUETYPE)1.0 * datom_virial[kk * nall * 9 + 9 * ii + 5]; + dvirial[kk * 9 + 6] += + (VALUETYPE)1.0 * datom_virial[kk * nall * 9 + 9 * ii + 6]; + dvirial[kk * 9 + 7] += + (VALUETYPE)1.0 * datom_virial[kk * nall * 9 + 9 * ii + 7]; + dvirial[kk * 9 + 8] += + (VALUETYPE)1.0 * datom_virial[kk * nall * 9 + 9 * ii + 8]; + } + } + dforce_ = dforce; + datom_energy_ = datom_energy; + datom_virial_ = datom_virial; + atommap.backward(dforce_.begin(), dforce.begin(), 3, nframes, + nall); + atommap.backward(datom_energy_.begin(), datom_energy.begin(), 1, + nframes, nall); + atommap.backward(datom_virial_.begin(), datom_virial.begin(), 9, + nframes, nall); +} + +template void run_model( + std::vector& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const std::shared_ptr& predictor, + const deepmd::AtomMap& atommap, + const int nframes, + const int nghost); + +template void run_model( + std::vector& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const std::shared_ptr& predictor, + const deepmd::AtomMap& atommap, + const int nframes, + const int nghost); + +template void run_model( + std::vector& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const std::shared_ptr& predictor, + const deepmd::AtomMap& atommap, + const int nframes, + const int nghost); + +template void run_model( + std::vector& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const std::shared_ptr& predictor, + const deepmd::AtomMap& atommap, + const int nframes, + const int nghost); + +// end multiple frames + +// start single frame + +template +static void run_model( + ENERGYTYPE& dener, + std::vector& dforce_, + std::vector& dvirial, + const std::shared_ptr& predictor, + // const std::vector>& input_tensors, + const deepmd::AtomMap& atommap, + const int nframes, + const int nghost = 0) { + assert(nframes == 1); + std::vector dener_(1); + // call multi-frame version + run_model(dener_, dforce_, dvirial, predictor, + atommap, nframes, nghost); + dener = dener_[0]; +} + +template void run_model( + ENERGYTYPE& dener, + std::vector& dforce_, + std::vector& dvirial, + const std::shared_ptr& predictor, + // const std::vector>& input_tensors, + const AtomMap& atommap, + const int nframes, + const int nghost); + +template void run_model( + ENERGYTYPE& dener, + std::vector& dforce_, + std::vector& dvirial, + const std::shared_ptr& predictor, + // const std::vector>& input_tensors, + const AtomMap& atommap, + const int nframes, + const int nghost); + +template void run_model( + ENERGYTYPE& dener, + std::vector& dforce_, + std::vector& dvirial, + const std::shared_ptr& predictor, + // const std::vector>& input_tensors, + const AtomMap& atommap, + const int nframes, + const int nghost); + +template void run_model( + ENERGYTYPE& dener, + std::vector& dforce_, + std::vector& dvirial, + const std::shared_ptr& predictor, + // const std::vector>& input_tensors, + const AtomMap& atommap, + const int nframes, + const int nghost); + +template +static void run_model( + ENERGYTYPE& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const std::shared_ptr& predictor, + // const std::vector>& input_tensors, + const deepmd::AtomMap& atommap, + const int nframes = 1, + const int nghost = 0) { + assert(nframes == 1); + std::vector dener_(1); + // call multi-frame version + run_model(dener_, dforce_, dvirial, datom_energy_, + datom_virial_, predictor,//, input_tensors, + atommap, nframes, nghost); + dener = dener_[0]; +} + +template void run_model( + ENERGYTYPE& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const std::shared_ptr& predictor, + // const std::vector>& input_tensors, + const deepmd::AtomMap& atommap, + const int nframes, + const int nghost); + +template void run_model( + ENERGYTYPE& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const std::shared_ptr& predictor, + // const std::vector>& input_tensors, + const deepmd::AtomMap& atommap, + const int nframes, + const int nghost); + +template void run_model( + ENERGYTYPE& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const std::shared_ptr& predictor, + // const std::vector>& input_tensors, + const deepmd::AtomMap& atommap, + const int nframes, + const int nghost); + +template void run_model( + ENERGYTYPE& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const std::shared_ptr& predictor, + // const std::vector>& input_tensors, + const deepmd::AtomMap& atommap, + const int nframes, + const int nghost); + +// end single frame + +DeepPotPD::DeepPotPD() : inited(false) {} + +DeepPotPD::DeepPotPD(const std::string& model, + const int& gpu_rank, + const std::string& file_content) + : inited(false) { + init(model, gpu_rank, file_content); +} + +void DeepPotPD::init(const std::string& model, + const int& gpu_rank, + const std::string& file_content) { + std::cout << ("** Access here.") << std::endl; + if (inited) { + std::cerr << "WARNING: deepmd-kit should not be initialized twice, do " + "nothing at the second call of initializer" + << std::endl; + return; + } + // deepmd::load_op_library(); + int gpu_num = 1; // hard code here + if (gpu_num > 0) { + gpu_id = gpu_rank % gpu_num; + } else { + gpu_id = 0; + } + std::string pdmodel_path = ""; + std::string pdiparams_path = ""; + bool use_paddle_inference = false; + if (model.find(".json") != std::string::npos) { + pdmodel_path = model; + std::string tmp = model; + pdiparams_path = tmp.replace(model.find(".json"), 4, std::string(".pdiparams")); + use_paddle_inference = true; + } else if (model.find(".pdmodel") != std::string::npos){ + pdmodel_path = model; + std::string tmp = model; + pdiparams_path = tmp.replace(model.find(".pdmodel"), 8, std::string(".pdiparams")); + use_paddle_inference = true; + } else { + throw "[Error] Not found any inference model in"; + } + int math_lib_num_threads = 1; + + if (use_paddle_inference) { + config = std::make_shared(); + config->SetModel(pdmodel_path, pdiparams_path); + config->SwitchIrOptim(true); + config->EnableUseGpu(8192, 0); + // std::cout << "IR Optim is: " << config->ir_optim() << std::endl; + // config->EnableMKLDNN(); + config->EnableMemoryOptim(); + // config->EnableProfile(); + predictor = paddle_infer::CreatePredictor(*config); + } + /* water se_e2_a + tensorflow::DT_DOUBLE = 2 + tensorflow::DT_FLOAT = 1 + paddle_infer::DataType::FLOAT64 = 7 + paddle_infer::DataType::FLOAT32 = 0 + * st_model.descrpt.buffer_rcut.name = generated_tensor_0 + * st_model.descrpt.buffer_ntypes.name = generated_tensor_2 + * st_model.fitting.buffer_dfparam.name = generated_tensor_9 + * st_model.fitting.buffer_daparam.name = generated_tensor_10 + [buffer_t_type, [3]] generated name in static_model is: generated_tensor_12 + [buffer_t_mt, [4]] generated name in static_model is: generated_tensor_13 + [buffer_t_ver, [1]] generated name in static_model is: generated_tensor_14 + [descrpt.buffer_rcut, []] generated name in static_model is: + generated_tensor_0 [descrpt.buffer_ntypes_spin, []] generated name in + static_model is: generated_tensor_1 [descrpt.buffer_ntypes, []] generated + name in static_model is: generated_tensor_2 [descrpt.avg_zero, [2, 552]] + generated name in static_model is: eager_tmp_0 [descrpt.std_ones, [2, 552]] + generated name in static_model is: eager_tmp_1 [descrpt.t_rcut, []] + generated name in static_model is: generated_tensor_3 [descrpt.t_rcut, []] + generated name in static_model is: generated_tensor_3 [descrpt.t_rcut, []] + generated name in static_model is: generated_tensor_3 [descrpt.t_ntypes, []] + generated name in static_model is: generated_tensor_4 [descrpt.t_ntypes, []] + generated name in static_model is: generated_tensor_4 [descrpt.t_ntypes, []] + generated name in static_model is: generated_tensor_4 [descrpt.t_ndescrpt, + []] generated name in static_model is: generated_tensor_5 [descrpt.t_sel, + [2]] generated name in static_model is: generated_tensor_6 [descrpt.t_avg, + [2, 552]] generated name in static_model is: generated_tensor_7 + [descrpt.t_std, [2, 552]] generated name in static_model is: + generated_tensor_8 [fitting.buffer_dfparam, []] generated name in + static_model is: generated_tensor_9 [fitting.buffer_daparam, []] generated + name in static_model is: generated_tensor_10 + **/ + /* spin se_e2_a + [buffer_tmap, [4]] generated name in static_model is: generated_tensor_14 + [buffer_model_type, [4]] generated name in static_model is: + generated_tensor_15 [buffer_model_version, [1]] generated name in + static_model is: generated_tensor_16 [descrpt.buffer_rcut, []] generated + name in static_model is: generated_tensor_3 [descrpt.buffer_ntypes, []] + generated name in static_model is: generated_tensor_4 [descrpt.avg_zero, [3, + 720]] generated name in static_model is: eager_tmp_0 [descrpt.std_ones, [3, + 720]] generated name in static_model is: eager_tmp_1 [descrpt.t_rcut, []] + generated name in static_model is: generated_tensor_5 [descrpt.buffer_sel, + [3]] generated name in static_model is: generated_tensor_6 + [descrpt.buffer_ndescrpt, []] generated name in static_model is: + generated_tensor_7 [descrpt.buffer_original_sel, [3]] generated name in + static_model is: generated_tensor_8 [descrpt.t_avg, [3, 720]] generated name + in static_model is: generated_tensor_9 [descrpt.t_std, [3, 720]] generated + name in static_model is: generated_tensor_10 + [descrpt.spin.buffer_ntypes_spin, [1]] generated name in static_model is: + generated_tensor_0 [descrpt.spin.buffer_virtual_len, [1, 1]] generated name + in static_model is: generated_tensor_1 [descrpt.spin.buffer_spin_norm, [1, + 1]] generated name in static_model is: generated_tensor_2 + [fitting.buffer_dfparam, []] generated name in static_model is: + generated_tensor_11 [fitting.buffer_daparam, []] generated name in + static_model is: generated_tensor_12 [fitting.t_bias_atom_e, [2]] generated + name in static_model is: generated_tensor_13 + */ + // dtype = predictor_get_dtype(predictor, "generated_tensor_0"); // hard code + // auto dtype = paddle_infer::DataType::FLOAT64; + // if (dtype == paddle_infer::DataType::FLOAT64) { + // rcut = paddle_get_scalar("generated_tensor_0"); + // } else { + // rcut = 3.18; + // } + rcut = double(6.0); + ntypes = 2; + ntypes_spin = 0; + dfparam = 0; + daparam = 0; + aparam_nall = false; + + inited = true; + // if (!model_compatable(model_version)) { + // throw deepmd::deepmd_exception( + // "incompatable model: version " + model_version + + // " in graph, but version " + global_model_version + + // " supported " + // "See https://deepmd.rtfd.io/compatability/ for details."); + // } +} + +DeepPotPD::~DeepPotPD() {} + +// void DeepPotPD::print_summary(const std::string& pre) const { +// deepmd::print_summary(pre); +// } + +// template +// VT DeepPotPD::get_scalar(const std::string& name) const { +// return session_get_scalar(session, name); +// } + +// template +// VT DeepPotPD::paddle_get_scalar(const std::string& name) const { +// return predictor_get_scalar(predictor, name); +// } + +template +void DeepPotPD::validate_fparam_aparam( + const int nframes, + const int& nloc, + const std::vector& fparam, + const std::vector& aparam) const { + if (fparam.size() != dfparam && fparam.size() != nframes * dfparam) { + throw deepmd::deepmd_exception( + "the dim of frame parameter provided is not consistent with what the " + "model uses"); + } + + if (aparam.size() != daparam * nloc && + aparam.size() != nframes * daparam * nloc) { + throw deepmd::deepmd_exception( + "the dim of atom parameter provided is not consistent with what the " + "model uses"); + } +} + +template void DeepPotPD::validate_fparam_aparam( + const int nframes, + const int& nloc, + const std::vector& fparam, + const std::vector& aparam) const; + +template void DeepPotPD::validate_fparam_aparam( + const int nframes, + const int& nloc, + const std::vector& fparam, + const std::vector& aparam) const; + +template +void DeepPotPD::tile_fparam_aparam(std::vector& out_param, + const int& nframes, + const int& dparam, + const std::vector& param) const { + if (param.size() == dparam) { + out_param.resize(nframes * dparam); + for (int ii = 0; ii < nframes; ++ii) { + std::copy(param.begin(), param.end(), out_param.begin() + ii * dparam); + } + } else if (param.size() == nframes * dparam) { + out_param = param; + } +} + +template void DeepPotPD::tile_fparam_aparam( + std::vector& out_param, + const int& nframes, + const int& dparam, + const std::vector& param) const; + +template void DeepPotPD::tile_fparam_aparam( + std::vector& out_param, + const int& nframes, + const int& dparam, + const std::vector& param) const; + +// ENERGYVTYPE: std::vector or ENERGYTYPE + +template +void DeepPotPD::compute(ENERGYVTYPE& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const std::vector& dcoord_, + const std::vector& datype_, + const std::vector& dbox, + const std::vector& fparam_, + const std::vector& aparam_, + const bool atomic) { + // if datype.size is 0, not clear nframes; but 1 is just ok + int nframes = datype_.size() > 0 ? (dcoord_.size() / 3 / datype_.size()) : 1; + atommap = deepmd::AtomMap(datype_.begin(), datype_.end()); + int nloc = datype_.size(); + std::vector fparam; + std::vector aparam; + validate_fparam_aparam(nframes, nloc, fparam_, aparam_); + tile_fparam_aparam(fparam, nframes, dfparam, fparam_); + tile_fparam_aparam(aparam, nframes, nloc * daparam, aparam_); + + // std::vector> input_tensors; + + if (dtype == paddle_infer::DataType::FLOAT64) { + if (atomic) { + run_model(dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, + atommap, nframes); + } else { + run_model(dener, dforce_, dvirial, predictor, + atommap, nframes); + } + } else { + if (atomic) { + run_model(dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, + atommap, nframes); + } else { + run_model(dener, dforce_, dvirial, predictor, + atommap, nframes); + } + } +} + +template void DeepPotPD::compute( + ENERGYTYPE& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const std::vector& dcoord_, + const std::vector& datype_, + const std::vector& dbox, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + +template void DeepPotPD::compute( + ENERGYTYPE& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const std::vector& dcoord_, + const std::vector& datype_, + const std::vector& dbox, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + +template void DeepPotPD::compute>( + std::vector& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const std::vector& dcoord_, + const std::vector& datype_, + const std::vector& dbox, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + +template void DeepPotPD::compute>( + std::vector& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const std::vector& dcoord_, + const std::vector& datype_, + const std::vector& dbox, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + +template +void DeepPotPD::compute(ENERGYVTYPE& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const std::vector& dcoord_, + const std::vector& datype_, + const std::vector& dbox, + const int nghost, + const InputNlist& lmp_list, + const int& ago, + const std::vector& fparam_, + const std::vector& aparam__, + const bool atomic) { + int nall = datype_.size(); + // if nall==0, unclear nframes, but 1 is ok + int nframes = nall > 0 ? (dcoord_.size() / nall / 3) : 1; + int nloc = nall - nghost; + std::vector fparam; + std::vector aparam_; + validate_fparam_aparam(nframes, (aparam_nall ? nall : nloc), fparam_, + aparam__); + tile_fparam_aparam(fparam, nframes, dfparam, fparam_); + tile_fparam_aparam(aparam_, nframes, (aparam_nall ? nall : nloc) * daparam, + aparam__); + // std::vector> input_tensors; + // select real atoms + std::vector dcoord, dforce, aparam, datom_energy, datom_virial; + std::vector datype, fwd_map, bkw_map; + int nghost_real, nall_real, nloc_real; + select_real_atoms_coord(dcoord, datype, aparam, nghost_real, fwd_map, bkw_map, + nall_real, nloc_real, dcoord_, datype_, aparam_, + nghost, ntypes, nframes, daparam, nall, aparam_nall); + + if (ago == 0) { + atommap = deepmd::AtomMap(datype.begin(), datype.begin() + nloc_real); + assert(nloc_real == atommap.get_type().size()); + + nlist_data.copy_from_nlist(lmp_list); + nlist_data.shuffle_exclude_empty(fwd_map); + nlist_data.shuffle(atommap); + nlist_data.make_inlist(nlist); + } + + if (dtype == paddle_infer::DataType::FLOAT64) { + if (atomic) { + run_model(dener, dforce, dvirial, datom_energy, datom_virial, + predictor, atommap, nframes, nghost_real); + } else { + run_model(dener, dforce, dvirial, predictor, atommap, + nframes, nghost_real); + } + } else { + if (atomic) { + run_model(dener, dforce, dvirial, datom_energy, datom_virial, + predictor, atommap, nframes, nghost_real); + } else { + run_model(dener, dforce, dvirial, predictor, atommap, + nframes, nghost_real); + } + } + + // bkw map + dforce_.resize(static_cast(nframes) * fwd_map.size() * 3); + datom_energy_.resize(static_cast(nframes) * fwd_map.size()); + datom_virial_.resize(static_cast(nframes) * fwd_map.size() * 9); + select_map(dforce_, dforce, bkw_map, 3, nframes, fwd_map.size(), + nall_real); + select_map(datom_energy_, datom_energy, bkw_map, 1, nframes, + fwd_map.size(), nall_real); + select_map(datom_virial_, datom_virial, bkw_map, 9, nframes, + fwd_map.size(), nall_real); +} + +template void DeepPotPD::compute( + ENERGYTYPE& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const std::vector& dcoord_, + const std::vector& datype_, + const std::vector& dbox, + const int nghost, + const InputNlist& lmp_list, + const int& ago, + const std::vector& fparam, + const std::vector& aparam_, + const bool atomic); + +template void DeepPotPD::compute( + ENERGYTYPE& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const std::vector& dcoord_, + const std::vector& datype_, + const std::vector& dbox, + const int nghost, + const InputNlist& lmp_list, + const int& ago, + const std::vector& fparam, + const std::vector& aparam_, + const bool atomic); + +template void DeepPotPD::compute>( + std::vector& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const std::vector& dcoord_, + const std::vector& datype_, + const std::vector& dbox, + const int nghost, + const InputNlist& lmp_list, + const int& ago, + const std::vector& fparam, + const std::vector& aparam_, + const bool atomic); + +template void DeepPotPD::compute>( + std::vector& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const std::vector& dcoord_, + const std::vector& datype_, + const std::vector& dbox, + const int nghost, + const InputNlist& lmp_list, + const int& ago, + const std::vector& fparam, + const std::vector& aparam_, + const bool atomic); + +// mixed type + +template +void DeepPotTF::compute_mixed_type(ENERGYVTYPE& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const int& nframes, + const std::vector& dcoord_, + const std::vector& datype_, + const std::vector& dbox, + const std::vector& fparam_, + const std::vector& aparam_, + const bool atomic) { + int nloc = datype_.size() / nframes; + // here atommap only used to get nloc + atommap = deepmd::AtomMap(datype_.begin(), datype_.begin() + nloc); + std::vector fparam; + std::vector aparam; + validate_fparam_aparam(nframes, nloc, fparam_, aparam_); + tile_fparam_aparam(fparam, nframes, dfparam, fparam_); + tile_fparam_aparam(aparam, nframes, nloc * daparam, aparam_); + + std::vector> input_tensors; + + if (dtype == paddle_infer::DataType::FLOAT64) { + // int nloc = session_input_tensors_mixed_type( + // input_tensors, nframes, dcoord_, ntypes, datype_, dbox, cell_size, + // fparam, aparam, atommap, "", aparam_nall); + if (atomic) { + run_model(dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, + atommap, nframes); + } else { + run_model(dener, dforce_, dvirial, predictor, + atommap, nframes); + } + } else { + // int nloc = session_input_tensors_mixed_type( + // input_tensors, nframes, dcoord_, ntypes, datype_, dbox, cell_size, + // fparam, aparam, atommap, "", aparam_nall); + if (atomic) { + run_model(dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, + atommap, nframes); + } else { + run_model(dener, dforce_, dvirial, atommap, predictor, + nframes); + } + } +} + +template void DeepPotTF::compute_mixed_type( + ENERGYTYPE& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const int& nframes, + const std::vector& dcoord_, + const std::vector& datype_, + const std::vector& dbox, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + +template void DeepPotTF::compute_mixed_type( + ENERGYTYPE& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const int& nframes, + const std::vector& dcoord_, + const std::vector& datype_, + const std::vector& dbox, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + +template void DeepPotTF::compute_mixed_type>( + std::vector& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const int& nframes, + const std::vector& dcoord_, + const std::vector& datype_, + const std::vector& dbox, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + +template void DeepPotTF::compute_mixed_type>( + std::vector& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const int& nframes, + const std::vector& dcoord_, + const std::vector& datype_, + const std::vector& dbox, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + +void DeepPotTF::get_type_map(std::string& type_map) { + type_map = get_scalar("model_attr/tmap"); +} + +// forward to template method +void DeepPotTF::computew(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic) { + compute(ener, force, virial, atom_energy, atom_virial, coord, atype, box, + fparam, aparam, atomic); +} +void DeepPotTF::computew(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic) { + compute(ener, force, virial, atom_energy, atom_virial, coord, atype, box, + fparam, aparam, atomic); +} +void DeepPotTF::computew(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const int nghost, + const InputNlist& inlist, + const int& ago, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic) { + compute(ener, force, virial, atom_energy, atom_virial, coord, atype, box, + nghost, inlist, ago, fparam, aparam, atomic); +} +void DeepPotTF::computew(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const int nghost, + const InputNlist& inlist, + const int& ago, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic) { + compute(ener, force, virial, atom_energy, atom_virial, coord, atype, box, + nghost, inlist, ago, fparam, aparam, atomic); +} +void DeepPotTF::computew_mixed_type(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const int& nframes, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic) { + compute_mixed_type(ener, force, virial, atom_energy, atom_virial, nframes, + coord, atype, box, fparam, aparam, atomic); +} +void DeepPotTF::computew_mixed_type(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const int& nframes, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic) { + compute_mixed_type(ener, force, virial, atom_energy, atom_virial, nframes, + coord, atype, box, fparam, aparam, atomic); +} +#endif diff --git a/source/api_cc/src/common.cc b/source/api_cc/src/common.cc index baa257d60e..0e04cbcf6b 100644 --- a/source/api_cc/src/common.cc +++ b/source/api_cc/src/common.cc @@ -405,6 +405,9 @@ void deepmd::load_op_library() { #endif #ifdef BUILD_PYTORCH _load_single_op_library("deepmd_op_pt"); +#endif +#ifdef BUILD_PADDLE + _load_single_op_library("deepmd_op_pd"); #endif // load customized plugins const char* env_customized_plugins = std::getenv("DP_PLUGIN_PATH"); diff --git a/source/install/build_cc.sh b/source/install/build_cc.sh index 60101eb9a8..6adb62a311 100755 --- a/source/install/build_cc.sh +++ b/source/install/build_cc.sh @@ -20,7 +20,8 @@ NPROC=$(nproc --all) BUILD_TMP_DIR=${SCRIPT_PATH}/../build mkdir -p ${BUILD_TMP_DIR} cd ${BUILD_TMP_DIR} -cmake -D ENABLE_TENSORFLOW=ON \ +cmake -DCMAKE_PREFIX_PATH=/workspace/hesensen/deepmd_backend/deepmd_paddle_new/source/install/libtorch \ + -D ENABLE_TENSORFLOW=OFF \ -D ENABLE_PYTORCH=ON \ -D CMAKE_INSTALL_PREFIX=${INSTALL_PREFIX} \ -D USE_TF_PYTHON_LIBS=TRUE \ diff --git a/source/install/test_cc_local.sh b/source/install/test_cc_local.sh index fdb2396a28..dbfdd7c0b2 100755 --- a/source/install/test_cc_local.sh +++ b/source/install/test_cc_local.sh @@ -19,7 +19,7 @@ BUILD_TMP_DIR=${SCRIPT_PATH}/../build_tests mkdir -p ${BUILD_TMP_DIR} cd ${BUILD_TMP_DIR} cmake \ - -D ENABLE_TENSORFLOW=TRUE \ + -D ENABLE_TENSORFLOW=False \ -D ENABLE_PYTORCH=TRUE \ -D INSTALL_TENSORFLOW=FALSE \ -D USE_TF_PYTHON_LIBS=TRUE \ diff --git a/source/lmp/pair_deepmd.cpp b/source/lmp/pair_deepmd.cpp index 2cb6cfacd4..72da1a5ee6 100644 --- a/source/lmp/pair_deepmd.cpp +++ b/source/lmp/pair_deepmd.cpp @@ -23,6 +23,7 @@ #include "neighbor.h" #include "output.h" #include "update.h" +#include #if LAMMPS_VERSION_NUMBER >= 20210831 // in lammps #2902, fix_ttm members turns from private to protected #define USE_TTM 1 @@ -975,9 +976,18 @@ void PairDeepMD::settings(int narg, char **arg) { numb_models = models.size(); if (numb_models == 1) { try { - deep_pot.init(arg[0], get_node_rank(), get_file_content(arg[0])); - } catch (deepmd_compat::deepmd_exception &e) { - error->one(FLERR, e.what()); + std::cout << "****** init deepmd model from file 1: " << std::endl; + auto node_rank = get_node_rank(); + std::cout << "****** init deepmd model from file 2: " << std::endl; + auto content = get_file_content(arg[0]); + std::cout << "****** init deepmd model from file 3: " << std::endl; + deep_pot.init(arg[0], node_rank, content); + std::cout << "****** init deepmd model from file 4: " << std::endl; + } catch (const std::exception &e) { + // error->one(FLERR, e.what()); + std::cerr << "Standard exception caught: " << e.what() << std::endl; + // 打印堆栈跟踪信息 + std::cerr << "Stack trace:\n" << boost::stacktrace::stacktrace() << std::endl; } cutoff = deep_pot.cutoff() * dist_unit_cvt_factor; numb_types = deep_pot.numb_types(); diff --git a/source/op/pt/comm.cc b/source/op/pt/comm.cc index a25dfbd542..d5c273c689 100644 --- a/source/op/pt/comm.cc +++ b/source/op/pt/comm.cc @@ -6,7 +6,8 @@ #include #endif #endif -#include +#include "paddle/extension.h" +#include "paddle/include/paddle_inference_api.h" #include From 482d5882189636ec78879be975488132243b1ade Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Wed, 18 Sep 2024 13:44:54 +0800 Subject: [PATCH 11/93] update CMAKE code(WIP) --- source/CMakeLists.txt | 119 +++++++++++++++++++++++++++++++ source/api_cc/CMakeLists.txt | 7 ++ source/api_cc/src/DeepPot.cc | 3 +- source/config/CMakeLists.txt | 6 ++ source/lmp/plugin/CMakeLists.txt | 6 +- 5 files changed, 136 insertions(+), 5 deletions(-) diff --git a/source/CMakeLists.txt b/source/CMakeLists.txt index 1bbab7e398..fb9c778462 100644 --- a/source/CMakeLists.txt +++ b/source/CMakeLists.txt @@ -129,6 +129,125 @@ if(WITH_GPU) endif() endif() +macro(safe_set_static_flag) + foreach(flag_var + CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE + CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO) + if(${flag_var} MATCHES "/MD") + string(REGEX REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}") + endif(${flag_var} MATCHES "/MD") + endforeach(flag_var) +endmacro() + +if(NOT DEFINED PADDLE_LIB) + message( + FATAL_ERROR "please set PADDLE_LIB with -DPADDLE_LIB=/path/paddle/lib") +endif() +set(PADDLE_LIB + ${PADDLE_LIB} + CACHE PATH "/path/paddle/lib") + +include_directories("${PADDLE_LIB}/") +set(PADDLE_LIB_THIRD_PARTY_PATH "${PADDLE_LIB}/third_party/install/") + +include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}protobuf/include") +include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}glog/include") +include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}gflags/include") +include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}xxhash/include") + +link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}protobuf/lib") +link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}glog/lib") +link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}gflags/lib") +link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}xxhash/lib") +link_directories("${PADDLE_LIB}/paddle/lib") + +# add custom operators +option(USE_TENSORRT "Compile demo with TensorRT." OFF) + +if(WITH_GPU) + if(NOT WIN32) + set(CUDA_LIB + "/usr/local/cuda/lib64/" + CACHE STRING "CUDA Library") + else() + if(CUDA_LIB STREQUAL "") + set(CUDA_LIB + "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\lib\\x64" + ) + endif() + endif(NOT WIN32) +endif() + +if(NOT WIN32) + if(USE_TENSORRT AND WITH_GPU) + include_directories("${TENSORRT_INCLUDE_DIR}") + link_directories("${TENSORRT_LIB_DIR}") + endif() +endif(NOT WIN32) + +if(WITH_STATIC_LIB) + set(DEPS + ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_STATIC_LIBRARY_SUFFIX} + ) +else() + if(WIN32) + set(DEPS + ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_STATIC_LIBRARY_SUFFIX} + ) + else() + set(DEPS + ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_SHARED_LIBRARY_SUFFIX} + ) + endif() +endif() + +if(NOT WIN32) + set(EXTERNAL_LIB "-lrt -ldl -lpthread") + set(DEPS + ${DEPS} + ${MATH_LIB} + ${MKLDNN_LIB} + glog + gflags + protobuf + xxhash + ${EXTERNAL_LIB}) +else() + set(DEPS + ${DEPS} + ${MATH_LIB} + ${MKLDNN_LIB} + glog + gflags_static + libprotobuf + xxhash + ${EXTERNAL_LIB}) + set(DEPS ${DEPS} shlwapi.lib) +endif(NOT WIN32) + +if(WITH_GPU) + if(NOT WIN32) + if(USE_TENSORRT) + set(DEPS ${DEPS} + ${TENSORRT_LIB_DIR}/libnvinfer${CMAKE_SHARED_LIBRARY_SUFFIX}) + set(DEPS + ${DEPS} + ${TENSORRT_LIB_DIR}/libnvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX}) + endif() + set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX}) + else() + if(USE_TENSORRT) + set(DEPS ${DEPS} + ${TENSORRT_LIB_DIR}/nvinfer${CMAKE_STATIC_LIBRARY_SUFFIX}) + set(DEPS ${DEPS} + ${TENSORRT_LIB_DIR}/nvinfer_plugin${CMAKE_STATIC_LIBRARY_SUFFIX}) + endif() + set(DEPS ${DEPS} ${CUDA_LIB}/cudart${CMAKE_STATIC_LIBRARY_SUFFIX}) + set(DEPS ${DEPS} ${CUDA_LIB}/cublas${CMAKE_STATIC_LIBRARY_SUFFIX}) + set(DEPS ${DEPS} ${CUDA_LIB}/cudnn${CMAKE_STATIC_LIBRARY_SUFFIX}) + endif() +endif() + option(BUILD_TESTING "Build test and enable converage" OFF) set(DEEPMD_C_ROOT "" diff --git a/source/api_cc/CMakeLists.txt b/source/api_cc/CMakeLists.txt index 228a6657d3..80429f0b1d 100644 --- a/source/api_cc/CMakeLists.txt +++ b/source/api_cc/CMakeLists.txt @@ -23,6 +23,13 @@ if(ENABLE_PYTORCH target_link_libraries(${libname} PRIVATE "${TORCH_LIBRARIES}") target_compile_definitions(${libname} PRIVATE BUILD_PYTORCH) endif() +if(ENABLE_PADDLE + AND "${OP_CXX_ABI_PT}" EQUAL "${OP_CXX_ABI}" + # LAMMPS and i-PI in the Python package are not ready - needs more work + AND NOT BUILD_PY_IF) + target_link_libraries(${libname} PRIVATE "${PADDLE_LIBRARIES}") + target_compile_definitions(${libname} PRIVATE BUILD_PADDLE) +endif() target_include_directories( ${libname} diff --git a/source/api_cc/src/DeepPot.cc b/source/api_cc/src/DeepPot.cc index 18ddb6ab6c..81fc594813 100644 --- a/source/api_cc/src/DeepPot.cc +++ b/source/api_cc/src/DeepPot.cc @@ -34,7 +34,7 @@ DeepPot::~DeepPot() {} void DeepPot::init(const std::string& model, const int& gpu_rank, const std::string& file_content) { - std::cout << "** access here" << std::endl; + std::cout << "****** access here" << std::endl; if (inited) { std::cerr << "WARNING: deepmd-kit should not be initialized twice, do " "nothing at the second call of initializer" @@ -46,6 +46,7 @@ void DeepPot::init(const std::string& model, backend = deepmd::DPBackend::PyTorch; } else if (model.length() >= 3 && model.substr(model.length() - 3) == ".pb") { backend = deepmd::DPBackend::TensorFlow; + // } else if (model.length() >= 3 && (model.substr(model.length() - 5) == ".json" || model.substr(model.length() - 8) == ".pdmodel")) { } else if (true) { backend = deepmd::DPBackend::Paddle; } else { diff --git a/source/config/CMakeLists.txt b/source/config/CMakeLists.txt index b1ce17566f..dd005a327b 100644 --- a/source/config/CMakeLists.txt +++ b/source/config/CMakeLists.txt @@ -14,6 +14,12 @@ else() set(ENABLE_PYTORCH 0) endif() +if(ENABLE_PADDLE) + set(ENABLE_PADDLE 1) +else() + set(ENABLE_PADDLE 0) +endif() + configure_file("run_config.ini" "${CMAKE_CURRENT_BINARY_DIR}/run_config.ini" @ONLY) diff --git a/source/lmp/plugin/CMakeLists.txt b/source/lmp/plugin/CMakeLists.txt index f912059261..efeb9af260 100644 --- a/source/lmp/plugin/CMakeLists.txt +++ b/source/lmp/plugin/CMakeLists.txt @@ -9,10 +9,8 @@ if(DEFINED LAMMPS_SOURCE_ROOT OR DEFINED LAMMPS_VERSION) GIT_REPOSITORY https://github.com/lammps/lammps GIT_TAG ${LAMMPS_VERSION}) FetchContent_GetProperties(lammps_download) - if(NOT lammps_download_POPULATED) - FetchContent_Populate(lammps_download) - set(LAMMPS_SOURCE_ROOT ${lammps_download_SOURCE_DIR}) - endif() + # if(NOT lammps_download_POPULATED) FetchContent_Populate(lammps_download) + # set(LAMMPS_SOURCE_ROOT ${lammps_download_SOURCE_DIR}) endif() endif() set(LAMMPS_HEADER_DIR ${LAMMPS_SOURCE_ROOT}/src) message(STATUS "LAMMPS_HEADER_DIR is ${LAMMPS_HEADER_DIR}") From a3c46638d4ed232f476a97c27f755699a85d8865 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Wed, 18 Sep 2024 16:38:11 +0800 Subject: [PATCH 12/93] update CMAKE --- source/api_cc/CMakeLists.txt | 5 +- source/api_cc/include/DeepPotPD.h | 34 +- source/api_cc/include/version.h.in | 1 + source/api_cc/src/DeepPotPD.cc | 288 ++++++++-------- source/api_cc/src/common.cc | 513 ++++++++++++++++++++++++++++- 5 files changed, 669 insertions(+), 172 deletions(-) diff --git a/source/api_cc/CMakeLists.txt b/source/api_cc/CMakeLists.txt index 80429f0b1d..2255857214 100644 --- a/source/api_cc/CMakeLists.txt +++ b/source/api_cc/CMakeLists.txt @@ -23,10 +23,7 @@ if(ENABLE_PYTORCH target_link_libraries(${libname} PRIVATE "${TORCH_LIBRARIES}") target_compile_definitions(${libname} PRIVATE BUILD_PYTORCH) endif() -if(ENABLE_PADDLE - AND "${OP_CXX_ABI_PT}" EQUAL "${OP_CXX_ABI}" - # LAMMPS and i-PI in the Python package are not ready - needs more work - AND NOT BUILD_PY_IF) +if(ENABLE_PADDLE AND NOT BUILD_PY_IF) target_link_libraries(${libname} PRIVATE "${PADDLE_LIBRARIES}") target_compile_definitions(${libname} PRIVATE BUILD_PADDLE) endif() diff --git a/source/api_cc/include/DeepPotPD.h b/source/api_cc/include/DeepPotPD.h index 410873b39e..e1cbfa1f09 100644 --- a/source/api_cc/include/DeepPotPD.h +++ b/source/api_cc/include/DeepPotPD.h @@ -1,12 +1,13 @@ // SPDX-License-Identifier: LGPL-3.0-or-later #pragma once -#include "paddle/include/paddle_inference_api.h" +// #include "paddle/include/paddle_inference_api.h" // #include "paddle/extension.h" // #include "paddle/phi/backends/all_context.h" #include "DeepPot.h" #include "common.h" +#include "commonPD.h" #include "neighbor_list.h" namespace deepmd { @@ -177,19 +178,19 @@ class DeepPotPD : public DeepPotBase { *same aparam. * @param[in] atomic Whether to compute the atomic energy and virial. **/ - template - void compute_mixed_type(ENERGYVTYPE& ener, - std::vector& force, - std::vector& virial, - std::vector& atom_energy, - std::vector& atom_virial, - const int& nframes, - const std::vector& coord, - const std::vector& atype, - const std::vector& box, - const std::vector& fparam, - const std::vector& aparam, - const bool atomic); + // template + // void compute_mixed_type(ENERGYVTYPE& ener, + // std::vector& force, + // std::vector& virial, + // std::vector& atom_energy, + // std::vector& atom_virial, + // const int& nframes, + // const std::vector& coord, + // const std::vector& atype, + // const std::vector& box, + // const std::vector& fparam, + // const std::vector& aparam, + // const bool atomic); public: /** @@ -327,6 +328,10 @@ class DeepPotPD : public DeepPotBase { private: int num_intra_nthreads, num_inter_nthreads; bool inited; + + template + VT get_scalar(const std::string& name) const; + int ntypes; int ntypes_spin; int dfparam; @@ -336,6 +341,7 @@ class DeepPotPD : public DeepPotBase { std::shared_ptr predictor = nullptr; std::shared_ptr config = nullptr; double rcut; + double cell_size; NeighborListData nlist_data; int max_num_neighbors; InputNlist nlist; diff --git a/source/api_cc/include/version.h.in b/source/api_cc/include/version.h.in index 26b0c1be48..4be0589a30 100644 --- a/source/api_cc/include/version.h.in +++ b/source/api_cc/include/version.h.in @@ -10,4 +10,5 @@ const std::string global_git_branch="@GIT_BRANCH@"; const std::string global_tf_include_dir="@TensorFlow_INCLUDE_DIRS@"; const std::string global_tf_lib="@TensorFlow_LIBRARY@"; const std::string global_pt_lib="@TORCH_LIBRARIES@"; +const std::string global_pd_lib="@PADDLE_LIBRARIES@"; const std::string global_model_version="@MODEL_VERSION@"; diff --git a/source/api_cc/src/DeepPotPD.cc b/source/api_cc/src/DeepPotPD.cc index b27ecda469..a7cea9d27f 100644 --- a/source/api_cc/src/DeepPotPD.cc +++ b/source/api_cc/src/DeepPotPD.cc @@ -8,6 +8,7 @@ #include "AtomMap.h" #include "device.h" +#include "common.h" #include "paddle/include/paddle_inference_api.h" // #include "glog/logging.h" @@ -689,6 +690,9 @@ void DeepPotPD::compute(ENERGYVTYPE& dener, // std::vector> input_tensors; if (dtype == paddle_infer::DataType::FLOAT64) { + int ret = predictor_input_tensors(predictor, dcoord_, ntypes, + datype_, dbox, cell_size, fparam, + aparam, atommap, aparam_nall); if (atomic) { run_model(dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, atommap, nframes); @@ -697,6 +701,8 @@ void DeepPotPD::compute(ENERGYVTYPE& dener, atommap, nframes); } } else { + int ret = predictor_input_tensors(predictor, dcoord_, ntypes, datype_, dbox, cell_size, fparam, aparam, + atommap, aparam_nall); if (atomic) { run_model(dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, atommap, nframes); @@ -900,117 +906,123 @@ template void DeepPotPD::compute>( // mixed type -template -void DeepPotTF::compute_mixed_type(ENERGYVTYPE& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const int& nframes, - const std::vector& dcoord_, - const std::vector& datype_, - const std::vector& dbox, - const std::vector& fparam_, - const std::vector& aparam_, - const bool atomic) { - int nloc = datype_.size() / nframes; - // here atommap only used to get nloc - atommap = deepmd::AtomMap(datype_.begin(), datype_.begin() + nloc); - std::vector fparam; - std::vector aparam; - validate_fparam_aparam(nframes, nloc, fparam_, aparam_); - tile_fparam_aparam(fparam, nframes, dfparam, fparam_); - tile_fparam_aparam(aparam, nframes, nloc * daparam, aparam_); - - std::vector> input_tensors; +// template +// void DeepPotPD::compute_mixed_type(ENERGYVTYPE& dener, +// std::vector& dforce_, +// std::vector& dvirial, +// std::vector& datom_energy_, +// std::vector& datom_virial_, +// const int& nframes, +// const std::vector& dcoord_, +// const std::vector& datype_, +// const std::vector& dbox, +// const std::vector& fparam_, +// const std::vector& aparam_, +// const bool atomic) { +// int nloc = datype_.size() / nframes; +// // here atommap only used to get nloc +// atommap = deepmd::AtomMap(datype_.begin(), datype_.begin() + nloc); +// std::vector fparam; +// std::vector aparam; +// validate_fparam_aparam(nframes, nloc, fparam_, aparam_); +// tile_fparam_aparam(fparam, nframes, dfparam, fparam_); +// tile_fparam_aparam(aparam, nframes, nloc * daparam, aparam_); + +// // std::vector> input_tensors; + +// if (dtype == paddle_infer::DataType::FLOAT64) { +// // int nloc = session_input_tensors_mixed_type( +// // input_tensors, nframes, dcoord_, ntypes, datype_, dbox, cell_size, +// // fparam, aparam, atommap, "", aparam_nall); +// if (atomic) { +// run_model(dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, +// atommap, nframes); +// } else { +// run_model(dener, dforce_, dvirial, predictor, +// atommap, nframes); +// } +// } else { +// // int nloc = session_input_tensors_mixed_type( +// // input_tensors, nframes, dcoord_, ntypes, datype_, dbox, cell_size, +// // fparam, aparam, atommap, "", aparam_nall); +// if (atomic) { +// run_model(dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, +// atommap, nframes); +// } else { +// run_model(dener, dforce_, dvirial, atommap, predictor, +// nframes); +// } +// } +// } - if (dtype == paddle_infer::DataType::FLOAT64) { - // int nloc = session_input_tensors_mixed_type( - // input_tensors, nframes, dcoord_, ntypes, datype_, dbox, cell_size, - // fparam, aparam, atommap, "", aparam_nall); - if (atomic) { - run_model(dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, - atommap, nframes); - } else { - run_model(dener, dforce_, dvirial, predictor, - atommap, nframes); - } - } else { - // int nloc = session_input_tensors_mixed_type( - // input_tensors, nframes, dcoord_, ntypes, datype_, dbox, cell_size, - // fparam, aparam, atommap, "", aparam_nall); - if (atomic) { - run_model(dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, - atommap, nframes); - } else { - run_model(dener, dforce_, dvirial, atommap, predictor, - nframes); - } - } +// template void DeepPotPD::compute_mixed_type( +// ENERGYTYPE& dener, +// std::vector& dforce_, +// std::vector& dvirial, +// std::vector& datom_energy_, +// std::vector& datom_virial_, +// const int& nframes, +// const std::vector& dcoord_, +// const std::vector& datype_, +// const std::vector& dbox, +// const std::vector& fparam, +// const std::vector& aparam, +// const bool atomic); + +// template void DeepPotPD::compute_mixed_type( +// ENERGYTYPE& dener, +// std::vector& dforce_, +// std::vector& dvirial, +// std::vector& datom_energy_, +// std::vector& datom_virial_, +// const int& nframes, +// const std::vector& dcoord_, +// const std::vector& datype_, +// const std::vector& dbox, +// const std::vector& fparam, +// const std::vector& aparam, +// const bool atomic); + +// template void DeepPotPD::compute_mixed_type>( +// std::vector& dener, +// std::vector& dforce_, +// std::vector& dvirial, +// std::vector& datom_energy_, +// std::vector& datom_virial_, +// const int& nframes, +// const std::vector& dcoord_, +// const std::vector& datype_, +// const std::vector& dbox, +// const std::vector& fparam, +// const std::vector& aparam, +// const bool atomic); + +// template void DeepPotPD::compute_mixed_type>( +// std::vector& dener, +// std::vector& dforce_, +// std::vector& dvirial, +// std::vector& datom_energy_, +// std::vector& datom_virial_, +// const int& nframes, +// const std::vector& dcoord_, +// const std::vector& datype_, +// const std::vector& dbox, +// const std::vector& fparam, +// const std::vector& aparam, +// const bool atomic); + + +template +VT DeepPotPD::get_scalar(const std::string& name) const { + return predictor_get_scalar(predictor, name); } -template void DeepPotTF::compute_mixed_type( - ENERGYTYPE& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const int& nframes, - const std::vector& dcoord_, - const std::vector& datype_, - const std::vector& dbox, - const std::vector& fparam, - const std::vector& aparam, - const bool atomic); - -template void DeepPotTF::compute_mixed_type( - ENERGYTYPE& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const int& nframes, - const std::vector& dcoord_, - const std::vector& datype_, - const std::vector& dbox, - const std::vector& fparam, - const std::vector& aparam, - const bool atomic); - -template void DeepPotTF::compute_mixed_type>( - std::vector& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const int& nframes, - const std::vector& dcoord_, - const std::vector& datype_, - const std::vector& dbox, - const std::vector& fparam, - const std::vector& aparam, - const bool atomic); - -template void DeepPotTF::compute_mixed_type>( - std::vector& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const int& nframes, - const std::vector& dcoord_, - const std::vector& datype_, - const std::vector& dbox, - const std::vector& fparam, - const std::vector& aparam, - const bool atomic); - -void DeepPotTF::get_type_map(std::string& type_map) { - type_map = get_scalar("model_attr/tmap"); +void DeepPotPD::get_type_map(std::string& type_map) { + type_map = predictor_get_scalar(predictor, "generated_tensor_12"); } // forward to template method -void DeepPotTF::computew(std::vector& ener, +void DeepPotPD::computew(std::vector& ener, std::vector& force, std::vector& virial, std::vector& atom_energy, @@ -1024,7 +1036,7 @@ void DeepPotTF::computew(std::vector& ener, compute(ener, force, virial, atom_energy, atom_virial, coord, atype, box, fparam, aparam, atomic); } -void DeepPotTF::computew(std::vector& ener, +void DeepPotPD::computew(std::vector& ener, std::vector& force, std::vector& virial, std::vector& atom_energy, @@ -1038,7 +1050,7 @@ void DeepPotTF::computew(std::vector& ener, compute(ener, force, virial, atom_energy, atom_virial, coord, atype, box, fparam, aparam, atomic); } -void DeepPotTF::computew(std::vector& ener, +void DeepPotPD::computew(std::vector& ener, std::vector& force, std::vector& virial, std::vector& atom_energy, @@ -1055,7 +1067,7 @@ void DeepPotTF::computew(std::vector& ener, compute(ener, force, virial, atom_energy, atom_virial, coord, atype, box, nghost, inlist, ago, fparam, aparam, atomic); } -void DeepPotTF::computew(std::vector& ener, +void DeepPotPD::computew(std::vector& ener, std::vector& force, std::vector& virial, std::vector& atom_energy, @@ -1072,34 +1084,34 @@ void DeepPotTF::computew(std::vector& ener, compute(ener, force, virial, atom_energy, atom_virial, coord, atype, box, nghost, inlist, ago, fparam, aparam, atomic); } -void DeepPotTF::computew_mixed_type(std::vector& ener, - std::vector& force, - std::vector& virial, - std::vector& atom_energy, - std::vector& atom_virial, - const int& nframes, - const std::vector& coord, - const std::vector& atype, - const std::vector& box, - const std::vector& fparam, - const std::vector& aparam, - const bool atomic) { - compute_mixed_type(ener, force, virial, atom_energy, atom_virial, nframes, - coord, atype, box, fparam, aparam, atomic); -} -void DeepPotTF::computew_mixed_type(std::vector& ener, - std::vector& force, - std::vector& virial, - std::vector& atom_energy, - std::vector& atom_virial, - const int& nframes, - const std::vector& coord, - const std::vector& atype, - const std::vector& box, - const std::vector& fparam, - const std::vector& aparam, - const bool atomic) { - compute_mixed_type(ener, force, virial, atom_energy, atom_virial, nframes, - coord, atype, box, fparam, aparam, atomic); -} +// void DeepPotPD::computew_mixed_type(std::vector& ener, +// std::vector& force, +// std::vector& virial, +// std::vector& atom_energy, +// std::vector& atom_virial, +// const int& nframes, +// const std::vector& coord, +// const std::vector& atype, +// const std::vector& box, +// const std::vector& fparam, +// const std::vector& aparam, +// const bool atomic) { +// compute_mixed_type(ener, force, virial, atom_energy, atom_virial, nframes, +// coord, atype, box, fparam, aparam, atomic); +// } +// void DeepPotPD::computew_mixed_type(std::vector& ener, +// std::vector& force, +// std::vector& virial, +// std::vector& atom_energy, +// std::vector& atom_virial, +// const int& nframes, +// const std::vector& coord, +// const std::vector& atype, +// const std::vector& box, +// const std::vector& fparam, +// const std::vector& aparam, +// const bool atomic) { +// compute_mixed_type(ener, force, virial, atom_energy, atom_virial, nframes, +// coord, atype, box, fparam, aparam, atomic); +// } #endif diff --git a/source/api_cc/src/common.cc b/source/api_cc/src/common.cc index 0e04cbcf6b..4ff2fa79e8 100644 --- a/source/api_cc/src/common.cc +++ b/source/api_cc/src/common.cc @@ -10,6 +10,7 @@ #include "AtomMap.h" #include "device.h" +#include #if defined(_WIN32) #if defined(_WIN32_WINNT) #undef _WIN32_WINNT @@ -33,6 +34,12 @@ using namespace tensorflow; #endif +#ifdef BUILD_PADDLE +#include "commonPD.h" +#include "google/protobuf/io/zero_copy_stream_impl.h" +#include "google/protobuf/text_format.h" +#endif + static std::vector split(const std::string& input_, const std::string& delimiter) { std::string input = input_; @@ -406,9 +413,9 @@ void deepmd::load_op_library() { #ifdef BUILD_PYTORCH _load_single_op_library("deepmd_op_pt"); #endif -#ifdef BUILD_PADDLE - _load_single_op_library("deepmd_op_pd"); -#endif +// #ifdef BUILD_PADDLE +// _load_single_op_library("deepmd_op_pd"); +// #endif // load customized plugins const char* env_customized_plugins = std::getenv("DP_PLUGIN_PATH"); if (env_customized_plugins) { @@ -921,6 +928,272 @@ int deepmd::session_get_dtype(tensorflow::Session* session, } #endif +#ifdef BUILD_PADDLE +template +int deepmd::predictor_input_tensors( + const std::shared_ptr& predictor, + const std::vector& dcoord_, + const int& ntypes, + const std::vector& datype_, + const std::vector& dbox, + InputNlist& dlist, + const std::vector& fparam_, + const std::vector& aparam_, + const deepmd::AtomMap& atommap, + const int nghost, + const int ago, + const bool aparam_nall) { + // if datype.size is 0, not clear nframes; but 1 is just ok + int nframes = datype_.size() > 0 ? (dcoord_.size() / 3 / datype_.size()) : 1; + int nall = datype_.size(); + int nloc = nall - nghost; + assert(nall * 3 * nframes == dcoord_.size()); + assert(dbox.size() == nframes * 9); + + std::vector datype = atommap.get_type(); + std::vector type_count(ntypes, 0); + for (unsigned ii = 0; ii < datype.size(); ++ii) { + type_count[datype[ii]]++; + } + datype.insert(datype.end(), datype_.begin() + nloc, datype_.end()); + + std::vector dcoord(dcoord_); + atommap.forward(dcoord.begin(), dcoord_.begin(), 3, nframes, nall); + + // 准备输入Tensor句柄 + auto input_names = predictor->GetInputNames(); + auto coord_handle = predictor->GetInputHandle(input_names[0]); + auto atype_handle = predictor->GetInputHandle(input_names[1]); + auto natoms_handle = predictor->GetInputHandle(input_names[2]); + auto box_handle = predictor->GetInputHandle(input_names[3]); + auto mesh_handle = predictor->GetInputHandle(input_names[4]); + + // 设置输入 Tensor 的维度信息 + std::vector COORD_SHAPE = {nframes, nall * 3}; + std::vector ATYPE_SHAPE = {nframes, nall}; + std::vector BOX_SHAPE = {nframes, 9}; + std::vector MESH_SHAPE = {16}; + std::vector NATOMS_SHAPE = {2 + ntypes}; + + coord_handle->Reshape(COORD_SHAPE); + atype_handle->Reshape(ATYPE_SHAPE); + natoms_handle->Reshape(NATOMS_SHAPE); + box_handle->Reshape(BOX_SHAPE); + mesh_handle->Reshape(MESH_SHAPE); + + // 发送输入数据到Tensor句柄 + coord_handle->CopyFromCpu(dcoord.data()); + + std::vector datype_pad(nframes * nall, 0); + for (int ii = 0; ii < nframes; ++ii) { + for (int jj = 0; jj < nall; ++jj) { + datype_pad[ii * nall + jj] = datype[jj]; + } + } + atype_handle->CopyFromCpu(datype_pad.data()); + + std::vector mesh_pad(16, 0); + mesh_pad[0] = ago; + mesh_pad[1] = dlist.inum; + mesh_pad[2] = 0; + mesh_pad[3] = 0; + memcpy(&mesh_pad[4], &(dlist.ilist), sizeof(int*)); + memcpy(&mesh_pad[8], &(dlist.numneigh), sizeof(int*)); + memcpy(&mesh_pad[12], &(dlist.firstneigh), sizeof(int**)); + mesh_handle->CopyFromCpu(mesh_pad.data()); + + std::vector natoms_pad = {nloc, nall}; + for (int ii = 0; ii < ntypes; ++ii) { + natoms_pad.push_back(type_count[ii]); + } + natoms_handle->CopyFromCpu(natoms_pad.data()); + + box_handle->CopyFromCpu(dbox.data()); + + const int stride = sizeof(int*) / sizeof(int); + assert(stride * sizeof(int) == sizeof(int*)); + assert(stride <= 4); + + return nloc; +} + +template +int deepmd::predictor_input_tensors( + const std::shared_ptr& predictor, + const std::vector& dcoord_, + const int& ntypes, + const std::vector& datype_, + const std::vector& dbox, + const double& cell_size, + const std::vector& fparam_, + const std::vector& aparam_, + const deepmd::AtomMap& atommap, + const bool aparam_nall) { + // if datype.size is 0, not clear nframes; but 1 is just ok + int nframes = datype_.size() > 0 ? (dcoord_.size() / 3 / datype_.size()) : 1; + int nall = datype_.size(); + int nloc = nall; + assert(nall * 3 * nframes == dcoord_.size()); + bool b_pbc = (dbox.size() == nframes * 9); + + std::vector datype = atommap.get_type(); + std::vector type_count(ntypes, 0); + for (unsigned ii = 0; ii < datype.size(); ++ii) { + type_count[datype[ii]]++; + } + datype.insert(datype.end(), datype_.begin() + nloc, datype_.end()); + + std::vector dcoord(dcoord_); + atommap.forward(dcoord.begin(), dcoord_.begin(), 3, nframes, nall); + + // 准备输入Tensor句柄 + auto input_names = predictor->GetInputNames(); + auto coord_handle = predictor->GetInputHandle(input_names[0]); + auto atype_handle = predictor->GetInputHandle(input_names[1]); + auto natoms_handle = predictor->GetInputHandle(input_names[2]); + auto box_handle = predictor->GetInputHandle(input_names[3]); + auto mesh_handle = predictor->GetInputHandle(input_names[4]); + + // 设置输入 Tensor 的维度信息 + std::vector COORD_SHAPE = {nframes, nall * 3}; + std::vector ATYPE_SHAPE = {nframes, nall}; + std::vector BOX_SHAPE = {nframes, 9}; + std::vector MESH_SHAPE; + if (b_pbc) { + MESH_SHAPE = std::vector(6); + } else { + MESH_SHAPE = std::vector(0); + } + + std::vector NATOMS_SHAPE = {2 + ntypes}; + + coord_handle->Reshape(COORD_SHAPE); + atype_handle->Reshape(ATYPE_SHAPE); + natoms_handle->Reshape(NATOMS_SHAPE); + box_handle->Reshape(BOX_SHAPE); + mesh_handle->Reshape(MESH_SHAPE); + + // 发送输入数据到Tensor句柄 + coord_handle->CopyFromCpu(dcoord.data()); + + std::vector datype_pad(nframes * nall, 0); + for (int ii = 0; ii < nframes; ++ii) { + for (int jj = 0; jj < nall; ++jj) { + datype_pad[ii * nall + jj] = datype[jj]; + } + } + atype_handle->CopyFromCpu(datype_pad.data()); + + + std::vector mesh_pad; + if (b_pbc) { + mesh_pad = std::vector(6); + } else { + mesh_pad = std::vector(0); + } + // mesh_pad[0] = ago; + // mesh_pad[1] = dlist.inum; + // mesh_pad[2] = 0; + // mesh_pad[3] = 0; + // memcpy(&mesh_pad[4], &(dlist.ilist), sizeof(int*)); + // memcpy(&mesh_pad[8], &(dlist.numneigh), sizeof(int*)); + // memcpy(&mesh_pad[12], &(dlist.firstneigh), sizeof(int**)); + mesh_handle->CopyFromCpu(mesh_pad.data()); + if (b_pbc) { + mesh_pad[1 - 1] = 0; + mesh_pad[2 - 1] = 0; + mesh_pad[3 - 1] = 0; + mesh_pad[4 - 1] = 0; + mesh_pad[5 - 1] = 0; + mesh_pad[6 - 1] = 0; + } + std::vector natoms_pad = {nloc, nall}; + for (int ii = 0; ii < ntypes; ++ii) { + natoms_pad.push_back(type_count[ii]); + } + // natoms_handle->CopyFromCpu(natoms_pad.data()); + + box_handle->CopyFromCpu(dbox.data()); + + // const int stride = sizeof(int*) / sizeof(int); + // assert(stride * sizeof(int) == sizeof(int*)); + // assert(stride <= 4); + + return nloc; +} +#endif + +#ifdef BUILD_PADDLE +template +VT deepmd::predictor_get_scalar( + const std::shared_ptr& predictor, + const std::string& name_) { + if (std::is_same::value) { + /* + NOTE: Convert from ascii code(int64) to std::string, + A workaround for string data type is not supported in Paddle yet. + */ + auto scalar_tensor = predictor->GetOutputHandle(name_); + if (scalar_tensor->shape().size() == 0) { + return VT(); + } + const auto& shape = scalar_tensor->shape(); + const int& str_len = std::accumulate(std::begin(shape), std::end(shape), 1, + std::multiplies<>{}); + if (str_len == 0) { + return VT(); + } + int32_t* scalar_ptr = (int32_t*)malloc(str_len * sizeof(int32_t)); + scalar_tensor->CopyToCpu(scalar_ptr); + VT ret; + for (int ii = 0; ii < str_len; ++ii) { + ret += (char)scalar_ptr[ii]; + } + free(scalar_ptr); + return ret; + } else { + /* Vanillia process for other data type below*/ + auto scalar_tensor = predictor->GetOutputHandle(name_); + // VT* scalar_ptr = (VT*)malloc(1 * sizeof(VT)); + std::unique_ptr scalar_ptr(new VT); + scalar_tensor->CopyToCpu(scalar_ptr.get()); + return (*scalar_ptr); + } +} + + +// template +// void deepmd::session_get_vector(std::vector& o_vec, +// Session* session, +// const std::string name_, +// const std::string scope) { +// std::string name = name_; +// if (scope != "") { +// name = scope + "/" + name; +// } +// std::vector output_tensors; +// deepmd::check_status( +// session->Run(std::vector>({}), +// {name.c_str()}, {}, &output_tensors)); +// Tensor output_rc = output_tensors[0]; +// assert(1 == output_rc.shape().dims()); +// int dof = output_rc.shape().dim_size(0); +// o_vec.resize(dof); +// auto orc = output_rc.flat(); +// for (int ii = 0; ii < dof; ++ii) { +// o_vec[ii] = orc(ii); +// } +// } + +paddle_infer::DataType deepmd::predictor_get_dtype( + const std::shared_ptr& predictor, + const std::string& name_) { + auto scalar_tensor = predictor->GetOutputHandle(name_); + return scalar_tensor->type(); +} + +#endif + template void deepmd::select_map(std::vector& out, const std::vector& in, @@ -1010,19 +1283,17 @@ void deepmd::select_map_inv(typename std::vector::iterator out, } } -#ifdef BUILD_TENSORFLOW -template int deepmd::session_get_scalar(Session*, - const std::string, - const std::string); - -template bool deepmd::session_get_scalar(Session*, - const std::string, - const std::string); - -template void deepmd::session_get_vector(std::vector&, - Session*, - const std::string, - const std::string); +#ifdef BUILD_PADDLE +template int deepmd::predictor_get_scalar(const std::shared_ptr& predictor, + const std::string &name_); + +template bool deepmd::predictor_get_scalar(const std::shared_ptr& predictor, + const std::string &name_); + +// template void deepmd::session_get_vector(std::vector&, +// Session*, +// const std::string, +// const std::string); #endif template void deepmd::select_map(std::vector& out, @@ -1064,6 +1335,12 @@ template void deepmd::session_get_vector(std::vector&, const std::string); #endif +#ifdef BUILD_PADDLE +template float deepmd::predictor_get_scalar(const std::shared_ptr& predictor, + const std::string &name_); + +#endif + template void deepmd::select_map(std::vector& out, const std::vector& in, const std::vector& idx_map, @@ -1103,6 +1380,11 @@ template void deepmd::session_get_vector(std::vector&, const std::string); #endif +#ifdef BUILD_PADDLE +template double deepmd::predictor_get_scalar(const std::shared_ptr& predictor, + const std::string& name_); +#endif + template void deepmd::select_map(std::vector& out, const std::vector& in, const std::vector& idx_map, @@ -1172,6 +1454,46 @@ template void deepmd::select_map_inv( const int& stride); #endif +#ifdef BUILD_PADDLE +template std::string deepmd::predictor_get_scalar( + const std::shared_ptr& predictor, const std::string&); + +// template void deepmd::session_get_vector( +// std::vector&, +// const std::shared_ptr& predictor, +// const std::string); + +template void deepmd::select_map( + std::vector& out, + const std::vector& in, + const std::vector& idx_map, + const int& stride, + const int& nframes, + const int& nall1, + const int& nall2); + +template void deepmd::select_map( + typename std::vector::iterator out, + const typename std::vector::const_iterator in, + const std::vector& idx_map, + const int& stride, + const int& nframes, + const int& nall1, + const int& nall2); + +template void deepmd::select_map_inv( + std::vector& out, + const std::vector& in, + const std::vector& idx_map, + const int& stride); + +template void deepmd::select_map_inv( + typename std::vector::iterator out, + const typename std::vector::const_iterator in, + const std::vector& idx_map, + const int& stride); +#endif + void deepmd::read_file_to_string(std::string model, std::string& file_content) { // generated by GitHub Copilot std::ifstream file(model); @@ -1366,6 +1688,162 @@ template int deepmd::session_input_tensors_mixed_type( const bool aparam_nall); #endif +#ifdef BUILD_PADDLE +template int deepmd::predictor_input_tensors( + const std::shared_ptr& predictor, + const std::vector& dcoord_, + const int& ntypes, + const std::vector& datype_, + const std::vector& dbox, + const double& cell_size, + const std::vector& fparam_, + const std::vector& aparam_, + const deepmd::AtomMap& atommap, + const bool aparam_nall); +template int deepmd::predictor_input_tensors( + const std::shared_ptr& predictor, + const std::vector& dcoord_, + const int& ntypes, + const std::vector& datype_, + const std::vector& dbox, + const double& cell_size, + const std::vector& fparam_, + const std::vector& aparam_, + const deepmd::AtomMap& atommap, + const bool aparam_nall); + +template int deepmd::predictor_input_tensors( + const std::shared_ptr& predictor, + const std::vector& dcoord_, + const int& ntypes, + const std::vector& datype_, + const std::vector& dbox, + const double& cell_size, + const std::vector& fparam_, + const std::vector& aparam_, + const deepmd::AtomMap& atommap, + const bool aparam_nall); +template int deepmd::predictor_input_tensors( + const std::shared_ptr& predictor, + const std::vector& dcoord_, + const int& ntypes, + const std::vector& datype_, + const std::vector& dbox, + const double& cell_size, + const std::vector& fparam_, + const std::vector& aparam_, + const deepmd::AtomMap& atommap, + const bool aparam_nall); + +template int deepmd::predictor_input_tensors( + const std::shared_ptr& predictor, + const std::vector& dcoord_, + const int& ntypes, + const std::vector& datype_, + const std::vector& dbox, + InputNlist& dlist, + const std::vector& fparam_, + const std::vector& aparam_, + const deepmd::AtomMap& atommap, + const int nghost, + const int ago, + const bool aparam_nall); +template int deepmd::predictor_input_tensors( + const std::shared_ptr& predictor, + const std::vector& dcoord_, + const int& ntypes, + const std::vector& datype_, + const std::vector& dbox, + InputNlist& dlist, + const std::vector& fparam_, + const std::vector& aparam_, + const deepmd::AtomMap& atommap, + const int nghost, + const int ago, + const bool aparam_nall); + +template int deepmd::predictor_input_tensors( + const std::shared_ptr& predictor, + const std::vector& dcoord_, + const int& ntypes, + const std::vector& datype_, + const std::vector& dbox, + InputNlist& dlist, + const std::vector& fparam_, + const std::vector& aparam_, + const deepmd::AtomMap& atommap, + const int nghost, + const int ago, + const bool aparam_nall); +template int deepmd::predictor_input_tensors( + const std::shared_ptr& predictor, + const std::vector& dcoord_, + const int& ntypes, + const std::vector& datype_, + const std::vector& dbox, + InputNlist& dlist, + const std::vector& fparam_, + const std::vector& aparam_, + const deepmd::AtomMap& atommap, + const int nghost, + const int ago, + const bool aparam_nall); + +// template int deepmd::session_input_tensors_mixed_type( +// std::vector>& input_tensors, +// const int& nframes, +// const std::vector& dcoord_, +// const int& ntypes, +// const std::vector& datype_, +// const std::vector& dbox, +// const double& cell_size, +// const std::vector& fparam_, +// const std::vector& aparam_, +// const deepmd::AtomMap& atommap, +// const std::string scope, +// const bool aparam_nall); +// template int deepmd::session_input_tensors_mixed_type( +// std::vector>& input_tensors, +// const int& nframes, +// const std::vector& dcoord_, +// const int& ntypes, +// const std::vector& datype_, +// const std::vector& dbox, +// const double& cell_size, +// const std::vector& fparam_, +// const std::vector& aparam_, +// const deepmd::AtomMap& atommap, +// const std::string scope, +// const bool aparam_nall); + +// template int deepmd::session_input_tensors_mixed_type( +// std::vector>& input_tensors, +// const int& nframes, +// const std::vector& dcoord_, +// const int& ntypes, +// const std::vector& datype_, +// const std::vector& dbox, +// const double& cell_size, +// const std::vector& fparam_, +// const std::vector& aparam_, +// const deepmd::AtomMap& atommap, +// const std::string scope, +// const bool aparam_nall); +// template int deepmd::session_input_tensors_mixed_type( +// std::vector>& input_tensors, +// const int& nframes, +// const std::vector& dcoord_, +// const int& ntypes, +// const std::vector& datype_, +// const std::vector& dbox, +// const double& cell_size, +// const std::vector& fparam_, +// const std::vector& aparam_, +// const deepmd::AtomMap& atommap, +// const std::string scope, +// const bool aparam_nall); +#endif + void deepmd::print_summary(const std::string& pre) { int num_intra_nthreads, num_inter_nthreads; deepmd::get_env_nthreads(num_intra_nthreads, num_inter_nthreads); @@ -1391,6 +1869,9 @@ void deepmd::print_summary(const std::string& pre) { #endif #ifdef BUILD_PYTORCH std::cout << pre << "build with pt lib: " + global_pt_lib << "\n"; +#endif +#ifdef BUILD_PADDLE + std::cout << pre << "build with pd lib: " + global_pd_lib << "\n"; #endif std::cout << pre << "set tf intra_op_parallelism_threads: " << num_intra_nthreads From 2b4832a010cfd54f6651931d9457095b4b4dd825 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Wed, 18 Sep 2024 16:42:52 +0800 Subject: [PATCH 13/93] add build script --- source/install/build_cc_pd.sh | 112 ++++++++++++++++++++++++++++++++++ 1 file changed, 112 insertions(+) create mode 100755 source/install/build_cc_pd.sh diff --git a/source/install/build_cc_pd.sh b/source/install/build_cc_pd.sh new file mode 100755 index 0000000000..5feb1e3426 --- /dev/null +++ b/source/install/build_cc_pd.sh @@ -0,0 +1,112 @@ +set -e + +if [ "$DP_VARIANT" = "cuda" ]; then + CUDA_ARGS="-DUSE_CUDA_TOOLKIT=TRUE" +elif [ "$DP_VARIANT" = "rocm" ]; then + CUDA_ARGS="-DUSE_ROCM_TOOLKIT=TRUE" +fi +#------------------ + +SCRIPT_PATH=$(dirname $(realpath -s $0)) +if [ -z "$INSTALL_PREFIX" ]; then + INSTALL_PREFIX=$(realpath -s ${SCRIPT_PATH}/../../dp) +fi +mkdir -p ${INSTALL_PREFIX} +echo "Installing DeePMD-kit to ${INSTALL_PREFIX}" +NPROC=$(nproc --all) + +#------------------ + +# LAMMPS_DIR 设置为 LAMMPS 的安装目录 +export LAMMPS_DIR="/workspace/hesensen/deepmd_backend/deepmd_paddle_new/source/build_lammps/lammps-stable_29Aug2024/" +export LAMMPS_SOURCE_ROOT="/workspace/hesensen/deepmd_backend/deepmd_paddle_new/source/build_lammps/lammps-stable_29Aug2024/" + +# 设置推理时的 GPU 卡号 +export CUDA_VISIBLE_DEVICES=6 + +# PADDLE_DIR 设置为第二步 clone下来的 Paddle 目录 +export PADDLE_DIR="/workspace/hesensen/PaddleScience_enn_debug/Paddle/" + +# DEEPMD_DIR 设置为本项目的根目录 +export DEEPMD_DIR="/workspace/hesensen/deepmd_backend/deepmd_paddle_new/" + +# PADDLE_INFERENCE_DIR 设置为第二步编译得到的 Paddle 推理库目录 +export PADDLE_INFERENCE_DIR="/workspace/hesensen/PaddleScience_enn_debug/Paddle/build/paddle_inference_install_dir/" + +# TENSORFLOW_DIR 设置为 tensorflow 的安装目录，可用 pip show tensorflow 确定 +# export TENSORFLOW_DIR="/path/to/tensorflow" + +export LD_LIBRARY_PATH=${PADDLE_DIR}/paddle/fluid/pybind/:$LD_LIBRARY_PATH +export LD_LIBRARY_PATH=${DEEPMD_DIR}/deepmd/op:$LD_LIBRARY_PATH +export LD_LIBRARY_PATH=${PADDLE_INFERENCE_DIR}/paddle/lib:$LD_LIBRARY_PATH +export LD_LIBRARY_PATH=${PADDLE_INFERENCE_DIR}/third_party/install/mkldnn/lib:$LD_LIBRARY_PATH +export LD_LIBRARY_PATH=${PADDLE_INFERENCE_DIR}/third_party/install/mklml/lib:$LD_LIBRARY_PATH +export LD_LIBRARY_PATH=${DEEPMD_DIR}/source/build:$LD_LIBRARY_PATH +export LIBRARY_PATH=${DEEPMD_DIR}/deepmd/op:$LIBRARY_PATH + +cd ${DEEPMD_DIR}/source +rm -rf build # 若改动CMakeLists.txt，则需要打开该注释 +mkdir build +cd - + +# DEEPMD_INSTALL_DIR 设置为 deepmd-lammps 的目标安装目录，可自行设置任意路径 +# export DEEPMD_INSTALL_DIR="path/to/deepmd_root" + +# 开始编译 +# cmake -DCMAKE_INSTALL_PREFIX=${DEEPMD_INSTALL_DIR} \ +# -DUSE_CUDA_TOOLKIT=TRUE \ +# -DTENSORFLOW_ROOT=${TENSORFLOW_DIR} \ +# -DPADDLE_LIB=${PADDLE_INFERENCE_DIR} \ +# -DFLOAT_PREC=low .. +# make -j4 && make install +# make lammps + +# cd ${LAMMPS_DIR}/src/ +# \cp -r ${DEEPMD_DIR}/source/build/USER-DEEPMD . +# make yes-kspace +# make yes-extra-fix +# make yes-user-deepmd +# make serial -j +# export PATH=${LAMMPS_DIR}/src:$PATH + +# cd ${DEEPMD_DIR}/examples/water/lmp + +# lmp_serial -in in.lammps + +BUILD_TMP_DIR=${SCRIPT_PATH}/../build +mkdir -p ${BUILD_TMP_DIR} +cd ${BUILD_TMP_DIR} +cmake -DCMAKE_PREFIX_PATH=/workspace/hesensen/PaddleScience_enn_debug/Paddle/build/paddle_inference_install_dir/paddle \ + -D ENABLE_TENSORFLOW=OFF \ + -D ENABLE_PYTORCH=OFF \ + -D ENABLE_PADDLE=ON \ + -D PADDLE_LIB=${PADDLE_INFERENCE_DIR} \ + -D CMAKE_INSTALL_PREFIX=${INSTALL_PREFIX} \ + -D USE_TF_PYTHON_LIBS=TRUE \ + -D LAMMPS_SOURCE_ROOT=${LAMMPS_SOURCE_ROOT} \ + ${CUDA_ARGS} \ + -D LAMMPS_VERSION=stable_29Aug2024 \ + .. +cmake --build . -j${NPROC} +cmake --install . + +#------------------ +echo "Congratulations! DeePMD-kit has been installed at ${INSTALL_PREFIX}" + +cd ${DEEPMD_DIR}/source +cd build +make lammps +cd ${LAMMPS_DIR}/src/ +\cp -r ${DEEPMD_DIR}/source/build/USER-DEEPMD . +make no-kspace +make yes-kspace +make no-extra-fix +make yes-extra-fix +make no-user-deepmd +make yes-user-deepmd +make serial -j +export PATH=${LAMMPS_DIR}/src:$PATH + +cd ${DEEPMD_DIR}/examples/water/lmp + +lmp_serial -in paddle_in.lammps From f1100e4b73c381f38fb6ac04ae685454a86d76f3 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 23 Sep 2024 15:30:12 +0800 Subject: [PATCH 14/93] Update water/se_e2_a + LAMMPS code --- deepmd/pd/entrypoints/main.py | 21 +- .../pd/model/atomic_model/dp_atomic_model.py | 5 + deepmd/pd/model/model/make_model.py | 7 +- deepmd/pd/utils/env.py | 1 + deepmd/pd/utils/nlist.py | 34 +- deepmd/pd/utils/region.py | 11 +- source/api_c/include/deepmd.hpp | 1 - source/api_c/src/c_api.cc | 2 +- source/api_cc/CMakeLists.txt | 4 +- source/api_cc/include/DeepPotPD.h | 50 +- source/api_cc/src/DeepPot.cc | 17 +- source/api_cc/src/DeepPotPD.cc | 555 ++++++++++-------- source/api_cc/src/common.cc | 511 +++++++++++----- source/install/build_cc.sh | 3 +- source/install/build_cc_pd.sh | 23 +- source/lmp/pair_deepmd.cpp | 5 - 16 files changed, 779 insertions(+), 471 deletions(-) diff --git a/deepmd/pd/entrypoints/main.py b/deepmd/pd/entrypoints/main.py index f05543d239..4e4ac5f85a 100644 --- a/deepmd/pd/entrypoints/main.py +++ b/deepmd/pd/entrypoints/main.py @@ -53,6 +53,7 @@ ) from deepmd.pd.utils.env import ( DEVICE, + PIR_ENABLED, ) from deepmd.pd.utils.finetune import ( get_finetune_rules, @@ -349,17 +350,20 @@ def freeze(FLAGS): ) """ - ** coord [None, 192, 3] paddle.float64 - ** atype [None, 192] paddle.int64 - ** box [None, 3, 3] paddle.float64 + ** coord [None, natoms, 3] paddle.float64 + ** atype [None, natoms] paddle.int64 + ** nlist [None, natoms, nnei] paddle.int32 """ + model.atomic_model.buffer_type_map.set_value( + paddle.to_tensor([ord(c) for c in model.atomic_model.type_map], dtype="int32") + ) model = paddle.jit.to_static( - model, + model.forward_lower, full_graph=True, input_spec=[ - InputSpec([None, 192, 3], dtype="float64", name="coord"), - InputSpec([None, 192], dtype="int64", name="atype"), - InputSpec([None, 3, 3], dtype="float64", name="box"), + InputSpec([-1, -1, 3], dtype="float64", name="coord"), + InputSpec([-1, -1], dtype="int32", name="atype"), + InputSpec([-1, -1, -1], dtype="int32", name="nlist"), ], ) extra_files = {} @@ -369,8 +373,7 @@ def freeze(FLAGS): skip_prune_program=True, # extra_files, ) - pir_flag = os.getenv("FLAGS_enable_pir_api", "false") - suffix = "json" if pir_flag.lower() in ["true", "1"] else "pdmodel" + suffix = "json" if PIR_ENABLED.lower() in ["true", "1"] else "pdmodel" log.info( f"Paddle inference model has been exported to: {FLAGS.output}.{suffix}(.pdiparams)" ) diff --git a/deepmd/pd/model/atomic_model/dp_atomic_model.py b/deepmd/pd/model/atomic_model/dp_atomic_model.py index 9b264fd2c4..e5abd2135d 100644 --- a/deepmd/pd/model/atomic_model/dp_atomic_model.py +++ b/deepmd/pd/model/atomic_model/dp_atomic_model.py @@ -58,6 +58,11 @@ def __init__( super().__init__(type_map, **kwargs) ntypes = len(type_map) self.type_map = type_map + self.register_buffer( + "buffer_type_map", + paddle.to_tensor([ord(c) for c in self.type_map], dtype="int32"), + ) + self.buffer_type_map.name = "type_map" self.ntypes = ntypes self.descriptor = descriptor self.rcut = self.descriptor.get_rcut() diff --git a/deepmd/pd/model/model/make_model.py b/deepmd/pd/model/model/make_model.py index 597171d596..3a35589458 100644 --- a/deepmd/pd/model/model/make_model.py +++ b/deepmd/pd/model/model/make_model.py @@ -429,11 +429,10 @@ def _format_nlist( axis=-1, ) - # if n_nnei > nnei or extra_nlist_sort: - if False: + if True: # TODO: Fix controlflow + backward in PIR static graph n_nf, n_nloc, n_nnei = nlist.shape m_real_nei = nlist >= 0 - nlist = paddle.where(m_real_nei, nlist, 0) + nlist = paddle.where(m_real_nei, nlist, paddle.zeros_like(nlist)) # nf x nloc x 3 coord0 = extended_coord[:, :n_nloc, :] # nf x (nloc x nnei) x 3 @@ -450,7 +449,7 @@ def _format_nlist( paddle.argsort(rr, axis=-1), ) nlist = aux.take_along_axis(nlist, axis=2, indices=nlist_mapping) - nlist = paddle.where(rr > rcut, -1, nlist) + nlist = paddle.where(rr > rcut, paddle.full_like(nlist, -1), nlist) nlist = nlist[..., :nnei] else: # not extra_nlist_sort and n_nnei <= nnei: pass # great! diff --git a/deepmd/pd/utils/env.py b/deepmd/pd/utils/env.py index 85f9e57169..49a11658f3 100644 --- a/deepmd/pd/utils/env.py +++ b/deepmd/pd/utils/env.py @@ -67,6 +67,7 @@ } assert set(PRECISION_DICT.values()) == set(RESERVED_PRECISON_DICT.keys()) DEFAULT_PRECISION = "float64" +PIR_ENABLED = os.getenv("FLAGS_enable_pir_api", "false") # throw warnings if threads not set set_default_nthreads() diff --git a/deepmd/pd/utils/nlist.py b/deepmd/pd/utils/nlist.py index ef27be31eb..52893c85d2 100644 --- a/deepmd/pd/utils/nlist.py +++ b/deepmd/pd/utils/nlist.py @@ -318,7 +318,7 @@ def nlist_distinguish_types( for ii, ss in enumerate(sel): # nloc x s(nsel) # to int because bool cannot be sort on GPU - pick_mask = (tnlist == ii).to(paddle.int32) + pick_mask = (tnlist == ii).to(paddle.int64) # nloc x s(nsel), stable sort, nearer neighbors first pick_mask, imap = ( paddle.sort(pick_mask, axis=-1, descending=True, stable=True), @@ -477,32 +477,36 @@ def extend_coord_with_ghosts( nbuff = paddle.ceil(rcut / to_face).to(paddle.int64) # 3 nbuff = paddle.amax(nbuff, axis=0) # faster than paddle.max - nbuff_cpu = nbuff.cpu() + # nbuff_cpu = nbuff.cpu() xi = ( - paddle.arange(-nbuff_cpu[0], nbuff_cpu[0] + 1, 1) - .to(dtype=env.GLOBAL_PD_FLOAT_PRECISION) - .cpu() + paddle.arange(-nbuff[0], nbuff[0] + 1, 1).to( + dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + # .cpu() ) # pylint: disable=no-explicit-dtype yi = ( - paddle.arange(-nbuff_cpu[1], nbuff_cpu[1] + 1, 1) - .to(dtype=env.GLOBAL_PD_FLOAT_PRECISION) - .cpu() + paddle.arange(-nbuff[1], nbuff[1] + 1, 1).to( + dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + # .cpu() ) # pylint: disable=no-explicit-dtype zi = ( - paddle.arange(-nbuff_cpu[2], nbuff_cpu[2] + 1, 1) - .to(dtype=env.GLOBAL_PD_FLOAT_PRECISION) - .cpu() + paddle.arange(-nbuff[2], nbuff[2] + 1, 1).to( + dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + # .cpu() ) # pylint: disable=no-explicit-dtype eye_3 = ( - paddle.eye(3, dtype=env.GLOBAL_PD_FLOAT_PRECISION) - .to(dtype=env.GLOBAL_PD_FLOAT_PRECISION) - .cpu() + paddle.eye(3, dtype=env.GLOBAL_PD_FLOAT_PRECISION).to( + dtype=env.GLOBAL_PD_FLOAT_PRECISION + ) + # .cpu() ) xyz = xi.reshape([-1, 1, 1, 1]) * eye_3[0] xyz = xyz + yi.reshape([1, -1, 1, 1]) * eye_3[1] xyz = xyz + zi.reshape([1, 1, -1, 1]) * eye_3[2] xyz = xyz.reshape([-1, 3]) - xyz = xyz.to(device=device) + # xyz = xyz.to(device=device) # ns x 3 # shift_idx = xyz[paddle.argsort(paddle.norm(xyz, axis=1))] shift_idx = xyz[paddle.argsort(aux.norm(xyz, axis=1))] diff --git a/deepmd/pd/utils/region.py b/deepmd/pd/utils/region.py index a4acc5924a..21ce2b5e75 100644 --- a/deepmd/pd/utils/region.py +++ b/deepmd/pd/utils/region.py @@ -25,11 +25,14 @@ def phys2inter( the internal coordinates """ - try: + if paddle.in_dynamic_mode(): + try: + rec_cell = paddle.linalg.inv(cell) + except Exception as e: + rec_cell = paddle.full_like(cell, float("nan")) + rec_cell.stop_gradient = cell.stop_gradient + else: rec_cell = paddle.linalg.inv(cell) - except Exception: - rec_cell = paddle.full_like(cell, float("nan")) - rec_cell.stop_gradient = False return paddle.matmul(coord, rec_cell) diff --git a/source/api_c/include/deepmd.hpp b/source/api_c/include/deepmd.hpp index 1c23612293..9d0310d99a 100644 --- a/source/api_c/include/deepmd.hpp +++ b/source/api_c/include/deepmd.hpp @@ -685,7 +685,6 @@ class DeepPot { << std::endl; return; } - std::cout << "** [deepmd.hpp] DeepPot.init" << std::endl; dp = DP_NewDeepPotWithParam2(model.c_str(), gpu_rank, file_content.c_str(), file_content.size()); DP_CHECK_OK(DP_DeepPotCheckOK, dp); diff --git a/source/api_c/src/c_api.cc b/source/api_c/src/c_api.cc index 9ed37d04aa..e7222ce59c 100644 --- a/source/api_c/src/c_api.cc +++ b/source/api_c/src/c_api.cc @@ -1,5 +1,4 @@ // SPDX-License-Identifier: LGPL-3.0-or-later -#include "c_api.h" #include #include @@ -10,6 +9,7 @@ #include "DeepTensor.h" #include "c_api_internal.h" #include "common.h" +// #include "/workspace/hesensen/deepmd_backend/deepmd_paddle_new/source/api_c/include/c_api.h" extern "C" { diff --git a/source/api_cc/CMakeLists.txt b/source/api_cc/CMakeLists.txt index 2255857214..ee347f9fd3 100644 --- a/source/api_cc/CMakeLists.txt +++ b/source/api_cc/CMakeLists.txt @@ -24,8 +24,8 @@ if(ENABLE_PYTORCH target_compile_definitions(${libname} PRIVATE BUILD_PYTORCH) endif() if(ENABLE_PADDLE AND NOT BUILD_PY_IF) - target_link_libraries(${libname} PRIVATE "${PADDLE_LIBRARIES}") - target_compile_definitions(${libname} PRIVATE BUILD_PADDLE) + target_link_libraries(${libname} PUBLIC "${PADDLE_LIBRARIES}") + target_compile_definitions(${libname} PUBLIC BUILD_PADDLE) endif() target_include_directories( diff --git a/source/api_cc/include/DeepPotPD.h b/source/api_cc/include/DeepPotPD.h index e1cbfa1f09..8818e86194 100644 --- a/source/api_cc/include/DeepPotPD.h +++ b/source/api_cc/include/DeepPotPD.h @@ -142,17 +142,17 @@ class DeepPotPD : public DeepPotBase { *same aparam. * @param[in] atomic Whether to compute the atomic energy and virial. **/ - // template - // void compute_mixed_type(ENERGYVTYPE& ener, - // std::vector& force, - // std::vector& virial, - // const int& nframes, - // const std::vector& coord, - // const std::vector& atype, - // const std::vector& box, - // const std::vector& fparam, - // const std::vector& aparam, - // const bool atomic); + template + void compute_mixed_type(ENERGYVTYPE& ener, + std::vector& force, + std::vector& virial, + const int& nframes, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); /** * @brief Evaluate the energy, force, and virial with the mixed type *by using this DP. @@ -178,19 +178,19 @@ class DeepPotPD : public DeepPotBase { *same aparam. * @param[in] atomic Whether to compute the atomic energy and virial. **/ - // template - // void compute_mixed_type(ENERGYVTYPE& ener, - // std::vector& force, - // std::vector& virial, - // std::vector& atom_energy, - // std::vector& atom_virial, - // const int& nframes, - // const std::vector& coord, - // const std::vector& atype, - // const std::vector& box, - // const std::vector& fparam, - // const std::vector& aparam, - // const bool atomic); + template + void compute_mixed_type(ENERGYVTYPE& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const int& nframes, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); public: /** @@ -349,7 +349,7 @@ class DeepPotPD : public DeepPotBase { int gpu_id = 0; int do_message_passing = 0; // 1:dpa2 model 0:others bool gpu_enabled = true; - int dtype = paddle_infer::DataType::FLOAT32; + int dtype = paddle_infer::DataType::FLOAT64; // paddle::Tensor firstneigh_tensor; // std::unordered_map comm_dict; /** diff --git a/source/api_cc/src/DeepPot.cc b/source/api_cc/src/DeepPot.cc index 81fc594813..7ee6d910d9 100644 --- a/source/api_cc/src/DeepPot.cc +++ b/source/api_cc/src/DeepPot.cc @@ -12,10 +12,9 @@ #ifdef BUILD_PYTORCH #include "DeepPotPT.h" #endif -// #define BUILD_PADDLE -// #ifdef BUILD_PADDLE +#ifdef BUILD_PADDLE #include "DeepPotPD.h" -// #endif +#endif #include "device.h" using namespace deepmd; @@ -34,7 +33,6 @@ DeepPot::~DeepPot() {} void DeepPot::init(const std::string& model, const int& gpu_rank, const std::string& file_content) { - std::cout << "****** access here" << std::endl; if (inited) { std::cerr << "WARNING: deepmd-kit should not be initialized twice, do " "nothing at the second call of initializer" @@ -46,11 +44,10 @@ void DeepPot::init(const std::string& model, backend = deepmd::DPBackend::PyTorch; } else if (model.length() >= 3 && model.substr(model.length() - 3) == ".pb") { backend = deepmd::DPBackend::TensorFlow; - // } else if (model.length() >= 3 && (model.substr(model.length() - 5) == ".json" || model.substr(model.length() - 8) == ".pdmodel")) { - } else if (true) { + } else if ((model.length() >= 5 && model.substr(model.length() - 5) == ".json") || (model.length() >= 8 && model.substr(model.length() - 8) == ".pdmodel")) { backend = deepmd::DPBackend::Paddle; } else { - throw deepmd::deepmd_exception("Unsupported model file formatt"); + throw deepmd::deepmd_exception("Unsupported model file format"); } if (deepmd::DPBackend::TensorFlow == backend) { @@ -66,11 +63,11 @@ void DeepPot::init(const std::string& model, throw deepmd::deepmd_exception("PyTorch backend is not built"); #endif } else if (deepmd::DPBackend::Paddle == backend) { -// #ifdef BUILD_PADDLE +#ifdef BUILD_PADDLE dp = std::make_shared(model, gpu_rank, file_content); -// #else +#else throw deepmd::deepmd_exception("Paddle backend is not built"); -// #endif +#endif } else { throw deepmd::deepmd_exception("Unknown file type"); } diff --git a/source/api_cc/src/DeepPotPD.cc b/source/api_cc/src/DeepPotPD.cc index a7cea9d27f..ebabfc66e1 100644 --- a/source/api_cc/src/DeepPotPD.cc +++ b/source/api_cc/src/DeepPotPD.cc @@ -20,12 +20,13 @@ static void run_model( std::vector& dforce_, std::vector& dvirial, const std::shared_ptr& predictor, - // const std::vector>& input_tensors, const AtomMap& atommap, const int nframes, const int nghost = 0) { + // printf("run_model 1 st\n"); unsigned nloc = atommap.get_type().size(); unsigned nall = nloc + nghost; + // printf("nloc = %d, nall = %d\n", nloc, nall); dener.resize(nframes); if (nloc == 0) { // no backward map needed @@ -39,14 +40,26 @@ static void run_model( } /* Running inference */ + // printf("Running inference st\n"); if (!predictor->Run()) { throw deepmd::deepmd_exception("Paddle inference failed"); } + // printf("Running inference ed\n"); auto output_names = predictor->GetOutputNames(); - auto output_e = predictor->GetOutputHandle(output_names[0]); - auto output_f = predictor->GetOutputHandle(output_names[1]); - auto output_virial_tensor = predictor->GetOutputHandle(output_names[3]); + // for (auto &name: output_names) + // { + // printf("output name: %s, shape: [", name.c_str()); + // auto shape = predictor->GetOutputHandle(name)->shape(); + // for (auto &dd: shape) + // { + // printf("%d, ", dd); + // } + // printf("]\n"); + // } + auto output_e = predictor->GetOutputHandle(output_names[1]); + auto output_f = predictor->GetOutputHandle(output_names[2]); + auto output_virial_tensor = predictor->GetOutputHandle(output_names[4]); // 获取 Output paddle::Tensor 的维度信息 std::vector output_energy_shape = output_e->shape(); @@ -61,29 +74,48 @@ static void run_model( int output_virial_size = std::accumulate(output_virial_shape.begin(), output_virial_shape.end(), 1, std::multiplies()); + // for (int i=0; i oe; + // printf("Resize st\n"); oe.resize(output_energy_size); + // printf("Resize ed\n"); + // printf("CopytoCpu st\n"); output_e->CopyToCpu(oe.data()); + // printf("Resize st\n"); + // printf("CopytoCpu ed\n"); // get data of output_force + // printf("of\n"); std::vector of; of.resize(output_force_size); output_f->CopyToCpu(of.data()); // get data of output_virial + // printf("oav\n"); std::vector oav; oav.resize(output_virial_size); + // printf("oav 2\n"); output_virial_tensor->CopyToCpu(oav.data()); + // printf("oav 22\n"); + // printf("dvirial\n"); std::vector dforce(nframes * 3 * nall); dvirial.resize(nframes * 9); for (int ii = 0; ii < nframes; ++ii) { + // printf("oe[%d] = %.5lf\n", ii, oe[ii]); dener[ii] = oe[ii]; } for (int ii = 0; ii < nframes * nall * 3; ++ii) { dforce[ii] = of[ii]; } // set dvirial to zero, prevent input vector is not zero (#1123) + // printf("fill\n"); std::fill(dvirial.begin(), dvirial.end(), (VALUETYPE)0.); for (int kk = 0; kk < nframes; ++kk) { for (int ii = 0; ii < nall; ++ii) { @@ -99,8 +131,10 @@ static void run_model( } } dforce_ = dforce; + // printf("atommap.backward\n"); atommap.backward(dforce_.begin(), dforce.begin(), 3, nframes, nall); + // printf("run_model 1 ed\n"); } template void run_model( @@ -151,10 +185,10 @@ static void run_model( std::vector& datom_energy_, std::vector& datom_virial_, const std::shared_ptr& predictor, - // const std::vector>& input_tensors, const deepmd::AtomMap& atommap, const int nframes, const int nghost = 0) { + // printf("run_model 2\n"); unsigned nloc = atommap.get_type().size(); unsigned nall = nloc + nghost; dener.resize(nframes); @@ -329,7 +363,6 @@ static void run_model( std::vector& dforce_, std::vector& dvirial, const std::shared_ptr& predictor, - // const std::vector>& input_tensors, const deepmd::AtomMap& atommap, const int nframes, const int nghost = 0) { @@ -389,7 +422,6 @@ static void run_model( std::vector& datom_energy_, std::vector& datom_virial_, const std::shared_ptr& predictor, - // const std::vector>& input_tensors, const deepmd::AtomMap& atommap, const int nframes = 1, const int nghost = 0) { @@ -464,7 +496,7 @@ DeepPotPD::DeepPotPD(const std::string& model, void DeepPotPD::init(const std::string& model, const int& gpu_rank, const std::string& file_content) { - std::cout << ("** Access here.") << std::endl; + // std::cout << ("** Access here.") << std::endl; if (inited) { std::cerr << "WARNING: deepmd-kit should not be initialized twice, do " "nothing at the second call of initializer" @@ -481,10 +513,12 @@ void DeepPotPD::init(const std::string& model, std::string pdmodel_path = ""; std::string pdiparams_path = ""; bool use_paddle_inference = false; + bool use_pir = false; if (model.find(".json") != std::string::npos) { + use_pir = true; pdmodel_path = model; std::string tmp = model; - pdiparams_path = tmp.replace(model.find(".json"), 4, std::string(".pdiparams")); + pdiparams_path = tmp.replace(model.find(".json"), 5, std::string(".pdiparams")); use_paddle_inference = true; } else if (model.find(".pdmodel") != std::string::npos){ pdmodel_path = model; @@ -497,15 +531,23 @@ void DeepPotPD::init(const std::string& model, int math_lib_num_threads = 1; if (use_paddle_inference) { + // printf("***** creating paddle predictor\n"); config = std::make_shared(); + config->DisableGlogInfo(); + // config->SwitchIrDebug(true); + if (use_pir) { + config->EnableNewExecutor(true); + config->EnableNewIR(true); + } config->SetModel(pdmodel_path, pdiparams_path); - config->SwitchIrOptim(true); + // config->SwitchIrOptim(true); config->EnableUseGpu(8192, 0); // std::cout << "IR Optim is: " << config->ir_optim() << std::endl; // config->EnableMKLDNN(); - config->EnableMemoryOptim(); + // config->EnableMemoryOptim(); // config->EnableProfile(); predictor = paddle_infer::CreatePredictor(*config); + // printf("***** created paddle predictor\n"); } /* water se_e2_a tensorflow::DT_DOUBLE = 2 @@ -586,6 +628,7 @@ void DeepPotPD::init(const std::string& model, // " supported " // "See https://deepmd.rtfd.io/compatability/ for details."); // } + // printf("***** initialized finished\n"); } DeepPotPD::~DeepPotPD() {} @@ -677,6 +720,7 @@ void DeepPotPD::compute(ENERGYVTYPE& dener, const std::vector& fparam_, const std::vector& aparam_, const bool atomic) { + // printf("compute 1\n"); // if datype.size is 0, not clear nframes; but 1 is just ok int nframes = datype_.size() > 0 ? (dcoord_.size() / 3 / datype_.size()) : 1; atommap = deepmd::AtomMap(datype_.begin(), datype_.end()); @@ -713,31 +757,31 @@ void DeepPotPD::compute(ENERGYVTYPE& dener, } } -template void DeepPotPD::compute( - ENERGYTYPE& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const std::vector& dcoord_, - const std::vector& datype_, - const std::vector& dbox, - const std::vector& fparam, - const std::vector& aparam, - const bool atomic); +// template void DeepPotPD::compute( +// ENERGYTYPE& dener, +// std::vector& dforce_, +// std::vector& dvirial, +// std::vector& datom_energy_, +// std::vector& datom_virial_, +// const std::vector& dcoord_, +// const std::vector& datype_, +// const std::vector& dbox, +// const std::vector& fparam, +// const std::vector& aparam, +// const bool atomic); -template void DeepPotPD::compute( - ENERGYTYPE& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const std::vector& dcoord_, - const std::vector& datype_, - const std::vector& dbox, - const std::vector& fparam, - const std::vector& aparam, - const bool atomic); +// template void DeepPotPD::compute( +// ENERGYTYPE& dener, +// std::vector& dforce_, +// std::vector& dvirial, +// std::vector& datom_energy_, +// std::vector& datom_virial_, +// const std::vector& dcoord_, +// const std::vector& datype_, +// const std::vector& dbox, +// const std::vector& fparam, +// const std::vector& aparam, +// const bool atomic); template void DeepPotPD::compute>( std::vector& dener, @@ -765,6 +809,16 @@ template void DeepPotPD::compute>( const std::vector& aparam, const bool atomic); +std::vector createNlistTensor(const std::vector>& data) { + std::vector ret; + + for (const auto& row : data) { + ret.insert(ret.end(), row.begin(), row.end()); + } + + return ret; +} + template void DeepPotPD::compute(ENERGYVTYPE& dener, std::vector& dforce_, @@ -780,68 +834,142 @@ void DeepPotPD::compute(ENERGYVTYPE& dener, const std::vector& fparam_, const std::vector& aparam__, const bool atomic) { - int nall = datype_.size(); - // if nall==0, unclear nframes, but 1 is ok - int nframes = nall > 0 ? (dcoord_.size() / nall / 3) : 1; - int nloc = nall - nghost; - std::vector fparam; - std::vector aparam_; - validate_fparam_aparam(nframes, (aparam_nall ? nall : nloc), fparam_, - aparam__); - tile_fparam_aparam(fparam, nframes, dfparam, fparam_); - tile_fparam_aparam(aparam_, nframes, (aparam_nall ? nall : nloc) * daparam, - aparam__); - // std::vector> input_tensors; + /*参考pytorch的推理代码如下*/ + int natoms = datype_.size(); // select real atoms - std::vector dcoord, dforce, aparam, datom_energy, datom_virial; + std::vector dcoord, dforce, aparam_, datom_energy, datom_virial; std::vector datype, fwd_map, bkw_map; int nghost_real, nall_real, nloc_real; - select_real_atoms_coord(dcoord, datype, aparam, nghost_real, fwd_map, bkw_map, - nall_real, nloc_real, dcoord_, datype_, aparam_, - nghost, ntypes, nframes, daparam, nall, aparam_nall); + int nall = natoms; + select_real_atoms_coord(dcoord, datype, aparam_, nghost_real, fwd_map, + bkw_map, nall_real, nloc_real, dcoord_, datype_, aparam__, + nghost, ntypes, 1, daparam, nall, aparam_nall); + int nloc = nall_real - nghost_real; + int nframes = 1; + std::vector coord_wrapped = dcoord; + auto coord_wrapped_Tensor = predictor->GetInputHandle("coord"); + coord_wrapped_Tensor->Reshape({1, nall_real, 3}); + coord_wrapped_Tensor->CopyFromCpu(coord_wrapped.data()); + + auto atype_Tensor = predictor->GetInputHandle("atype"); + atype_Tensor->Reshape({1, nall_real}); + atype_Tensor->CopyFromCpu(datype.data()); if (ago == 0) { - atommap = deepmd::AtomMap(datype.begin(), datype.begin() + nloc_real); - assert(nloc_real == atommap.get_type().size()); - nlist_data.copy_from_nlist(lmp_list); nlist_data.shuffle_exclude_empty(fwd_map); - nlist_data.shuffle(atommap); - nlist_data.make_inlist(nlist); + nlist_data.padding(); } + std::vector firstneigh = createNlistTensor(nlist_data.jlist); + auto firstneigh_tensor = predictor->GetInputHandle("nlist"); + firstneigh_tensor->Reshape({1, nloc, firstneigh.size() / nloc}); + firstneigh_tensor->CopyFromCpu(firstneigh.data()); - if (dtype == paddle_infer::DataType::FLOAT64) { - if (atomic) { - run_model(dener, dforce, dvirial, datom_energy, datom_virial, - predictor, atommap, nframes, nghost_real); - } else { - run_model(dener, dforce, dvirial, predictor, atommap, - nframes, nghost_real); - } - } else { - if (atomic) { - run_model(dener, dforce, dvirial, datom_energy, datom_virial, - predictor, atommap, nframes, nghost_real); - } else { - run_model(dener, dforce, dvirial, predictor, atommap, - nframes, nghost_real); - } + + if (!predictor->Run()) { + throw deepmd::deepmd_exception("Paddle inference failed"); } + auto output_names = predictor->GetOutputNames(); + + auto print_shape = [](const std::vector &shape, const std::string &name=""){ + printf("shape of %s: [", name.c_str()); + for (int i=0; iGetOutputHandle(output_names[1]); + auto output_f = predictor->GetOutputHandle(output_names[2]); + auto output_virial_tensor = predictor->GetOutputHandle(output_names[3]); + // print_shape(output_e->shape(), "ener"); + // print_shape(output_f->shape(), "force"); + // print_shape(output_virial_tensor->shape(), "virial"); + std::vector output_energy_shape = output_e->shape(); + int output_energy_size = + std::accumulate(output_energy_shape.begin(), output_energy_shape.end(), 1, + std::multiplies()); + std::vector output_force_shape = output_f->shape(); + int output_force_size = + std::accumulate(output_force_shape.begin(), output_force_shape.end(), 1, + std::multiplies()); + std::vector output_virial_shape = output_virial_tensor->shape(); + int output_virial_size = + std::accumulate(output_virial_shape.begin(), output_virial_shape.end(), 1, + std::multiplies()); + std::vector oe; + oe.resize(output_energy_size); + output_e->CopyToCpu(oe.data()); + + std::vector of; + of.resize(output_force_size); + output_f->CopyToCpu(of.data()); + std::vector oav; + oav.resize(output_virial_size); + output_virial_tensor->CopyToCpu(oav.data()); + + dvirial.resize(nframes * 9); + dener.assign(oe.begin(), oe.end()); + dforce.resize(nframes * 3 * nall); + for (int ii = 0; ii < nframes * nall * 3; ++ii) { + dforce[ii] = of[ii]; + } + std::fill(dvirial.begin(), dvirial.end(), (VALUETYPE)0.); + dvirial.assign(oav.begin(), oav.end()); + // for (int kk = 0; kk < nframes; ++kk) { + // for (int ii = 0; ii < nall; ++ii) { + // dvirial[kk * 9 + 0] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 0]; + // dvirial[kk * 9 + 1] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 1]; + // dvirial[kk * 9 + 2] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 2]; + // dvirial[kk * 9 + 3] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 3]; + // dvirial[kk * 9 + 4] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 4]; + // dvirial[kk * 9 + 5] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 5]; + // dvirial[kk * 9 + 6] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 6]; + // dvirial[kk * 9 + 7] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 7]; + // dvirial[kk * 9 + 8] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 8]; + // } + // } // bkw map dforce_.resize(static_cast(nframes) * fwd_map.size() * 3); - datom_energy_.resize(static_cast(nframes) * fwd_map.size()); - datom_virial_.resize(static_cast(nframes) * fwd_map.size() * 9); select_map(dforce_, dforce, bkw_map, 3, nframes, fwd_map.size(), nall_real); - select_map(datom_energy_, datom_energy, bkw_map, 1, nframes, - fwd_map.size(), nall_real); - select_map(datom_virial_, datom_virial, bkw_map, 9, nframes, - fwd_map.size(), nall_real); } -template void DeepPotPD::compute( - ENERGYTYPE& dener, +// template void DeepPotPD::compute( +// ENERGYTYPE& dener, +// std::vector& dforce_, +// std::vector& dvirial, +// std::vector& datom_energy_, +// std::vector& datom_virial_, +// const std::vector& dcoord_, +// const std::vector& datype_, +// const std::vector& dbox, +// const int nghost, +// const InputNlist& lmp_list, +// const int& ago, +// const std::vector& fparam, +// const std::vector& aparam_, +// const bool atomic); + +// template void DeepPotPD::compute( +// ENERGYTYPE& dener, +// std::vector& dforce_, +// std::vector& dvirial, +// std::vector& datom_energy_, +// std::vector& datom_virial_, +// const std::vector& dcoord_, +// const std::vector& datype_, +// const std::vector& dbox, +// const int nghost, +// const InputNlist& lmp_list, +// const int& ago, +// const std::vector& fparam, +// const std::vector& aparam_, +// const bool atomic); + +template void DeepPotPD::compute>( + std::vector& dener, std::vector& dforce_, std::vector& dvirial, std::vector& datom_energy_, @@ -856,8 +984,8 @@ template void DeepPotPD::compute( const std::vector& aparam_, const bool atomic); -template void DeepPotPD::compute( - ENERGYTYPE& dener, +template void DeepPotPD::compute>( + std::vector& dener, std::vector& dforce_, std::vector& dvirial, std::vector& datom_energy_, @@ -872,144 +1000,110 @@ template void DeepPotPD::compute( const std::vector& aparam_, const bool atomic); -template void DeepPotPD::compute>( - std::vector& dener, +// mixed type + +template +void DeepPotPD::compute_mixed_type(ENERGYVTYPE& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const int& nframes, + const std::vector& dcoord_, + const std::vector& datype_, + const std::vector& dbox, + const std::vector& fparam_, + const std::vector& aparam_, + const bool atomic) { + int nloc = datype_.size() / nframes; + // here atommap only used to get nloc + atommap = deepmd::AtomMap(datype_.begin(), datype_.begin() + nloc); + std::vector fparam; + std::vector aparam; + validate_fparam_aparam(nframes, nloc, fparam_, aparam_); + tile_fparam_aparam(fparam, nframes, dfparam, fparam_); + tile_fparam_aparam(aparam, nframes, nloc * daparam, aparam_); + + if (dtype == paddle_infer::DataType::FLOAT64) { + int nloc = predictor_input_tensors_mixed_type( + predictor, nframes, dcoord_, ntypes, datype_, dbox, cell_size, + fparam, aparam, atommap, aparam_nall); + if (atomic) { + run_model(dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, + atommap, nframes); + } else { + run_model(dener, dforce_, dvirial, predictor, + atommap, nframes); + } + } else { + int nloc = predictor_input_tensors_mixed_type( + predictor, nframes, dcoord_, ntypes, datype_, dbox, cell_size, + fparam, aparam, atommap, aparam_nall); + if (atomic) { + run_model(dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, + atommap, nframes); + } else { + run_model(dener, dforce_, dvirial, predictor, atommap, + nframes); + } + } +} + +template void DeepPotPD::compute_mixed_type( + ENERGYTYPE& dener, std::vector& dforce_, std::vector& dvirial, std::vector& datom_energy_, std::vector& datom_virial_, + const int& nframes, const std::vector& dcoord_, const std::vector& datype_, const std::vector& dbox, - const int nghost, - const InputNlist& lmp_list, - const int& ago, const std::vector& fparam, - const std::vector& aparam_, + const std::vector& aparam, const bool atomic); -template void DeepPotPD::compute>( - std::vector& dener, +template void DeepPotPD::compute_mixed_type( + ENERGYTYPE& dener, std::vector& dforce_, std::vector& dvirial, std::vector& datom_energy_, std::vector& datom_virial_, + const int& nframes, const std::vector& dcoord_, const std::vector& datype_, const std::vector& dbox, - const int nghost, - const InputNlist& lmp_list, - const int& ago, const std::vector& fparam, - const std::vector& aparam_, + const std::vector& aparam, const bool atomic); -// mixed type - -// template -// void DeepPotPD::compute_mixed_type(ENERGYVTYPE& dener, -// std::vector& dforce_, -// std::vector& dvirial, -// std::vector& datom_energy_, -// std::vector& datom_virial_, -// const int& nframes, -// const std::vector& dcoord_, -// const std::vector& datype_, -// const std::vector& dbox, -// const std::vector& fparam_, -// const std::vector& aparam_, -// const bool atomic) { -// int nloc = datype_.size() / nframes; -// // here atommap only used to get nloc -// atommap = deepmd::AtomMap(datype_.begin(), datype_.begin() + nloc); -// std::vector fparam; -// std::vector aparam; -// validate_fparam_aparam(nframes, nloc, fparam_, aparam_); -// tile_fparam_aparam(fparam, nframes, dfparam, fparam_); -// tile_fparam_aparam(aparam, nframes, nloc * daparam, aparam_); - -// // std::vector> input_tensors; - -// if (dtype == paddle_infer::DataType::FLOAT64) { -// // int nloc = session_input_tensors_mixed_type( -// // input_tensors, nframes, dcoord_, ntypes, datype_, dbox, cell_size, -// // fparam, aparam, atommap, "", aparam_nall); -// if (atomic) { -// run_model(dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, -// atommap, nframes); -// } else { -// run_model(dener, dforce_, dvirial, predictor, -// atommap, nframes); -// } -// } else { -// // int nloc = session_input_tensors_mixed_type( -// // input_tensors, nframes, dcoord_, ntypes, datype_, dbox, cell_size, -// // fparam, aparam, atommap, "", aparam_nall); -// if (atomic) { -// run_model(dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, -// atommap, nframes); -// } else { -// run_model(dener, dforce_, dvirial, atommap, predictor, -// nframes); -// } -// } -// } - -// template void DeepPotPD::compute_mixed_type( -// ENERGYTYPE& dener, -// std::vector& dforce_, -// std::vector& dvirial, -// std::vector& datom_energy_, -// std::vector& datom_virial_, -// const int& nframes, -// const std::vector& dcoord_, -// const std::vector& datype_, -// const std::vector& dbox, -// const std::vector& fparam, -// const std::vector& aparam, -// const bool atomic); - -// template void DeepPotPD::compute_mixed_type( -// ENERGYTYPE& dener, -// std::vector& dforce_, -// std::vector& dvirial, -// std::vector& datom_energy_, -// std::vector& datom_virial_, -// const int& nframes, -// const std::vector& dcoord_, -// const std::vector& datype_, -// const std::vector& dbox, -// const std::vector& fparam, -// const std::vector& aparam, -// const bool atomic); - -// template void DeepPotPD::compute_mixed_type>( -// std::vector& dener, -// std::vector& dforce_, -// std::vector& dvirial, -// std::vector& datom_energy_, -// std::vector& datom_virial_, -// const int& nframes, -// const std::vector& dcoord_, -// const std::vector& datype_, -// const std::vector& dbox, -// const std::vector& fparam, -// const std::vector& aparam, -// const bool atomic); +template void DeepPotPD::compute_mixed_type>( + std::vector& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const int& nframes, + const std::vector& dcoord_, + const std::vector& datype_, + const std::vector& dbox, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); -// template void DeepPotPD::compute_mixed_type>( -// std::vector& dener, -// std::vector& dforce_, -// std::vector& dvirial, -// std::vector& datom_energy_, -// std::vector& datom_virial_, -// const int& nframes, -// const std::vector& dcoord_, -// const std::vector& datype_, -// const std::vector& dbox, -// const std::vector& fparam, -// const std::vector& aparam, -// const bool atomic); +template void DeepPotPD::compute_mixed_type>( + std::vector& dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + const int& nframes, + const std::vector& dcoord_, + const std::vector& datype_, + const std::vector& dbox, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); template @@ -1018,7 +1112,8 @@ VT DeepPotPD::get_scalar(const std::string& name) const { } void DeepPotPD::get_type_map(std::string& type_map) { - type_map = predictor_get_scalar(predictor, "generated_tensor_12"); + type_map = "O H "; + // type_map = predictor_get_scalar(predictor, "type_map"); } // forward to template method @@ -1084,34 +1179,34 @@ void DeepPotPD::computew(std::vector& ener, compute(ener, force, virial, atom_energy, atom_virial, coord, atype, box, nghost, inlist, ago, fparam, aparam, atomic); } -// void DeepPotPD::computew_mixed_type(std::vector& ener, -// std::vector& force, -// std::vector& virial, -// std::vector& atom_energy, -// std::vector& atom_virial, -// const int& nframes, -// const std::vector& coord, -// const std::vector& atype, -// const std::vector& box, -// const std::vector& fparam, -// const std::vector& aparam, -// const bool atomic) { -// compute_mixed_type(ener, force, virial, atom_energy, atom_virial, nframes, -// coord, atype, box, fparam, aparam, atomic); -// } -// void DeepPotPD::computew_mixed_type(std::vector& ener, -// std::vector& force, -// std::vector& virial, -// std::vector& atom_energy, -// std::vector& atom_virial, -// const int& nframes, -// const std::vector& coord, -// const std::vector& atype, -// const std::vector& box, -// const std::vector& fparam, -// const std::vector& aparam, -// const bool atomic) { -// compute_mixed_type(ener, force, virial, atom_energy, atom_virial, nframes, -// coord, atype, box, fparam, aparam, atomic); -// } +void DeepPotPD::computew_mixed_type(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const int& nframes, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic) { + compute_mixed_type(ener, force, virial, atom_energy, atom_virial, nframes, + coord, atype, box, fparam, aparam, atomic); +} +void DeepPotPD::computew_mixed_type(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const int& nframes, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic) { + compute_mixed_type(ener, force, virial, atom_energy, atom_virial, nframes, + coord, atype, box, fparam, aparam, atomic); +} #endif diff --git a/source/api_cc/src/common.cc b/source/api_cc/src/common.cc index 4ff2fa79e8..378b50a71c 100644 --- a/source/api_cc/src/common.cc +++ b/source/api_cc/src/common.cc @@ -929,6 +929,7 @@ int deepmd::session_get_dtype(tensorflow::Session* session, #endif #ifdef BUILD_PADDLE + template int deepmd::predictor_input_tensors( const std::shared_ptr& predictor, @@ -936,19 +937,17 @@ int deepmd::predictor_input_tensors( const int& ntypes, const std::vector& datype_, const std::vector& dbox, - InputNlist& dlist, + const double& cell_size, const std::vector& fparam_, - const std::vector& aparam_, + const std::vector& aparam__, const deepmd::AtomMap& atommap, - const int nghost, - const int ago, const bool aparam_nall) { // if datype.size is 0, not clear nframes; but 1 is just ok int nframes = datype_.size() > 0 ? (dcoord_.size() / 3 / datype_.size()) : 1; int nall = datype_.size(); - int nloc = nall - nghost; + int nloc = nall; assert(nall * 3 * nframes == dcoord_.size()); - assert(dbox.size() == nframes * 9); + bool b_pbc = (dbox.size() == nframes * 9); std::vector datype = atommap.get_type(); std::vector type_count(ntypes, 0); @@ -957,62 +956,86 @@ int deepmd::predictor_input_tensors( } datype.insert(datype.end(), datype_.begin() + nloc, datype_.end()); - std::vector dcoord(dcoord_); - atommap.forward(dcoord.begin(), dcoord_.begin(), 3, nframes, nall); - // 准备输入Tensor句柄 auto input_names = predictor->GetInputNames(); auto coord_handle = predictor->GetInputHandle(input_names[0]); - auto atype_handle = predictor->GetInputHandle(input_names[1]); + auto type_handle = predictor->GetInputHandle(input_names[1]); auto natoms_handle = predictor->GetInputHandle(input_names[2]); auto box_handle = predictor->GetInputHandle(input_names[3]); auto mesh_handle = predictor->GetInputHandle(input_names[4]); // 设置输入 Tensor 的维度信息 - std::vector COORD_SHAPE = {nframes, nall * 3}; - std::vector ATYPE_SHAPE = {nframes, nall}; - std::vector BOX_SHAPE = {nframes, 9}; - std::vector MESH_SHAPE = {16}; - std::vector NATOMS_SHAPE = {2 + ntypes}; - - coord_handle->Reshape(COORD_SHAPE); - atype_handle->Reshape(ATYPE_SHAPE); - natoms_handle->Reshape(NATOMS_SHAPE); - box_handle->Reshape(BOX_SHAPE); - mesh_handle->Reshape(MESH_SHAPE); + std::vector coord_shape = {nframes, nall * 3}; + std::vector atype_shape = {nframes, nall}; + std::vector box_shape = {nframes, 9}; + std::vector mesh_shape; + if (b_pbc) { + mesh_shape = std::vector({6}); + } else { + mesh_shape = std::vector({0}); + } + + std::vector natoms_shape = {2 + ntypes}; + + coord_handle->Reshape(coord_shape); + type_handle->Reshape(atype_shape); + natoms_handle->Reshape(natoms_shape); + box_handle->Reshape(box_shape); + mesh_handle->Reshape(mesh_shape); + + paddle_infer::DataType model_type; + if (std::is_same::value) { + model_type = paddle_infer::DataType::FLOAT64; + } else if (std::is_same::value) { + model_type = paddle_infer::DataType::FLOAT32; + } else { + throw deepmd::deepmd_exception("unsupported data type"); + } + + std::vector dcoord(dcoord_); + atommap.forward(dcoord.begin(), dcoord_.begin(), 3, nframes, nall); + std::vector aparam_(aparam__); + if ((aparam_nall ? nall : nloc) > 0) { + atommap.forward( + aparam_.begin(), aparam__.begin(), + aparam__.size() / nframes / (aparam_nall ? nall : nloc), nframes, + (aparam_nall ? nall : nloc)); + } // 发送输入数据到Tensor句柄 coord_handle->CopyFromCpu(dcoord.data()); - - std::vector datype_pad(nframes * nall, 0); + if (b_pbc) { + box_handle->CopyFromCpu(dbox.data()); + } else { + std::vector zero = dbox; + std::fill(zero.begin(), zero.end(), 0); + box_handle->CopyFromCpu(zero.data()); + } + std::vector datype_rep(nframes * nall, 0); for (int ii = 0; ii < nframes; ++ii) { for (int jj = 0; jj < nall; ++jj) { - datype_pad[ii * nall + jj] = datype[jj]; + datype_rep[ii * nall + jj] = datype[jj]; } } - atype_handle->CopyFromCpu(datype_pad.data()); - - std::vector mesh_pad(16, 0); - mesh_pad[0] = ago; - mesh_pad[1] = dlist.inum; - mesh_pad[2] = 0; - mesh_pad[3] = 0; - memcpy(&mesh_pad[4], &(dlist.ilist), sizeof(int*)); - memcpy(&mesh_pad[8], &(dlist.numneigh), sizeof(int*)); - memcpy(&mesh_pad[12], &(dlist.firstneigh), sizeof(int**)); - mesh_handle->CopyFromCpu(mesh_pad.data()); - - std::vector natoms_pad = {nloc, nall}; + type_handle->CopyFromCpu(datype_rep.data()); + std::vector mesh; + if (b_pbc) { + mesh = std::vector(6); + mesh[1 - 1] = 0; + mesh[2 - 1] = 0; + mesh[3 - 1] = 0; + mesh[4 - 1] = 0; + mesh[5 - 1] = 0; + mesh[6 - 1] = 0; + } else { + mesh = std::vector(0); + } + mesh_handle->CopyFromCpu(mesh.data()); + std::vector natoms = {nloc, nall}; for (int ii = 0; ii < ntypes; ++ii) { - natoms_pad.push_back(type_count[ii]); + natoms.push_back(type_count[ii]); } - natoms_handle->CopyFromCpu(natoms_pad.data()); - - box_handle->CopyFromCpu(dbox.data()); - - const int stride = sizeof(int*) / sizeof(int); - assert(stride * sizeof(int) == sizeof(int*)); - assert(stride <= 4); + natoms_handle->CopyFromCpu(natoms.data()); return nloc; } @@ -1024,103 +1047,285 @@ int deepmd::predictor_input_tensors( const int& ntypes, const std::vector& datype_, const std::vector& dbox, - const double& cell_size, + InputNlist& dlist, const std::vector& fparam_, - const std::vector& aparam_, + const std::vector& aparam__, const deepmd::AtomMap& atommap, + const int nghost, + const int ago, const bool aparam_nall) { // if datype.size is 0, not clear nframes; but 1 is just ok int nframes = datype_.size() > 0 ? (dcoord_.size() / 3 / datype_.size()) : 1; int nall = datype_.size(); - int nloc = nall; + int nloc = nall - nghost; assert(nall * 3 * nframes == dcoord_.size()); - bool b_pbc = (dbox.size() == nframes * 9); + assert(dbox.size() == nframes * 9); std::vector datype = atommap.get_type(); + // for (int i=0; i type_count(ntypes, 0); for (unsigned ii = 0; ii < datype.size(); ++ii) { type_count[datype[ii]]++; } datype.insert(datype.end(), datype_.begin() + nloc, datype_.end()); - std::vector dcoord(dcoord_); - atommap.forward(dcoord.begin(), dcoord_.begin(), 3, nframes, nall); - // 准备输入Tensor句柄 auto input_names = predictor->GetInputNames(); + // for (auto &ss: input_names) + // { + // std::cout << "input_name: " << " " << ss << std::endl; + // } auto coord_handle = predictor->GetInputHandle(input_names[0]); - auto atype_handle = predictor->GetInputHandle(input_names[1]); - auto natoms_handle = predictor->GetInputHandle(input_names[2]); - auto box_handle = predictor->GetInputHandle(input_names[3]); - auto mesh_handle = predictor->GetInputHandle(input_names[4]); + auto type_handle = predictor->GetInputHandle(input_names[1]); + // auto natoms_handle = predictor->GetInputHandle(input_names[2]); + auto box_handle = predictor->GetInputHandle(input_names[2]); + // auto mesh_handle = predictor->GetInputHandle(input_names[4]); // 设置输入 Tensor 的维度信息 - std::vector COORD_SHAPE = {nframes, nall * 3}; - std::vector ATYPE_SHAPE = {nframes, nall}; - std::vector BOX_SHAPE = {nframes, 9}; - std::vector MESH_SHAPE; - if (b_pbc) { - MESH_SHAPE = std::vector(6); + std::vector coord_shape = {nframes, nall, 3}; + std::vector coord_shape_flat = {nframes, nall * 3}; + + std::vector atype_shape = {nframes, nall}; + std::vector atype_shape_flat = {nframes, nall}; + + std::vector box_shape = {nframes, 3, 3}; + std::vector box_shape_flat = {nframes * 9}; + // std::vector mesh_shape = std::vector({16}); + // std::vector natoms_shape = {2 + ntypes}; + + paddle_infer::DataType model_type; + if (std::is_same::value) { + model_type = paddle_infer::DataType::FLOAT64; + } else if (std::is_same::value) { + model_type = paddle_infer::DataType::FLOAT32; } else { - MESH_SHAPE = std::vector(0); + throw deepmd::deepmd_exception("unsupported data type"); } - std::vector NATOMS_SHAPE = {2 + ntypes}; + coord_handle->Reshape(coord_shape_flat); + box_handle->Reshape(box_shape_flat); + type_handle->Reshape(atype_shape_flat); + // printf("coord.shape = ["); + // for (auto &d: coord_shape) + // { + // printf("%d, ", d); + // } + // printf("]\n"); + + // printf("type.shape = ["); + // for (auto &d: atype_shape) + // { + // printf("%d, ", d); + // } + // printf("]\n"); + + // printf("box.shape = ["); + // for (auto &d: box_shape) + // { + // printf("%d, ", d); + // } + // printf("]\n"); + // mesh_handle->Reshape(mesh_shape); + // natoms_handle->Reshape(natoms_shape); - coord_handle->Reshape(COORD_SHAPE); - atype_handle->Reshape(ATYPE_SHAPE); - natoms_handle->Reshape(NATOMS_SHAPE); - box_handle->Reshape(BOX_SHAPE); - mesh_handle->Reshape(MESH_SHAPE); + std::vector dcoord(dcoord_); + atommap.forward(dcoord.begin(), dcoord_.begin(), 3, nframes, nall); //012 + std::vector aparam_(aparam__); + if ((aparam_nall ? nall : nloc) > 0) { + atommap.forward( + aparam_.begin(), aparam__.begin(), + aparam__.size() / nframes / (aparam_nall ? nall : nloc), nframes, + (aparam_nall ? nall : nloc)); + } + + // const std::string filename = "/workspace/hesensen/deepmd_backend/deepmd_paddle_new/examples/water/lmp/coord_torch.log"; + // std::ifstream inputFile(filename); + // VALUETYPE number; + // int iii = 0; + // while (inputFile >> number) { + // dcoord[iii] = number; + // ++iii; + // } + // printf("dcoord finished, iii = %d\n", iii); + // inputFile.close(); // 发送输入数据到Tensor句柄 coord_handle->CopyFromCpu(dcoord.data()); - - std::vector datype_pad(nframes * nall, 0); + coord_handle->Reshape(coord_shape); + box_handle->CopyFromCpu(dbox.data()); + box_handle->Reshape(box_shape); + // for (int i = 0; i < dcoord.size(); ++i) + // { + // printf("dcoord[%d] = %.6lf\n", i, dcoord[i]); + // } + std::vector datype_rep(nframes * nall, 0); for (int ii = 0; ii < nframes; ++ii) { for (int jj = 0; jj < nall; ++jj) { - datype_pad[ii * nall + jj] = datype[jj]; + datype_rep[ii * nall + jj] = datype[jj]; } } - atype_handle->CopyFromCpu(datype_pad.data()); + // const std::string filename1 = "/workspace/hesensen/deepmd_backend/deepmd_paddle_new/examples/water/lmp/type_torch.log"; + // std::ifstream inputFile1(filename1); + // int number_int; + // iii = 0; + // while (inputFile1 >> number_int) { + // datype_rep[iii] = number_int; + // ++iii; + // } + // printf("atype finishied, iii = %d\n", iii); + // inputFile1.close(); + + type_handle->CopyFromCpu(datype_rep.data()); + // for (int i = 0; i < datype_rep.size(); ++i) + // { + // printf("%d\n", datype_rep[i]); + // } + type_handle->Reshape(atype_shape); + // std::vector mesh(mesh_shape[0], 0); + // for (int ii = 0; ii < 16; ++ii) { + // mesh[ii] = 0; + // } + // const int stride = sizeof(int*) / sizeof(int); + // assert(stride * sizeof(int) == sizeof(int*)); + // assert(stride <= 4); + // mesh[0] = ago; + // mesh[1] = dlist.inum; + // mesh[2] = 0; + // mesh[3] = 0; + // memcpy(&mesh[4], &(dlist.ilist), sizeof(int*)); + // memcpy(&mesh[8], &(dlist.numneigh), sizeof(int*)); + // memcpy(&mesh[12], &(dlist.firstneigh), sizeof(int**)); + // mesh_handle->CopyFromCpu(mesh.data()); + + // std::vector natoms = {nloc, nall}; + // for (int ii = 0; ii < ntypes; ++ii) { + // natoms.push_back(type_count[ii]); + // } + // natoms_handle->CopyFromCpu(natoms.data()); + // printf("finished predictor_input_tensors\n"); + // printf("nloc = %d\n", nloc); + return nloc; +} + +template +int deepmd::predictor_input_tensors_mixed_type( + const std::shared_ptr& predictor, + const int& nframes, + const std::vector& dcoord_, + const int& ntypes, + const std::vector& datype_, + const std::vector& dbox, + const double& cell_size, + const std::vector& fparam_, + const std::vector& aparam__, + const deepmd::AtomMap& atommap, + const bool aparam_nall) { + int nall = datype_.size() / nframes; + int nloc = nall; + assert(nall * 3 * nframes == dcoord_.size()); + bool b_pbc = (dbox.size() == nframes * 9); + std::vector datype(datype_); + atommap.forward(datype.begin(), datype_.begin(), 1, nframes, nall); - std::vector mesh_pad; + auto input_names = predictor->GetInputNames(); + auto coord_handle = predictor->GetInputHandle(input_names[0]); + auto type_handle = predictor->GetInputHandle(input_names[1]); + auto box_handle = predictor->GetInputHandle(input_names[3]); + auto mesh_handle = predictor->GetInputHandle(input_names[4]); + auto natoms_handle = predictor->GetInputHandle(input_names[2]); + + // 设置输入 Tensor 的维度信息 + std::vector coord_shape = {nframes, nall * 3}; + std::vector atype_shape = {nframes, nall}; + std::vector box_shape = {nframes, 9}; + std::vector mesh_shape; if (b_pbc) { - mesh_pad = std::vector(6); + mesh_shape = std::vector({7}); } else { - mesh_pad = std::vector(0); - } - // mesh_pad[0] = ago; - // mesh_pad[1] = dlist.inum; - // mesh_pad[2] = 0; - // mesh_pad[3] = 0; - // memcpy(&mesh_pad[4], &(dlist.ilist), sizeof(int*)); - // memcpy(&mesh_pad[8], &(dlist.numneigh), sizeof(int*)); - // memcpy(&mesh_pad[12], &(dlist.firstneigh), sizeof(int**)); - mesh_handle->CopyFromCpu(mesh_pad.data()); - if (b_pbc) { - mesh_pad[1 - 1] = 0; - mesh_pad[2 - 1] = 0; - mesh_pad[3 - 1] = 0; - mesh_pad[4 - 1] = 0; - mesh_pad[5 - 1] = 0; - mesh_pad[6 - 1] = 0; - } - std::vector natoms_pad = {nloc, nall}; - for (int ii = 0; ii < ntypes; ++ii) { - natoms_pad.push_back(type_count[ii]); + mesh_shape = std::vector({1}); } - // natoms_handle->CopyFromCpu(natoms_pad.data()); + std::vector natoms_shape = {2 + ntypes}; - box_handle->CopyFromCpu(dbox.data()); + coord_handle->Reshape(coord_shape); + type_handle->Reshape(atype_shape); + box_handle->Reshape(box_shape); + mesh_handle->Reshape(mesh_shape); + natoms_handle->Reshape(natoms_shape); - // const int stride = sizeof(int*) / sizeof(int); - // assert(stride * sizeof(int) == sizeof(int*)); - // assert(stride <= 4); + paddle_infer::DataType model_type; + if (std::is_same::value) { + model_type = paddle_infer::DataType::FLOAT64; + } else if (std::is_same::value) { + model_type = paddle_infer::DataType::FLOAT32; + } else { + throw deepmd::deepmd_exception("unsupported data type"); + } + std::vector dcoord(dcoord_); + atommap.forward(dcoord.begin(), dcoord_.begin(), 3, nframes, nall); + std::vector aparam_(aparam__); + if ((aparam_nall ? nall : nloc) > 0) { + atommap.forward( + aparam_.begin(), aparam__.begin(), + aparam__.size() / nframes / (aparam_nall ? nall : nloc), nframes, + (aparam_nall ? nall : nloc)); + } + // coord + coord_handle->CopyFromCpu(dcoord.data()); + + // box + if (b_pbc) { + box_handle->CopyFromCpu(dbox.data()); + } else { + std::vector zero = dbox; + std::fill(zero.begin(), zero.end(), 0); + box_handle->CopyFromCpu(zero.data()); + } + + // datype + std::vector datype_rep(nframes * nall, 0); + for (int ii = 0; ii < nframes; ++ii) { + for (int jj = 0; jj < nall; ++jj) { + datype_rep[ii * nall + jj] = datype[jj]; + } + } + type_handle->CopyFromCpu(datype_rep.data()); + // mesh + std::vector mesh; + if (b_pbc) { + mesh = std::vector(7, 0); + mesh[1 - 1] = 0; + mesh[2 - 1] = 0; + mesh[3 - 1] = 0; + mesh[4 - 1] = 0; + mesh[5 - 1] = 0; + mesh[6 - 1] = 0; + mesh[7 - 1] = 0; + } else { + mesh = std::vector(1, 0); + mesh[1 - 1] = 0; + } + mesh_handle->CopyFromCpu(mesh.data()); + //natoms + std::vector natoms_pad = {nloc, nall, nall}; + if (ntypes > 1) { + for (int ii = 0; ii < ntypes; ++ii) { + natoms_pad.push_back(0); + } + } + natoms_handle->CopyFromCpu(natoms_pad.data()); + + // if (fparam_.size() > 0) { + // input_tensors.push_back({prefix + "t_fparam", fparam_tensor}); + // } + // if (aparam_.size() > 0) { + // input_tensors.push_back({prefix + "t_aparam", aparam_tensor}); + // } return nloc; } + #endif #ifdef BUILD_PADDLE @@ -1456,7 +1661,7 @@ template void deepmd::select_map_inv( #ifdef BUILD_PADDLE template std::string deepmd::predictor_get_scalar( - const std::shared_ptr& predictor, const std::string&); + const std::shared_ptr& predictor, const std::string &name_); // template void deepmd::session_get_vector( // std::vector&, @@ -1789,59 +1994,55 @@ template int deepmd::predictor_input_tensors( const int ago, const bool aparam_nall); -// template int deepmd::session_input_tensors_mixed_type( -// std::vector>& input_tensors, -// const int& nframes, -// const std::vector& dcoord_, -// const int& ntypes, -// const std::vector& datype_, -// const std::vector& dbox, -// const double& cell_size, -// const std::vector& fparam_, -// const std::vector& aparam_, -// const deepmd::AtomMap& atommap, -// const std::string scope, -// const bool aparam_nall); -// template int deepmd::session_input_tensors_mixed_type( -// std::vector>& input_tensors, -// const int& nframes, -// const std::vector& dcoord_, -// const int& ntypes, -// const std::vector& datype_, -// const std::vector& dbox, -// const double& cell_size, -// const std::vector& fparam_, -// const std::vector& aparam_, -// const deepmd::AtomMap& atommap, -// const std::string scope, -// const bool aparam_nall); - -// template int deepmd::session_input_tensors_mixed_type( -// std::vector>& input_tensors, -// const int& nframes, -// const std::vector& dcoord_, -// const int& ntypes, -// const std::vector& datype_, -// const std::vector& dbox, -// const double& cell_size, -// const std::vector& fparam_, -// const std::vector& aparam_, -// const deepmd::AtomMap& atommap, -// const std::string scope, -// const bool aparam_nall); -// template int deepmd::session_input_tensors_mixed_type( -// std::vector>& input_tensors, -// const int& nframes, -// const std::vector& dcoord_, -// const int& ntypes, -// const std::vector& datype_, -// const std::vector& dbox, -// const double& cell_size, -// const std::vector& fparam_, -// const std::vector& aparam_, -// const deepmd::AtomMap& atommap, -// const std::string scope, -// const bool aparam_nall); +template int deepmd::predictor_input_tensors_mixed_type( + const std::shared_ptr& predictor, + const int& nframes, + const std::vector& dcoord_, + const int& ntypes, + const std::vector& datype_, + const std::vector& dbox, + const double& cell_size, + const std::vector& fparam_, + const std::vector& aparam_, + const deepmd::AtomMap& atommap, + const bool aparam_nall); +template int deepmd::predictor_input_tensors_mixed_type( + const std::shared_ptr& predictor, + const int& nframes, + const std::vector& dcoord_, + const int& ntypes, + const std::vector& datype_, + const std::vector& dbox, + const double& cell_size, + const std::vector& fparam_, + const std::vector& aparam_, + const deepmd::AtomMap& atommap, + const bool aparam_nall); + +template int deepmd::predictor_input_tensors_mixed_type( + const std::shared_ptr& predictor, + const int& nframes, + const std::vector& dcoord_, + const int& ntypes, + const std::vector& datype_, + const std::vector& dbox, + const double& cell_size, + const std::vector& fparam_, + const std::vector& aparam_, + const deepmd::AtomMap& atommap, + const bool aparam_nall); +template int deepmd::predictor_input_tensors_mixed_type( + const std::shared_ptr& predictor, + const int& nframes, + const std::vector& dcoord_, + const int& ntypes, + const std::vector& datype_, + const std::vector& dbox, + const double& cell_size, + const std::vector& fparam_, + const std::vector& aparam_, + const deepmd::AtomMap& atommap, + const bool aparam_nall); #endif void deepmd::print_summary(const std::string& pre) { diff --git a/source/install/build_cc.sh b/source/install/build_cc.sh index 6adb62a311..60101eb9a8 100755 --- a/source/install/build_cc.sh +++ b/source/install/build_cc.sh @@ -20,8 +20,7 @@ NPROC=$(nproc --all) BUILD_TMP_DIR=${SCRIPT_PATH}/../build mkdir -p ${BUILD_TMP_DIR} cd ${BUILD_TMP_DIR} -cmake -DCMAKE_PREFIX_PATH=/workspace/hesensen/deepmd_backend/deepmd_paddle_new/source/install/libtorch \ - -D ENABLE_TENSORFLOW=OFF \ +cmake -D ENABLE_TENSORFLOW=ON \ -D ENABLE_PYTORCH=ON \ -D CMAKE_INSTALL_PREFIX=${INSTALL_PREFIX} \ -D USE_TF_PYTHON_LIBS=TRUE \ diff --git a/source/install/build_cc_pd.sh b/source/install/build_cc_pd.sh index 5feb1e3426..335a394e5b 100755 --- a/source/install/build_cc_pd.sh +++ b/source/install/build_cc_pd.sh @@ -22,7 +22,9 @@ export LAMMPS_DIR="/workspace/hesensen/deepmd_backend/deepmd_paddle_new/source/b export LAMMPS_SOURCE_ROOT="/workspace/hesensen/deepmd_backend/deepmd_paddle_new/source/build_lammps/lammps-stable_29Aug2024/" # 设置推理时的 GPU 卡号 -export CUDA_VISIBLE_DEVICES=6 +export CUDA_VISIBLE_DEVICES=3 +# export FLAGS_benchmark=1 +# export GLOG_v=6 # PADDLE_DIR 设置为第二步 clone下来的 Paddle 目录 export PADDLE_DIR="/workspace/hesensen/PaddleScience_enn_debug/Paddle/" @@ -43,11 +45,11 @@ export LD_LIBRARY_PATH=${PADDLE_INFERENCE_DIR}/third_party/install/mkldnn/lib:$L export LD_LIBRARY_PATH=${PADDLE_INFERENCE_DIR}/third_party/install/mklml/lib:$LD_LIBRARY_PATH export LD_LIBRARY_PATH=${DEEPMD_DIR}/source/build:$LD_LIBRARY_PATH export LIBRARY_PATH=${DEEPMD_DIR}/deepmd/op:$LIBRARY_PATH - -cd ${DEEPMD_DIR}/source -rm -rf build # 若改动CMakeLists.txt，则需要打开该注释 -mkdir build -cd - +# export FLAGS_check_nan_inf=1 +# cd ${DEEPMD_DIR}/source +# rm -rf build # 若改动CMakeLists.txt，则需要打开该注释 +# mkdir build +# cd - # DEEPMD_INSTALL_DIR 设置为 deepmd-lammps 的目标安装目录，可自行设置任意路径 # export DEEPMD_INSTALL_DIR="path/to/deepmd_root" @@ -84,6 +86,8 @@ cmake -DCMAKE_PREFIX_PATH=/workspace/hesensen/PaddleScience_enn_debug/Paddle/bui -D CMAKE_INSTALL_PREFIX=${INSTALL_PREFIX} \ -D USE_TF_PYTHON_LIBS=TRUE \ -D LAMMPS_SOURCE_ROOT=${LAMMPS_SOURCE_ROOT} \ + -D ENABLE_IPI=OFF \ + -D PADDLE_LIBRARIES=/workspace/hesensen/PaddleScience_enn_debug/Paddle/build/paddle_inference_install_dir/paddle/lib/libpaddle_inference.so \ ${CUDA_ARGS} \ -D LAMMPS_VERSION=stable_29Aug2024 \ .. @@ -104,9 +108,12 @@ make no-extra-fix make yes-extra-fix make no-user-deepmd make yes-user-deepmd -make serial -j +# make serial -j +make mpi -j 20 export PATH=${LAMMPS_DIR}/src:$PATH cd ${DEEPMD_DIR}/examples/water/lmp -lmp_serial -in paddle_in.lammps +echo "START INFERENCE..." +# lmp_serial -in paddle_in.lammps 2>&1 | tee paddle_infer.log +mpirun -np 1 lmp_mpi -in paddle_in.lammps 2>&1 | tee paddle_infer.log diff --git a/source/lmp/pair_deepmd.cpp b/source/lmp/pair_deepmd.cpp index 72da1a5ee6..2112c12ac7 100644 --- a/source/lmp/pair_deepmd.cpp +++ b/source/lmp/pair_deepmd.cpp @@ -495,7 +495,6 @@ void PairDeepMD::compute(int eflag, int vflag) { } } } - vector dtype(nall); for (int ii = 0; ii < nall; ++ii) { dtype[ii] = type_idx_map[type[ii] - 1]; @@ -976,13 +975,9 @@ void PairDeepMD::settings(int narg, char **arg) { numb_models = models.size(); if (numb_models == 1) { try { - std::cout << "****** init deepmd model from file 1: " << std::endl; auto node_rank = get_node_rank(); - std::cout << "****** init deepmd model from file 2: " << std::endl; auto content = get_file_content(arg[0]); - std::cout << "****** init deepmd model from file 3: " << std::endl; deep_pot.init(arg[0], node_rank, content); - std::cout << "****** init deepmd model from file 4: " << std::endl; } catch (const std::exception &e) { // error->one(FLERR, e.what()); std::cerr << "Standard exception caught: " << e.what() << std::endl; From 68a2d6214e44a0849b2ee7132e58bb1baeed0e59 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 23 Sep 2024 15:43:09 +0800 Subject: [PATCH 15/93] fix get_pd_version --- backend/find_paddle.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/backend/find_paddle.py b/backend/find_paddle.py index e4a5ee8aed..0f24dd3788 100644 --- a/backend/find_paddle.py +++ b/backend/find_paddle.py @@ -142,4 +142,4 @@ def get_pd_version(pd_path: Optional[Union[str, Path]]) -> str: spec = importlib.util.spec_from_file_location("paddle.version", version_file) module = importlib.util.module_from_spec(spec) spec.loader.exec_module(module) - return module.__version__ + return module.full_version From ff7e0efbbe87e919b4f46ce3257977b725536c3a Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 23 Sep 2024 15:48:34 +0800 Subject: [PATCH 16/93] fix read_env.py --- backend/read_env.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/backend/read_env.py b/backend/read_env.py index edfb7d16b5..8c595c34c3 100644 --- a/backend/read_env.py +++ b/backend/read_env.py @@ -134,7 +134,7 @@ def get_argument_from_env() -> Tuple[str, list, list, dict, str, str]: ) else: cmake_args.append("-DENABLE_PADDLE=OFF") - pt_version = None + pd_version = None cmake_args = [ "-DBUILD_PY_IF:BOOL=TRUE", @@ -147,6 +147,7 @@ def get_argument_from_env() -> Tuple[str, list, list, dict, str, str]: extra_scripts, tf_version, pt_version, + pd_version, ) From 023ba5332813948705f3ad5ee9b277a99210d116 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 23 Sep 2024 15:50:14 +0800 Subject: [PATCH 17/93] fix suffix --- deepmd/backend/paddle.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepmd/backend/paddle.py b/deepmd/backend/paddle.py index 9647bb137c..7f97e4feed 100644 --- a/deepmd/backend/paddle.py +++ b/deepmd/backend/paddle.py @@ -41,7 +41,7 @@ class PaddleBackend(Backend): | Backend.Feature.IO ) """The features of the backend.""" - suffixes: ClassVar[List[str]] = [".pdparams", ".pd"] + suffixes: ClassVar[List[str]] = [".json", ".pd"] """The suffixes of the backend.""" def is_available(self) -> bool: From 8a1834f4ae24df3b8ed5dfa07bd2c12649393d66 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 23 Sep 2024 15:53:38 +0800 Subject: [PATCH 18/93] fix pd/cxx_op.py --- deepmd/pd/__init__.py | 1 + deepmd/pd/cxx_op.py | 30 +++++++++++++++--------------- 2 files changed, 16 insertions(+), 15 deletions(-) diff --git a/deepmd/pd/__init__.py b/deepmd/pd/__init__.py index 5437d02d43..bd8b881bc5 100644 --- a/deepmd/pd/__init__.py +++ b/deepmd/pd/__init__.py @@ -14,5 +14,6 @@ import paddle +# enable primitive mode for eager/static graph paddle.framework.core.set_prim_eager_enabled(True) paddle.framework.core._set_prim_all_enabled(True) diff --git a/deepmd/pd/cxx_op.py b/deepmd/pd/cxx_op.py index 8239e94c7c..92aed25f65 100644 --- a/deepmd/pd/cxx_op.py +++ b/deepmd/pd/cxx_op.py @@ -40,15 +40,15 @@ def load_library(module_name: str) -> bool: except OSError as e: # check: CXX11_ABI_FLAG; version # from our op - PT_VERSION = GLOBAL_CONFIG["pt_version"] - PT_CXX11_ABI_FLAG = int(GLOBAL_CONFIG["pt_cxx11_abi_flag"]) + PD_VERSION = GLOBAL_CONFIG["pd_version"] + PD_CXX11_ABI_FLAG = int(GLOBAL_CONFIG["pd_cxx11_abi_flag"]) # from paddle # strip the local version - pt_py_version = Version(paddle.__version__).public - # pt_cxx11_abi_flag = int(paddle.compiled_with_cxx11_abi()) - pt_cxx11_abi_flag = 0 + pd_py_version = Version(paddle.__version__).public + # pd_cxx11_abi_flag = int(paddle.compiled_with_cxx11_abi()) + pd_cxx11_abi_flag = 0 - if PT_CXX11_ABI_FLAG != pt_cxx11_abi_flag: + if PD_CXX11_ABI_FLAG != pd_cxx11_abi_flag: raise RuntimeError( "This deepmd-kit package was compiled with " "CXX11_ABI_FLAG=%d, but Paddle runtime was compiled " @@ -57,21 +57,21 @@ def load_library(module_name: str) -> bool: "You need to rebuild deepmd-kit against this Paddle " "runtime." % ( - PT_CXX11_ABI_FLAG, - pt_cxx11_abi_flag, + PD_CXX11_ABI_FLAG, + pd_cxx11_abi_flag, module_name, ) ) from e # different versions may cause incompatibility, see TF - if PT_VERSION != pt_py_version: + if PD_VERSION != pd_py_version: raise RuntimeError( "The version of Paddle used to compile this " - f"deepmd-kit package is {PT_VERSION}, but the version of Paddle " - f"runtime you are using is {pt_py_version}. These two versions are " + f"deepmd-kit package is {PD_VERSION}, but the version of Paddle " + f"runtime you are using is {pd_py_version}. These two versions are " f"incompatible and thus an error is raised when loading {module_name}. " - f"You need to install Paddle {PT_VERSION}, or rebuild deepmd-kit " - f"against Paddle {pt_py_version}.\nIf you are using a wheel from " + f"You need to install Paddle {PD_VERSION}, or rebuild deepmd-kit " + f"against Paddle {pd_py_version}.\nIf you are using a wheel from " "PyPI, you may consider to install deepmd-kit execuating " "`DP_ENABLE_Paddle=1 pip install deepmd-kit --no-binary deepmd-kit` " "instead." @@ -82,7 +82,7 @@ def load_library(module_name: str) -> bool: "You need to rebuild deepmd-kit against this Paddle " "runtime." ) - if PT_CXX11_ABI_FLAG == 1: + if PD_CXX11_ABI_FLAG == 1: # #1791 error_message += ( "\nWARNING: devtoolset on RHEL6 and RHEL7 does not support _GLIBCXX_USE_CXX11_ABI=1. " @@ -93,7 +93,7 @@ def load_library(module_name: str) -> bool: return False -ENABLE_CUSTOMIZED_OP = load_library("deepmd_op_pt") +ENABLE_CUSTOMIZED_OP = load_library("deepmd_op_pd") __all__ = [ "ENABLE_CUSTOMIZED_OP", From 396bd54e3d9e3e49d3ae10613ac8fdea7c367a85 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 23 Sep 2024 15:59:59 +0800 Subject: [PATCH 19/93] fix main.py --- deepmd/pd/entrypoints/main.py | 33 +++++++++++++++++---------------- 1 file changed, 17 insertions(+), 16 deletions(-) diff --git a/deepmd/pd/entrypoints/main.py b/deepmd/pd/entrypoints/main.py index 4e4ac5f85a..214f150850 100644 --- a/deepmd/pd/entrypoints/main.py +++ b/deepmd/pd/entrypoints/main.py @@ -385,15 +385,15 @@ def show(FLAGS): if "model" in state_dict: state_dict = state_dict["model"] model_params = state_dict["_extra_state"]["model_params"] - elif FLAGS.INPUT.split(".")[-1] == "pdmodel": - model_params_string = paddle.jit.load( - FLAGS.INPUT[: -len(".pdmodel")] - ).model_def_script - model_params = json.loads(model_params_string) + # elif FLAGS.INPUT.split(".")[-1] == "pdmodel": + # model_params_string = paddle.jit.load( + # FLAGS.INPUT[: -len(".pdmodel")] + # ).model_def_script + # model_params = json.loads(model_params_string) else: raise RuntimeError( - "The model provided must be a checkpoint file with a .pdparams extension " - "or a frozen model with a .pdmodel extension" + "The model provided must be a checkpoint file with a .pd extension" + # "or a frozen model with a .pdmodel extension" ) model_is_multi_task = "model_dict" in model_params log.info("This is a multitask model") if model_is_multi_task else log.info( @@ -449,16 +449,16 @@ def change_bias(FLAGS): old_state_dict = paddle.load(FLAGS.INPUT) model_state_dict = copy.deepcopy(old_state_dict.get("model", old_state_dict)) model_params = model_state_dict["_extra_state"]["model_params"] - elif FLAGS.INPUT.endswith(".pdmodel"): - old_model = paddle.jit.load(FLAGS.INPUT[: -len(".pdmodel")]) - model_params_string = old_model.get_model_def_script() - model_params = json.loads(model_params_string) - old_state_dict = old_model.state_dict() - model_state_dict = old_state_dict + # elif FLAGS.INPUT.endswith(".pdmodel"): + # old_model = paddle.jit.load(FLAGS.INPUT[: -len(".pdmodel")]) + # model_params_string = old_model.get_model_def_script() + # model_params = json.loads(model_params_string) + # old_state_dict = old_model.state_dict() + # model_state_dict = old_state_dict else: raise RuntimeError( - "The model provided must be a checkpoint file with a .pd extension " - "or a frozen model with a .pdparams extension" + "The model provided must be a checkpoint file with a .pd extension" + # "or a frozen model with a .pdparams extension" ) multi_task = "model_dict" in model_params model_branch = FLAGS.model_branch @@ -556,7 +556,8 @@ def change_bias(FLAGS): old_state_dict["_extra_state"] = model_state_dict["_extra_state"] paddle.save(old_state_dict, output_path) else: - # for .pdparams + raise NotImplementedError + # for .json output_path = ( FLAGS.output if FLAGS.output is not None From 66734bce536bd39304042e14b7e1f6cf73e54e80 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 23 Sep 2024 16:12:40 +0800 Subject: [PATCH 20/93] fix get_item_paddle --- deepmd/pd/infer/inference.py | 3 ++- deepmd/utils/batch_size.py | 3 +-- deepmd/utils/data.py | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/deepmd/pd/infer/inference.py b/deepmd/pd/infer/inference.py index 71602d990c..1ebadd24c9 100644 --- a/deepmd/pd/infer/inference.py +++ b/deepmd/pd/infer/inference.py @@ -61,5 +61,6 @@ def __init__( # Model Wrapper self.wrapper = ModelWrapper(self.model) # inference only if JIT: - self.wrapper = paddle.jit.to_static(self.wrapper) + raise NotImplementedError + # self.wrapper = paddle.jit.to_static(self.wrapper) self.wrapper.set_state_dict(state_dict) diff --git a/deepmd/utils/batch_size.py b/deepmd/utils/batch_size.py index 0cace10abe..46ed40720e 100644 --- a/deepmd/utils/batch_size.py +++ b/deepmd/utils/batch_size.py @@ -182,7 +182,7 @@ def execute_with_batch_size( ) -> Tuple[int, Tuple[np.ndarray]]: end_index = start_index + batch_size end_index = min(end_index, total_size) - result = callable( + return (end_index - start_index), callable( *[ ( vv[start_index:end_index, ...] @@ -208,7 +208,6 @@ def execute_with_batch_size( for kk, vv in kwargs.items() }, ) - return (end_index - start_index), result index = 0 results = None diff --git a/deepmd/utils/data.py b/deepmd/utils/data.py index 4ce2a7d3b3..cbe1f7caf5 100644 --- a/deepmd/utils/data.py +++ b/deepmd/utils/data.py @@ -259,7 +259,7 @@ def get_item_paddle(self, index: int) -> dict: i = bisect.bisect_right(self.prefix_sum, index) frames = self._load_set(self.dirs[i]) frame = self._get_subdata(frames, index - self.prefix_sum[i]) - frame = self.reformat_data_torch(frame) + frame = self.reformat_data_paddle(frame) frame["fid"] = index return frame From ba02ae80704c301abfb7cfab7c9f98368ddade26 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 23 Sep 2024 16:13:22 +0800 Subject: [PATCH 21/93] restore in.lammps --- examples/water/lmp/in.lammps | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/water/lmp/in.lammps b/examples/water/lmp/in.lammps index 805ef8bed0..ea3b5d52cd 100644 --- a/examples/water/lmp/in.lammps +++ b/examples/water/lmp/in.lammps @@ -12,7 +12,7 @@ mass 1 16 mass 2 2 # See https://deepmd.rtfd.io/lammps/ for usage -pair_style deepmd /workspace/hesensen/deepmd_backend/deepmd_paddle_new/examples/water/se_e2_a/torch_infer.pth +pair_style deepmd frozen_model.pb # If atom names (O H in this example) are not set in the pair_coeff command, the type_map defined by the training parameter will be used by default. pair_coeff * * O H @@ -21,7 +21,7 @@ velocity all create 330.0 23456789 fix 1 all nvt temp 330.0 330.0 0.5 timestep 0.0005 thermo_style custom step pe ke etotal temp press vol -thermo 1 -dump 1 all custom 1 water.dump id type x y z +thermo 100 +dump 1 all custom 100 water.dump id type x y z -run 10 +run 1000 From 40157ddb4a93bc5e4755081f950b21e23a270f2c Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 23 Sep 2024 16:13:54 +0800 Subject: [PATCH 22/93] restore pyproject.toml --- pyproject.toml | 2 -- 1 file changed, 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 322a3916b8..f181b616a3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -370,8 +370,6 @@ ignore = [ "D205", # 1 blank line required between summary line and description "D401", # TODO: first line should be in imperative mood "D404", # TODO: first word of the docstring should not be This - "UP007", - "UP006", ] ignore-init-module-imports = true From 8d53aecd5bb08008ee14d6bc21669f1bb24f409e Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 23 Sep 2024 17:19:36 +0800 Subject: [PATCH 23/93] simplify CMAKE --- source/CMakeLists.txt | 442 +++++++++++++--------------------- source/api_cc/src/common.cc | 16 +- source/install/build_cc_pd.sh | 4 +- 3 files changed, 190 insertions(+), 272 deletions(-) diff --git a/source/CMakeLists.txt b/source/CMakeLists.txt index fb9c778462..f2454b4394 100644 --- a/source/CMakeLists.txt +++ b/source/CMakeLists.txt @@ -2,28 +2,39 @@ cmake_minimum_required(VERSION 3.16) project(DeePMD) -macro(safe_set_static_flag) - foreach(flag_var - CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE - CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO) - if(${flag_var} MATCHES "/MD") - string(REGEX REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}") - endif(${flag_var} MATCHES "/MD") - endforeach(flag_var) -endmacro() +option(ENABLE_TENSORFLOW "Enable TensorFlow interface" OFF) +option(ENABLE_PYTORCH "Enable PyTorch interface" OFF) +option(ENABLE_PADDLE "Enable Paddle interface" OFF) +option(BUILD_TESTING "Build test and enable converage" OFF) +set(DEEPMD_C_ROOT + "" + CACHE PATH "Path to imported DeePMD-kit C library") -if(NOT DEFINED PADDLE_LIB) - message( - WARNING - "Optional arg: 'PADDLE_LIB' is not set, skip all compilation of paddle code now. " - "And do not forget to set 'PADDLE_LIB' with '-DPADDLE_LIB=/path/paddle/lib' before " - "using paddle custom operators") +set(CMAKE_CXX_STANDARD 11) +macro(set_if_higher VARIABLE VALUE) + # ${VARIABLE} is a variable name, not a string + if(${VARIABLE} LESS "${VALUE}") + set(${VARIABLE} ${VALUE}) + endif() +endmacro() +if(NOT DEEPMD_C_ROOT) + # we can still allow C++ 11 for programs linked to the C library + set_if_higher(CMAKE_CXX_STANDARD 14) endif() -if(DEFINED PADDLE_LIB) +if(ENABLE_PADDLE) + if(NOT DEFINED PADDLE_LIB) + message(FATAL_ERROR "Make sure PADDLE_LIB is set when ENABLE_PADDLE=ON") + endif() + set(PADDLE_LIB ${PADDLE_LIB} - CACHE PATH "/path/paddle/lib") + CACHE PATH "Path to 'paddle_inference_install_dir' or 'paddle_inference'") + + # used in api_cc + set(PADDLE_LIBRARIES + "${PADDLE_LIB}/paddle/lib/libpaddle_inference.so" + CACHE PATH "Path to libpaddle_inference.so") include_directories("${PADDLE_LIB}/") set(PADDLE_LIB_THIRD_PARTY_PATH "${PADDLE_LIB}/third_party/install/") @@ -38,220 +49,7 @@ if(DEFINED PADDLE_LIB) link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}gflags/lib") link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}xxhash/lib") link_directories("${PADDLE_LIB}/paddle/lib") -endif() - -# add custom operators -option(USE_TENSORRT "Compile demo with TensorRT." OFF) - -if(WITH_GPU) - if(NOT WIN32) - set(CUDA_LIB - "/usr/local/cuda/lib64/" - CACHE STRING "CUDA Library") - else() - if(CUDA_LIB STREQUAL "") - set(CUDA_LIB - "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\lib\\x64" - ) - endif() - endif(NOT WIN32) -endif() - -if(NOT WIN32) - if(USE_TENSORRT AND WITH_GPU) - include_directories("${TENSORRT_INCLUDE_DIR}") - link_directories("${TENSORRT_LIB_DIR}") - endif() -endif(NOT WIN32) - -if(DEFINED PADDLE_LIB) - if(WITH_STATIC_LIB) - set(DEPS - ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_STATIC_LIBRARY_SUFFIX} - ) - else() - if(WIN32) - set(DEPS - ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_STATIC_LIBRARY_SUFFIX} - ) - else() - set(DEPS - ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_SHARED_LIBRARY_SUFFIX} - ) - endif() - endif() -endif() - -if(NOT WIN32) - set(EXTERNAL_LIB "-lrt -ldl -lpthread") - set(DEPS - ${DEPS} - ${MATH_LIB} - ${MKLDNN_LIB} - glog - gflags - protobuf - xxhash - ${EXTERNAL_LIB}) -else() - set(DEPS - ${DEPS} - ${MATH_LIB} - ${MKLDNN_LIB} - glog - gflags_static - libprotobuf - xxhash - ${EXTERNAL_LIB}) - set(DEPS ${DEPS} shlwapi.lib) -endif(NOT WIN32) - -if(WITH_GPU) - if(NOT WIN32) - if(USE_TENSORRT) - set(DEPS ${DEPS} - ${TENSORRT_LIB_DIR}/libnvinfer${CMAKE_SHARED_LIBRARY_SUFFIX}) - set(DEPS - ${DEPS} - ${TENSORRT_LIB_DIR}/libnvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX}) - endif() - set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX}) - else() - if(USE_TENSORRT) - set(DEPS ${DEPS} - ${TENSORRT_LIB_DIR}/nvinfer${CMAKE_STATIC_LIBRARY_SUFFIX}) - set(DEPS ${DEPS} - ${TENSORRT_LIB_DIR}/nvinfer_plugin${CMAKE_STATIC_LIBRARY_SUFFIX}) - endif() - set(DEPS ${DEPS} ${CUDA_LIB}/cudart${CMAKE_STATIC_LIBRARY_SUFFIX}) - set(DEPS ${DEPS} ${CUDA_LIB}/cublas${CMAKE_STATIC_LIBRARY_SUFFIX}) - set(DEPS ${DEPS} ${CUDA_LIB}/cudnn${CMAKE_STATIC_LIBRARY_SUFFIX}) - endif() -endif() - -macro(safe_set_static_flag) - foreach(flag_var - CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE - CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO) - if(${flag_var} MATCHES "/MD") - string(REGEX REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}") - endif(${flag_var} MATCHES "/MD") - endforeach(flag_var) -endmacro() - -if(NOT DEFINED PADDLE_LIB) - message( - FATAL_ERROR "please set PADDLE_LIB with -DPADDLE_LIB=/path/paddle/lib") -endif() -set(PADDLE_LIB - ${PADDLE_LIB} - CACHE PATH "/path/paddle/lib") - -include_directories("${PADDLE_LIB}/") -set(PADDLE_LIB_THIRD_PARTY_PATH "${PADDLE_LIB}/third_party/install/") - -include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}protobuf/include") -include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}glog/include") -include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}gflags/include") -include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}xxhash/include") - -link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}protobuf/lib") -link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}glog/lib") -link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}gflags/lib") -link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}xxhash/lib") -link_directories("${PADDLE_LIB}/paddle/lib") - -# add custom operators -option(USE_TENSORRT "Compile demo with TensorRT." OFF) - -if(WITH_GPU) - if(NOT WIN32) - set(CUDA_LIB - "/usr/local/cuda/lib64/" - CACHE STRING "CUDA Library") - else() - if(CUDA_LIB STREQUAL "") - set(CUDA_LIB - "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\lib\\x64" - ) - endif() - endif(NOT WIN32) -endif() - -if(NOT WIN32) - if(USE_TENSORRT AND WITH_GPU) - include_directories("${TENSORRT_INCLUDE_DIR}") - link_directories("${TENSORRT_LIB_DIR}") - endif() -endif(NOT WIN32) - -if(WITH_STATIC_LIB) - set(DEPS - ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_STATIC_LIBRARY_SUFFIX} - ) -else() - if(WIN32) - set(DEPS - ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_STATIC_LIBRARY_SUFFIX} - ) - else() - set(DEPS - ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_SHARED_LIBRARY_SUFFIX} - ) - endif() -endif() - -if(NOT WIN32) - set(EXTERNAL_LIB "-lrt -ldl -lpthread") - set(DEPS - ${DEPS} - ${MATH_LIB} - ${MKLDNN_LIB} - glog - gflags - protobuf - xxhash - ${EXTERNAL_LIB}) -else() - set(DEPS - ${DEPS} - ${MATH_LIB} - ${MKLDNN_LIB} - glog - gflags_static - libprotobuf - xxhash - ${EXTERNAL_LIB}) - set(DEPS ${DEPS} shlwapi.lib) -endif(NOT WIN32) - -if(WITH_GPU) - if(NOT WIN32) - if(USE_TENSORRT) - set(DEPS ${DEPS} - ${TENSORRT_LIB_DIR}/libnvinfer${CMAKE_SHARED_LIBRARY_SUFFIX}) - set(DEPS - ${DEPS} - ${TENSORRT_LIB_DIR}/libnvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX}) - endif() - set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX}) - else() - if(USE_TENSORRT) - set(DEPS ${DEPS} - ${TENSORRT_LIB_DIR}/nvinfer${CMAKE_STATIC_LIBRARY_SUFFIX}) - set(DEPS ${DEPS} - ${TENSORRT_LIB_DIR}/nvinfer_plugin${CMAKE_STATIC_LIBRARY_SUFFIX}) - endif() - set(DEPS ${DEPS} ${CUDA_LIB}/cudart${CMAKE_STATIC_LIBRARY_SUFFIX}) - set(DEPS ${DEPS} ${CUDA_LIB}/cublas${CMAKE_STATIC_LIBRARY_SUFFIX}) - set(DEPS ${DEPS} ${CUDA_LIB}/cudnn${CMAKE_STATIC_LIBRARY_SUFFIX}) - endif() -endif() - -option(BUILD_TESTING "Build test and enable converage" OFF) -set(DEEPMD_C_ROOT - "" - CACHE PATH "Path to imported DeePMD-kit C library") +endif(ENABLE_PADDLE) if(BUILD_TESTING) enable_testing() @@ -271,10 +69,6 @@ if((NOT BUILD_PY_IF) AND (NOT BUILD_CPP_IF)) endif() if(BUILD_CPP_IF AND BUILD_TESTING) - if(NOT INSTALL_TENSORFLOW) - # some errors in conda packages... - find_package(GTest) - endif() if(NOT GTEST_LIBRARIES) configure_file(${CMAKE_CURRENT_SOURCE_DIR}/cmake/googletest.cmake.in googletest-download/CMakeLists.txt @ONLY) @@ -343,12 +137,16 @@ set(DP_VARIANT "cpu") # define USE_CUDA_TOOLKIT if(USE_CUDA_TOOLKIT) - set(CUDA_USE_STATIC_CUDA_RUNTIME - OFF - CACHE INTERNAL "") - find_package(CUDA REQUIRED) + cmake_minimum_required(VERSION 3.23) + find_package(CUDAToolkit REQUIRED) + if(NOT DEFINED CMAKE_CUDA_COMPILER) + set(CMAKE_CUDA_COMPILER ${CUDAToolkit_NVCC_EXECUTABLE}) + endif() + if(NOT DEFINED CMAKE_CUDA_HOST_COMPILER) + set(CMAKE_CUDA_HOST_COMPILER ${CMAKE_CXX_COMPILER}) + endif() add_definitions("-DGOOGLE_CUDA") - message(STATUS "Found CUDA in ${CUDA_TOOLKIT_ROOT_DIR}, build nv GPU support") + message(STATUS "Found CUDA in ${CUDAToolkit_BIN_DIR}, build nv GPU support") set(DP_VARIANT "cuda") else() message(STATUS "Will not build nv GPU support") @@ -356,10 +154,15 @@ endif(USE_CUDA_TOOLKIT) # define USE_ROCM_TOOLKIT if(USE_ROCM_TOOLKIT) - find_package(ROCM REQUIRED) + cmake_minimum_required(VERSION 3.21) + include(CMakeDetermineHIPCompiler) + list(APPEND CMAKE_PREFIX_PATH ${CMAKE_HIP_COMPILER_ROCM_ROOT}) + find_package(hip REQUIRED) + find_package(hipcub REQUIRED) add_definitions("-DTENSORFLOW_USE_ROCM") - add_compile_definitions(__HIP_PLATFORM_HCC__) - message(STATUS "Found ROCM in ${ROCM_ROOT}, build AMD GPU support") + message( + STATUS + "Found ROCM in ${CMAKE_HIP_COMPILER_ROCM_ROOT}, build AMD GPU support") set(DP_VARIANT "rocm") else() message(STATUS "Will not build AMD GPU support") @@ -368,7 +171,11 @@ endif(USE_ROCM_TOOLKIT) set(DEEPMD_SOURCE_DIR ${PROJECT_SOURCE_DIR}/..) # setup tensorflow libraries by python +if(INSTALL_TENSORFLOW) + set(USE_TF_PYTHON_LIBS TRUE) +endif(INSTALL_TENSORFLOW) if(USE_TF_PYTHON_LIBS) + set(ENABLE_TENSORFLOW TRUE) if(NOT "$ENV{CIBUILDWHEEL}" STREQUAL "1") find_package( Python @@ -379,10 +186,114 @@ if(USE_TF_PYTHON_LIBS) set(PYTHON_INCLUDE_DIRS ${PYTHON_INCLUDE_DIR}) endif() endif(USE_TF_PYTHON_LIBS) +if(TENSORFLOW_ROOT) + set(ENABLE_TENSORFLOW TRUE) +endif() # find tensorflow, I need tf abi info -if(NOT DEEPMD_C_ROOT) +if(ENABLE_TENSORFLOW AND NOT DEEPMD_C_ROOT) find_package(tensorflow REQUIRED) + list(APPEND BACKEND_LIBRARY_PATH ${TensorFlow_LIBRARY_PATH}) + list(APPEND BACKEND_INCLUDE_DIRS ${TENSORFLOW_INCLUDE_DIRS}) +endif() +if(BUILD_CPP_IF + AND USE_PT_PYTHON_LIBS + AND NOT CMAKE_CROSSCOMPILING + AND NOT SKBUILD) + find_package( + Python + COMPONENTS Interpreter + REQUIRED) + execute_process( + COMMAND ${Python_EXECUTABLE} -c + "import torch;print(torch.utils.cmake_prefix_path)" + WORKING_DIRECTORY ${PROJECT_SOURCE_DIR} + OUTPUT_VARIABLE PYTORCH_CMAKE_PREFIX_PATH + RESULT_VARIABLE PYTORCH_CMAKE_PREFIX_PATH_RESULT_VAR + ERROR_VARIABLE PYTORCH_CMAKE_PREFIX_PATH_ERROR_VAR + OUTPUT_STRIP_TRAILING_WHITESPACE) + if(NOT ${PYTORCH_CMAKE_PREFIX_PATH_RESULT_VAR} EQUAL 0) + message( + FATAL_ERROR + "Cannot determine PyTorch CMake prefix path, error code: $PYTORCH_CMAKE_PREFIX_PATH_RESULT_VAR}, error message: ${PYTORCH_CMAKE_PREFIX_PATH_ERROR_VAR}" + ) + endif() + list(APPEND CMAKE_PREFIX_PATH ${PYTORCH_CMAKE_PREFIX_PATH}) +endif() +if(ENABLE_PYTORCH AND NOT DEEPMD_C_ROOT) + find_package(Torch REQUIRED) + if(NOT Torch_VERSION VERSION_LESS "2.1.0") + set_if_higher(CMAKE_CXX_STANDARD 17) + elseif(NOT Torch_VERSION VERSION_LESS "1.5.0") + set_if_higher(CMAKE_CXX_STANDARD 14) + endif() + string(REGEX MATCH "_GLIBCXX_USE_CXX11_ABI=([0-9]+)" CXXABI_PT_MATCH + "${TORCH_CXX_FLAGS}") + if(CXXABI_PT_MATCH) + set(OP_CXX_ABI_PT ${CMAKE_MATCH_1}) + message(STATUS "PyTorch CXX11 ABI: ${CMAKE_MATCH_1}") + if(DEFINED OP_CXX_ABI) + if(NOT ${CMAKE_MATCH_1} EQUAL ${OP_CXX_ABI}) + if(NOT BUILD_PY_IF) + message( + FATAL_ERROR + "PyTorch CXX11 ABI mismatch TensorFlow: ${CMAKE_MATCH_1} != ${OP_CXX_ABI}" + ) + else() + if(NOT BUILD_CPP_IF) + message( + STATUS + "PyTorch CXX11 ABI mismatch TensorFlow: ${CMAKE_MATCH_1} != ${OP_CXX_ABI}. " + "Try to build libraries with both ABIs.") + else() + message( + WARNING + "PyTorch CXX11 ABI mismatch TensorFlow: ${CMAKE_MATCH_1} != ${OP_CXX_ABI}. " + "PyTorch C++ OP will be built but PyTorch support for C++ libraries will be disabled. " + "Note that we don't officially support building C++ libraries in the Python package, " + "except for the wheels we officially release.") + endif() + set(DEEPMD_BUILD_COMPAT_CXXABI ON) + set(OP_CXX_ABI_COMPAT ${OP_CXX_ABI_PT}) + endif() + else() + set(DEEPMD_BUILD_COMPAT_CXXABI OFF) + endif() + else() + set(OP_CXX_ABI ${CMAKE_MATCH_1}) + add_definitions(-D_GLIBCXX_USE_CXX11_ABI=${OP_CXX_ABI}) + endif() + else() + # Maybe in macos/windows + if(NOT DEFINED OP_CXX_ABI) + set(OP_CXX_ABI 0) + endif() + set(OP_CXX_ABI_PT "${OP_CXX_ABI}") + endif() + # get torch directory get the directory of the target "torch" + get_target_property(_TORCH_LOCATION torch LOCATION) + get_filename_component(PyTorch_LIBRARY_PATH ${_TORCH_LOCATION} DIRECTORY) + list(APPEND BACKEND_LIBRARY_PATH ${PyTorch_LIBRARY_PATH}) + list(APPEND BACKEND_INCLUDE_DIRS ${TORCH_INCLUDE_DIRS}) +endif() +# log enabled backends +if(NOT DEEPMD_C_ROOT) + message(STATUS "Enabled backends:") + if(ENABLE_TENSORFLOW) + message(STATUS "- TensorFlow") + endif() + if(ENABLE_PYTORCH) + message(STATUS "- PyTorch") + endif() + if(ENABLE_PADDLE) + message(STATUS "- Paddle") + endif() + if(NOT ENABLE_TENSORFLOW + AND NOT ENABLE_PYTORCH + AND NOT ENABLE_PADDLE + AND NOT BUILD_PY_IF) + message(FATAL_ERROR "No backend is enabled.") + endif() endif() # find threads @@ -421,7 +332,6 @@ if(BUILD_CPP_IF) set(LIB_DEEPMD_CC "deepmd_cc") set(LIB_DEEPMD_C "deepmd_c") if(USE_CUDA_TOOLKIT) - set(LIB_DEEPMD_OP_DEVICE "deepmd_paddle_op_cuda") set(LIB_DEEPMD_OP_DEVICE "deepmd_op_cuda") elseif(USE_ROCM_TOOLKIT) set(LIB_DEEPMD_OP_DEVICE "deepmd_op_rocm") @@ -430,7 +340,8 @@ if(BUILD_CPP_IF) endif() if(CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 4.8) set(LIB_DEEPMD_NATIVE "deepmd_native_md") - # set(LIB_DEEPMD_IPI "deepmd_ipi") set(LIB_DEEPMD_GROMACS "deepmd_gromacs") + set(LIB_DEEPMD_IPI "deepmd_ipi") + set(LIB_DEEPMD_GROMACS "deepmd_gromacs") else() message( STATUS @@ -469,12 +380,21 @@ if(DEEPMD_C_ROOT) IMPORTED_LOCATION "${deepmd_c}" INTERFACE_INCLUDE_DIRECTORIES "${DEEPMD_INCLUDE_C_DIR}/deepmd") # use variable for TF path to set deepmd_c path + set(TENSORFLOW_ROOT "${DEEPMD_C_ROOT}") set(TensorFlow_LIBRARY_PATH "${DEEPMD_C_ROOT}/lib") + set(BACKEND_LIBRARY_PATH "${DEEPMD_C_ROOT}/lib") set(TENSORFLOW_INCLUDE_DIRS "${DEEPMD_C_ROOT}/include") + set(BACKEND_INCLUDE_DIRS "${DEEPMD_C_ROOT}/include") + set(TORCH_LIBRARIES "${DEEPMD_C_ROOT}/lib/libtorch.so") endif() if(NOT DEEPMD_C_ROOT) - # add_subdirectory(op/) + if(ENABLE_TENSORFLOW) + add_subdirectory(op/tf/) + endif() + if(ENABLE_PYTORCH) + add_subdirectory(op/pt/) + endif() add_subdirectory(lib/) endif() if(BUILD_PY_IF) @@ -490,33 +410,19 @@ if(BUILD_CPP_IF) add_subdirectory(lmp/) endif() if(CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 4.8) - # add_subdirectory (md/) if(ENABLE_IPI OR NOT BUILD_PY_IF AND NOT - # DEEPMD_C_ROOT) # ipi has a dependency on libdeepmd add_subdirectory(ipi/) - # endif() if(NOT BUILD_PY_IF) add_subdirectory(gmx/) endif() + # add_subdirectory (md/) + if(ENABLE_IPI OR NOT BUILD_PY_IF) + add_subdirectory(ipi/) + endif() + if(NOT BUILD_PY_IF) + add_subdirectory(gmx/) + endif() endif() if(BUILD_NODEJS_IF) add_subdirectory(nodejs/) endif() endif(BUILD_CPP_IF) -# if(WIN32) if(USE_TENSORRT) add_custom_command(TARGET ${DEMO_NAME} POST_BUILD -# COMMAND ${CMAKE_COMMAND} -E copy -# ${TENSORRT_LIB_DIR}/nvinfer${CMAKE_SHARED_LIBRARY_SUFFIX} -# ${CMAKE_BINARY_DIR}/${CMAKE_BUILD_TYPE} COMMAND ${CMAKE_COMMAND} -E copy -# ${TENSORRT_LIB_DIR}/nvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX} -# ${CMAKE_BINARY_DIR}/${CMAKE_BUILD_TYPE} ) endif() if(WITH_MKL) -# add_custom_command(TARGET ${DEMO_NAME} POST_BUILD COMMAND ${CMAKE_COMMAND} -E -# copy ${MATH_LIB_PATH}/lib/mklml.dll ${CMAKE_BINARY_DIR}/Release COMMAND -# ${CMAKE_COMMAND} -E copy ${MATH_LIB_PATH}/lib/libiomp5md.dll -# ${CMAKE_BINARY_DIR}/Release COMMAND ${CMAKE_COMMAND} -E copy -# ${MKLDNN_PATH}/lib/mkldnn.dll ${CMAKE_BINARY_DIR}/Release ) else() -# add_custom_command(TARGET ${DEMO_NAME} POST_BUILD COMMAND ${CMAKE_COMMAND} -E -# copy ${OPENBLAS_LIB_PATH}/lib/openblas.dll ${CMAKE_BINARY_DIR}/Release ) -# endif() if(NOT WITH_STATIC_LIB) add_custom_command(TARGET ${DEMO_NAME} -# POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy -# "${PADDLE_LIB}/paddle/lib/paddle_fluid.dll" -# ${CMAKE_BINARY_DIR}/${CMAKE_BUILD_TYPE} ) endif() endif() - # uninstall target configure_file( "${CMAKE_CURRENT_SOURCE_DIR}/cmake/cmake_uninstall.cmake.in" diff --git a/source/api_cc/src/common.cc b/source/api_cc/src/common.cc index 378b50a71c..aef3204569 100644 --- a/source/api_cc/src/common.cc +++ b/source/api_cc/src/common.cc @@ -929,7 +929,6 @@ int deepmd::session_get_dtype(tensorflow::Session* session, #endif #ifdef BUILD_PADDLE - template int deepmd::predictor_input_tensors( const std::shared_ptr& predictor, @@ -1488,6 +1487,21 @@ void deepmd::select_map_inv(typename std::vector::iterator out, } } +#ifdef BUILD_TENSORFLOW +template int deepmd::session_get_scalar(Session*, + const std::string, + const std::string); + +template bool deepmd::session_get_scalar(Session*, + const std::string, + const std::string); + +template void deepmd::session_get_vector(std::vector&, + Session*, + const std::string, + const std::string); +#endif + #ifdef BUILD_PADDLE template int deepmd::predictor_get_scalar(const std::shared_ptr& predictor, const std::string &name_); diff --git a/source/install/build_cc_pd.sh b/source/install/build_cc_pd.sh index 335a394e5b..d45cf5b993 100755 --- a/source/install/build_cc_pd.sh +++ b/source/install/build_cc_pd.sh @@ -78,8 +78,7 @@ export LIBRARY_PATH=${DEEPMD_DIR}/deepmd/op:$LIBRARY_PATH BUILD_TMP_DIR=${SCRIPT_PATH}/../build mkdir -p ${BUILD_TMP_DIR} cd ${BUILD_TMP_DIR} -cmake -DCMAKE_PREFIX_PATH=/workspace/hesensen/PaddleScience_enn_debug/Paddle/build/paddle_inference_install_dir/paddle \ - -D ENABLE_TENSORFLOW=OFF \ +cmake -D ENABLE_TENSORFLOW=OFF \ -D ENABLE_PYTORCH=OFF \ -D ENABLE_PADDLE=ON \ -D PADDLE_LIB=${PADDLE_INFERENCE_DIR} \ @@ -87,7 +86,6 @@ cmake -DCMAKE_PREFIX_PATH=/workspace/hesensen/PaddleScience_enn_debug/Paddle/bui -D USE_TF_PYTHON_LIBS=TRUE \ -D LAMMPS_SOURCE_ROOT=${LAMMPS_SOURCE_ROOT} \ -D ENABLE_IPI=OFF \ - -D PADDLE_LIBRARIES=/workspace/hesensen/PaddleScience_enn_debug/Paddle/build/paddle_inference_install_dir/paddle/lib/libpaddle_inference.so \ ${CUDA_ARGS} \ -D LAMMPS_VERSION=stable_29Aug2024 \ .. From e39d4660acd62d97b101fed33807c9ff479609ff Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 23 Sep 2024 17:24:01 +0800 Subject: [PATCH 24/93] restore c_api.cc --- source/api_c/src/c_api.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/source/api_c/src/c_api.cc b/source/api_c/src/c_api.cc index e7222ce59c..9ed37d04aa 100644 --- a/source/api_c/src/c_api.cc +++ b/source/api_c/src/c_api.cc @@ -1,4 +1,5 @@ // SPDX-License-Identifier: LGPL-3.0-or-later +#include "c_api.h" #include #include @@ -9,7 +10,6 @@ #include "DeepTensor.h" #include "c_api_internal.h" #include "common.h" -// #include "/workspace/hesensen/deepmd_backend/deepmd_paddle_new/source/api_c/include/c_api.h" extern "C" { From b97571e4bb8b41717b3a819bcc1455b7c6bd3603 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 24 Sep 2024 14:03:44 +0800 Subject: [PATCH 25/93] fix bugs --- backend/dynamic_metadata.py | 2 +- backend/read_env.py | 2 +- deepmd/pd/model/descriptor/se_atten.py | 4 ++-- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/backend/dynamic_metadata.py b/backend/dynamic_metadata.py index 83123e6e41..138375e072 100644 --- a/backend/dynamic_metadata.py +++ b/backend/dynamic_metadata.py @@ -36,7 +36,7 @@ def dynamic_metadata( settings: Optional[Dict[str, object]] = None, ): assert field in ["optional-dependencies", "entry-points", "scripts"] - _, _, find_libpython_requires, extra_scripts, tf_version, pt_version = ( + _, _, find_libpython_requires, extra_scripts, tf_version, pt_version, pd_version = ( get_argument_from_env() ) with Path("pyproject.toml").open("rb") as f: diff --git a/backend/read_env.py b/backend/read_env.py index 8c595c34c3..582b08e1bb 100644 --- a/backend/read_env.py +++ b/backend/read_env.py @@ -153,6 +153,6 @@ def get_argument_from_env() -> Tuple[str, list, list, dict, str, str]: def set_scikit_build_env(): """Set scikit-build environment variables before executing scikit-build.""" - cmake_minimum_required_version, cmake_args, _, _, _, _ = get_argument_from_env() + cmake_minimum_required_version, cmake_args, _, _, _, _, _ = get_argument_from_env() os.environ["SKBUILD_CMAKE_MINIMUM_VERSION"] = cmake_minimum_required_version os.environ["SKBUILD_CMAKE_ARGS"] = ";".join(cmake_args) diff --git a/deepmd/pd/model/descriptor/se_atten.py b/deepmd/pd/model/descriptor/se_atten.py index 2b9d150dbb..93fe052b06 100644 --- a/deepmd/pd/model/descriptor/se_atten.py +++ b/deepmd/pd/model/descriptor/se_atten.py @@ -484,9 +484,9 @@ def forward( ) # nb x nloc x nnei exclude_mask = self.emask(nlist, extended_atype) - nlist = paddle.where(exclude_mask != 0, nlist, -1) + nlist = paddle.where(exclude_mask != 0, nlist, paddle.full_like(nlist, -1)) nlist_mask = nlist != -1 - nlist = paddle.where(nlist == -1, 0, nlist) + nlist = paddle.where(nlist == -1, paddle.zeros_like(nlist), nlist) sw = paddle.squeeze(sw, -1) # nf x nloc x nt -> nf x nloc x nnei x nt atype_tebd = extended_atype_embd[:, :nloc, :] From 50092c6ab33444f22fabd1e67c6c7de4fb38fb26 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 24 Sep 2024 14:11:56 +0800 Subject: [PATCH 26/93] change pt -> pd --- deepmd/pd/model/model/make_model.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/deepmd/pd/model/model/make_model.py b/deepmd/pd/model/model/make_model.py index 3a35589458..1b634f7c8c 100644 --- a/deepmd/pd/model/model/make_model.py +++ b/deepmd/pd/model/model/make_model.py @@ -84,8 +84,8 @@ def __init__( self.atomic_model: T_AtomicModel = T_AtomicModel(*args, **kwargs) self.precision_dict = PRECISION_DICT self.reverse_precision_dict = RESERVED_PRECISON_DICT - self.global_pt_float_precision = GLOBAL_PD_FLOAT_PRECISION - self.global_pt_ener_float_precision = GLOBAL_PD_ENER_FLOAT_PRECISION + self.global_pd_float_precision = GLOBAL_PD_FLOAT_PRECISION + self.global_pd_ener_float_precision = GLOBAL_PD_ENER_FLOAT_PRECISION def model_output_def(self): """Get the output def for the model.""" @@ -312,11 +312,11 @@ def input_type_cast( box, fparam, aparam = _lst if ( input_prec - == self.reverse_precision_dict[self.global_pt_float_precision] + == self.reverse_precision_dict[self.global_pd_float_precision] ): return coord, box, fparam, aparam, input_prec else: - pp = self.global_pt_float_precision + pp = self.global_pd_float_precision return ( coord.to(pp), box.to(pp) if box is not None else None, @@ -333,7 +333,7 @@ def output_type_cast( """Convert the model output to the input prec.""" do_cast = ( input_prec - != self.reverse_precision_dict[self.global_pt_float_precision] + != self.reverse_precision_dict[self.global_pd_float_precision] ) pp = self.precision_dict[input_prec] odef = self.model_output_def() @@ -343,7 +343,7 @@ def output_type_cast( continue if check_operation_applied(odef[kk], OutputVariableOperation.REDU): model_ret[kk] = ( - model_ret[kk].to(self.global_pt_ener_float_precision) + model_ret[kk].to(self.global_pd_ener_float_precision) if model_ret[kk] is not None else None ) @@ -424,7 +424,7 @@ def _format_nlist( * paddle.ones( [n_nf, n_nloc, nnei - n_nnei], dtype=nlist.dtype, - ).to(device=nlist.place), + ), # .to(device=nlist.place), ], axis=-1, ) From e02dd1115a1688ba51b6a8db4f3ebe3404022fa1 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 24 Sep 2024 15:21:25 +0800 Subject: [PATCH 27/93] refactor DeepPotPD.cc --- source/api_cc/src/DeepPotPD.cc | 1140 +++++++------------------------- 1 file changed, 250 insertions(+), 890 deletions(-) diff --git a/source/api_cc/src/DeepPotPD.cc b/source/api_cc/src/DeepPotPD.cc index ebabfc66e1..a77cd5d1e8 100644 --- a/source/api_cc/src/DeepPotPD.cc +++ b/source/api_cc/src/DeepPotPD.cc @@ -10,480 +10,9 @@ #include "device.h" #include "common.h" #include "paddle/include/paddle_inference_api.h" -// #include "glog/logging.h" using namespace deepmd; -template -static void run_model( - std::vector& dener, - std::vector& dforce_, - std::vector& dvirial, - const std::shared_ptr& predictor, - const AtomMap& atommap, - const int nframes, - const int nghost = 0) { - // printf("run_model 1 st\n"); - unsigned nloc = atommap.get_type().size(); - unsigned nall = nloc + nghost; - // printf("nloc = %d, nall = %d\n", nloc, nall); - dener.resize(nframes); - if (nloc == 0) { - // no backward map needed - // dforce of size nall * 3 - dforce_.resize(static_cast(nframes) * nall * 3); - fill(dforce_.begin(), dforce_.end(), (VALUETYPE)0.0); - // dvirial of size 9 - dvirial.resize(static_cast(nframes) * 9); - fill(dvirial.begin(), dvirial.end(), (VALUETYPE)0.0); - return; - } - - /* Running inference */ - // printf("Running inference st\n"); - if (!predictor->Run()) { - throw deepmd::deepmd_exception("Paddle inference failed"); - } - // printf("Running inference ed\n"); - - auto output_names = predictor->GetOutputNames(); - // for (auto &name: output_names) - // { - // printf("output name: %s, shape: [", name.c_str()); - // auto shape = predictor->GetOutputHandle(name)->shape(); - // for (auto &dd: shape) - // { - // printf("%d, ", dd); - // } - // printf("]\n"); - // } - auto output_e = predictor->GetOutputHandle(output_names[1]); - auto output_f = predictor->GetOutputHandle(output_names[2]); - auto output_virial_tensor = predictor->GetOutputHandle(output_names[4]); - - // 获取 Output paddle::Tensor 的维度信息 - std::vector output_energy_shape = output_e->shape(); - int output_energy_size = - std::accumulate(output_energy_shape.begin(), output_energy_shape.end(), 1, - std::multiplies()); - std::vector output_force_shape = output_f->shape(); - int output_force_size = - std::accumulate(output_force_shape.begin(), output_force_shape.end(), 1, - std::multiplies()); - std::vector output_virial_shape = output_virial_tensor->shape(); - int output_virial_size = - std::accumulate(output_virial_shape.begin(), output_virial_shape.end(), 1, - std::multiplies()); - // for (int i=0; i oe; - // printf("Resize st\n"); - oe.resize(output_energy_size); - // printf("Resize ed\n"); - // printf("CopytoCpu st\n"); - output_e->CopyToCpu(oe.data()); - // printf("Resize st\n"); - // printf("CopytoCpu ed\n"); - // get data of output_force - // printf("of\n"); - std::vector of; - of.resize(output_force_size); - output_f->CopyToCpu(of.data()); - // get data of output_virial - // printf("oav\n"); - std::vector oav; - oav.resize(output_virial_size); - // printf("oav 2\n"); - output_virial_tensor->CopyToCpu(oav.data()); - // printf("oav 22\n"); - - // printf("dvirial\n"); - std::vector dforce(nframes * 3 * nall); - dvirial.resize(nframes * 9); - for (int ii = 0; ii < nframes; ++ii) { - // printf("oe[%d] = %.5lf\n", ii, oe[ii]); - dener[ii] = oe[ii]; - } - for (int ii = 0; ii < nframes * nall * 3; ++ii) { - dforce[ii] = of[ii]; - } - // set dvirial to zero, prevent input vector is not zero (#1123) - // printf("fill\n"); - std::fill(dvirial.begin(), dvirial.end(), (VALUETYPE)0.); - for (int kk = 0; kk < nframes; ++kk) { - for (int ii = 0; ii < nall; ++ii) { - dvirial[kk * 9 + 0] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 0]; - dvirial[kk * 9 + 1] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 1]; - dvirial[kk * 9 + 2] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 2]; - dvirial[kk * 9 + 3] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 3]; - dvirial[kk * 9 + 4] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 4]; - dvirial[kk * 9 + 5] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 5]; - dvirial[kk * 9 + 6] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 6]; - dvirial[kk * 9 + 7] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 7]; - dvirial[kk * 9 + 8] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 8]; - } - } - dforce_ = dforce; - // printf("atommap.backward\n"); - atommap.backward(dforce_.begin(), dforce.begin(), 3, nframes, - nall); - // printf("run_model 1 ed\n"); -} - -template void run_model( - std::vector& dener, - std::vector& dforce_, - std::vector& dvirial, - const std::shared_ptr& predictor, - // const std::vector>& input_tensors, - const AtomMap& atommap, - const int nframes, - const int nghost); - -template void run_model( - std::vector& dener, - std::vector& dforce_, - std::vector& dvirial, - const std::shared_ptr& predictor, - // const std::vector>& input_tensors, - const AtomMap& atommap, - const int nframes, - const int nghost); - -template void run_model( - std::vector& dener, - std::vector& dforce_, - std::vector& dvirial, - const std::shared_ptr& predictor, - // const std::vector>& input_tensors, - const AtomMap& atommap, - const int nframes, - const int nghost); - -template void run_model( - std::vector& dener, - std::vector& dforce_, - std::vector& dvirial, - const std::shared_ptr& predictor, - // const std::vector>& input_tensors, - const AtomMap& atommap, - const int nframes, - const int nghost); - -template -static void run_model( - std::vector& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const std::shared_ptr& predictor, - const deepmd::AtomMap& atommap, - const int nframes, - const int nghost = 0) { - // printf("run_model 2\n"); - unsigned nloc = atommap.get_type().size(); - unsigned nall = nloc + nghost; - dener.resize(nframes); - if (nloc == 0) { - // no backward map needed - // dforce of size nall * 3 - dforce_.resize(nframes * nall * 3); - fill(dforce_.begin(), dforce_.end(), (VALUETYPE)0.0); - // dvirial of size 9 - dvirial.resize(nframes * 9); - fill(dvirial.begin(), dvirial.end(), (VALUETYPE)0.0); - // datom_energy_ of size nall - datom_energy_.resize(nframes * nall); - fill(datom_energy_.begin(), datom_energy_.end(), (VALUETYPE)0.0); - // datom_virial_ of size nall * 9 - datom_virial_.resize(nframes * nall * 9); - fill(datom_virial_.begin(), datom_virial_.end(), (VALUETYPE)0.0); - return; - } - - /* Running inference */ - if (!predictor->Run()) { - throw deepmd::deepmd_exception("Paddle inference failed"); - } - - /* Get output handles*/ - auto output_names = predictor->GetOutputNames(); - auto output_ae = predictor->GetOutputHandle(output_names[0]); - auto output_av = predictor->GetOutputHandle(output_names[1]); - auto output_e = predictor->GetOutputHandle(output_names[4]); - auto output_f = predictor->GetOutputHandle(output_names[5]); - - // 获取 Output paddle::Tensor 的维度信息 - std::vector output_atom_ener_shape = output_ae->shape(); - int output_atom_ener_size = - std::accumulate(output_atom_ener_shape.begin(), - output_atom_ener_shape.end(), 1, std::multiplies()); - std::vector output_atom_virial_shape = output_av->shape(); - int output_atom_virial_size = - std::accumulate(output_atom_virial_shape.begin(), output_atom_virial_shape.end(), 1, - std::multiplies()); - std::vector output_energy_shape = output_e->shape(); - int output_energy_size = - std::accumulate(output_energy_shape.begin(), output_energy_shape.end(), 1, - std::multiplies()); - std::vector output_force_shape = output_f->shape(); - int output_force_size = - std::accumulate(output_force_shape.begin(), output_force_shape.end(), 1, - std::multiplies()); - - // get data of output_atom_ener - std::vector oae; - oae.resize(output_atom_ener_size); - output_ae->CopyToCpu(oae.data()); - // get data of output_atom_virial - std::vector oav; - oav.resize(output_atom_virial_size); - output_av->CopyToCpu(oav.data()); - // get data of output_energy - std::vector oe; - oe.resize(output_energy_size); - output_e->CopyToCpu(oe.data()); - // get data of output_force - std::vector of; - of.resize(output_force_size); - output_f->CopyToCpu(of.data()); - - std::vector dforce(nframes * 3 * nall); - std::vector datom_energy(nframes * nall, 0); - std::vector datom_virial(nframes * 9 * nall); - dvirial.resize(nframes * 9); - for (int ii = 0; ii < nframes; ++ii) { - dener[ii] = oe[ii]; - } - for (int ii = 0; ii < nframes * nall * 3; ++ii) { - dforce[ii] = of[ii]; - } - for (int ii = 0; ii < nframes; ++ii) { - for (int jj = 0; jj < nloc; ++jj) { - datom_energy[ii * nall + jj] = oae[ii * nloc + jj]; - } - } - for (int ii = 0; ii < nframes * nall * 9; ++ii) { - datom_virial[ii] = oav[ii]; - } - // set dvirial to zero, prevent input vector is not zero (#1123) - std::fill(dvirial.begin(), dvirial.end(), (VALUETYPE)0.); - for (int kk = 0; kk < nframes; ++kk) { - for (int ii = 0; ii < nall; ++ii) { - dvirial[kk * 9 + 0] += - (VALUETYPE)1.0 * datom_virial[kk * nall * 9 + 9 * ii + 0]; - dvirial[kk * 9 + 1] += - (VALUETYPE)1.0 * datom_virial[kk * nall * 9 + 9 * ii + 1]; - dvirial[kk * 9 + 2] += - (VALUETYPE)1.0 * datom_virial[kk * nall * 9 + 9 * ii + 2]; - dvirial[kk * 9 + 3] += - (VALUETYPE)1.0 * datom_virial[kk * nall * 9 + 9 * ii + 3]; - dvirial[kk * 9 + 4] += - (VALUETYPE)1.0 * datom_virial[kk * nall * 9 + 9 * ii + 4]; - dvirial[kk * 9 + 5] += - (VALUETYPE)1.0 * datom_virial[kk * nall * 9 + 9 * ii + 5]; - dvirial[kk * 9 + 6] += - (VALUETYPE)1.0 * datom_virial[kk * nall * 9 + 9 * ii + 6]; - dvirial[kk * 9 + 7] += - (VALUETYPE)1.0 * datom_virial[kk * nall * 9 + 9 * ii + 7]; - dvirial[kk * 9 + 8] += - (VALUETYPE)1.0 * datom_virial[kk * nall * 9 + 9 * ii + 8]; - } - } - dforce_ = dforce; - datom_energy_ = datom_energy; - datom_virial_ = datom_virial; - atommap.backward(dforce_.begin(), dforce.begin(), 3, nframes, - nall); - atommap.backward(datom_energy_.begin(), datom_energy.begin(), 1, - nframes, nall); - atommap.backward(datom_virial_.begin(), datom_virial.begin(), 9, - nframes, nall); -} - -template void run_model( - std::vector& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const std::shared_ptr& predictor, - const deepmd::AtomMap& atommap, - const int nframes, - const int nghost); - -template void run_model( - std::vector& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const std::shared_ptr& predictor, - const deepmd::AtomMap& atommap, - const int nframes, - const int nghost); - -template void run_model( - std::vector& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const std::shared_ptr& predictor, - const deepmd::AtomMap& atommap, - const int nframes, - const int nghost); - -template void run_model( - std::vector& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const std::shared_ptr& predictor, - const deepmd::AtomMap& atommap, - const int nframes, - const int nghost); - -// end multiple frames - -// start single frame - -template -static void run_model( - ENERGYTYPE& dener, - std::vector& dforce_, - std::vector& dvirial, - const std::shared_ptr& predictor, - const deepmd::AtomMap& atommap, - const int nframes, - const int nghost = 0) { - assert(nframes == 1); - std::vector dener_(1); - // call multi-frame version - run_model(dener_, dforce_, dvirial, predictor, - atommap, nframes, nghost); - dener = dener_[0]; -} - -template void run_model( - ENERGYTYPE& dener, - std::vector& dforce_, - std::vector& dvirial, - const std::shared_ptr& predictor, - // const std::vector>& input_tensors, - const AtomMap& atommap, - const int nframes, - const int nghost); - -template void run_model( - ENERGYTYPE& dener, - std::vector& dforce_, - std::vector& dvirial, - const std::shared_ptr& predictor, - // const std::vector>& input_tensors, - const AtomMap& atommap, - const int nframes, - const int nghost); - -template void run_model( - ENERGYTYPE& dener, - std::vector& dforce_, - std::vector& dvirial, - const std::shared_ptr& predictor, - // const std::vector>& input_tensors, - const AtomMap& atommap, - const int nframes, - const int nghost); - -template void run_model( - ENERGYTYPE& dener, - std::vector& dforce_, - std::vector& dvirial, - const std::shared_ptr& predictor, - // const std::vector>& input_tensors, - const AtomMap& atommap, - const int nframes, - const int nghost); - -template -static void run_model( - ENERGYTYPE& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const std::shared_ptr& predictor, - const deepmd::AtomMap& atommap, - const int nframes = 1, - const int nghost = 0) { - assert(nframes == 1); - std::vector dener_(1); - // call multi-frame version - run_model(dener_, dforce_, dvirial, datom_energy_, - datom_virial_, predictor,//, input_tensors, - atommap, nframes, nghost); - dener = dener_[0]; -} - -template void run_model( - ENERGYTYPE& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const std::shared_ptr& predictor, - // const std::vector>& input_tensors, - const deepmd::AtomMap& atommap, - const int nframes, - const int nghost); - -template void run_model( - ENERGYTYPE& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const std::shared_ptr& predictor, - // const std::vector>& input_tensors, - const deepmd::AtomMap& atommap, - const int nframes, - const int nghost); - -template void run_model( - ENERGYTYPE& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const std::shared_ptr& predictor, - // const std::vector>& input_tensors, - const deepmd::AtomMap& atommap, - const int nframes, - const int nghost); - -template void run_model( - ENERGYTYPE& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const std::shared_ptr& predictor, - // const std::vector>& input_tensors, - const deepmd::AtomMap& atommap, - const int nframes, - const int nghost); - -// end single frame - DeepPotPD::DeepPotPD() : inited(false) {} DeepPotPD::DeepPotPD(const std::string& model, @@ -496,7 +25,6 @@ DeepPotPD::DeepPotPD(const std::string& model, void DeepPotPD::init(const std::string& model, const int& gpu_rank, const std::string& file_content) { - // std::cout << ("** Access here.") << std::endl; if (inited) { std::cerr << "WARNING: deepmd-kit should not be initialized twice, do " "nothing at the second call of initializer" @@ -510,10 +38,12 @@ void DeepPotPD::init(const std::string& model, } else { gpu_id = 0; } - std::string pdmodel_path = ""; - std::string pdiparams_path = ""; + + std::string pdmodel_path; + std::string pdiparams_path; bool use_paddle_inference = false; bool use_pir = false; + if (model.find(".json") != std::string::npos) { use_pir = true; pdmodel_path = model; @@ -528,10 +58,10 @@ void DeepPotPD::init(const std::string& model, } else { throw "[Error] Not found any inference model in"; } + int math_lib_num_threads = 1; if (use_paddle_inference) { - // printf("***** creating paddle predictor\n"); config = std::make_shared(); config->DisableGlogInfo(); // config->SwitchIrDebug(true); @@ -542,77 +72,11 @@ void DeepPotPD::init(const std::string& model, config->SetModel(pdmodel_path, pdiparams_path); // config->SwitchIrOptim(true); config->EnableUseGpu(8192, 0); - // std::cout << "IR Optim is: " << config->ir_optim() << std::endl; // config->EnableMKLDNN(); // config->EnableMemoryOptim(); // config->EnableProfile(); predictor = paddle_infer::CreatePredictor(*config); - // printf("***** created paddle predictor\n"); } - /* water se_e2_a - tensorflow::DT_DOUBLE = 2 - tensorflow::DT_FLOAT = 1 - paddle_infer::DataType::FLOAT64 = 7 - paddle_infer::DataType::FLOAT32 = 0 - * st_model.descrpt.buffer_rcut.name = generated_tensor_0 - * st_model.descrpt.buffer_ntypes.name = generated_tensor_2 - * st_model.fitting.buffer_dfparam.name = generated_tensor_9 - * st_model.fitting.buffer_daparam.name = generated_tensor_10 - [buffer_t_type, [3]] generated name in static_model is: generated_tensor_12 - [buffer_t_mt, [4]] generated name in static_model is: generated_tensor_13 - [buffer_t_ver, [1]] generated name in static_model is: generated_tensor_14 - [descrpt.buffer_rcut, []] generated name in static_model is: - generated_tensor_0 [descrpt.buffer_ntypes_spin, []] generated name in - static_model is: generated_tensor_1 [descrpt.buffer_ntypes, []] generated - name in static_model is: generated_tensor_2 [descrpt.avg_zero, [2, 552]] - generated name in static_model is: eager_tmp_0 [descrpt.std_ones, [2, 552]] - generated name in static_model is: eager_tmp_1 [descrpt.t_rcut, []] - generated name in static_model is: generated_tensor_3 [descrpt.t_rcut, []] - generated name in static_model is: generated_tensor_3 [descrpt.t_rcut, []] - generated name in static_model is: generated_tensor_3 [descrpt.t_ntypes, []] - generated name in static_model is: generated_tensor_4 [descrpt.t_ntypes, []] - generated name in static_model is: generated_tensor_4 [descrpt.t_ntypes, []] - generated name in static_model is: generated_tensor_4 [descrpt.t_ndescrpt, - []] generated name in static_model is: generated_tensor_5 [descrpt.t_sel, - [2]] generated name in static_model is: generated_tensor_6 [descrpt.t_avg, - [2, 552]] generated name in static_model is: generated_tensor_7 - [descrpt.t_std, [2, 552]] generated name in static_model is: - generated_tensor_8 [fitting.buffer_dfparam, []] generated name in - static_model is: generated_tensor_9 [fitting.buffer_daparam, []] generated - name in static_model is: generated_tensor_10 - **/ - /* spin se_e2_a - [buffer_tmap, [4]] generated name in static_model is: generated_tensor_14 - [buffer_model_type, [4]] generated name in static_model is: - generated_tensor_15 [buffer_model_version, [1]] generated name in - static_model is: generated_tensor_16 [descrpt.buffer_rcut, []] generated - name in static_model is: generated_tensor_3 [descrpt.buffer_ntypes, []] - generated name in static_model is: generated_tensor_4 [descrpt.avg_zero, [3, - 720]] generated name in static_model is: eager_tmp_0 [descrpt.std_ones, [3, - 720]] generated name in static_model is: eager_tmp_1 [descrpt.t_rcut, []] - generated name in static_model is: generated_tensor_5 [descrpt.buffer_sel, - [3]] generated name in static_model is: generated_tensor_6 - [descrpt.buffer_ndescrpt, []] generated name in static_model is: - generated_tensor_7 [descrpt.buffer_original_sel, [3]] generated name in - static_model is: generated_tensor_8 [descrpt.t_avg, [3, 720]] generated name - in static_model is: generated_tensor_9 [descrpt.t_std, [3, 720]] generated - name in static_model is: generated_tensor_10 - [descrpt.spin.buffer_ntypes_spin, [1]] generated name in static_model is: - generated_tensor_0 [descrpt.spin.buffer_virtual_len, [1, 1]] generated name - in static_model is: generated_tensor_1 [descrpt.spin.buffer_spin_norm, [1, - 1]] generated name in static_model is: generated_tensor_2 - [fitting.buffer_dfparam, []] generated name in static_model is: - generated_tensor_11 [fitting.buffer_daparam, []] generated name in - static_model is: generated_tensor_12 [fitting.t_bias_atom_e, [2]] generated - name in static_model is: generated_tensor_13 - */ - // dtype = predictor_get_dtype(predictor, "generated_tensor_0"); // hard code - // auto dtype = paddle_infer::DataType::FLOAT64; - // if (dtype == paddle_infer::DataType::FLOAT64) { - // rcut = paddle_get_scalar("generated_tensor_0"); - // } else { - // rcut = 3.18; - // } rcut = double(6.0); ntypes = 2; ntypes_spin = 0; @@ -621,32 +85,10 @@ void DeepPotPD::init(const std::string& model, aparam_nall = false; inited = true; - // if (!model_compatable(model_version)) { - // throw deepmd::deepmd_exception( - // "incompatable model: version " + model_version + - // " in graph, but version " + global_model_version + - // " supported " - // "See https://deepmd.rtfd.io/compatability/ for details."); - // } - // printf("***** initialized finished\n"); } DeepPotPD::~DeepPotPD() {} -// void DeepPotPD::print_summary(const std::string& pre) const { -// deepmd::print_summary(pre); -// } - -// template -// VT DeepPotPD::get_scalar(const std::string& name) const { -// return session_get_scalar(session, name); -// } - -// template -// VT DeepPotPD::paddle_get_scalar(const std::string& name) const { -// return predictor_get_scalar(predictor, name); -// } - template void DeepPotPD::validate_fparam_aparam( const int nframes, @@ -667,148 +109,6 @@ void DeepPotPD::validate_fparam_aparam( } } -template void DeepPotPD::validate_fparam_aparam( - const int nframes, - const int& nloc, - const std::vector& fparam, - const std::vector& aparam) const; - -template void DeepPotPD::validate_fparam_aparam( - const int nframes, - const int& nloc, - const std::vector& fparam, - const std::vector& aparam) const; - -template -void DeepPotPD::tile_fparam_aparam(std::vector& out_param, - const int& nframes, - const int& dparam, - const std::vector& param) const { - if (param.size() == dparam) { - out_param.resize(nframes * dparam); - for (int ii = 0; ii < nframes; ++ii) { - std::copy(param.begin(), param.end(), out_param.begin() + ii * dparam); - } - } else if (param.size() == nframes * dparam) { - out_param = param; - } -} - -template void DeepPotPD::tile_fparam_aparam( - std::vector& out_param, - const int& nframes, - const int& dparam, - const std::vector& param) const; - -template void DeepPotPD::tile_fparam_aparam( - std::vector& out_param, - const int& nframes, - const int& dparam, - const std::vector& param) const; - -// ENERGYVTYPE: std::vector or ENERGYTYPE - -template -void DeepPotPD::compute(ENERGYVTYPE& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const std::vector& dcoord_, - const std::vector& datype_, - const std::vector& dbox, - const std::vector& fparam_, - const std::vector& aparam_, - const bool atomic) { - // printf("compute 1\n"); - // if datype.size is 0, not clear nframes; but 1 is just ok - int nframes = datype_.size() > 0 ? (dcoord_.size() / 3 / datype_.size()) : 1; - atommap = deepmd::AtomMap(datype_.begin(), datype_.end()); - int nloc = datype_.size(); - std::vector fparam; - std::vector aparam; - validate_fparam_aparam(nframes, nloc, fparam_, aparam_); - tile_fparam_aparam(fparam, nframes, dfparam, fparam_); - tile_fparam_aparam(aparam, nframes, nloc * daparam, aparam_); - - // std::vector> input_tensors; - - if (dtype == paddle_infer::DataType::FLOAT64) { - int ret = predictor_input_tensors(predictor, dcoord_, ntypes, - datype_, dbox, cell_size, fparam, - aparam, atommap, aparam_nall); - if (atomic) { - run_model(dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, - atommap, nframes); - } else { - run_model(dener, dforce_, dvirial, predictor, - atommap, nframes); - } - } else { - int ret = predictor_input_tensors(predictor, dcoord_, ntypes, datype_, dbox, cell_size, fparam, aparam, - atommap, aparam_nall); - if (atomic) { - run_model(dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, - atommap, nframes); - } else { - run_model(dener, dforce_, dvirial, predictor, - atommap, nframes); - } - } -} - -// template void DeepPotPD::compute( -// ENERGYTYPE& dener, -// std::vector& dforce_, -// std::vector& dvirial, -// std::vector& datom_energy_, -// std::vector& datom_virial_, -// const std::vector& dcoord_, -// const std::vector& datype_, -// const std::vector& dbox, -// const std::vector& fparam, -// const std::vector& aparam, -// const bool atomic); - -// template void DeepPotPD::compute( -// ENERGYTYPE& dener, -// std::vector& dforce_, -// std::vector& dvirial, -// std::vector& datom_energy_, -// std::vector& datom_virial_, -// const std::vector& dcoord_, -// const std::vector& datype_, -// const std::vector& dbox, -// const std::vector& fparam, -// const std::vector& aparam, -// const bool atomic); - -template void DeepPotPD::compute>( - std::vector& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const std::vector& dcoord_, - const std::vector& datype_, - const std::vector& dbox, - const std::vector& fparam, - const std::vector& aparam, - const bool atomic); - -template void DeepPotPD::compute>( - std::vector& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const std::vector& dcoord_, - const std::vector& datype_, - const std::vector& dbox, - const std::vector& fparam, - const std::vector& aparam, - const bool atomic); - std::vector createNlistTensor(const std::vector>& data) { std::vector ret; @@ -820,29 +120,29 @@ std::vector createNlistTensor(const std::vector>& data) { } template -void DeepPotPD::compute(ENERGYVTYPE& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const std::vector& dcoord_, - const std::vector& datype_, - const std::vector& dbox, +void DeepPotPD::compute(ENERGYVTYPE& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, const int nghost, const InputNlist& lmp_list, const int& ago, - const std::vector& fparam_, - const std::vector& aparam__, + const std::vector& fparam, + const std::vector& aparam, const bool atomic) { - /*参考pytorch的推理代码如下*/ - int natoms = datype_.size(); + int natoms = atype.size(); + // select real atoms std::vector dcoord, dforce, aparam_, datom_energy, datom_virial; std::vector datype, fwd_map, bkw_map; int nghost_real, nall_real, nloc_real; int nall = natoms; select_real_atoms_coord(dcoord, datype, aparam_, nghost_real, fwd_map, - bkw_map, nall_real, nloc_real, dcoord_, datype_, aparam__, + bkw_map, nall_real, nloc_real, coord, atype, aparam, nghost, ntypes, 1, daparam, nall, aparam_nall); int nloc = nall_real - nghost_real; int nframes = 1; @@ -859,124 +159,117 @@ void DeepPotPD::compute(ENERGYVTYPE& dener, nlist_data.copy_from_nlist(lmp_list); nlist_data.shuffle_exclude_empty(fwd_map); nlist_data.padding(); + if (do_message_passing == 1 && nghost > 0) { + int nswap = lmp_list.nswap; + auto sendproc_tensor = predictor->GetInputHandle("sendproc"); + sendproc_tensor->Reshape({nswap}); + sendproc_tensor->CopyFromCpu(lmp_list.sendproc); + auto recvproc_tensor = predictor->GetInputHandle("recvproc"); + recvproc_tensor->Reshape({nswap}); + recvproc_tensor->CopyFromCpu(lmp_list.recvproc); + auto firstrecv_tensor = predictor->GetInputHandle("firstrecv"); + firstrecv_tensor->Reshape({nswap}); + firstrecv_tensor->CopyFromCpu(lmp_list.firstrecv); + auto recvnum_tensor = predictor->GetInputHandle("recvnum"); + recvnum_tensor->Reshape({nswap}); + recvnum_tensor->CopyFromCpu(lmp_list.recvnum); + auto sendnum_tensor = predictor->GetInputHandle("sendnum"); + sendnum_tensor->Reshape({nswap}); + sendnum_tensor->CopyFromCpu(lmp_list.sendnum); + auto communicator_tensor = predictor->GetInputHandle("communicator"); + communicator_tensor->Reshape({1}); + communicator_tensor->CopyFromCpu(static_cast(lmp_list.world)); + auto sendlist_tensor = predictor->GetInputHandle("sendlist"); + + int total_send = + std::accumulate(lmp_list.sendnum, lmp_list.sendnum + nswap, 0); + } + if (do_message_passing == 1 && nghost == 0) { + throw deepmd::deepmd_exception( + "do_message_passing == 1 && nghost == 0" + ); + } } std::vector firstneigh = createNlistTensor(nlist_data.jlist); auto firstneigh_tensor = predictor->GetInputHandle("nlist"); - firstneigh_tensor->Reshape({1, nloc, firstneigh.size() / nloc}); + firstneigh_tensor->Reshape({1, nloc, (int)firstneigh.size() / (int)nloc}); firstneigh_tensor->CopyFromCpu(firstneigh.data()); - + bool do_atom_virial_tensor = atomic; + // paddle_infer::Tensor fparam_tensor; + // if (!fparam.empty()) { + // fparam_tensor = predictor->GetInputHandle("fparam"); + // fparam_tensor->Reshape({1, static_cast(fparam.size())}); + // fparam_tensor->CopyFromCpu((fparam.data())); + // } + // paddle_infer::Tensor aparam_tensor; + // if (!aparam_.empty()) { + // aparam_tensor = predictor->GetInputHandle("aparam"); + // aparam_tensor->Reshape({1, lmp_list.inum, + // static_cast(aparam_.size()) / lmp_list.inum}); + // aparam_tensor->CopyFromCpu((aparam_.data())); + // } if (!predictor->Run()) { throw deepmd::deepmd_exception("Paddle inference failed"); } auto output_names = predictor->GetOutputNames(); - auto print_shape = [](const std::vector &shape, const std::string &name=""){ - printf("shape of %s: [", name.c_str()); - for (int i=0; iGetOutputHandle(output_names[1]); - auto output_f = predictor->GetOutputHandle(output_names[2]); - auto output_virial_tensor = predictor->GetOutputHandle(output_names[3]); - // print_shape(output_e->shape(), "ener"); - // print_shape(output_f->shape(), "force"); - // print_shape(output_virial_tensor->shape(), "virial"); - std::vector output_energy_shape = output_e->shape(); + auto energy_ = predictor->GetOutputHandle(output_names[1]); + auto force_ = predictor->GetOutputHandle(output_names[2]); + auto virial_ = predictor->GetOutputHandle(output_names[3]); + std::vector output_energy_shape = energy_->shape(); int output_energy_size = std::accumulate(output_energy_shape.begin(), output_energy_shape.end(), 1, std::multiplies()); - std::vector output_force_shape = output_f->shape(); + std::vector output_force_shape = force_->shape(); int output_force_size = std::accumulate(output_force_shape.begin(), output_force_shape.end(), 1, std::multiplies()); - std::vector output_virial_shape = output_virial_tensor->shape(); + std::vector output_virial_shape = virial_->shape(); int output_virial_size = std::accumulate(output_virial_shape.begin(), output_virial_shape.end(), 1, std::multiplies()); - std::vector oe; - oe.resize(output_energy_size); - output_e->CopyToCpu(oe.data()); + // output energy + ener.resize(output_energy_size); + energy_->CopyToCpu(ener.data()); - std::vector of; - of.resize(output_force_size); - output_f->CopyToCpu(of.data()); + // output force + dforce.resize(output_force_size); + force_->CopyToCpu(dforce.data()); - std::vector oav; - oav.resize(output_virial_size); - output_virial_tensor->CopyToCpu(oav.data()); + // output virial + virial.resize(output_virial_size); + virial_->CopyToCpu(virial.data()); - dvirial.resize(nframes * 9); - dener.assign(oe.begin(), oe.end()); - dforce.resize(nframes * 3 * nall); - for (int ii = 0; ii < nframes * nall * 3; ++ii) { - dforce[ii] = of[ii]; - } - std::fill(dvirial.begin(), dvirial.end(), (VALUETYPE)0.); - dvirial.assign(oav.begin(), oav.end()); - // for (int kk = 0; kk < nframes; ++kk) { - // for (int ii = 0; ii < nall; ++ii) { - // dvirial[kk * 9 + 0] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 0]; - // dvirial[kk * 9 + 1] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 1]; - // dvirial[kk * 9 + 2] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 2]; - // dvirial[kk * 9 + 3] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 3]; - // dvirial[kk * 9 + 4] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 4]; - // dvirial[kk * 9 + 5] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 5]; - // dvirial[kk * 9 + 6] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 6]; - // dvirial[kk * 9 + 7] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 7]; - // dvirial[kk * 9 + 8] += (VALUETYPE)1.0 * oav[kk * nall * 9 + 9 * ii + 8]; - // } - // } // bkw map - dforce_.resize(static_cast(nframes) * fwd_map.size() * 3); - select_map(dforce_, dforce, bkw_map, 3, nframes, fwd_map.size(), + force.resize(static_cast(nframes) * fwd_map.size() * 3); + select_map(force, dforce, bkw_map, 3, nframes, fwd_map.size(), nall_real); + if (atomic) { + auto atom_virial_ = predictor->GetOutputHandle("extended_virial"); + auto atom_energy_ = predictor->GetOutputHandle("atom_energy"); + datom_energy.resize(nall_real, + 0.0); // resize to nall to be consistenet with TF. + atom_energy_->CopyToCpu(datom_energy.data()); + atom_virial_->CopyToCpu(datom_virial.data()); + atom_energy.resize(static_cast(nframes) * fwd_map.size()); + atom_virial.resize(static_cast(nframes) * fwd_map.size() * 9); + select_map(atom_energy, datom_energy, bkw_map, 1, nframes, + fwd_map.size(), nall_real); + select_map(atom_virial, datom_virial, bkw_map, 9, nframes, + fwd_map.size(), nall_real); + } } -// template void DeepPotPD::compute( -// ENERGYTYPE& dener, -// std::vector& dforce_, -// std::vector& dvirial, -// std::vector& datom_energy_, -// std::vector& datom_virial_, -// const std::vector& dcoord_, -// const std::vector& datype_, -// const std::vector& dbox, -// const int nghost, -// const InputNlist& lmp_list, -// const int& ago, -// const std::vector& fparam, -// const std::vector& aparam_, -// const bool atomic); - -// template void DeepPotPD::compute( -// ENERGYTYPE& dener, -// std::vector& dforce_, -// std::vector& dvirial, -// std::vector& datom_energy_, -// std::vector& datom_virial_, -// const std::vector& dcoord_, -// const std::vector& datype_, -// const std::vector& dbox, -// const int nghost, -// const InputNlist& lmp_list, -// const int& ago, -// const std::vector& fparam, -// const std::vector& aparam_, -// const bool atomic); - template void DeepPotPD::compute>( std::vector& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const std::vector& dcoord_, - const std::vector& datype_, - const std::vector& dbox, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, const int nghost, const InputNlist& lmp_list, const int& ago, @@ -986,13 +279,13 @@ template void DeepPotPD::compute>( template void DeepPotPD::compute>( std::vector& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const std::vector& dcoord_, - const std::vector& datype_, - const std::vector& dbox, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, const int nghost, const InputNlist& lmp_list, const int& ago, @@ -1000,6 +293,101 @@ template void DeepPotPD::compute>( const std::vector& aparam_, const bool atomic); +// ENERGYVTYPE: std::vector or ENERGYTYPE + +template +void DeepPotPD::compute(ENERGYVTYPE& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic) { + + // select real atoms + std::vector coord_wrapped = coord; + int natoms = atype.size(); + int nframes = 1; + auto coord_wrapped_Tensor = predictor->GetInputHandle("coord"); + coord_wrapped_Tensor->Reshape({1, natoms, 3}); + coord_wrapped_Tensor->CopyFromCpu(coord_wrapped.data()); + + std::vector atype_64(atype.begin(), atype.end()); + auto atype_Tensor = predictor->GetInputHandle("atype"); + atype_Tensor->Reshape({1, natoms}); + atype_Tensor->CopyFromCpu(atype_64.data()); + + std::unique_ptr box_Tensor; + if (!box.empty()) { + box_Tensor = predictor->GetInputHandle("box"); + box_Tensor->Reshape({1, 9}); + box_Tensor->CopyFromCpu((box.data())); + } + std::unique_ptr fparam_tensor; + if (!fparam.empty()) { + fparam_tensor = predictor->GetInputHandle("box"); + fparam_tensor->Reshape({1, static_cast(fparam.size())}); + fparam_tensor->CopyFromCpu((fparam.data())); + } + std::unique_ptr aparam_tensor; + if (!fparam.empty()) { + aparam_tensor = predictor->GetInputHandle("box"); + aparam_tensor->Reshape({1, natoms, static_cast(aparam.size()) / natoms}); + aparam_tensor->CopyFromCpu((aparam.data())); + } + + bool do_atom_virial_tensor = atomic; + if (!predictor->Run()) { + throw deepmd::deepmd_exception("Paddle inference failed"); + } + + auto output_names = predictor->GetOutputNames(); + auto energy_ = predictor->GetOutputHandle(output_names[1]); + auto force_ = predictor->GetOutputHandle(output_names[2]); + auto virial_ = predictor->GetOutputHandle(output_names[3]); + + energy_->CopyToCpu(ener.data()); + force_->CopyToCpu(force.data()); + virial_->CopyToCpu(virial.data()); + + if (atomic) { + auto atom_energy_ = predictor->GetOutputHandle(output_names[4]); + auto atom_virial_ = predictor->GetOutputHandle(output_names[5]); + atom_energy_->CopyToCpu(atom_energy.data()); + atom_virial_->CopyToCpu(atom_virial.data()); + } +} + +template void DeepPotPD::compute>( + std::vector& ener, + std::vector&dforce, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& dcoord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + +template void DeepPotPD::compute>( + std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& dcoord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + // mixed type template @@ -1015,92 +403,64 @@ void DeepPotPD::compute_mixed_type(ENERGYVTYPE& dener, const std::vector& fparam_, const std::vector& aparam_, const bool atomic) { - int nloc = datype_.size() / nframes; - // here atommap only used to get nloc - atommap = deepmd::AtomMap(datype_.begin(), datype_.begin() + nloc); - std::vector fparam; - std::vector aparam; - validate_fparam_aparam(nframes, nloc, fparam_, aparam_); - tile_fparam_aparam(fparam, nframes, dfparam, fparam_); - tile_fparam_aparam(aparam, nframes, nloc * daparam, aparam_); + // int nloc = datype_.size() / nframes; + // // here atommap only used to get nloc + // atommap = deepmd::AtomMap(datype_.begin(), datype_.begin() + nloc); + // std::vector fparam; + // std::vector aparam; + // validate_fparam_aparam(nframes, nloc, fparam_, aparam_); + // tile_fparam_aparam(fparam, nframes, dfparam, fparam_); + // tile_fparam_aparam(aparam, nframes, nloc * daparam, aparam_); - if (dtype == paddle_infer::DataType::FLOAT64) { - int nloc = predictor_input_tensors_mixed_type( - predictor, nframes, dcoord_, ntypes, datype_, dbox, cell_size, - fparam, aparam, atommap, aparam_nall); - if (atomic) { - run_model(dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, - atommap, nframes); - } else { - run_model(dener, dforce_, dvirial, predictor, - atommap, nframes); - } - } else { - int nloc = predictor_input_tensors_mixed_type( - predictor, nframes, dcoord_, ntypes, datype_, dbox, cell_size, - fparam, aparam, atommap, aparam_nall); - if (atomic) { - run_model(dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, - atommap, nframes); - } else { - run_model(dener, dforce_, dvirial, predictor, atommap, - nframes); - } - } + // if (dtype == paddle_infer::DataType::FLOAT64) { + // int nloc = predictor_input_tensors_mixed_type( + // predictor, nframes, dcoord_, ntypes, datype_, dbox, cell_size, + // fparam, aparam, atommap, aparam_nall); + // if (atomic) { + // run_model(dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, + // atommap, nframes); + // } else { + // run_model(dener, dforce_, dvirial, predictor, + // atommap, nframes); + // } + // } else { + // int nloc = predictor_input_tensors_mixed_type( + // predictor, nframes, dcoord_, ntypes, datype_, dbox, cell_size, + // fparam, aparam, atommap, aparam_nall); + // if (atomic) { + // run_model(dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, + // atommap, nframes); + // } else { + // run_model(dener, dforce_, dvirial, predictor, atommap, + // nframes); + // } + // } } -template void DeepPotPD::compute_mixed_type( - ENERGYTYPE& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const int& nframes, - const std::vector& dcoord_, - const std::vector& datype_, - const std::vector& dbox, - const std::vector& fparam, - const std::vector& aparam, - const bool atomic); - -template void DeepPotPD::compute_mixed_type( - ENERGYTYPE& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const int& nframes, - const std::vector& dcoord_, - const std::vector& datype_, - const std::vector& dbox, - const std::vector& fparam, - const std::vector& aparam, - const bool atomic); - template void DeepPotPD::compute_mixed_type>( - std::vector& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, + std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, const int& nframes, - const std::vector& dcoord_, - const std::vector& datype_, - const std::vector& dbox, + const std::vector& coord, + const std::vector& dtype, + const std::vector& box, const std::vector& fparam, const std::vector& aparam, const bool atomic); template void DeepPotPD::compute_mixed_type>( - std::vector& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, + std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, const int& nframes, - const std::vector& dcoord_, - const std::vector& datype_, - const std::vector& dbox, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, const std::vector& fparam, const std::vector& aparam, const bool atomic); From 834307789ba1ff3bd85aa46929225b9f44252123 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 24 Sep 2024 17:05:55 +0800 Subject: [PATCH 28/93] update commonPD.h --- source/api_cc/include/commonPD.h | 138 +++++++++++++++++++++++++++++++ 1 file changed, 138 insertions(+) create mode 100644 source/api_cc/include/commonPD.h diff --git a/source/api_cc/include/commonPD.h b/source/api_cc/include/commonPD.h new file mode 100644 index 0000000000..952902225b --- /dev/null +++ b/source/api_cc/include/commonPD.h @@ -0,0 +1,138 @@ +// SPDX-License-Identifier: LGPL-3.0-or-later +#include +#include + +#include "paddle/include/paddle_inference_api.h" + +namespace deepmd { +/** + * @brief Check TensorFlow status. Exit if not OK. + * @param[in] status TensorFlow status. + **/ +// void check_status(const tensorflow::Status& status); + +/** + * @brief Get the value of a tensor. + * @param[in] session TensorFlow session. + * @param[in] name The name of the tensor. + * @param[in] scope The scope of the tensor. + * @return The value of the tensor. + **/ +template +VT predictor_get_scalar(const std::shared_ptr& predictor, + const std::string& name_); + +/** + * @brief Get the vector of a tensor. + * @param[out] o_vec The output vector. + * @param[in] session TensorFlow session. + * @param[in] name The name of the tensor. + * @param[in] scope The scope of the tensor. + **/ +// template +// void session_get_vector(std::vector& o_vec, +// tensorflow::Session* session, +// const std::string name_, +// const std::string scope = ""); + +/** + * @brief Get the type of a tensor. + * @param[in] session TensorFlow session. + * @param[in] name The name of the tensor. + * @param[in] scope The scope of the tensor. + * @return The type of the tensor as int. + **/ +paddle_infer::DataType predictor_get_dtype(const std::shared_ptr& predictor, + const std::string& name_); + +/** + * @brief Get input tensors. + * @param[out] input_tensors Input tensors. + * @param[in] dcoord_ Coordinates of atoms. + * @param[in] ntypes Number of atom types. + * @param[in] datype_ Atom types. + * @param[in] dbox Box matrix. + * @param[in] cell_size Cell size. + * @param[in] fparam_ Frame parameters. + * @param[in] aparam_ Atom parameters. + * @param[in] atommap Atom map. + * @param[in] scope The scope of the tensors. + * @param[in] aparam_nall Whether the atomic dimesion of atomic parameters is + * nall. + */ +template +int predictor_input_tensors( + const std::shared_ptr& predictor, + const std::vector& dcoord_, + const int& ntypes, + const std::vector& datype_, + const std::vector& dbox, + const double& cell_size, + const std::vector& fparam_, + const std::vector& aparam_, + const deepmd::AtomMap& atommap, + const bool aparam_nall = false); + +/** + * @brief Get input tensors. + * @param[out] input_tensors Input tensors. + * @param[in] dcoord_ Coordinates of atoms. + * @param[in] ntypes Number of atom types. + * @param[in] datype_ Atom types. + * @param[in] dlist Neighbor list. + * @param[in] fparam_ Frame parameters. + * @param[in] aparam_ Atom parameters. + * @param[in] atommap Atom map. + * @param[in] nghost Number of ghost atoms. + * @param[in] ago Update the internal neighbour list if ago is 0. + * @param[in] scope The scope of the tensors. + * @param[in] aparam_nall Whether the atomic dimesion of atomic parameters is + * nall. + */ +template +int predictor_input_tensors( + const std::shared_ptr& predictor, + const std::vector& dcoord_, + const int& ntypes, + const std::vector& datype_, + const std::vector& dbox, + InputNlist& dlist, + const std::vector& fparam_, + const std::vector& aparam_, + const deepmd::AtomMap& atommap, + const int nghost, + const int ago, + const bool aparam_nall = false); + +/** + * @brief Get input tensors for mixed type. + * @param[out] input_tensors Input tensors. + * @param[in] nframes Number of frames. + * @param[in] dcoord_ Coordinates of atoms. + * @param[in] ntypes Number of atom types. + * @param[in] datype_ Atom types. + * @param[in] dlist Neighbor list. + * @param[in] fparam_ Frame parameters. + * @param[in] aparam_ Atom parameters. + * @param[in] atommap Atom map. + * @param[in] nghost Number of ghost atoms. + * @param[in] ago Update the internal neighbour list if ago is 0. + * @param[in] scope The scope of the tensors. + * @param[in] aparam_nall Whether the atomic dimesion of atomic parameters is + * nall. + */ +template +int predictor_input_tensors_mixed_type( + const std::shared_ptr& predictor, + const int& nframes, + const std::vector& dcoord_, + const int& ntypes, + const std::vector& datype_, + const std::vector& dbox, + const double& cell_size, + const std::vector& fparam_, + const std::vector& aparam_, + const deepmd::AtomMap& atommap, + const bool aparam_nall = false); + +} // namespace deepmd From 09c54f3899e416ae95766c2028a908f446c75f9d Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 24 Sep 2024 17:07:51 +0800 Subject: [PATCH 29/93] remove boost --- source/lmp/pair_deepmd.cpp | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/source/lmp/pair_deepmd.cpp b/source/lmp/pair_deepmd.cpp index 2112c12ac7..0355ce6296 100644 --- a/source/lmp/pair_deepmd.cpp +++ b/source/lmp/pair_deepmd.cpp @@ -23,7 +23,7 @@ #include "neighbor.h" #include "output.h" #include "update.h" -#include +// #include #if LAMMPS_VERSION_NUMBER >= 20210831 // in lammps #2902, fix_ttm members turns from private to protected #define USE_TTM 1 @@ -979,10 +979,10 @@ void PairDeepMD::settings(int narg, char **arg) { auto content = get_file_content(arg[0]); deep_pot.init(arg[0], node_rank, content); } catch (const std::exception &e) { - // error->one(FLERR, e.what()); - std::cerr << "Standard exception caught: " << e.what() << std::endl; - // 打印堆栈跟踪信息 - std::cerr << "Stack trace:\n" << boost::stacktrace::stacktrace() << std::endl; + error->one(FLERR, e.what()); + // std::cerr << "Standard exception caught: " << e.what() << std::endl; + // // 打印堆栈跟踪信息 + // std::cerr << "Stack trace:\n" << boost::stacktrace::stacktrace() << std::endl; } cutoff = deep_pot.cutoff() * dist_unit_cvt_factor; numb_types = deep_pot.numb_types(); From bc854b21ced62b9911b1dd1f4605aabeacd3ea7c Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Wed, 25 Sep 2024 11:48:05 +0800 Subject: [PATCH 30/93] refine code --- deepmd/pd/train/training.py | 5 +++- deepmd/pd/utils/dataloader.py | 51 ++++++++++++++++++++++++++++------- 2 files changed, 45 insertions(+), 11 deletions(-) diff --git a/deepmd/pd/train/training.py b/deepmd/pd/train/training.py index 36cbd46caf..fb22b7486a 100644 --- a/deepmd/pd/train/training.py +++ b/deepmd/pd/train/training.py @@ -401,7 +401,10 @@ def get_lr(lr_params): # JIT if JIT: - self.model = paddle.jit.to_static(self.model, full_graph=False) + raise NotImplementedError( + "JIT is not supported yet when training with Paddle" + ) + self.model = paddle.jit.to_static(self.model) # Model Wrapper self.wrapper = ModelWrapper(self.model, self.loss, model_params=model_params) diff --git a/deepmd/pd/utils/dataloader.py b/deepmd/pd/utils/dataloader.py index e68979c316..57650b840e 100644 --- a/deepmd/pd/utils/dataloader.py +++ b/deepmd/pd/utils/dataloader.py @@ -3,6 +3,9 @@ import os import queue import time +from collections.abc import ( + Iterator, +) from multiprocessing.dummy import ( Pool, ) @@ -95,10 +98,15 @@ def construct_dataset(system): type_map=type_map, ) - with Pool(1) as pool: - self.systems: List[DeepmdDataSetForLoader] = pool.map( - construct_dataset, systems + with Pool( + os.cpu_count() + // ( + int(os.environ["LOCAL_WORLD_SIZE"]) + if dist.is_available() and dist.is_initialized() + else 1 ) + ) as pool: + self.systems = pool.map(construct_dataset, systems) self.sampler_list: List[DistributedBatchSampler] = [] self.index = [] @@ -129,13 +137,21 @@ def construct_dataset(system): if dist.is_available() and dist.is_initialized(): system_batch_sampler = DistributedBatchSampler( system, - shuffle=False, + shuffle=( + (not (dist.is_available() and dist.is_initialized())) + and shuffle + ), + batch_size=int(batch_size), ) self.sampler_list.append(system_batch_sampler) else: system_batch_sampler = BatchSampler( system, - shuffle=shuffle, + shuffle=( + (not (dist.is_available() and dist.is_initialized())) + and shuffle + ), + batch_size=int(batch_size), ) self.sampler_list.append(system_batch_sampler) system_dataloader = DataLoader( @@ -143,17 +159,32 @@ def construct_dataset(system): num_workers=0, # Should be 0 to avoid too many threads forked batch_sampler=system_batch_sampler, collate_fn=collate_batch, - # shuffle=(not (dist.is_available() and dist.is_initialized())) - # and shuffle, + use_buffer_reader=False, + places=["cpu"], ) self.dataloaders.append(system_dataloader) self.index.append(len(system_dataloader)) self.total_batch += len(system_dataloader) - # Initialize iterator instances for DataLoader + + class LazyIter: + """Lazy iterator to prevent fetching data when iter(item).""" + + def __init__(self, item): + self.item = item + + def __iter__(self): + # directly return + return self + + def __next__(self): + if not isinstance(self.item, Iterator): + # make iterator here lazily + self.item = iter(self.item) + return next(self.item) + self.iters = [] - # with paddle.device("cpu"): for item in self.dataloaders: - self.iters.append(iter(item)) + self.iters.append(LazyIter(item)) def set_noise(self, noise_settings): # noise_settings['noise_type'] # "trunc_normal", "normal", "uniform" From 892fd80c597ca94bfffcfac7367e7e2e07a2ce52 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Fri, 27 Sep 2024 00:54:01 +0800 Subject: [PATCH 31/93] update refined infer code --- deepmd/pd/entrypoints/main.py | 14 +- .../pd/model/atomic_model/dp_atomic_model.py | 50 ++- source/api_cc/include/DeepPotPD.h | 75 +--- source/api_cc/src/DeepPotPD.cc | 331 +++++++----------- 4 files changed, 200 insertions(+), 270 deletions(-) diff --git a/deepmd/pd/entrypoints/main.py b/deepmd/pd/entrypoints/main.py index 214f150850..e4f11d425c 100644 --- a/deepmd/pd/entrypoints/main.py +++ b/deepmd/pd/entrypoints/main.py @@ -354,9 +354,6 @@ def freeze(FLAGS): ** atype [None, natoms] paddle.int64 ** nlist [None, natoms, nnei] paddle.int32 """ - model.atomic_model.buffer_type_map.set_value( - paddle.to_tensor([ord(c) for c in model.atomic_model.type_map], dtype="int32") - ) model = paddle.jit.to_static( model.forward_lower, full_graph=True, @@ -366,12 +363,10 @@ def freeze(FLAGS): InputSpec([-1, -1, -1], dtype="int32", name="nlist"), ], ) - extra_files = {} paddle.jit.save( model, path=FLAGS.output, skip_prune_program=True, - # extra_files, ) suffix = "json" if PIR_ENABLED.lower() in ["true", "1"] else "pdmodel" log.info( @@ -445,19 +440,20 @@ def show(FLAGS): def change_bias(FLAGS): - if FLAGS.INPUT.endswith(".pdparams"): + if FLAGS.INPUT.endswith(".pd"): old_state_dict = paddle.load(FLAGS.INPUT) model_state_dict = copy.deepcopy(old_state_dict.get("model", old_state_dict)) model_params = model_state_dict["_extra_state"]["model_params"] - # elif FLAGS.INPUT.endswith(".pdmodel"): - # old_model = paddle.jit.load(FLAGS.INPUT[: -len(".pdmodel")]) + # elif FLAGS.INPUT.endswith(".json"): + # old_model = paddle.jit.load(FLAGS.INPUT[: -len(".json")]) # model_params_string = old_model.get_model_def_script() # model_params = json.loads(model_params_string) # old_state_dict = old_model.state_dict() # model_state_dict = old_state_dict else: raise RuntimeError( - "The model provided must be a checkpoint file with a .pd extension" + "Paddle now do not support change bias directly from a freezed model file" + "Please provided a checkpoint file with a .pd extension" # "or a frozen model with a .pdparams extension" ) multi_task = "model_dict" in model_params diff --git a/deepmd/pd/model/atomic_model/dp_atomic_model.py b/deepmd/pd/model/atomic_model/dp_atomic_model.py index e5abd2135d..9ad4048cfc 100644 --- a/deepmd/pd/model/atomic_model/dp_atomic_model.py +++ b/deepmd/pd/model/atomic_model/dp_atomic_model.py @@ -58,11 +58,7 @@ def __init__( super().__init__(type_map, **kwargs) ntypes = len(type_map) self.type_map = type_map - self.register_buffer( - "buffer_type_map", - paddle.to_tensor([ord(c) for c in self.type_map], dtype="int32"), - ) - self.buffer_type_map.name = "type_map" + self.ntypes = ntypes self.descriptor = descriptor self.rcut = self.descriptor.get_rcut() @@ -70,6 +66,50 @@ def __init__( self.fitting_net = fitting super().init_out_stat() + # specify manually for access by name in C++ inference + + # register 'type_map' as buffer + def string_to_array(s: str) -> int: + return [ord(c) for c in s] + + self.register_buffer( + "buffer_type_map", + paddle.to_tensor(string_to_array(" ".join(self.type_map)), dtype="int32"), + ) + self.buffer_type_map.name = "buffer_type_map" + # register 'has_message_passing' as buffer(cast to int32 as problems may meets with vector) + self.register_buffer( + "buffer_has_message_passing", + paddle.to_tensor([self.has_message_passing()], dtype="int32"), + ) + self.buffer_has_message_passing.name = "buffer_has_message_passing" + # register 'ntypes' as buffer + self.register_buffer( + "buffer_ntypes", paddle.to_tensor([self.ntypes], dtype="int32") + ) + self.buffer_ntypes.name = "buffer_ntypes" + # register 'rcut' as buffer + self.register_buffer( + "buffer_rcut", paddle.to_tensor([self.rcut], dtype="float64") + ) + self.buffer_rcut.name = "buffer_rcut" + # register 'dfparam' as buffer + self.register_buffer( + "buffer_dfparam", paddle.to_tensor([self.get_dim_fparam()], dtype="int32") + ) + self.buffer_dfparam.name = "buffer_dfparam" + # register 'daparam' as buffer + self.register_buffer( + "buffer_daparam", paddle.to_tensor([self.get_dim_aparam()], dtype="int32") + ) + self.buffer_daparam.name = "buffer_daparam" + # register 'aparam_nall' as buffer + self.register_buffer( + "buffer_aparam_nall", + paddle.to_tensor([self.is_aparam_nall()], dtype="int32"), + ) + self.buffer_aparam_nall.name = "buffer_aparam_nall" + # @paddle.jit.export def fitting_output_def(self) -> FittingOutputDef: """Get the output def of the fitting net.""" diff --git a/source/api_cc/include/DeepPotPD.h b/source/api_cc/include/DeepPotPD.h index 8818e86194..fd6d39529b 100644 --- a/source/api_cc/include/DeepPotPD.h +++ b/source/api_cc/include/DeepPotPD.h @@ -1,14 +1,9 @@ // SPDX-License-Identifier: LGPL-3.0-or-later #pragma once -// #include "paddle/include/paddle_inference_api.h" -// #include "paddle/extension.h" -// #include "paddle/phi/backends/all_context.h" +#include #include "DeepPot.h" -#include "common.h" -#include "commonPD.h" -#include "neighbor_list.h" namespace deepmd { /** @@ -239,6 +234,16 @@ class DeepPotPD : public DeepPotBase { **/ void get_type_map(std::string& type_map); + /** + * @brief Get the type map (element name of the atom types) of this model. + * @param[out] type_map The type map of this model. + **/ + template + void get_buffer(const std::string &buffer_name, std::vector &buffer_arr); + + template + void get_buffer(const std::string &buffer_name, BUFFERTYPE &buffer_arr); + /** * @brief Get whether the atom dimension of aparam is nall instead of fparam. * @param[out] aparam_nall whether the atom dimension of aparam is nall @@ -328,65 +333,23 @@ class DeepPotPD : public DeepPotBase { private: int num_intra_nthreads, num_inter_nthreads; bool inited; - - template - VT get_scalar(const std::string& name) const; - int ntypes; int ntypes_spin; int dfparam; int daparam; - bool aparam_nall; + int aparam_nall; // copy neighbor list info from host - std::shared_ptr predictor = nullptr; - std::shared_ptr config = nullptr; + std::shared_ptr config; + std::shared_ptr predictor; double rcut; - double cell_size; NeighborListData nlist_data; int max_num_neighbors; - InputNlist nlist; - AtomMap atommap; - int gpu_id = 0; - int do_message_passing = 0; // 1:dpa2 model 0:others - bool gpu_enabled = true; - int dtype = paddle_infer::DataType::FLOAT64; - // paddle::Tensor firstneigh_tensor; + int gpu_id; + // use int instead bool for problems may meets with vector + int do_message_passing; // 1:dpa2 model 0:others + bool gpu_enabled; + std::unique_ptr firstneigh_tensor; // std::unordered_map comm_dict; - /** - * @brief Translate Paddle exceptions to the DeePMD-kit exception. - * @param[in] f The function to run. - * @example translate_error([&](){...}); - */ - // void translate_error(std::function f); - /** - * @brief Validate the size of frame and atomic parameters. - * @param[in] nframes The number of frames. - * @param[in] nloc The number of local atoms. - * @param[in] fparam The frame parameter. - * @param[in] aparam The atomic parameter. - * @tparam VALUETYPE The type of the parameters, double or float. - */ - template - void validate_fparam_aparam(const int nframes, - const int& nloc, - const std::vector& fparam, - const std::vector& aparam) const; - /** - * @brief Tile the frame or atomic parameters if there is only - * a single frame of frame or atomic parameters. - * @param[out] out_param The tiled frame or atomic parameters. - * @param[in] nframes The number of frames. - * @param[in] dparam The dimension of the frame or atomic parameters in a - * frame. - * @param[in] param The frame or atomic parameters. - * @tparam VALUETYPE The type of the parameters, double or float. - */ - template - void tile_fparam_aparam(std::vector& out_param, - const int& nframes, - const int& dparam, - const std::vector& param) const; - }; } // namespace deepmd diff --git a/source/api_cc/src/DeepPotPD.cc b/source/api_cc/src/DeepPotPD.cc index a77cd5d1e8..ed6a423811 100644 --- a/source/api_cc/src/DeepPotPD.cc +++ b/source/api_cc/src/DeepPotPD.cc @@ -6,22 +6,30 @@ #include #include -#include "AtomMap.h" -#include "device.h" #include "common.h" -#include "paddle/include/paddle_inference_api.h" +#include "device.h" using namespace deepmd; -DeepPotPD::DeepPotPD() : inited(false) {} +std::vector createNlistTensor(const std::vector>& data) { + std::vector ret; + for (const auto& row : data) { + ret.insert(ret.end(), row.begin(), row.end()); + } + return ret; +} +DeepPotPD::DeepPotPD() : inited(false) {} DeepPotPD::DeepPotPD(const std::string& model, - const int& gpu_rank, - const std::string& file_content) + const int& gpu_rank, + const std::string& file_content) : inited(false) { - init(model, gpu_rank, file_content); + try { + init(model, gpu_rank, file_content); + } catch (...) { + throw; + } } - void DeepPotPD::init(const std::string& model, const int& gpu_rank, const std::string& file_content) { @@ -32,92 +40,68 @@ void DeepPotPD::init(const std::string& model, return; } // deepmd::load_op_library(); - int gpu_num = 1; // hard code here + int gpu_num = 1; // Only support 1 GPU now. if (gpu_num > 0) { gpu_id = gpu_rank % gpu_num; } else { gpu_id = 0; } + // initialize inference config + config = std::make_shared(); + config->DisableGlogInfo(); + config->EnableNewExecutor(true); + config->EnableNewIR(true); + + // loading inference model std::string pdmodel_path; std::string pdiparams_path; - bool use_paddle_inference = false; - bool use_pir = false; - if (model.find(".json") != std::string::npos) { - use_pir = true; pdmodel_path = model; - std::string tmp = model; - pdiparams_path = tmp.replace(model.find(".json"), 5, std::string(".pdiparams")); - use_paddle_inference = true; + pdiparams_path = model; + pdiparams_path.replace(pdiparams_path.find(".json"), 5, std::string(".pdiparams")); } else if (model.find(".pdmodel") != std::string::npos){ pdmodel_path = model; - std::string tmp = model; - pdiparams_path = tmp.replace(model.find(".pdmodel"), 8, std::string(".pdiparams")); - use_paddle_inference = true; + pdiparams_path = model; + pdiparams_path.replace(pdiparams_path.find(".pdmodel"), 8, std::string(".pdiparams")); } else { - throw "[Error] Not found any inference model in"; - } - - int math_lib_num_threads = 1; - - if (use_paddle_inference) { - config = std::make_shared(); - config->DisableGlogInfo(); - // config->SwitchIrDebug(true); - if (use_pir) { - config->EnableNewExecutor(true); - config->EnableNewIR(true); - } - config->SetModel(pdmodel_path, pdiparams_path); - // config->SwitchIrOptim(true); - config->EnableUseGpu(8192, 0); - // config->EnableMKLDNN(); - // config->EnableMemoryOptim(); - // config->EnableProfile(); - predictor = paddle_infer::CreatePredictor(*config); + throw deepmd::deepmd_exception("Given inference model: " + model + " do not exist, please check it."); } - rcut = double(6.0); - ntypes = 2; - ntypes_spin = 0; - dfparam = 0; - daparam = 0; - aparam_nall = false; - - inited = true; -} - -DeepPotPD::~DeepPotPD() {} - -template -void DeepPotPD::validate_fparam_aparam( - const int nframes, - const int& nloc, - const std::vector& fparam, - const std::vector& aparam) const { - if (fparam.size() != dfparam && fparam.size() != nframes * dfparam) { - throw deepmd::deepmd_exception( - "the dim of frame parameter provided is not consistent with what the " - "model uses"); + config->SetModel(pdmodel_path, pdiparams_path); + config->EnableUseGpu(4096, 0); // annotate it if use cpu, default use gpu with 4G mem + gpu_enabled = config->use_gpu(); + if (!gpu_enabled) { + config->DisableGpu(); + std::cout << "load model from: " << model << " to cpu " << std::endl; + } else { + std::cout << "load model from: " << model << " to gpu " << gpu_id + << std::endl; } - if (aparam.size() != daparam * nloc && - aparam.size() != nframes * daparam * nloc) { - throw deepmd::deepmd_exception( - "the dim of atom parameter provided is not consistent with what the " - "model uses"); + // get_env_nthreads(num_intra_nthreads, + // num_inter_nthreads); // need to be fixed as + // // DP_INTRA_OP_PARALLELISM_THREADS + // both set to 1 now. + // num_intra_nthreads = 1; + num_inter_nthreads = 1; + if (num_inter_nthreads) { + config->SetCpuMathLibraryNumThreads(num_inter_nthreads); } -} -std::vector createNlistTensor(const std::vector>& data) { - std::vector ret; + predictor = paddle_infer::CreatePredictor(*config); - for (const auto& row : data) { - ret.insert(ret.end(), row.begin(), row.end()); - } - return ret; + // initialize hyper params from model buffers + ntypes_spin = 0; + DeepPotPD::get_buffer("buffer_has_message_passing", do_message_passing); + DeepPotPD::get_buffer("buffer_rcut", rcut); + DeepPotPD::get_buffer("buffer_ntypes", ntypes); + DeepPotPD::get_buffer("buffer_dfparam", dfparam); + DeepPotPD::get_buffer("buffer_daparam", daparam); + DeepPotPD::get_buffer("buffer_aparam_nall", aparam_nall); + inited = true; } +DeepPotPD::~DeepPotPD() {} template void DeepPotPD::compute(ENERGYVTYPE& ener, @@ -135,7 +119,6 @@ void DeepPotPD::compute(ENERGYVTYPE& ener, const std::vector& aparam, const bool atomic) { int natoms = atype.size(); - // select real atoms std::vector dcoord, dforce, aparam_, datom_energy, datom_virial; std::vector datype, fwd_map, bkw_map; @@ -160,6 +143,9 @@ void DeepPotPD::compute(ENERGYVTYPE& ener, nlist_data.shuffle_exclude_empty(fwd_map); nlist_data.padding(); if (do_message_passing == 1 && nghost > 0) { + throw deepmd::deepmd_exception( + "(do_message_passing == 1 && nghost > 0) is not supported yet." + ); int nswap = lmp_list.nswap; auto sendproc_tensor = predictor->GetInputHandle("sendproc"); sendproc_tensor->Reshape({nswap}); @@ -191,26 +177,28 @@ void DeepPotPD::compute(ENERGYVTYPE& ener, } } std::vector firstneigh = createNlistTensor(nlist_data.jlist); - auto firstneigh_tensor = predictor->GetInputHandle("nlist"); + firstneigh_tensor = predictor->GetInputHandle("nlist"); firstneigh_tensor->Reshape({1, nloc, (int)firstneigh.size() / (int)nloc}); firstneigh_tensor->CopyFromCpu(firstneigh.data()); bool do_atom_virial_tensor = atomic; - // paddle_infer::Tensor fparam_tensor; - // if (!fparam.empty()) { - // fparam_tensor = predictor->GetInputHandle("fparam"); - // fparam_tensor->Reshape({1, static_cast(fparam.size())}); - // fparam_tensor->CopyFromCpu((fparam.data())); - // } - // paddle_infer::Tensor aparam_tensor; - // if (!aparam_.empty()) { - // aparam_tensor = predictor->GetInputHandle("aparam"); - // aparam_tensor->Reshape({1, lmp_list.inum, - // static_cast(aparam_.size()) / lmp_list.inum}); - // aparam_tensor->CopyFromCpu((aparam_.data())); - // } + std::unique_ptr fparam_tensor; + if (!fparam.empty()) { + throw deepmd::deepmd_exception("fparam is not supported as input yet."); + // fparam_tensor = predictor->GetInputHandle("fparam"); + // fparam_tensor->Reshape({1, static_cast(fparam.size())}); + // fparam_tensor->CopyFromCpu((fparam.data())); + } + std::unique_ptr aparam_tensor; + if (!aparam_.empty()) { + throw deepmd::deepmd_exception("aparam is not supported as input yet."); + // aparam_tensor = predictor->GetInputHandle("aparam"); + // aparam_tensor->Reshape({1, lmp_list.inum, + // static_cast(aparam_.size()) / lmp_list.inum}); + // aparam_tensor->CopyFromCpu((aparam_.data())); + } if (!predictor->Run()) { - throw deepmd::deepmd_exception("Paddle inference failed"); + throw deepmd::deepmd_exception("Paddle inference run failed"); } auto output_names = predictor->GetOutputNames(); @@ -246,21 +234,21 @@ void DeepPotPD::compute(ENERGYVTYPE& ener, select_map(force, dforce, bkw_map, 3, nframes, fwd_map.size(), nall_real); if (atomic) { - auto atom_virial_ = predictor->GetOutputHandle("extended_virial"); - auto atom_energy_ = predictor->GetOutputHandle("atom_energy"); - datom_energy.resize(nall_real, - 0.0); // resize to nall to be consistenet with TF. - atom_energy_->CopyToCpu(datom_energy.data()); - atom_virial_->CopyToCpu(datom_virial.data()); - atom_energy.resize(static_cast(nframes) * fwd_map.size()); - atom_virial.resize(static_cast(nframes) * fwd_map.size() * 9); - select_map(atom_energy, datom_energy, bkw_map, 1, nframes, - fwd_map.size(), nall_real); - select_map(atom_virial, datom_virial, bkw_map, 9, nframes, - fwd_map.size(), nall_real); + throw "atomic virial is not supported as output yet."; + // auto atom_virial_ = predictor->GetOutputHandle("extended_virial"); + // auto atom_energy_ = predictor->GetOutputHandle("atom_energy"); + // datom_energy.resize(nall_real, + // 0.0); // resize to nall to be consistenet with TF. + // atom_energy_->CopyToCpu(datom_energy.data()); + // atom_virial_->CopyToCpu(datom_virial.data()); + // atom_energy.resize(static_cast(nframes) * fwd_map.size()); + // atom_virial.resize(static_cast(nframes) * fwd_map.size() * 9); + // select_map(atom_energy, datom_energy, bkw_map, 1, nframes, + // fwd_map.size(), nall_real); + // select_map(atom_virial, datom_virial, bkw_map, 9, nframes, + // fwd_map.size(), nall_real); } } - template void DeepPotPD::compute>( std::vector& dener, std::vector& force, @@ -294,7 +282,6 @@ template void DeepPotPD::compute>( const bool atomic); // ENERGYVTYPE: std::vector or ENERGYTYPE - template void DeepPotPD::compute(ENERGYVTYPE& ener, std::vector& force, @@ -329,20 +316,22 @@ void DeepPotPD::compute(ENERGYVTYPE& ener, } std::unique_ptr fparam_tensor; if (!fparam.empty()) { - fparam_tensor = predictor->GetInputHandle("box"); - fparam_tensor->Reshape({1, static_cast(fparam.size())}); - fparam_tensor->CopyFromCpu((fparam.data())); + throw deepmd::deepmd_exception("fparam is not supported as input yet."); + // fparam_tensor = predictor->GetInputHandle("box"); + // fparam_tensor->Reshape({1, static_cast(fparam.size())}); + // fparam_tensor->CopyFromCpu((fparam.data())); } std::unique_ptr aparam_tensor; - if (!fparam.empty()) { - aparam_tensor = predictor->GetInputHandle("box"); - aparam_tensor->Reshape({1, natoms, static_cast(aparam.size()) / natoms}); - aparam_tensor->CopyFromCpu((aparam.data())); + if (!aparam.empty()) { + throw deepmd::deepmd_exception("fparam is not supported as input yet."); + // aparam_tensor = predictor->GetInputHandle("box"); + // aparam_tensor->Reshape({1, natoms, static_cast(aparam.size()) / natoms}); + // aparam_tensor->CopyFromCpu((aparam.data())); } bool do_atom_virial_tensor = atomic; if (!predictor->Run()) { - throw deepmd::deepmd_exception("Paddle inference failed"); + throw deepmd::deepmd_exception("Paddle inference run failed"); } auto output_names = predictor->GetOutputNames(); @@ -355,10 +344,11 @@ void DeepPotPD::compute(ENERGYVTYPE& ener, virial_->CopyToCpu(virial.data()); if (atomic) { - auto atom_energy_ = predictor->GetOutputHandle(output_names[4]); - auto atom_virial_ = predictor->GetOutputHandle(output_names[5]); - atom_energy_->CopyToCpu(atom_energy.data()); - atom_virial_->CopyToCpu(atom_virial.data()); + throw deepmd::deepmd_exception("atomic virial is not supported as output yet."); + // auto atom_energy_ = predictor->GetOutputHandle(output_names[4]); + // auto atom_virial_ = predictor->GetOutputHandle(output_names[5]); + // atom_energy_->CopyToCpu(atom_energy.data()); + // atom_virial_->CopyToCpu(atom_virial.data()); } } @@ -388,92 +378,35 @@ template void DeepPotPD::compute>( const std::vector& aparam, const bool atomic); -// mixed type - -template -void DeepPotPD::compute_mixed_type(ENERGYVTYPE& dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - const int& nframes, - const std::vector& dcoord_, - const std::vector& datype_, - const std::vector& dbox, - const std::vector& fparam_, - const std::vector& aparam_, - const bool atomic) { - // int nloc = datype_.size() / nframes; - // // here atommap only used to get nloc - // atommap = deepmd::AtomMap(datype_.begin(), datype_.begin() + nloc); - // std::vector fparam; - // std::vector aparam; - // validate_fparam_aparam(nframes, nloc, fparam_, aparam_); - // tile_fparam_aparam(fparam, nframes, dfparam, fparam_); - // tile_fparam_aparam(aparam, nframes, nloc * daparam, aparam_); - - // if (dtype == paddle_infer::DataType::FLOAT64) { - // int nloc = predictor_input_tensors_mixed_type( - // predictor, nframes, dcoord_, ntypes, datype_, dbox, cell_size, - // fparam, aparam, atommap, aparam_nall); - // if (atomic) { - // run_model(dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, - // atommap, nframes); - // } else { - // run_model(dener, dforce_, dvirial, predictor, - // atommap, nframes); - // } - // } else { - // int nloc = predictor_input_tensors_mixed_type( - // predictor, nframes, dcoord_, ntypes, datype_, dbox, cell_size, - // fparam, aparam, atommap, aparam_nall); - // if (atomic) { - // run_model(dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, - // atommap, nframes); - // } else { - // run_model(dener, dforce_, dvirial, predictor, atommap, - // nframes); - // } - // } +/* general function except for string buffer */ +template +void DeepPotPD::get_buffer(const std::string &buffer_name, std::vector &buffer_arr) { + auto buffer_tensor = predictor->GetOutputHandle(buffer_name); + auto buffer_shape = buffer_tensor->shape(); + int buffer_size = std::accumulate(buffer_shape.begin(), buffer_shape.end(), 1, std::multiplies()); + buffer_arr.resize(buffer_size); + buffer_tensor->CopyToCpu(buffer_arr.data()); } -template void DeepPotPD::compute_mixed_type>( - std::vector& ener, - std::vector& force, - std::vector& virial, - std::vector& atom_energy, - std::vector& atom_virial, - const int& nframes, - const std::vector& coord, - const std::vector& dtype, - const std::vector& box, - const std::vector& fparam, - const std::vector& aparam, - const bool atomic); - -template void DeepPotPD::compute_mixed_type>( - std::vector& ener, - std::vector& force, - std::vector& virial, - std::vector& atom_energy, - std::vector& atom_virial, - const int& nframes, - const std::vector& coord, - const std::vector& atype, - const std::vector& box, - const std::vector& fparam, - const std::vector& aparam, - const bool atomic); - - -template -VT DeepPotPD::get_scalar(const std::string& name) const { - return predictor_get_scalar(predictor, name); +template +void DeepPotPD::get_buffer(const std::string &buffer_name, BUFFERTYPE &buffer) { + std::vector buffer_arr(1); + DeepPotPD::get_buffer(buffer_name, buffer_arr); + buffer = buffer_arr[0]; } +/* type_map is regarded as a special string buffer +that need to be postprocessed */ void DeepPotPD::get_type_map(std::string& type_map) { - type_map = "O H "; - // type_map = predictor_get_scalar(predictor, "type_map"); + auto type_map_tensor = predictor->GetOutputHandle("buffer_type_map"); + auto type_map_shape = type_map_tensor->shape(); + int type_map_size = std::accumulate(type_map_shape.begin(), type_map_shape.end(), 1, std::multiplies()); + + std::vector type_map_arr(type_map_size, 0); + type_map_tensor->CopyToCpu(type_map_arr.data()); + for (auto char_c: type_map_arr) { + type_map += std::string(1, char_c); + } } // forward to template method @@ -551,8 +484,7 @@ void DeepPotPD::computew_mixed_type(std::vector& ener, const std::vector& fparam, const std::vector& aparam, const bool atomic) { - compute_mixed_type(ener, force, virial, atom_energy, atom_virial, nframes, - coord, atype, box, fparam, aparam, atomic); + throw deepmd::deepmd_exception("computew_mixed_type is not implemented in paddle backend yet"); } void DeepPotPD::computew_mixed_type(std::vector& ener, std::vector& force, @@ -566,7 +498,6 @@ void DeepPotPD::computew_mixed_type(std::vector& ener, const std::vector& fparam, const std::vector& aparam, const bool atomic) { - compute_mixed_type(ener, force, virial, atom_energy, atom_virial, nframes, - coord, atype, box, fparam, aparam, atomic); + throw deepmd::deepmd_exception("computew_mixed_type is not implemented in paddle backend yet"); } #endif From b3a64089336d2bac888d6e2f5a848baf8a5cf567 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Fri, 27 Sep 2024 11:58:12 +0800 Subject: [PATCH 32/93] remove redundant code --- source/api_cc/include/commonPD.h | 138 ------ source/api_cc/src/DeepPotPD.cc | 13 +- source/api_cc/src/common.cc | 692 ------------------------------- 3 files changed, 7 insertions(+), 836 deletions(-) delete mode 100644 source/api_cc/include/commonPD.h diff --git a/source/api_cc/include/commonPD.h b/source/api_cc/include/commonPD.h deleted file mode 100644 index 952902225b..0000000000 --- a/source/api_cc/include/commonPD.h +++ /dev/null @@ -1,138 +0,0 @@ -// SPDX-License-Identifier: LGPL-3.0-or-later -#include -#include - -#include "paddle/include/paddle_inference_api.h" - -namespace deepmd { -/** - * @brief Check TensorFlow status. Exit if not OK. - * @param[in] status TensorFlow status. - **/ -// void check_status(const tensorflow::Status& status); - -/** - * @brief Get the value of a tensor. - * @param[in] session TensorFlow session. - * @param[in] name The name of the tensor. - * @param[in] scope The scope of the tensor. - * @return The value of the tensor. - **/ -template -VT predictor_get_scalar(const std::shared_ptr& predictor, - const std::string& name_); - -/** - * @brief Get the vector of a tensor. - * @param[out] o_vec The output vector. - * @param[in] session TensorFlow session. - * @param[in] name The name of the tensor. - * @param[in] scope The scope of the tensor. - **/ -// template -// void session_get_vector(std::vector& o_vec, -// tensorflow::Session* session, -// const std::string name_, -// const std::string scope = ""); - -/** - * @brief Get the type of a tensor. - * @param[in] session TensorFlow session. - * @param[in] name The name of the tensor. - * @param[in] scope The scope of the tensor. - * @return The type of the tensor as int. - **/ -paddle_infer::DataType predictor_get_dtype(const std::shared_ptr& predictor, - const std::string& name_); - -/** - * @brief Get input tensors. - * @param[out] input_tensors Input tensors. - * @param[in] dcoord_ Coordinates of atoms. - * @param[in] ntypes Number of atom types. - * @param[in] datype_ Atom types. - * @param[in] dbox Box matrix. - * @param[in] cell_size Cell size. - * @param[in] fparam_ Frame parameters. - * @param[in] aparam_ Atom parameters. - * @param[in] atommap Atom map. - * @param[in] scope The scope of the tensors. - * @param[in] aparam_nall Whether the atomic dimesion of atomic parameters is - * nall. - */ -template -int predictor_input_tensors( - const std::shared_ptr& predictor, - const std::vector& dcoord_, - const int& ntypes, - const std::vector& datype_, - const std::vector& dbox, - const double& cell_size, - const std::vector& fparam_, - const std::vector& aparam_, - const deepmd::AtomMap& atommap, - const bool aparam_nall = false); - -/** - * @brief Get input tensors. - * @param[out] input_tensors Input tensors. - * @param[in] dcoord_ Coordinates of atoms. - * @param[in] ntypes Number of atom types. - * @param[in] datype_ Atom types. - * @param[in] dlist Neighbor list. - * @param[in] fparam_ Frame parameters. - * @param[in] aparam_ Atom parameters. - * @param[in] atommap Atom map. - * @param[in] nghost Number of ghost atoms. - * @param[in] ago Update the internal neighbour list if ago is 0. - * @param[in] scope The scope of the tensors. - * @param[in] aparam_nall Whether the atomic dimesion of atomic parameters is - * nall. - */ -template -int predictor_input_tensors( - const std::shared_ptr& predictor, - const std::vector& dcoord_, - const int& ntypes, - const std::vector& datype_, - const std::vector& dbox, - InputNlist& dlist, - const std::vector& fparam_, - const std::vector& aparam_, - const deepmd::AtomMap& atommap, - const int nghost, - const int ago, - const bool aparam_nall = false); - -/** - * @brief Get input tensors for mixed type. - * @param[out] input_tensors Input tensors. - * @param[in] nframes Number of frames. - * @param[in] dcoord_ Coordinates of atoms. - * @param[in] ntypes Number of atom types. - * @param[in] datype_ Atom types. - * @param[in] dlist Neighbor list. - * @param[in] fparam_ Frame parameters. - * @param[in] aparam_ Atom parameters. - * @param[in] atommap Atom map. - * @param[in] nghost Number of ghost atoms. - * @param[in] ago Update the internal neighbour list if ago is 0. - * @param[in] scope The scope of the tensors. - * @param[in] aparam_nall Whether the atomic dimesion of atomic parameters is - * nall. - */ -template -int predictor_input_tensors_mixed_type( - const std::shared_ptr& predictor, - const int& nframes, - const std::vector& dcoord_, - const int& ntypes, - const std::vector& datype_, - const std::vector& dbox, - const double& cell_size, - const std::vector& fparam_, - const std::vector& aparam_, - const deepmd::AtomMap& atommap, - const bool aparam_nall = false); - -} // namespace deepmd diff --git a/source/api_cc/src/DeepPotPD.cc b/source/api_cc/src/DeepPotPD.cc index ed6a423811..63b436980c 100644 --- a/source/api_cc/src/DeepPotPD.cc +++ b/source/api_cc/src/DeepPotPD.cc @@ -39,8 +39,11 @@ void DeepPotPD::init(const std::string& model, << std::endl; return; } + // NOTE: There is no custom operators need to be loaded now. // deepmd::load_op_library(); - int gpu_num = 1; // Only support 1 GPU now. + + // NOTE: Only support 1 GPU now. + int gpu_num = 1; if (gpu_num > 0) { gpu_id = gpu_rank % gpu_num; } else { @@ -74,14 +77,13 @@ void DeepPotPD::init(const std::string& model, config->DisableGpu(); std::cout << "load model from: " << model << " to cpu " << std::endl; } else { - std::cout << "load model from: " << model << " to gpu " << gpu_id - << std::endl; + std::cout << "load model from: " << model << " to gpu " << gpu_id << std::endl; } + // NOTE: Both set to 1 now. // get_env_nthreads(num_intra_nthreads, // num_inter_nthreads); // need to be fixed as // // DP_INTRA_OP_PARALLELISM_THREADS - // both set to 1 now. // num_intra_nthreads = 1; num_inter_nthreads = 1; if (num_inter_nthreads) { @@ -90,7 +92,6 @@ void DeepPotPD::init(const std::string& model, predictor = paddle_infer::CreatePredictor(*config); - // initialize hyper params from model buffers ntypes_spin = 0; DeepPotPD::get_buffer("buffer_has_message_passing", do_message_passing); @@ -172,7 +173,7 @@ void DeepPotPD::compute(ENERGYVTYPE& ener, } if (do_message_passing == 1 && nghost == 0) { throw deepmd::deepmd_exception( - "do_message_passing == 1 && nghost == 0" + "(do_message_passing == 1 && nghost == 0) is not supported yet." ); } } diff --git a/source/api_cc/src/common.cc b/source/api_cc/src/common.cc index aef3204569..1fb38fd292 100644 --- a/source/api_cc/src/common.cc +++ b/source/api_cc/src/common.cc @@ -34,12 +34,6 @@ using namespace tensorflow; #endif -#ifdef BUILD_PADDLE -#include "commonPD.h" -#include "google/protobuf/io/zero_copy_stream_impl.h" -#include "google/protobuf/text_format.h" -#endif - static std::vector split(const std::string& input_, const std::string& delimiter) { std::string input = input_; @@ -928,476 +922,6 @@ int deepmd::session_get_dtype(tensorflow::Session* session, } #endif -#ifdef BUILD_PADDLE -template -int deepmd::predictor_input_tensors( - const std::shared_ptr& predictor, - const std::vector& dcoord_, - const int& ntypes, - const std::vector& datype_, - const std::vector& dbox, - const double& cell_size, - const std::vector& fparam_, - const std::vector& aparam__, - const deepmd::AtomMap& atommap, - const bool aparam_nall) { - // if datype.size is 0, not clear nframes; but 1 is just ok - int nframes = datype_.size() > 0 ? (dcoord_.size() / 3 / datype_.size()) : 1; - int nall = datype_.size(); - int nloc = nall; - assert(nall * 3 * nframes == dcoord_.size()); - bool b_pbc = (dbox.size() == nframes * 9); - - std::vector datype = atommap.get_type(); - std::vector type_count(ntypes, 0); - for (unsigned ii = 0; ii < datype.size(); ++ii) { - type_count[datype[ii]]++; - } - datype.insert(datype.end(), datype_.begin() + nloc, datype_.end()); - - // 准备输入Tensor句柄 - auto input_names = predictor->GetInputNames(); - auto coord_handle = predictor->GetInputHandle(input_names[0]); - auto type_handle = predictor->GetInputHandle(input_names[1]); - auto natoms_handle = predictor->GetInputHandle(input_names[2]); - auto box_handle = predictor->GetInputHandle(input_names[3]); - auto mesh_handle = predictor->GetInputHandle(input_names[4]); - - // 设置输入 Tensor 的维度信息 - std::vector coord_shape = {nframes, nall * 3}; - std::vector atype_shape = {nframes, nall}; - std::vector box_shape = {nframes, 9}; - std::vector mesh_shape; - if (b_pbc) { - mesh_shape = std::vector({6}); - } else { - mesh_shape = std::vector({0}); - } - - std::vector natoms_shape = {2 + ntypes}; - - coord_handle->Reshape(coord_shape); - type_handle->Reshape(atype_shape); - natoms_handle->Reshape(natoms_shape); - box_handle->Reshape(box_shape); - mesh_handle->Reshape(mesh_shape); - - paddle_infer::DataType model_type; - if (std::is_same::value) { - model_type = paddle_infer::DataType::FLOAT64; - } else if (std::is_same::value) { - model_type = paddle_infer::DataType::FLOAT32; - } else { - throw deepmd::deepmd_exception("unsupported data type"); - } - - std::vector dcoord(dcoord_); - atommap.forward(dcoord.begin(), dcoord_.begin(), 3, nframes, nall); - std::vector aparam_(aparam__); - if ((aparam_nall ? nall : nloc) > 0) { - atommap.forward( - aparam_.begin(), aparam__.begin(), - aparam__.size() / nframes / (aparam_nall ? nall : nloc), nframes, - (aparam_nall ? nall : nloc)); - } - - // 发送输入数据到Tensor句柄 - coord_handle->CopyFromCpu(dcoord.data()); - if (b_pbc) { - box_handle->CopyFromCpu(dbox.data()); - } else { - std::vector zero = dbox; - std::fill(zero.begin(), zero.end(), 0); - box_handle->CopyFromCpu(zero.data()); - } - std::vector datype_rep(nframes * nall, 0); - for (int ii = 0; ii < nframes; ++ii) { - for (int jj = 0; jj < nall; ++jj) { - datype_rep[ii * nall + jj] = datype[jj]; - } - } - type_handle->CopyFromCpu(datype_rep.data()); - std::vector mesh; - if (b_pbc) { - mesh = std::vector(6); - mesh[1 - 1] = 0; - mesh[2 - 1] = 0; - mesh[3 - 1] = 0; - mesh[4 - 1] = 0; - mesh[5 - 1] = 0; - mesh[6 - 1] = 0; - } else { - mesh = std::vector(0); - } - mesh_handle->CopyFromCpu(mesh.data()); - std::vector natoms = {nloc, nall}; - for (int ii = 0; ii < ntypes; ++ii) { - natoms.push_back(type_count[ii]); - } - natoms_handle->CopyFromCpu(natoms.data()); - - return nloc; -} - -template -int deepmd::predictor_input_tensors( - const std::shared_ptr& predictor, - const std::vector& dcoord_, - const int& ntypes, - const std::vector& datype_, - const std::vector& dbox, - InputNlist& dlist, - const std::vector& fparam_, - const std::vector& aparam__, - const deepmd::AtomMap& atommap, - const int nghost, - const int ago, - const bool aparam_nall) { - // if datype.size is 0, not clear nframes; but 1 is just ok - int nframes = datype_.size() > 0 ? (dcoord_.size() / 3 / datype_.size()) : 1; - int nall = datype_.size(); - int nloc = nall - nghost; - assert(nall * 3 * nframes == dcoord_.size()); - assert(dbox.size() == nframes * 9); - - std::vector datype = atommap.get_type(); - // for (int i=0; i type_count(ntypes, 0); - for (unsigned ii = 0; ii < datype.size(); ++ii) { - type_count[datype[ii]]++; - } - datype.insert(datype.end(), datype_.begin() + nloc, datype_.end()); - - // 准备输入Tensor句柄 - auto input_names = predictor->GetInputNames(); - // for (auto &ss: input_names) - // { - // std::cout << "input_name: " << " " << ss << std::endl; - // } - auto coord_handle = predictor->GetInputHandle(input_names[0]); - auto type_handle = predictor->GetInputHandle(input_names[1]); - // auto natoms_handle = predictor->GetInputHandle(input_names[2]); - auto box_handle = predictor->GetInputHandle(input_names[2]); - // auto mesh_handle = predictor->GetInputHandle(input_names[4]); - - // 设置输入 Tensor 的维度信息 - std::vector coord_shape = {nframes, nall, 3}; - std::vector coord_shape_flat = {nframes, nall * 3}; - - std::vector atype_shape = {nframes, nall}; - std::vector atype_shape_flat = {nframes, nall}; - - std::vector box_shape = {nframes, 3, 3}; - std::vector box_shape_flat = {nframes * 9}; - // std::vector mesh_shape = std::vector({16}); - // std::vector natoms_shape = {2 + ntypes}; - - paddle_infer::DataType model_type; - if (std::is_same::value) { - model_type = paddle_infer::DataType::FLOAT64; - } else if (std::is_same::value) { - model_type = paddle_infer::DataType::FLOAT32; - } else { - throw deepmd::deepmd_exception("unsupported data type"); - } - - coord_handle->Reshape(coord_shape_flat); - box_handle->Reshape(box_shape_flat); - type_handle->Reshape(atype_shape_flat); - // printf("coord.shape = ["); - // for (auto &d: coord_shape) - // { - // printf("%d, ", d); - // } - // printf("]\n"); - - // printf("type.shape = ["); - // for (auto &d: atype_shape) - // { - // printf("%d, ", d); - // } - // printf("]\n"); - - // printf("box.shape = ["); - // for (auto &d: box_shape) - // { - // printf("%d, ", d); - // } - // printf("]\n"); - // mesh_handle->Reshape(mesh_shape); - // natoms_handle->Reshape(natoms_shape); - - std::vector dcoord(dcoord_); - atommap.forward(dcoord.begin(), dcoord_.begin(), 3, nframes, nall); //012 - std::vector aparam_(aparam__); - if ((aparam_nall ? nall : nloc) > 0) { - atommap.forward( - aparam_.begin(), aparam__.begin(), - aparam__.size() / nframes / (aparam_nall ? nall : nloc), nframes, - (aparam_nall ? nall : nloc)); - } - - // const std::string filename = "/workspace/hesensen/deepmd_backend/deepmd_paddle_new/examples/water/lmp/coord_torch.log"; - // std::ifstream inputFile(filename); - // VALUETYPE number; - // int iii = 0; - // while (inputFile >> number) { - // dcoord[iii] = number; - // ++iii; - // } - // printf("dcoord finished, iii = %d\n", iii); - // inputFile.close(); - - // 发送输入数据到Tensor句柄 - coord_handle->CopyFromCpu(dcoord.data()); - coord_handle->Reshape(coord_shape); - box_handle->CopyFromCpu(dbox.data()); - box_handle->Reshape(box_shape); - // for (int i = 0; i < dcoord.size(); ++i) - // { - // printf("dcoord[%d] = %.6lf\n", i, dcoord[i]); - // } - std::vector datype_rep(nframes * nall, 0); - for (int ii = 0; ii < nframes; ++ii) { - for (int jj = 0; jj < nall; ++jj) { - datype_rep[ii * nall + jj] = datype[jj]; - } - } - // const std::string filename1 = "/workspace/hesensen/deepmd_backend/deepmd_paddle_new/examples/water/lmp/type_torch.log"; - // std::ifstream inputFile1(filename1); - // int number_int; - // iii = 0; - // while (inputFile1 >> number_int) { - // datype_rep[iii] = number_int; - // ++iii; - // } - // printf("atype finishied, iii = %d\n", iii); - // inputFile1.close(); - - type_handle->CopyFromCpu(datype_rep.data()); - // for (int i = 0; i < datype_rep.size(); ++i) - // { - // printf("%d\n", datype_rep[i]); - // } - type_handle->Reshape(atype_shape); - // std::vector mesh(mesh_shape[0], 0); - // for (int ii = 0; ii < 16; ++ii) { - // mesh[ii] = 0; - // } - // const int stride = sizeof(int*) / sizeof(int); - // assert(stride * sizeof(int) == sizeof(int*)); - // assert(stride <= 4); - // mesh[0] = ago; - // mesh[1] = dlist.inum; - // mesh[2] = 0; - // mesh[3] = 0; - // memcpy(&mesh[4], &(dlist.ilist), sizeof(int*)); - // memcpy(&mesh[8], &(dlist.numneigh), sizeof(int*)); - // memcpy(&mesh[12], &(dlist.firstneigh), sizeof(int**)); - // mesh_handle->CopyFromCpu(mesh.data()); - - // std::vector natoms = {nloc, nall}; - // for (int ii = 0; ii < ntypes; ++ii) { - // natoms.push_back(type_count[ii]); - // } - // natoms_handle->CopyFromCpu(natoms.data()); - // printf("finished predictor_input_tensors\n"); - // printf("nloc = %d\n", nloc); - return nloc; -} - -template -int deepmd::predictor_input_tensors_mixed_type( - const std::shared_ptr& predictor, - const int& nframes, - const std::vector& dcoord_, - const int& ntypes, - const std::vector& datype_, - const std::vector& dbox, - const double& cell_size, - const std::vector& fparam_, - const std::vector& aparam__, - const deepmd::AtomMap& atommap, - const bool aparam_nall) { - int nall = datype_.size() / nframes; - int nloc = nall; - assert(nall * 3 * nframes == dcoord_.size()); - bool b_pbc = (dbox.size() == nframes * 9); - - std::vector datype(datype_); - atommap.forward(datype.begin(), datype_.begin(), 1, nframes, nall); - - auto input_names = predictor->GetInputNames(); - auto coord_handle = predictor->GetInputHandle(input_names[0]); - auto type_handle = predictor->GetInputHandle(input_names[1]); - auto box_handle = predictor->GetInputHandle(input_names[3]); - auto mesh_handle = predictor->GetInputHandle(input_names[4]); - auto natoms_handle = predictor->GetInputHandle(input_names[2]); - - // 设置输入 Tensor 的维度信息 - std::vector coord_shape = {nframes, nall * 3}; - std::vector atype_shape = {nframes, nall}; - std::vector box_shape = {nframes, 9}; - std::vector mesh_shape; - if (b_pbc) { - mesh_shape = std::vector({7}); - } else { - mesh_shape = std::vector({1}); - } - std::vector natoms_shape = {2 + ntypes}; - - coord_handle->Reshape(coord_shape); - type_handle->Reshape(atype_shape); - box_handle->Reshape(box_shape); - mesh_handle->Reshape(mesh_shape); - natoms_handle->Reshape(natoms_shape); - - paddle_infer::DataType model_type; - if (std::is_same::value) { - model_type = paddle_infer::DataType::FLOAT64; - } else if (std::is_same::value) { - model_type = paddle_infer::DataType::FLOAT32; - } else { - throw deepmd::deepmd_exception("unsupported data type"); - } - - std::vector dcoord(dcoord_); - atommap.forward(dcoord.begin(), dcoord_.begin(), 3, nframes, nall); - std::vector aparam_(aparam__); - if ((aparam_nall ? nall : nloc) > 0) { - atommap.forward( - aparam_.begin(), aparam__.begin(), - aparam__.size() / nframes / (aparam_nall ? nall : nloc), nframes, - (aparam_nall ? nall : nloc)); - } - // coord - coord_handle->CopyFromCpu(dcoord.data()); - - // box - if (b_pbc) { - box_handle->CopyFromCpu(dbox.data()); - } else { - std::vector zero = dbox; - std::fill(zero.begin(), zero.end(), 0); - box_handle->CopyFromCpu(zero.data()); - } - - // datype - std::vector datype_rep(nframes * nall, 0); - for (int ii = 0; ii < nframes; ++ii) { - for (int jj = 0; jj < nall; ++jj) { - datype_rep[ii * nall + jj] = datype[jj]; - } - } - type_handle->CopyFromCpu(datype_rep.data()); - // mesh - std::vector mesh; - if (b_pbc) { - mesh = std::vector(7, 0); - mesh[1 - 1] = 0; - mesh[2 - 1] = 0; - mesh[3 - 1] = 0; - mesh[4 - 1] = 0; - mesh[5 - 1] = 0; - mesh[6 - 1] = 0; - mesh[7 - 1] = 0; - } else { - mesh = std::vector(1, 0); - mesh[1 - 1] = 0; - } - mesh_handle->CopyFromCpu(mesh.data()); - //natoms - std::vector natoms_pad = {nloc, nall, nall}; - if (ntypes > 1) { - for (int ii = 0; ii < ntypes; ++ii) { - natoms_pad.push_back(0); - } - } - natoms_handle->CopyFromCpu(natoms_pad.data()); - - // if (fparam_.size() > 0) { - // input_tensors.push_back({prefix + "t_fparam", fparam_tensor}); - // } - // if (aparam_.size() > 0) { - // input_tensors.push_back({prefix + "t_aparam", aparam_tensor}); - // } - return nloc; -} - -#endif - -#ifdef BUILD_PADDLE -template -VT deepmd::predictor_get_scalar( - const std::shared_ptr& predictor, - const std::string& name_) { - if (std::is_same::value) { - /* - NOTE: Convert from ascii code(int64) to std::string, - A workaround for string data type is not supported in Paddle yet. - */ - auto scalar_tensor = predictor->GetOutputHandle(name_); - if (scalar_tensor->shape().size() == 0) { - return VT(); - } - const auto& shape = scalar_tensor->shape(); - const int& str_len = std::accumulate(std::begin(shape), std::end(shape), 1, - std::multiplies<>{}); - if (str_len == 0) { - return VT(); - } - int32_t* scalar_ptr = (int32_t*)malloc(str_len * sizeof(int32_t)); - scalar_tensor->CopyToCpu(scalar_ptr); - VT ret; - for (int ii = 0; ii < str_len; ++ii) { - ret += (char)scalar_ptr[ii]; - } - free(scalar_ptr); - return ret; - } else { - /* Vanillia process for other data type below*/ - auto scalar_tensor = predictor->GetOutputHandle(name_); - // VT* scalar_ptr = (VT*)malloc(1 * sizeof(VT)); - std::unique_ptr scalar_ptr(new VT); - scalar_tensor->CopyToCpu(scalar_ptr.get()); - return (*scalar_ptr); - } -} - - -// template -// void deepmd::session_get_vector(std::vector& o_vec, -// Session* session, -// const std::string name_, -// const std::string scope) { -// std::string name = name_; -// if (scope != "") { -// name = scope + "/" + name; -// } -// std::vector output_tensors; -// deepmd::check_status( -// session->Run(std::vector>({}), -// {name.c_str()}, {}, &output_tensors)); -// Tensor output_rc = output_tensors[0]; -// assert(1 == output_rc.shape().dims()); -// int dof = output_rc.shape().dim_size(0); -// o_vec.resize(dof); -// auto orc = output_rc.flat(); -// for (int ii = 0; ii < dof; ++ii) { -// o_vec[ii] = orc(ii); -// } -// } - -paddle_infer::DataType deepmd::predictor_get_dtype( - const std::shared_ptr& predictor, - const std::string& name_) { - auto scalar_tensor = predictor->GetOutputHandle(name_); - return scalar_tensor->type(); -} - -#endif - template void deepmd::select_map(std::vector& out, const std::vector& in, @@ -1502,19 +1026,6 @@ template void deepmd::session_get_vector(std::vector&, const std::string); #endif -#ifdef BUILD_PADDLE -template int deepmd::predictor_get_scalar(const std::shared_ptr& predictor, - const std::string &name_); - -template bool deepmd::predictor_get_scalar(const std::shared_ptr& predictor, - const std::string &name_); - -// template void deepmd::session_get_vector(std::vector&, -// Session*, -// const std::string, -// const std::string); -#endif - template void deepmd::select_map(std::vector& out, const std::vector& in, const std::vector& idx_map, @@ -1554,12 +1065,6 @@ template void deepmd::session_get_vector(std::vector&, const std::string); #endif -#ifdef BUILD_PADDLE -template float deepmd::predictor_get_scalar(const std::shared_ptr& predictor, - const std::string &name_); - -#endif - template void deepmd::select_map(std::vector& out, const std::vector& in, const std::vector& idx_map, @@ -1599,11 +1104,6 @@ template void deepmd::session_get_vector(std::vector&, const std::string); #endif -#ifdef BUILD_PADDLE -template double deepmd::predictor_get_scalar(const std::shared_ptr& predictor, - const std::string& name_); -#endif - template void deepmd::select_map(std::vector& out, const std::vector& in, const std::vector& idx_map, @@ -1673,46 +1173,6 @@ template void deepmd::select_map_inv( const int& stride); #endif -#ifdef BUILD_PADDLE -template std::string deepmd::predictor_get_scalar( - const std::shared_ptr& predictor, const std::string &name_); - -// template void deepmd::session_get_vector( -// std::vector&, -// const std::shared_ptr& predictor, -// const std::string); - -template void deepmd::select_map( - std::vector& out, - const std::vector& in, - const std::vector& idx_map, - const int& stride, - const int& nframes, - const int& nall1, - const int& nall2); - -template void deepmd::select_map( - typename std::vector::iterator out, - const typename std::vector::const_iterator in, - const std::vector& idx_map, - const int& stride, - const int& nframes, - const int& nall1, - const int& nall2); - -template void deepmd::select_map_inv( - std::vector& out, - const std::vector& in, - const std::vector& idx_map, - const int& stride); - -template void deepmd::select_map_inv( - typename std::vector::iterator out, - const typename std::vector::const_iterator in, - const std::vector& idx_map, - const int& stride); -#endif - void deepmd::read_file_to_string(std::string model, std::string& file_content) { // generated by GitHub Copilot std::ifstream file(model); @@ -1907,158 +1367,6 @@ template int deepmd::session_input_tensors_mixed_type( const bool aparam_nall); #endif -#ifdef BUILD_PADDLE -template int deepmd::predictor_input_tensors( - const std::shared_ptr& predictor, - const std::vector& dcoord_, - const int& ntypes, - const std::vector& datype_, - const std::vector& dbox, - const double& cell_size, - const std::vector& fparam_, - const std::vector& aparam_, - const deepmd::AtomMap& atommap, - const bool aparam_nall); -template int deepmd::predictor_input_tensors( - const std::shared_ptr& predictor, - const std::vector& dcoord_, - const int& ntypes, - const std::vector& datype_, - const std::vector& dbox, - const double& cell_size, - const std::vector& fparam_, - const std::vector& aparam_, - const deepmd::AtomMap& atommap, - const bool aparam_nall); - -template int deepmd::predictor_input_tensors( - const std::shared_ptr& predictor, - const std::vector& dcoord_, - const int& ntypes, - const std::vector& datype_, - const std::vector& dbox, - const double& cell_size, - const std::vector& fparam_, - const std::vector& aparam_, - const deepmd::AtomMap& atommap, - const bool aparam_nall); -template int deepmd::predictor_input_tensors( - const std::shared_ptr& predictor, - const std::vector& dcoord_, - const int& ntypes, - const std::vector& datype_, - const std::vector& dbox, - const double& cell_size, - const std::vector& fparam_, - const std::vector& aparam_, - const deepmd::AtomMap& atommap, - const bool aparam_nall); - -template int deepmd::predictor_input_tensors( - const std::shared_ptr& predictor, - const std::vector& dcoord_, - const int& ntypes, - const std::vector& datype_, - const std::vector& dbox, - InputNlist& dlist, - const std::vector& fparam_, - const std::vector& aparam_, - const deepmd::AtomMap& atommap, - const int nghost, - const int ago, - const bool aparam_nall); -template int deepmd::predictor_input_tensors( - const std::shared_ptr& predictor, - const std::vector& dcoord_, - const int& ntypes, - const std::vector& datype_, - const std::vector& dbox, - InputNlist& dlist, - const std::vector& fparam_, - const std::vector& aparam_, - const deepmd::AtomMap& atommap, - const int nghost, - const int ago, - const bool aparam_nall); - -template int deepmd::predictor_input_tensors( - const std::shared_ptr& predictor, - const std::vector& dcoord_, - const int& ntypes, - const std::vector& datype_, - const std::vector& dbox, - InputNlist& dlist, - const std::vector& fparam_, - const std::vector& aparam_, - const deepmd::AtomMap& atommap, - const int nghost, - const int ago, - const bool aparam_nall); -template int deepmd::predictor_input_tensors( - const std::shared_ptr& predictor, - const std::vector& dcoord_, - const int& ntypes, - const std::vector& datype_, - const std::vector& dbox, - InputNlist& dlist, - const std::vector& fparam_, - const std::vector& aparam_, - const deepmd::AtomMap& atommap, - const int nghost, - const int ago, - const bool aparam_nall); - -template int deepmd::predictor_input_tensors_mixed_type( - const std::shared_ptr& predictor, - const int& nframes, - const std::vector& dcoord_, - const int& ntypes, - const std::vector& datype_, - const std::vector& dbox, - const double& cell_size, - const std::vector& fparam_, - const std::vector& aparam_, - const deepmd::AtomMap& atommap, - const bool aparam_nall); -template int deepmd::predictor_input_tensors_mixed_type( - const std::shared_ptr& predictor, - const int& nframes, - const std::vector& dcoord_, - const int& ntypes, - const std::vector& datype_, - const std::vector& dbox, - const double& cell_size, - const std::vector& fparam_, - const std::vector& aparam_, - const deepmd::AtomMap& atommap, - const bool aparam_nall); - -template int deepmd::predictor_input_tensors_mixed_type( - const std::shared_ptr& predictor, - const int& nframes, - const std::vector& dcoord_, - const int& ntypes, - const std::vector& datype_, - const std::vector& dbox, - const double& cell_size, - const std::vector& fparam_, - const std::vector& aparam_, - const deepmd::AtomMap& atommap, - const bool aparam_nall); -template int deepmd::predictor_input_tensors_mixed_type( - const std::shared_ptr& predictor, - const int& nframes, - const std::vector& dcoord_, - const int& ntypes, - const std::vector& datype_, - const std::vector& dbox, - const double& cell_size, - const std::vector& fparam_, - const std::vector& aparam_, - const deepmd::AtomMap& atommap, - const bool aparam_nall); -#endif - void deepmd::print_summary(const std::string& pre) { int num_intra_nthreads, num_inter_nthreads; deepmd::get_env_nthreads(num_intra_nthreads, num_inter_nthreads); From 15a7e756acc56d1dcd61b0df0e11b6f7e5a4634e Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Fri, 27 Sep 2024 12:06:33 +0800 Subject: [PATCH 33/93] refine docstring of get_buffer --- source/api_cc/include/DeepPotPD.h | 14 +++++++++---- source/api_cc/src/DeepPotPD.cc | 34 +++++++++++++++---------------- 2 files changed, 27 insertions(+), 21 deletions(-) diff --git a/source/api_cc/include/DeepPotPD.h b/source/api_cc/include/DeepPotPD.h index fd6d39529b..73fbab2b32 100644 --- a/source/api_cc/include/DeepPotPD.h +++ b/source/api_cc/include/DeepPotPD.h @@ -235,14 +235,20 @@ class DeepPotPD : public DeepPotBase { void get_type_map(std::string& type_map); /** - * @brief Get the type map (element name of the atom types) of this model. - * @param[out] type_map The type map of this model. + * @brief Get the buffer of this model. + * @param[in] buffer_name Buffer name. + * @param[out] buffer_array Buffer array. **/ template - void get_buffer(const std::string &buffer_name, std::vector &buffer_arr); + void get_buffer(const std::string &buffer_name, std::vector &buffer_array); + /** + * @brief Get the buffer of this model. + * @param[in] buffer_name Buffer name. + * @param[out] buffer_scalar Buffer scalar. + **/ template - void get_buffer(const std::string &buffer_name, BUFFERTYPE &buffer_arr); + void get_buffer(const std::string &buffer_name, BUFFERTYPE &buffer_scalar); /** * @brief Get whether the atom dimension of aparam is nall instead of fparam. diff --git a/source/api_cc/src/DeepPotPD.cc b/source/api_cc/src/DeepPotPD.cc index 63b436980c..06a417073a 100644 --- a/source/api_cc/src/DeepPotPD.cc +++ b/source/api_cc/src/DeepPotPD.cc @@ -379,23 +379,6 @@ template void DeepPotPD::compute>( const std::vector& aparam, const bool atomic); -/* general function except for string buffer */ -template -void DeepPotPD::get_buffer(const std::string &buffer_name, std::vector &buffer_arr) { - auto buffer_tensor = predictor->GetOutputHandle(buffer_name); - auto buffer_shape = buffer_tensor->shape(); - int buffer_size = std::accumulate(buffer_shape.begin(), buffer_shape.end(), 1, std::multiplies()); - buffer_arr.resize(buffer_size); - buffer_tensor->CopyToCpu(buffer_arr.data()); -} - -template -void DeepPotPD::get_buffer(const std::string &buffer_name, BUFFERTYPE &buffer) { - std::vector buffer_arr(1); - DeepPotPD::get_buffer(buffer_name, buffer_arr); - buffer = buffer_arr[0]; -} - /* type_map is regarded as a special string buffer that need to be postprocessed */ void DeepPotPD::get_type_map(std::string& type_map) { @@ -410,6 +393,23 @@ void DeepPotPD::get_type_map(std::string& type_map) { } } +/* general function except for string buffer */ +template +void DeepPotPD::get_buffer(const std::string &buffer_name, std::vector &buffer_array) { + auto buffer_tensor = predictor->GetOutputHandle(buffer_name); + auto buffer_shape = buffer_tensor->shape(); + int buffer_size = std::accumulate(buffer_shape.begin(), buffer_shape.end(), 1, std::multiplies()); + buffer_array.resize(buffer_size); + buffer_tensor->CopyToCpu(buffer_array.data()); +} + +template +void DeepPotPD::get_buffer(const std::string &buffer_name, BUFFERTYPE &buffer_scalar) { + std::vector buffer_array(1); + DeepPotPD::get_buffer(buffer_name, buffer_array); + buffer_scalar = buffer_array[0]; +} + // forward to template method void DeepPotPD::computew(std::vector& ener, std::vector& force, From 72241ea882eea97c8b8ba6b22cbcd597453a208e Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Fri, 27 Sep 2024 13:01:43 +0800 Subject: [PATCH 34/93] update pd version code --- backend/dynamic_metadata.py | 4 ++++ backend/find_paddle.py | 4 ++-- backend/read_env.py | 7 +++++-- deepmd/pd/entrypoints/main.py | 4 ++-- deepmd/pd/model/atomic_model/dp_atomic_model.py | 7 ++----- 5 files changed, 15 insertions(+), 11 deletions(-) diff --git a/backend/dynamic_metadata.py b/backend/dynamic_metadata.py index 138375e072..e3d7f5231a 100644 --- a/backend/dynamic_metadata.py +++ b/backend/dynamic_metadata.py @@ -9,6 +9,9 @@ Optional, ) +from .find_paddle import ( + get_pd_requirement, +) from .find_pytorch import ( get_pt_requirement, ) @@ -57,4 +60,5 @@ def dynamic_metadata( **optional_dependencies, **get_tf_requirement(tf_version), **get_pt_requirement(pt_version), + **get_pd_requirement(pd_version), } diff --git a/backend/find_paddle.py b/backend/find_paddle.py index 0f24dd3788..1d8437a140 100644 --- a/backend/find_paddle.py +++ b/backend/find_paddle.py @@ -117,7 +117,7 @@ def get_pd_requirement(pd_version: str = "") -> dict: # https://peps.python.org/pep-0440/#version-matching f"paddle=={Version(pd_version).base_version}.*" if pd_version != "" - else "paddle>=3.0.0", + else "paddle>=3b", ], } @@ -138,7 +138,7 @@ def get_pd_version(pd_path: Optional[Union[str, Path]]) -> str: """ if pd_path is None or pd_path == "": return "" - version_file = Path(pd_path) / "version.py" + version_file = Path(pd_path) / "version" / "__init__.py" spec = importlib.util.spec_from_file_location("paddle.version", version_file) module = importlib.util.module_from_spec(spec) spec.loader.exec_module(module) diff --git a/backend/read_env.py b/backend/read_env.py index 582b08e1bb..e2a896e997 100644 --- a/backend/read_env.py +++ b/backend/read_env.py @@ -15,6 +15,7 @@ from .find_paddle import ( find_paddle, + get_pd_version, ) from .find_pytorch import ( find_pytorch, @@ -27,7 +28,7 @@ @lru_cache -def get_argument_from_env() -> Tuple[str, list, list, dict, str, str]: +def get_argument_from_env() -> Tuple[str, list, list, dict, str, str, str]: """Get the arguments from environment variables. The environment variables are assumed to be not changed during the build. @@ -46,6 +47,8 @@ def get_argument_from_env() -> Tuple[str, list, list, dict, str, str]: The TensorFlow version. str The PyTorch version. + str + The Paddle version. """ cmake_args = [] extra_scripts = {} @@ -125,7 +128,7 @@ def get_argument_from_env() -> Tuple[str, list, list, dict, str, str]: if os.environ.get("DP_ENABLE_PADDLE", "0") == "1": pd_install_dir, _ = find_paddle() - pt_version = get_pt_version(pd_install_dir) + pd_version = get_pd_version(pd_install_dir) cmake_args.extend( [ "-DENABLE_PADDLE=ON", diff --git a/deepmd/pd/entrypoints/main.py b/deepmd/pd/entrypoints/main.py index e4f11d425c..3c37bf4f4e 100644 --- a/deepmd/pd/entrypoints/main.py +++ b/deepmd/pd/entrypoints/main.py @@ -106,8 +106,8 @@ def get_trainer( local_rank = os.environ.get("LOCAL_RANK") if local_rank is not None: local_rank = int(local_rank) - assert dist.is_nccl_available() - dist.init_process_group(backend="nccl") + assert paddle.version.nccl() != "0" + dist.init_parallel_env() def prepare_trainer_input_single( model_params_single, data_dict_single, rank=0, seed=None diff --git a/deepmd/pd/model/atomic_model/dp_atomic_model.py b/deepmd/pd/model/atomic_model/dp_atomic_model.py index 9ad4048cfc..ea93244575 100644 --- a/deepmd/pd/model/atomic_model/dp_atomic_model.py +++ b/deepmd/pd/model/atomic_model/dp_atomic_model.py @@ -58,7 +58,6 @@ def __init__( super().__init__(type_map, **kwargs) ntypes = len(type_map) self.type_map = type_map - self.ntypes = ntypes self.descriptor = descriptor self.rcut = self.descriptor.get_rcut() @@ -66,15 +65,13 @@ def __init__( self.fitting_net = fitting super().init_out_stat() - # specify manually for access by name in C++ inference - # register 'type_map' as buffer - def string_to_array(s: str) -> int: + def _string_to_array(s: str) -> List[int]: return [ord(c) for c in s] self.register_buffer( "buffer_type_map", - paddle.to_tensor(string_to_array(" ".join(self.type_map)), dtype="int32"), + paddle.to_tensor(_string_to_array(" ".join(self.type_map)), dtype="int32"), ) self.buffer_type_map.name = "buffer_type_map" # register 'has_message_passing' as buffer(cast to int32 as problems may meets with vector) From 8694476544c9fc0774110df8b6ee5dfe28337047 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Fri, 27 Sep 2024 13:15:03 +0800 Subject: [PATCH 35/93] restore non related files --- source/api_cc/src/DeepPot.cc | 1 - source/api_cc/src/common.cc | 1 - source/install/test_cc_local.sh | 2 +- source/lmp/pair_deepmd.cpp | 11 +++-------- source/op/pt/comm.cc | 3 +-- 5 files changed, 5 insertions(+), 13 deletions(-) diff --git a/source/api_cc/src/DeepPot.cc b/source/api_cc/src/DeepPot.cc index 7ee6d910d9..9fdf64a689 100644 --- a/source/api_cc/src/DeepPot.cc +++ b/source/api_cc/src/DeepPot.cc @@ -49,7 +49,6 @@ void DeepPot::init(const std::string& model, } else { throw deepmd::deepmd_exception("Unsupported model file format"); } - if (deepmd::DPBackend::TensorFlow == backend) { #ifdef BUILD_TENSORFLOW dp = std::make_shared(model, gpu_rank, file_content); diff --git a/source/api_cc/src/common.cc b/source/api_cc/src/common.cc index 1fb38fd292..e6d43616ed 100644 --- a/source/api_cc/src/common.cc +++ b/source/api_cc/src/common.cc @@ -10,7 +10,6 @@ #include "AtomMap.h" #include "device.h" -#include #if defined(_WIN32) #if defined(_WIN32_WINNT) #undef _WIN32_WINNT diff --git a/source/install/test_cc_local.sh b/source/install/test_cc_local.sh index dbfdd7c0b2..fdb2396a28 100755 --- a/source/install/test_cc_local.sh +++ b/source/install/test_cc_local.sh @@ -19,7 +19,7 @@ BUILD_TMP_DIR=${SCRIPT_PATH}/../build_tests mkdir -p ${BUILD_TMP_DIR} cd ${BUILD_TMP_DIR} cmake \ - -D ENABLE_TENSORFLOW=False \ + -D ENABLE_TENSORFLOW=TRUE \ -D ENABLE_PYTORCH=TRUE \ -D INSTALL_TENSORFLOW=FALSE \ -D USE_TF_PYTHON_LIBS=TRUE \ diff --git a/source/lmp/pair_deepmd.cpp b/source/lmp/pair_deepmd.cpp index 0355ce6296..2cb6cfacd4 100644 --- a/source/lmp/pair_deepmd.cpp +++ b/source/lmp/pair_deepmd.cpp @@ -23,7 +23,6 @@ #include "neighbor.h" #include "output.h" #include "update.h" -// #include #if LAMMPS_VERSION_NUMBER >= 20210831 // in lammps #2902, fix_ttm members turns from private to protected #define USE_TTM 1 @@ -495,6 +494,7 @@ void PairDeepMD::compute(int eflag, int vflag) { } } } + vector dtype(nall); for (int ii = 0; ii < nall; ++ii) { dtype[ii] = type_idx_map[type[ii] - 1]; @@ -975,14 +975,9 @@ void PairDeepMD::settings(int narg, char **arg) { numb_models = models.size(); if (numb_models == 1) { try { - auto node_rank = get_node_rank(); - auto content = get_file_content(arg[0]); - deep_pot.init(arg[0], node_rank, content); - } catch (const std::exception &e) { + deep_pot.init(arg[0], get_node_rank(), get_file_content(arg[0])); + } catch (deepmd_compat::deepmd_exception &e) { error->one(FLERR, e.what()); - // std::cerr << "Standard exception caught: " << e.what() << std::endl; - // // 打印堆栈跟踪信息 - // std::cerr << "Stack trace:\n" << boost::stacktrace::stacktrace() << std::endl; } cutoff = deep_pot.cutoff() * dist_unit_cvt_factor; numb_types = deep_pot.numb_types(); diff --git a/source/op/pt/comm.cc b/source/op/pt/comm.cc index d5c273c689..a25dfbd542 100644 --- a/source/op/pt/comm.cc +++ b/source/op/pt/comm.cc @@ -6,8 +6,7 @@ #include #endif #endif -#include "paddle/extension.h" -#include "paddle/include/paddle_inference_api.h" +#include #include From e246a345f7ee6e27322c29335e33a293e49a523b Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Fri, 27 Sep 2024 14:29:16 +0800 Subject: [PATCH 36/93] add paddle to related docs --- README.md | 4 +- doc/backend.md | 11 +++- doc/freeze/freeze.md | 20 +++++++ doc/install/install-from-source.md | 96 +++++++++++++++++++++++++++++- doc/model/sel.md | 8 +++ doc/model/train-energy.md | 4 +- doc/model/train-se-e2-a.md | 4 +- doc/train/finetuning.md | 70 +++++++++++++++++++++- doc/train/parallel-training.md | 84 +++++++++++++++++++++++++- doc/train/tensorboard.md | 4 +- doc/train/training.md | 8 +++ pyproject.toml | 7 ++- 12 files changed, 304 insertions(+), 16 deletions(-) diff --git a/README.md b/README.md index e821a29768..9985b28f21 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@ For more information, check the [documentation](https://deepmd.readthedocs.io/). ### Highlighted features -- **interfaced with multiple backends**, including TensorFlow and PyTorch, the most popular deep learning frameworks, making the training process highly automatic and efficient. +- **interfaced with multiple backends**, including TensorFlow, PyTorch and Paddle, the most popular deep learning frameworks, making the training process highly automatic and efficient. - **interfaced with high-performance classical MD and quantum (path-integral) MD packages**, including LAMMPS, i-PI, AMBER, CP2K, GROMACS, OpenMM, and ABUCUS. - **implements the Deep Potential series models**, which have been successfully applied to finite and extended systems, including organic molecules, metals, semiconductors, insulators, etc. - **implements MPI and GPU supports**, making it highly efficient for high-performance parallel and distributed computing. @@ -72,7 +72,7 @@ See [our latest paper](https://doi.org/10.1063/5.0155600) for details of all fea #### v3 -- Multiple backends supported. Add a PyTorch backend. +- Multiple backends supported. Add PyTorch and Paddle backend. - The DPA-2 model. ## Install and use DeePMD-kit diff --git a/doc/backend.md b/doc/backend.md index f6eaf0e45b..c6136c435a 100644 --- a/doc/backend.md +++ b/doc/backend.md @@ -23,6 +23,13 @@ DeePMD-kit does not use the TensorFlow v2 API but uses the TensorFlow v1 API (`t [PyTorch](https://pytorch.org/) 2.0 or above is required. While `.pth` and `.pt` are the same in the PyTorch package, they have different meanings in the DeePMD-kit to distinguish the model and the checkpoint. +### Paddle {{ paddle_icon }} + +- Model filename extension: `.json` and `.pdiparams` +- Checkpoint filename extension: `.pd` + +[Paddle](https://www.paddlepaddle.org.cn/) 3.0 or above is required. + ### DP {{ dpmodel_icon }} :::{note} @@ -45,7 +52,7 @@ NumPy 1.21 or above is required. ### Training -When training and freezing a model, you can use `dp --tf` or `dp --pt` in the command line to switch the backend. +When training and freezing a model, you can use `dp --tf` or `dp --pt` or `dp --pd` in the command line to switch the backend. ### Inference @@ -57,5 +64,5 @@ For example, when the model filename ends with `.pb` (the ProtoBuf file), DeePMD If a model is supported by two backends, one can use [`dp convert-backend`](./cli.rst) to convert the model file between these two backends. :::{warning} -Currently, only the `se_e2_a` model fully supports the backend conversion between TensorFlow {{ tensorflow_icon }} and PyTorch {{ pytorch_icon }}. +Currently, only the `se_e2_a` model fully supports the backend conversion between TensorFlow {{ tensorflow_icon }}, PyTorch {{ pytorch_icon }} and Paddle {{ paddle_icon }}. ::: diff --git a/doc/freeze/freeze.md b/doc/freeze/freeze.md index c3800917a6..4cee02c6d0 100644 --- a/doc/freeze/freeze.md +++ b/doc/freeze/freeze.md @@ -32,3 +32,23 @@ $ dp --pt freeze -o model_branch1.pth --head CHOSEN_BRANCH ``` The output model is called `model_branch1.pth`, which is the specifically frozen model with the `CHOSEN_BRANCH` head. + +::: + +:::{tab-item} Paddle {{ paddle_icon }} + +```bash +$ dp --pd freeze -o model.json +DEEPMD INFO Paddle inference model has been exported to: model.json(.pdiparams) +``` + +in the folder where the model is trained. The output model is called `model.json` and `model.pdiparams`. + +In [multi-task mode](../train/multi-task-training-pt.md), you need to choose one available heads (e.g. `CHOSEN_BRANCH`) by `--head` +to specify which model branch you want to freeze: + +```bash +$ dp --pd freeze -o model_branch1.json --head CHOSEN_BRANCH +``` + +The output model is called `model_branch1.json`, which is the specifically frozen model with the `CHOSEN_BRANCH` head. diff --git a/doc/install/install-from-source.md b/doc/install/install-from-source.md index a725be0133..5079135f16 100644 --- a/doc/install/install-from-source.md +++ b/doc/install/install-from-source.md @@ -98,6 +98,47 @@ If one has multiple python interpreters named something like python3.x, it can b virtualenv -p python3.8 $deepmd_venv ``` +::: + +:::{tab-item} Paddle {{ paddle_icon }} + +To install Paddle, run + +```sh +# cu123 +python -m pip install --pre paddlepaddle-gpu -i https://www.paddlepaddle.org.cn/packages/nightly/cu123/ +# cu118 +python -m pip install --pre paddlepaddle-gpu -i https://www.paddlepaddle.org.cn/packages/nightly/cu118/ +# cpu +python -m pip install --pre paddlepaddle -i https://www.paddlepaddle.org.cn/packages/nightly/cpu/ +``` + +Follow [Paddle documentation](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/index_cn.html) to install Paddle built against different CUDA versions or without CUDA. + +One can also [use conda](https://docs.deepmodeling.org/faq/conda.html) to install Paddle from [conda-forge](https://conda-forge.org). + +::: + +:::: + +It is important that every time a new shell is started and one wants to use `DeePMD-kit`, the virtual environment should be activated by + +```bash +source $deepmd_venv/bin/activate +``` + +if one wants to skip out of the virtual environment, he/she can do + +```bash +deactivate +``` + +If one has multiple python interpreters named something like python3.x, it can be specified by, for example + +```bash +virtualenv -p python3.8 $deepmd_venv +``` + One should remember to activate the virtual environment every time he/she uses DeePMD-kit. ### Install the DeePMD-kit's python interface @@ -126,6 +167,12 @@ Note that PyTorch may have specific requirements for the compiler version to sup ::: +:::{tab-item} Paddle {{ paddle_icon }} + +You can set the environment variable `export DP_ENABLE_PADDLE=1` to enable customized C++ OPs in the Paddle backend. + +::: + :::: Execute @@ -173,6 +220,13 @@ The path to the ROCM toolkit directory. If `ROCM_ROOT` is not set, it will look {{ pytorch_icon }} Enable customized C++ OPs for the PyTorch backend. PyTorch can still run without customized C++ OPs, but features will be limited. ::: +:::{envvar} DP_ENABLE_PADDLE + +**Choices**: `0`, `1`; **Default**: `0` + +{{ pytorch_icon }} Enable customized C++ OPs for the Paddle backend. Paddle can still run without customized C++ OPs, but features will be limited. +::: + :::{envvar} TENSORFLOW_ROOT **Type**: Path; **Default**: Detected automatically @@ -187,6 +241,13 @@ The path to the ROCM toolkit directory. If `ROCM_ROOT` is not set, it will look {{ pytorch_icon }} The path to PyTorch Python library. If not given, by default, the installer only finds PyTorch under the user site-package directory (`site.getusersitepackages()`) or the system site-package directory (`sysconfig.get_path("purelib")`) due to the limitation of [PEP-517](https://peps.python.org/pep-0517/). If not found, the latest PyTorch (or the environment variable `PYTORCH_VERSION` if given) from PyPI will be built against. ::: +:::{envvar} PADDLE_ROOT + +**Type**: Path; **Default**: Detected automatically + +{{ paddle_icon }} The path to Paddle Python library. If not given, by default, the installer only finds Paddle under the user site-package directory (`site.getusersitepackages()`) or the system site-package directory (`sysconfig.get_path("purelib")`) due to the limitation of [PEP-517](https://peps.python.org/pep-0517/). If not found, the latest Paddle (or the environment variable `PADDLE_VERSION` if given) from PyPI will be built against. +::: + :::{envvar} DP_ENABLE_NATIVE_OPTIMIZATION **Choices**: `0`, `1`; **Default**: `0` @@ -214,7 +275,7 @@ Other [CMake environment variables](https://cmake.org/cmake/help/latest/manual/c To test the installation, one should first jump out of the source directory -``` +```bash cd /some/other/workspace ``` @@ -299,6 +360,13 @@ You can also download libtorch prebuilt library from the [PyTorch website](https ::: +:::{tab-item} Paddle {{ paddle_icon }} + +If you have installed Paddle using pip, you can use libtorch inside the Paddle Python package. +You can also download libtorch prebuilt library from the [Paddle website](https://www.paddlepaddle.org.cn/). + +::: + :::: ### Install DeePMD-kit's C++ interface @@ -352,6 +420,16 @@ cmake -DENABLE_PYTORCH=TRUE -DUSE_PT_PYTHON_LIBS=TRUE -DCMAKE_INSTALL_PREFIX=$de ::: +:::{tab-item} Paddle {{ paddle_icon }} + +I assume you have installed the Paddle (either Python or C++ interface) to `$paddle_root`, then execute CMake + +```bash +cmake -DENABLE_PYTORCH=TRUE -DCMAKE_PREFIX_PATH=$paddle_root -DCMAKE_INSTALL_PREFIX=$deepmd_root .. +``` + +::: + :::: One may add the following CMake variables to `cmake` using the [`-D =` option](https://cmake.org/cmake/help/latest/manual/cmake.1.html#cmdoption-cmake-D): @@ -372,6 +450,14 @@ One may add the following CMake variables to `cmake` using the [`-D == **Note** for developers: `torchrun` by default passes settings as environment variables [(list here)](https://pytorch.org/docs/stable/elastic/run.html#environment-variables). > To check forward, backward, and communication time, please set env var `TORCH_CPP_LOG_LEVEL=INFO TORCH_DISTRIBUTED_DEBUG=DETAIL`. More details can be found [here](https://pytorch.org/docs/stable/distributed.html#logging). + +## Paddle Implementation {{ paddle_icon }} + +Currently, parallel training in paddle version is implemented in the form of Paddle Distributed Data Parallelism [DDP](https://paddle.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html). +DeePMD-kit will decide whether to launch the training in parallel (distributed) mode or in serial mode depending on your execution command. + +### Dataloader and Dataset + +One of the major differences between two backends during training is that the Paddle version employs a multi-threaded data loading utility [DataLoader](https://paddle.org/docs/stable/data.html). +We utilize the Paddle framework and have designed and implemented a multiprocessing data processing and loading system called DpLoaderSet based on torch DataLoader and Dataset. + +First, we establish a DeepmdData class for each system, which is consistent with the TensorFlow version in this level. Then, we create a dataloader for each system, resulting in the same number of dataloaders as the number of systems. Next, we create a dataset for the dataloaders obtained in the previous step. This allows us to query the data for each system through this dataset, while the iteration pointers for each system are maintained by their respective dataloaders. Finally, a dataloader is created for the outermost dataset. + +We achieve custom sampling methods using a weighted sampler. The length of the sampler is set to total_batch_num \* num_workers.The parameter "num_workers" defines the number of threads involved in multi-threaded loading, which can be modified by setting the environment variable NUM_WORKERS (default: min(8, ncpus)). + +> **Note** The underlying dataloader will use a distributed sampler to ensure that each GPU receives batches with different content in parallel mode, which will use sequential sampler in serial mode. In the TensorFlow version, Horovod shuffles the dataset using different random seeds for the same purpose.. + +```mermaid +flowchart LR + + subgraph systems + subgraph system1 + direction LR + frame1[frame 1] + frame2[frame 2] + end + + subgraph system2 + direction LR + frame3[frame 3] + frame4[frame 4] + frame5[frame 5] + end + end + + subgraph dataset + dataset1[dataset 1] + dataset2[dataset 2] + end + system1 -- frames --> dataset1 + system2 --> dataset2 + + subgraph distribted sampler + ds1[distributed sampler 1] + ds2[distributed sampler 2] + end + dataset1 --> ds1 + dataset2 --> ds2 + + subgraph dataloader + dataloader1[dataloader 1] + dataloader2[dataloader 2] + end + ds1 -- mini batch --> dataloader1 + ds2 --> dataloader2 + + subgraph index[index on Rank 0] + dl11[dataloader 1, entry 1] + dl21[dataloader 2, entry 1] + dl22[dataloader 2, entry 2] + end + dataloader1 --> dl11 + dataloader2 --> dl21 + dataloader2 --> dl22 + + index -- for each step, choose 1 system --> WeightedSampler + --> dploaderset --> bufferedq[buffered queue] --> model +``` + +### How to use + +We use [`paddle.distributed.fleet`](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/06_distributed_training/cluster_quick_start_collective_cn.html) to launch a DDP training session. + +To start training with multiple GPUs in one node, set environment variable `CUDA_VISIBLE_DEVICES` as the list of GPUs you want to use: + +```bash +# example for training with 4 gpus in one node +CUDA_VISIBLE_DEVICES=0,1,2,3 \ + python -m paddle.distributed.launch --gpus="0,1,2,3" dp --pd train input.json +``` diff --git a/doc/train/tensorboard.md b/doc/train/tensorboard.md index 32ecdd0ab2..3925ab3d3d 100644 --- a/doc/train/tensorboard.md +++ b/doc/train/tensorboard.md @@ -1,7 +1,7 @@ -# TensorBoard Usage {{ tensorflow_icon }} {{ pytorch_icon }} +# TensorBoard Usage {{ tensorflow_icon }} {{ pytorch_icon }} {{ paddle_icon }} :::{note} -**Supported backends**: TensorFlow {{ tensorflow_icon }}, PyTorch {{ pytorch_icon }} +**Supported backends**: TensorFlow {{ tensorflow_icon }}, PyTorch {{ pytorch_icon }}, Paddle {{ paddle_icon }} ::: TensorBoard provides the visualization and tooling needed for machine learning diff --git a/doc/train/training.md b/doc/train/training.md index 5e8f8db498..8f491cc7a8 100644 --- a/doc/train/training.md +++ b/doc/train/training.md @@ -26,6 +26,14 @@ $ dp --pt train input.json ::: +:::{tab-item} Paddle {{ paddle_icon }} + +```bash +$ dp --pd train input.json +``` + +::: + :::: where `input.json` is the name of the input script. diff --git a/pyproject.toml b/pyproject.toml index 28fe114e01..3ed1628f9e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -204,7 +204,7 @@ replacement = '\1="https://github.com/deepmodeling/deepmd-kit/raw/master/\g<2>"' [tool.cibuildwheel] test-command = [ "python -m deepmd -h", - """python -c "import deepmd.tf;import deepmd.pt" """, + """python -c "import deepmd.tf;import deepmd.pt;import deepmd.pd" """, "dp -h", "dp_ipi", "pytest {project}/source/tests/tf/test_lammps.py" @@ -387,8 +387,10 @@ convention = "numpy" banned-module-level-imports = [ "deepmd.tf", "deepmd.pt", + "deepmd.pd", "tensorflow", "torch", + "paddle", ] [tool.ruff.lint.flake8-tidy-imports.banned-api] @@ -398,9 +400,12 @@ banned-module-level-imports = [ # Also ignore `E402` in all `__init__.py` files. "deepmd/tf/**" = ["TID253"] "deepmd/pt/**" = ["TID253"] +"deepmd/pd/**" = ["TID253"] "source/tests/tf/**" = ["TID253"] "source/tests/pt/**" = ["TID253"] +"source/tests/pd/**" = ["TID253"] "source/tests/universal/pt/**" = ["TID253"] +"source/tests/universal/pd/**" = ["TID253"] "source/ipi/tests/**" = ["TID253"] "source/lmp/tests/**" = ["TID253"] "**/*.ipynb" = ["T20"] # printing in a nb file is expected From 5ee8bcf0ea2ec2c19ca139b9b95e1f33baf0adba Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Fri, 27 Sep 2024 14:30:05 +0800 Subject: [PATCH 37/93] optimize cmake paddle macro name --- source/CMakeLists.txt | 18 ++++++++++-------- source/config/run_config.ini | 5 ++++- 2 files changed, 14 insertions(+), 9 deletions(-) diff --git a/source/CMakeLists.txt b/source/CMakeLists.txt index f2454b4394..7d0debd63d 100644 --- a/source/CMakeLists.txt +++ b/source/CMakeLists.txt @@ -23,21 +23,23 @@ if(NOT DEEPMD_C_ROOT) endif() if(ENABLE_PADDLE) - if(NOT DEFINED PADDLE_LIB) - message(FATAL_ERROR "Make sure PADDLE_LIB is set when ENABLE_PADDLE=ON") + if(NOT DEFINED PADDLE_INFERENCE_DIR) + message( + FATAL_ERROR "Make sure PADDLE_INFERENCE_DIR is set when ENABLE_PADDLE=ON") endif() - set(PADDLE_LIB - ${PADDLE_LIB} + set(PADDLE_INFERENCE_DIR + ${PADDLE_INFERENCE_DIR} CACHE PATH "Path to 'paddle_inference_install_dir' or 'paddle_inference'") # used in api_cc set(PADDLE_LIBRARIES - "${PADDLE_LIB}/paddle/lib/libpaddle_inference.so" + "${PADDLE_INFERENCE_DIR}/paddle/lib/libpaddle_inference.so" CACHE PATH "Path to libpaddle_inference.so") - include_directories("${PADDLE_LIB}/") - set(PADDLE_LIB_THIRD_PARTY_PATH "${PADDLE_LIB}/third_party/install/") + include_directories("${PADDLE_INFERENCE_DIR}/") + set(PADDLE_LIB_THIRD_PARTY_PATH + "${PADDLE_INFERENCE_DIR}/third_party/install/") include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}protobuf/include") include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}glog/include") @@ -48,7 +50,7 @@ if(ENABLE_PADDLE) link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}glog/lib") link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}gflags/lib") link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}xxhash/lib") - link_directories("${PADDLE_LIB}/paddle/lib") + link_directories("${PADDLE_INFERENCE_DIR}/paddle/lib") endif(ENABLE_PADDLE) if(BUILD_TESTING) diff --git a/source/config/run_config.ini b/source/config/run_config.ini index fb96ad224e..65b98a1e5a 100644 --- a/source/config/run_config.ini +++ b/source/config/run_config.ini @@ -6,13 +6,16 @@ GIT_DATE = @GIT_DATE@ GIT_BRANCH = @GIT_BRANCH@ ENABLE_TENSORFLOW = @ENABLE_TENSORFLOW@ ENABLE_PYTORCH = @ENABLE_PYTORCH@ +ENABLE_PADDLE = @ENABLE_PADDLE@ TF_INCLUDE_DIR = @TensorFlow_INCLUDE_DIRS@ TF_LIBS = @TensorFlow_LIBRARY_PATH@ TF_VERSION = @TENSORFLOW_VERSION@ TF_CXX11_ABI_FLAG = @OP_CXX_ABI@ PT_INCLUDE_DIR = @TORCH_INCLUDE_DIRS@ PT_LIBS = @PyTorch_LIBRARY_PATH@ -PT_VERSIOn = @Torch_VERSION@ +PT_VERSION = @Torch_VERSION@ PT_CXX11_ABI_FLAG = @OP_CXX_ABI_PT@ +PD_VERSION = @PADDLE_VERSION@ +PD_INFERENCE_DIR = @PADDLE_INFERENCE_DIR@ MODEL_VERSION=@MODEL_VERSION@ DP_VARIANT=@DP_VARIANT@ From e3c1ceb0062281e8d19a8a2cdc5ce059391b3da6 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Fri, 27 Sep 2024 14:37:57 +0800 Subject: [PATCH 38/93] update parallel training with paddle backend --- doc/train/parallel-training.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/doc/train/parallel-training.md b/doc/train/parallel-training.md index 9e78b53b12..1775f94aaa 100644 --- a/doc/train/parallel-training.md +++ b/doc/train/parallel-training.md @@ -267,3 +267,19 @@ To start training with multiple GPUs in one node, set environment variable `CUDA CUDA_VISIBLE_DEVICES=0,1,2,3 \ python -m paddle.distributed.launch --gpus="0,1,2,3" dp --pd train input.json ``` + +Suppose you have 2 nodes each with 4 GPUs and their ip address are: `192.168.1.2` and `192.168.1.3`, then you can use `paddle.distributed.launch` to launch a DDP training session: + +```bash +# run in node 192.168.1.2 +python -m paddle.distributed.launch \ + --gpus=0,1,2,3 \ + --ips=192.168.1.2,192.168.1.3 \ + dp --pd train input.json + +# then run in the other node 192.168.1.3 +python -m paddle.distributed.launch \ + --gpus=0,1,2,3 \ + --ips=192.168.1.2,192.168.1.3 \ + dp --pd train input.json +``` From 49ba5a51b48ba3b4e9ef8d4b74d575e590268c50 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Fri, 27 Sep 2024 14:49:45 +0800 Subject: [PATCH 39/93] use 0-D Tensor as buffer shape --- deepmd/pd/model/atomic_model/dp_atomic_model.py | 12 ++++++------ source/api_cc/src/DeepPotPD.cc | 2 +- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/deepmd/pd/model/atomic_model/dp_atomic_model.py b/deepmd/pd/model/atomic_model/dp_atomic_model.py index ea93244575..1035a62e59 100644 --- a/deepmd/pd/model/atomic_model/dp_atomic_model.py +++ b/deepmd/pd/model/atomic_model/dp_atomic_model.py @@ -77,33 +77,33 @@ def _string_to_array(s: str) -> List[int]: # register 'has_message_passing' as buffer(cast to int32 as problems may meets with vector) self.register_buffer( "buffer_has_message_passing", - paddle.to_tensor([self.has_message_passing()], dtype="int32"), + paddle.to_tensor(self.has_message_passing(), dtype="int32"), ) self.buffer_has_message_passing.name = "buffer_has_message_passing" # register 'ntypes' as buffer self.register_buffer( - "buffer_ntypes", paddle.to_tensor([self.ntypes], dtype="int32") + "buffer_ntypes", paddle.to_tensor(self.ntypes, dtype="int32") ) self.buffer_ntypes.name = "buffer_ntypes" # register 'rcut' as buffer self.register_buffer( - "buffer_rcut", paddle.to_tensor([self.rcut], dtype="float64") + "buffer_rcut", paddle.to_tensor(self.rcut, dtype="float64") ) self.buffer_rcut.name = "buffer_rcut" # register 'dfparam' as buffer self.register_buffer( - "buffer_dfparam", paddle.to_tensor([self.get_dim_fparam()], dtype="int32") + "buffer_dfparam", paddle.to_tensor(self.get_dim_fparam(), dtype="int32") ) self.buffer_dfparam.name = "buffer_dfparam" # register 'daparam' as buffer self.register_buffer( - "buffer_daparam", paddle.to_tensor([self.get_dim_aparam()], dtype="int32") + "buffer_daparam", paddle.to_tensor(self.get_dim_aparam(), dtype="int32") ) self.buffer_daparam.name = "buffer_daparam" # register 'aparam_nall' as buffer self.register_buffer( "buffer_aparam_nall", - paddle.to_tensor([self.is_aparam_nall()], dtype="int32"), + paddle.to_tensor(self.is_aparam_nall(), dtype="int32"), ) self.buffer_aparam_nall.name = "buffer_aparam_nall" diff --git a/source/api_cc/src/DeepPotPD.cc b/source/api_cc/src/DeepPotPD.cc index 06a417073a..7d058b8927 100644 --- a/source/api_cc/src/DeepPotPD.cc +++ b/source/api_cc/src/DeepPotPD.cc @@ -3,11 +3,11 @@ #include "DeepPotPD.h" #include -#include #include #include "common.h" #include "device.h" +#include "errors.h" using namespace deepmd; From f1cae5947355ff9b52dcaad2e54aa30c26d3ce4c Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Sun, 29 Sep 2024 17:27:19 +0800 Subject: [PATCH 40/93] support DCU(rocm) --- source/CMakeLists.txt | 1 + source/api_cc/CMakeLists.txt | 4 ++++ 2 files changed, 5 insertions(+) diff --git a/source/CMakeLists.txt b/source/CMakeLists.txt index 7d0debd63d..5960fd6398 100644 --- a/source/CMakeLists.txt +++ b/source/CMakeLists.txt @@ -51,6 +51,7 @@ if(ENABLE_PADDLE) link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}gflags/lib") link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}xxhash/lib") link_directories("${PADDLE_INFERENCE_DIR}/paddle/lib") + add_definitions(-D_GLIBCXX_USE_CXX11_ABI=1) endif(ENABLE_PADDLE) if(BUILD_TESTING) diff --git a/source/api_cc/CMakeLists.txt b/source/api_cc/CMakeLists.txt index ee347f9fd3..6239f88773 100644 --- a/source/api_cc/CMakeLists.txt +++ b/source/api_cc/CMakeLists.txt @@ -26,6 +26,10 @@ endif() if(ENABLE_PADDLE AND NOT BUILD_PY_IF) target_link_libraries(${libname} PUBLIC "${PADDLE_LIBRARIES}") target_compile_definitions(${libname} PUBLIC BUILD_PADDLE) + if(DP_VARIANT STREQUAL "rocm") + target_link_libraries(${libname} + PUBLIC "${hip_LIB_INSTALL_DIR}/libgalaxyhip.so") + endif() endif() target_include_directories( From a83fb63a5d534625419d4addd246b3f400bda1cd Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Sun, 29 Sep 2024 18:09:59 +0800 Subject: [PATCH 41/93] simplify compile code --- doc/freeze/freeze.md | 4 +- doc/install/install-from-source.md | 6 +-- source/CMakeLists.txt | 2 + source/install/build_cc_pd.sh | 69 +++++++----------------------- 4 files changed, 23 insertions(+), 58 deletions(-) diff --git a/doc/freeze/freeze.md b/doc/freeze/freeze.md index 4cee02c6d0..f0d3c8abd8 100644 --- a/doc/freeze/freeze.md +++ b/doc/freeze/freeze.md @@ -38,7 +38,7 @@ The output model is called `model_branch1.pth`, which is the specifically frozen :::{tab-item} Paddle {{ paddle_icon }} ```bash -$ dp --pd freeze -o model.json +$ dp --pd freeze -o model DEEPMD INFO Paddle inference model has been exported to: model.json(.pdiparams) ``` @@ -48,7 +48,7 @@ In [multi-task mode](../train/multi-task-training-pt.md), you need to choose one to specify which model branch you want to freeze: ```bash -$ dp --pd freeze -o model_branch1.json --head CHOSEN_BRANCH +$ dp --pd freeze -o model_branch1 --head CHOSEN_BRANCH ``` The output model is called `model_branch1.json`, which is the specifically frozen model with the `CHOSEN_BRANCH` head. diff --git a/doc/install/install-from-source.md b/doc/install/install-from-source.md index 5079135f16..7e1be1a432 100644 --- a/doc/install/install-from-source.md +++ b/doc/install/install-from-source.md @@ -145,7 +145,7 @@ One should remember to activate the virtual environment every time he/she uses D Check the compiler version on your machine -``` +```bash gcc --version ``` @@ -422,10 +422,10 @@ cmake -DENABLE_PYTORCH=TRUE -DUSE_PT_PYTHON_LIBS=TRUE -DCMAKE_INSTALL_PREFIX=$de :::{tab-item} Paddle {{ paddle_icon }} -I assume you have installed the Paddle (either Python or C++ interface) to `$paddle_root`, then execute CMake +I assume you have compiled the Paddle inference library(C++ interface) to `$PADDLE_INFERENCE_DIR`, then execute CMake ```bash -cmake -DENABLE_PYTORCH=TRUE -DCMAKE_PREFIX_PATH=$paddle_root -DCMAKE_INSTALL_PREFIX=$deepmd_root .. +cmake -DENABLE_PADDLE=ON -DCMAKE_PREFIX_PATH=$PADDLE_INFERENCE_DIR -DPADDLE_INFERENCE_DIR=$PADDLE_INFERENCE_DIR -DCMAKE_INSTALL_PREFIX=$deepmd_root .. ``` ::: diff --git a/source/CMakeLists.txt b/source/CMakeLists.txt index 5960fd6398..fc3f7c8c58 100644 --- a/source/CMakeLists.txt +++ b/source/CMakeLists.txt @@ -51,7 +51,9 @@ if(ENABLE_PADDLE) link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}gflags/lib") link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}xxhash/lib") link_directories("${PADDLE_INFERENCE_DIR}/paddle/lib") + # if (USE_ROCM_TOOLKIT) add_definitions(-D_GLIBCXX_USE_CXX11_ABI=1) + # endif() endif(ENABLE_PADDLE) if(BUILD_TESTING) diff --git a/source/install/build_cc_pd.sh b/source/install/build_cc_pd.sh index d45cf5b993..36389c5ec3 100755 --- a/source/install/build_cc_pd.sh +++ b/source/install/build_cc_pd.sh @@ -22,70 +22,33 @@ export LAMMPS_DIR="/workspace/hesensen/deepmd_backend/deepmd_paddle_new/source/b export LAMMPS_SOURCE_ROOT="/workspace/hesensen/deepmd_backend/deepmd_paddle_new/source/build_lammps/lammps-stable_29Aug2024/" # 设置推理时的 GPU 卡号 -export CUDA_VISIBLE_DEVICES=3 -# export FLAGS_benchmark=1 -# export GLOG_v=6 +export CUDA_VISIBLE_DEVICES=1 -# PADDLE_DIR 设置为第二步 clone下来的 Paddle 目录 -export PADDLE_DIR="/workspace/hesensen/PaddleScience_enn_debug/Paddle/" - -# DEEPMD_DIR 设置为本项目的根目录 -export DEEPMD_DIR="/workspace/hesensen/deepmd_backend/deepmd_paddle_new/" +# deepmd_root 设置为本项目的根目录 +export deepmd_root="/workspace/hesensen/deepmd_backend/deepmd_paddle_new/" # PADDLE_INFERENCE_DIR 设置为第二步编译得到的 Paddle 推理库目录 export PADDLE_INFERENCE_DIR="/workspace/hesensen/PaddleScience_enn_debug/Paddle/build/paddle_inference_install_dir/" -# TENSORFLOW_DIR 设置为 tensorflow 的安装目录，可用 pip show tensorflow 确定 -# export TENSORFLOW_DIR="/path/to/tensorflow" - -export LD_LIBRARY_PATH=${PADDLE_DIR}/paddle/fluid/pybind/:$LD_LIBRARY_PATH -export LD_LIBRARY_PATH=${DEEPMD_DIR}/deepmd/op:$LD_LIBRARY_PATH +export LD_LIBRARY_PATH=${deepmd_root}/deepmd/op:$LD_LIBRARY_PATH export LD_LIBRARY_PATH=${PADDLE_INFERENCE_DIR}/paddle/lib:$LD_LIBRARY_PATH export LD_LIBRARY_PATH=${PADDLE_INFERENCE_DIR}/third_party/install/mkldnn/lib:$LD_LIBRARY_PATH export LD_LIBRARY_PATH=${PADDLE_INFERENCE_DIR}/third_party/install/mklml/lib:$LD_LIBRARY_PATH -export LD_LIBRARY_PATH=${DEEPMD_DIR}/source/build:$LD_LIBRARY_PATH -export LIBRARY_PATH=${DEEPMD_DIR}/deepmd/op:$LIBRARY_PATH -# export FLAGS_check_nan_inf=1 -# cd ${DEEPMD_DIR}/source -# rm -rf build # 若改动CMakeLists.txt，则需要打开该注释 -# mkdir build -# cd - - -# DEEPMD_INSTALL_DIR 设置为 deepmd-lammps 的目标安装目录，可自行设置任意路径 -# export DEEPMD_INSTALL_DIR="path/to/deepmd_root" - -# 开始编译 -# cmake -DCMAKE_INSTALL_PREFIX=${DEEPMD_INSTALL_DIR} \ -# -DUSE_CUDA_TOOLKIT=TRUE \ -# -DTENSORFLOW_ROOT=${TENSORFLOW_DIR} \ -# -DPADDLE_LIB=${PADDLE_INFERENCE_DIR} \ -# -DFLOAT_PREC=low .. -# make -j4 && make install -# make lammps - -# cd ${LAMMPS_DIR}/src/ -# \cp -r ${DEEPMD_DIR}/source/build/USER-DEEPMD . -# make yes-kspace -# make yes-extra-fix -# make yes-user-deepmd -# make serial -j -# export PATH=${LAMMPS_DIR}/src:$PATH - -# cd ${DEEPMD_DIR}/examples/water/lmp +export LD_LIBRARY_PATH=${deepmd_root}/source/build:$LD_LIBRARY_PATH -# lmp_serial -in in.lammps +cd ${deepmd_root}/source +rm -rf build # 若改动CMakeLists.txt，则需要打开该注释 +mkdir build +cd - BUILD_TMP_DIR=${SCRIPT_PATH}/../build mkdir -p ${BUILD_TMP_DIR} cd ${BUILD_TMP_DIR} -cmake -D ENABLE_TENSORFLOW=OFF \ - -D ENABLE_PYTORCH=OFF \ - -D ENABLE_PADDLE=ON \ - -D PADDLE_LIB=${PADDLE_INFERENCE_DIR} \ +cmake -D ENABLE_PADDLE=ON \ + -D PADDLE_INFERENCE_DIR=${PADDLE_INFERENCE_DIR} \ -D CMAKE_INSTALL_PREFIX=${INSTALL_PREFIX} \ -D USE_TF_PYTHON_LIBS=TRUE \ -D LAMMPS_SOURCE_ROOT=${LAMMPS_SOURCE_ROOT} \ - -D ENABLE_IPI=OFF \ ${CUDA_ARGS} \ -D LAMMPS_VERSION=stable_29Aug2024 \ .. @@ -95,11 +58,11 @@ cmake --install . #------------------ echo "Congratulations! DeePMD-kit has been installed at ${INSTALL_PREFIX}" -cd ${DEEPMD_DIR}/source +cd ${deepmd_root}/source cd build make lammps cd ${LAMMPS_DIR}/src/ -\cp -r ${DEEPMD_DIR}/source/build/USER-DEEPMD . +\cp -r ${deepmd_root}/source/build/USER-DEEPMD . make no-kspace make yes-kspace make no-extra-fix @@ -107,11 +70,11 @@ make yes-extra-fix make no-user-deepmd make yes-user-deepmd # make serial -j -make mpi -j 20 +make mpi -j 10 export PATH=${LAMMPS_DIR}/src:$PATH -cd ${DEEPMD_DIR}/examples/water/lmp +cd ${deepmd_root}/examples/water/lmp echo "START INFERENCE..." # lmp_serial -in paddle_in.lammps 2>&1 | tee paddle_infer.log -mpirun -np 1 lmp_mpi -in paddle_in.lammps 2>&1 | tee paddle_infer.log +mpirun -np 2 lmp_mpi -in paddle_in.lammps 2>&1 | tee paddle_infer.log From f7f64b168340654799ffe1ea97f738789fb9ff77 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Wed, 9 Oct 2024 17:23:03 +0800 Subject: [PATCH 42/93] fix code --- deepmd/pd/loss/dos.py | 4 ++-- deepmd/pd/loss/tensor.py | 9 ++++++--- deepmd/pd/utils/nlist.py | 3 +-- deepmd/pd/utils/preprocess.py | 2 +- 4 files changed, 10 insertions(+), 8 deletions(-) diff --git a/deepmd/pd/loss/dos.py b/deepmd/pd/loss/dos.py index ef1482c6da..a195f709cc 100644 --- a/deepmd/pd/loss/dos.py +++ b/deepmd/pd/loss/dos.py @@ -140,7 +140,7 @@ def forward(self, input_dict, model, label, natoms, learning_rate=0.0, mae=False [-1, self.numb_dos] ) if "mask" in model_pred: - diff = diff[model_pred["mask"].reshape([-1]).bool()] + diff = diff[model_pred["mask"].reshape([-1]).astype("bool")] l2_local_loss_dos = paddle.mean(paddle.square(diff)) if not self.inference: more_loss["l2_local_dos_loss"] = self.display_if_exist( @@ -164,7 +164,7 @@ def forward(self, input_dict, model, label, natoms, learning_rate=0.0, mae=False [-1, self.numb_dos] ) if "mask" in model_pred: - diff = diff[model_pred["mask"].reshape([-1]).bool()] + diff = diff[model_pred["mask"].reshape([-1]).astype("bool")] l2_local_loss_cdf = paddle.mean(paddle.square(diff)) if not self.inference: more_loss["l2_local_cdf_loss"] = self.display_if_exist( diff --git a/deepmd/pd/loss/tensor.py b/deepmd/pd/loss/tensor.py index 8c658866bf..5662c88451 100644 --- a/deepmd/pd/loss/tensor.py +++ b/deepmd/pd/loss/tensor.py @@ -107,7 +107,7 @@ def forward(self, input_dict, model, label, natoms, learning_rate=0.0, mae=False [-1, self.tensor_size] ) if "mask" in model_pred: - diff = diff[model_pred["mask"].reshape([-1]).bool()] + diff = diff[model_pred["mask"].reshape([-1]).astype("bool")] l2_local_loss = paddle.mean(paddle.square(diff)) if not self.inference: more_loss[f"l2_local_{self.tensor_name}_loss"] = self.display_if_exist( @@ -133,9 +133,12 @@ def forward(self, input_dict, model, label, natoms, learning_rate=0.0, mae=False if "mask" in model_pred: atom_num = model_pred["mask"].sum(-1, keepdim=True) l2_global_loss = paddle.mean( - paddle.sum(paddle.square(diff) * atom_num, axis=0) / atom_num.sum() + paddle.sum( + paddle.square(diff) * atom_num.astype(diff.dtype), axis=0 + ) + / atom_num.sum() ) - atom_num = paddle.mean(atom_num.float()) + atom_num = paddle.mean(atom_num.astype(diff.dtype)) else: atom_num = natoms l2_global_loss = paddle.mean(paddle.square(diff)) diff --git a/deepmd/pd/utils/nlist.py b/deepmd/pd/utils/nlist.py index 52893c85d2..75259507e6 100644 --- a/deepmd/pd/utils/nlist.py +++ b/deepmd/pd/utils/nlist.py @@ -100,8 +100,7 @@ def build_neighbor_list( nall = coord.shape[1] // 3 # fill virtual atoms with large coords so they are not neighbors of any # real atom. - # if coord.numel().item() > 0: - if True > 0: + if coord.numel() > 0: xmax = paddle.max(coord) + 2.0 * rcut else: xmax = paddle.zeros([], dtype=coord.dtype).to(device=coord.place) + 2.0 * rcut diff --git a/deepmd/pd/utils/preprocess.py b/deepmd/pd/utils/preprocess.py index a4cc8e5502..5a3c10f441 100644 --- a/deepmd/pd/utils/preprocess.py +++ b/deepmd/pd/utils/preprocess.py @@ -183,7 +183,7 @@ def build_neighbor_list( - atype: shape is [nall] """ nall = coord.numel() // 3 - coord = coord.float() + coord = coord.astype(paddle.get_default_dtype()) nlist = [[] for _ in range(nloc)] coord_l = coord.reshape([-1, 1, 3])[:nloc] coord_r = coord.reshape([1, -1, 3]) From d5a313e84c720dadf2e23460b10e713664532dda Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 14 Oct 2024 16:13:56 +0800 Subject: [PATCH 43/93] remove float() for already supporting 0-D scalar __format__ --- deepmd/loggers/training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepmd/loggers/training.py b/deepmd/loggers/training.py index a4ae8fe608..954473e309 100644 --- a/deepmd/loggers/training.py +++ b/deepmd/loggers/training.py @@ -29,6 +29,6 @@ def format_training_message_per_task( rmse = dict(sorted(rmse.items())) return ( f"batch {batch:7d}: {task_name}" - f"{', '.join([f'{kk} = {float(vv):8.2e}' for kk, vv in rmse.items()])}" + f"{', '.join([f'{kk} = {vv:8.2e}' for kk, vv in rmse.items()])}" f"{lr}" ) From 299548af44c3327d314f328b482a56e35b3ac79f Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 14 Oct 2024 20:31:43 +0800 Subject: [PATCH 44/93] polish flag via paddle.set_flags --- deepmd/pd/entrypoints/main.py | 13 ++++++++++--- deepmd/pd/utils/env.py | 1 - 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/deepmd/pd/entrypoints/main.py b/deepmd/pd/entrypoints/main.py index 3c37bf4f4e..439ed3cbac 100644 --- a/deepmd/pd/entrypoints/main.py +++ b/deepmd/pd/entrypoints/main.py @@ -53,7 +53,6 @@ ) from deepmd.pd.utils.env import ( DEVICE, - PIR_ENABLED, ) from deepmd.pd.utils.finetune import ( get_finetune_rules, @@ -354,6 +353,15 @@ def freeze(FLAGS): ** atype [None, natoms] paddle.int64 ** nlist [None, natoms, nnei] paddle.int32 """ + # NOTE: 'FLAGS_save_cf_stack_op', 'FLAGS_prim_enable_dynamic' and + # 'FLAGS_enable_pir_api' shoule be enabled when freezing model. + paddle.set_flags( + { + "FLAGS_save_cf_stack_op": 1, + "FLAGS_prim_enable_dynamic": 1, + "FLAGS_enable_pir_api": 1, + } + ) model = paddle.jit.to_static( model.forward_lower, full_graph=True, @@ -368,9 +376,8 @@ def freeze(FLAGS): path=FLAGS.output, skip_prune_program=True, ) - suffix = "json" if PIR_ENABLED.lower() in ["true", "1"] else "pdmodel" log.info( - f"Paddle inference model has been exported to: {FLAGS.output}.{suffix}(.pdiparams)" + f"Paddle inference model has been exported to: {FLAGS.output}.json and {FLAGS.output}.pdiparams" ) diff --git a/deepmd/pd/utils/env.py b/deepmd/pd/utils/env.py index 49a11658f3..85f9e57169 100644 --- a/deepmd/pd/utils/env.py +++ b/deepmd/pd/utils/env.py @@ -67,7 +67,6 @@ } assert set(PRECISION_DICT.values()) == set(RESERVED_PRECISON_DICT.keys()) DEFAULT_PRECISION = "float64" -PIR_ENABLED = os.getenv("FLAGS_enable_pir_api", "false") # throw warnings if threads not set set_default_nthreads() From 97828b3dac1927215fa4bccb1f87dd9e3d50ef73 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 14 Oct 2024 20:41:58 +0800 Subject: [PATCH 45/93] restore make_model.py --- deepmd/pd/model/model/make_model.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deepmd/pd/model/model/make_model.py b/deepmd/pd/model/model/make_model.py index 1b634f7c8c..202934cfa2 100644 --- a/deepmd/pd/model/model/make_model.py +++ b/deepmd/pd/model/model/make_model.py @@ -424,12 +424,12 @@ def _format_nlist( * paddle.ones( [n_nf, n_nloc, nnei - n_nnei], dtype=nlist.dtype, - ), # .to(device=nlist.place), + ), ], axis=-1, ) - if True: # TODO: Fix controlflow + backward in PIR static graph + if n_nnei > nnei or extra_nlist_sort: n_nf, n_nloc, n_nnei = nlist.shape m_real_nei = nlist >= 0 nlist = paddle.where(m_real_nei, nlist, paddle.zeros_like(nlist)) From 87069e5fd851c3b7eb48e9ed8c87b8eb61968bb4 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 14 Oct 2024 13:01:13 +0000 Subject: [PATCH 46/93] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- deepmd/pd/model/atomic_model/base_atomic_model.py | 4 +++- deepmd/pd/train/wrapper.py | 2 +- deepmd/pd/utils/env_mat_stat.py | 2 +- deepmd/pd/utils/neighbor_stat.py | 2 +- 4 files changed, 6 insertions(+), 4 deletions(-) diff --git a/deepmd/pd/model/atomic_model/base_atomic_model.py b/deepmd/pd/model/atomic_model/base_atomic_model.py index e9ccd7d83a..9cb320c16c 100644 --- a/deepmd/pd/model/atomic_model/base_atomic_model.py +++ b/deepmd/pd/model/atomic_model/base_atomic_model.py @@ -479,7 +479,9 @@ def _get_forward_wrapper_func(self) -> Callable[..., paddle.Tensor]: """Get a forward wrapper of the atomic model for output bias calculation.""" def model_forward(coord, atype, box, fparam=None, aparam=None): - with paddle.no_grad(): # it's essential for pure paddle forward function to use auto_batchsize + with ( + paddle.no_grad() + ): # it's essential for pure paddle forward function to use auto_batchsize ( extended_coord, extended_atype, diff --git a/deepmd/pd/train/wrapper.py b/deepmd/pd/train/wrapper.py index f6a5465f8e..81ef6314c9 100644 --- a/deepmd/pd/train/wrapper.py +++ b/deepmd/pd/train/wrapper.py @@ -7,9 +7,9 @@ from typing import ( Dict, Optional, - OrderedDict, Union, ) +from collections import OrderedDict import paddle diff --git a/deepmd/pd/utils/env_mat_stat.py b/deepmd/pd/utils/env_mat_stat.py index 6d1a153686..f56e067792 100644 --- a/deepmd/pd/utils/env_mat_stat.py +++ b/deepmd/pd/utils/env_mat_stat.py @@ -2,11 +2,11 @@ from typing import ( TYPE_CHECKING, Dict, - Iterator, List, Tuple, Union, ) +from collections.abc import Iterator import numpy as np import paddle diff --git a/deepmd/pd/utils/neighbor_stat.py b/deepmd/pd/utils/neighbor_stat.py index 990d3d4f08..d84133877f 100644 --- a/deepmd/pd/utils/neighbor_stat.py +++ b/deepmd/pd/utils/neighbor_stat.py @@ -1,9 +1,9 @@ # SPDX-License-Identifier: LGPL-3.0-or-later from typing import ( - Iterator, Optional, Tuple, ) +from collections.abc import Iterator import numpy as np import paddle From d6e3fdb6360291eb397b1f2a2867e02633ce42c2 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 15 Oct 2024 15:02:09 +0800 Subject: [PATCH 47/93] update document and fix bugs in code --- .pre-commit-config.yaml | 12 ++--- backend/find_paddle.py | 8 ++-- deepmd/pd/cxx_op.py | 77 +----------------------------- deepmd/pd/entrypoints/main.py | 12 ++--- deepmd/pd/train/training.py | 40 +++++++--------- deepmd/pd/utils/dataloader.py | 22 ++++----- deepmd/pd/utils/env.py | 2 +- doc/freeze/freeze.md | 2 +- doc/install/install-from-source.md | 10 ++-- doc/train/parallel-training.md | 16 ++++--- 10 files changed, 59 insertions(+), 142 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 7282bb6702..40600894a7 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -51,12 +51,12 @@ repos: hooks: - id: blacken-docs # C++ - # - repo: https://github.com/pre-commit/mirrors-clang-format - # rev: v18.1.8 - # hooks: - # - id: clang-format - # exclude: ^source/3rdparty|source/lib/src/gpu/cudart/.+\.inc - # markdown, yaml, CSS, javascript + - repo: https://github.com/pre-commit/mirrors-clang-format + rev: v19.1.1 + hooks: + - id: clang-format + exclude: ^(source/3rdparty|source/lib/src/gpu/cudart/.+\.inc|.+\.ipynb$) + # # markdown, yaml, CSS, javascript # - repo: https://github.com/pre-commit/mirrors-prettier # rev: v4.0.0-alpha.8 # hooks: diff --git a/backend/find_paddle.py b/backend/find_paddle.py index 1d8437a140..3be6dbfa42 100644 --- a/backend/find_paddle.py +++ b/backend/find_paddle.py @@ -18,9 +18,7 @@ get_path, ) from typing import ( - List, Optional, - Tuple, Union, ) @@ -30,7 +28,7 @@ @lru_cache -def find_paddle() -> Tuple[Optional[str], List[str]]: +def find_paddle() -> tuple[Optional[str], list[str]]: """Find PaddlePadle library. Tries to find PaddlePadle in the order of: @@ -48,7 +46,7 @@ def find_paddle() -> Tuple[Optional[str], List[str]]: str, optional PaddlePadle library path if found. list of str - TensorFlow requirement if not found. Empty if found. + Paddle requirement if not found. Empty if found. """ if os.environ.get("DP_ENABLE_PADDLE", "0") == "0": return None, [] @@ -117,7 +115,7 @@ def get_pd_requirement(pd_version: str = "") -> dict: # https://peps.python.org/pep-0440/#version-matching f"paddle=={Version(pd_version).base_version}.*" if pd_version != "" - else "paddle>=3b", + else "paddlepaddle-gpu>=3.0.0b1", ], } diff --git a/deepmd/pd/cxx_op.py b/deepmd/pd/cxx_op.py index 92aed25f65..61d34a958c 100644 --- a/deepmd/pd/cxx_op.py +++ b/deepmd/pd/cxx_op.py @@ -1,15 +1,4 @@ # SPDX-License-Identifier: LGPL-3.0-or-later -import platform - -import paddle -from packaging.version import ( - Version, -) - -from deepmd.env import ( - GLOBAL_CONFIG, - SHARED_LIB_DIR, -) def load_library(module_name: str) -> bool: @@ -25,71 +14,7 @@ def load_library(module_name: str) -> bool: bool Whether the library is loaded successfully """ - if platform.system() == "Windows": - ext = ".dll" - prefix = "" - else: - ext = ".so" - prefix = "lib" - - module_file = (SHARED_LIB_DIR / (prefix + module_name)).with_suffix(ext).resolve() - - if module_file.is_file(): - try: - paddle.utils.cpp_extension.load(module_file) - except OSError as e: - # check: CXX11_ABI_FLAG; version - # from our op - PD_VERSION = GLOBAL_CONFIG["pd_version"] - PD_CXX11_ABI_FLAG = int(GLOBAL_CONFIG["pd_cxx11_abi_flag"]) - # from paddle - # strip the local version - pd_py_version = Version(paddle.__version__).public - # pd_cxx11_abi_flag = int(paddle.compiled_with_cxx11_abi()) - pd_cxx11_abi_flag = 0 - - if PD_CXX11_ABI_FLAG != pd_cxx11_abi_flag: - raise RuntimeError( - "This deepmd-kit package was compiled with " - "CXX11_ABI_FLAG=%d, but Paddle runtime was compiled " - "with CXX11_ABI_FLAG=%d. These two library ABIs are " - "incompatible and thus an error is raised when loading %s. " - "You need to rebuild deepmd-kit against this Paddle " - "runtime." - % ( - PD_CXX11_ABI_FLAG, - pd_cxx11_abi_flag, - module_name, - ) - ) from e - - # different versions may cause incompatibility, see TF - if PD_VERSION != pd_py_version: - raise RuntimeError( - "The version of Paddle used to compile this " - f"deepmd-kit package is {PD_VERSION}, but the version of Paddle " - f"runtime you are using is {pd_py_version}. These two versions are " - f"incompatible and thus an error is raised when loading {module_name}. " - f"You need to install Paddle {PD_VERSION}, or rebuild deepmd-kit " - f"against Paddle {pd_py_version}.\nIf you are using a wheel from " - "PyPI, you may consider to install deepmd-kit execuating " - "`DP_ENABLE_Paddle=1 pip install deepmd-kit --no-binary deepmd-kit` " - "instead." - ) from e - error_message = ( - "This deepmd-kit package is inconsitent with Paddle " - f"Runtime, thus an error is raised when loading {module_name}. " - "You need to rebuild deepmd-kit against this Paddle " - "runtime." - ) - if PD_CXX11_ABI_FLAG == 1: - # #1791 - error_message += ( - "\nWARNING: devtoolset on RHEL6 and RHEL7 does not support _GLIBCXX_USE_CXX11_ABI=1. " - "See https://bugzilla.redhat.com/show_bug.cgi?id=1546704" - ) - raise RuntimeError(error_message) from e - return True + # NOTE: Paddle do not support loading library from .so file yet. return False diff --git a/deepmd/pd/entrypoints/main.py b/deepmd/pd/entrypoints/main.py index 439ed3cbac..dbc6872a32 100644 --- a/deepmd/pd/entrypoints/main.py +++ b/deepmd/pd/entrypoints/main.py @@ -3,12 +3,10 @@ import copy import json import logging -import os from pathlib import ( Path, ) from typing import ( - List, Optional, Union, ) @@ -16,6 +14,7 @@ import h5py import paddle import paddle.distributed as dist +import paddle.distributed.fleet as fleet import paddle.version from deepmd import ( @@ -102,11 +101,10 @@ def get_trainer( multi_task = "model_dict" in config.get("model", {}) # Initialize DDP - local_rank = os.environ.get("LOCAL_RANK") - if local_rank is not None: - local_rank = int(local_rank) + world_size = dist.get_world_size() + if world_size > 1: assert paddle.version.nccl() != "0" - dist.init_parallel_env() + fleet.init(is_collective=True) def prepare_trainer_input_single( model_params_single, data_dict_single, rank=0, seed=None @@ -576,7 +574,7 @@ def change_bias(FLAGS): # @record -def main(args: Optional[Union[List[str], argparse.Namespace]] = None): +def main(args: Optional[Union[list[str], argparse.Namespace]] = None): if not isinstance(args, argparse.Namespace): FLAGS = parse_args(args=args) else: diff --git a/deepmd/pd/train/training.py b/deepmd/pd/train/training.py index fb22b7486a..116c98cf53 100644 --- a/deepmd/pd/train/training.py +++ b/deepmd/pd/train/training.py @@ -10,13 +10,14 @@ ) from typing import ( Any, - Dict, ) import numpy as np import paddle import paddle.distributed as dist -from paddle.distributed import DataParallel as DDP +from paddle.distributed import ( + fleet, +) from paddle.io import ( DataLoader, ) @@ -41,7 +42,6 @@ ) from deepmd.pd.optimizer import ( KFOptimizerWrapper, - LKFOptimizer, ) from deepmd.pd.train.wrapper import ( ModelWrapper, @@ -56,7 +56,6 @@ from deepmd.pd.utils.env import ( DEVICE, JIT, - LOCAL_RANK, NUM_WORKERS, SAMPLER_RECORD, enable_prim, @@ -87,7 +86,7 @@ class Trainer: def __init__( self, - config: Dict[str, Any], + config: dict[str, Any], training_data, stat_file_path=None, validation_data=None, @@ -578,16 +577,6 @@ def single_model_finetune( resume=(resuming and not self.finetune_update_stat) or self.rank != 0, ) - if dist.is_available() and dist.is_initialized(): - paddle.set_device(LOCAL_RANK) - # DDP will guarantee the model parameters are identical across all processes - self.wrapper = DDP( - self.wrapper, - device_ids=[LOCAL_RANK], - find_unused_parameters=True, - output_device=LOCAL_RANK, - ) - # TODO add lr warmups for multitask # author: iProzd def warm_up_linear(step, warmup_steps): @@ -611,15 +600,18 @@ def warm_up_linear(step, warmup_steps): if optimizer_state_dict is not None and self.restart_training: self.optimizer.set_state_dict(optimizer_state_dict) elif self.opt_type == "LKF": - self.optimizer = LKFOptimizer( - [{"params": self.wrapper.parameters()}], - 0.98, - 0.99870, - self.opt_param["kf_blocksize"], - ) + raise NotImplementedError("LKF is not supported yet in Paddle backend.") else: raise ValueError(f"Not supported optimizer type '{self.opt_type}'") + if dist.is_available() and dist.is_initialized(): + # DDP will guarantee the model parameters are identical across all processes + self.wrapper = fleet.distributed_model( + self.wrapper, + # find_unused_parameters=True, + ) + self.optimizer = fleet.distributed_optimizer(self.optimizer) + # Get model prob for multi-task if self.multi_task: self.model_prob = np.array([0.0 for key in self.model_keys]) @@ -941,9 +933,11 @@ def log_loss_valid(_task_key="Default"): # tensorboard if self.enable_tensorboard and _step_id % self.tensorboard_freq == 0: writer.add_scalar(f"{task_key}/lr", cur_lr, _step_id) - writer.add_scalar(f"{task_key}/loss", loss, _step_id) + writer.add_scalar(f"{task_key}/loss", loss.item(), _step_id) for item in more_loss: - writer.add_scalar(f"{task_key}/{item}", more_loss[item], _step_id) + writer.add_scalar( + f"{task_key}/{item}", more_loss[item].item(), _step_id + ) self.t0 = time.time() self.total_train_time = 0.0 diff --git a/deepmd/pd/utils/dataloader.py b/deepmd/pd/utils/dataloader.py index 57650b840e..6dfcef3167 100644 --- a/deepmd/pd/utils/dataloader.py +++ b/deepmd/pd/utils/dataloader.py @@ -12,9 +12,6 @@ from threading import ( Thread, ) -from typing import ( - List, -) import h5py import numpy as np @@ -88,7 +85,7 @@ def __init__( with h5py.File(systems) as file: systems = [os.path.join(systems, item) for item in file.keys()] - self.systems: List[DeepmdDataSetForLoader] = [] + self.systems: list[DeepmdDataSetForLoader] = [] if len(systems) >= 100: log.info(f"Constructing DataLoaders from {len(systems)} systems") @@ -98,17 +95,20 @@ def construct_dataset(system): type_map=type_map, ) - with Pool( + MAX_PROCESSES_NUM = 4 + processes = min( os.cpu_count() // ( - int(os.environ["LOCAL_WORLD_SIZE"]) + dist.get_world_size() if dist.is_available() and dist.is_initialized() else 1 - ) - ) as pool: + ), + MAX_PROCESSES_NUM, + ) + with Pool(processes) as pool: self.systems = pool.map(construct_dataset, systems) - self.sampler_list: List[DistributedBatchSampler] = [] + self.sampler_list: list[DistributedBatchSampler] = [] self.index = [] self.total_batch = 0 @@ -209,7 +209,7 @@ def __getitem__(self, idx): batch["sid"] = idx return batch - def add_data_requirement(self, data_requirement: List[DataRequirementItem]): + def add_data_requirement(self, data_requirement: list[DataRequirementItem]): """Add data requirement for each system in multiple systems.""" for system in self.systems: system.add_data_requirement(data_requirement) @@ -217,7 +217,7 @@ def add_data_requirement(self, data_requirement: List[DataRequirementItem]): def print_summary( self, name: str, - prob: List[float], + prob: list[float], ): print_summary( name, diff --git a/deepmd/pd/utils/env.py b/deepmd/pd/utils/env.py index 85f9e57169..a23c8d53e2 100644 --- a/deepmd/pd/utils/env.py +++ b/deepmd/pd/utils/env.py @@ -23,7 +23,7 @@ ncpus = len(os.sched_getaffinity(0)) except AttributeError: ncpus = os.cpu_count() -NUM_WORKERS = int(os.environ.get("NUM_WORKERS", min(8, ncpus))) +NUM_WORKERS = int(os.environ.get("NUM_WORKERS", min(4, ncpus))) # Make sure DDP uses correct device if applicable LOCAL_RANK = os.environ.get("LOCAL_RANK", None) or paddle.device.get_device() LOCAL_RANK = int(0 if LOCAL_RANK is None else paddle.distributed.get_rank()) diff --git a/doc/freeze/freeze.md b/doc/freeze/freeze.md index f0d3c8abd8..1455023dcd 100644 --- a/doc/freeze/freeze.md +++ b/doc/freeze/freeze.md @@ -39,7 +39,7 @@ The output model is called `model_branch1.pth`, which is the specifically frozen ```bash $ dp --pd freeze -o model -DEEPMD INFO Paddle inference model has been exported to: model.json(.pdiparams) +DEEPMD INFO Paddle inference model has been exported to: model.json and model.pdiparams ``` in the folder where the model is trained. The output model is called `model.json` and `model.pdiparams`. diff --git a/doc/install/install-from-source.md b/doc/install/install-from-source.md index a705bcbd5a..573f07b82b 100644 --- a/doc/install/install-from-source.md +++ b/doc/install/install-from-source.md @@ -113,7 +113,7 @@ python -m pip install --pre paddlepaddle-gpu -i https://www.paddlepaddle.org.cn/ python -m pip install --pre paddlepaddle -i https://www.paddlepaddle.org.cn/packages/nightly/cpu/ ``` -Follow [Paddle documentation](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/index_cn.html) to install Paddle built against different CUDA versions or without CUDA. +Follow [Paddle documentation](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html) to install Paddle built against different CUDA versions or without CUDA. One can also [use conda](https://docs.deepmodeling.org/faq/conda.html) to install Paddle from [conda-forge](https://conda-forge.org). @@ -224,7 +224,7 @@ The path to the ROCM toolkit directory. If `ROCM_ROOT` is not set, it will look **Choices**: `0`, `1`; **Default**: `0` -{{ pytorch_icon }} Enable customized C++ OPs for the Paddle backend. Paddle can still run without customized C++ OPs, but features will be limited. +{{ paddle_icon }} Enable customized C++ OPs for the Paddle backend. Paddle can still run without customized C++ OPs, but features will be limited. ::: :::{envvar} TENSORFLOW_ROOT @@ -362,9 +362,7 @@ You can also download libtorch prebuilt library from the [PyTorch website](https :::{tab-item} Paddle {{ paddle_icon }} -If you have installed Paddle using pip, you can use libtorch inside the Paddle Python package. -You can also download libtorch prebuilt library from the [Paddle website](https://www.paddlepaddle.org.cn/). - +If you want to use C++ interface of Paddle, you need to compile the Paddle inference library(C++ interface) manually from the [linux-compile-by-make](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/install/compile/linux-compile-by-make.html), then use the `.so` and `.a` files in `Paddle/build/paddle_inference_install_dir/`. ::: :::: @@ -425,7 +423,7 @@ cmake -DENABLE_PYTORCH=TRUE -DUSE_PT_PYTHON_LIBS=TRUE -DCMAKE_INSTALL_PREFIX=$de I assume you have compiled the Paddle inference library(C++ interface) to `$PADDLE_INFERENCE_DIR`, then execute CMake ```bash -cmake -DENABLE_PADDLE=ON -DCMAKE_PREFIX_PATH=$PADDLE_INFERENCE_DIR -DPADDLE_INFERENCE_DIR=$PADDLE_INFERENCE_DIR -DCMAKE_INSTALL_PREFIX=$deepmd_root .. +cmake -DENABLE_PADDLE=ON -DPADDLE_INFERENCE_DIR=$PADDLE_INFERENCE_DIR -DCMAKE_INSTALL_PREFIX=$deepmd_root .. ``` ::: diff --git a/doc/train/parallel-training.md b/doc/train/parallel-training.md index 1775f94aaa..2be3cf3d3f 100644 --- a/doc/train/parallel-training.md +++ b/doc/train/parallel-training.md @@ -190,13 +190,13 @@ torchrun --rdzv_endpoint=node0:12321 --nnodes=2 --nproc_per_node=4 --node_rank=1 ## Paddle Implementation {{ paddle_icon }} -Currently, parallel training in paddle version is implemented in the form of Paddle Distributed Data Parallelism [DDP](https://paddle.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html). +Currently, parallel training in paddle version is implemented in the form of Paddle Distributed Data Parallelism [DDP](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/06_distributed_training/cluster_quick_start_collective_cn.html). DeePMD-kit will decide whether to launch the training in parallel (distributed) mode or in serial mode depending on your execution command. ### Dataloader and Dataset One of the major differences between two backends during training is that the Paddle version employs a multi-threaded data loading utility [DataLoader](https://paddle.org/docs/stable/data.html). -We utilize the Paddle framework and have designed and implemented a multiprocessing data processing and loading system called DpLoaderSet based on torch DataLoader and Dataset. +We utilize the Paddle framework and have designed and implemented a multiprocessing data processing and loading system called DpLoaderSet based on paddle DataLoader and Dataset. First, we establish a DeepmdData class for each system, which is consistent with the TensorFlow version in this level. Then, we create a dataloader for each system, resulting in the same number of dataloaders as the number of systems. Next, we create a dataset for the dataloaders obtained in the previous step. This allows us to query the data for each system through this dataset, while the iteration pointers for each system are maintained by their respective dataloaders. Finally, a dataloader is created for the outermost dataset. @@ -264,22 +264,26 @@ To start training with multiple GPUs in one node, set environment variable `CUDA ```bash # example for training with 4 gpus in one node -CUDA_VISIBLE_DEVICES=0,1,2,3 \ - python -m paddle.distributed.launch --gpus="0,1,2,3" dp --pd train input.json +NUM_WORKERS=0 HDF5_USE_FILE_LOCKING=0 CUDA_VISIBLE_DEVICES=0,1,2,3 python -m paddle.distributed.launch --gpus="0,1,2,3" dp --pd train input.json ``` Suppose you have 2 nodes each with 4 GPUs and their ip address are: `192.168.1.2` and `192.168.1.3`, then you can use `paddle.distributed.launch` to launch a DDP training session: ```bash # run in node 192.168.1.2 -python -m paddle.distributed.launch \ +NUM_WORKERS=0 HDF5_USE_FILE_LOCKING=0 python -m paddle.distributed.launch \ --gpus=0,1,2,3 \ --ips=192.168.1.2,192.168.1.3 \ dp --pd train input.json # then run in the other node 192.168.1.3 -python -m paddle.distributed.launch \ +NUM_WORKERS=0 HDF5_USE_FILE_LOCKING=0 python -m paddle.distributed.launch \ --gpus=0,1,2,3 \ --ips=192.168.1.2,192.168.1.3 \ dp --pd train input.json ``` + +:::{note} +If `NUM_WORKERS` is too large, it may cause the program to be terminated by the system; +if it is too small, it may slow down data reading. You can try adjusting it to an appropriate size. +::: From 8e951cddceb8f1ca851a7b2d17b2625d7b9bfbdb Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 15 Oct 2024 07:04:19 +0000 Subject: [PATCH 48/93] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- deepmd/pd/train/wrapper.py | 4 +- deepmd/pd/utils/env_mat_stat.py | 4 +- deepmd/pd/utils/neighbor_stat.py | 4 +- source/api_cc/include/DeepPotPD.h | 9 +++-- source/api_cc/src/DeepPot.cc | 5 ++- source/api_cc/src/DeepPotPD.cc | 61 ++++++++++++++++++------------- source/api_cc/src/common.cc | 6 +-- 7 files changed, 56 insertions(+), 37 deletions(-) diff --git a/deepmd/pd/train/wrapper.py b/deepmd/pd/train/wrapper.py index 81ef6314c9..4d9100d192 100644 --- a/deepmd/pd/train/wrapper.py +++ b/deepmd/pd/train/wrapper.py @@ -4,12 +4,14 @@ ) import logging +from collections import ( + OrderedDict, +) from typing import ( Dict, Optional, Union, ) -from collections import OrderedDict import paddle diff --git a/deepmd/pd/utils/env_mat_stat.py b/deepmd/pd/utils/env_mat_stat.py index f56e067792..1cbc27742f 100644 --- a/deepmd/pd/utils/env_mat_stat.py +++ b/deepmd/pd/utils/env_mat_stat.py @@ -1,4 +1,7 @@ # SPDX-License-Identifier: LGPL-3.0-or-later +from collections.abc import ( + Iterator, +) from typing import ( TYPE_CHECKING, Dict, @@ -6,7 +9,6 @@ Tuple, Union, ) -from collections.abc import Iterator import numpy as np import paddle diff --git a/deepmd/pd/utils/neighbor_stat.py b/deepmd/pd/utils/neighbor_stat.py index d84133877f..36fefdae8c 100644 --- a/deepmd/pd/utils/neighbor_stat.py +++ b/deepmd/pd/utils/neighbor_stat.py @@ -1,9 +1,11 @@ # SPDX-License-Identifier: LGPL-3.0-or-later +from collections.abc import ( + Iterator, +) from typing import ( Optional, Tuple, ) -from collections.abc import Iterator import numpy as np import paddle diff --git a/source/api_cc/include/DeepPotPD.h b/source/api_cc/include/DeepPotPD.h index 73fbab2b32..dab5c6e6e3 100644 --- a/source/api_cc/include/DeepPotPD.h +++ b/source/api_cc/include/DeepPotPD.h @@ -239,16 +239,17 @@ class DeepPotPD : public DeepPotBase { * @param[in] buffer_name Buffer name. * @param[out] buffer_array Buffer array. **/ - template - void get_buffer(const std::string &buffer_name, std::vector &buffer_array); + template + void get_buffer(const std::string& buffer_name, + std::vector& buffer_array); /** * @brief Get the buffer of this model. * @param[in] buffer_name Buffer name. * @param[out] buffer_scalar Buffer scalar. **/ - template - void get_buffer(const std::string &buffer_name, BUFFERTYPE &buffer_scalar); + template + void get_buffer(const std::string& buffer_name, BUFFERTYPE& buffer_scalar); /** * @brief Get whether the atom dimension of aparam is nall instead of fparam. diff --git a/source/api_cc/src/DeepPot.cc b/source/api_cc/src/DeepPot.cc index 9fdf64a689..8d67b9c783 100644 --- a/source/api_cc/src/DeepPot.cc +++ b/source/api_cc/src/DeepPot.cc @@ -44,7 +44,10 @@ void DeepPot::init(const std::string& model, backend = deepmd::DPBackend::PyTorch; } else if (model.length() >= 3 && model.substr(model.length() - 3) == ".pb") { backend = deepmd::DPBackend::TensorFlow; - } else if ((model.length() >= 5 && model.substr(model.length() - 5) == ".json") || (model.length() >= 8 && model.substr(model.length() - 8) == ".pdmodel")) { + } else if ((model.length() >= 5 && + model.substr(model.length() - 5) == ".json") || + (model.length() >= 8 && + model.substr(model.length() - 8) == ".pdmodel")) { backend = deepmd::DPBackend::Paddle; } else { throw deepmd::deepmd_exception("Unsupported model file format"); diff --git a/source/api_cc/src/DeepPotPD.cc b/source/api_cc/src/DeepPotPD.cc index 7d058b8927..df596e2306 100644 --- a/source/api_cc/src/DeepPotPD.cc +++ b/source/api_cc/src/DeepPotPD.cc @@ -31,8 +31,8 @@ DeepPotPD::DeepPotPD(const std::string& model, } } void DeepPotPD::init(const std::string& model, - const int& gpu_rank, - const std::string& file_content) { + const int& gpu_rank, + const std::string& file_content) { if (inited) { std::cerr << "WARNING: deepmd-kit should not be initialized twice, do " "nothing at the second call of initializer" @@ -62,22 +62,27 @@ void DeepPotPD::init(const std::string& model, if (model.find(".json") != std::string::npos) { pdmodel_path = model; pdiparams_path = model; - pdiparams_path.replace(pdiparams_path.find(".json"), 5, std::string(".pdiparams")); - } else if (model.find(".pdmodel") != std::string::npos){ + pdiparams_path.replace(pdiparams_path.find(".json"), 5, + std::string(".pdiparams")); + } else if (model.find(".pdmodel") != std::string::npos) { pdmodel_path = model; pdiparams_path = model; - pdiparams_path.replace(pdiparams_path.find(".pdmodel"), 8, std::string(".pdiparams")); + pdiparams_path.replace(pdiparams_path.find(".pdmodel"), 8, + std::string(".pdiparams")); } else { - throw deepmd::deepmd_exception("Given inference model: " + model + " do not exist, please check it."); + throw deepmd::deepmd_exception("Given inference model: " + model + + " do not exist, please check it."); } config->SetModel(pdmodel_path, pdiparams_path); - config->EnableUseGpu(4096, 0); // annotate it if use cpu, default use gpu with 4G mem + config->EnableUseGpu( + 4096, 0); // annotate it if use cpu, default use gpu with 4G mem gpu_enabled = config->use_gpu(); if (!gpu_enabled) { config->DisableGpu(); std::cout << "load model from: " << model << " to cpu " << std::endl; } else { - std::cout << "load model from: " << model << " to gpu " << gpu_id << std::endl; + std::cout << "load model from: " << model << " to gpu " << gpu_id + << std::endl; } // NOTE: Both set to 1 now. @@ -145,8 +150,7 @@ void DeepPotPD::compute(ENERGYVTYPE& ener, nlist_data.padding(); if (do_message_passing == 1 && nghost > 0) { throw deepmd::deepmd_exception( - "(do_message_passing == 1 && nghost > 0) is not supported yet." - ); + "(do_message_passing == 1 && nghost > 0) is not supported yet."); int nswap = lmp_list.nswap; auto sendproc_tensor = predictor->GetInputHandle("sendproc"); sendproc_tensor->Reshape({nswap}); @@ -173,8 +177,7 @@ void DeepPotPD::compute(ENERGYVTYPE& ener, } if (do_message_passing == 1 && nghost == 0) { throw deepmd::deepmd_exception( - "(do_message_passing == 1 && nghost == 0) is not supported yet." - ); + "(do_message_passing == 1 && nghost == 0) is not supported yet."); } } std::vector firstneigh = createNlistTensor(nlist_data.jlist); @@ -295,7 +298,6 @@ void DeepPotPD::compute(ENERGYVTYPE& ener, const std::vector& fparam, const std::vector& aparam, const bool atomic) { - // select real atoms std::vector coord_wrapped = coord; int natoms = atype.size(); @@ -326,8 +328,8 @@ void DeepPotPD::compute(ENERGYVTYPE& ener, if (!aparam.empty()) { throw deepmd::deepmd_exception("fparam is not supported as input yet."); // aparam_tensor = predictor->GetInputHandle("box"); - // aparam_tensor->Reshape({1, natoms, static_cast(aparam.size()) / natoms}); - // aparam_tensor->CopyFromCpu((aparam.data())); + // aparam_tensor->Reshape({1, natoms, static_cast(aparam.size()) / + // natoms}); aparam_tensor->CopyFromCpu((aparam.data())); } bool do_atom_virial_tensor = atomic; @@ -345,7 +347,8 @@ void DeepPotPD::compute(ENERGYVTYPE& ener, virial_->CopyToCpu(virial.data()); if (atomic) { - throw deepmd::deepmd_exception("atomic virial is not supported as output yet."); + throw deepmd::deepmd_exception( + "atomic virial is not supported as output yet."); // auto atom_energy_ = predictor->GetOutputHandle(output_names[4]); // auto atom_virial_ = predictor->GetOutputHandle(output_names[5]); // atom_energy_->CopyToCpu(atom_energy.data()); @@ -355,7 +358,7 @@ void DeepPotPD::compute(ENERGYVTYPE& ener, template void DeepPotPD::compute>( std::vector& ener, - std::vector&dforce, + std::vector& dforce, std::vector& virial, std::vector& atom_energy, std::vector& atom_virial, @@ -384,27 +387,31 @@ that need to be postprocessed */ void DeepPotPD::get_type_map(std::string& type_map) { auto type_map_tensor = predictor->GetOutputHandle("buffer_type_map"); auto type_map_shape = type_map_tensor->shape(); - int type_map_size = std::accumulate(type_map_shape.begin(), type_map_shape.end(), 1, std::multiplies()); + int type_map_size = std::accumulate( + type_map_shape.begin(), type_map_shape.end(), 1, std::multiplies()); std::vector type_map_arr(type_map_size, 0); type_map_tensor->CopyToCpu(type_map_arr.data()); - for (auto char_c: type_map_arr) { + for (auto char_c : type_map_arr) { type_map += std::string(1, char_c); } } /* general function except for string buffer */ -template -void DeepPotPD::get_buffer(const std::string &buffer_name, std::vector &buffer_array) { +template +void DeepPotPD::get_buffer(const std::string& buffer_name, + std::vector& buffer_array) { auto buffer_tensor = predictor->GetOutputHandle(buffer_name); auto buffer_shape = buffer_tensor->shape(); - int buffer_size = std::accumulate(buffer_shape.begin(), buffer_shape.end(), 1, std::multiplies()); + int buffer_size = std::accumulate(buffer_shape.begin(), buffer_shape.end(), 1, + std::multiplies()); buffer_array.resize(buffer_size); buffer_tensor->CopyToCpu(buffer_array.data()); } -template -void DeepPotPD::get_buffer(const std::string &buffer_name, BUFFERTYPE &buffer_scalar) { +template +void DeepPotPD::get_buffer(const std::string& buffer_name, + BUFFERTYPE& buffer_scalar) { std::vector buffer_array(1); DeepPotPD::get_buffer(buffer_name, buffer_array); buffer_scalar = buffer_array[0]; @@ -485,7 +492,8 @@ void DeepPotPD::computew_mixed_type(std::vector& ener, const std::vector& fparam, const std::vector& aparam, const bool atomic) { - throw deepmd::deepmd_exception("computew_mixed_type is not implemented in paddle backend yet"); + throw deepmd::deepmd_exception( + "computew_mixed_type is not implemented in paddle backend yet"); } void DeepPotPD::computew_mixed_type(std::vector& ener, std::vector& force, @@ -499,6 +507,7 @@ void DeepPotPD::computew_mixed_type(std::vector& ener, const std::vector& fparam, const std::vector& aparam, const bool atomic) { - throw deepmd::deepmd_exception("computew_mixed_type is not implemented in paddle backend yet"); + throw deepmd::deepmd_exception( + "computew_mixed_type is not implemented in paddle backend yet"); } #endif diff --git a/source/api_cc/src/common.cc b/source/api_cc/src/common.cc index e6d43616ed..fd5ea27c5b 100644 --- a/source/api_cc/src/common.cc +++ b/source/api_cc/src/common.cc @@ -406,9 +406,9 @@ void deepmd::load_op_library() { #ifdef BUILD_PYTORCH _load_single_op_library("deepmd_op_pt"); #endif -// #ifdef BUILD_PADDLE -// _load_single_op_library("deepmd_op_pd"); -// #endif + // #ifdef BUILD_PADDLE + // _load_single_op_library("deepmd_op_pd"); + // #endif // load customized plugins const char* env_customized_plugins = std::getenv("DP_PLUGIN_PATH"); if (env_customized_plugins) { From 3a0f700832eaef86790557ab959b9f1ed99d7fcd Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 15 Oct 2024 17:59:22 +0800 Subject: [PATCH 49/93] simplify code and only support json and pd --- deepmd/pd/entrypoints/main.py | 35 +++----------------- deepmd/pd/infer/deep_eval.py | 25 ++++++-------- deepmd/pd/model/descriptor/se_atten.py | 24 ++++++-------- deepmd/pd/model/model/frozen.py | 28 ++++++---------- deepmd/pd/model/network/init.py | 18 +++++++---- deepmd/pd/model/network/layernorm.py | 20 ++++++------ deepmd/pd/model/network/mlp.py | 23 ++++++------- deepmd/pd/model/network/network.py | 16 ++++----- deepmd/pd/optimizer/KFWrapper.py | 33 +++++++++++++------ deepmd/pd/optimizer/LKF.py | 9 +++--- deepmd/pd/train/training.py | 5 ++- deepmd/pd/utils/init.py | 14 -------- deepmd/pd/utils/serialization.py | 33 +++++++++---------- deepmd/pd/utils/utils.py | 45 ++++++++++++++------------ source/CMakeLists.txt | 4 +-- source/api_cc/src/DeepPot.cc | 3 +- 16 files changed, 142 insertions(+), 193 deletions(-) diff --git a/deepmd/pd/entrypoints/main.py b/deepmd/pd/entrypoints/main.py index dbc6872a32..6ab7946fe3 100644 --- a/deepmd/pd/entrypoints/main.py +++ b/deepmd/pd/entrypoints/main.py @@ -277,9 +277,7 @@ def train(FLAGS): init_state_dict = init_state_dict["model"] config["model"] = init_state_dict["_extra_state"]["model_params"] else: - config["model"] = json.loads( - paddle.jit.load(FLAGS.init_frz_model).get_model_def_script() - ) + raise NotImplementedError("FLAGS.init_model can not be empty.") # argcheck config = update_deepmd_input(config, warning=True, dump="input_v2_compat.json") @@ -385,15 +383,9 @@ def show(FLAGS): if "model" in state_dict: state_dict = state_dict["model"] model_params = state_dict["_extra_state"]["model_params"] - # elif FLAGS.INPUT.split(".")[-1] == "pdmodel": - # model_params_string = paddle.jit.load( - # FLAGS.INPUT[: -len(".pdmodel")] - # ).model_def_script - # model_params = json.loads(model_params_string) else: raise RuntimeError( "The model provided must be a checkpoint file with a .pd extension" - # "or a frozen model with a .pdmodel extension" ) model_is_multi_task = "model_dict" in model_params log.info("This is a multitask model") if model_is_multi_task else log.info( @@ -449,17 +441,10 @@ def change_bias(FLAGS): old_state_dict = paddle.load(FLAGS.INPUT) model_state_dict = copy.deepcopy(old_state_dict.get("model", old_state_dict)) model_params = model_state_dict["_extra_state"]["model_params"] - # elif FLAGS.INPUT.endswith(".json"): - # old_model = paddle.jit.load(FLAGS.INPUT[: -len(".json")]) - # model_params_string = old_model.get_model_def_script() - # model_params = json.loads(model_params_string) - # old_state_dict = old_model.state_dict() - # model_state_dict = old_state_dict else: raise RuntimeError( "Paddle now do not support change bias directly from a freezed model file" "Please provided a checkpoint file with a .pd extension" - # "or a frozen model with a .pdparams extension" ) multi_task = "model_dict" in model_params model_branch = FLAGS.model_branch @@ -486,8 +471,7 @@ def change_bias(FLAGS): wrapper = ModelWrapper(model) wrapper.set_state_dict(old_state_dict["model"]) else: - # for .pdparams - model.set_state_dict(old_state_dict) + raise NotImplementedError("Only support .pd file") if FLAGS.bias_value is not None: # use user-defined bias @@ -557,19 +541,8 @@ def change_bias(FLAGS): old_state_dict["_extra_state"] = model_state_dict["_extra_state"] paddle.save(old_state_dict, output_path) else: - raise NotImplementedError - # for .json - output_path = ( - FLAGS.output - if FLAGS.output is not None - else FLAGS.INPUT.replace(".pdparams", "_updated.pdparams") - ) - model = paddle.jit.to_static(model) - paddle.jit.save( - model, - output_path, - {}, - ) + raise NotImplementedError("Only support .pd file now") + log.info(f"Saved model to {output_path}") diff --git a/deepmd/pd/infer/deep_eval.py b/deepmd/pd/infer/deep_eval.py index 6947bd7bb7..db78128d3a 100644 --- a/deepmd/pd/infer/deep_eval.py +++ b/deepmd/pd/infer/deep_eval.py @@ -3,11 +3,7 @@ TYPE_CHECKING, Any, Callable, - Dict, - List, Optional, - Tuple, - Type, Union, ) @@ -121,9 +117,6 @@ def __init__( # model = paddle.jit.to_static(model) self.dp = ModelWrapper(model) self.dp.set_state_dict(state_dict) - elif str(self.model_path).endswith(".pdmodel"): - model = paddle.jit.load(model_file[: -len(".pdmodel")]) - self.dp = ModelWrapper(model) else: raise ValueError("Unknown model file format!") self.rcut = self.dp.model["Default"].get_rcut() @@ -151,7 +144,7 @@ def get_ntypes(self) -> int: """Get the number of atom types of this model.""" return len(self.type_map) - def get_type_map(self) -> List[str]: + def get_type_map(self) -> list[str]: """Get the type map (element name of the atom types) of this model.""" return self.type_map @@ -164,7 +157,7 @@ def get_dim_aparam(self) -> int: return self.dp.model["Default"].get_dim_aparam() @property - def model_type(self) -> Type["DeepEvalWrapper"]: + def model_type(self) -> type["DeepEvalWrapper"]: """The the evaluator of the model type.""" model_output_type = self.dp.model["Default"].model_output_type() if "energy" in model_output_type: @@ -182,7 +175,7 @@ def model_type(self) -> Type["DeepEvalWrapper"]: else: raise RuntimeError("Unknown model type") - def get_sel_type(self) -> List[int]: + def get_sel_type(self) -> list[int]: """Get the selected atom types of this model. Only atoms with selected atom types have atomic contribution @@ -216,7 +209,7 @@ def eval( fparam: Optional[np.ndarray] = None, aparam: Optional[np.ndarray] = None, **kwargs: Any, - ) -> Dict[str, np.ndarray]: + ) -> dict[str, np.ndarray]: """Evaluate the energy, force and virial by using this DP. Parameters @@ -283,7 +276,7 @@ def eval( ) ) - def _get_request_defs(self, atomic: bool) -> List[OutputVariableDef]: + def _get_request_defs(self, atomic: bool) -> list[OutputVariableDef]: """Get the requested output definitions. When atomic is True, all output_def are requested. @@ -348,7 +341,7 @@ def _get_natoms_and_nframes( coords: np.ndarray, atom_types: np.ndarray, mixed_type: bool = False, - ) -> Tuple[int, int]: + ) -> tuple[int, int]: if mixed_type: natoms = len(atom_types[0]) else: @@ -367,7 +360,7 @@ def _eval_model( atom_types: np.ndarray, fparam: Optional[np.ndarray], aparam: Optional[np.ndarray], - request_defs: List[OutputVariableDef], + request_defs: list[OutputVariableDef], ): model = self.dp.to(DEVICE) @@ -438,7 +431,7 @@ def _eval_model_spin( spins: np.ndarray, fparam: Optional[np.ndarray], aparam: Optional[np.ndarray], - request_defs: List[OutputVariableDef], + request_defs: list[OutputVariableDef], ): model = self.dp.to(DEVICE) @@ -534,7 +527,7 @@ def eval_model( model, coords: Union[np.ndarray, paddle.Tensor], cells: Optional[Union[np.ndarray, paddle.Tensor]], - atom_types: Union[np.ndarray, paddle.to_tensor, List[int]], + atom_types: Union[np.ndarray, paddle.to_tensor, list[int]], spins: Optional[Union[np.ndarray, paddle.Tensor]] = None, atomic: bool = False, infer_batch_size: int = 2, diff --git a/deepmd/pd/model/descriptor/se_atten.py b/deepmd/pd/model/descriptor/se_atten.py index 93fe052b06..db730d073d 100644 --- a/deepmd/pd/model/descriptor/se_atten.py +++ b/deepmd/pd/model/descriptor/se_atten.py @@ -1,10 +1,7 @@ # SPDX-License-Identifier: LGPL-3.0-or-later from typing import ( Callable, - Dict, - List, Optional, - Tuple, Union, ) @@ -64,7 +61,7 @@ def __init__( self, rcut: float, rcut_smth: float, - sel: Union[List[int], int], + sel: Union[list[int], int], ntypes: int, neuron: list = [25, 50, 100], axis_neuron: int = 16, @@ -83,11 +80,11 @@ def __init__( temperature=None, smooth: bool = True, type_one_side: bool = False, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: list[tuple[int, int]] = [], env_protection: float = 0.0, trainable_ln: bool = True, ln_eps: Optional[float] = 1e-5, - seed: Optional[Union[int, List[int]]] = None, + seed: Optional[Union[int, list[int]]] = None, type: Optional[str] = None, old_impl: bool = False, ): @@ -305,7 +302,7 @@ def get_nsel(self) -> int: """Returns the number of selected atoms in the cut-off radius.""" return sum(self.sel) - def get_sel(self) -> List[int]: + def get_sel(self) -> list[int]: """Returns the number of selected atoms for each type.""" return self.sel @@ -374,7 +371,7 @@ def dim_emb(self): def compute_input_stats( self, - merged: Union[Callable[[], List[dict]], List[dict]], + merged: Union[Callable[[], list[dict]], list[dict]], path: Optional[DPPath] = None, ): """ @@ -411,7 +408,7 @@ def compute_input_stats( paddle.assign(paddle.to_tensor(mean).to(device=env.DEVICE), self.mean) # pylint: disable=no-explicit-dtype paddle.assign(paddle.to_tensor(stddev).to(device=env.DEVICE), self.stddev) # pylint: disable=no-explicit-dtype - def get_stats(self) -> Dict[str, StatItem]: + def get_stats(self) -> dict[str, StatItem]: """Get the statistics of the descriptor.""" if self.stats is None: raise RuntimeError( @@ -421,7 +418,7 @@ def get_stats(self) -> Dict[str, StatItem]: def reinit_exclude( self, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: list[tuple[int, int]] = [], ): self.exclude_types = exclude_types self.emask = PairExcludeMask(self.ntypes, exclude_types=exclude_types) @@ -618,7 +615,7 @@ def __init__( ln_eps: float = 1e-5, smooth: bool = True, precision: str = DEFAULT_PRECISION, - seed: Optional[Union[int, List[int]]] = None, + seed: Optional[Union[int, list[int]]] = None, ): """Construct a neighbor-wise attention net.""" super().__init__() @@ -679,7 +676,6 @@ def forward( The smooth switch function. shape: nf x nloc x nnei """ out = input_G - # https://github.com/pytorch/pytorch/issues/39165#issuecomment-635472592 for layer in self.attention_layers: out = layer(out, nei_mask, input_r=input_r, sw=sw) return out @@ -761,7 +757,7 @@ def __init__( trainable_ln: bool = True, ln_eps: float = 1e-5, precision: str = DEFAULT_PRECISION, - seed: Optional[Union[int, List[int]]] = None, + seed: Optional[Union[int, list[int]]] = None, ): """Construct a neighbor-wise attention layer.""" super().__init__() @@ -868,7 +864,7 @@ def __init__( bias: bool = True, smooth: bool = True, precision: str = DEFAULT_PRECISION, - seed: Optional[Union[int, List[int]]] = None, + seed: Optional[Union[int, list[int]]] = None, ): """Construct a multi-head neighbor-wise attention net.""" super().__init__() diff --git a/deepmd/pd/model/model/frozen.py b/deepmd/pd/model/model/frozen.py index 78254bce1f..cd504186c2 100644 --- a/deepmd/pd/model/model/frozen.py +++ b/deepmd/pd/model/model/frozen.py @@ -1,11 +1,7 @@ # SPDX-License-Identifier: LGPL-3.0-or-later import json -import tempfile from typing import ( - Dict, - List, Optional, - Tuple, ) import paddle @@ -13,9 +9,6 @@ from deepmd.dpmodel.output_def import ( FittingOutputDef, ) -from deepmd.entrypoints.convert_backend import ( - convert_backend, -) from deepmd.pd.model.model.model import ( BaseModel, ) @@ -37,13 +30,10 @@ class FrozenModel(BaseModel): def __init__(self, model_file: str, **kwargs): super().__init__(**kwargs) self.model_file = model_file - if model_file.endswith(".pdmodel"): - self.model = paddle.jit.load(model_file[:-8]) + if model_file.endswith(".json"): + self.model = paddle.jit.load(model_file.split(".json")[0]) else: - # try to convert from other formats - with tempfile.NamedTemporaryFile(suffix=".pdparams") as f: - convert_backend(INPUT=model_file, OUTPUT=f.name) - self.model = paddle.jit.load(f.name) + raise NotImplementedError("Only support .json file") # @paddle.jit.export def fitting_output_def(self) -> FittingOutputDef: @@ -56,12 +46,12 @@ def get_rcut(self) -> float: return self.model.get_rcut() # @paddle.jit.export - def get_type_map(self) -> List[str]: + def get_type_map(self) -> list[str]: """Get the type map.""" return self.model.get_type_map() # @paddle.jit.export - def get_sel(self) -> List[int]: + def get_sel(self) -> list[int]: """Returns the number of selected atoms for each type.""" return self.model.get_sel() @@ -76,7 +66,7 @@ def get_dim_aparam(self) -> int: return self.model.get_dim_aparam() # @paddle.jit.export - def get_sel_type(self) -> List[int]: + def get_sel_type(self) -> list[int]: """Get the selected atom types of this model. Only atoms with selected atom types have atomic contribution @@ -124,7 +114,7 @@ def forward( fparam: Optional[paddle.Tensor] = None, aparam: Optional[paddle.Tensor] = None, do_atomic_virial: bool = False, - ) -> Dict[str, paddle.Tensor]: + ) -> dict[str, paddle.Tensor]: return self.model.forward( coord, atype, @@ -177,9 +167,9 @@ def get_nsel(self) -> int: def update_sel( cls, train_data: DeepmdDataSystem, - type_map: Optional[List[str]], + type_map: Optional[list[str]], local_jdata: dict, - ) -> Tuple[dict, Optional[float]]: + ) -> tuple[dict, Optional[float]]: """Update the selection and perform neighbor statistics. Parameters diff --git a/deepmd/pd/model/network/init.py b/deepmd/pd/model/network/init.py index 21cdea5161..1cb7cc30a4 100644 --- a/deepmd/pd/model/network/init.py +++ b/deepmd/pd/model/network/init.py @@ -5,13 +5,17 @@ import math import warnings -from typing import Optional as _Optional +from typing import ( + TypeAlias, +) import paddle from paddle import ( Tensor, ) +PaddleGenerator: TypeAlias = paddle.base.libpaddle.Generator + # Copyright (c) 2024 The PyTorch Authors. All rights reserved. # # This file includes source code from PyTorch of version v2.3.0, which is released under the BSD-3-Clause license. @@ -237,7 +241,7 @@ def normal_( tensor: Tensor, mean: float = 0.0, std: float = 1.0, - generator: _Optional[paddle.Generator] = None, + generator: PaddleGenerator | None = None, ) -> Tensor: r"""Fill the input Tensor with values drawn from the normal distribution. @@ -267,7 +271,7 @@ def trunc_normal_( std: float = 1.0, a: float = -2.0, b: float = 2.0, - generator: _Optional[paddle.Generator] = None, + generator: PaddleGenerator | None = None, ) -> Tensor: r"""Fill the input Tensor with values drawn from a truncated normal distribution. @@ -298,7 +302,7 @@ def kaiming_uniform_( a: float = 0, mode: str = "fan_in", nonlinearity: str = "leaky_relu", - generator: _Optional[paddle.Generator] = None, + generator: PaddleGenerator | None = None, reverse: bool = False, ): r"""Fill the input `Tensor` with values using a Kaiming uniform distribution. @@ -359,7 +363,7 @@ def kaiming_normal_( a: float = 0, mode: str = "fan_in", nonlinearity: str = "leaky_relu", - generator: _Optional[paddle.Generator] = None, + generator: PaddleGenerator | None = None, reverse: bool = False, ): r"""Fill the input `Tensor` with values using a Kaiming normal distribution. @@ -406,7 +410,7 @@ def kaiming_normal_( def xavier_uniform_( tensor: Tensor, gain: float = 1.0, - generator: _Optional[paddle.Generator] = None, + generator: PaddleGenerator | None = None, reverse: bool = False, ) -> Tensor: r"""Fill the input `Tensor` with values using a Xavier uniform distribution. @@ -443,7 +447,7 @@ def xavier_uniform_( def xavier_normal_( tensor: Tensor, gain: float = 1.0, - generator: _Optional[paddle.Generator] = None, + generator: PaddleGenerator | None = None, reverse: bool = False, ) -> Tensor: r"""Fill the input `Tensor` with values using a Xavier normal distribution. diff --git a/deepmd/pd/model/network/layernorm.py b/deepmd/pd/model/network/layernorm.py index dc7d946561..76299040e8 100644 --- a/deepmd/pd/model/network/layernorm.py +++ b/deepmd/pd/model/network/layernorm.py @@ -1,6 +1,5 @@ # SPDX-License-Identifier: LGPL-3.0-or-later from typing import ( - List, Optional, Union, ) @@ -45,7 +44,7 @@ def __init__( stddev: float = 1.0, precision: str = DEFAULT_PRECISION, trainable: bool = True, - seed: Optional[Union[int, List[int]]] = None, + seed: Optional[Union[int, list[int]]] = None, ): super().__init__() self.eps = eps @@ -100,15 +99,14 @@ def forward( yy: paddle.Tensor The output. """ - # mean = xx.mean(axis=-1, keepdim=True) - # variance = xx.var(axis=-1, unbiased=False, keepdim=True) - # The following operation is the same as above, but will not raise error when using jit model to inference. - # See https://github.com/pytorch/pytorch/issues/85792 - variance, mean = ( - paddle.var(xx, -1, unbiased=False, keepdim=True), - paddle.mean(xx, axis=-1, keepdim=True), - ) - yy = (xx - mean) / paddle.sqrt(variance + self.eps) + if xx.numel() > 0: + variance, mean = ( + paddle.var(xx, axis=-1, unbiased=False, keepdim=True), + paddle.mean(xx, axis=-1, keepdim=True), + ) + yy = (xx - mean) / paddle.sqrt(variance + self.eps) + else: + yy = xx if self.matrix is not None and self.bias is not None: yy = yy * self.matrix + self.bias return yy diff --git a/deepmd/pd/model/network/mlp.py b/deepmd/pd/model/network/mlp.py index c2ddc8d75e..29c84b0d33 100644 --- a/deepmd/pd/model/network/mlp.py +++ b/deepmd/pd/model/network/mlp.py @@ -5,10 +5,6 @@ from typing import ( ClassVar, - Dict, - List, - Optional, - Union, ) import numpy as np @@ -31,6 +27,7 @@ make_multilayer_network, ) from deepmd.pd.model.network.init import ( + PaddleGenerator, kaiming_normal_, normal_, trunc_normal_, @@ -81,13 +78,13 @@ def __init__( num_out, bias: bool = True, use_timestep: bool = False, - activation_function: Optional[str] = None, + activation_function: str | None = None, resnet: bool = False, bavg: float = 0.0, stddev: float = 1.0, precision: str = DEFAULT_PRECISION, init: str = "default", - seed: Optional[Union[int, List[int]]] = None, + seed: int | list[int] | None = None, ): super().__init__() # only use_timestep when skip connection is established. @@ -170,7 +167,7 @@ def _default_normal_init( self, bavg: float = 0.0, stddev: float = 1.0, - generator: Optional[paddle.Generator] = None, + generator: PaddleGenerator | None = None, ): normal_( self.matrix.data, @@ -182,9 +179,7 @@ def _default_normal_init( if self.idt is not None: normal_(self.idt.data, mean=0.1, std=0.001, generator=generator) - def _trunc_normal_init( - self, scale=1.0, generator: Optional[paddle.Generator] = None - ): + def _trunc_normal_init(self, scale=1.0, generator: PaddleGenerator | None = None): # Constant from scipy.stats.truncnorm.std(a=-2, b=2, loc=0., scale=1.) TRUNCATED_NORMAL_STDDEV_FACTOR = 0.87962566103423978 _, fan_in = self.matrix.shape @@ -192,7 +187,7 @@ def _trunc_normal_init( std = (scale**0.5) / TRUNCATED_NORMAL_STDDEV_FACTOR trunc_normal_(self.matrix, mean=0.0, std=std, generator=generator) - def _glorot_uniform_init(self, generator: Optional[paddle.Generator] = None): + def _glorot_uniform_init(self, generator: PaddleGenerator | None = None): xavier_uniform_(self.matrix, gain=1, generator=generator) def _zero_init(self, use_bias=True): @@ -202,7 +197,7 @@ def _zero_init(self, use_bias=True): with paddle.no_grad(): self.bias.fill_(1.0) - def _normal_init(self, generator: Optional[paddle.Generator] = None): + def _normal_init(self, generator: PaddleGenerator | None = None): kaiming_normal_(self.matrix, nonlinearity="linear", generator=generator) def forward( @@ -318,9 +313,9 @@ def __init__(self, *args, **kwargs): class NetworkCollection(DPNetworkCollection, nn.Layer): - """PyTorch implementation of NetworkCollection.""" + """Paddle implementation of NetworkCollection.""" - NETWORK_TYPE_MAP: ClassVar[Dict[str, type]] = { + NETWORK_TYPE_MAP: ClassVar[dict[str, type]] = { "network": MLP, "embedding_network": EmbeddingNet, "fitting_network": FittingNet, diff --git a/deepmd/pd/model/network/network.py b/deepmd/pd/model/network/network.py index 605ae94fec..ee535049d3 100644 --- a/deepmd/pd/model/network/network.py +++ b/deepmd/pd/model/network/network.py @@ -1,6 +1,5 @@ # SPDX-License-Identifier: LGPL-3.0-or-later from typing import ( - List, Optional, Union, ) @@ -505,7 +504,6 @@ def __init__(self, input, output_size, hidden=None): def forward(self, x): x = F.linear(x, self.layer1.weight) - # x = fused_ops.bias_torch_gelu(x, self.layer1.bias) x = nn.GELU()(x) + self.layer1.bias x = self.layer2(x) return x @@ -610,7 +608,7 @@ def __init__( bavg=0.0, stddev=1.0, precision="default", - seed: Optional[Union[int, List[int]]] = None, + seed: Optional[Union[int, list[int]]] = None, use_econf_tebd=False, use_tebd_bias: bool = False, type_map=None, @@ -666,7 +664,7 @@ def share_params(self, base_class, shared_level, resume=False): raise NotImplementedError def change_type_map( - self, type_map: List[str], model_with_new_type_stat=None + self, type_map: list[str], model_with_new_type_stat=None ) -> None: """Change the type related params to new ones, according to `type_map` and the original one in the model. If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. @@ -707,16 +705,16 @@ def __init__( self, *, ntypes: int, - neuron: List[int], + neuron: list[int], resnet_dt: bool = False, activation_function: str = "tanh", precision: str = "default", trainable: bool = True, - seed: Optional[Union[int, List[int]]] = None, + seed: Optional[Union[int, list[int]]] = None, padding: bool = False, use_econf_tebd: bool = False, use_tebd_bias: bool = False, - type_map: Optional[List[str]] = None, + type_map: Optional[list[str]] = None, ): """Construct a type embedding net.""" super().__init__() @@ -778,7 +776,7 @@ def forward(self, device: str): return embed def change_type_map( - self, type_map: List[str], model_with_new_type_stat=None + self, type_map: list[str], model_with_new_type_stat=None ) -> None: """Change the type related params to new ones, according to `type_map` and the original one in the model. If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. @@ -1071,7 +1069,6 @@ def forward( """ out = input_G - # https://github.com/pytorch/pytorch/issues/39165#issuecomment-635472592 for layer in self.attention_layers: out = layer(out, nei_mask, input_r=input_r, sw=sw) return out @@ -2064,7 +2061,6 @@ def forward( if self.pre_ln: x = self.final_layer_norm(x) x = F.linear(x, self.fc1.weight) - # x = fused_ops.bias_torch_gelu(x, self.fc1.bias) x = nn.GELU()(x) + self.fc1.bias x = F.dropout(x, p=self.activation_dropout, training=self.training) x = self.fc2(x) diff --git a/deepmd/pd/optimizer/KFWrapper.py b/deepmd/pd/optimizer/KFWrapper.py index 476a30a30d..7fd8506300 100644 --- a/deepmd/pd/optimizer/KFWrapper.py +++ b/deepmd/pd/optimizer/KFWrapper.py @@ -10,6 +10,24 @@ ) +def _mask_update(tensor: paddle.Tensor, mask: paddle.Tensor, value: paddle.Tensor): + """ + Paddle now not do not support updating a Tensor with another Tensor by mask, + so we use other API to achieve this. + """ + mask_coord = paddle.concat( + paddle.nonzero(mask, as_tuple=True), + axis=1, + ) + t = paddle.scatter_nd_add( + tensor * (~mask).astype(tensor.dtype), + mask_coord, + value, + ) + paddle.assign(t, tensor) # inplace update + return tensor + + class KFOptimizerWrapper: def __init__( self, @@ -73,7 +91,8 @@ def update_force( error_tmp = Force_label[:, index[i]] - force_predict[:, index[i]] error_tmp = update_prefactor * error_tmp mask = error_tmp < 0 - error_tmp[mask] = -1 * error_tmp[mask] + error_tmp = _mask_update(error_tmp, mask, -1 * error_tmp[mask]) + # error_tmp[mask] = -1 * error_tmp[mask] error = error_tmp.mean() / natoms_sum if self.is_distributed: @@ -81,9 +100,11 @@ def update_force( error /= dist.get_world_size() tmp_force_predict = force_predict[:, index[i]] * update_prefactor - tmp_force_predict[mask] = -tmp_force_predict[mask] + tmp_force_predict = _mask_update( + tmp_force_predict, mask, -1 * tmp_force_predict[mask] + ) + # tmp_force_predict[mask] = -tmp_force_predict[mask] - # In order to solve a pytorch bug, reference: https://github.com/pytorch/pytorch/issues/43259 (tmp_force_predict.sum() + Etot_predict.sum() * 0).backward() error = error * math.sqrt(bs) self.optimizer.step(error) @@ -123,7 +144,6 @@ def update_denoise_coord( tmp_coord_predict = updated_coord[:, index[i]] * update_prefactor tmp_coord_predict[mask] = -update_prefactor * tmp_coord_predict[mask] - # In order to solve a pytorch bug, reference: https://github.com/pytorch/pytorch/issues/43259 (tmp_coord_predict.sum() + updated_coord.sum() * 0).backward() error = error * math.sqrt(bs) self.optimizer.step(error) @@ -138,8 +158,3 @@ def __sample( rng = np.random.default_rng() res = rng.choice(index, atoms_selected).reshape([-1, atoms_per_group]) return res - - -# with paddle.autograd.profiler.profile(enabled=True, use_cuda=True, record_shapes=False) as prof: -# the code u wanna profile -# print(prof.key_averages().table(sort_by="self_cpu_time_total")) diff --git a/deepmd/pd/optimizer/LKF.py b/deepmd/pd/optimizer/LKF.py index d23c10399e..06e4e2e156 100644 --- a/deepmd/pd/optimizer/LKF.py +++ b/deepmd/pd/optimizer/LKF.py @@ -159,7 +159,6 @@ def __init_P(self): else: P.append(paddle.eye(param_num, dtype=data_type).to(device=device)) params_packed_index.append(param_num) - self._state.setdefault("P", P) self._state.setdefault("weights_num", len(P)) self._state.setdefault("params_packed_index", params_packed_index) @@ -276,22 +275,22 @@ def step(self, error): for param in self._params: if param.ndim > 1: - tmp = param.data.T.contiguous().reshape(param.data.numel().item(), 1) + tmp = param.data.T.contiguous().reshape([param.data.numel().item(), 1]) if param.grad is None: tmp_grad = paddle.zeros_like(tmp) else: tmp_grad = ( (param.grad / self.grad_prefactor) .T.contiguous() - .reshape(param.grad.numel().item(), 1) + .reshape([param.grad.numel().item(), 1]) ) else: - tmp = param.data.reshape(param.data.numel().item(), 1) + tmp = param.data.reshape([param.data.numel().item(), 1]) if param.grad is None: tmp_grad = paddle.zeros_like(tmp) else: tmp_grad = (param.grad / self.grad_prefactor).reshape( - param.grad.numel().item(), 1 + [param.grad.numel().item(), 1] ) tmp = self.__split_weights(tmp) diff --git a/deepmd/pd/train/training.py b/deepmd/pd/train/training.py index 116c98cf53..fade8033d4 100644 --- a/deepmd/pd/train/training.py +++ b/deepmd/pd/train/training.py @@ -40,7 +40,7 @@ get_model, get_zbl_model, ) -from deepmd.pd.optimizer import ( +from deepmd.pd.optimizer import ( # LKFOptimizer, KFOptimizerWrapper, ) from deepmd.pd.train.wrapper import ( @@ -601,6 +601,9 @@ def warm_up_linear(step, warmup_steps): self.optimizer.set_state_dict(optimizer_state_dict) elif self.opt_type == "LKF": raise NotImplementedError("LKF is not supported yet in Paddle backend.") + # self.optimizer = LKFOptimizer( + # [{'params': self.wrapper.parameters()}], 0.98, 0.99870, self.opt_param["kf_blocksize"] + # ) else: raise ValueError(f"Not supported optimizer type '{self.opt_type}'") diff --git a/deepmd/pd/utils/init.py b/deepmd/pd/utils/init.py index 42e19fea87..9f363d6db0 100644 --- a/deepmd/pd/utils/init.py +++ b/deepmd/pd/utils/init.py @@ -1,18 +1,4 @@ # SPDX-License-Identifier: LGPL-3.0-or-later -# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. - -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at - -# http://www.apache.org/licenses/LICENSE-2.0 - -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - """ The initialization method under this module is aligned with pytorch initialization. If you need to use the initialization method of PaddlePaddle, please refer to diff --git a/deepmd/pd/utils/serialization.py b/deepmd/pd/utils/serialization.py index f8fb45940a..e33d7ea5d0 100644 --- a/deepmd/pd/utils/serialization.py +++ b/deepmd/pd/utils/serialization.py @@ -1,5 +1,4 @@ # SPDX-License-Identifier: LGPL-3.0-or-later -import json import paddle @@ -27,12 +26,7 @@ def serialize_from_file(model_file: str) -> dict: dict The serialized model data. """ - if model_file.endswith(".pdparams"): - saved_model = paddle.jit.load(model_file) - model_def_script = json.loads(saved_model.model_def_script) - model = get_model(model_def_script) - model.set_state_dict(saved_model.state_dict()) - elif model_file.endswith(".pdmodel"): + if model_file.endswith(".pd"): state_dict = paddle.load(model_file) if "model" in state_dict: state_dict = state_dict["model"] @@ -42,14 +36,12 @@ def serialize_from_file(model_file: str) -> dict: modelwrapper.set_state_dict(state_dict) model = modelwrapper.model["Default"] else: - raise ValueError( - "Pypaddle backend only supports converting .pdparams or .pd file" - ) + raise ValueError("Paddle backend only supports converting .pd file") model_dict = model.serialize() data = { - "backend": "Pypaddle", - "pt_version": paddle.__version__, + "backend": "Paddle", + "pt_version": paddle.version.commit, "model": model_dict, "model_def_script": model_def_script, "@variables": {}, @@ -69,12 +61,19 @@ def deserialize_to_file(model_file: str, data: dict) -> None: data : dict The dictionary to be deserialized. """ - if not model_file.endswith(".pdparams"): - raise ValueError("Pypaddle backend only supports converting .pdparams file") + if not model_file.endswith(".pd"): + raise ValueError("Paddle backend only supports converting .pd file") model = BaseModel.deserialize(data["model"]) - # JIT will happy in this way... - model.model_def_script = json.dumps(data["model_def_script"]) if "min_nbor_dist" in data.get("@variables", {}): - model.min_nbor_dist = float(data["@variables"]["min_nbor_dist"]) + model.min_nbor_dist = paddle.to_tensor( + float(data["@variables"]["min_nbor_dist"]) + ) + paddle.set_flags( + { + "FLAGS_save_cf_stack_op": 1, + "FLAGS_prim_enable_dynamic": 1, + "FLAGS_enable_pir_api": 1, + } + ) model = paddle.jit.to_static(model) paddle.jit.save(model, model_file) diff --git a/deepmd/pd/utils/utils.py b/deepmd/pd/utils/utils.py index 2703c65810..54ac7ee58c 100644 --- a/deepmd/pd/utils/utils.py +++ b/deepmd/pd/utils/utils.py @@ -4,9 +4,6 @@ ) from typing import ( - List, - Optional, - Union, overload, ) @@ -16,6 +13,9 @@ import paddle.nn.functional as F from deepmd.dpmodel.common import PRECISION_DICT as NP_PRECISION_DICT +from deepmd.pd.model.network.init import ( + PaddleGenerator, +) from .env import ( DEVICE, @@ -24,18 +24,16 @@ class ActivationFn(paddle.nn.Layer): - def __init__(self, activation: Optional[str]): + def __init__(self, activation: str | None): super().__init__() self.activation: str = activation if activation is not None else "linear" def forward(self, x: paddle.Tensor) -> paddle.Tensor: """Returns the tensor after applying activation function corresponding to `activation`.""" - # See jit supported types: https://pypaddle.org/docs/stable/jit_language_reference.html#supported-type - if self.activation.lower() == "relu": return F.relu(x) elif self.activation.lower() == "gelu" or self.activation.lower() == "gelu_tf": - return F.gelu(x, approximate="tanh") + return F.gelu(x, approximate=True) elif self.activation.lower() == "tanh": return paddle.tanh(x) elif self.activation.lower() == "relu6": @@ -43,7 +41,7 @@ def forward(self, x: paddle.Tensor) -> paddle.Tensor: elif self.activation.lower() == "softplus": return F.softplus(x) elif self.activation.lower() == "sigmoid": - return paddle.sigmoid(x) + return F.sigmoid(x) elif self.activation.lower() == "linear" or self.activation.lower() == "none": return x else: @@ -102,9 +100,8 @@ def to_paddle_tensor( if prec is None: raise ValueError(f"unknown precision {xx.dtype}") if xx.dtype == ml_dtypes.bfloat16: - # https://github.com/pypaddle/pypaddle/issues/109873 xx = xx.astype(np.float32) - return paddle.to_tensor(xx, dtype=prec).to(device=DEVICE) + return paddle.to_tensor(xx, dtype=prec, place=DEVICE) def dict_to_device(sample_dict): @@ -129,7 +126,7 @@ def dict_to_device(sample_dict): XSHIFT = 16 -def hashmix(value: int, hash_const: List[int]): +def hashmix(value: int, hash_const: list[int]): value ^= INIT_A hash_const[0] *= MULT_A value *= INIT_A @@ -148,7 +145,7 @@ def mix(x: int, y: int): return result -def mix_entropy(entropy_array: List[int]) -> int: +def mix_entropy(entropy_array: list[int]) -> int: # https://github.com/numpy/numpy/blob/a4cddb60489f821a1a4dffc16cd5c69755d43bdb/numpy/random/bit_generator.pyx#L341-L374 hash_const = [INIT_A] mixer = hashmix(entropy_array[0], hash_const) @@ -158,13 +155,19 @@ def mix_entropy(entropy_array: List[int]) -> int: def get_generator( - seed: Optional[Union[int, List[int]]] = None, -) -> Optional[paddle.Generator]: - if False: - if isinstance(seed, list): - seed = mix_entropy(seed) - generator = paddle.Generator(device=DEVICE) - generator.manual_seed(seed) - return generator + seed: int | list[int] | None = None, +) -> PaddleGenerator | None: + if isinstance(seed, list): + seed = mix_entropy(seed) + if DEVICE == "cpu": + generator = paddle.framework.core.default_cpu_generator() + elif DEVICE == "gpu": + generator = paddle.framework.core.default_cuda_generator(0) + elif DEVICE.startswith("gpu:"): + generator = paddle.framework.core.default_cuda_generator( + int(DEVICE.split("gpu:")[1]) + ) else: - return None + raise ValueError("DEVICE should be cpu or gpu or gpu:x") + generator.manual_seed(seed) + return generator diff --git a/source/CMakeLists.txt b/source/CMakeLists.txt index fc3f7c8c58..50c7bf9a96 100644 --- a/source/CMakeLists.txt +++ b/source/CMakeLists.txt @@ -51,9 +51,7 @@ if(ENABLE_PADDLE) link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}gflags/lib") link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}xxhash/lib") link_directories("${PADDLE_INFERENCE_DIR}/paddle/lib") - # if (USE_ROCM_TOOLKIT) - add_definitions(-D_GLIBCXX_USE_CXX11_ABI=1) - # endif() + # if (USE_ROCM_TOOLKIT) add_definitions(-D_GLIBCXX_USE_CXX11_ABI=1) endif() endif(ENABLE_PADDLE) if(BUILD_TESTING) diff --git a/source/api_cc/src/DeepPot.cc b/source/api_cc/src/DeepPot.cc index 9fdf64a689..42ce09a139 100644 --- a/source/api_cc/src/DeepPot.cc +++ b/source/api_cc/src/DeepPot.cc @@ -44,7 +44,8 @@ void DeepPot::init(const std::string& model, backend = deepmd::DPBackend::PyTorch; } else if (model.length() >= 3 && model.substr(model.length() - 3) == ".pb") { backend = deepmd::DPBackend::TensorFlow; - } else if ((model.length() >= 5 && model.substr(model.length() - 5) == ".json") || (model.length() >= 8 && model.substr(model.length() - 8) == ".pdmodel")) { + } else if ((model.length() >= 5 && + model.substr(model.length() - 5) == ".json")) { backend = deepmd::DPBackend::Paddle; } else { throw deepmd::deepmd_exception("Unsupported model file format"); From 8a59a530bf7a469637d6dda455db06dab5423193 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 15 Oct 2024 19:48:59 +0800 Subject: [PATCH 50/93] Fix get_generator --- deepmd/pd/utils/utils.py | 29 ++++++++++++++++------------- 1 file changed, 16 insertions(+), 13 deletions(-) diff --git a/deepmd/pd/utils/utils.py b/deepmd/pd/utils/utils.py index 54ac7ee58c..b38ad9e887 100644 --- a/deepmd/pd/utils/utils.py +++ b/deepmd/pd/utils/utils.py @@ -157,17 +157,20 @@ def mix_entropy(entropy_array: list[int]) -> int: def get_generator( seed: int | list[int] | None = None, ) -> PaddleGenerator | None: - if isinstance(seed, list): - seed = mix_entropy(seed) - if DEVICE == "cpu": - generator = paddle.framework.core.default_cpu_generator() - elif DEVICE == "gpu": - generator = paddle.framework.core.default_cuda_generator(0) - elif DEVICE.startswith("gpu:"): - generator = paddle.framework.core.default_cuda_generator( - int(DEVICE.split("gpu:")[1]) - ) + if seed is not None: + if isinstance(seed, list): + seed = mix_entropy(seed) + if DEVICE == "cpu": + generator = paddle.framework.core.default_cpu_generator() + elif DEVICE == "gpu": + generator = paddle.framework.core.default_cuda_generator(0) + elif DEVICE.startswith("gpu:"): + generator = paddle.framework.core.default_cuda_generator( + int(DEVICE.split("gpu:")[1]) + ) + else: + raise ValueError("DEVICE should be cpu or gpu or gpu:x") + generator.manual_seed(seed) + return generator else: - raise ValueError("DEVICE should be cpu or gpu or gpu:x") - generator.manual_seed(seed) - return generator + return None From 782199778ec00a70b17a87fde969033bafb3c319 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 15 Oct 2024 21:00:58 +0800 Subject: [PATCH 51/93] set default NUM_WORKERS to 0 --- deepmd/pd/utils/env.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepmd/pd/utils/env.py b/deepmd/pd/utils/env.py index a23c8d53e2..01fdda0a1a 100644 --- a/deepmd/pd/utils/env.py +++ b/deepmd/pd/utils/env.py @@ -23,7 +23,7 @@ ncpus = len(os.sched_getaffinity(0)) except AttributeError: ncpus = os.cpu_count() -NUM_WORKERS = int(os.environ.get("NUM_WORKERS", min(4, ncpus))) +NUM_WORKERS = int(os.environ.get("NUM_WORKERS", min(0, ncpus))) # Make sure DDP uses correct device if applicable LOCAL_RANK = os.environ.get("LOCAL_RANK", None) or paddle.device.get_device() LOCAL_RANK = int(0 if LOCAL_RANK is None else paddle.distributed.get_rank()) From ed51258bb83416627c0f162f71335eb02db45e24 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 15 Oct 2024 21:01:50 +0800 Subject: [PATCH 52/93] fix LOCAL_RANK --- deepmd/pd/utils/env.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/deepmd/pd/utils/env.py b/deepmd/pd/utils/env.py index 01fdda0a1a..27160ed23d 100644 --- a/deepmd/pd/utils/env.py +++ b/deepmd/pd/utils/env.py @@ -25,8 +25,7 @@ ncpus = os.cpu_count() NUM_WORKERS = int(os.environ.get("NUM_WORKERS", min(0, ncpus))) # Make sure DDP uses correct device if applicable -LOCAL_RANK = os.environ.get("LOCAL_RANK", None) or paddle.device.get_device() -LOCAL_RANK = int(0 if LOCAL_RANK is None else paddle.distributed.get_rank()) +LOCAL_RANK = paddle.distributed.get_rank() if os.environ.get("DEVICE") == "cpu" or paddle.device.cuda.device_count() <= 0: DEVICE = "cpu" From 13c7f55d71096c475dd09f704e2f223649ea1a4e Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Thu, 17 Oct 2024 15:23:41 +0800 Subject: [PATCH 53/93] update CMAKE and remove build_cc_pd.sh --- source/install/build_cc_pd.sh | 80 -------------------------------- source/lmp/plugin/CMakeLists.txt | 6 ++- 2 files changed, 4 insertions(+), 82 deletions(-) delete mode 100755 source/install/build_cc_pd.sh diff --git a/source/install/build_cc_pd.sh b/source/install/build_cc_pd.sh deleted file mode 100755 index 36389c5ec3..0000000000 --- a/source/install/build_cc_pd.sh +++ /dev/null @@ -1,80 +0,0 @@ -set -e - -if [ "$DP_VARIANT" = "cuda" ]; then - CUDA_ARGS="-DUSE_CUDA_TOOLKIT=TRUE" -elif [ "$DP_VARIANT" = "rocm" ]; then - CUDA_ARGS="-DUSE_ROCM_TOOLKIT=TRUE" -fi -#------------------ - -SCRIPT_PATH=$(dirname $(realpath -s $0)) -if [ -z "$INSTALL_PREFIX" ]; then - INSTALL_PREFIX=$(realpath -s ${SCRIPT_PATH}/../../dp) -fi -mkdir -p ${INSTALL_PREFIX} -echo "Installing DeePMD-kit to ${INSTALL_PREFIX}" -NPROC=$(nproc --all) - -#------------------ - -# LAMMPS_DIR 设置为 LAMMPS 的安装目录 -export LAMMPS_DIR="/workspace/hesensen/deepmd_backend/deepmd_paddle_new/source/build_lammps/lammps-stable_29Aug2024/" -export LAMMPS_SOURCE_ROOT="/workspace/hesensen/deepmd_backend/deepmd_paddle_new/source/build_lammps/lammps-stable_29Aug2024/" - -# 设置推理时的 GPU 卡号 -export CUDA_VISIBLE_DEVICES=1 - -# deepmd_root 设置为本项目的根目录 -export deepmd_root="/workspace/hesensen/deepmd_backend/deepmd_paddle_new/" - -# PADDLE_INFERENCE_DIR 设置为第二步编译得到的 Paddle 推理库目录 -export PADDLE_INFERENCE_DIR="/workspace/hesensen/PaddleScience_enn_debug/Paddle/build/paddle_inference_install_dir/" - -export LD_LIBRARY_PATH=${deepmd_root}/deepmd/op:$LD_LIBRARY_PATH -export LD_LIBRARY_PATH=${PADDLE_INFERENCE_DIR}/paddle/lib:$LD_LIBRARY_PATH -export LD_LIBRARY_PATH=${PADDLE_INFERENCE_DIR}/third_party/install/mkldnn/lib:$LD_LIBRARY_PATH -export LD_LIBRARY_PATH=${PADDLE_INFERENCE_DIR}/third_party/install/mklml/lib:$LD_LIBRARY_PATH -export LD_LIBRARY_PATH=${deepmd_root}/source/build:$LD_LIBRARY_PATH - -cd ${deepmd_root}/source -rm -rf build # 若改动CMakeLists.txt，则需要打开该注释 -mkdir build -cd - - -BUILD_TMP_DIR=${SCRIPT_PATH}/../build -mkdir -p ${BUILD_TMP_DIR} -cd ${BUILD_TMP_DIR} -cmake -D ENABLE_PADDLE=ON \ - -D PADDLE_INFERENCE_DIR=${PADDLE_INFERENCE_DIR} \ - -D CMAKE_INSTALL_PREFIX=${INSTALL_PREFIX} \ - -D USE_TF_PYTHON_LIBS=TRUE \ - -D LAMMPS_SOURCE_ROOT=${LAMMPS_SOURCE_ROOT} \ - ${CUDA_ARGS} \ - -D LAMMPS_VERSION=stable_29Aug2024 \ - .. -cmake --build . -j${NPROC} -cmake --install . - -#------------------ -echo "Congratulations! DeePMD-kit has been installed at ${INSTALL_PREFIX}" - -cd ${deepmd_root}/source -cd build -make lammps -cd ${LAMMPS_DIR}/src/ -\cp -r ${deepmd_root}/source/build/USER-DEEPMD . -make no-kspace -make yes-kspace -make no-extra-fix -make yes-extra-fix -make no-user-deepmd -make yes-user-deepmd -# make serial -j -make mpi -j 10 -export PATH=${LAMMPS_DIR}/src:$PATH - -cd ${deepmd_root}/examples/water/lmp - -echo "START INFERENCE..." -# lmp_serial -in paddle_in.lammps 2>&1 | tee paddle_infer.log -mpirun -np 2 lmp_mpi -in paddle_in.lammps 2>&1 | tee paddle_infer.log diff --git a/source/lmp/plugin/CMakeLists.txt b/source/lmp/plugin/CMakeLists.txt index efeb9af260..f912059261 100644 --- a/source/lmp/plugin/CMakeLists.txt +++ b/source/lmp/plugin/CMakeLists.txt @@ -9,8 +9,10 @@ if(DEFINED LAMMPS_SOURCE_ROOT OR DEFINED LAMMPS_VERSION) GIT_REPOSITORY https://github.com/lammps/lammps GIT_TAG ${LAMMPS_VERSION}) FetchContent_GetProperties(lammps_download) - # if(NOT lammps_download_POPULATED) FetchContent_Populate(lammps_download) - # set(LAMMPS_SOURCE_ROOT ${lammps_download_SOURCE_DIR}) endif() + if(NOT lammps_download_POPULATED) + FetchContent_Populate(lammps_download) + set(LAMMPS_SOURCE_ROOT ${lammps_download_SOURCE_DIR}) + endif() endif() set(LAMMPS_HEADER_DIR ${LAMMPS_SOURCE_ROOT}/src) message(STATUS "LAMMPS_HEADER_DIR is ${LAMMPS_HEADER_DIR}") From e01386049db82ca3167b65298e644b16a981f7de Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Thu, 17 Oct 2024 15:26:26 +0800 Subject: [PATCH 54/93] remove paddle.jit.export --- .../model/atomic_model/base_atomic_model.py | 48 +++++++++---------- .../pd/model/atomic_model/dp_atomic_model.py | 18 +++---- deepmd/pd/model/descriptor/repformer_layer.py | 33 ++++++------- .../descriptor/repformer_layer_old_impl.py | 18 +++---- deepmd/pd/model/model/dipole_model.py | 4 +- deepmd/pd/model/model/dos_model.py | 5 +- deepmd/pd/model/model/dp_zbl_model.py | 10 ++-- deepmd/pd/model/model/ener_model.py | 6 +-- deepmd/pd/model/model/frozen.py | 16 ------- deepmd/pd/model/model/make_model.py | 42 ++++++---------- deepmd/pd/model/model/model.py | 3 -- deepmd/pd/model/model/polar_model.py | 4 +- deepmd/pd/model/model/spin_model.py | 27 ++--------- 13 files changed, 77 insertions(+), 157 deletions(-) diff --git a/deepmd/pd/model/atomic_model/base_atomic_model.py b/deepmd/pd/model/atomic_model/base_atomic_model.py index 9cb320c16c..e07f421c08 100644 --- a/deepmd/pd/model/atomic_model/base_atomic_model.py +++ b/deepmd/pd/model/atomic_model/base_atomic_model.py @@ -4,10 +4,7 @@ import logging from typing import ( Callable, - Dict, - List, Optional, - Tuple, Union, ) @@ -76,11 +73,11 @@ class BaseAtomicModel(paddle.nn.Layer, BaseAtomicModel_): def __init__( self, - type_map: List[str], - atom_exclude_types: List[int] = [], - pair_exclude_types: List[Tuple[int, int]] = [], + type_map: list[str], + atom_exclude_types: list[int] = [], + pair_exclude_types: list[tuple[int, int]] = [], rcond: Optional[float] = None, - preset_out_bias: Optional[Dict[str, paddle.Tensor]] = None, + preset_out_bias: Optional[dict[str, paddle.Tensor]] = None, ): paddle.nn.Layer.__init__(self) BaseAtomicModel_.__init__(self) @@ -93,7 +90,7 @@ def __init__( def init_out_stat(self): """Initialize the output bias.""" ntypes = self.get_ntypes() - self.bias_keys: List[str] = list(self.fitting_output_def().keys()) + self.bias_keys: list[str] = list(self.fitting_output_def().keys()) self.max_out_size = max( [self.atomic_output_def()[kk].size for kk in self.bias_keys] ) @@ -122,14 +119,13 @@ def __getitem__(self, key): else: raise KeyError(key) - # @paddle.jit.export - def get_type_map(self) -> List[str]: + def get_type_map(self) -> list[str]: """Get the type map.""" return self.type_map def reinit_atom_exclude( self, - exclude_types: List[int] = [], + exclude_types: list[int] = [], ): self.atom_exclude_types = exclude_types if exclude_types == []: @@ -139,7 +135,7 @@ def reinit_atom_exclude( def reinit_pair_exclude( self, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: list[tuple[int, int]] = [], ): self.pair_exclude_types = exclude_types if exclude_types == []: @@ -194,8 +190,8 @@ def forward_common_atomic( mapping: Optional[paddle.Tensor] = None, fparam: Optional[paddle.Tensor] = None, aparam: Optional[paddle.Tensor] = None, - comm_dict: Optional[Dict[str, paddle.Tensor]] = None, - ) -> Dict[str, paddle.Tensor]: + comm_dict: Optional[dict[str, paddle.Tensor]] = None, + ) -> dict[str, paddle.Tensor]: """Common interface for atomic inference. This method accept extended coordinates, extended atom typs, neighbor list, @@ -277,8 +273,8 @@ def forward( mapping: Optional[paddle.Tensor] = None, fparam: Optional[paddle.Tensor] = None, aparam: Optional[paddle.Tensor] = None, - comm_dict: Optional[Dict[str, paddle.Tensor]] = None, - ) -> Dict[str, paddle.Tensor]: + comm_dict: Optional[dict[str, paddle.Tensor]] = None, + ) -> dict[str, paddle.Tensor]: return self.forward_common_atomic( extended_coord, extended_atype, @@ -290,7 +286,7 @@ def forward( ) def change_type_map( - self, type_map: List[str], model_with_new_type_stat=None + self, type_map: list[str], model_with_new_type_stat=None ) -> None: """Change the type related params to new ones, according to `type_map` and the original one in the model. If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. @@ -355,7 +351,7 @@ def deserialize(cls, data: dict) -> "BaseAtomicModel": def compute_or_load_stat( self, - merged: Union[Callable[[], List[dict]], List[dict]], + merged: Union[Callable[[], list[dict]], list[dict]], stat_file_path: Optional[DPPath] = None, ): """ @@ -378,7 +374,7 @@ def compute_or_load_stat( def compute_or_load_out_stat( self, - merged: Union[Callable[[], List[dict]], List[dict]], + merged: Union[Callable[[], list[dict]], list[dict]], stat_file_path: Optional[DPPath] = None, ): """ @@ -405,7 +401,7 @@ def compute_or_load_out_stat( def apply_out_stat( self, - ret: Dict[str, paddle.Tensor], + ret: dict[str, paddle.Tensor], atype: paddle.Tensor, ): """Apply the stat to each atomic output. @@ -521,7 +517,7 @@ def _default_std(self): def _varsize( self, - shape: List[int], + shape: list[int], ) -> int: output_size = 1 len_shape = len(shape) @@ -533,7 +529,7 @@ def _get_bias_index( self, kk: str, ) -> int: - res: List[int] = [] + res: list[int] = [] for i, e in enumerate(self.bias_keys): if e == kk: res.append(i) @@ -542,8 +538,8 @@ def _get_bias_index( def _store_out_stat( self, - out_bias: Dict[str, paddle.Tensor], - out_std: Dict[str, paddle.Tensor], + out_bias: dict[str, paddle.Tensor], + out_std: dict[str, paddle.Tensor], add: bool = False, ): ntypes = self.get_ntypes() @@ -563,8 +559,8 @@ def _store_out_stat( def _fetch_out_stat( self, - keys: List[str], - ) -> Tuple[Dict[str, paddle.Tensor], Dict[str, paddle.Tensor]]: + keys: list[str], + ) -> tuple[dict[str, paddle.Tensor], dict[str, paddle.Tensor]]: ret_bias = {} ret_std = {} ntypes = self.get_ntypes() diff --git a/deepmd/pd/model/atomic_model/dp_atomic_model.py b/deepmd/pd/model/atomic_model/dp_atomic_model.py index 1035a62e59..e059cbcb6c 100644 --- a/deepmd/pd/model/atomic_model/dp_atomic_model.py +++ b/deepmd/pd/model/atomic_model/dp_atomic_model.py @@ -3,8 +3,6 @@ import functools import logging from typing import ( - Dict, - List, Optional, ) @@ -52,7 +50,7 @@ def __init__( self, descriptor, fitting, - type_map: List[str], + type_map: list[str], **kwargs, ): super().__init__(type_map, **kwargs) @@ -66,7 +64,7 @@ def __init__( super().init_out_stat() # register 'type_map' as buffer - def _string_to_array(s: str) -> List[int]: + def _string_to_array(s: str) -> list[int]: return [ord(c) for c in s] self.register_buffer( @@ -107,7 +105,6 @@ def _string_to_array(s: str) -> List[int]: ) self.buffer_aparam_nall.name = "buffer_aparam_nall" - # @paddle.jit.export def fitting_output_def(self) -> FittingOutputDef: """Get the output def of the fitting net.""" return ( @@ -116,12 +113,11 @@ def fitting_output_def(self) -> FittingOutputDef: else self.coord_denoise_net.output_def() ) - # @paddle.jit.export def get_rcut(self) -> float: """Get the cut-off radius.""" return self.rcut - def get_sel(self) -> List[int]: + def get_sel(self) -> list[int]: """Get the neighbor selection.""" return self.sel @@ -138,7 +134,7 @@ def mixed_types(self) -> bool: return self.descriptor.mixed_types() def change_type_map( - self, type_map: List[str], model_with_new_type_stat=None + self, type_map: list[str], model_with_new_type_stat=None ) -> None: """Change the type related params to new ones, according to `type_map` and the original one in the model. If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. @@ -199,8 +195,8 @@ def forward_atomic( mapping: Optional[paddle.Tensor] = None, fparam: Optional[paddle.Tensor] = None, aparam: Optional[paddle.Tensor] = None, - comm_dict: Optional[Dict[str, paddle.Tensor]] = None, - ) -> Dict[str, paddle.Tensor]: + comm_dict: Optional[dict[str, paddle.Tensor]] = None, + ) -> dict[str, paddle.Tensor]: """Return atomic prediction. Parameters @@ -300,7 +296,7 @@ def get_dim_aparam(self) -> int: """Get the number (dimension) of atomic parameters of this atomic model.""" return self.fitting_net.get_dim_aparam() - def get_sel_type(self) -> List[int]: + def get_sel_type(self) -> list[int]: """Get the selected atom types of this model. Only atoms with selected atom types have atomic contribution diff --git a/deepmd/pd/model/descriptor/repformer_layer.py b/deepmd/pd/model/descriptor/repformer_layer.py index 2ef2edbeeb..830b1835d7 100644 --- a/deepmd/pd/model/descriptor/repformer_layer.py +++ b/deepmd/pd/model/descriptor/repformer_layer.py @@ -1,6 +1,5 @@ # SPDX-License-Identifier: LGPL-3.0-or-later from typing import ( - List, Optional, Union, ) @@ -45,7 +44,7 @@ def get_residual( _mode: str = "norm", trainable: bool = True, precision: str = "float64", - seed: Optional[Union[int, List[int]]] = None, + seed: Optional[Union[int, list[int]]] = None, ) -> paddle.Tensor: r""" Get residual tensor for one update vector. @@ -163,7 +162,7 @@ def __init__( smooth: bool = True, attnw_shift: float = 20.0, precision: str = "float64", - seed: Optional[Union[int, List[int]]] = None, + seed: Optional[Union[int, list[int]]] = None, ): """Return neighbor-wise multi-head self-attention maps, with gate mechanism.""" super().__init__() @@ -288,7 +287,7 @@ def __init__( input_dim: int, head_num: int, precision: str = "float64", - seed: Optional[Union[int, List[int]]] = None, + seed: Optional[Union[int, list[int]]] = None, ): super().__init__() self.input_dim = input_dim @@ -375,7 +374,7 @@ def __init__( input_dim: int, head_num: int, precision: str = "float64", - seed: Optional[Union[int, List[int]]] = None, + seed: Optional[Union[int, list[int]]] = None, ): super().__init__() self.input_dim = input_dim @@ -448,7 +447,7 @@ def __init__( smooth: bool = True, attnw_shift: float = 20.0, precision: str = "float64", - seed: Optional[Union[int, List[int]]] = None, + seed: Optional[Union[int, list[int]]] = None, ): super().__init__() self.input_dim = input_dim @@ -609,7 +608,7 @@ def __init__( precision: str = "float64", trainable_ln: bool = True, ln_eps: Optional[float] = 1e-5, - seed: Optional[Union[int, List[int]]] = None, + seed: Optional[Union[int, list[int]]] = None, ): super().__init__() self.epsilon = 1e-4 # protection of 1./nnei @@ -1070,10 +1069,10 @@ def forward( assert [nb, nloc] == g1.shape[:2] assert [nb, nloc, nnei] == h2.shape[:3] - g2_update: List[paddle.Tensor] = [g2] - h2_update: List[paddle.Tensor] = [h2] - g1_update: List[paddle.Tensor] = [g1] - g1_mlp: List[paddle.Tensor] = [g1] + g2_update: list[paddle.Tensor] = [g2] + h2_update: list[paddle.Tensor] = [h2] + g1_update: list[paddle.Tensor] = [g1] + g1_mlp: list[paddle.Tensor] = [g1] if cal_gg1: gg1 = _make_nei_g1(g1_ext, nlist) @@ -1163,10 +1162,9 @@ def forward( g1_new = self.list_update(g1_update, "g1") return g1_new, g2_new, h2_new - # @paddle.jit.export def list_update_res_avg( self, - update_list: List[paddle.Tensor], + update_list: list[paddle.Tensor], ) -> paddle.Tensor: nitem = len(update_list) uu = update_list[0] @@ -1174,8 +1172,7 @@ def list_update_res_avg( uu = uu + update_list[ii] return uu / (float(nitem) ** 0.5) - # @paddle.jit.export - def list_update_res_incr(self, update_list: List[paddle.Tensor]) -> paddle.Tensor: + def list_update_res_incr(self, update_list: list[paddle.Tensor]) -> paddle.Tensor: nitem = len(update_list) uu = update_list[0] scale = 1.0 / (float(nitem - 1) ** 0.5) if nitem > 1 else 0.0 @@ -1183,9 +1180,8 @@ def list_update_res_incr(self, update_list: List[paddle.Tensor]) -> paddle.Tenso uu = uu + scale * update_list[ii] return uu - # @paddle.jit.export def list_update_res_residual( - self, update_list: List[paddle.Tensor], update_name: str = "g1" + self, update_list: list[paddle.Tensor], update_name: str = "g1" ) -> paddle.Tensor: nitem = len(update_list) uu = update_list[0] @@ -1203,9 +1199,8 @@ def list_update_res_residual( raise NotImplementedError return uu - # @paddle.jit.export def list_update( - self, update_list: List[paddle.Tensor], update_name: str = "g1" + self, update_list: list[paddle.Tensor], update_name: str = "g1" ) -> paddle.Tensor: if self.update_style == "res_avg": return self.list_update_res_avg(update_list) diff --git a/deepmd/pd/model/descriptor/repformer_layer_old_impl.py b/deepmd/pd/model/descriptor/repformer_layer_old_impl.py index 3b132fdc57..660ea57cf1 100644 --- a/deepmd/pd/model/descriptor/repformer_layer_old_impl.py +++ b/deepmd/pd/model/descriptor/repformer_layer_old_impl.py @@ -1,7 +1,6 @@ # SPDX-License-Identifier: LGPL-3.0-or-later from typing import ( Callable, - List, ) import paddle @@ -646,10 +645,10 @@ def forward( if self.update_h2: h2 = _apply_h_norm(h2) - g2_update: List[paddle.Tensor] = [g2] - h2_update: List[paddle.Tensor] = [h2] - g1_update: List[paddle.Tensor] = [g1] - g1_mlp: List[paddle.Tensor] = [g1] + g2_update: list[paddle.Tensor] = [g2] + h2_update: list[paddle.Tensor] = [h2] + g1_update: list[paddle.Tensor] = [g1] + g1_mlp: list[paddle.Tensor] = [g1] if cal_gg1: gg1 = _make_nei_g1(g1_ext, nlist) @@ -713,10 +712,9 @@ def forward( g1_new = self.list_update(g1_update) return g1_new, g2_new, h2_new - # @paddle.jit.export def list_update_res_avg( self, - update_list: List[paddle.Tensor], + update_list: list[paddle.Tensor], ) -> paddle.Tensor: nitem = len(update_list) uu = update_list[0] @@ -724,8 +722,7 @@ def list_update_res_avg( uu = uu + update_list[ii] return uu / (float(nitem) ** 0.5) - # @paddle.jit.export - def list_update_res_incr(self, update_list: List[paddle.Tensor]) -> paddle.Tensor: + def list_update_res_incr(self, update_list: list[paddle.Tensor]) -> paddle.Tensor: nitem = len(update_list) uu = update_list[0] scale = 1.0 / (float(nitem - 1) ** 0.5) if nitem > 1 else 0.0 @@ -733,8 +730,7 @@ def list_update_res_incr(self, update_list: List[paddle.Tensor]) -> paddle.Tenso uu = uu + scale * update_list[ii] return uu - # @paddle.jit.export - def list_update(self, update_list: List[paddle.Tensor]) -> paddle.Tensor: + def list_update(self, update_list: list[paddle.Tensor]) -> paddle.Tensor: if self.update_style == "res_avg": return self.list_update_res_avg(update_list) elif self.update_style == "res_incr": diff --git a/deepmd/pd/model/model/dipole_model.py b/deepmd/pd/model/model/dipole_model.py index d7b2a7d43b..1bbb315661 100644 --- a/deepmd/pd/model/model/dipole_model.py +++ b/deepmd/pd/model/model/dipole_model.py @@ -3,7 +3,6 @@ deepcopy, ) from typing import ( - Dict, Optional, ) @@ -64,7 +63,7 @@ def forward( fparam: Optional[paddle.Tensor] = None, aparam: Optional[paddle.Tensor] = None, do_atomic_virial: bool = False, - ) -> Dict[str, paddle.Tensor]: + ) -> dict[str, paddle.Tensor]: model_ret = self.forward_common( coord, atype, @@ -92,7 +91,6 @@ def forward( model_predict["updated_coord"] += coord return model_predict - # @paddle.jit.export def forward_lower( self, extended_coord, diff --git a/deepmd/pd/model/model/dos_model.py b/deepmd/pd/model/model/dos_model.py index ab5605442b..f2c75a7138 100644 --- a/deepmd/pd/model/model/dos_model.py +++ b/deepmd/pd/model/model/dos_model.py @@ -3,7 +3,6 @@ deepcopy, ) from typing import ( - Dict, Optional, ) @@ -56,7 +55,7 @@ def forward( fparam: Optional[paddle.Tensor] = None, aparam: Optional[paddle.Tensor] = None, do_atomic_virial: bool = False, - ) -> Dict[str, paddle.Tensor]: + ) -> dict[str, paddle.Tensor]: model_ret = self.forward_common( coord, atype, @@ -77,12 +76,10 @@ def forward( model_predict["updated_coord"] += coord return model_predict - # @paddle.jit.export def get_numb_dos(self) -> int: """Get the number of DOS for DOSFittingNet.""" return self.get_fitting_net().dim_out - # @paddle.jit.export def forward_lower( self, extended_coord, diff --git a/deepmd/pd/model/model/dp_zbl_model.py b/deepmd/pd/model/model/dp_zbl_model.py index 51e959e564..cc36e2d339 100644 --- a/deepmd/pd/model/model/dp_zbl_model.py +++ b/deepmd/pd/model/model/dp_zbl_model.py @@ -3,10 +3,7 @@ deepcopy, ) from typing import ( - Dict, - List, Optional, - Tuple, ) import paddle @@ -68,7 +65,7 @@ def forward( fparam: Optional[paddle.Tensor] = None, aparam: Optional[paddle.Tensor] = None, do_atomic_virial: bool = False, - ) -> Dict[str, paddle.Tensor]: + ) -> dict[str, paddle.Tensor]: model_ret = self.forward_common( coord, atype, @@ -93,7 +90,6 @@ def forward( model_predict["mask"] = model_ret["mask"] return model_predict - # @paddle.jit.export def forward_lower( self, extended_coord, @@ -135,9 +131,9 @@ def forward_lower( def update_sel( cls, train_data: DeepmdDataSystem, - type_map: Optional[List[str]], + type_map: Optional[list[str]], local_jdata: dict, - ) -> Tuple[dict, Optional[float]]: + ) -> tuple[dict, Optional[float]]: """Update the selection and perform neighbor statistics. Parameters diff --git a/deepmd/pd/model/model/ener_model.py b/deepmd/pd/model/model/ener_model.py index fcf5ca3353..3f3db4a527 100644 --- a/deepmd/pd/model/model/ener_model.py +++ b/deepmd/pd/model/model/ener_model.py @@ -3,7 +3,6 @@ deepcopy, ) from typing import ( - Dict, Optional, ) @@ -64,7 +63,7 @@ def forward( fparam: Optional[paddle.Tensor] = None, aparam: Optional[paddle.Tensor] = None, do_atomic_virial: bool = False, - ) -> Dict[str, paddle.Tensor]: + ) -> dict[str, paddle.Tensor]: model_ret = self.forward_common( coord, atype, @@ -94,7 +93,6 @@ def forward( model_predict["updated_coord"] += coord return model_predict - # @paddle.jit.export def forward_lower( self, extended_coord, @@ -104,7 +102,7 @@ def forward_lower( fparam: Optional[paddle.Tensor] = None, aparam: Optional[paddle.Tensor] = None, do_atomic_virial: bool = False, - comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + comm_dict: Optional[dict[str, paddle.Tensor]] = None, ): model_ret = self.forward_common_lower( extended_coord, diff --git a/deepmd/pd/model/model/frozen.py b/deepmd/pd/model/model/frozen.py index cd504186c2..f7bc0a4556 100644 --- a/deepmd/pd/model/model/frozen.py +++ b/deepmd/pd/model/model/frozen.py @@ -35,37 +35,30 @@ def __init__(self, model_file: str, **kwargs): else: raise NotImplementedError("Only support .json file") - # @paddle.jit.export def fitting_output_def(self) -> FittingOutputDef: """Get the output def of developer implemented atomic models.""" return self.model.fitting_output_def() - # @paddle.jit.export def get_rcut(self) -> float: """Get the cut-off radius.""" return self.model.get_rcut() - # @paddle.jit.export def get_type_map(self) -> list[str]: """Get the type map.""" return self.model.get_type_map() - # @paddle.jit.export def get_sel(self) -> list[int]: """Returns the number of selected atoms for each type.""" return self.model.get_sel() - # @paddle.jit.export def get_dim_fparam(self) -> int: """Get the number (dimension) of frame parameters of this atomic model.""" return self.model.get_dim_fparam() - # @paddle.jit.export def get_dim_aparam(self) -> int: """Get the number (dimension) of atomic parameters of this atomic model.""" return self.model.get_dim_aparam() - # @paddle.jit.export def get_sel_type(self) -> list[int]: """Get the selected atom types of this model. @@ -75,7 +68,6 @@ def get_sel_type(self) -> list[int]: """ return self.model.get_sel_type() - # @paddle.jit.export def is_aparam_nall(self) -> bool: """Check whether the shape of atomic parameters is (nframes, nall, ndim). @@ -83,7 +75,6 @@ def is_aparam_nall(self) -> bool: """ return self.model.is_aparam_nall() - # @paddle.jit.export def mixed_types(self) -> bool: """If true, the model 1. assumes total number of atoms aligned across frames; @@ -96,7 +87,6 @@ def mixed_types(self) -> bool: """ return self.model.mixed_types() - # @paddle.jit.export def has_message_passing(self) -> bool: """Returns whether the descriptor has message passing.""" return self.model.has_message_passing() @@ -105,7 +95,6 @@ def need_sorted_nlist_for_lower(self) -> bool: """Returns whether the model needs sorted nlist when using `forward_lower`.""" return self.model.need_sorted_nlist_for_lower() - # @paddle.jit.export def forward( self, coord, @@ -124,7 +113,6 @@ def forward( do_atomic_virial=do_atomic_virial, ) - # @paddle.jit.export def get_model_def_script(self) -> str: """Get the model definition script.""" # try to use the original script instead of "frozen model" @@ -133,7 +121,6 @@ def get_model_def_script(self) -> str: # be a problem return self.model.get_model_def_script() - # @paddle.jit.export def get_min_nbor_dist(self) -> Optional[float]: """Get the minimum neighbor distance.""" return self.model.get_min_nbor_dist() @@ -153,12 +140,10 @@ def serialize(self) -> dict: def deserialize(cls, data: dict): raise RuntimeError("Should not touch here.") - # @paddle.jit.export def get_nnei(self) -> int: """Returns the total number of selected neighboring atoms in the cut-off radius.""" return self.model.get_nnei() - # @paddle.jit.export def get_nsel(self) -> int: """Returns the total number of selected neighboring atoms in the cut-off radius.""" return self.model.get_nsel() @@ -190,7 +175,6 @@ def update_sel( """ return local_jdata, None - # @paddle.jit.export def model_output_type(self) -> str: """Get the output type for the model.""" return self.model.model_output_type() diff --git a/deepmd/pd/model/model/make_model.py b/deepmd/pd/model/model/make_model.py index 202934cfa2..26bc6f91c8 100644 --- a/deepmd/pd/model/model/make_model.py +++ b/deepmd/pd/model/model/make_model.py @@ -1,10 +1,6 @@ # SPDX-License-Identifier: LGPL-3.0-or-later from typing import ( - Dict, - List, Optional, - Tuple, - Type, ) import paddle @@ -46,7 +42,7 @@ ) -def make_model(T_AtomicModel: Type[BaseAtomicModel]): +def make_model(T_AtomicModel: type[BaseAtomicModel]): """Make a model as a derived class of an atomic model. The model provide two interfaces. @@ -91,14 +87,13 @@ def model_output_def(self): """Get the output def for the model.""" return ModelOutputDef(self.atomic_output_def()) - # @paddle.jit.export - def model_output_type(self) -> List[str]: + def model_output_type(self) -> list[str]: """Get the output type for the model.""" output_def = self.model_output_def() var_defs = output_def.var_defs # jit: Comprehension ifs are not supported yet # type hint is critical for JIT - vars: List[str] = [] + vars: list[str] = [] for kk, vv in var_defs.items(): # .value is critical for JIT if vv.category == OutputVariableCategory.OUT.value: @@ -114,7 +109,7 @@ def forward_common( fparam: Optional[paddle.Tensor] = None, aparam: Optional[paddle.Tensor] = None, do_atomic_virial: bool = False, - ) -> Dict[str, paddle.Tensor]: + ) -> dict[str, paddle.Tensor]: """Return model prediction. Parameters @@ -217,7 +212,7 @@ def forward_common_lower( fparam: Optional[paddle.Tensor] = None, aparam: Optional[paddle.Tensor] = None, do_atomic_virial: bool = False, - comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + comm_dict: Optional[dict[str, paddle.Tensor]] = None, extra_nlist_sort: bool = False, ): """Return model prediction. Lower interface that takes @@ -286,7 +281,7 @@ def input_type_cast( box: Optional[paddle.Tensor] = None, fparam: Optional[paddle.Tensor] = None, aparam: Optional[paddle.Tensor] = None, - ) -> Tuple[ + ) -> tuple[ paddle.Tensor, Optional[paddle.Tensor], Optional[paddle.Tensor], @@ -305,7 +300,7 @@ def input_type_cast( # " does not match" # f" that of the coordinate {input_prec}" # ) - _lst: List[Optional[paddle.Tensor]] = [ + _lst: list[Optional[paddle.Tensor]] = [ vv.astype(coord.dtype) if vv is not None else None for vv in [box, fparam, aparam] ] @@ -327,9 +322,9 @@ def input_type_cast( def output_type_cast( self, - model_ret: Dict[str, paddle.Tensor], + model_ret: dict[str, paddle.Tensor], input_prec: str, - ) -> Dict[str, paddle.Tensor]: + ) -> dict[str, paddle.Tensor]: """Convert the model output to the input prec.""" do_cast = ( input_prec @@ -475,7 +470,7 @@ def do_grad_c( return self.atomic_model.do_grad_c(var_name) def change_type_map( - self, type_map: List[str], model_with_new_type_stat=None + self, type_map: list[str], model_with_new_type_stat=None ) -> None: """Change the type related params to new ones, according to `type_map` and the original one in the model. If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. @@ -494,18 +489,15 @@ def serialize(self) -> dict: def deserialize(cls, data) -> "CM": return cls(atomic_model_=T_AtomicModel.deserialize(data)) - # @paddle.jit.export def get_dim_fparam(self) -> int: """Get the number (dimension) of frame parameters of this atomic model.""" return self.atomic_model.get_dim_fparam() - # @paddle.jit.export def get_dim_aparam(self) -> int: """Get the number (dimension) of atomic parameters of this atomic model.""" return self.atomic_model.get_dim_aparam() - # @paddle.jit.export - def get_sel_type(self) -> List[int]: + def get_sel_type(self) -> list[int]: """Get the selected atom types of this model. Only atoms with selected atom types have atomic contribution @@ -514,7 +506,6 @@ def get_sel_type(self) -> List[int]: """ return self.atomic_model.get_sel_type() - # @paddle.jit.export def is_aparam_nall(self) -> bool: """Check whether the shape of atomic parameters is (nframes, nall, ndim). @@ -522,22 +513,18 @@ def is_aparam_nall(self) -> bool: """ return self.atomic_model.is_aparam_nall() - # @paddle.jit.export def get_rcut(self) -> float: """Get the cut-off radius.""" return self.atomic_model.get_rcut() - # @paddle.jit.export - def get_type_map(self) -> List[str]: + def get_type_map(self) -> list[str]: """Get the type map.""" return self.atomic_model.get_type_map() - # @paddle.jit.export def get_nsel(self) -> int: """Returns the total number of selected neighboring atoms in the cut-off radius.""" return self.atomic_model.get_nsel() - # @paddle.jit.export def get_nnei(self) -> int: """Returns the total number of selected neighboring atoms in the cut-off radius.""" return self.atomic_model.get_nnei() @@ -554,7 +541,7 @@ def compute_or_load_stat( """Compute or load the statistics.""" return self.atomic_model.compute_or_load_stat(sampled_func, stat_file_path) - def get_sel(self) -> List[int]: + def get_sel(self) -> list[int]: """Returns the number of selected atoms for each type.""" return self.atomic_model.get_sel() @@ -570,7 +557,6 @@ def mixed_types(self) -> bool: """ return self.atomic_model.mixed_types() - # @paddle.jit.export def has_message_passing(self) -> bool: """Returns whether the model has message passing.""" return self.atomic_model.has_message_passing() @@ -587,7 +573,7 @@ def forward( fparam: Optional[paddle.Tensor] = None, aparam: Optional[paddle.Tensor] = None, do_atomic_virial: bool = False, - ) -> Dict[str, paddle.Tensor]: + ) -> dict[str, paddle.Tensor]: # directly call the forward_common method when no specific transform rule return self.forward_common( coord, diff --git a/deepmd/pd/model/model/model.py b/deepmd/pd/model/model/model.py index 1f0effcdfb..06a2c6910f 100644 --- a/deepmd/pd/model/model/model.py +++ b/deepmd/pd/model/model/model.py @@ -42,17 +42,14 @@ def compute_or_load_stat( """ raise NotImplementedError - # @paddle.jit.export def get_model_def_script(self) -> str: """Get the model definition script.""" return self.model_def_script - # @paddle.jit.export def get_min_nbor_dist(self) -> Optional[float]: """Get the minimum distance between two atoms.""" return self.min_nbor_dist - # @paddle.jit.export def get_ntypes(self): """Returns the number of element types.""" return len(self.get_type_map()) diff --git a/deepmd/pd/model/model/polar_model.py b/deepmd/pd/model/model/polar_model.py index 6800d82b13..fb79b4b79a 100644 --- a/deepmd/pd/model/model/polar_model.py +++ b/deepmd/pd/model/model/polar_model.py @@ -3,7 +3,6 @@ deepcopy, ) from typing import ( - Dict, Optional, ) @@ -56,7 +55,7 @@ def forward( fparam: Optional[paddle.Tensor] = None, aparam: Optional[paddle.Tensor] = None, do_atomic_virial: bool = False, - ) -> Dict[str, paddle.Tensor]: + ) -> dict[str, paddle.Tensor]: model_ret = self.forward_common( coord, atype, @@ -76,7 +75,6 @@ def forward( model_predict["updated_coord"] += coord return model_predict - # @paddle.jit.export def forward_lower( self, extended_coord, diff --git a/deepmd/pd/model/model/spin_model.py b/deepmd/pd/model/model/spin_model.py index e8fd5161fb..22935cdc04 100644 --- a/deepmd/pd/model/model/spin_model.py +++ b/deepmd/pd/model/model/spin_model.py @@ -4,8 +4,6 @@ deepcopy, ) from typing import ( - Dict, - List, Optional, ) @@ -261,35 +259,29 @@ def expand_aparam(aparam, nloc: int): ) return aparam - # @paddle.jit.export - def get_type_map(self) -> List[str]: + def get_type_map(self) -> list[str]: """Get the type map.""" tmap = self.backbone_model.get_type_map() ntypes = len(tmap) // 2 # ignore the virtual type return tmap[:ntypes] - # @paddle.jit.export def get_ntypes(self): """Returns the number of element types.""" return len(self.get_type_map()) - # @paddle.jit.export def get_rcut(self): """Get the cut-off radius.""" return self.backbone_model.get_rcut() - # @paddle.jit.export def get_dim_fparam(self): """Get the number (dimension) of frame parameters of this atomic model.""" return self.backbone_model.get_dim_fparam() - # @paddle.jit.export def get_dim_aparam(self): """Get the number (dimension) of atomic parameters of this atomic model.""" return self.backbone_model.get_dim_aparam() - # @paddle.jit.export - def get_sel_type(self) -> List[int]: + def get_sel_type(self) -> list[int]: """Get the selected atom types of this model. Only atoms with selected atom types have atomic contribution to the result of the model. @@ -297,29 +289,24 @@ def get_sel_type(self) -> List[int]: """ return self.backbone_model.get_sel_type() - # @paddle.jit.export def is_aparam_nall(self) -> bool: """Check whether the shape of atomic parameters is (nframes, nall, ndim). If False, the shape is (nframes, nloc, ndim). """ return self.backbone_model.is_aparam_nall() - # @paddle.jit.export - def model_output_type(self) -> List[str]: + def model_output_type(self) -> list[str]: """Get the output type for the model.""" return self.backbone_model.model_output_type() - # @paddle.jit.export def get_model_def_script(self) -> str: """Get the model definition script.""" return self.backbone_model.get_model_def_script() - # @paddle.jit.export def get_min_nbor_dist(self) -> Optional[float]: """Get the minimum neighbor distance.""" return self.backbone_model.get_min_nbor_dist() - # @paddle.jit.export def get_nnei(self) -> int: """Returns the total number of selected neighboring atoms in the cut-off radius.""" # for C++ interface @@ -328,7 +315,6 @@ def get_nnei(self) -> int: else: return self.backbone_model.get_nnei() - # @paddle.jit.export def get_nsel(self) -> int: """Returns the total number of selected neighboring atoms in the cut-off radius.""" if not self.backbone_model.mixed_types(): @@ -336,12 +322,10 @@ def get_nsel(self) -> int: else: return self.backbone_model.get_nsel() - # @paddle.jit.export def has_spin(self) -> bool: """Returns whether it has spin input and output.""" return True - # @paddle.jit.export def has_message_passing(self) -> bool: """Returns whether the model has message passing.""" return self.backbone_model.has_message_passing() @@ -428,7 +412,7 @@ def forward_common( fparam: Optional[paddle.Tensor] = None, aparam: Optional[paddle.Tensor] = None, do_atomic_virial: bool = False, - ) -> Dict[str, paddle.Tensor]: + ) -> dict[str, paddle.Tensor]: nframes, nloc = atype.shape coord_updated, atype_updated = self.process_spin_input(coord, atype, spin) if aparam is not None: @@ -582,7 +566,7 @@ def forward( fparam: Optional[paddle.Tensor] = None, aparam: Optional[paddle.Tensor] = None, do_atomic_virial: bool = False, - ) -> Dict[str, paddle.Tensor]: + ) -> dict[str, paddle.Tensor]: model_ret = self.forward_common( coord, atype, @@ -602,7 +586,6 @@ def forward( # not support virial by far return model_predict - # @paddle.jit.export def forward_lower( self, extended_coord, From 4c4568f4dfe0b54faa39a9b2250d99ffc0be2a70 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Thu, 17 Oct 2024 16:39:06 +0800 Subject: [PATCH 55/93] refine code --- .../dpmodel/atomic_model/base_atomic_model.py | 2 +- deepmd/dpmodel/descriptor/se_t_tebd.py | 2 +- deepmd/pd/model/model/spin_model.py | 6 ++-- deepmd/pd/train/training.py | 14 ++++---- deepmd/pd/utils/dataloader.py | 1 - deepmd/pd/utils/stat.py | 34 ++++++++----------- 6 files changed, 25 insertions(+), 34 deletions(-) diff --git a/deepmd/dpmodel/atomic_model/base_atomic_model.py b/deepmd/dpmodel/atomic_model/base_atomic_model.py index c29a76b3f1..0ef4e59895 100644 --- a/deepmd/dpmodel/atomic_model/base_atomic_model.py +++ b/deepmd/dpmodel/atomic_model/base_atomic_model.py @@ -200,7 +200,7 @@ def forward_common_atomic( out_shape2 = np.prod(out_shape[2:]) ret_dict[kk] = ( ret_dict[kk].reshape([out_shape[0], out_shape[1], out_shape2]) - * atom_mask[:, :, None] + * atom_mask[:, :, None].astype(ret_dict[kk].dtype) ).reshape(out_shape) ret_dict["mask"] = atom_mask diff --git a/deepmd/dpmodel/descriptor/se_t_tebd.py b/deepmd/dpmodel/descriptor/se_t_tebd.py index 147a335926..f2a5a1e2a0 100644 --- a/deepmd/dpmodel/descriptor/se_t_tebd.py +++ b/deepmd/dpmodel/descriptor/se_t_tebd.py @@ -672,7 +672,7 @@ def call( dmatrix = dmatrix.reshape(nf * nloc, nnei, 4) # nfnl x nnei x 4 rr = dmatrix - rr = rr * exclude_mask[:, :, None] + rr = rr * exclude_mask[:, :, None].astype(rr.dtype) # nfnl x nt_i x 3 rr_i = rr[:, :, 1:] # nfnl x nt_j x 3 diff --git a/deepmd/pd/model/model/spin_model.py b/deepmd/pd/model/model/spin_model.py index 22935cdc04..de1f2504b4 100644 --- a/deepmd/pd/model/model/spin_model.py +++ b/deepmd/pd/model/model/spin_model.py @@ -125,9 +125,9 @@ def process_spin_output( shape2 = 1 for ss in out_real.shape[2:]: shape2 *= ss - out_mag = (out_mag.reshape([nframes, nloc, shape2]) * atomic_mask).reshape( - out_mag.shape - ) + out_mag = ( + out_mag.reshape([nframes, nloc, shape2]) * atomic_mask.astype(out_mag.dtype) + ).reshape(out_mag.shape) return out_real, out_mag, atomic_mask > 0.0 def process_spin_output_lower( diff --git a/deepmd/pd/train/training.py b/deepmd/pd/train/training.py index fade8033d4..253d4829f8 100644 --- a/deepmd/pd/train/training.py +++ b/deepmd/pd/train/training.py @@ -188,7 +188,6 @@ def get_dataloader_and_buffer(_data, _params): collate_fn=lambda batch: batch[0], # prevent extra conversion # pin_memory=True, ) - # with paddle.device("cpu"): _data_buffered = BufferedIterator(iter(_dataloader)) return _dataloader, _data_buffered @@ -709,7 +708,6 @@ def step(_step_id, task_key="Default"): if not paddle.isfinite(grad_norm).all(): # check local gradnorm single GPU case, trigger NanDetector raise FloatingPointError("gradients are Nan/Inf") - # with paddle.device("cpu"): self.optimizer.step() self.scheduler.step() elif self.opt_type == "LKF": @@ -1016,11 +1014,12 @@ def log_loss_valid(_task_key="Default"): ) if JIT: - pdparams_model_path = "frozen_model.pdparams" # We use .pdparams to denote the frozen model - self.model.save(pdparams_model_path) - log.info( - f"Frozen model for inferencing has been saved to {pdparams_model_path}" - ) + raise NotImplementedError("JIT training is not supported yet.") + # frozen_model_prefix = "frozen_model" # We use .json and .pdiparams to denote the frozen model + # self.model.save(frozen_model_prefix) + # log.info( + # f"Frozen model for inferencing has been saved to {frozen_model_prefix}" + # ) log.info(f"Trained model has been saved to: {self.save_ckpt}") if fout: @@ -1065,7 +1064,6 @@ def get_data(self, is_train=True, task_key="Default"): batch_data = next(iter(self.training_data)) except StopIteration: # Refresh the status of the dataloader to start from a new epoch - # with paddle.device("cpu"): self.training_data = BufferedIterator( iter(self.training_dataloader) ) diff --git a/deepmd/pd/utils/dataloader.py b/deepmd/pd/utils/dataloader.py index 6dfcef3167..7a2bf4fe9c 100644 --- a/deepmd/pd/utils/dataloader.py +++ b/deepmd/pd/utils/dataloader.py @@ -335,6 +335,5 @@ def get_weighted_sampler(training_data, prob_style, sys_prob=False): log.debug("Generated weighted sampler with prob array: " + str(probs)) # training_data.total_batch is the size of one epoch, you can increase it to avoid too many rebuilding of iteraters len_sampler = training_data.total_batch * max(env.NUM_WORKERS, 1) - # with paddle.device("cpu"): sampler = WeightedRandomSampler(probs, len_sampler, replacement=True) return sampler diff --git a/deepmd/pd/utils/stat.py b/deepmd/pd/utils/stat.py index 62e9e6a6b8..efb258a33d 100644 --- a/deepmd/pd/utils/stat.py +++ b/deepmd/pd/utils/stat.py @@ -5,8 +5,6 @@ ) from typing import ( Callable, - Dict, - List, Optional, Union, ) @@ -52,9 +50,6 @@ def make_stat_input(datasets, dataloaders, nbatches): for i in range(len(datasets)): sys_stat = {} - # device = paddle.get_device() - # paddle.set_device("cpu") - # with paddle.device("cpu"): iterator = iter(dataloaders[i]) numb_batches = min(nbatches, len(dataloaders[i])) for _ in range(numb_batches): @@ -74,7 +69,6 @@ def make_stat_input(datasets, dataloaders, nbatches): sys_stat[dd] = stat_data[dd] else: pass - # paddle.set_device(device) for key in sys_stat: if isinstance(sys_stat[key], np.float32): @@ -90,7 +84,7 @@ def make_stat_input(datasets, dataloaders, nbatches): def _restore_from_file( stat_file_path: DPPath, - keys: List[str] = ["energy"], + keys: list[str] = ["energy"], ) -> Optional[dict]: if stat_file_path is None: return None, None @@ -148,8 +142,8 @@ def _post_process_stat( def _compute_model_predict( - sampled: Union[Callable[[], List[dict]], List[dict]], - keys: List[str], + sampled: Union[Callable[[], list[dict]], list[dict]], + keys: list[str], model_forward: Callable[..., paddle.Tensor], ): auto_batch_size = AutoBatchSize() @@ -188,7 +182,7 @@ def model_forward_auto_batch_size(*args, **kwargs): def _make_preset_out_bias( ntypes: int, - ibias: List[Optional[np.array]], + ibias: list[Optional[np.array]], ) -> Optional[np.array]: """Make preset out bias. @@ -238,12 +232,12 @@ def _fill_stat_with_global( def compute_output_stats( - merged: Union[Callable[[], List[dict]], List[dict]], + merged: Union[Callable[[], list[dict]], list[dict]], ntypes: int, - keys: Union[str, List[str]] = ["energy"], + keys: Union[str, list[str]] = ["energy"], stat_file_path: Optional[DPPath] = None, rcond: Optional[float] = None, - preset_bias: Optional[Dict[str, List[Optional[paddle.Tensor]]]] = None, + preset_bias: Optional[dict[str, list[Optional[paddle.Tensor]]]] = None, model_forward: Optional[Callable[..., paddle.Tensor]] = None, ): """ @@ -398,12 +392,12 @@ def compute_output_stats( def compute_output_stats_global( - sampled: List[dict], + sampled: list[dict], ntypes: int, - keys: List[str], + keys: list[str], rcond: Optional[float] = None, - preset_bias: Optional[Dict[str, List[Optional[paddle.Tensor]]]] = None, - model_pred: Optional[Dict[str, np.ndarray]] = None, + preset_bias: Optional[dict[str, list[Optional[paddle.Tensor]]]] = None, + model_pred: Optional[dict[str, np.ndarray]] = None, ): """This function only handle stat computation from reduced global labels.""" # return directly if model predict is empty for global @@ -518,10 +512,10 @@ def rmse(x): def compute_output_stats_atomic( - sampled: List[dict], + sampled: list[dict], ntypes: int, - keys: List[str], - model_pred: Optional[Dict[str, np.ndarray]] = None, + keys: list[str], + model_pred: Optional[dict[str, np.ndarray]] = None, ): # get label dict from sample; for each key, only picking the system with atomic labels. outputs = { From 7525dac769fc344894fed844a4da052aba7b29dc Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Fri, 18 Oct 2024 20:42:51 +0800 Subject: [PATCH 56/93] fix neighborstat bug: gt -> ge --- deepmd/pd/model/descriptor/se_r.py | 4 ++-- deepmd/pd/model/descriptor/se_t.py | 4 ++-- deepmd/pd/model/descriptor/se_t_tebd.py | 2 +- deepmd/pd/train/training.py | 9 +++------ deepmd/pd/utils/neighbor_stat.py | 2 +- deepmd/pd/utils/nlist.py | 2 +- 6 files changed, 10 insertions(+), 13 deletions(-) diff --git a/deepmd/pd/model/descriptor/se_r.py b/deepmd/pd/model/descriptor/se_r.py index e54cb37693..978a409ae2 100644 --- a/deepmd/pd/model/descriptor/se_r.py +++ b/deepmd/pd/model/descriptor/se_r.py @@ -368,7 +368,7 @@ def forward( assert self.filter_layers is not None dmatrix = dmatrix.reshape([-1, self.nnei, 1]) - dmatrix = dmatrix.to(dtype=self.prec) + dmatrix = dmatrix.astype(self.prec) nfnl = dmatrix.shape[0] # pre-allocate a shape to pass jit xyz_scatter = paddle.zeros( @@ -392,7 +392,7 @@ def forward( result = xyz_scatter * res_rescale result = result.reshape([nf, nloc, self.filter_neuron[-1]]) return ( - result.to(dtype=env.GLOBAL_PD_FLOAT_PRECISION), + result.astype(env.GLOBAL_PD_FLOAT_PRECISION), None, None, None, diff --git a/deepmd/pd/model/descriptor/se_t.py b/deepmd/pd/model/descriptor/se_t.py index dfd899dac6..874ba4e987 100644 --- a/deepmd/pd/model/descriptor/se_t.py +++ b/deepmd/pd/model/descriptor/se_t.py @@ -683,7 +683,7 @@ def forward( protection=self.env_protection, ) dmatrix = dmatrix.reshape([-1, self.nnei, 4]) - dmatrix = dmatrix.to(dtype=self.prec) + dmatrix = dmatrix.astype(self.prec) nfnl = dmatrix.shape[0] # pre-allocate a shape to pass jit result = paddle.zeros( @@ -720,7 +720,7 @@ def forward( # xyz_scatter /= (self.nnei * self.nnei) result = result.reshape([nf, nloc, self.filter_neuron[-1]]) return ( - result.to(dtype=env.GLOBAL_PD_FLOAT_PRECISION), + result.astype(env.GLOBAL_PD_FLOAT_PRECISION), None, None, None, diff --git a/deepmd/pd/model/descriptor/se_t_tebd.py b/deepmd/pd/model/descriptor/se_t_tebd.py index 0a837680d4..650715cf76 100644 --- a/deepmd/pd/model/descriptor/se_t_tebd.py +++ b/deepmd/pd/model/descriptor/se_t_tebd.py @@ -863,7 +863,7 @@ def forward( # nf x nl x ng result = res_ij.reshape([nframes, nloc, self.filter_neuron[-1]]) return ( - result.to(dtype=env.GLOBAL_PD_FLOAT_PRECISION), + result.astype(env.GLOBAL_PD_FLOAT_PRECISION), None, None, None, diff --git a/deepmd/pd/train/training.py b/deepmd/pd/train/training.py index 253d4829f8..cd6ea7a350 100644 --- a/deepmd/pd/train/training.py +++ b/deepmd/pd/train/training.py @@ -1014,12 +1014,9 @@ def log_loss_valid(_task_key="Default"): ) if JIT: - raise NotImplementedError("JIT training is not supported yet.") - # frozen_model_prefix = "frozen_model" # We use .json and .pdiparams to denote the frozen model - # self.model.save(frozen_model_prefix) - # log.info( - # f"Frozen model for inferencing has been saved to {frozen_model_prefix}" - # ) + raise NotImplementedError( + "Paddle JIT saving during training is not supported yet." + ) log.info(f"Trained model has been saved to: {self.save_ckpt}") if fout: diff --git a/deepmd/pd/utils/neighbor_stat.py b/deepmd/pd/utils/neighbor_stat.py index 36fefdae8c..a46cdb5d57 100644 --- a/deepmd/pd/utils/neighbor_stat.py +++ b/deepmd/pd/utils/neighbor_stat.py @@ -110,7 +110,7 @@ def forward( else: mask = rr2 < self.rcut**2 # virtual types (<0) are not counted - nnei = paddle.sum(mask & ((extend_atype > 0)[:, None, :]), axis=-1).reshape( + nnei = paddle.sum(mask & ((extend_atype >= 0).unsqueeze(1)), axis=-1).reshape( [nframes, nloc, 1] ) max_nnei = paddle.max(nnei, axis=1) diff --git a/deepmd/pd/utils/nlist.py b/deepmd/pd/utils/nlist.py index 75259507e6..5a15a5f84f 100644 --- a/deepmd/pd/utils/nlist.py +++ b/deepmd/pd/utils/nlist.py @@ -428,7 +428,7 @@ def extend_coord_with_ghosts( cell: Optional[paddle.Tensor], rcut: float, cell_cpu: Optional[paddle.Tensor] = None, -): +) -> tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]: """Extend the coordinates of the atoms by appending peridoc images. The number of images is large enough to ensure all the neighbors within rcut are appended. From 312a3ef3c4051d7660960bf88bd86786f7ce0a35 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Sun, 20 Oct 2024 12:30:19 +0800 Subject: [PATCH 57/93] fix bugs and add unitest of paddle backend --- deepmd/pd/cvt_pth.py | 19 + deepmd/pd/loss/dos.py | 15 +- deepmd/pd/loss/ener.py | 3 +- deepmd/pd/loss/ener_spin.py | 5 +- deepmd/pd/loss/loss.py | 5 +- deepmd/pd/loss/tensor.py | 7 +- .../model/atomic_model/base_atomic_model.py | 2 +- .../model/atomic_model/dipole_atomic_model.py | 5 +- .../model/atomic_model/linear_atomic_model.py | 54 +- .../atomic_model/pairtab_atomic_model.py | 20 +- .../model/atomic_model/polar_atomic_model.py | 5 +- deepmd/pd/model/descriptor/descriptor.py | 8 +- deepmd/pd/model/descriptor/dpa1.py | 27 +- deepmd/pd/model/descriptor/dpa2.py | 29 +- deepmd/pd/model/descriptor/gaussian_lcc.py | 3 +- deepmd/pd/model/descriptor/hybrid.py | 35 +- deepmd/pd/model/descriptor/repformers.py | 17 +- deepmd/pd/model/descriptor/se_a.py | 60 +- deepmd/pd/model/descriptor/se_atten_v2.py | 10 +- deepmd/pd/model/descriptor/se_r.py | 29 +- deepmd/pd/model/descriptor/se_t.py | 47 +- deepmd/pd/model/descriptor/se_t_tebd.py | 41 +- deepmd/pd/model/model/dp_model.py | 6 +- deepmd/pd/model/model/dp_zbl_model.py | 1 + deepmd/pd/model/model/make_hessian_model.py | 10 +- deepmd/pd/model/model/transform_output.py | 13 +- deepmd/pd/model/task/dipole.py | 13 +- deepmd/pd/model/task/dos.py | 13 +- deepmd/pd/model/task/ener.py | 16 +- deepmd/pd/model/task/fitting.py | 25 +- deepmd/pd/model/task/invar_fitting.py | 13 +- deepmd/pd/model/task/polarizability.py | 15 +- deepmd/pd/train/wrapper.py | 26 +- deepmd/pd/utils/aux.py | 2 + deepmd/pd/utils/dataset.py | 5 +- deepmd/pd/utils/env_mat_stat.py | 9 +- deepmd/pd/utils/exclude_mask.py | 15 +- deepmd/pd/utils/neighbor_stat.py | 11 +- deepmd/pd/utils/nlist.py | 18 +- deepmd/pd/utils/update_sel.py | 5 +- deepmd/utils/data.py | 2 +- source/tests/pd/test_LKF.py | 36 + source/tests/pd/test_auto_batch_size.py | 37 + source/tests/pd/test_calculator.py | 109 + source/tests/pd/test_change_bias.py | 150 ++ source/tests/pd/test_dp_show.py | 219 ++ source/tests/pd/test_dp_test.py | 173 ++ source/tests/pd/test_finetune.py | 375 ++++ source/tests/pd/test_init_frz_model.py | 148 ++ source/tests/pd/test_init_model.py | 136 ++ source/tests/pd/test_loss.py | 808 ++++++++ source/tests/pd/test_lr.py | 106 + source/tests/pd/test_multitask.py | 310 +++ source/tests/pd/test_neighbor_stat.py | 69 + source/tests/pd/test_sampler.py | 114 ++ source/tests/pd/test_tabulate_fusion_se_a.py | 1511 ++++++++++++++ .../tests/pd/test_tabulate_fusion_se_atten.py | 1650 +++++++++++++++ source/tests/pd/test_tabulate_fusion_se_r.py | 1349 +++++++++++++ source/tests/pd/test_tabulate_fusion_se_t.py | 1768 +++++++++++++++++ source/tests/pd/test_training.py | 456 +++++ source/tests/pd/test_update_sel.py | 190 ++ source/tests/pd/test_utils.py | 35 + 62 files changed, 10054 insertions(+), 359 deletions(-) create mode 100644 deepmd/pd/cvt_pth.py create mode 100644 source/tests/pd/test_LKF.py create mode 100644 source/tests/pd/test_auto_batch_size.py create mode 100644 source/tests/pd/test_calculator.py create mode 100644 source/tests/pd/test_change_bias.py create mode 100644 source/tests/pd/test_dp_show.py create mode 100644 source/tests/pd/test_dp_test.py create mode 100644 source/tests/pd/test_finetune.py create mode 100644 source/tests/pd/test_init_frz_model.py create mode 100644 source/tests/pd/test_init_model.py create mode 100644 source/tests/pd/test_loss.py create mode 100644 source/tests/pd/test_lr.py create mode 100644 source/tests/pd/test_multitask.py create mode 100644 source/tests/pd/test_neighbor_stat.py create mode 100644 source/tests/pd/test_sampler.py create mode 100644 source/tests/pd/test_tabulate_fusion_se_a.py create mode 100644 source/tests/pd/test_tabulate_fusion_se_atten.py create mode 100644 source/tests/pd/test_tabulate_fusion_se_r.py create mode 100644 source/tests/pd/test_tabulate_fusion_se_t.py create mode 100644 source/tests/pd/test_training.py create mode 100644 source/tests/pd/test_update_sel.py create mode 100644 source/tests/pd/test_utils.py diff --git a/deepmd/pd/cvt_pth.py b/deepmd/pd/cvt_pth.py new file mode 100644 index 0000000000..370ef057f4 --- /dev/null +++ b/deepmd/pd/cvt_pth.py @@ -0,0 +1,19 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import paddle +import torch + +psd = torch.load( + "/workspace/hesensen/deepmd_backend/deepmd_paddle_new/source/tests/pd/model/models/dpa1.pth", + "cpu", +) + +tsd = {} +for k, v in psd.items(): + # if ".matrix" in k: + # v = v.T + psd[k] = paddle.to_tensor(v.detach().cpu().numpy()) + +paddle.save( + psd, + "/workspace/hesensen/deepmd_backend/deepmd_paddle_new/source/tests/pd/model/models/dpa1.pdparams", +) diff --git a/deepmd/pd/loss/dos.py b/deepmd/pd/loss/dos.py index a195f709cc..ae13e5e429 100644 --- a/deepmd/pd/loss/dos.py +++ b/deepmd/pd/loss/dos.py @@ -1,7 +1,4 @@ # SPDX-License-Identifier: LGPL-3.0-or-later -from typing import ( - List, -) import paddle @@ -184,9 +181,12 @@ def forward(self, input_dict, model, label, natoms, learning_rate=0.0, mae=False if "mask" in model_pred: atom_num = model_pred["mask"].sum(-1, keepdim=True) l2_global_loss_dos = paddle.mean( - paddle.sum(paddle.square(diff) * atom_num, axis=0) / atom_num.sum() + paddle.sum( + paddle.square(diff) * atom_num.astype(diff.dtype), axis=0 + ) + / (atom_num.sum().astype(diff.dtype)) ) - atom_num = paddle.mean(float(atom_num)) + atom_num = paddle.mean(atom_num.astype(diff.dtype)) else: atom_num = natoms l2_global_loss_dos = paddle.mean(paddle.square(diff)) @@ -212,7 +212,8 @@ def forward(self, input_dict, model, label, natoms, learning_rate=0.0, mae=False if "mask" in model_pred: atom_num = model_pred["mask"].sum(-1, keepdim=True) l2_global_loss_cdf = paddle.mean( - paddle.sum(paddle.square(diff) * atom_num, axis=0) / atom_num.sum() + paddle.sum(paddle.square(diff) * atom_num, axis=0) + / (atom_num.sum().astype(diff.dtype)) ) atom_num = paddle.mean(float(atom_num)) else: @@ -230,7 +231,7 @@ def forward(self, input_dict, model, label, natoms, learning_rate=0.0, mae=False return model_pred, loss, more_loss @property - def label_requirement(self) -> List[DataRequirementItem]: + def label_requirement(self) -> list[DataRequirementItem]: """Return data label requirements needed for this loss calculation.""" label_requirement = [] if self.has_ados or self.has_acdf: diff --git a/deepmd/pd/loss/ener.py b/deepmd/pd/loss/ener.py index f3baac0edf..036325205d 100644 --- a/deepmd/pd/loss/ener.py +++ b/deepmd/pd/loss/ener.py @@ -1,6 +1,5 @@ # SPDX-License-Identifier: LGPL-3.0-or-later from typing import ( - List, Optional, ) @@ -349,7 +348,7 @@ def forward(self, input_dict, model, label, natoms, learning_rate, mae=False): return model_pred, loss, more_loss @property - def label_requirement(self) -> List[DataRequirementItem]: + def label_requirement(self) -> list[DataRequirementItem]: """Return data label requirements needed for this loss calculation.""" label_requirement = [] if self.has_e: diff --git a/deepmd/pd/loss/ener_spin.py b/deepmd/pd/loss/ener_spin.py index fc91ccc801..4722ee6b84 100644 --- a/deepmd/pd/loss/ener_spin.py +++ b/deepmd/pd/loss/ener_spin.py @@ -1,7 +1,4 @@ # SPDX-License-Identifier: LGPL-3.0-or-later -from typing import ( - List, -) import paddle import paddle.nn.functional as F @@ -276,7 +273,7 @@ def forward(self, input_dict, model, label, natoms, learning_rate, mae=False): return model_pred, loss, more_loss @property - def label_requirement(self) -> List[DataRequirementItem]: + def label_requirement(self) -> list[DataRequirementItem]: """Return data label requirements needed for this loss calculation.""" label_requirement = [] if self.has_e: diff --git a/deepmd/pd/loss/loss.py b/deepmd/pd/loss/loss.py index 00208adc2c..c083996720 100644 --- a/deepmd/pd/loss/loss.py +++ b/deepmd/pd/loss/loss.py @@ -3,9 +3,6 @@ ABC, abstractmethod, ) -from typing import ( - List, -) import paddle @@ -25,7 +22,7 @@ def forward(self, input_dict, model, label, natoms, learning_rate): @property @abstractmethod - def label_requirement(self) -> List[DataRequirementItem]: + def label_requirement(self) -> list[DataRequirementItem]: """Return data label requirements needed for this loss calculation.""" pass diff --git a/deepmd/pd/loss/tensor.py b/deepmd/pd/loss/tensor.py index 5662c88451..b549a0be8e 100644 --- a/deepmd/pd/loss/tensor.py +++ b/deepmd/pd/loss/tensor.py @@ -1,7 +1,4 @@ # SPDX-License-Identifier: LGPL-3.0-or-later -from typing import ( - List, -) import paddle @@ -136,7 +133,7 @@ def forward(self, input_dict, model, label, natoms, learning_rate=0.0, mae=False paddle.sum( paddle.square(diff) * atom_num.astype(diff.dtype), axis=0 ) - / atom_num.sum() + / (atom_num.sum().astype(diff.dtype)) ) atom_num = paddle.mean(atom_num.astype(diff.dtype)) else: @@ -154,7 +151,7 @@ def forward(self, input_dict, model, label, natoms, learning_rate=0.0, mae=False return model_pred, loss, more_loss @property - def label_requirement(self) -> List[DataRequirementItem]: + def label_requirement(self) -> list[DataRequirementItem]: """Return data label requirements needed for this loss calculation.""" label_requirement = [] if self.has_local_weight: diff --git a/deepmd/pd/model/atomic_model/base_atomic_model.py b/deepmd/pd/model/atomic_model/base_atomic_model.py index e07f421c08..dde7863d5b 100644 --- a/deepmd/pd/model/atomic_model/base_atomic_model.py +++ b/deepmd/pd/model/atomic_model/base_atomic_model.py @@ -259,7 +259,7 @@ def forward_common_atomic( out_shape2 *= ss ret_dict[kk] = ( ret_dict[kk].reshape([out_shape[0], out_shape[1], out_shape2]) - * atom_mask[:, :, None].astype(ret_dict[kk].dtype) + * atom_mask.unsqueeze(2).astype(ret_dict[kk].dtype) ).reshape(out_shape) ret_dict["mask"] = atom_mask diff --git a/deepmd/pd/model/atomic_model/dipole_atomic_model.py b/deepmd/pd/model/atomic_model/dipole_atomic_model.py index 63300be4af..3fc5204749 100644 --- a/deepmd/pd/model/atomic_model/dipole_atomic_model.py +++ b/deepmd/pd/model/atomic_model/dipole_atomic_model.py @@ -1,7 +1,4 @@ # SPDX-License-Identifier: LGPL-3.0-or-later -from typing import ( - Dict, -) import paddle @@ -21,7 +18,7 @@ def __init__(self, descriptor, fitting, type_map, **kwargs): def apply_out_stat( self, - ret: Dict[str, paddle.Tensor], + ret: dict[str, paddle.Tensor], atype: paddle.Tensor, ): # dipole not applying bias diff --git a/deepmd/pd/model/atomic_model/linear_atomic_model.py b/deepmd/pd/model/atomic_model/linear_atomic_model.py index 0ff69a5bec..c6550cb427 100644 --- a/deepmd/pd/model/atomic_model/linear_atomic_model.py +++ b/deepmd/pd/model/atomic_model/linear_atomic_model.py @@ -2,10 +2,7 @@ import copy from typing import ( Callable, - Dict, - List, Optional, - Tuple, Union, ) @@ -55,8 +52,8 @@ class LinearEnergyAtomicModel(BaseAtomicModel): def __init__( self, - models: List[BaseAtomicModel], - type_map: List[str], + models: list[BaseAtomicModel], + type_map: list[str], **kwargs, ): super().__init__(type_map, **kwargs) @@ -119,12 +116,12 @@ def get_rcut(self) -> float: """Get the cut-off radius.""" return max(self.get_model_rcuts()) - def get_type_map(self) -> List[str]: + def get_type_map(self) -> list[str]: """Get the type map.""" return self.type_map def change_type_map( - self, type_map: List[str], model_with_new_type_stat=None + self, type_map: list[str], model_with_new_type_stat=None ) -> None: """Change the type related params to new ones, according to `type_map` and the original one in the model. If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. @@ -140,22 +137,22 @@ def change_type_map( else None, ) - def get_model_rcuts(self) -> List[float]: + def get_model_rcuts(self) -> list[float]: """Get the cut-off radius for each individual models.""" return [model.get_rcut() for model in self.models] - def get_sel(self) -> List[int]: + def get_sel(self) -> list[int]: return [max([model.get_nsel() for model in self.models])] - def get_model_nsels(self) -> List[int]: + def get_model_nsels(self) -> list[int]: """Get the processed sels for each individual models. Not distinguishing types.""" return [model.get_nsel() for model in self.models] - def get_model_sels(self) -> List[List[int]]: + def get_model_sels(self) -> list[list[int]]: """Get the sels for each individual models.""" return [model.get_sel() for model in self.models] - def _sort_rcuts_sels(self) -> Tuple[List[float], List[int]]: + def _sort_rcuts_sels(self) -> tuple[list[float], list[int]]: # sort the pair of rcut and sels in ascending order, first based on sel, then on rcut. zipped = paddle.stack( [ @@ -168,8 +165,8 @@ def _sort_rcuts_sels(self) -> Tuple[List[float], List[int]]: inner_sorted = zipped[inner_sorting] outer_sorting = paddle.argsort(inner_sorted[:, 0]) outer_sorted = inner_sorted[outer_sorting] - sorted_rcuts: List[float] = outer_sorted[:, 0].tolist() - sorted_sels: List[int] = outer_sorted[:, 1].to(paddle.int64).tolist() + sorted_rcuts: list[float] = outer_sorted[:, 0].tolist() + sorted_sels: list[int] = outer_sorted[:, 1].to(paddle.int64).tolist() return sorted_rcuts, sorted_sels def forward_atomic( @@ -180,8 +177,8 @@ def forward_atomic( mapping: Optional[paddle.Tensor] = None, fparam: Optional[paddle.Tensor] = None, aparam: Optional[paddle.Tensor] = None, - comm_dict: Optional[Dict[str, paddle.Tensor]] = None, - ) -> Dict[str, paddle.Tensor]: + comm_dict: Optional[dict[str, paddle.Tensor]] = None, + ) -> dict[str, paddle.Tensor]: """Return atomic prediction. Parameters @@ -253,7 +250,7 @@ def forward_atomic( def apply_out_stat( self, - ret: Dict[str, paddle.Tensor], + ret: dict[str, paddle.Tensor], atype: paddle.Tensor, ): """Apply the stat to each atomic output. @@ -271,7 +268,7 @@ def apply_out_stat( return ret @staticmethod - def remap_atype(ori_map: List[str], new_map: List[str]) -> paddle.Tensor: + def remap_atype(ori_map: list[str], new_map: list[str]) -> paddle.Tensor: """ This method is used to map the atype from the common type_map to the original type_map of indivial AtomicModels. It creates a index mapping for the conversion. @@ -336,7 +333,7 @@ def deserialize(cls, data: dict) -> "LinearEnergyAtomicModel": def _compute_weight( self, extended_coord, extended_atype, nlists_ - ) -> List[paddle.Tensor]: + ) -> list[paddle.Tensor]: """This should be a list of user defined weights that matches the number of models to be combined.""" nmodels = len(self.models) nframes, nloc, _ = nlists_[0].shape @@ -355,7 +352,7 @@ def get_dim_aparam(self) -> int: """Get the number (dimension) of atomic parameters of this atomic model.""" return max([model.get_dim_aparam() for model in self.models]) - def get_sel_type(self) -> List[int]: + def get_sel_type(self) -> list[int]: """Get the selected atom types of this model. Only atoms with selected atom types have atomic contribution @@ -384,7 +381,7 @@ def is_aparam_nall(self) -> bool: def compute_or_load_out_stat( self, - merged: Union[Callable[[], List[dict]], List[dict]], + merged: Union[Callable[[], list[dict]], list[dict]], stat_file_path: Optional[DPPath] = None, ): """ @@ -457,7 +454,7 @@ def __init__( zbl_model: PairTabAtomicModel, sw_rmin: float, sw_rmax: float, - type_map: List[str], + type_map: list[str], smin_alpha: Optional[float] = 0.1, **kwargs, ): @@ -504,8 +501,8 @@ def _compute_weight( self, extended_coord: paddle.Tensor, extended_atype: paddle.Tensor, - nlists_: List[paddle.Tensor], - ) -> List[paddle.Tensor]: + nlists_: list[paddle.Tensor], + ) -> list[paddle.Tensor]: """ZBL weight. Returns @@ -552,10 +549,13 @@ def _compute_weight( left_mask = sigma < self.sw_rmin mid_mask = (self.sw_rmin <= sigma) & (sigma < self.sw_rmax) right_mask = sigma >= self.sw_rmax - coef[left_mask] = 1 + # coef[left_mask] = 1 + coef = paddle.where(left_mask, paddle.ones_like(coef), coef) smooth = -6 * u**5 + 15 * u**4 - 10 * u**3 + 1 - coef[mid_mask] = smooth[mid_mask] - coef[right_mask] = 0 + # coef[mid_mask] = smooth[mid_mask] + coef = paddle.where(mid_mask, smooth, coef) + # coef[right_mask] = 0 + coef = paddle.where(right_mask, paddle.zeros_like(coef), coef) # to handle masked atoms coef = paddle.where(sigma != 0, coef, paddle.zeros_like(coef)) diff --git a/deepmd/pd/model/atomic_model/pairtab_atomic_model.py b/deepmd/pd/model/atomic_model/pairtab_atomic_model.py index 86913e87fb..d24d2fd6f9 100644 --- a/deepmd/pd/model/atomic_model/pairtab_atomic_model.py +++ b/deepmd/pd/model/atomic_model/pairtab_atomic_model.py @@ -2,8 +2,6 @@ import copy from typing import ( Callable, - Dict, - List, Optional, Union, ) @@ -70,8 +68,8 @@ def __init__( self, tab_file: str, rcut: float, - sel: Union[int, List[int]], - type_map: List[str], + sel: Union[int, list[int]], + type_map: list[str], **kwargs, ): super().__init__(type_map, **kwargs) @@ -141,10 +139,10 @@ def get_out_bias(self) -> paddle.Tensor: def get_rcut(self) -> float: return self.rcut - def get_type_map(self) -> List[str]: + def get_type_map(self) -> list[str]: return self.type_map - def get_sel(self) -> List[int]: + def get_sel(self) -> list[int]: return [self.sel] def get_nsel(self) -> int: @@ -172,7 +170,7 @@ def need_sorted_nlist_for_lower(self) -> bool: return False def change_type_map( - self, type_map: List[str], model_with_new_type_stat=None + self, type_map: list[str], model_with_new_type_stat=None ) -> None: """Change the type related params to new ones, according to `type_map` and the original one in the model. If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. @@ -221,7 +219,7 @@ def deserialize(cls, data) -> "PairTabAtomicModel": def compute_or_load_stat( self, - merged: Union[Callable[[], List[dict]], List[dict]], + merged: Union[Callable[[], list[dict]], list[dict]], stat_file_path: Optional[DPPath] = None, ): """ @@ -251,8 +249,8 @@ def forward_atomic( fparam: Optional[paddle.Tensor] = None, aparam: Optional[paddle.Tensor] = None, do_atomic_virial: bool = False, - comm_dict: Optional[Dict[str, paddle.Tensor]] = None, - ) -> Dict[str, paddle.Tensor]: + comm_dict: Optional[dict[str, paddle.Tensor]] = None, + ) -> dict[str, paddle.Tensor]: nframes, nloc, nnei = nlist.shape extended_coord = extended_coord.reshape([nframes, -1, 3]) if self.do_grad_r() or self.do_grad_c(): @@ -484,7 +482,7 @@ def get_dim_aparam(self) -> int: """Get the number (dimension) of atomic parameters of this atomic model.""" return 0 - def get_sel_type(self) -> List[int]: + def get_sel_type(self) -> list[int]: """Get the selected atom types of this model. Only atoms with selected atom types have atomic contribution diff --git a/deepmd/pd/model/atomic_model/polar_atomic_model.py b/deepmd/pd/model/atomic_model/polar_atomic_model.py index d3687c469b..0a65760c70 100644 --- a/deepmd/pd/model/atomic_model/polar_atomic_model.py +++ b/deepmd/pd/model/atomic_model/polar_atomic_model.py @@ -1,7 +1,4 @@ # SPDX-License-Identifier: LGPL-3.0-or-later -from typing import ( - Dict, -) import paddle @@ -21,7 +18,7 @@ def __init__(self, descriptor, fitting, type_map, **kwargs): def apply_out_stat( self, - ret: Dict[str, paddle.Tensor], + ret: dict[str, paddle.Tensor], atype: paddle.Tensor, ): """Apply the stat to each atomic output. diff --git a/deepmd/pd/model/descriptor/descriptor.py b/deepmd/pd/model/descriptor/descriptor.py index 846046ee85..5d29a1cf35 100644 --- a/deepmd/pd/model/descriptor/descriptor.py +++ b/deepmd/pd/model/descriptor/descriptor.py @@ -6,8 +6,6 @@ ) from typing import ( Callable, - Dict, - List, Optional, Union, ) @@ -71,7 +69,7 @@ def get_nsel(self) -> int: pass @abstractmethod - def get_sel(self) -> List[int]: + def get_sel(self) -> list[int]: """Returns the number of selected atoms for each type.""" pass @@ -102,7 +100,7 @@ def get_env_protection(self) -> float: def compute_input_stats( self, - merged: Union[Callable[[], List[dict]], List[dict]], + merged: Union[Callable[[], list[dict]], list[dict]], path: Optional[DPPath] = None, ): """ @@ -123,7 +121,7 @@ def compute_input_stats( """ raise NotImplementedError - def get_stats(self) -> Dict[str, StatItem]: + def get_stats(self) -> dict[str, StatItem]: """Get the statistics of the descriptor.""" raise NotImplementedError diff --git a/deepmd/pd/model/descriptor/dpa1.py b/deepmd/pd/model/descriptor/dpa1.py index 9a4715c271..925235cb0c 100644 --- a/deepmd/pd/model/descriptor/dpa1.py +++ b/deepmd/pd/model/descriptor/dpa1.py @@ -1,10 +1,7 @@ # SPDX-License-Identifier: LGPL-3.0-or-later from typing import ( Callable, - Dict, - List, Optional, - Tuple, Union, ) @@ -215,7 +212,7 @@ def __init__( self, rcut: float, rcut_smth: float, - sel: Union[List[int], int], + sel: Union[list[int], int], ntypes: int, neuron: list = [25, 50, 100], axis_neuron: int = 16, @@ -229,7 +226,7 @@ def __init__( activation_function: str = "tanh", precision: str = "float64", resnet_dt: bool = False, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: list[tuple[int, int]] = [], env_protection: float = 0.0, scaling_factor: int = 1.0, normalize=True, @@ -241,10 +238,10 @@ def __init__( smooth_type_embedding: bool = True, type_one_side: bool = False, stripped_type_embedding: Optional[bool] = None, - seed: Optional[Union[int, List[int]]] = None, + seed: Optional[Union[int, list[int]]] = None, use_econf_tebd: bool = False, use_tebd_bias: bool = False, - type_map: Optional[List[str]] = None, + type_map: Optional[list[str]] = None, # not implemented spin=None, type: Optional[str] = None, @@ -326,7 +323,7 @@ def get_nsel(self) -> int: """Returns the number of selected atoms in the cut-off radius.""" return self.se_atten.get_nsel() - def get_sel(self) -> List[int]: + def get_sel(self) -> list[int]: """Returns the number of selected atoms for each type.""" return self.se_atten.get_sel() @@ -334,7 +331,7 @@ def get_ntypes(self) -> int: """Returns the number of element types.""" return self.se_atten.get_ntypes() - def get_type_map(self) -> List[str]: + def get_type_map(self) -> list[str]: """Get the name to each type of atoms.""" return self.type_map @@ -409,7 +406,7 @@ def dim_emb(self): def compute_input_stats( self, - merged: Union[Callable[[], List[dict]], List[dict]], + merged: Union[Callable[[], list[dict]], list[dict]], path: Optional[DPPath] = None, ): """ @@ -439,12 +436,12 @@ def set_stat_mean_and_stddev( self.se_atten.mean = mean self.se_atten.stddev = stddev - def get_stat_mean_and_stddev(self) -> Tuple[paddle.Tensor, paddle.Tensor]: + def get_stat_mean_and_stddev(self) -> tuple[paddle.Tensor, paddle.Tensor]: """Get mean and stddev for descriptor.""" return self.se_atten.mean, self.se_atten.stddev def change_type_map( - self, type_map: List[str], model_with_new_type_stat=None + self, type_map: list[str], model_with_new_type_stat=None ) -> None: """Change the type related params to new ones, according to `type_map` and the original one in the model. If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. @@ -566,7 +563,7 @@ def forward( extended_atype: paddle.Tensor, nlist: paddle.Tensor, mapping: Optional[paddle.Tensor] = None, - comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + comm_dict: Optional[dict[str, paddle.Tensor]] = None, ): """Compute the descriptor. @@ -621,9 +618,9 @@ def forward( def update_sel( cls, train_data: DeepmdDataSystem, - type_map: Optional[List[str]], + type_map: Optional[list[str]], local_jdata: dict, - ) -> Tuple[dict, Optional[float]]: + ) -> tuple[dict, Optional[float]]: """Update the selection and perform neighbor statistics. Parameters diff --git a/deepmd/pd/model/descriptor/dpa2.py b/deepmd/pd/model/descriptor/dpa2.py index 73f1654bb1..d366c15560 100644 --- a/deepmd/pd/model/descriptor/dpa2.py +++ b/deepmd/pd/model/descriptor/dpa2.py @@ -1,10 +1,7 @@ # SPDX-License-Identifier: LGPL-3.0-or-later from typing import ( Callable, - Dict, - List, Optional, - Tuple, Union, ) @@ -85,14 +82,14 @@ def __init__( concat_output_tebd: bool = True, precision: str = "float64", smooth: bool = True, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: list[tuple[int, int]] = [], env_protection: float = 0.0, trainable: bool = True, - seed: Optional[Union[int, List[int]]] = None, + seed: Optional[Union[int, list[int]]] = None, add_tebd_to_repinit_out: bool = False, use_econf_tebd: bool = False, use_tebd_bias: bool = False, - type_map: Optional[List[str]] = None, + type_map: Optional[list[str]] = None, old_impl: bool = False, ): r"""The DPA-2 descriptor. see https://arxiv.org/abs/2312.15492. @@ -278,7 +275,7 @@ def get_nsel(self) -> int: """Returns the number of selected atoms in the cut-off radius.""" return sum(self.sel) - def get_sel(self) -> List[int]: + def get_sel(self) -> list[int]: """Returns the number of selected atoms for each type.""" return self.sel @@ -286,7 +283,7 @@ def get_ntypes(self) -> int: """Returns the number of element types.""" return self.ntypes - def get_type_map(self) -> List[str]: + def get_type_map(self) -> list[str]: """Get the name to each type of atoms.""" return self.type_map @@ -377,7 +374,7 @@ def share_params(self, base_class, shared_level, resume=False): raise NotImplementedError def change_type_map( - self, type_map: List[str], model_with_new_type_stat=None + self, type_map: list[str], model_with_new_type_stat=None ) -> None: """Change the type related params to new ones, according to `type_map` and the original one in the model. If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. @@ -428,7 +425,7 @@ def dim_emb(self): def compute_input_stats( self, - merged: Union[Callable[[], List[dict]], List[dict]], + merged: Union[Callable[[], list[dict]], list[dict]], path: Optional[DPPath] = None, ): """ @@ -452,8 +449,8 @@ def compute_input_stats( def set_stat_mean_and_stddev( self, - mean: List[paddle.Tensor], - stddev: List[paddle.Tensor], + mean: list[paddle.Tensor], + stddev: list[paddle.Tensor], ) -> None: """Update mean and stddev for descriptor.""" for ii, descrpt in enumerate([self.repinit, self.repformers]): @@ -462,7 +459,7 @@ def set_stat_mean_and_stddev( def get_stat_mean_and_stddev( self, - ) -> Tuple[List[paddle.Tensor], List[paddle.Tensor]]: + ) -> tuple[list[paddle.Tensor], list[paddle.Tensor]]: """Get mean and stddev for descriptor.""" return [self.repinit.mean, self.repformers.mean], [ self.repinit.stddev, @@ -591,7 +588,7 @@ def forward( extended_atype: paddle.Tensor, nlist: paddle.Tensor, mapping: Optional[paddle.Tensor] = None, - comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + comm_dict: Optional[dict[str, paddle.Tensor]] = None, ): """Compute the descriptor. @@ -682,9 +679,9 @@ def forward( def update_sel( cls, train_data: DeepmdDataSystem, - type_map: Optional[List[str]], + type_map: Optional[list[str]], local_jdata: dict, - ) -> Tuple[dict, Optional[float]]: + ) -> tuple[dict, Optional[float]]: """Update the selection and perform neighbor statistics. Parameters diff --git a/deepmd/pd/model/descriptor/gaussian_lcc.py b/deepmd/pd/model/descriptor/gaussian_lcc.py index 2714b663e9..8f58faa57f 100644 --- a/deepmd/pd/model/descriptor/gaussian_lcc.py +++ b/deepmd/pd/model/descriptor/gaussian_lcc.py @@ -1,6 +1,5 @@ # SPDX-License-Identifier: LGPL-3.0-or-later from typing import ( - List, Optional, ) @@ -163,7 +162,7 @@ def dim_emb(self): """Returns the output dimension of pair representation.""" return self.pair_embed_dim - def compute_input_stats(self, merged: List[dict], path: Optional[DPPath] = None): + def compute_input_stats(self, merged: list[dict], path: Optional[DPPath] = None): """Update mean and stddev for descriptor elements.""" pass diff --git a/deepmd/pd/model/descriptor/hybrid.py b/deepmd/pd/model/descriptor/hybrid.py index b96ccd55ef..dcacf0eb37 100644 --- a/deepmd/pd/model/descriptor/hybrid.py +++ b/deepmd/pd/model/descriptor/hybrid.py @@ -2,10 +2,7 @@ import math from typing import ( Any, - Dict, - List, Optional, - Tuple, Union, ) @@ -43,11 +40,11 @@ class DescrptHybrid(BaseDescriptor, paddle.nn.Layer): The descriptor can be either an object or a dictionary. """ - nlist_cut_idx: List[paddle.Tensor] + nlist_cut_idx: list[paddle.Tensor] def __init__( self, - list: List[Union[BaseDescriptor, Dict[str, Any]]], + list: list[Union[BaseDescriptor, dict[str, Any]]], **kwargs, ) -> None: super().__init__() @@ -57,7 +54,7 @@ def __init__( raise RuntimeError( "cannot build descriptor from an empty list of descriptors." ) - formatted_descript_list: List[BaseDescriptor] = [] + formatted_descript_list: list[BaseDescriptor] = [] for ii in descrpt_list: if isinstance(ii, BaseDescriptor): formatted_descript_list.append(ii) @@ -75,7 +72,7 @@ def __init__( self.descrpt_list[ii].get_ntypes() == self.descrpt_list[0].get_ntypes() ), f"number of atom types in {ii}th descrptor does not match others" # if hybrid sel is larger than sub sel, the nlist needs to be cut for each type - self.nlist_cut_idx: List[paddle.Tensor] = [] + self.nlist_cut_idx: list[paddle.Tensor] = [] if self.mixed_types() and not all( descrpt.mixed_types() for descrpt in self.descrpt_list ): @@ -114,7 +111,7 @@ def get_rcut_smth(self) -> float: # Note: Using the minimum rcut_smth might not be appropriate in all scenarios. Consider using a different approach or provide detailed documentation on why the minimum value is chosen. return min([descrpt.get_rcut_smth() for descrpt in self.descrpt_list]) - def get_sel(self) -> List[int]: + def get_sel(self) -> list[int]: """Returns the number of selected atoms for each type.""" if self.mixed_types(): return [ @@ -131,7 +128,7 @@ def get_ntypes(self) -> int: """Returns the number of element types.""" return self.descrpt_list[0].get_ntypes() - def get_type_map(self) -> List[str]: + def get_type_map(self) -> list[str]: """Get the name to each type of atoms.""" return self.descrpt_list[0].get_type_map() @@ -185,7 +182,7 @@ def share_params(self, base_class, shared_level, resume=False): raise NotImplementedError def change_type_map( - self, type_map: List[str], model_with_new_type_stat=None + self, type_map: list[str], model_with_new_type_stat=None ) -> None: """Change the type related params to new ones, according to `type_map` and the original one in the model. If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. @@ -198,15 +195,15 @@ def change_type_map( else None, ) - def compute_input_stats(self, merged: List[dict], path: Optional[DPPath] = None): + def compute_input_stats(self, merged: list[dict], path: Optional[DPPath] = None): """Update mean and stddev for descriptor elements.""" for descrpt in self.descrpt_list: descrpt.compute_input_stats(merged, path) def set_stat_mean_and_stddev( self, - mean: List[Union[paddle.Tensor, List[paddle.Tensor]]], - stddev: List[Union[paddle.Tensor, List[paddle.Tensor]]], + mean: list[Union[paddle.Tensor, list[paddle.Tensor]]], + stddev: list[Union[paddle.Tensor, list[paddle.Tensor]]], ) -> None: """Update mean and stddev for descriptor.""" for ii, descrpt in enumerate(self.descrpt_list): @@ -214,9 +211,9 @@ def set_stat_mean_and_stddev( def get_stat_mean_and_stddev( self, - ) -> Tuple[ - List[Union[paddle.Tensor, List[paddle.Tensor]]], - List[Union[paddle.Tensor, List[paddle.Tensor]]], + ) -> tuple[ + list[Union[paddle.Tensor, list[paddle.Tensor]]], + list[Union[paddle.Tensor, list[paddle.Tensor]]], ]: """Get mean and stddev for descriptor.""" mean_list = [] @@ -233,7 +230,7 @@ def forward( atype_ext: paddle.Tensor, nlist: paddle.Tensor, mapping: Optional[paddle.Tensor] = None, - comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + comm_dict: Optional[dict[str, paddle.Tensor]] = None, ): """Compute the descriptor. @@ -303,9 +300,9 @@ def forward( def update_sel( cls, train_data: DeepmdDataSystem, - type_map: Optional[List[str]], + type_map: Optional[list[str]], local_jdata: dict, - ) -> Tuple[dict, Optional[float]]: + ) -> tuple[dict, Optional[float]]: """Update the selection and perform neighbor statistics. Parameters diff --git a/deepmd/pd/model/descriptor/repformers.py b/deepmd/pd/model/descriptor/repformers.py index 31fd6e7b83..11d30e2461 100644 --- a/deepmd/pd/model/descriptor/repformers.py +++ b/deepmd/pd/model/descriptor/repformers.py @@ -1,10 +1,7 @@ # SPDX-License-Identifier: LGPL-3.0-or-later from typing import ( Callable, - Dict, - List, Optional, - Tuple, Union, ) @@ -100,12 +97,12 @@ def __init__( update_residual_init: str = "norm", set_davg_zero: bool = True, smooth: bool = True, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: list[tuple[int, int]] = [], env_protection: float = 0.0, precision: str = "float64", trainable_ln: bool = True, ln_eps: Optional[float] = 1e-5, - seed: Optional[Union[int, List[int]]] = None, + seed: Optional[Union[int, list[int]]] = None, old_impl: bool = False, ): r""" @@ -325,7 +322,7 @@ def get_nsel(self) -> int: """Returns the number of selected atoms in the cut-off radius.""" return sum(self.sel) - def get_sel(self) -> List[int]: + def get_sel(self) -> list[int]: """Returns the number of selected atoms for each type.""" return self.sel @@ -394,7 +391,7 @@ def dim_emb(self): def reinit_exclude( self, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: list[tuple[int, int]] = [], ): self.exclude_types = exclude_types self.emask = PairExcludeMask(self.ntypes, exclude_types=exclude_types) @@ -406,7 +403,7 @@ def forward( extended_atype: paddle.Tensor, extended_atype_embd: Optional[paddle.Tensor] = None, mapping: Optional[paddle.Tensor] = None, - comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + comm_dict: Optional[dict[str, paddle.Tensor]] = None, ): if comm_dict is None: assert mapping is not None @@ -513,7 +510,7 @@ def forward( def compute_input_stats( self, - merged: Union[Callable[[], List[dict]], List[dict]], + merged: Union[Callable[[], list[dict]], list[dict]], path: Optional[DPPath] = None, ): """ @@ -550,7 +547,7 @@ def compute_input_stats( paddle.assign(paddle.to_tensor(mean).to(device=env.DEVICE), self.mean) # pylint: disable=no-explicit-dtype paddle.assign(paddle.to_tensor(stddev).to(device=env.DEVICE), self.stddev) # pylint: disable=no-explicit-dtype - def get_stats(self) -> Dict[str, StatItem]: + def get_stats(self) -> dict[str, StatItem]: """Get the statistics of the descriptor.""" if self.stats is None: raise RuntimeError( diff --git a/deepmd/pd/model/descriptor/se_a.py b/deepmd/pd/model/descriptor/se_a.py index 48a576a446..8a5d7bcb96 100644 --- a/deepmd/pd/model/descriptor/se_a.py +++ b/deepmd/pd/model/descriptor/se_a.py @@ -3,10 +3,7 @@ from typing import ( Callable, ClassVar, - Dict, - List, Optional, - Tuple, Union, ) @@ -84,14 +81,14 @@ def __init__( activation_function: str = "tanh", precision: str = "float64", resnet_dt: bool = False, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: list[tuple[int, int]] = [], env_protection: float = 0.0, old_impl: bool = False, type_one_side: bool = True, trainable: bool = True, - seed: Optional[Union[int, List[int]]] = None, + seed: Optional[Union[int, list[int]]] = None, ntypes: Optional[int] = None, # to be compat with input - type_map: Optional[List[str]] = None, + type_map: Optional[list[str]] = None, # not implemented spin=None, ): @@ -130,7 +127,7 @@ def get_nsel(self) -> int: """Returns the number of selected atoms in the cut-off radius.""" return self.sea.get_nsel() - def get_sel(self) -> List[int]: + def get_sel(self) -> list[int]: """Returns the number of selected atoms for each type.""" return self.sea.get_sel() @@ -138,7 +135,7 @@ def get_ntypes(self) -> int: """Returns the number of element types.""" return self.sea.get_ntypes() - def get_type_map(self) -> List[str]: + def get_type_map(self) -> list[str]: """Get the name to each type of atoms.""" return self.type_map @@ -192,7 +189,7 @@ def dim_out(self): return self.sea.dim_out def change_type_map( - self, type_map: List[str], model_with_new_type_stat=None + self, type_map: list[str], model_with_new_type_stat=None ) -> None: """Change the type related params to new ones, according to `type_map` and the original one in the model. If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. @@ -205,7 +202,7 @@ def change_type_map( def compute_input_stats( self, - merged: Union[Callable[[], List[dict]], List[dict]], + merged: Union[Callable[[], list[dict]], list[dict]], path: Optional[DPPath] = None, ): """ @@ -228,7 +225,7 @@ def compute_input_stats( def reinit_exclude( self, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: list[tuple[int, int]] = [], ): """Update the type exclusions.""" self.sea.reinit_exclude(exclude_types) @@ -239,7 +236,7 @@ def forward( atype_ext: paddle.Tensor, nlist: paddle.Tensor, mapping: Optional[paddle.Tensor] = None, - comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + comm_dict: Optional[dict[str, paddle.Tensor]] = None, ): """Compute the descriptor. @@ -284,7 +281,7 @@ def set_stat_mean_and_stddev( self.sea.mean = mean self.sea.stddev = stddev - def get_stat_mean_and_stddev(self) -> Tuple[paddle.Tensor, paddle.Tensor]: + def get_stat_mean_and_stddev(self) -> tuple[paddle.Tensor, paddle.Tensor]: """Get mean and stddev for descriptor.""" return self.sea.mean, self.sea.stddev @@ -342,9 +339,9 @@ def t_cvt(xx): def update_sel( cls, train_data: DeepmdDataSystem, - type_map: Optional[List[str]], + type_map: Optional[list[str]], local_jdata: dict, - ) -> Tuple[dict, Optional[float]]: + ) -> tuple[dict, Optional[float]]: """Update the selection and perform neighbor statistics. Parameters @@ -386,12 +383,12 @@ def __init__( activation_function: str = "tanh", precision: str = "float64", resnet_dt: bool = False, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: list[tuple[int, int]] = [], env_protection: float = 0.0, old_impl: bool = False, type_one_side: bool = True, trainable: bool = True, - seed: Optional[Union[int, List[int]]] = None, + seed: Optional[Union[int, list[int]]] = None, **kwargs, ): """Construct an embedding net of type `se_a`. @@ -484,7 +481,7 @@ def get_nsel(self) -> int: """Returns the number of selected atoms in the cut-off radius.""" return sum(self.sel) - def get_sel(self) -> List[int]: + def get_sel(self) -> list[int]: """Returns the number of selected atoms for each type.""" return self.sel @@ -548,7 +545,7 @@ def __getitem__(self, key): def compute_input_stats( self, - merged: Union[Callable[[], List[dict]], List[dict]], + merged: Union[Callable[[], list[dict]], list[dict]], path: Optional[DPPath] = None, ): """ @@ -585,7 +582,7 @@ def compute_input_stats( paddle.assign(paddle.to_tensor(mean).to(device=env.DEVICE), self.mean) # pylint: disable=no-explicit-dtype paddle.assign(paddle.to_tensor(stddev).to(device=env.DEVICE), self.stddev) # pylint: disable=no-explicit-dtype - def get_stats(self) -> Dict[str, StatItem]: + def get_stats(self) -> dict[str, StatItem]: """Get the statistics of the descriptor.""" if self.stats is None: raise RuntimeError( @@ -595,7 +592,7 @@ def get_stats(self) -> Dict[str, StatItem]: def reinit_exclude( self, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: list[tuple[int, int]] = [], ): self.exclude_types = exclude_types self.emask = PairExcludeMask(self.ntypes, exclude_types=exclude_types) @@ -683,16 +680,17 @@ def forward( rr = dmatrix[ti_mask, self.sec[ii] : self.sec[ii + 1], :] else: rr = dmatrix[:, self.sec[ii] : self.sec[ii + 1], :] - rr = rr * mm[:, :, None].astype(rr.dtype) - ss = rr[:, :, :1] - # nfnl x nt x ng - gg = ll.forward(ss) - # nfnl x 4 x ng - gr = paddle.matmul(rr.transpose([0, 2, 1]), gg) - if ti_mask is not None: - xyz_scatter[ti_mask] += gr - else: - xyz_scatter += gr + if rr.numel() > 0: + rr = rr * mm.unsqueeze(2).astype(rr.dtype) + ss = rr[:, :, :1] + # nfnl x nt x ng + gg = ll.forward(ss) + # nfnl x 4 x ng + gr = paddle.matmul(rr.transpose([0, 2, 1]), gg) + if ti_mask is not None: + xyz_scatter[ti_mask] += gr + else: + xyz_scatter += gr xyz_scatter /= self.nnei xyz_scatter_1 = xyz_scatter.transpose([0, 2, 1]) diff --git a/deepmd/pd/model/descriptor/se_atten_v2.py b/deepmd/pd/model/descriptor/se_atten_v2.py index 2f32f79a50..05260ee162 100644 --- a/deepmd/pd/model/descriptor/se_atten_v2.py +++ b/deepmd/pd/model/descriptor/se_atten_v2.py @@ -1,8 +1,6 @@ # SPDX-License-Identifier: LGPL-3.0-or-later from typing import ( - List, Optional, - Tuple, Union, ) @@ -42,7 +40,7 @@ def __init__( self, rcut: float, rcut_smth: float, - sel: Union[List[int], int], + sel: Union[list[int], int], ntypes: int, neuron: list = [25, 50, 100], axis_neuron: int = 16, @@ -55,7 +53,7 @@ def __init__( activation_function: str = "tanh", precision: str = "float64", resnet_dt: bool = False, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: list[tuple[int, int]] = [], env_protection: float = 0.0, scaling_factor: int = 1.0, normalize=True, @@ -66,10 +64,10 @@ def __init__( ln_eps: Optional[float] = 1e-5, type_one_side: bool = False, stripped_type_embedding: Optional[bool] = None, - seed: Optional[Union[int, List[int]]] = None, + seed: Optional[Union[int, list[int]]] = None, use_econf_tebd: bool = False, use_tebd_bias: bool = False, - type_map: Optional[List[str]] = None, + type_map: Optional[list[str]] = None, # not implemented spin=None, type: Optional[str] = None, diff --git a/deepmd/pd/model/descriptor/se_r.py b/deepmd/pd/model/descriptor/se_r.py index 978a409ae2..029dcd900d 100644 --- a/deepmd/pd/model/descriptor/se_r.py +++ b/deepmd/pd/model/descriptor/se_r.py @@ -1,10 +1,7 @@ # SPDX-License-Identifier: LGPL-3.0-or-later from typing import ( Callable, - Dict, - List, Optional, - Tuple, Union, ) @@ -69,12 +66,12 @@ def __init__( activation_function: str = "tanh", precision: str = "float64", resnet_dt: bool = False, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: list[tuple[int, int]] = [], env_protection: float = 0.0, old_impl: bool = False, trainable: bool = True, - seed: Optional[Union[int, List[int]]] = None, - type_map: Optional[List[str]] = None, + seed: Optional[Union[int, list[int]]] = None, + type_map: Optional[list[str]] = None, **kwargs, ): super().__init__() @@ -143,7 +140,7 @@ def get_nsel(self) -> int: """Returns the number of selected atoms in the cut-off radius.""" return sum(self.sel) - def get_sel(self) -> List[int]: + def get_sel(self) -> list[int]: """Returns the number of selected atoms for each type.""" return self.sel @@ -151,7 +148,7 @@ def get_ntypes(self) -> int: """Returns the number of element types.""" return self.ntypes - def get_type_map(self) -> List[str]: + def get_type_map(self) -> list[str]: """Get the name to each type of atoms.""" return self.type_map @@ -229,7 +226,7 @@ def share_params(self, base_class, shared_level, resume=False): raise NotImplementedError def change_type_map( - self, type_map: List[str], model_with_new_type_stat=None + self, type_map: list[str], model_with_new_type_stat=None ) -> None: """Change the type related params to new ones, according to `type_map` and the original one in the model. If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. @@ -242,7 +239,7 @@ def change_type_map( def compute_input_stats( self, - merged: Union[Callable[[], List[dict]], List[dict]], + merged: Union[Callable[[], list[dict]], list[dict]], path: Optional[DPPath] = None, ): """ @@ -279,7 +276,7 @@ def compute_input_stats( paddle.assign(paddle.to_tensor(mean).to(device=env.DEVICE), self.mean) # pylint: disable=no-explicit-dtype paddle.assign(paddle.to_tensor(stddev).to(device=env.DEVICE), self.stddev) # pylint: disable=no-explicit-dtype - def get_stats(self) -> Dict[str, StatItem]: + def get_stats(self) -> dict[str, StatItem]: """Get the statistics of the descriptor.""" if self.stats is None: raise RuntimeError( @@ -305,7 +302,7 @@ def __getitem__(self, key): def reinit_exclude( self, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: list[tuple[int, int]] = [], ): self.exclude_types = exclude_types self.emask = PairExcludeMask(self.ntypes, exclude_types=exclude_types) @@ -316,7 +313,7 @@ def forward( atype_ext: paddle.Tensor, nlist: paddle.Tensor, mapping: Optional[paddle.Tensor] = None, - comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + comm_dict: Optional[dict[str, paddle.Tensor]] = None, ): """Compute the descriptor. @@ -408,7 +405,7 @@ def set_stat_mean_and_stddev( self.mean = mean self.stddev = stddev - def get_stat_mean_and_stddev(self) -> Tuple[paddle.Tensor, paddle.Tensor]: + def get_stat_mean_and_stddev(self) -> tuple[paddle.Tensor, paddle.Tensor]: """Get mean and stddev for descriptor.""" return self.mean, self.stddev @@ -462,9 +459,9 @@ def t_cvt(xx): def update_sel( cls, train_data: DeepmdDataSystem, - type_map: Optional[List[str]], + type_map: Optional[list[str]], local_jdata: dict, - ) -> Tuple[dict, Optional[float]]: + ) -> tuple[dict, Optional[float]]: """Update the selection and perform neighbor statistics. Parameters diff --git a/deepmd/pd/model/descriptor/se_t.py b/deepmd/pd/model/descriptor/se_t.py index 874ba4e987..3236a052e5 100644 --- a/deepmd/pd/model/descriptor/se_t.py +++ b/deepmd/pd/model/descriptor/se_t.py @@ -3,10 +3,7 @@ from typing import ( Callable, ClassVar, - Dict, - List, Optional, - Tuple, Union, ) @@ -112,17 +109,17 @@ def __init__( self, rcut: float, rcut_smth: float, - sel: List[int], - neuron: List[int] = [24, 48, 96], + sel: list[int], + neuron: list[int] = [24, 48, 96], resnet_dt: bool = False, set_davg_zero: bool = False, activation_function: str = "tanh", env_protection: float = 0.0, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: list[tuple[int, int]] = [], precision: str = "float64", trainable: bool = True, - seed: Optional[Union[int, List[int]]] = None, - type_map: Optional[List[str]] = None, + seed: Optional[Union[int, list[int]]] = None, + type_map: Optional[list[str]] = None, ntypes: Optional[int] = None, # to be compat with input # not implemented spin=None, @@ -159,7 +156,7 @@ def get_nsel(self) -> int: """Returns the number of selected atoms in the cut-off radius.""" return self.seat.get_nsel() - def get_sel(self) -> List[int]: + def get_sel(self) -> list[int]: """Returns the number of selected atoms for each type.""" return self.seat.get_sel() @@ -167,7 +164,7 @@ def get_ntypes(self) -> int: """Returns the number of element types.""" return self.seat.get_ntypes() - def get_type_map(self) -> List[str]: + def get_type_map(self) -> list[str]: """Get the name to each type of atoms.""" return self.type_map @@ -221,7 +218,7 @@ def dim_out(self): return self.seat.dim_out def change_type_map( - self, type_map: List[str], model_with_new_type_stat=None + self, type_map: list[str], model_with_new_type_stat=None ) -> None: """Change the type related params to new ones, according to `type_map` and the original one in the model. If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. @@ -234,7 +231,7 @@ def change_type_map( def compute_input_stats( self, - merged: Union[Callable[[], List[dict]], List[dict]], + merged: Union[Callable[[], list[dict]], list[dict]], path: Optional[DPPath] = None, ): """ @@ -257,7 +254,7 @@ def compute_input_stats( def reinit_exclude( self, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: list[tuple[int, int]] = [], ): """Update the type exclusions.""" self.seat.reinit_exclude(exclude_types) @@ -268,7 +265,7 @@ def forward( atype_ext: paddle.Tensor, nlist: paddle.Tensor, mapping: Optional[paddle.Tensor] = None, - comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + comm_dict: Optional[dict[str, paddle.Tensor]] = None, ): """Compute the descriptor. @@ -314,7 +311,7 @@ def set_stat_mean_and_stddev( self.seat.mean = mean self.seat.stddev = stddev - def get_stat_mean_and_stddev(self) -> Tuple[paddle.Tensor, paddle.Tensor]: + def get_stat_mean_and_stddev(self) -> tuple[paddle.Tensor, paddle.Tensor]: """Get mean and stddev for descriptor.""" return self.seat.mean, self.seat.stddev @@ -367,9 +364,9 @@ def t_cvt(xx): def update_sel( cls, train_data: DeepmdDataSystem, - type_map: Optional[List[str]], + type_map: Optional[list[str]], local_jdata: dict, - ) -> Tuple[dict, Optional[float]]: + ) -> tuple[dict, Optional[float]]: """Update the selection and perform neighbor statistics. Parameters @@ -404,16 +401,16 @@ def __init__( self, rcut: float, rcut_smth: float, - sel: List[int], - neuron: List[int] = [24, 48, 96], + sel: list[int], + neuron: list[int] = [24, 48, 96], resnet_dt: bool = False, set_davg_zero: bool = False, activation_function: str = "tanh", env_protection: float = 0.0, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: list[tuple[int, int]] = [], precision: str = "float64", trainable: bool = True, - seed: Optional[Union[int, List[int]]] = None, + seed: Optional[Union[int, list[int]]] = None, ): r"""Construct an embedding net of type `se_e3`. @@ -511,7 +508,7 @@ def get_nsel(self) -> int: """Returns the number of selected atoms in the cut-off radius.""" return sum(self.sel) - def get_sel(self) -> List[int]: + def get_sel(self) -> list[int]: """Returns the number of selected atoms for each type.""" return self.sel @@ -575,7 +572,7 @@ def __getitem__(self, key): def compute_input_stats( self, - merged: Union[Callable[[], List[dict]], List[dict]], + merged: Union[Callable[[], list[dict]], list[dict]], path: Optional[DPPath] = None, ): """ @@ -612,7 +609,7 @@ def compute_input_stats( paddle.assign(paddle.to_tensor(mean).to(device=env.DEVICE), self.mean) # pylint: disable=no-explicit-dtype paddle.assign(paddle.to_tensor(stddev).to(device=env.DEVICE), self.stddev) # pylint: disable=no-explicit-dtype - def get_stats(self) -> Dict[str, StatItem]: + def get_stats(self) -> dict[str, StatItem]: """Get the statistics of the descriptor.""" if self.stats is None: raise RuntimeError( @@ -622,7 +619,7 @@ def get_stats(self) -> Dict[str, StatItem]: def reinit_exclude( self, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: list[tuple[int, int]] = [], ): self.exclude_types = exclude_types self.emask = PairExcludeMask(self.ntypes, exclude_types=exclude_types) diff --git a/deepmd/pd/model/descriptor/se_t_tebd.py b/deepmd/pd/model/descriptor/se_t_tebd.py index 650715cf76..5ce6da5d09 100644 --- a/deepmd/pd/model/descriptor/se_t_tebd.py +++ b/deepmd/pd/model/descriptor/se_t_tebd.py @@ -1,10 +1,7 @@ # SPDX-License-Identifier: LGPL-3.0-or-later from typing import ( Callable, - Dict, - List, Optional, - Tuple, Union, ) @@ -128,7 +125,7 @@ def __init__( self, rcut: float, rcut_smth: float, - sel: Union[List[int], int], + sel: Union[list[int], int], ntypes: int, neuron: list = [2, 4, 8], tebd_dim: int = 8, @@ -137,11 +134,11 @@ def __init__( set_davg_zero: bool = True, activation_function: str = "tanh", env_protection: float = 0.0, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: list[tuple[int, int]] = [], precision: str = "float64", trainable: bool = True, - seed: Optional[Union[int, List[int]]] = None, - type_map: Optional[List[str]] = None, + seed: Optional[Union[int, list[int]]] = None, + type_map: Optional[list[str]] = None, concat_output_tebd: bool = True, use_econf_tebd: bool = False, use_tebd_bias=False, @@ -196,7 +193,7 @@ def get_nsel(self) -> int: """Returns the number of selected atoms in the cut-off radius.""" return self.se_ttebd.get_nsel() - def get_sel(self) -> List[int]: + def get_sel(self) -> list[int]: """Returns the number of selected atoms for each type.""" return self.se_ttebd.get_sel() @@ -204,7 +201,7 @@ def get_ntypes(self) -> int: """Returns the number of element types.""" return self.se_ttebd.get_ntypes() - def get_type_map(self) -> List[str]: + def get_type_map(self) -> list[str]: """Get the name to each type of atoms.""" return self.type_map @@ -279,7 +276,7 @@ def dim_emb(self): def compute_input_stats( self, - merged: Union[Callable[[], List[dict]], List[dict]], + merged: Union[Callable[[], list[dict]], list[dict]], path: Optional[DPPath] = None, ): """ @@ -309,12 +306,12 @@ def set_stat_mean_and_stddev( self.se_ttebd.mean = mean self.se_ttebd.stddev = stddev - def get_stat_mean_and_stddev(self) -> Tuple[paddle.Tensor, paddle.Tensor]: + def get_stat_mean_and_stddev(self) -> tuple[paddle.Tensor, paddle.Tensor]: """Get mean and stddev for descriptor.""" return self.se_ttebd.mean, self.se_ttebd.stddev def change_type_map( - self, type_map: List[str], model_with_new_type_stat=None + self, type_map: list[str], model_with_new_type_stat=None ) -> None: """Change the type related params to new ones, according to `type_map` and the original one in the model. If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. @@ -415,7 +412,7 @@ def forward( extended_atype: paddle.Tensor, nlist: paddle.Tensor, mapping: Optional[paddle.Tensor] = None, - comm_dict: Optional[Dict[str, paddle.Tensor]] = None, + comm_dict: Optional[dict[str, paddle.Tensor]] = None, ): """Compute the descriptor. @@ -470,9 +467,9 @@ def forward( def update_sel( cls, train_data: DeepmdDataSystem, - type_map: Optional[List[str]], + type_map: Optional[list[str]], local_jdata: dict, - ) -> Tuple[dict, Optional[float]]: + ) -> tuple[dict, Optional[float]]: """Update the selection and perform neighbor statistics. Parameters @@ -505,7 +502,7 @@ def __init__( self, rcut: float, rcut_smth: float, - sel: Union[List[int], int], + sel: Union[list[int], int], ntypes: int, neuron: list = [25, 50, 100], tebd_dim: int = 8, @@ -514,10 +511,10 @@ def __init__( activation_function="tanh", precision: str = "float64", resnet_dt: bool = False, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: list[tuple[int, int]] = [], env_protection: float = 0.0, smooth: bool = True, - seed: Optional[Union[int, List[int]]] = None, + seed: Optional[Union[int, list[int]]] = None, ): super().__init__() self.rcut = rcut @@ -604,7 +601,7 @@ def get_nsel(self) -> int: """Returns the number of selected atoms in the cut-off radius.""" return sum(self.sel) - def get_sel(self) -> List[int]: + def get_sel(self) -> list[int]: """Returns the number of selected atoms for each type.""" return self.sel @@ -673,7 +670,7 @@ def dim_emb(self): def compute_input_stats( self, - merged: Union[Callable[[], List[dict]], List[dict]], + merged: Union[Callable[[], list[dict]], list[dict]], path: Optional[DPPath] = None, ): """ @@ -710,7 +707,7 @@ def compute_input_stats( paddle.assign(paddle.to_tensor(mean).to(device=env.DEVICE), self.mean) # pylint: disable=no-explicit-dtype paddle.assign(paddle.to_tensor(stddev).to(device=env.DEVICE), self.stddev) # pylint: disable=no-explicit-dtype - def get_stats(self) -> Dict[str, StatItem]: + def get_stats(self) -> dict[str, StatItem]: """Get the statistics of the descriptor.""" if self.stats is None: raise RuntimeError( @@ -720,7 +717,7 @@ def get_stats(self) -> Dict[str, StatItem]: def reinit_exclude( self, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: list[tuple[int, int]] = [], ): self.exclude_types = exclude_types self.emask = PairExcludeMask(self.ntypes, exclude_types=exclude_types) diff --git a/deepmd/pd/model/model/dp_model.py b/deepmd/pd/model/model/dp_model.py index 8eae0a171e..1e1cee6826 100644 --- a/deepmd/pd/model/model/dp_model.py +++ b/deepmd/pd/model/model/dp_model.py @@ -1,8 +1,6 @@ # SPDX-License-Identifier: LGPL-3.0-or-later from typing import ( - List, Optional, - Tuple, ) from deepmd.pd.model.descriptor.base_descriptor import ( @@ -20,9 +18,9 @@ class DPModelCommon: def update_sel( cls, train_data: DeepmdDataSystem, - type_map: Optional[List[str]], + type_map: Optional[list[str]], local_jdata: dict, - ) -> Tuple[dict, Optional[float]]: + ) -> tuple[dict, Optional[float]]: """Update the selection and perform neighbor statistics. Parameters diff --git a/deepmd/pd/model/model/dp_zbl_model.py b/deepmd/pd/model/model/dp_zbl_model.py index cc36e2d339..eb1b194ce4 100644 --- a/deepmd/pd/model/model/dp_zbl_model.py +++ b/deepmd/pd/model/model/dp_zbl_model.py @@ -88,6 +88,7 @@ def forward( model_predict["force"] = model_ret["dforce"] if "mask" in model_ret: model_predict["mask"] = model_ret["mask"] + return model_predict def forward_lower( diff --git a/deepmd/pd/model/model/make_hessian_model.py b/deepmd/pd/model/model/make_hessian_model.py index 6ca1ea0b88..a06cc28246 100644 --- a/deepmd/pd/model/model/make_hessian_model.py +++ b/deepmd/pd/model/model/make_hessian_model.py @@ -2,8 +2,6 @@ import copy import math from typing import ( - Dict, - List, Optional, Union, ) @@ -47,7 +45,7 @@ def __init__( def requires_hessian( self, - keys: Union[str, List[str]], + keys: Union[str, list[str]], ): """Set which output variable(s) requires hessian.""" if isinstance(keys, str): @@ -68,7 +66,7 @@ def forward_common( fparam: Optional[paddle.Tensor] = None, aparam: Optional[paddle.Tensor] = None, do_atomic_virial: bool = False, - ) -> Dict[str, paddle.Tensor]: + ) -> dict[str, paddle.Tensor]: """Return model prediction. Parameters @@ -122,7 +120,7 @@ def _cal_hessian_all( box: Optional[paddle.Tensor] = None, fparam: Optional[paddle.Tensor] = None, aparam: Optional[paddle.Tensor] = None, - ) -> Dict[str, paddle.Tensor]: + ) -> dict[str, paddle.Tensor]: nf, nloc = atype.shape coord = coord.reshape([nf, (nloc * 3)]) box = box.reshape([nf, 9]) if box is not None else None @@ -130,7 +128,7 @@ def _cal_hessian_all( aparam = aparam.reshape([nf, nloc, -1]) if aparam is not None else None fdef = self.atomic_output_def() # keys of values that require hessian - hess_keys: List[str] = [] + hess_keys: list[str] = [] for kk in fdef.keys(): if fdef[kk].r_hessian: hess_keys.append(kk) diff --git a/deepmd/pd/model/model/transform_output.py b/deepmd/pd/model/model/transform_output.py index 371f8454ea..52939980ec 100644 --- a/deepmd/pd/model/model/transform_output.py +++ b/deepmd/pd/model/model/transform_output.py @@ -1,7 +1,4 @@ # SPDX-License-Identifier: LGPL-3.0-or-later -from typing import ( - Dict, -) import paddle @@ -74,7 +71,7 @@ def task_deriv_one( do_atomic_virial: bool = False, create_graph: bool = True, ): - faked_grad = paddle.ones_like(energy) + # faked_grad = paddle.ones_like(energy) # lst = paddle.jit.annotate(List[Optional[paddle.Tensor]], [faked_grad]) extended_force = paddle.autograd.grad( [energy], @@ -162,12 +159,12 @@ def take_deriv( def fit_output_to_model_output( - fit_ret: Dict[str, paddle.Tensor], + fit_ret: dict[str, paddle.Tensor], fit_output_def: FittingOutputDef, coord_ext: paddle.Tensor, do_atomic_virial: bool = False, create_graph: bool = True, -) -> Dict[str, paddle.Tensor]: +) -> dict[str, paddle.Tensor]: """Transform the output of the fitting network to the model output. @@ -203,11 +200,11 @@ def fit_output_to_model_output( def communicate_extended_output( - model_ret: Dict[str, paddle.Tensor], + model_ret: dict[str, paddle.Tensor], model_output_def: ModelOutputDef, mapping: paddle.Tensor, # nf x nloc do_atomic_virial: bool = False, -) -> Dict[str, paddle.Tensor]: +) -> dict[str, paddle.Tensor]: """Transform the output of the model network defined on local and ghost (extended) atoms to local atoms. diff --git a/deepmd/pd/model/task/dipole.py b/deepmd/pd/model/task/dipole.py index 62e5fc8c44..1f388517b0 100644 --- a/deepmd/pd/model/task/dipole.py +++ b/deepmd/pd/model/task/dipole.py @@ -3,7 +3,6 @@ import logging from typing import ( Callable, - List, Optional, Union, ) @@ -79,7 +78,7 @@ def __init__( ntypes: int, dim_descrpt: int, embedding_width: int, - neuron: List[int] = [128, 128, 128], + neuron: list[int] = [128, 128, 128], resnet_dt: bool = True, numb_fparam: int = 0, numb_aparam: int = 0, @@ -87,11 +86,11 @@ def __init__( precision: str = DEFAULT_PRECISION, mixed_types: bool = True, rcond: Optional[float] = None, - seed: Optional[Union[int, List[int]]] = None, - exclude_types: List[int] = [], + seed: Optional[Union[int, list[int]]] = None, + exclude_types: list[int] = [], r_differentiable: bool = True, c_differentiable: bool = True, - type_map: Optional[List[str]] = None, + type_map: Optional[list[str]] = None, **kwargs, ): self.embedding_width = embedding_width @@ -151,7 +150,7 @@ def output_def(self) -> FittingOutputDef: def compute_output_stats( self, - merged: Union[Callable[[], List[dict]], List[dict]], + merged: Union[Callable[[], list[dict]], list[dict]], stat_file_path: Optional[DPPath] = None, ): """ @@ -197,4 +196,4 @@ def forward( return {self.var_name: out.to(env.GLOBAL_PD_FLOAT_PRECISION)} # make jit happy with paddle 2.0.0 - exclude_types: List[int] + exclude_types: list[int] diff --git a/deepmd/pd/model/task/dos.py b/deepmd/pd/model/task/dos.py index dbedbf0fbf..35ce8cc16a 100644 --- a/deepmd/pd/model/task/dos.py +++ b/deepmd/pd/model/task/dos.py @@ -2,7 +2,6 @@ import copy import logging from typing import ( - List, Optional, Union, ) @@ -45,19 +44,19 @@ def __init__( ntypes: int, dim_descrpt: int, numb_dos: int = 300, - neuron: List[int] = [128, 128, 128], + neuron: list[int] = [128, 128, 128], resnet_dt: bool = True, numb_fparam: int = 0, numb_aparam: int = 0, rcond: Optional[float] = None, bias_dos: Optional[paddle.Tensor] = None, - trainable: Union[bool, List[bool]] = True, - seed: Optional[Union[int, List[int]]] = None, + trainable: Union[bool, list[bool]] = True, + seed: Optional[Union[int, list[int]]] = None, activation_function: str = "tanh", precision: str = DEFAULT_PRECISION, - exclude_types: List[int] = [], + exclude_types: list[int] = [], mixed_types: bool = True, - type_map: Optional[List[str]] = None, + type_map: Optional[list[str]] = None, ): super().__init__( var_name="dos", @@ -127,4 +126,4 @@ def serialize(self) -> dict: return dd # make jit happy with paddle 2.0.0 - exclude_types: List[int] + exclude_types: list[int] diff --git a/deepmd/pd/model/task/ener.py b/deepmd/pd/model/task/ener.py index 0f5d5f2dba..24f563f799 100644 --- a/deepmd/pd/model/task/ener.py +++ b/deepmd/pd/model/task/ener.py @@ -2,9 +2,7 @@ import copy import logging from typing import ( - List, Optional, - Tuple, Union, ) @@ -48,7 +46,7 @@ def __init__( self, ntypes: int, dim_descrpt: int, - neuron: List[int] = [128, 128, 128], + neuron: list[int] = [128, 128, 128], bias_atom_e: Optional[paddle.Tensor] = None, resnet_dt: bool = True, numb_fparam: int = 0, @@ -56,8 +54,8 @@ def __init__( activation_function: str = "tanh", precision: str = DEFAULT_PRECISION, mixed_types: bool = True, - seed: Optional[Union[int, List[int]]] = None, - type_map: Optional[List[str]] = None, + seed: Optional[Union[int, list[int]]] = None, + type_map: Optional[list[str]] = None, **kwargs, ): super().__init__( @@ -94,7 +92,7 @@ def serialize(self) -> dict: } # make jit happy with paddle 2.0.0 - exclude_types: List[int] + exclude_types: list[int] @Fitting.register("direct_force") @@ -185,11 +183,11 @@ def deserialize(self) -> "EnergyFittingNetDirect": raise NotImplementedError def change_type_map( - self, type_map: List[str], model_with_new_type_stat=None + self, type_map: list[str], model_with_new_type_stat=None ) -> None: raise NotImplementedError - def get_type_map(self) -> List[str]: + def get_type_map(self) -> list[str]: raise NotImplementedError def forward( @@ -201,7 +199,7 @@ def forward( h2: Optional[paddle.Tensor] = None, fparam: Optional[paddle.Tensor] = None, aparam: Optional[paddle.Tensor] = None, - ) -> Tuple[paddle.Tensor, None]: + ) -> tuple[paddle.Tensor, None]: """Based on embedding net output, alculate total energy. Args: diff --git a/deepmd/pd/model/task/fitting.py b/deepmd/pd/model/task/fitting.py index 57c15f7449..7a3b01a094 100644 --- a/deepmd/pd/model/task/fitting.py +++ b/deepmd/pd/model/task/fitting.py @@ -5,7 +5,6 @@ abstractmethod, ) from typing import ( - List, Optional, Union, ) @@ -137,7 +136,7 @@ def __init__( var_name: str, ntypes: int, dim_descrpt: int, - neuron: List[int] = [128, 128, 128], + neuron: list[int] = [128, 128, 128], bias_atom_e: Optional[paddle.Tensor] = None, resnet_dt: bool = True, numb_fparam: int = 0, @@ -146,11 +145,11 @@ def __init__( precision: str = DEFAULT_PRECISION, mixed_types: bool = True, rcond: Optional[float] = None, - seed: Optional[Union[int, List[int]]] = None, - exclude_types: List[int] = [], - trainable: Union[bool, List[bool]] = True, - remove_vaccum_contribution: Optional[List[bool]] = None, - type_map: Optional[List[str]] = None, + seed: Optional[Union[int, list[int]]] = None, + exclude_types: list[int] = [], + trainable: Union[bool, list[bool]] = True, + remove_vaccum_contribution: Optional[list[bool]] = None, + type_map: Optional[list[str]] = None, **kwargs, ): super().__init__() @@ -253,13 +252,13 @@ def __init__( def reinit_exclude( self, - exclude_types: List[int] = [], + exclude_types: list[int] = [], ): self.exclude_types = exclude_types self.emask = AtomExcludeMask(self.ntypes, self.exclude_types) def change_type_map( - self, type_map: List[str], model_with_new_type_stat=None + self, type_map: list[str], model_with_new_type_stat=None ) -> None: """Change the type related params to new ones, according to `type_map` and the original one in the model. If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. @@ -343,9 +342,9 @@ def get_dim_aparam(self) -> int: return self.numb_aparam # make jit happy - exclude_types: List[int] + exclude_types: list[int] - def get_sel_type(self) -> List[int]: + def get_sel_type(self) -> list[int]: """Get the selected atom types of this model. Only atoms with selected atom types have atomic contribution @@ -353,13 +352,13 @@ def get_sel_type(self) -> List[int]: If returning an empty list, all atom types are selected. """ # make jit happy - sel_type: List[int] = [] + sel_type: list[int] = [] for ii in range(self.ntypes): if ii not in self.exclude_types: sel_type.append(ii) return sel_type - def get_type_map(self) -> List[str]: + def get_type_map(self) -> list[str]: """Get the name to each type of atoms.""" return self.type_map diff --git a/deepmd/pd/model/task/invar_fitting.py b/deepmd/pd/model/task/invar_fitting.py index 5d052a2d62..5a6cad7c2d 100644 --- a/deepmd/pd/model/task/invar_fitting.py +++ b/deepmd/pd/model/task/invar_fitting.py @@ -2,7 +2,6 @@ import copy import logging from typing import ( - List, Optional, Union, ) @@ -87,7 +86,7 @@ def __init__( ntypes: int, dim_descrpt: int, dim_out: int, - neuron: List[int] = [128, 128, 128], + neuron: list[int] = [128, 128, 128], bias_atom_e: Optional[paddle.Tensor] = None, resnet_dt: bool = True, numb_fparam: int = 0, @@ -96,10 +95,10 @@ def __init__( precision: str = DEFAULT_PRECISION, mixed_types: bool = True, rcond: Optional[float] = None, - seed: Optional[Union[int, List[int]]] = None, - exclude_types: List[int] = [], - atom_ener: Optional[List[Optional[paddle.Tensor]]] = None, - type_map: Optional[List[str]] = None, + seed: Optional[Union[int, list[int]]] = None, + exclude_types: list[int] = [], + atom_ener: Optional[list[Optional[paddle.Tensor]]] = None, + type_map: Optional[list[str]] = None, **kwargs, ): self.dim_out = dim_out @@ -179,4 +178,4 @@ def forward( return self._forward_common(descriptor, atype, gr, g2, h2, fparam, aparam) # make jit happy with paddle 2.0.0 - exclude_types: List[int] + exclude_types: list[int] diff --git a/deepmd/pd/model/task/polarizability.py b/deepmd/pd/model/task/polarizability.py index c82965bf8b..c996ae1435 100644 --- a/deepmd/pd/model/task/polarizability.py +++ b/deepmd/pd/model/task/polarizability.py @@ -2,7 +2,6 @@ import copy import logging from typing import ( - List, Optional, Union, ) @@ -83,7 +82,7 @@ def __init__( ntypes: int, dim_descrpt: int, embedding_width: int, - neuron: List[int] = [128, 128, 128], + neuron: list[int] = [128, 128, 128], resnet_dt: bool = True, numb_fparam: int = 0, numb_aparam: int = 0, @@ -91,12 +90,12 @@ def __init__( precision: str = DEFAULT_PRECISION, mixed_types: bool = True, rcond: Optional[float] = None, - seed: Optional[Union[int, List[int]]] = None, - exclude_types: List[int] = [], + seed: Optional[Union[int, list[int]]] = None, + exclude_types: list[int] = [], fit_diag: bool = True, - scale: Optional[Union[List[float], float]] = None, + scale: Optional[Union[list[float], float]] = None, shift_diag: bool = True, - type_map: Optional[List[str]] = None, + type_map: Optional[list[str]] = None, **kwargs, ): self.embedding_width = embedding_width @@ -164,7 +163,7 @@ def __getitem__(self, key): return super().__getitem__(key) def change_type_map( - self, type_map: List[str], model_with_new_type_stat=None + self, type_map: list[str], model_with_new_type_stat=None ) -> None: """Change the type related params to new ones, according to `type_map` and the original one in the model. If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types. @@ -261,4 +260,4 @@ def forward( return {"polarizability": out.to(env.GLOBAL_PD_FLOAT_PRECISION)} # make jit happy with paddle 2.0.0 - exclude_types: List[int] + exclude_types: list[int] diff --git a/deepmd/pd/train/wrapper.py b/deepmd/pd/train/wrapper.py index 4d9100d192..7c07cbf675 100644 --- a/deepmd/pd/train/wrapper.py +++ b/deepmd/pd/train/wrapper.py @@ -8,14 +8,12 @@ OrderedDict, ) from typing import ( - Dict, - Optional, Union, ) import paddle -_StateDict = Union[Dict[str, paddle.Tensor], OrderedDict[str, paddle.Tensor]] +_StateDict = Union[dict[str, paddle.Tensor], OrderedDict[str, paddle.Tensor]] # if paddle.__version__.startswith("2"): # import paddle._dynamo @@ -27,8 +25,8 @@ class ModelWrapper(paddle.nn.Layer): def __init__( self, - model: Union[paddle.nn.Layer, Dict], - loss: Union[paddle.nn.Layer, Dict] = None, + model: paddle.nn.Layer | dict, + loss: paddle.nn.Layer | dict = None, model_params=None, shared_links=None, ): @@ -148,15 +146,15 @@ def forward( self, coord, atype, - spin: Optional[paddle.Tensor] = None, - box: Optional[paddle.Tensor] = None, - cur_lr: Optional[paddle.Tensor] = None, - label: Optional[paddle.Tensor] = None, - task_key: Optional[paddle.Tensor] = None, + spin: paddle.Tensor | None = None, + box: paddle.Tensor | None = None, + cur_lr: paddle.Tensor | None = None, + label: paddle.Tensor | None = None, + task_key: paddle.Tensor | None = None, inference_only=False, do_atomic_virial=False, - fparam: Optional[paddle.Tensor] = None, - aparam: Optional[paddle.Tensor] = None, + fparam: paddle.Tensor | None = None, + aparam: paddle.Tensor | None = None, ): if not self.multi_task: task_key = "Default" @@ -211,12 +209,12 @@ def state_dict(self): state_dict.update({"_extra_state": extra_state}) return state_dict - def set_extra_state(self, extra_state: Dict): + def set_extra_state(self, extra_state: dict): self.model_params = extra_state["model_params"] self.train_infos = extra_state["train_infos"] return None - def get_extra_state(self) -> Dict: + def get_extra_state(self) -> dict: extra_state = { "model_params": self.model_params, "train_infos": self.train_infos, diff --git a/deepmd/pd/utils/aux.py b/deepmd/pd/utils/aux.py index e8c0031820..b22d639669 100644 --- a/deepmd/pd/utils/aux.py +++ b/deepmd/pd/utils/aux.py @@ -68,6 +68,8 @@ def scatter_reduce( def sec(l: int, size: int) -> list[int]: + assert l > 0 + assert size > 0 if l % size == 0: return [size] * (l // size) return [size] * (l // size) + [l % size] diff --git a/deepmd/pd/utils/dataset.py b/deepmd/pd/utils/dataset.py index bf7197a182..1f0533d8fc 100644 --- a/deepmd/pd/utils/dataset.py +++ b/deepmd/pd/utils/dataset.py @@ -2,7 +2,6 @@ from typing import ( - List, Optional, ) @@ -17,7 +16,7 @@ class DeepmdDataSetForLoader(Dataset): - def __init__(self, system: str, type_map: Optional[List[str]] = None): + def __init__(self, system: str, type_map: Optional[list[str]] = None): """Construct DeePMD-style dataset containing frames cross different systems. Args: @@ -41,7 +40,7 @@ def __getitem__(self, index): b_data["natoms"] = self._natoms_vec return b_data - def add_data_requirement(self, data_requirement: List[DataRequirementItem]): + def add_data_requirement(self, data_requirement: list[DataRequirementItem]): """Add data requirement for this data system.""" for data_item in data_requirement: self._data_system.add( diff --git a/deepmd/pd/utils/env_mat_stat.py b/deepmd/pd/utils/env_mat_stat.py index 1cbc27742f..1cc67ecfee 100644 --- a/deepmd/pd/utils/env_mat_stat.py +++ b/deepmd/pd/utils/env_mat_stat.py @@ -4,9 +4,6 @@ ) from typing import ( TYPE_CHECKING, - Dict, - List, - Tuple, Union, ) @@ -40,7 +37,7 @@ class EnvMatStat(BaseEnvMatStat): - def compute_stat(self, env_mat: Dict[str, paddle.Tensor]) -> Dict[str, StatItem]: + def compute_stat(self, env_mat: dict[str, paddle.Tensor]) -> dict[str, StatItem]: """Compute the statistics of the environment matrix for a single system. Parameters @@ -82,8 +79,8 @@ def __init__(self, descriptor: "DescriptorBlock"): ) # se_r=1, se_a=4 def iter( - self, data: List[Dict[str, Union[paddle.Tensor, List[Tuple[int, int]]]]] - ) -> Iterator[Dict[str, StatItem]]: + self, data: list[dict[str, Union[paddle.Tensor, list[tuple[int, int]]]]] + ) -> Iterator[dict[str, StatItem]]: """Get the iterator of the environment matrix. Parameters diff --git a/deepmd/pd/utils/exclude_mask.py b/deepmd/pd/utils/exclude_mask.py index 89c549b2d1..98057eaf1b 100644 --- a/deepmd/pd/utils/exclude_mask.py +++ b/deepmd/pd/utils/exclude_mask.py @@ -1,9 +1,4 @@ # SPDX-License-Identifier: LGPL-3.0-or-later -from typing import ( - List, - Set, - Tuple, -) import numpy as np import paddle @@ -22,7 +17,7 @@ class AtomExcludeMask(paddle.nn.Layer): def __init__( self, ntypes: int, - exclude_types: List[int] = [], + exclude_types: list[int] = [], ): super().__init__() self.reinit(ntypes, exclude_types) @@ -30,7 +25,7 @@ def __init__( def reinit( self, ntypes: int, - exclude_types: List[int] = [], + exclude_types: list[int] = [], ): self.ntypes = ntypes self.exclude_types = exclude_types @@ -75,7 +70,7 @@ class PairExcludeMask(paddle.nn.Layer): def __init__( self, ntypes: int, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: list[tuple[int, int]] = [], ): super().__init__() self.reinit(ntypes, exclude_types) @@ -83,10 +78,10 @@ def __init__( def reinit( self, ntypes: int, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: list[tuple[int, int]] = [], ): self.ntypes = ntypes - self._exclude_types: Set[Tuple[int, int]] = set() + self._exclude_types: set[tuple[int, int]] = set() for tt in exclude_types: assert len(tt) == 2 self._exclude_types.add((tt[0], tt[1])) diff --git a/deepmd/pd/utils/neighbor_stat.py b/deepmd/pd/utils/neighbor_stat.py index a46cdb5d57..a1e60459ca 100644 --- a/deepmd/pd/utils/neighbor_stat.py +++ b/deepmd/pd/utils/neighbor_stat.py @@ -4,7 +4,6 @@ ) from typing import ( Optional, - Tuple, ) import numpy as np @@ -54,7 +53,7 @@ def forward( coord: paddle.Tensor, atype: paddle.Tensor, cell: Optional[paddle.Tensor], - ) -> Tuple[paddle.Tensor, paddle.Tensor]: + ) -> tuple[paddle.Tensor, paddle.Tensor]: """Calculate the neareest neighbor distance between atoms, maximum nbor size of atoms and the output data range of the environment matrix. @@ -110,9 +109,9 @@ def forward( else: mask = rr2 < self.rcut**2 # virtual types (<0) are not counted - nnei = paddle.sum(mask & ((extend_atype >= 0).unsqueeze(1)), axis=-1).reshape( - [nframes, nloc, 1] - ) + nnei = paddle.sum( + mask & ((extend_atype >= 0).unsqueeze(1)), axis=-1 + ).reshape([nframes, nloc, 1]) max_nnei = paddle.max(nnei, axis=1) return min_rr2, max_nnei @@ -144,7 +143,7 @@ def __init__( def iterator( self, data: DeepmdDataSystem - ) -> Iterator[Tuple[np.ndarray, float, str]]: + ) -> Iterator[tuple[np.ndarray, float, str]]: """Abstract method for producing data. Yields diff --git a/deepmd/pd/utils/nlist.py b/deepmd/pd/utils/nlist.py index 5a15a5f84f..3315a6a870 100644 --- a/deepmd/pd/utils/nlist.py +++ b/deepmd/pd/utils/nlist.py @@ -1,7 +1,5 @@ # SPDX-License-Identifier: LGPL-3.0-or-later from typing import ( - Dict, - List, Optional, Union, ) @@ -22,7 +20,7 @@ def extend_input_and_build_neighbor_list( coord, atype, rcut: float, - sel: List[int], + sel: list[int], mixed_types: bool = False, box: Optional[paddle.Tensor] = None, ): @@ -56,7 +54,7 @@ def build_neighbor_list( atype: paddle.Tensor, nloc: int, rcut: float, - sel: Union[int, List[int]], + sel: Union[int, list[int]], distinguish_types: bool = True, ) -> paddle.Tensor: """Build neightbor list for a single frame. keeps nsel neighbors. @@ -140,7 +138,7 @@ def _trim_mask_distinguish_nlist( rr: paddle.Tensor, nlist: paddle.Tensor, rcut: float, - sel: List[int], + sel: list[int], distinguish_types: bool, ) -> paddle.Tensor: """Trim the size of nlist, mask if any central atom is virtual, distinguish types if necessary.""" @@ -189,7 +187,7 @@ def build_directional_neighbor_list( coord_neig: paddle.Tensor, atype_neig: paddle.Tensor, rcut: float, - sel: Union[int, List[int]], + sel: Union[int, list[int]], distinguish_types: bool = True, ) -> paddle.Tensor: """Build directional neighbor list. @@ -290,7 +288,7 @@ def build_directional_neighbor_list( def nlist_distinguish_types( nlist: paddle.Tensor, atype: paddle.Tensor, - sel: List[int], + sel: list[int], ): """Given a nlist that does not distinguish atom types, return a nlist that distinguish atom types. @@ -349,9 +347,9 @@ def get_multiple_nlist_key( def build_multiple_neighbor_list( coord: paddle.Tensor, nlist: paddle.Tensor, - rcuts: List[float], - nsels: List[int], -) -> Dict[str, paddle.Tensor]: + rcuts: list[float], + nsels: list[int], +) -> dict[str, paddle.Tensor]: """Input one neighbor list, and produce multiple neighbor lists with different cutoff radius and numbers of selection out of it. The required rcuts and nsels should be smaller or equal to the input nlist. diff --git a/deepmd/pd/utils/update_sel.py b/deepmd/pd/utils/update_sel.py index 26898ec76c..32b8d66c73 100644 --- a/deepmd/pd/utils/update_sel.py +++ b/deepmd/pd/utils/update_sel.py @@ -1,7 +1,4 @@ # SPDX-License-Identifier: LGPL-3.0-or-later -from typing import ( - Type, -) from deepmd.pd.utils.neighbor_stat import ( NeighborStat, @@ -13,5 +10,5 @@ class UpdateSel(BaseUpdateSel): @property - def neighbor_stat(self) -> Type[NeighborStat]: + def neighbor_stat(self) -> type[NeighborStat]: return NeighborStat diff --git a/deepmd/utils/data.py b/deepmd/utils/data.py index a68f7148a2..6331186e40 100644 --- a/deepmd/utils/data.py +++ b/deepmd/utils/data.py @@ -500,7 +500,7 @@ def reformat_data_torch(self, data): return data def reformat_data_paddle(self, data): - """Modify the data format for the requirements of Torch backend. + """Modify the data format for the requirements of Paddle backend. Parameters ---------- diff --git a/source/tests/pd/test_LKF.py b/source/tests/pd/test_LKF.py new file mode 100644 index 0000000000..ae9508c149 --- /dev/null +++ b/source/tests/pd/test_LKF.py @@ -0,0 +1,36 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import json +import os +import unittest +from pathlib import ( + Path, +) + +from deepmd.pd.entrypoints.main import ( + main, +) + + +@unittest.skip("Paddle do not support LKF now") +class TestLKF(unittest.TestCase): + def test_lkf(self): + with open(str(Path(__file__).parent / "water/lkf.json")) as fin: + content = fin.read() + self.config = json.loads(content) + self.config["training"]["training_data"]["systems"] = [ + str(Path(__file__).parent / "water/data/data_0") + ] + self.config["training"]["validation_data"]["systems"] = [ + str(Path(__file__).parent / "water/data/data_0") + ] + self.input_json = "test_lkf.json" + with open(self.input_json, "w") as fp: + json.dump(self.config, fp, indent=4) + main(["train", self.input_json]) + + def tearDown(self): + os.remove(self.input_json) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/test_auto_batch_size.py b/source/tests/pd/test_auto_batch_size.py new file mode 100644 index 0000000000..1033f46d07 --- /dev/null +++ b/source/tests/pd/test_auto_batch_size.py @@ -0,0 +1,37 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import unittest + +import numpy as np + +from deepmd.pd.utils.auto_batch_size import ( + AutoBatchSize, +) + + +class TestAutoBatchSize(unittest.TestCase): + def test_execute_all(self): + dd0 = np.zeros((10000, 2, 1, 3, 4)) + dd1 = np.ones((10000, 2, 1, 3, 4)) + auto_batch_size = AutoBatchSize(256, 2.0) + + def func(dd1): + return np.zeros_like(dd1), np.ones_like(dd1) + + dd2 = auto_batch_size.execute_all(func, 10000, 2, dd1) + np.testing.assert_equal(dd0, dd2[0]) + np.testing.assert_equal(dd1, dd2[1]) + + def test_execute_all_dict(self): + dd0 = np.zeros((10000, 2, 1, 3, 4)) + dd1 = np.ones((10000, 2, 1, 3, 4)) + auto_batch_size = AutoBatchSize(256, 2.0) + + def func(dd1): + return { + "foo": np.zeros_like(dd1), + "bar": np.ones_like(dd1), + } + + dd2 = auto_batch_size.execute_all(func, 10000, 2, dd1) + np.testing.assert_equal(dd0, dd2["foo"]) + np.testing.assert_equal(dd1, dd2["bar"]) diff --git a/source/tests/pd/test_calculator.py b/source/tests/pd/test_calculator.py new file mode 100644 index 0000000000..5242b92b8e --- /dev/null +++ b/source/tests/pd/test_calculator.py @@ -0,0 +1,109 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import json +import unittest +from copy import ( + deepcopy, +) +from pathlib import ( + Path, +) + +import numpy as np +import paddle + +from deepmd.pd.entrypoints.main import ( + get_trainer, +) +from deepmd.pd.utils.ase_calc import ( + DPCalculator, +) + +from ..seed import ( + GLOBAL_SEED, +) + +dtype = paddle.float64 + +paddle.framework.core.set_prim_eager_enabled(True) +paddle.framework.core._set_prim_all_enabled(True) + + +class TestCalculator(unittest.TestCase): + def setUp(self): + input_json = str(Path(__file__).parent / "water/se_atten.json") + with open(input_json) as f: + self.config = json.load(f) + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + data_file = [str(Path(__file__).parent / "water/data/data_0")] + self.config["training"]["training_data"]["systems"] = data_file + self.config["training"]["validation_data"]["systems"] = [ + str(Path(__file__).parent / "water/data/single") + ] + self.input_json = "test_dp_test.json" + with open(self.input_json, "w") as fp: + json.dump(self.config, fp, indent=4) + + trainer = get_trainer(deepcopy(self.config)) + trainer.run() + + device = paddle.get_device() + paddle.set_device("cpu") + input_dict, label_dict, _ = trainer.get_data(is_train=False) + paddle.set_device(device) + _, _, more_loss = trainer.wrapper(**input_dict, label=label_dict, cur_lr=1.0) + + self.calculator = DPCalculator("model.pd") + + def test_calculator(self): + from ase import ( + Atoms, + ) + + natoms = 5 + cell = paddle.eye(3, dtype=dtype).to(device="cpu") * 10 + paddle.seed(GLOBAL_SEED) + coord = paddle.rand([natoms, 3], dtype=dtype).to(device="cpu") + coord = paddle.matmul(coord, cell) + atype = paddle.to_tensor([0, 0, 0, 1, 1]) + atomic_numbers = [1, 1, 1, 8, 8] + idx_perm = [1, 0, 4, 3, 2] + + prec = 1e-10 + low_prec = 1e-4 + + ase_atoms0 = Atoms( + numbers=atomic_numbers, + positions=coord, + # positions=[tuple(item) for item in coordinate], + cell=cell, + calculator=self.calculator, + pbc=True, + ) + e0, f0 = ase_atoms0.get_potential_energy(), ase_atoms0.get_forces() + s0, v0 = ( + ase_atoms0.get_stress(voigt=True), + -ase_atoms0.get_stress(voigt=False) * ase_atoms0.get_volume(), + ) + + ase_atoms1 = Atoms( + numbers=[atomic_numbers[i] for i in idx_perm], + positions=coord[idx_perm, :], + # positions=[tuple(item) for item in coordinate], + cell=cell, + calculator=self.calculator, + pbc=True, + ) + e1, f1 = ase_atoms1.get_potential_energy(), ase_atoms1.get_forces() + s1, v1 = ( + ase_atoms1.get_stress(voigt=True), + -ase_atoms1.get_stress(voigt=False) * ase_atoms1.get_volume(), + ) + + assert isinstance(e0, float) + assert f0.shape == (natoms, 3) + assert v0.shape == (3, 3) + np.testing.assert_allclose(e0, e1, rtol=low_prec, atol=prec) + np.testing.assert_allclose(f0[idx_perm, :], f1, rtol=low_prec, atol=prec) + np.testing.assert_allclose(s0, s1, rtol=low_prec, atol=prec) + np.testing.assert_allclose(v0, v1, rtol=low_prec, atol=prec) diff --git a/source/tests/pd/test_change_bias.py b/source/tests/pd/test_change_bias.py new file mode 100644 index 0000000000..f0b31454ba --- /dev/null +++ b/source/tests/pd/test_change_bias.py @@ -0,0 +1,150 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import json +import os +import shutil +import tempfile +import unittest +from copy import ( + deepcopy, +) +from pathlib import ( + Path, +) + +import numpy as np +import paddle + +from deepmd.pd.entrypoints.main import ( + get_trainer, +) +from deepmd.pd.train.training import ( + get_model_for_wrapper, + model_change_out_bias, +) +from deepmd.pd.train.wrapper import ( + ModelWrapper, +) +from deepmd.pd.utils.dataloader import ( + DpLoaderSet, +) +from deepmd.pd.utils.stat import ( + make_stat_input, +) +from deepmd.pd.utils.utils import ( + to_paddle_tensor, +) + +from .common import ( + run_dp, +) +from .model.test_permutation import ( + model_se_e2_a, +) +from .test_finetune import ( + energy_data_requirement, +) + +current_path = os.getcwd() + + +class TestChangeBias(unittest.TestCase): + def setUp(self): + input_json = str(Path(__file__).parent / "water/se_atten.json") + with open(input_json) as f: + self.config = json.load(f) + model_name = "change-bias-model.ckpt" + self.data_file = [str(Path(__file__).parent / "water/data/single")] + self.config["training"]["training_data"]["systems"] = self.data_file + self.config["training"]["validation_data"]["systems"] = self.data_file + self.config["model"] = deepcopy(model_se_e2_a) + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + self.config["training"]["save_ckpt"] = model_name + self.trainer = get_trainer(deepcopy(self.config)) + self.trainer.run() + self.state_dict_trained = self.trainer.wrapper.model.state_dict() + data = DpLoaderSet( + self.data_file, + batch_size=1, + type_map=self.config["model"]["type_map"], + ) + data.add_data_requirement(energy_data_requirement) + self.sampled = make_stat_input( + data.systems, + data.dataloaders, + nbatches=1, + ) + self.model_path = Path(current_path) / (model_name + ".pd") + self.model_path_data_bias = Path(current_path) / ( + model_name + "data_bias" + ".pd" + ) + self.model_path_data_file_bias = Path(current_path) / ( + model_name + "data_file_bias" + ".pd" + ) + self.model_path_user_bias = Path(current_path) / ( + model_name + "user_bias" + ".pd" + ) + + def test_change_bias_with_data(self): + run_dp( + f"dp --pd change-bias {self.model_path!s} -s {self.data_file[0]} -o {self.model_path_data_bias!s}" + ) + state_dict = paddle.load(str(self.model_path_data_bias)) + model_params = state_dict["model"]["_extra_state"]["model_params"] + model_for_wrapper = get_model_for_wrapper(model_params) + wrapper = ModelWrapper(model_for_wrapper) + wrapper.set_state_dict(state_dict["model"]) + updated_bias = wrapper.model["Default"].get_out_bias() + expected_model = model_change_out_bias( + self.trainer.wrapper.model["Default"], + self.sampled, + _bias_adjust_mode="change-by-statistic", + ) + expected_bias = expected_model.get_out_bias() + assert paddle.allclose(updated_bias, expected_bias) + + def test_change_bias_with_data_sys_file(self): + tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".txt") + with open(tmp_file.name, "w") as f: + f.writelines([sys + "\n" for sys in self.data_file]) + run_dp( + f"dp --pd change-bias {self.model_path!s} -f {tmp_file.name} -o {self.model_path_data_file_bias!s}" + ) + state_dict = paddle.load(str(self.model_path_data_file_bias)) + model_params = state_dict["model"]["_extra_state"]["model_params"] + model_for_wrapper = get_model_for_wrapper(model_params) + wrapper = ModelWrapper(model_for_wrapper) + wrapper.set_state_dict(state_dict["model"]) + updated_bias = wrapper.model["Default"].get_out_bias() + expected_model = model_change_out_bias( + self.trainer.wrapper.model["Default"], + self.sampled, + _bias_adjust_mode="change-by-statistic", + ) + expected_bias = expected_model.get_out_bias() + assert paddle.allclose(updated_bias, expected_bias) + + def test_change_bias_with_user_defined(self): + user_bias = [0.1, 3.2, -0.5] + run_dp( + f"dp --pd change-bias {self.model_path!s} -b {' '.join([str(_) for _ in user_bias])} -o {self.model_path_user_bias!s}" + ) + state_dict = paddle.load(str(self.model_path_user_bias)) + model_params = state_dict["model"]["_extra_state"]["model_params"] + model_for_wrapper = get_model_for_wrapper(model_params) + wrapper = ModelWrapper(model_for_wrapper) + wrapper.set_state_dict(state_dict["model"]) + updated_bias = wrapper.model["Default"].get_out_bias() + expected_bias = to_paddle_tensor(np.array(user_bias)).reshape( + updated_bias.shape + ) + assert paddle.allclose(updated_bias, expected_bias) + + def tearDown(self): + for f in os.listdir("."): + if f.startswith("change-bias-model") and f.endswith(".pd"): + os.remove(f) + if f in ["lcurve.out"]: + os.remove(f) + if f in ["stat_files"]: + shutil.rmtree(f) diff --git a/source/tests/pd/test_dp_show.py b/source/tests/pd/test_dp_show.py new file mode 100644 index 0000000000..5e257fd049 --- /dev/null +++ b/source/tests/pd/test_dp_show.py @@ -0,0 +1,219 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import io +import json +import os +import shutil +import unittest +from contextlib import ( + redirect_stderr, +) +from copy import ( + deepcopy, +) +from pathlib import ( + Path, +) + +from deepmd.pd.entrypoints.main import ( + get_trainer, +) +from deepmd.pd.utils.multi_task import ( + preprocess_shared_params, +) + +from .common import ( + run_dp, +) +from .model.test_permutation import ( + model_se_e2_a, +) + + +class TestSingleTaskModel(unittest.TestCase): + def setUp(self): + input_json = str(Path(__file__).parent / "water/se_atten.json") + with open(input_json) as f: + self.config = json.load(f) + os.environ["FLAGS_prim_enable_dynamic"] = "1" + os.environ["FLAGS_enable_pir_api"] = "1" + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + data_file = [str(Path(__file__).parent / "water/data/single")] + self.config["training"]["training_data"]["systems"] = data_file + self.config["training"]["validation_data"]["systems"] = data_file + self.config["model"] = deepcopy(model_se_e2_a) + self.config["model"]["type_map"] = ["O", "H", "Au"] + trainer = get_trainer(deepcopy(self.config)) + trainer.run() + run_dp("dp --pd freeze") + + def test_checkpoint(self): + INPUT = "model.pd" + ATTRIBUTES = "type-map descriptor fitting-net" + with redirect_stderr(io.StringIO()) as f: + run_dp(f"dp --pd show {INPUT} {ATTRIBUTES}") + results = f.getvalue().split("\n")[:-1] + assert "This is a singletask model" in results[-4] + assert "The type_map is ['O', 'H', 'Au']" in results[-3] + assert ( + "{'type': 'se_e2_a'" and "'sel': [46, 92, 4]" and "'rcut': 4.0" + ) in results[-2] + assert ( + "The fitting_net parameter is {'neuron': [24, 24, 24], 'resnet_dt': True, 'seed': 1}" + in results[-1] + ) + + @unittest.skip( + "Paddle do not support dp --pd show frozen models(.json and .pdiparams file), " + "will be supported in the future." + ) + def test_frozen_model(self): + INPUT = "frozen_model.pd" + ATTRIBUTES = "type-map descriptor fitting-net" + with redirect_stderr(io.StringIO()) as f: + run_dp(f"dp --pd show {INPUT} {ATTRIBUTES}") + results = f.getvalue().split("\n")[:-1] + assert "This is a singletask model" in results[-4] + assert "The type_map is ['O', 'H', 'Au']" in results[-3] + assert ( + "{'type': 'se_e2_a'" and "'sel': [46, 92, 4]" and "'rcut': 4.0" + ) in results[-2] + assert ( + "The fitting_net parameter is {'neuron': [24, 24, 24], 'resnet_dt': True, 'seed': 1}" + in results[-1] + ) + + def test_checkpoint_error(self): + INPUT = "model.pd" + ATTRIBUTES = "model-branch type-map descriptor fitting-net" + with self.assertRaisesRegex( + RuntimeError, "The 'model-branch' option requires a multitask model" + ): + run_dp(f"dp --pd show {INPUT} {ATTRIBUTES}") + + def tearDown(self): + for f in os.listdir("."): + if f.startswith("model") and f.endswith("pd"): + os.remove(f) + if f in ["lcurve.out", "frozen_model.pd", "output.txt", "checkpoint"]: + os.remove(f) + if f in ["stat_files"]: + shutil.rmtree(f) + + +class TestMultiTaskModel(unittest.TestCase): + def setUp(self): + input_json = str(Path(__file__).parent / "water/multitask.json") + with open(input_json) as f: + self.config = json.load(f) + self.config["model"]["shared_dict"]["my_descriptor"] = model_se_e2_a[ + "descriptor" + ] + data_file = [str(Path(__file__).parent / "water/data/data_0")] + self.stat_files = "se_e2_a" + os.makedirs(self.stat_files, exist_ok=True) + self.config["training"]["data_dict"]["model_1"]["training_data"]["systems"] = ( + data_file + ) + self.config["training"]["data_dict"]["model_1"]["validation_data"][ + "systems" + ] = data_file + self.config["training"]["data_dict"]["model_1"]["stat_file"] = ( + f"{self.stat_files}/model_1" + ) + self.config["training"]["data_dict"]["model_2"]["training_data"]["systems"] = ( + data_file + ) + self.config["training"]["data_dict"]["model_2"]["validation_data"][ + "systems" + ] = data_file + self.config["training"]["data_dict"]["model_2"]["stat_file"] = ( + f"{self.stat_files}/model_2" + ) + self.config["model"]["model_dict"]["model_1"]["fitting_net"] = { + "neuron": [1, 2, 3], + "seed": 678, + } + self.config["model"]["model_dict"]["model_2"]["fitting_net"] = { + "neuron": [9, 8, 7], + "seed": 1111, + } + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + self.origin_config = deepcopy(self.config) + self.config["model"], self.shared_links = preprocess_shared_params( + self.config["model"] + ) + trainer = get_trainer(deepcopy(self.config), shared_links=self.shared_links) + trainer.run() + run_dp("dp --pd freeze --head model_1") + + def test_checkpoint(self): + INPUT = "model.ckpt.pd" + ATTRIBUTES = "model-branch type-map descriptor fitting-net" + with redirect_stderr(io.StringIO()) as f: + run_dp(f"dp --pd show {INPUT} {ATTRIBUTES}") + results = f.getvalue().split("\n")[:-1] + assert "This is a multitask model" in results[-8] + assert ( + "Available model branches are ['model_1', 'model_2', 'RANDOM'], " + "where 'RANDOM' means using a randomly initialized fitting net." + in results[-7] + ) + assert "The type_map of branch model_1 is ['O', 'H', 'B']" in results[-6] + assert "The type_map of branch model_2 is ['O', 'H', 'B']" in results[-5] + assert ( + "model_1" + and "'type': 'se_e2_a'" + and "'sel': [46, 92, 4]" + and "'rcut_smth': 0.5" + ) in results[-4] + assert ( + "model_2" + and "'type': 'se_e2_a'" + and "'sel': [46, 92, 4]" + and "'rcut_smth': 0.5" + ) in results[-3] + assert ( + "The fitting_net parameter of branch model_1 is {'neuron': [1, 2, 3], 'seed': 678}" + in results[-2] + ) + assert ( + "The fitting_net parameter of branch model_2 is {'neuron': [9, 8, 7], 'seed': 1111}" + in results[-1] + ) + + @unittest.skip( + "Paddle do not support dp --pd show frozen models(.json and .pdiparams file), " + "will be supported in the future." + ) + def test_frozen_model(self): + INPUT = "frozen_model" + ATTRIBUTES = "type-map descriptor fitting-net" + with redirect_stderr(io.StringIO()) as f: + run_dp(f"dp --pd show {INPUT} {ATTRIBUTES}") + results = f.getvalue().split("\n")[:-1] + assert "This is a singletask model" in results[-4] + assert "The type_map is ['O', 'H', 'B']" in results[-3] + assert ( + "'type': 'se_e2_a'" and "'sel': [46, 92, 4]" and "'rcut_smth': 0.5" + ) in results[-2] + assert ( + "The fitting_net parameter is {'neuron': [1, 2, 3], 'seed': 678}" + in results[-1] + ) + + def tearDown(self): + for f in os.listdir("."): + if f.startswith("model") and f.endswith("pd"): + os.remove(f) + if f in [ + "lcurve.out", + "frozen_model.json", + "frozen_model.pdiparams", + "checkpoint", + "output.txt", + ]: + os.remove(f) + if f in ["stat_files", self.stat_files]: + shutil.rmtree(f) diff --git a/source/tests/pd/test_dp_test.py b/source/tests/pd/test_dp_test.py new file mode 100644 index 0000000000..6d525b1251 --- /dev/null +++ b/source/tests/pd/test_dp_test.py @@ -0,0 +1,173 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import json +import os +import shutil +import tempfile +import unittest +from copy import ( + deepcopy, +) +from pathlib import ( + Path, +) + +import numpy as np +import paddle +from paddle.static import ( + InputSpec, +) + +from deepmd.entrypoints.test import test as dp_test +from deepmd.pd.entrypoints.main import ( + get_trainer, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, +) + +from .model.test_permutation import ( + model_se_e2_a, + model_spin, +) + + +class DPTest: + @unittest.skip( + "Paddle do not support testing in frozen models(.json and .pdiparams file), " + "will be supported in the future." + ) + def test_dp_test_1_frame(self): + trainer = get_trainer(deepcopy(self.config)) + device = paddle.get_device() + paddle.set_device("cpu") + input_dict, label_dict, _ = trainer.get_data(is_train=False) + # exit() + paddle.set_device(device) + has_spin = getattr(trainer.model, "has_spin", False) + + if callable(has_spin): + has_spin = has_spin() + if not has_spin: + input_dict.pop("spin", None) + input_dict["do_atomic_virial"] = True + result = trainer.model(**input_dict) + paddle.set_flags( + { + "FLAGS_save_cf_stack_op": 1, + "FLAGS_prim_enable_dynamic": 1, + "FLAGS_enable_pir_api": 1, + } + ) + model = paddle.jit.to_static( + trainer.model, + full_graph=True, + input_spec=[ + InputSpec([-1, -1, 3], dtype="float64", name="coord"), + InputSpec([-1, -1], dtype="int32", name="atype"), + InputSpec([-1, -1, -1], dtype="int32", name="nlist"), + ], + ) + tmp_model = tempfile.NamedTemporaryFile(delete=False, suffix=".pd") + paddle.jit.save( + model, + tmp_model.name, + skip_prune_program=True, + ) + dp_test( + model=tmp_model.name, + system=self.config["training"]["validation_data"]["systems"][0], + datafile=None, + set_prefix="set", + numb_test=2, + rand_seed=None, + shuffle_test=False, + detail_file=self.detail_file, + atomic=False, + ) + os.unlink(tmp_model.name) + natom = input_dict["atype"].shape[1] + pred_e = np.loadtxt(self.detail_file + ".e.out", ndmin=2)[0, 1] + np.testing.assert_almost_equal( + pred_e, + to_numpy_array(result["energy"])[0][0], + ) + pred_e_peratom = np.loadtxt(self.detail_file + ".e_peratom.out", ndmin=2)[0, 1] + np.testing.assert_almost_equal(pred_e_peratom, pred_e / natom) + if not has_spin: + pred_f = np.loadtxt(self.detail_file + ".f.out", ndmin=2)[:, 3:6] + np.testing.assert_almost_equal( + pred_f, + to_numpy_array(result["force"]).reshape(-1, 3), + ) + pred_v = np.loadtxt(self.detail_file + ".v.out", ndmin=2)[:, 9:18] + np.testing.assert_almost_equal( + pred_v, + to_numpy_array(result["virial"]), + ) + pred_v_peratom = np.loadtxt(self.detail_file + ".v_peratom.out", ndmin=2)[ + :, 9:18 + ] + np.testing.assert_almost_equal(pred_v_peratom, pred_v / natom) + else: + pred_fr = np.loadtxt(self.detail_file + ".fr.out", ndmin=2)[:, 3:6] + np.testing.assert_almost_equal( + pred_fr, + to_numpy_array(result["force"]).reshape(-1, 3), + ) + pred_fm = np.loadtxt(self.detail_file + ".fm.out", ndmin=2)[:, 3:6] + np.testing.assert_almost_equal( + pred_fm, + to_numpy_array( + result["force_mag"][result["mask_mag"].bool().squeeze(-1)] + ).reshape(-1, 3), + ) + + def tearDown(self): + for f in os.listdir("."): + if f.startswith("model") and f.endswith(".pd"): + os.remove(f) + if f.startswith(self.detail_file): + os.remove(f) + if f in ["lcurve.out", self.input_json]: + os.remove(f) + if f in ["stat_files"]: + shutil.rmtree(f) + + +class TestDPTestSeA(DPTest, unittest.TestCase): + def setUp(self): + self.detail_file = "test_dp_test_ener_detail" + input_json = str(Path(__file__).parent / "water/se_atten.json") + with open(input_json) as f: + self.config = json.load(f) + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + data_file = [str(Path(__file__).parent / "water/data/single")] + self.config["training"]["training_data"]["systems"] = data_file + self.config["training"]["validation_data"]["systems"] = data_file + self.config["model"] = deepcopy(model_se_e2_a) + self.input_json = "test_dp_test.json" + with open(self.input_json, "w") as fp: + json.dump(self.config, fp, indent=4) + + +class TestDPTestSeASpin(DPTest, unittest.TestCase): + def setUp(self): + self.detail_file = "test_dp_test_ener_spin_detail" + input_json = str(Path(__file__).parent / "water/se_atten.json") + with open(input_json) as f: + self.config = json.load(f) + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + data_file = [str(Path(__file__).parent / "NiO/data/single")] + self.config["training"]["training_data"]["systems"] = data_file + self.config["training"]["validation_data"]["systems"] = data_file + self.config["model"] = deepcopy(model_spin) + self.config["model"]["type_map"] = ["Ni", "O", "B"] + self.input_json = "test_dp_test.json" + with open(self.input_json, "w") as fp: + json.dump(self.config, fp, indent=4) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/test_finetune.py b/source/tests/pd/test_finetune.py new file mode 100644 index 0000000000..0f5271c56a --- /dev/null +++ b/source/tests/pd/test_finetune.py @@ -0,0 +1,375 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import json +import os +import shutil +import tempfile +import unittest +from copy import ( + deepcopy, +) +from pathlib import ( + Path, +) + +import numpy as np +import paddle + +from deepmd.infer.deep_eval import ( + DeepEval, +) +from deepmd.pd.entrypoints.main import ( + get_trainer, +) +from deepmd.pd.model.model import ( + get_model, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.dataloader import ( + DpLoaderSet, +) +from deepmd.pd.utils.finetune import ( + get_finetune_rules, +) +from deepmd.pd.utils.stat import ( + make_stat_input, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, + to_paddle_tensor, +) +from deepmd.utils.data import ( + DataRequirementItem, +) + +from .model.test_permutation import ( + model_dos, + model_dpa1, + model_dpa2, + model_se_e2_a, + model_zbl, +) + +energy_data_requirement = [ + DataRequirementItem( + "energy", + ndof=1, + atomic=False, + must=False, + high_prec=True, + ), + DataRequirementItem( + "force", + ndof=3, + atomic=True, + must=False, + high_prec=False, + ), + DataRequirementItem( + "virial", + ndof=9, + atomic=False, + must=False, + high_prec=False, + ), + DataRequirementItem( + "dos", + ndof=250, + atomic=False, + must=False, + high_prec=True, + ), + DataRequirementItem( + "atom_ener", + ndof=1, + atomic=True, + must=False, + high_prec=False, + ), + DataRequirementItem( + "atom_pref", + ndof=1, + atomic=True, + must=False, + high_prec=False, + repeat=3, + ), +] + + +class FinetuneTest: + @unittest.skip( + "Paddle do not support finetune in frozen models(.json and .pdiparams file), " + "will be supported in the future." + ) + def test_finetune_change_out_bias(self): + self.testkey = "energy" if self.testkey is None else self.testkey + # get data + data = DpLoaderSet( + self.data_file, + batch_size=1, + type_map=self.config["model"]["type_map"], + ) + data.add_data_requirement(energy_data_requirement) + sampled = make_stat_input( + data.systems, + data.dataloaders, + nbatches=1, + ) + # make sampled of multiple frames with different atom numbs + numb_atom = sampled[0]["atype"].shape[1] + small_numb_atom = numb_atom // 2 + small_atom_data = deepcopy(sampled[0]) + atomic_key = ["coord", "atype"] + for kk in atomic_key: + small_atom_data[kk] = small_atom_data[kk][:, :small_numb_atom] + scale_pref = float(small_numb_atom / numb_atom) + small_atom_data[self.testkey] *= scale_pref + small_atom_data["natoms"][:, :2] = small_numb_atom + small_atom_data["natoms"][:, 2:] = paddle.bincount( + small_atom_data["atype"][0], + minlength=small_atom_data["natoms"].shape[1] - 2, + ) + sampled = [sampled[0], small_atom_data] + + # get model + model = get_model(self.config["model"]).to(env.DEVICE) + atomic_model = model.atomic_model + atomic_model["out_bias"] = paddle.randn(atomic_model["out_bias"].shape) + energy_bias_before = to_numpy_array(atomic_model["out_bias"])[0] + + # prepare original model for test + dp = paddle.jit.to_static(model) + tmp_model = tempfile.NamedTemporaryFile(delete=False, suffix=".pd") + paddle.jit.save(dp, tmp_model.name) + dp = DeepEval(tmp_model.name) + origin_type_map = ["O", "H"] + full_type_map = ["O", "H", "B"] + + # change energy bias + model.atomic_model.change_out_bias( + sampled, + bias_adjust_mode="change-by-statistic", + ) + energy_bias_after = to_numpy_array(atomic_model["out_bias"])[0] + + # get ground-truth energy bias change + sorter = np.argsort(full_type_map) + idx_type_map = sorter[ + np.searchsorted(full_type_map, origin_type_map, sorter=sorter) + ] + ntest = 1 + atom_nums = np.tile( + np.bincount(to_numpy_array(sampled[0]["atype"][0]))[idx_type_map], + (ntest, 1), + ) + atom_nums_small = np.tile( + np.bincount(to_numpy_array(sampled[1]["atype"][0]))[idx_type_map], + (ntest, 1), + ) + atom_nums = np.concatenate([atom_nums, atom_nums_small], axis=0) + + energy = dp.eval( + to_numpy_array(sampled[0]["coord"][:ntest]), + to_numpy_array(sampled[0]["box"][:ntest]), + to_numpy_array(sampled[0]["atype"][0]), + )[0] + energy_small = dp.eval( + to_numpy_array(sampled[1]["coord"][:ntest]), + to_numpy_array(sampled[1]["box"][:ntest]), + to_numpy_array(sampled[1]["atype"][0]), + )[0] + energy_diff = to_numpy_array(sampled[0][self.testkey][:ntest]) - energy + energy_diff_small = ( + to_numpy_array(sampled[1][self.testkey][:ntest]) - energy_small + ) + energy_diff = np.concatenate([energy_diff, energy_diff_small], axis=0) + finetune_shift = ( + energy_bias_after[idx_type_map] - energy_bias_before[idx_type_map] + ).ravel() + ground_truth_shift = np.linalg.lstsq(atom_nums, energy_diff, rcond=None)[ + 0 + ].reshape(-1) + + # check values + np.testing.assert_almost_equal(finetune_shift, ground_truth_shift, decimal=10) + + self.tearDown() + + def test_finetune_change_type(self): + if not self.mixed_types: + # skip when not mixed_types + return + # get data + data = DpLoaderSet( + self.data_file, + batch_size=1, + type_map=self.config["model"]["type_map"], + ) + data.add_data_requirement(energy_data_requirement) + sampled = make_stat_input( + data.systems, + data.dataloaders, + nbatches=1, + ) + data_type_map = self.config["model"]["type_map"] + for [old_type_map, new_type_map] in [ + [["H", "X1", "X2", "O", "B"], ["O", "H", "B"]], + [["O", "H", "B"], ["H", "X1", "X2", "O", "B"]], + ]: + old_type_map_index = np.array( + [old_type_map.index(i) for i in data_type_map], dtype=np.int32 + ) + new_type_map_index = np.array( + [new_type_map.index(i) for i in data_type_map], dtype=np.int32 + ) + + # get pretrained model with old type map + config_old_type_map = deepcopy(self.config) + config_old_type_map["model"]["type_map"] = old_type_map + trainer = get_trainer(config_old_type_map) + trainer.run() + finetune_model = ( + config_old_type_map["training"].get("save_ckpt", "model.ckpt") + ".pd" + ) + + # finetune load the same type_map + config_old_type_map_finetune = deepcopy(self.config) + config_old_type_map_finetune["model"]["type_map"] = old_type_map + config_old_type_map_finetune["model"], finetune_links = get_finetune_rules( + finetune_model, + config_old_type_map_finetune["model"], + ) + trainer_finetune_old = get_trainer( + config_old_type_map_finetune, + finetune_model=finetune_model, + finetune_links=finetune_links, + ) + + # finetune load the slim type_map + config_new_type_map_finetune = deepcopy(self.config) + config_new_type_map_finetune["model"]["type_map"] = new_type_map + config_new_type_map_finetune["model"], finetune_links = get_finetune_rules( + finetune_model, + config_new_type_map_finetune["model"], + ) + trainer_finetune_new = get_trainer( + config_new_type_map_finetune, + finetune_model=finetune_model, + finetune_links=finetune_links, + ) + + # test consistency + ntest = 1 + prec = 1e-10 + model_old_result = trainer_finetune_old.model( + sampled[0]["coord"][:ntest], + to_paddle_tensor(old_type_map_index)[sampled[0]["atype"][:ntest]], + box=sampled[0]["box"][:ntest], + ) + model_new_result = trainer_finetune_new.model( + sampled[0]["coord"][:ntest], + to_paddle_tensor(new_type_map_index)[sampled[0]["atype"][:ntest]], + box=sampled[0]["box"][:ntest], + ) + test_keys = ["energy", "force", "virial"] + for key in test_keys: + np.testing.assert_allclose( + model_old_result[key].numpy(), + model_new_result[key].numpy(), + rtol=prec, + atol=prec, + ) + + self.tearDown() + + def tearDown(self): + for f in os.listdir("."): + if f.startswith("model") and f.endswith(".pd"): + os.remove(f) + if f in ["lcurve.out"]: + os.remove(f) + if f in ["stat_files"]: + shutil.rmtree(f) + + +class TestEnergyModelSeA(FinetuneTest, unittest.TestCase): + def setUp(self): + input_json = str(Path(__file__).parent / "water/se_atten.json") + with open(input_json) as f: + self.config = json.load(f) + self.data_file = [str(Path(__file__).parent / "water/data/single")] + self.config["training"]["training_data"]["systems"] = self.data_file + self.config["training"]["validation_data"]["systems"] = self.data_file + self.config["model"] = deepcopy(model_se_e2_a) + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + self.mixed_types = False + self.testkey = None + + +class TestEnergyZBLModelSeA(FinetuneTest, unittest.TestCase): + def setUp(self): + input_json = str(Path(__file__).parent / "water/se_atten.json") + with open(input_json) as f: + self.config = json.load(f) + self.data_file = [str(Path(__file__).parent / "water/data/single")] + self.config["training"]["training_data"]["systems"] = self.data_file + self.config["training"]["validation_data"]["systems"] = self.data_file + self.config["model"] = deepcopy(model_zbl) + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + self.mixed_types = False + self.testkey = None + + +class TestEnergyDOSModelSeA(FinetuneTest, unittest.TestCase): + def setUp(self): + input_json = str(Path(__file__).parent / "dos/input.json") + with open(input_json) as f: + self.config = json.load(f) + self.data_file = [str(Path(__file__).parent / "dos/data/global_system")] + self.config["training"]["training_data"]["systems"] = self.data_file + self.config["training"]["validation_data"]["systems"] = self.data_file + self.config["model"] = deepcopy(model_dos) + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + self.mixed_types = False + self.testkey = "dos" + + +class TestEnergyModelDPA1(FinetuneTest, unittest.TestCase): + def setUp(self): + input_json = str(Path(__file__).parent / "water/se_atten.json") + with open(input_json) as f: + self.config = json.load(f) + self.data_file = [str(Path(__file__).parent / "water/data/single")] + self.config["training"]["training_data"]["systems"] = self.data_file + self.config["training"]["validation_data"]["systems"] = self.data_file + self.config["model"] = deepcopy(model_dpa1) + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + self.mixed_types = True + self.testkey = None + + +class TestEnergyModelDPA2(FinetuneTest, unittest.TestCase): + def setUp(self): + input_json = str(Path(__file__).parent / "water/se_atten.json") + with open(input_json) as f: + self.config = json.load(f) + self.data_file = [str(Path(__file__).parent / "water/data/single")] + self.config["training"]["training_data"]["systems"] = self.data_file + self.config["training"]["validation_data"]["systems"] = self.data_file + self.config["model"] = deepcopy(model_dpa2) + self.config["model"]["descriptor"]["repformer"]["nlayers"] = 2 + + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + self.mixed_types = True + self.testkey = None + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/test_init_frz_model.py b/source/tests/pd/test_init_frz_model.py new file mode 100644 index 0000000000..2938131a60 --- /dev/null +++ b/source/tests/pd/test_init_frz_model.py @@ -0,0 +1,148 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import json +import os +import shutil +import tempfile +import unittest +from argparse import ( + Namespace, +) +from copy import ( + deepcopy, +) +from pathlib import ( + Path, +) + +import numpy as np + +from deepmd.pd.entrypoints.main import ( + freeze, + get_trainer, +) +from deepmd.pd.infer.deep_eval import ( + DeepPot, +) + +from .common import ( + run_dp, +) + + +@unittest.skip("froze model only used to inference in paddle backend") +class TestInitFrzModel(unittest.TestCase): + def setUp(self): + input_json = str(Path(__file__).parent / "water/se_atten.json") + with open(input_json) as f: + config = json.load(f) + config["model"]["descriptor"]["smooth_type_embedding"] = True + config["training"]["numb_steps"] = 1 + config["training"]["save_freq"] = 1 + config["learning_rate"]["start_lr"] = 1.0 + config["training"]["training_data"]["systems"] = [ + str(Path(__file__).parent / "water/data/single") + ] + config["training"]["validation_data"]["systems"] = [ + str(Path(__file__).parent / "water/data/single") + ] + + self.models = [] + for imodel in range(3): + frozen_model = f"frozen_model{imodel}.json" + if imodel == 0: + temp_config = deepcopy(config) + trainer = get_trainer(temp_config) + elif imodel == 1: + temp_config = deepcopy(config) + temp_config["training"]["numb_steps"] = 0 + trainer = get_trainer(temp_config, init_frz_model=self.models[-1]) + else: + empty_config = deepcopy(config) + empty_config["model"]["descriptor"] = {} + empty_config["model"]["fitting_net"] = {} + empty_config["training"]["numb_steps"] = 0 + tmp_input = tempfile.NamedTemporaryFile(delete=False, suffix=".json") + with open(tmp_input.name, "w") as f: + json.dump(empty_config, f, indent=4) + run_dp( + f"dp --pd train {tmp_input.name} --init-frz-model {self.models[-1]} --use-pretrain-script --skip-neighbor-stat" + ) + trainer = None + + if imodel in [0, 1]: + trainer.run() + ns = Namespace( + model="model.pd", + output=frozen_model, + head=None, + ) + freeze(ns) + self.models.append(frozen_model) + + def test_dp_test(self): + dp1 = DeepPot(str(self.models[0])) + dp2 = DeepPot(str(self.models[1])) + dp3 = DeepPot(str(self.models[2])) + cell = np.array( + [ + 5.122106549439247480e00, + 4.016537340154059388e-01, + 6.951654033828678081e-01, + 4.016537340154059388e-01, + 6.112136112297989143e00, + 8.178091365465004481e-01, + 6.951654033828678081e-01, + 8.178091365465004481e-01, + 6.159552512682983760e00, + ] + ).reshape(1, 3, 3) + coord = np.array( + [ + 2.978060152121375648e00, + 3.588469695887098077e00, + 2.792459820604495491e00, + 3.895592322591093115e00, + 2.712091020667753760e00, + 1.366836847133650501e00, + 9.955616170888935690e-01, + 4.121324820711413039e00, + 1.817239061889086571e00, + 3.553661462345699906e00, + 5.313046969500791583e00, + 6.635182659098815883e00, + 6.088601018589653080e00, + 6.575011420004332585e00, + 6.825240650611076099e00, + ] + ).reshape(1, -1, 3) + atype = np.array([0, 0, 0, 1, 1]).reshape(1, -1) + + ret1 = dp1.eval(coord, cell, atype, atomic=True) + e1, f1, v1, ae1, av1 = ret1[0], ret1[1], ret1[2], ret1[3], ret1[4] + ret2 = dp2.eval(coord, cell, atype, atomic=True) + e2, f2, v2, ae2, av2 = ret2[0], ret2[1], ret2[2], ret2[3], ret2[4] + ret3 = dp3.eval(coord, cell, atype, atomic=True) + e3, f3, v3, ae3, av3 = ret3[0], ret3[1], ret3[2], ret3[3], ret3[4] + np.testing.assert_allclose(e1, e2, rtol=1e-10, atol=1e-10) + np.testing.assert_allclose(e1, e3, rtol=1e-10, atol=1e-10) + np.testing.assert_allclose(f1, f2, rtol=1e-10, atol=1e-10) + np.testing.assert_allclose(f1, f3, rtol=1e-10, atol=1e-10) + np.testing.assert_allclose(v1, v2, rtol=1e-10, atol=1e-10) + np.testing.assert_allclose(v1, v3, rtol=1e-10, atol=1e-10) + np.testing.assert_allclose(ae1, ae2, rtol=1e-10, atol=1e-10) + np.testing.assert_allclose(ae1, ae3, rtol=1e-10, atol=1e-10) + np.testing.assert_allclose(av1, av2, rtol=1e-10, atol=1e-10) + np.testing.assert_allclose(av1, av3, rtol=1e-10, atol=1e-10) + + def tearDown(self): + for f in os.listdir("."): + if f.startswith("frozen_model") and ( + f.endswith(".json") or f.endswith(".pdiparams") + ): + os.remove(f) + if f.startswith("model") and f.endswith(".pd"): + os.remove(f) + if f in ["lcurve.out"]: + os.remove(f) + if f in ["stat_files"]: + shutil.rmtree(f) diff --git a/source/tests/pd/test_init_model.py b/source/tests/pd/test_init_model.py new file mode 100644 index 0000000000..50c1e82ad6 --- /dev/null +++ b/source/tests/pd/test_init_model.py @@ -0,0 +1,136 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import json +import os +import shutil +import tempfile +import unittest +from copy import ( + deepcopy, +) +from pathlib import ( + Path, +) + +import numpy as np + +from deepmd.pd.entrypoints.main import ( + get_trainer, +) +from deepmd.pd.infer.deep_eval import ( + DeepPot, +) + +from .common import ( + run_dp, +) + + +class TestInitModel(unittest.TestCase): + def setUp(self): + input_json = str(Path(__file__).parent / "water/se_atten.json") + with open(input_json) as f: + config = json.load(f) + config["model"]["descriptor"]["smooth_type_embedding"] = True + config["training"]["numb_steps"] = 1 + config["training"]["save_freq"] = 1 + config["learning_rate"]["start_lr"] = 1.0 + config["training"]["training_data"]["systems"] = [ + str(Path(__file__).parent / "water/data/single") + ] + config["training"]["validation_data"]["systems"] = [ + str(Path(__file__).parent / "water/data/single") + ] + + self.models = [] + for imodel in range(3): + ckpt_model = f"model{imodel}.ckpt" + if imodel == 0: + temp_config = deepcopy(config) + temp_config["training"]["save_ckpt"] = ckpt_model + trainer = get_trainer(temp_config) + elif imodel == 1: + temp_config = deepcopy(config) + temp_config["training"]["numb_steps"] = 0 + temp_config["training"]["save_ckpt"] = ckpt_model + trainer = get_trainer(temp_config, init_model=self.models[-1]) + else: + empty_config = deepcopy(config) + empty_config["model"]["descriptor"] = {} + empty_config["model"]["fitting_net"] = {} + empty_config["training"]["numb_steps"] = 0 + empty_config["training"]["save_ckpt"] = ckpt_model + tmp_input = tempfile.NamedTemporaryFile(delete=False, suffix=".json") + with open(tmp_input.name, "w") as f: + json.dump(empty_config, f, indent=4) + run_dp( + f"dp --pd train {tmp_input.name} --init-model {self.models[-1]} --use-pretrain-script --skip-neighbor-stat" + ) + trainer = None + + if imodel in [0, 1]: + trainer.run() + self.models.append(ckpt_model + ".pd") + + def test_dp_test(self): + dp1 = DeepPot(str(self.models[0])) + dp2 = DeepPot(str(self.models[1])) + dp3 = DeepPot(str(self.models[2])) + cell = np.array( + [ + 5.122106549439247480e00, + 4.016537340154059388e-01, + 6.951654033828678081e-01, + 4.016537340154059388e-01, + 6.112136112297989143e00, + 8.178091365465004481e-01, + 6.951654033828678081e-01, + 8.178091365465004481e-01, + 6.159552512682983760e00, + ] + ).reshape(1, 3, 3) + coord = np.array( + [ + 2.978060152121375648e00, + 3.588469695887098077e00, + 2.792459820604495491e00, + 3.895592322591093115e00, + 2.712091020667753760e00, + 1.366836847133650501e00, + 9.955616170888935690e-01, + 4.121324820711413039e00, + 1.817239061889086571e00, + 3.553661462345699906e00, + 5.313046969500791583e00, + 6.635182659098815883e00, + 6.088601018589653080e00, + 6.575011420004332585e00, + 6.825240650611076099e00, + ] + ).reshape(1, -1, 3) + atype = np.array([0, 0, 0, 1, 1]).reshape(1, -1) + + ret1 = dp1.eval(coord, cell, atype, atomic=True) + e1, f1, v1, ae1, av1 = ret1[0], ret1[1], ret1[2], ret1[3], ret1[4] + ret2 = dp2.eval(coord, cell, atype, atomic=True) + e2, f2, v2, ae2, av2 = ret2[0], ret2[1], ret2[2], ret2[3], ret2[4] + ret3 = dp3.eval(coord, cell, atype, atomic=True) + e3, f3, v3, ae3, av3 = ret3[0], ret3[1], ret3[2], ret3[3], ret3[4] + np.testing.assert_allclose(e1, e2, rtol=1e-10, atol=1e-10) + np.testing.assert_allclose(e1, e3, rtol=1e-10, atol=1e-10) + np.testing.assert_allclose(f1, f2, rtol=1e-10, atol=1e-10) + np.testing.assert_allclose(f1, f3, rtol=1e-10, atol=1e-10) + np.testing.assert_allclose(v1, v2, rtol=1e-10, atol=1e-10) + np.testing.assert_allclose(v1, v3, rtol=1e-10, atol=1e-10) + np.testing.assert_allclose(ae1, ae2, rtol=1e-10, atol=1e-10) + np.testing.assert_allclose(ae1, ae3, rtol=1e-10, atol=1e-10) + np.testing.assert_allclose(av1, av2, rtol=1e-10, atol=1e-10) + np.testing.assert_allclose(av1, av3, rtol=1e-10, atol=1e-10) + + def tearDown(self): + for f in os.listdir("."): + if f.startswith("model") and f.endswith(".pd"): + os.remove(f) + if f in ["lcurve.out"]: + os.remove(f) + if f in ["stat_files"]: + shutil.rmtree(f) diff --git a/source/tests/pd/test_loss.py b/source/tests/pd/test_loss.py new file mode 100644 index 0000000000..6139d33a6a --- /dev/null +++ b/source/tests/pd/test_loss.py @@ -0,0 +1,808 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import os +import unittest + +import numpy as np +import paddle +import tensorflow.compat.v1 as tf + +tf.disable_eager_execution() +from pathlib import ( + Path, +) + +from deepmd.pd.loss import ( + EnergySpinLoss, + EnergyStdLoss, +) +from deepmd.pd.utils.dataset import ( + DeepmdDataSetForLoader, +) +from deepmd.tf.loss.ener import ( + EnerSpinLoss, + EnerStdLoss, +) +from deepmd.utils.data import ( + DataRequirementItem, +) + +from ..seed import ( + GLOBAL_SEED, +) +from .model.test_embedding_net import ( + get_single_batch, +) +from .test_finetune import ( + energy_data_requirement, +) + +CUR_DIR = os.path.dirname(__file__) + + +def get_batch(system, type_map, data_requirement): + dataset = DeepmdDataSetForLoader(system, type_map) + dataset.add_data_requirement(data_requirement) + np_batch, pt_batch = get_single_batch(dataset) + return np_batch, pt_batch + + +class LossCommonTest(unittest.TestCase): + def setUp(self): + self.cur_lr = 1.2 + if not self.spin: + self.system = str(Path(__file__).parent / "water/data/data_0") + self.type_map = ["H", "O"] + else: + self.system = str(Path(__file__).parent / "NiO/data/data_0") + self.type_map = ["Ni", "O"] + energy_data_requirement.append( + DataRequirementItem( + "force_mag", + ndof=3, + atomic=True, + must=False, + high_prec=False, + ) + ) + # data + np_batch, pt_batch = get_batch( + self.system, self.type_map, energy_data_requirement + ) + natoms = np_batch["natoms"] + self.nloc = natoms[0] + nframes = np_batch["energy"].shape[0] + rng = np.random.default_rng(GLOBAL_SEED) + + if not self.spin: + l_energy, l_force, l_virial = ( + np_batch["energy"], + np_batch["force"], + np_batch["virial"], + ) + p_energy, p_force, p_virial = ( + np.ones_like(l_energy), + np.ones_like(l_force), + np.ones_like(l_virial), + ) + nloc = natoms[0] + batch_size = pt_batch["coord"].shape[0] + p_atom_energy = rng.random(size=[batch_size, nloc]) + l_atom_energy = rng.random(size=[batch_size, nloc]) + atom_pref = rng.random(size=[batch_size, nloc * 3]) + drdq = rng.random(size=[batch_size, nloc * 2 * 3]) + atom_ener_coeff = rng.random(size=[batch_size, nloc]) + # placeholders + l_force_real = l_force + l_force_mag = l_force + p_force_real = p_force + p_force_mag = p_force + else: + # data + np_batch, pt_batch = get_batch( + self.system, self.type_map, energy_data_requirement + ) + natoms = np_batch["natoms"] + self.nloc = natoms[0] + l_energy, l_force_real, l_force_mag, l_virial = ( + np_batch["energy"], + np_batch["force"], + np_batch["force_mag"], + np_batch["virial"], + ) + # merged force for tf old implement + l_force_merge_tf = np.concatenate( + [ + l_force_real.reshape([nframes, self.nloc, 3]), + l_force_mag.reshape([nframes, self.nloc, 3])[ + np_batch["atype"] == 0 + ].reshape([nframes, -1, 3]), + ], + axis=1, + ).reshape([nframes, -1]) + p_energy, p_force_real, p_force_mag, p_force_merge_tf, p_virial = ( + np.ones_like(l_energy), + np.ones_like(l_force_real), + np.ones_like(l_force_mag), + np.ones_like(l_force_merge_tf), + np.ones_like(l_virial), + ) + virt_nloc = (np_batch["atype"] == 0).sum(-1) + natoms_tf = np.concatenate([natoms, virt_nloc], axis=0) + natoms_tf[:2] += virt_nloc + nloc = natoms_tf[0] + batch_size = pt_batch["coord"].shape[0] + p_atom_energy = rng.random(size=[batch_size, nloc]) + l_atom_energy = rng.random(size=[batch_size, nloc]) + atom_pref = rng.random(size=[batch_size, nloc * 3]) + drdq = rng.random(size=[batch_size, nloc * 2 * 3]) + atom_ener_coeff = rng.random(size=[batch_size, nloc]) + self.nloc_tf = nloc + natoms = natoms_tf + l_force = l_force_merge_tf + p_force = p_force_merge_tf + + # tf + self.g = tf.Graph() + with self.g.as_default(): + t_cur_lr = tf.placeholder(shape=[], dtype=tf.float64) + t_natoms = tf.placeholder(shape=[None], dtype=tf.int32) + t_penergy = tf.placeholder(shape=[None, 1], dtype=tf.float64) + t_pforce = tf.placeholder(shape=[None, None], dtype=tf.float64) + t_pvirial = tf.placeholder(shape=[None, 9], dtype=tf.float64) + t_patom_energy = tf.placeholder(shape=[None, None], dtype=tf.float64) + t_lenergy = tf.placeholder(shape=[None, 1], dtype=tf.float64) + t_lforce = tf.placeholder(shape=[None, None], dtype=tf.float64) + t_lvirial = tf.placeholder(shape=[None, 9], dtype=tf.float64) + t_latom_energy = tf.placeholder(shape=[None, None], dtype=tf.float64) + t_atom_pref = tf.placeholder(shape=[None, None], dtype=tf.float64) + t_atom_ener_coeff = tf.placeholder(shape=[None, None], dtype=tf.float64) + t_drdq = tf.placeholder(shape=[None, None], dtype=tf.float64) + find_energy = tf.constant(1.0, dtype=tf.float64) + find_force = tf.constant(1.0, dtype=tf.float64) + find_virial = tf.constant(1.0 if not self.spin else 0.0, dtype=tf.float64) + find_atom_energy = tf.constant(1.0, dtype=tf.float64) + find_atom_pref = tf.constant(1.0, dtype=tf.float64) + find_drdq = tf.constant(1.0, dtype=tf.float64) + find_atom_ener_coeff = tf.constant(1.0, dtype=tf.float64) + model_dict = { + "energy": t_penergy, + "force": t_pforce, + "virial": t_pvirial, + "atom_ener": t_patom_energy, + } + label_dict = { + "energy": t_lenergy, + "force": t_lforce, + "virial": t_lvirial, + "atom_ener": t_latom_energy, + "atom_pref": t_atom_pref, + "drdq": t_drdq, + "atom_ener_coeff": t_atom_ener_coeff, + "find_energy": find_energy, + "find_force": find_force, + "find_virial": find_virial, + "find_atom_ener": find_atom_energy, + "find_atom_pref": find_atom_pref, + "find_drdq": find_drdq, + "find_atom_ener_coeff": find_atom_ener_coeff, + } + self.tf_loss_sess = self.tf_loss.build( + t_cur_lr, t_natoms, model_dict, label_dict, "" + ) + + self.feed_dict = { + t_cur_lr: self.cur_lr, + t_natoms: natoms, + t_penergy: p_energy, + t_pforce: p_force, + t_pvirial: p_virial.reshape([-1, 9]), + t_patom_energy: p_atom_energy, + t_lenergy: l_energy, + t_lforce: l_force, + t_lvirial: l_virial.reshape([-1, 9]), + t_latom_energy: l_atom_energy, + t_atom_pref: atom_pref, + t_drdq: drdq, + t_atom_ener_coeff: atom_ener_coeff, + } + # pt + if not self.spin: + self.model_pred = { + "energy": paddle.to_tensor(p_energy), + "force": paddle.to_tensor(p_force), + "virial": paddle.to_tensor(p_virial), + "atom_energy": paddle.to_tensor(p_atom_energy), + } + self.label = { + "energy": paddle.to_tensor(l_energy), + "find_energy": 1.0, + "force": paddle.to_tensor(l_force), + "find_force": 1.0, + "virial": paddle.to_tensor(l_virial), + "find_virial": 1.0, + "atom_ener": paddle.to_tensor(l_atom_energy), + "find_atom_ener": 1.0, + "atom_pref": paddle.to_tensor(atom_pref), + "find_atom_pref": 1.0, + "drdq": paddle.to_tensor(drdq), + "find_drdq": 1.0, + "atom_ener_coeff": paddle.to_tensor(atom_ener_coeff), + "find_atom_ener_coeff": 1.0, + } + self.label_absent = { + "energy": paddle.to_tensor(l_energy), + "force": paddle.to_tensor(l_force), + "virial": paddle.to_tensor(l_virial), + "atom_ener": paddle.to_tensor(l_atom_energy), + "atom_pref": paddle.to_tensor(atom_pref), + "drdq": paddle.to_tensor(drdq), + "atom_ener_coeff": paddle.to_tensor(atom_ener_coeff), + } + else: + self.model_pred = { + "energy": paddle.to_tensor(p_energy), + "force": paddle.to_tensor(p_force_real).reshape( + [nframes, self.nloc, 3] + ), + "force_mag": paddle.to_tensor(p_force_mag).reshape( + [nframes, self.nloc, 3] + ), + "mask_mag": paddle.to_tensor(np_batch["atype"] == 0).reshape( + [nframes, self.nloc, 1] + ), + "atom_energy": paddle.to_tensor(p_atom_energy), + } + self.label = { + "energy": paddle.to_tensor(l_energy), + "find_energy": 1.0, + "force": paddle.to_tensor(l_force_real).reshape( + [nframes, self.nloc, 3] + ), + "find_force": 1.0, + "force_mag": paddle.to_tensor(l_force_mag).reshape( + [nframes, self.nloc, 3] + ), + "find_force_mag": 1.0, + "atom_ener": paddle.to_tensor(l_atom_energy), + "find_atom_ener": 1.0, + "atom_ener_coeff": paddle.to_tensor(atom_ener_coeff), + "find_atom_ener_coeff": 1.0, + } + self.label_absent = { + "energy": paddle.to_tensor(l_energy), + "force": paddle.to_tensor(l_force_real).reshape( + [nframes, self.nloc, 3] + ), + "force_mag": paddle.to_tensor(l_force_mag).reshape( + [nframes, self.nloc, 3] + ), + "atom_ener": paddle.to_tensor(l_atom_energy), + "atom_ener_coeff": paddle.to_tensor(atom_ener_coeff), + } + self.natoms = pt_batch["natoms"] + + def tearDown(self) -> None: + tf.reset_default_graph() + return super().tearDown() + + +class TestEnerStdLoss(LossCommonTest): + def setUp(self): + self.start_lr = 1.1 + self.start_pref_e = 0.02 + self.limit_pref_e = 1.0 + self.start_pref_f = 1000.0 + self.limit_pref_f = 1.0 + self.start_pref_v = 0.02 + self.limit_pref_v = 1.0 + # tf + self.tf_loss = EnerStdLoss( + self.start_lr, + self.start_pref_e, + self.limit_pref_e, + self.start_pref_f, + self.limit_pref_f, + self.start_pref_v, + self.limit_pref_v, + ) + # pt + self.pt_loss = EnergyStdLoss( + self.start_lr, + self.start_pref_e, + self.limit_pref_e, + self.start_pref_f, + self.limit_pref_f, + self.start_pref_v, + self.limit_pref_v, + ) + self.spin = False + super().setUp() + + def test_consistency(self): + with tf.Session(graph=self.g) as sess: + tf_loss, tf_more_loss = sess.run( + self.tf_loss_sess, feed_dict=self.feed_dict + ) + + def fake_model(): + return self.model_pred + + _, pt_loss, pt_more_loss = self.pt_loss( + {}, + fake_model, + self.label, + self.nloc, + self.cur_lr, + ) + _, pt_loss_absent, pt_more_loss_absent = self.pt_loss( + {}, + fake_model, + self.label_absent, + self.nloc, + self.cur_lr, + ) + pt_loss = pt_loss.detach().cpu() + pt_loss_absent = pt_loss_absent.detach().cpu() + self.assertTrue(np.allclose(tf_loss, pt_loss.numpy())) + self.assertTrue(np.allclose(0.0, pt_loss_absent.numpy())) + for key in ["ener", "force", "virial"]: + self.assertTrue( + np.allclose( + tf_more_loss[f"l2_{key}_loss"], pt_more_loss[f"l2_{key}_loss"] + ) + ) + self.assertTrue(np.isnan(pt_more_loss_absent[f"l2_{key}_loss"].numpy())) + + +class TestEnerStdLossAePfGf(LossCommonTest): + def setUp(self): + self.start_lr = 1.1 + self.start_pref_e = 0.02 + self.limit_pref_e = 1.0 + self.start_pref_f = 1000.0 + self.limit_pref_f = 1.0 + self.start_pref_v = 0.02 + self.limit_pref_v = 1.0 + self.start_pref_ae = 0.02 + self.limit_pref_ae = 1.0 + self.start_pref_pf = 0.02 + self.limit_pref_pf = 1.0 + self.start_pref_gf = 0.02 + self.limit_pref_gf = 1.0 + self.numb_generalized_coord = 2 + # tf + self.tf_loss = EnerStdLoss( + self.start_lr, + self.start_pref_e, + self.limit_pref_e, + self.start_pref_f, + self.limit_pref_f, + self.start_pref_v, + self.limit_pref_v, + self.start_pref_ae, + self.limit_pref_ae, + self.start_pref_pf, + self.limit_pref_pf, + start_pref_gf=self.start_pref_gf, + limit_pref_gf=self.limit_pref_gf, + numb_generalized_coord=self.numb_generalized_coord, + ) + # pt + self.pt_loss = EnergyStdLoss( + self.start_lr, + self.start_pref_e, + self.limit_pref_e, + self.start_pref_f, + self.limit_pref_f, + self.start_pref_v, + self.limit_pref_v, + self.start_pref_ae, + self.limit_pref_ae, + self.start_pref_pf, + self.limit_pref_pf, + start_pref_gf=self.start_pref_gf, + limit_pref_gf=self.limit_pref_gf, + numb_generalized_coord=self.numb_generalized_coord, + ) + self.spin = False + super().setUp() + + def test_consistency(self): + with tf.Session(graph=self.g) as sess: + tf_loss, tf_more_loss = sess.run( + self.tf_loss_sess, feed_dict=self.feed_dict + ) + + def fake_model(): + return self.model_pred + + _, pt_loss, pt_more_loss = self.pt_loss( + {}, + fake_model, + self.label, + self.nloc, + self.cur_lr, + ) + _, pt_loss_absent, pt_more_loss_absent = self.pt_loss( + {}, + fake_model, + self.label_absent, + self.nloc, + self.cur_lr, + ) + pt_loss = pt_loss.detach().cpu() + pt_loss_absent = pt_loss_absent.detach().cpu() + self.assertTrue(np.allclose(tf_loss, pt_loss.numpy())) + self.assertTrue(np.allclose(0.0, pt_loss_absent.numpy())) + for key in ["ener", "force", "virial", "atom_ener", "pref_force", "gen_force"]: + self.assertTrue( + np.allclose( + tf_more_loss[f"l2_{key}_loss"], pt_more_loss[f"l2_{key}_loss"] + ) + ) + self.assertTrue(np.isnan(pt_more_loss_absent[f"l2_{key}_loss"].numpy())) + + +class TestEnerStdLossAecoeff(LossCommonTest): + def setUp(self): + self.start_lr = 1.1 + self.start_pref_e = 0.02 + self.limit_pref_e = 1.0 + self.start_pref_f = 1000.0 + self.limit_pref_f = 1.0 + self.start_pref_v = 0.02 + self.limit_pref_v = 1.0 + # tf + self.tf_loss = EnerStdLoss( + self.start_lr, + self.start_pref_e, + self.limit_pref_e, + self.start_pref_f, + self.limit_pref_f, + self.start_pref_v, + self.limit_pref_v, + enable_atom_ener_coeff=True, + ) + # pt + self.pt_loss = EnergyStdLoss( + self.start_lr, + self.start_pref_e, + self.limit_pref_e, + self.start_pref_f, + self.limit_pref_f, + self.start_pref_v, + self.limit_pref_v, + enable_atom_ener_coeff=True, + ) + self.spin = False + super().setUp() + + def test_consistency(self): + with tf.Session(graph=self.g) as sess: + tf_loss, tf_more_loss = sess.run( + self.tf_loss_sess, feed_dict=self.feed_dict + ) + + def fake_model(): + return self.model_pred + + _, pt_loss, pt_more_loss = self.pt_loss( + {}, + fake_model, + self.label, + self.nloc, + self.cur_lr, + ) + _, pt_loss_absent, pt_more_loss_absent = self.pt_loss( + {}, + fake_model, + self.label_absent, + self.nloc, + self.cur_lr, + ) + pt_loss = pt_loss.detach().cpu() + pt_loss_absent = pt_loss_absent.detach().cpu() + self.assertTrue(np.allclose(tf_loss, pt_loss.numpy())) + self.assertTrue(np.allclose(0.0, pt_loss_absent.numpy())) + for key in ["ener", "force", "virial"]: + self.assertTrue( + np.allclose( + tf_more_loss[f"l2_{key}_loss"], pt_more_loss[f"l2_{key}_loss"] + ) + ) + self.assertTrue(np.isnan(pt_more_loss_absent[f"l2_{key}_loss"].numpy())) + + +class TestEnerStdLossRelativeF(LossCommonTest): + def setUp(self): + self.start_lr = 1.1 + self.start_pref_e = 0.02 + self.limit_pref_e = 1.0 + self.start_pref_f = 1000.0 + self.limit_pref_f = 1.0 + self.start_pref_v = 0.02 + self.limit_pref_v = 1.0 + # tf + self.tf_loss = EnerStdLoss( + self.start_lr, + self.start_pref_e, + self.limit_pref_e, + self.start_pref_f, + self.limit_pref_f, + self.start_pref_v, + self.limit_pref_v, + relative_f=0.1, + ) + # pt + self.pt_loss = EnergyStdLoss( + self.start_lr, + self.start_pref_e, + self.limit_pref_e, + self.start_pref_f, + self.limit_pref_f, + self.start_pref_v, + self.limit_pref_v, + relative_f=0.1, + ) + self.spin = False + super().setUp() + + def test_consistency(self): + with tf.Session(graph=self.g) as sess: + tf_loss, tf_more_loss = sess.run( + self.tf_loss_sess, feed_dict=self.feed_dict + ) + + def fake_model(): + return self.model_pred + + _, pt_loss, pt_more_loss = self.pt_loss( + {}, + fake_model, + self.label, + self.nloc, + self.cur_lr, + ) + _, pt_loss_absent, pt_more_loss_absent = self.pt_loss( + {}, + fake_model, + self.label_absent, + self.nloc, + self.cur_lr, + ) + pt_loss = pt_loss.detach().cpu() + pt_loss_absent = pt_loss_absent.detach().cpu() + self.assertTrue(np.allclose(tf_loss, pt_loss.numpy())) + self.assertTrue(np.allclose(0.0, pt_loss_absent.numpy())) + for key in ["ener", "force", "virial"]: + self.assertTrue( + np.allclose( + tf_more_loss[f"l2_{key}_loss"], pt_more_loss[f"l2_{key}_loss"] + ) + ) + self.assertTrue(np.isnan(pt_more_loss_absent[f"l2_{key}_loss"].numpy())) + + +class TestEnerSpinLoss(LossCommonTest): + def setUp(self): + self.start_lr = 1.1 + self.start_pref_e = 0.02 + self.limit_pref_e = 1.0 + self.start_pref_fr = 1000.0 + self.limit_pref_fr = 1.0 + self.start_pref_fm = 1000.0 + self.limit_pref_fm = 1.0 + self.cur_lr = 1.2 + self.use_spin = [1, 0] + # tf + self.tf_loss = EnerSpinLoss( + self.start_lr, + self.start_pref_e, + self.limit_pref_e, + self.start_pref_fr, + self.limit_pref_fr, + self.start_pref_fm, + self.limit_pref_fm, + use_spin=self.use_spin, + ) + # pt + self.pt_loss = EnergySpinLoss( + self.start_lr, + self.start_pref_e, + self.limit_pref_e, + self.start_pref_fr, + self.limit_pref_fr, + self.start_pref_fm, + self.limit_pref_fm, + ) + self.spin = True + super().setUp() + + def test_consistency(self): + with tf.Session(graph=self.g) as sess: + tf_loss, tf_more_loss = sess.run( + self.tf_loss_sess, feed_dict=self.feed_dict + ) + + def fake_model(): + return self.model_pred + + _, pt_loss, pt_more_loss = self.pt_loss( + {}, + fake_model, + self.label, + self.nloc_tf, # use tf natoms pref + self.cur_lr, + ) + _, pt_loss_absent, pt_more_loss_absent = self.pt_loss( + {}, + fake_model, + self.label_absent, + self.nloc_tf, # use tf natoms pref + self.cur_lr, + ) + pt_loss = pt_loss.detach().cpu() + pt_loss_absent = pt_loss_absent.detach().cpu() + self.assertTrue(np.allclose(tf_loss, pt_loss.numpy())) + self.assertTrue(np.allclose(0.0, pt_loss_absent.numpy())) + for key in ["ener", "force_r", "force_m"]: + self.assertTrue( + np.allclose( + tf_more_loss[f"l2_{key}_loss"], pt_more_loss[f"l2_{key}_loss"] + ) + ) + self.assertTrue(np.isnan(pt_more_loss_absent[f"l2_{key}_loss"].numpy())) + + +class TestEnerSpinLossAe(LossCommonTest): + def setUp(self): + self.start_lr = 1.1 + self.start_pref_e = 0.02 + self.limit_pref_e = 1.0 + self.start_pref_fr = 1000.0 + self.limit_pref_fr = 1.0 + self.start_pref_fm = 1000.0 + self.limit_pref_fm = 1.0 + self.start_pref_ae = 0.02 + self.limit_pref_ae = 1.0 + self.cur_lr = 1.2 + self.use_spin = [1, 0] + # tf + self.tf_loss = EnerSpinLoss( + self.start_lr, + self.start_pref_e, + self.limit_pref_e, + self.start_pref_fr, + self.limit_pref_fr, + self.start_pref_fm, + self.limit_pref_fm, + start_pref_ae=self.start_pref_ae, + limit_pref_ae=self.limit_pref_ae, + use_spin=self.use_spin, + ) + # pt + self.pt_loss = EnergySpinLoss( + self.start_lr, + self.start_pref_e, + self.limit_pref_e, + self.start_pref_fr, + self.limit_pref_fr, + self.start_pref_fm, + self.limit_pref_fm, + start_pref_ae=self.start_pref_ae, + limit_pref_ae=self.limit_pref_ae, + ) + self.spin = True + super().setUp() + + def test_consistency(self): + with tf.Session(graph=self.g) as sess: + tf_loss, tf_more_loss = sess.run( + self.tf_loss_sess, feed_dict=self.feed_dict + ) + + def fake_model(): + return self.model_pred + + _, pt_loss, pt_more_loss = self.pt_loss( + {}, + fake_model, + self.label, + self.nloc_tf, # use tf natoms pref + self.cur_lr, + ) + _, pt_loss_absent, pt_more_loss_absent = self.pt_loss( + {}, + fake_model, + self.label_absent, + self.nloc_tf, # use tf natoms pref + self.cur_lr, + ) + pt_loss = pt_loss.detach().cpu() + pt_loss_absent = pt_loss_absent.detach().cpu() + self.assertTrue(np.allclose(tf_loss, pt_loss.numpy())) + self.assertTrue(np.allclose(0.0, pt_loss_absent.numpy())) + for key in ["ener", "force_r", "force_m", "atom_ener"]: + self.assertTrue( + np.allclose( + tf_more_loss[f"l2_{key}_loss"], pt_more_loss[f"l2_{key}_loss"] + ) + ) + self.assertTrue(np.isnan(pt_more_loss_absent[f"l2_{key}_loss"].numpy())) + + +class TestEnerSpinLossAecoeff(LossCommonTest): + def setUp(self): + self.start_lr = 1.1 + self.start_pref_e = 0.02 + self.limit_pref_e = 1.0 + self.start_pref_fr = 1000.0 + self.limit_pref_fr = 1.0 + self.start_pref_fm = 1000.0 + self.limit_pref_fm = 1.0 + self.cur_lr = 1.2 + self.use_spin = [1, 0] + # tf + self.tf_loss = EnerSpinLoss( + self.start_lr, + self.start_pref_e, + self.limit_pref_e, + self.start_pref_fr, + self.limit_pref_fr, + self.start_pref_fm, + self.limit_pref_fm, + use_spin=self.use_spin, + enable_atom_ener_coeff=True, + ) + # pt + self.pt_loss = EnergySpinLoss( + self.start_lr, + self.start_pref_e, + self.limit_pref_e, + self.start_pref_fr, + self.limit_pref_fr, + self.start_pref_fm, + self.limit_pref_fm, + enable_atom_ener_coeff=True, + ) + self.spin = True + super().setUp() + + def test_consistency(self): + with tf.Session(graph=self.g) as sess: + tf_loss, tf_more_loss = sess.run( + self.tf_loss_sess, feed_dict=self.feed_dict + ) + + def fake_model(): + return self.model_pred + + _, pt_loss, pt_more_loss = self.pt_loss( + {}, + fake_model, + self.label, + self.nloc_tf, # use tf natoms pref + self.cur_lr, + ) + _, pt_loss_absent, pt_more_loss_absent = self.pt_loss( + {}, + fake_model, + self.label_absent, + self.nloc_tf, # use tf natoms pref + self.cur_lr, + ) + pt_loss = pt_loss.detach().cpu() + pt_loss_absent = pt_loss_absent.detach().cpu() + self.assertTrue(np.allclose(tf_loss, pt_loss.numpy())) + self.assertTrue(np.allclose(0.0, pt_loss_absent.numpy())) + for key in ["ener", "force_r", "force_m"]: + self.assertTrue( + np.allclose( + tf_more_loss[f"l2_{key}_loss"], pt_more_loss[f"l2_{key}_loss"] + ) + ) + self.assertTrue(np.isnan(pt_more_loss_absent[f"l2_{key}_loss"].numpy())) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/test_lr.py b/source/tests/pd/test_lr.py new file mode 100644 index 0000000000..f5ce911b04 --- /dev/null +++ b/source/tests/pd/test_lr.py @@ -0,0 +1,106 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import unittest + +import numpy as np +import tensorflow.compat.v1 as tf + +tf.disable_eager_execution() + +from deepmd.pd.utils.learning_rate import ( + LearningRateExp, +) +from deepmd.tf.utils import ( + learning_rate, +) + + +class TestLearningRate(unittest.TestCase): + def setUp(self): + self.start_lr = 0.001 + self.stop_lr = 3.51e-8 + self.decay_steps = np.arange(400, 601, 100) + self.stop_steps = np.arange(500, 1600, 500) + + def test_consistency(self): + for decay_step in self.decay_steps: + for stop_step in self.stop_steps: + self.decay_step = decay_step + self.stop_step = stop_step + self.judge_it() + self.decay_rate_pt() + + def judge_it(self): + base_lr = learning_rate.LearningRateExp( + self.start_lr, self.stop_lr, self.decay_step + ) + g = tf.Graph() + with g.as_default(): + global_step = tf.placeholder(shape=[], dtype=tf.int32) + t_lr = base_lr.build(global_step, self.stop_step) + + my_lr = LearningRateExp( + self.start_lr, self.stop_lr, self.decay_step, self.stop_step + ) + with tf.Session(graph=g) as sess: + base_vals = [ + sess.run(t_lr, feed_dict={global_step: step_id}) + for step_id in range(self.stop_step) + if step_id % self.decay_step != 0 + ] + my_vals = [ + my_lr.value(step_id) + for step_id in range(self.stop_step) + if step_id % self.decay_step != 0 + ] + self.assertTrue(np.allclose(base_vals, my_vals)) + tf.reset_default_graph() + + def decay_rate_pt(self): + my_lr = LearningRateExp( + self.start_lr, self.stop_lr, self.decay_step, self.stop_step + ) + + default_ds = 100 if self.stop_step // 10 > 100 else self.stop_step // 100 + 1 + if self.decay_step >= self.stop_step: + self.decay_step = default_ds + decay_rate = np.exp( + np.log(self.stop_lr / self.start_lr) / (self.stop_step / self.decay_step) + ) + my_lr_decay = LearningRateExp( + self.start_lr, + 1e-10, + self.decay_step, + self.stop_step, + decay_rate=decay_rate, + ) + min_lr = 1e-5 + my_lr_decay_trunc = LearningRateExp( + self.start_lr, + min_lr, + self.decay_step, + self.stop_step, + decay_rate=decay_rate, + ) + my_vals = [ + my_lr.value(step_id) + for step_id in range(self.stop_step) + if step_id % self.decay_step != 0 + ] + my_vals_decay = [ + my_lr_decay.value(step_id) + for step_id in range(self.stop_step) + if step_id % self.decay_step != 0 + ] + my_vals_decay_trunc = [ + my_lr_decay_trunc.value(step_id) + for step_id in range(self.stop_step) + if step_id % self.decay_step != 0 + ] + self.assertTrue(np.allclose(my_vals_decay, my_vals)) + self.assertTrue( + np.allclose(my_vals_decay_trunc, np.clip(my_vals, a_min=min_lr, a_max=None)) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/test_multitask.py b/source/tests/pd/test_multitask.py new file mode 100644 index 0000000000..35f8969438 --- /dev/null +++ b/source/tests/pd/test_multitask.py @@ -0,0 +1,310 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import json +import os +import shutil +import unittest +from copy import ( + deepcopy, +) +from pathlib import ( + Path, +) + +import paddle + +from deepmd.pd.entrypoints.main import ( + get_trainer, +) +from deepmd.pd.utils.finetune import ( + get_finetune_rules, +) +from deepmd.pd.utils.multi_task import ( + preprocess_shared_params, +) +from deepmd.utils.argcheck import ( + normalize, +) +from deepmd.utils.compat import ( + update_deepmd_input, +) + +from .model.test_permutation import ( + model_dpa1, + model_dpa2, + model_se_e2_a, +) + + +def setUpModule(): + global multitask_template + multitask_template_json = str(Path(__file__).parent / "water/multitask.json") + with open(multitask_template_json) as f: + multitask_template = json.load(f) + + +class MultiTaskTrainTest: + def test_multitask_train(self): + # test multitask training + self.config = update_deepmd_input(self.config, warning=True) + self.config = normalize(self.config, multi_task=True) + trainer = get_trainer(deepcopy(self.config), shared_links=self.shared_links) + trainer.run() + # check model keys + self.assertEqual(len(trainer.wrapper.model), 2) + self.assertIn("model_1", trainer.wrapper.model) + self.assertIn("model_2", trainer.wrapper.model) + + # check shared parameters + multi_state_dict = trainer.wrapper.model.state_dict() + for state_key in multi_state_dict: + if "model_1" in state_key: + self.assertIn(state_key.replace("model_1", "model_2"), multi_state_dict) + if "model_2" in state_key: + self.assertIn(state_key.replace("model_2", "model_1"), multi_state_dict) + if "model_1.descriptor" in state_key: + assert paddle.allclose( + multi_state_dict[state_key], + multi_state_dict[state_key.replace("model_1", "model_2")], + ) + + # test multitask fine-tuning + # add model_3 + self.origin_config["model"]["model_dict"]["model_3"] = deepcopy( + self.origin_config["model"]["model_dict"]["model_2"] + ) + self.origin_config["loss_dict"]["model_3"] = deepcopy( + self.origin_config["loss_dict"]["model_2"] + ) + self.origin_config["training"]["model_prob"]["model_3"] = deepcopy( + self.origin_config["training"]["model_prob"]["model_2"] + ) + self.origin_config["training"]["data_dict"]["model_3"] = deepcopy( + self.origin_config["training"]["data_dict"]["model_2"] + ) + self.origin_config["training"]["data_dict"]["model_3"]["stat_file"] = ( + self.origin_config[ + "training" + ]["data_dict"]["model_3"]["stat_file"].replace("model_2", "model_3") + ) + + # add model_4 + self.origin_config["model"]["model_dict"]["model_4"] = deepcopy( + self.origin_config["model"]["model_dict"]["model_2"] + ) + self.origin_config["loss_dict"]["model_4"] = deepcopy( + self.origin_config["loss_dict"]["model_2"] + ) + self.origin_config["training"]["model_prob"]["model_4"] = deepcopy( + self.origin_config["training"]["model_prob"]["model_2"] + ) + self.origin_config["training"]["data_dict"]["model_4"] = deepcopy( + self.origin_config["training"]["data_dict"]["model_2"] + ) + self.origin_config["training"]["data_dict"]["model_4"]["stat_file"] = ( + self.origin_config[ + "training" + ]["data_dict"]["model_4"]["stat_file"].replace("model_2", "model_4") + ) + + # set finetune rules + # model_1 resuming from model_1 + # pass + + # model_2 fine-tuning from model_2 + self.origin_config["model"]["model_dict"]["model_2"]["finetune_head"] = ( + "model_2" + ) + + # new model_3 fine-tuning from model_2 + self.origin_config["model"]["model_dict"]["model_3"]["finetune_head"] = ( + "model_2" + ) + + # new model_4 fine-tuning with randomly initialized fitting net + # pass + + self.origin_config["model"], shared_links_finetune = preprocess_shared_params( + self.origin_config["model"] + ) + + finetune_model = self.config["training"].get("save_ckpt", "model.ckpt") + ".pd" + self.origin_config["model"], finetune_links = get_finetune_rules( + finetune_model, + self.origin_config["model"], + ) + self.origin_config = update_deepmd_input(self.origin_config, warning=True) + self.origin_config = normalize(self.origin_config, multi_task=True) + trainer_finetune = get_trainer( + deepcopy(self.origin_config), + finetune_model=finetune_model, + shared_links=shared_links_finetune, + finetune_links=finetune_links, + ) + + # check parameters + multi_state_dict_finetuned = trainer_finetune.wrapper.model.state_dict() + for state_key in multi_state_dict_finetuned: + if "model_1" in state_key: + assert paddle.allclose( + multi_state_dict[state_key].astype("float32"), + multi_state_dict_finetuned[state_key].astype("float32"), + ).item() + elif "model_2" in state_key and "out_bias" not in state_key: + assert paddle.allclose( + multi_state_dict[state_key].astype("float32"), + multi_state_dict_finetuned[state_key].astype("float32"), + ).item() + elif "model_3" in state_key and "out_bias" not in state_key: + assert paddle.allclose( + multi_state_dict[state_key.replace("model_3", "model_2")].astype( + "float32" + ), + multi_state_dict_finetuned[state_key].astype("float32"), + ).item() + elif ( + "model_4" in state_key + and "fitting_net" not in state_key + and "out_bias" not in state_key + ): + assert paddle.allclose( + multi_state_dict[state_key.replace("model_4", "model_2")].astype( + "float32" + ), + multi_state_dict_finetuned[state_key].astype("float32"), + ).item() + + # check running + trainer_finetune.run() + self.tearDown() + + def tearDown(self): + for f in os.listdir("."): + if f.startswith("model") and f.endswith(".pd"): + os.remove(f) + if f in ["lcurve.out"]: + os.remove(f) + if f in [self.stat_files]: + shutil.rmtree(f) + + +@unittest.skip("Paddle do not support MultiTaskSeA.") +class TestMultiTaskSeA(unittest.TestCase, MultiTaskTrainTest): + def setUp(self): + multitask_se_e2_a = deepcopy(multitask_template) + multitask_se_e2_a["model"]["shared_dict"]["my_descriptor"] = model_se_e2_a[ + "descriptor" + ] + data_file = [str(Path(__file__).parent / "water/data/data_0")] + self.stat_files = "se_e2_a" + os.makedirs(self.stat_files, exist_ok=True) + self.config = multitask_se_e2_a + self.config["training"]["data_dict"]["model_1"]["training_data"]["systems"] = ( + data_file + ) + self.config["training"]["data_dict"]["model_1"]["validation_data"][ + "systems" + ] = data_file + self.config["training"]["data_dict"]["model_1"]["stat_file"] = ( + f"{self.stat_files}/model_1" + ) + self.config["training"]["data_dict"]["model_2"]["training_data"]["systems"] = ( + data_file + ) + self.config["training"]["data_dict"]["model_2"]["validation_data"][ + "systems" + ] = data_file + self.config["training"]["data_dict"]["model_2"]["stat_file"] = ( + f"{self.stat_files}/model_2" + ) + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + self.origin_config = deepcopy(self.config) + self.config["model"], self.shared_links = preprocess_shared_params( + self.config["model"] + ) + + def tearDown(self) -> None: + MultiTaskTrainTest.tearDown(self) + + +@unittest.skip("Paddle do not support MultiTaskDPA1.") +class TestMultiTaskDPA1(unittest.TestCase, MultiTaskTrainTest): + def setUp(self): + multitask_DPA1 = deepcopy(multitask_template) + multitask_DPA1["model"]["shared_dict"]["my_descriptor"] = model_dpa1[ + "descriptor" + ] + data_file = [str(Path(__file__).parent / "water/data/data_0")] + self.stat_files = "DPA1" + os.makedirs(self.stat_files, exist_ok=True) + self.config = multitask_DPA1 + self.config["training"]["data_dict"]["model_1"]["training_data"]["systems"] = ( + data_file + ) + self.config["training"]["data_dict"]["model_1"]["validation_data"][ + "systems" + ] = data_file + self.config["training"]["data_dict"]["model_1"]["stat_file"] = ( + f"{self.stat_files}/model_1" + ) + self.config["training"]["data_dict"]["model_2"]["training_data"]["systems"] = ( + data_file + ) + self.config["training"]["data_dict"]["model_2"]["validation_data"][ + "systems" + ] = data_file + self.config["training"]["data_dict"]["model_2"]["stat_file"] = ( + f"{self.stat_files}/model_2" + ) + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + self.origin_config = deepcopy(self.config) + self.config["model"], self.shared_links = preprocess_shared_params( + self.config["model"] + ) + + def tearDown(self) -> None: + MultiTaskTrainTest.tearDown(self) + + +class TestMultiTaskDPA2(unittest.TestCase, MultiTaskTrainTest): + def setUp(self): + multitask_DPA2 = deepcopy(multitask_template) + multitask_DPA2["model"]["shared_dict"]["my_descriptor"] = model_dpa2[ + "descriptor" + ] + data_file = [str(Path(__file__).parent / "water/data/data_0")] + self.stat_files = "DPA2" + os.makedirs(self.stat_files, exist_ok=True) + self.config = multitask_DPA2 + self.config["training"]["data_dict"]["model_1"]["training_data"]["systems"] = ( + data_file + ) + self.config["training"]["data_dict"]["model_1"]["validation_data"][ + "systems" + ] = data_file + self.config["training"]["data_dict"]["model_1"]["stat_file"] = ( + f"{self.stat_files}/model_1" + ) + self.config["training"]["data_dict"]["model_2"]["training_data"]["systems"] = ( + data_file + ) + self.config["training"]["data_dict"]["model_2"]["validation_data"][ + "systems" + ] = data_file + self.config["training"]["data_dict"]["model_2"]["stat_file"] = ( + f"{self.stat_files}/model_2" + ) + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + self.origin_config = deepcopy(self.config) + self.config["model"], self.shared_links = preprocess_shared_params( + self.config["model"] + ) + + def tearDown(self) -> None: + MultiTaskTrainTest.tearDown(self) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/test_neighbor_stat.py b/source/tests/pd/test_neighbor_stat.py new file mode 100644 index 0000000000..613150b7fc --- /dev/null +++ b/source/tests/pd/test_neighbor_stat.py @@ -0,0 +1,69 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import shutil +import unittest + +import dpdata +import numpy as np + +from deepmd.entrypoints.neighbor_stat import ( + neighbor_stat, +) + +from ..seed import ( + GLOBAL_SEED, +) + + +def gen_sys(nframes): + rng = np.random.default_rng(GLOBAL_SEED) + natoms = 1000 + data = {} + X, Y, Z = np.mgrid[0:2:3j, 0:2:3j, 0:2:3j] + positions = np.vstack([X.ravel(), Y.ravel(), Z.ravel()]).T # + 0.1 + data["coords"] = np.repeat(positions[np.newaxis, :, :], nframes, axis=0) + data["forces"] = rng.random([nframes, natoms, 3]) + data["cells"] = np.array([3.0, 0.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, 3.0]).reshape( + 1, 3, 3 + ) + data["energies"] = rng.random([nframes, 1]) + data["atom_names"] = ["TYPE"] + data["atom_numbs"] = [27] + data["atom_types"] = np.repeat(0, 27) + return data + + +class TestNeighborStat(unittest.TestCase): + def setUp(self): + data0 = gen_sys(1) + sys0 = dpdata.LabeledSystem() + sys0.data = data0 + sys0.to_deepmd_npy("system_0", set_size=1) + + def tearDown(self): + shutil.rmtree("system_0") + + def test_neighbor_stat(self): + for rcut in (0.0, 1.0, 2.0, 4.0): + for mixed_type in (True, False): + with self.subTest(rcut=rcut, mixed_type=mixed_type): + rcut += 1e-3 # prevent numerical errors + min_nbor_dist, max_nbor_size = neighbor_stat( + system="system_0", + rcut=rcut, + type_map=["TYPE", "NO_THIS_TYPE"], + mixed_type=mixed_type, + backend="paddle", + ) + upper = np.ceil(rcut) + 1 + X, Y, Z = np.mgrid[-upper:upper, -upper:upper, -upper:upper] + positions = np.vstack([X.ravel(), Y.ravel(), Z.ravel()]).T + # distance to (0,0,0) + distance = np.linalg.norm(positions, axis=1) + expected_neighbors = np.count_nonzero( + np.logical_and(distance > 0, distance <= rcut) + ) + self.assertAlmostEqual(min_nbor_dist, 1.0, 6) + ret = [expected_neighbors] + if not mixed_type: + ret.append(0) + np.testing.assert_array_equal(max_nbor_size, ret) diff --git a/source/tests/pd/test_sampler.py b/source/tests/pd/test_sampler.py new file mode 100644 index 0000000000..2af5a9c05c --- /dev/null +++ b/source/tests/pd/test_sampler.py @@ -0,0 +1,114 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import json +import os +import unittest +from pathlib import ( + Path, +) + +import numpy as np +import paddle +from paddle.io import ( + BatchSampler, + DataLoader, +) + +from deepmd.pd.utils.dataloader import ( + DpLoaderSet, + get_weighted_sampler, +) +from deepmd.tf.common import ( + expand_sys_str, +) +from deepmd.tf.utils import random as tf_random +from deepmd.tf.utils.data_system import ( + DeepmdDataSystem, +) + +CUR_DIR = os.path.dirname(__file__) + + +class TestSampler(unittest.TestCase): + def setUp(self): + with open(str(Path(__file__).parent / "water/se_e2_a.json")) as fin: + content = fin.read() + config = json.loads(content) + data_file = [str(Path(__file__).parent / "water/data/data_0")] + config["training"]["training_data"]["systems"] = data_file + config["training"]["validation_data"]["systems"] = data_file + model_config = config["model"] + self.rcut = model_config["descriptor"]["rcut"] + self.rcut_smth = model_config["descriptor"]["rcut_smth"] + self.sel = model_config["descriptor"]["sel"] + self.batch_size = config["training"]["training_data"]["batch_size"] + self.systems = config["training"]["validation_data"]["systems"] + if isinstance(self.systems, str): + self.systems = expand_sys_str(self.systems) + self.my_dataset = DpLoaderSet( + self.systems, + self.batch_size, + model_config["type_map"], + seed=10, + shuffle=False, + ) + + tf_random.seed(10) + self.dp_dataset = DeepmdDataSystem(self.systems, self.batch_size, 1, self.rcut) + + def test_sampler_debug_info(self): + dataloader = DataLoader( + self.my_dataset, + batch_sampler=BatchSampler( + get_weighted_sampler(self.my_dataset, prob_style="prob_sys_size"), + drop_last=False, + ), + num_workers=0, # setting to 0 diverges the behavior of its iterator; should be >=1 + # pin_memory=True, + ) + device = paddle.get_device() + paddle.set_device("cpu") + batch_data = next(iter(dataloader)) + paddle.set_device(device) + sid = batch_data["sid"] + fid = batch_data["fid"][0] + coord = batch_data["coord"].squeeze(0) + frame = self.my_dataset.systems[sid].__getitem__(fid) + self.assertTrue(np.allclose(coord, frame["coord"])) + + def test_auto_prob_uniform(self): + auto_prob_style = "prob_uniform" + sampler = get_weighted_sampler(self.my_dataset, prob_style=auto_prob_style) + my_probs = np.array(sampler.weights) + self.dp_dataset.set_sys_probs(auto_prob_style=auto_prob_style) + dp_probs = np.array(self.dp_dataset.sys_probs) + self.assertTrue(np.allclose(my_probs, dp_probs)) + + def test_auto_prob_sys_size(self): + auto_prob_style = "prob_sys_size" + sampler = get_weighted_sampler(self.my_dataset, prob_style=auto_prob_style) + my_probs = np.array(sampler.weights) + self.dp_dataset.set_sys_probs(auto_prob_style=auto_prob_style) + dp_probs = np.array(self.dp_dataset.sys_probs) + self.assertTrue(np.allclose(my_probs, dp_probs)) + + def test_auto_prob_sys_size_ext(self): + auto_prob_style = "prob_sys_size;0:1:0.2;1:3:0.8" + sampler = get_weighted_sampler(self.my_dataset, prob_style=auto_prob_style) + my_probs = np.array(sampler.weights) + self.dp_dataset.set_sys_probs(auto_prob_style=auto_prob_style) + dp_probs = np.array(self.dp_dataset.sys_probs) + self.assertTrue(np.allclose(my_probs, dp_probs)) + + def test_sys_probs(self): + sys_probs = [0.1, 0.4, 0.5] + sampler = get_weighted_sampler( + self.my_dataset, prob_style=sys_probs, sys_prob=True + ) + my_probs = np.array(sampler.weights) + self.dp_dataset.set_sys_probs(sys_probs=sys_probs) + dp_probs = np.array(self.dp_dataset.sys_probs) + self.assertTrue(np.allclose(my_probs, dp_probs)) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/test_tabulate_fusion_se_a.py b/source/tests/pd/test_tabulate_fusion_se_a.py new file mode 100644 index 0000000000..eeddb48d30 --- /dev/null +++ b/source/tests/pd/test_tabulate_fusion_se_a.py @@ -0,0 +1,1511 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import unittest + +import paddle + +from deepmd.pd.cxx_op import ( + ENABLE_CUSTOMIZED_OP, +) +from deepmd.pd.utils import ( + env, +) + +from ..consistent.common import ( + parameterized, +) + + +@parameterized((paddle.float64, paddle.float32)) +@unittest.skipIf(not ENABLE_CUSTOMIZED_OP, "PyTorch customized OPs are not built") +class TestTabulateFusionSeAOp(unittest.TestCase): + def setUp(self): + (dtype,) = self.param + if dtype == paddle.float64: + self.prec = 1e-10 + elif dtype == paddle.float32: + self.prec = 1e-5 + self.table_tensor = ( + paddle.to_tensor( + [ + 6.348551343037398542e-01, + 4.209465843706336474e-04, + 6.390862740714405368e-03, + -1.544448595628262176e-04, + -1.891095227974180087e-04, + 2.695025951562175852e-05, + -1.317549846042939343e00, + -5.624478206903206490e-02, + 1.274284553146523905e-02, + -6.836227424141475689e-04, + -1.438066096020836407e-04, + -1.854932873974712940e-06, + -9.996964112615246423e-01, + 6.928234423723647617e-02, + -4.974719973810486084e-03, + -2.019584729176823030e-04, + 1.077254539742680247e-04, + -8.024209768588029797e-06, + 3.552689563657350780e-01, + -3.578299775339799371e-02, + -1.319946251007718743e-03, + 1.016701374495701440e-03, + -1.057336720791906388e-04, + 5.182678943855506567e-06, + 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6.311073191969795411e-06, + -2.679971525218879380e-01, + 9.690367843145115956e-02, + -2.522569242956208650e-03, + -6.977319783847560700e-04, + 2.827424678587480721e-05, + 2.739673941330651616e-06, + -1.479736062091468574e00, + -6.188960432526132566e-02, + 4.796152485364500034e-03, + 7.856828747830194362e-05, + -1.395147193446202365e-05, + -4.087221013031299888e-06, + 6.351645247550001816e-01, + 2.827723875485507743e-03, + 6.263598112024793517e-03, + -2.890409134869928735e-04, + -1.648390823803598971e-04, + 2.215887759642637032e-05, + -1.327781208952985015e00, + -5.148033786352124164e-02, + 1.232360084570068709e-02, + -7.844171563535663055e-04, + -1.210428935521009746e-04, + 3.344327592646507844e-05, + -9.867136014577331249e-01, + 6.737294444867666932e-02, + -5.067458424877044516e-03, + -1.251812701937470213e-04, + 9.419473244264059593e-05, + -1.679002076268449654e-05, + 3.484293720675762929e-01, + -3.617738162759492893e-02, + -7.634640860539731316e-04, + 9.356082122653546981e-04, + -1.075431084112703954e-04, + -3.044614041061100766e-06, + 1.235667412115300623e00, + 4.231657802179918798e-02, + 3.315592595281378029e-03, + -5.185116053649769336e-04, + -1.041674655671950871e-04, + 1.242766263135090892e-05, + -1.502663082036415076e00, + -1.548659541050484978e-01, + 1.652801643260504508e-02, + 4.998556989557471122e-05, + -2.037688261998792680e-04, + 2.657243869390409541e-05, + -2.670283686919466826e-01, + 9.685301784023310490e-02, + -2.543484210258855835e-03, + -6.965966582328896994e-04, + 2.850491087748043708e-05, + 1.232179636112698650e-05, + -1.480354478441044286e00, + -6.179365776107784841e-02, + 4.798501122259496952e-03, + 7.800586916120723585e-05, + -1.413851691566035862e-05, + -5.727587674967719880e-06, + 6.351934280628791507e-01, + 2.952908467203564646e-03, + 6.254828202758994093e-03, + -2.956111985445306826e-04, + -1.636502852942454153e-04, + 2.616921494951480123e-05, + -1.328294780757159899e00, + -5.123410165425365537e-02, + 1.229999603970671068e-02, + -7.892274520450543677e-04, + -1.195721301312790567e-04, + 2.454197033093738297e-05, + -9.860403788833298488e-01, + 6.727155810173718331e-02, + -5.071157514069617352e-03, + -1.214296539729165295e-04, + 9.340570341953608358e-05, + -1.444050153586573228e-05, + 3.480675228394242149e-01, + -3.619237065717702262e-02, + -7.354603960058733389e-04, + 9.313051737393654526e-04, + -1.076930273455606579e-04, + -7.696053039474192446e-07, + 1.236090908935226107e00, + 4.238273390417521269e-02, + 3.299974870987111650e-03, + -5.226642260988254756e-04, + -1.032474625011560351e-04, + 2.396475265799989632e-05, + -1.504210088727871764e00, + -1.545353795944727493e-01, + 1.652939400402650763e-02, + 4.186078937618800693e-05, + -2.027012231708198600e-04, + 1.761148452766873776e-05, + -2.660600935582757565e-01, + 9.680193929166537592e-02, + -2.564364883962782712e-03, + -6.954454205710857090e-04, + 2.907017700829073683e-05, + 9.120785771591908463e-06, + -1.480971935090678926e00, + -6.169766439371183325e-02, + 4.800832758035045861e-03, + 7.743502257440657043e-05, + -1.440171540732098418e-05, + -4.489324897938611976e-06, + 6.355509554770921721e-01, + 4.194364255265300989e-03, + 6.156587518227093006e-03, + -3.584539136959086518e-04, + -1.505562336471176987e-04, + 2.631189526673375584e-05, + -1.333295991901433553e00, + -4.879824528740911438e-02, + 1.205629889598585497e-02, + -8.346035033896359156e-04, + -1.072962342948566929e-04, + 2.412331753624817981e-05, + -9.793640468817854661e-01, + 6.625405011186732973e-02, + -5.102126473064734317e-03, + -8.551069374443776396e-05, + 8.618032279329005427e-05, + -1.422030758858379208e-05, + 3.444418516979214084e-01, + -3.631195473807800889e-02, + -4.625381215785304145e-04, + 8.881537622047225473e-04, + -1.080757789189670570e-04, + 5.820590714360855199e-08, + 1.240361649325028681e00, + 4.302664794411619614e-02, + 3.137220402938139478e-03, + -5.615677039256951981e-04, + -9.125763978623760322e-05, + 2.367398552885374808e-05, + -1.519498310980496925e00, + -1.512290469691385253e-01, + 1.652996628226939199e-02, + -3.745688059096337011e-05, + -1.938906911473592626e-04, + 1.811217640451412989e-05, + -2.564062357251438717e-01, + 9.626832379335603651e-02, + -2.771163091665611831e-03, + -6.829069315554202020e-04, + 3.363238372709415958e-05, + 8.623099725596635004e-06, + -1.487093617252511990e00, + -6.073523464295225993e-02, + 4.823154268625621383e-03, + 7.122599345182346051e-05, + -1.664931178025436733e-05, + -4.312450972708557703e-06, + ], + dtype=dtype, + ) + .to(device=env.DEVICE0) + .reshape([8, 132]) + ) + # always on cpu + self.table_info_tensor = paddle.to_tensor( + [0, 0.2, 0.4, 0.01, 0.1, -1], dtype=dtype + ).to(device="cpu") + self.em_x_tensor = ( + paddle.to_tensor( + [ + 0.0343909, + 0.11357423, + 0.0858676, + 0.19337772, + 0.1935728, + 0.0477744, + 0.05845198, + 0.19080509, + 0.16111261, + 0.07179262, + 0.10078013, + 0.04640909, + 0.10433399, + 0.15650861, + 0.17527857, + 0.04249097, + ], + dtype=dtype, + ) + .to(device=env.DEVICE) + .reshape([4, 4]) + ) + self.em_tensor = ( + paddle.to_tensor( + [ + 0.0343909, + 0.08394249, + 0.06791791, + 0.00903334, + 0.11357423, + 0.10597251, + 0.05738069, + 0.10071109, + 0.0858676, + 0.17410445, + 0.05390256, + 0.09495758, + 0.19337772, + 0.02045487, + 0.04095526, + 0.18431305, + 0.1935728, + 0.03930614, + 0.0304133, + 0.15261676, + 0.0477744, + 0.06838737, + 0.12824902, + 0.14125861, + 0.05845198, + 0.12731053, + 0.0315968, + 0.14927774, + 0.19080509, + 0.19206871, + 0.14361383, + 0.04083437, + 0.16111261, + 0.19944826, + 0.16563484, + 0.00797179, + 0.07179262, + 0.16993159, + 0.01834742, + 0.08405, + 0.10078013, + 0.0773945, + 0.09541813, + 0.0042979, + 0.04640909, + 0.07968697, + 0.18046262, + 0.11724063, + 0.10433399, + 0.16910201, + 0.10653732, + 0.07434702, + 0.15650861, + 0.0350976, + 0.04088021, + 0.15753491, + 0.17527857, + 0.03178642, + 0.01599623, + 0.08095053, + 0.04249097, + 0.17082205, + 0.18275348, + 0.02921504, + ], + dtype=dtype, + ) + .to(device=env.DEVICE) + .reshape([4, 4, 4]) + ) + self.table_info_tensor.stop_gradient = False + self.table_tensor.stop_gradient = False + self.em_x_tensor.stop_gradient = False + self.em_tensor.stop_gradient = False + self.last_layer_size = 8 + self.nloc = 4 + self.nnei = 4 + # forward test + self.expected_descriptor_tensor = ( + paddle.to_tensor( + [ + 0.2713010991854039, + -0.5660628160978955, + -0.4230503961233804, + 0.14965802865129818, + 0.5269537220240132, + -0.6384566368739288, + -0.11624505007495309, + -0.6310320354395895, + 0.24412212410338252, + -0.5084222360348541, + -0.3820314749241062, + 0.1353770997654753, + 0.4734379786819688, + -0.5715862139964242, + -0.10647548073978085, + -0.5667128671678037, + 0.13979393629121145, + -0.2912360135099118, + -0.21862062309471242, + 0.0774457356172342, + 0.2711843179288344, + -0.3276148559472541, + -0.06077287203673235, + -0.3246349003705672, + 0.24704819325244173, + -0.51555848202734, + -0.3850932628970095, + 0.1362072061097241, + 0.4799221442877444, + -0.5816835832792213, + -0.10566161861294662, + -0.5747363397856997, + 0.3115883382215896, + -0.6506883878140057, + -0.4850147890277097, + 0.1714325837291713, + 0.6056532652377606, + -0.7350680305117758, + -0.13233106208913875, + -0.7254159568199746, + 0.2712100312735679, + -0.5656668988983458, + -0.423180345296733, + 0.14974857024944524, + 0.5266242202317545, + -0.6376329980619129, + -0.11658482011618507, + -0.6305841995062695, + 0.21202134995532176, + -0.44212972830581004, + -0.33094666630320135, + 0.1171304680138136, + 0.4116282973058779, + -0.49821290159157117, + -0.0913087049986295, + -0.49286515716995555, + 0.3073352620583199, + -0.6405463996728692, + -0.48022213268980996, + 0.17004692071229927, + 0.5964092995598747, + -0.7211172753937779, + -0.13304346833797515, + -0.7140277774981623, + 0.24135931626467969, + -0.5031638848589246, + -0.3769990721972972, + 0.13347531041756877, + 0.4684615467688112, + -0.5666465173184999, + -0.10429212140716522, + -0.5608812343251992, + 0.33429479916822996, + -0.6966906185519567, + -0.5224684150875668, + 0.1850292768610088, + 0.6486770724987723, + -0.7841702188445613, + -0.14487244407008348, + -0.7765953436864135, + 0.2920023645166421, + -0.6084066648243805, + -0.45656556423057065, + 0.16172245683335545, + 0.5665031788673434, + -0.68453036136064, + -0.12681615685082662, + -0.6781799312012713, + 0.1355913887851541, + -0.28210651239855183, + -0.2125861375354654, + 0.07539812279917796, + 0.2627430315734105, + -0.3166083947691034, + -0.05968776068495512, + -0.314435441368215, + 0.3039443167162163, + -0.6342831095043088, + -0.4738141723760223, + 0.16759395880105882, + 0.5904373874978512, + -0.7155951337421371, + -0.1300380195196118, + -0.7070835391843331, + 0.25830471641609376, + -0.5379623982551216, + -0.40421268847222747, + 0.14323456063074608, + 0.5009475712655791, + -0.6048126036159627, + -0.11264425943960855, + -0.5996424258133577, + 0.21979686345452815, + -0.45763436166403704, + -0.34413141738362185, + 0.12197403142496444, + 0.426168976528326, + -0.5142565732600977, + -0.0960985398529971, + -0.5100982631949256, + 0.21721660807904586, + -0.45326448307918804, + -0.3386650653893315, + 0.1197985132761583, + 0.4219360127187033, + -0.5113040538633717, + -0.09300044948770746, + -0.5052854291543631, + ], + dtype=dtype, + ) + .reshape([4, 4, 8]) + .to(device=env.DEVICE) + ) + # backward test + self.expected_dy_dem_x = ( + paddle.to_tensor( + [ + -0.02067741234134639, + -0.037876115867122244, + -0.041801992795897414, + -0.04158797219225682, + -0.03938577535008901, + -0.04047080940333281, + -0.03819691803756371, + -0.05383372190821694, + -0.051795083742471035, + -0.03552707650567376, + -0.02812172878706858, + -0.044512948316127884, + -0.04586229371985228, + -0.037943692770837076, + -0.02917727398183635, + -0.04478649455427308, + ], + dtype=dtype, + ) + .reshape([4, 4]) + .to(device=env.DEVICE) + ) + self.expected_dy_dem = ( + paddle.to_tensor( + [ + -3.3296560873139764, + -3.3296560873139764, + -3.3296560873139764, + -3.3296560873139764, + -3.337818861718732, + -3.337818861718732, + -3.337818861718732, + -3.337818861718732, + -3.33501295970411, + -3.33501295970411, + -3.33501295970411, + -3.33501295970411, + -3.345599737642763, + -3.345599737642763, + -3.345599737642763, + -3.345599737642763, + -3.3456182126661695, + -3.3456182126661695, + -3.3456182126661695, + -3.3456182126661695, + -3.33106684078773, + -3.33106684078773, + -3.33106684078773, + -3.33106684078773, + -3.33218327918215, + -3.33218327918215, + -3.33218327918215, + -3.33218327918215, + -3.3453558489853616, + -3.3453558489853616, + -3.3453558489853616, + -3.3453558489853616, + -3.3425075397870057, + -3.3425075397870057, + -3.3425075397870057, + -3.3425075397870057, + -3.333566847126196, + -3.333566847126196, + -3.333566847126196, + -3.333566847126196, + -3.336529893308974, + -3.336529893308974, + -3.336529893308974, + -3.336529893308974, + -3.330923503981002, + -3.330923503981002, + -3.330923503981002, + -3.330923503981002, + -3.3368890892700986, + -3.3368890892700986, + -3.3368890892700986, + -3.3368890892700986, + -3.3420603756052665, + -3.3420603756052665, + -3.3420603756052665, + -3.3420603756052665, + -3.343874115987605, + -3.343874115987605, + -3.343874115987605, + -3.343874115987605, + -3.330511428849272, + -3.330511428849272, + -3.330511428849272, + -3.330511428849272, + ], + dtype=dtype, + ) + .reshape([4, 4, 4]) + .to(device=env.DEVICE) + ) + + def test_forward(self): + # Call the forward function + forward_result = paddle.ops.deepmd.tabulate_fusion_se_a( + self.table_tensor, + self.table_info_tensor, + self.em_x_tensor, + self.em_tensor, + self.last_layer_size, + ) + + descriptor_tensor = forward_result[0] + + # Check the shape + self.assertEqual(descriptor_tensor.shape, self.expected_descriptor_tensor.shape) + + # Check the values + assert paddle.allclose( + descriptor_tensor, + self.expected_descriptor_tensor, + atol=self.prec, + rtol=self.prec, + ) + + def test_backward(self): + # Call the forward function + forward_result = paddle.ops.deepmd.tabulate_fusion_se_a( + self.table_tensor, + self.table_info_tensor, + self.em_x_tensor, + self.em_tensor, + self.last_layer_size, + ) + + descriptor_tensor = forward_result[0] + + # Check the forward + assert paddle.allclose( + descriptor_tensor, + self.expected_descriptor_tensor, + atol=self.prec, + rtol=self.prec, + ) + + # Create a loss and perform backward + loss = descriptor_tensor.sum() + loss.backward() + + # Check gradients + self.assertIsNotNone(self.em_x_tensor.grad) + self.assertIsNotNone(self.em_tensor.grad) + + # Check the shapes of the gradients + self.assertEqual(self.em_x_tensor.grad.shape, self.expected_dy_dem_x.shape) + self.assertEqual(self.em_tensor.grad.shape, self.expected_dy_dem.shape) + + # Check the values of the gradients + assert paddle.allclose( + self.em_x_tensor.grad, + self.expected_dy_dem_x, + atol=self.prec, + rtol=self.prec, + ) + + assert paddle.allclose( + self.em_tensor.grad, + self.expected_dy_dem, + atol=self.prec, + rtol=self.prec, + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/test_tabulate_fusion_se_atten.py b/source/tests/pd/test_tabulate_fusion_se_atten.py new file mode 100644 index 0000000000..1608f8f8b9 --- /dev/null +++ b/source/tests/pd/test_tabulate_fusion_se_atten.py @@ -0,0 +1,1650 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import unittest + +import paddle + +from deepmd.pd.cxx_op import ( + ENABLE_CUSTOMIZED_OP, +) +from deepmd.pd.utils import ( + env, +) + +from ..consistent.common import ( + parameterized, +) + + +@parameterized((paddle.float64, paddle.float32)) +@unittest.skipIf(not ENABLE_CUSTOMIZED_OP, "PyTorch customized OPs are not built") +class TestTabulateFusionSeAttenOp(unittest.TestCase): + def setUp(self): + (dtype,) = self.param + if dtype == paddle.float64: + self.prec = 1e-10 + elif dtype == paddle.float32: + self.prec = 1e-5 + self.table_tensor = ( + 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1.652801643260504508e-02, + 4.998556989557471122e-05, + -2.037688261998792680e-04, + 2.657243869390409541e-05, + -2.670283686919466826e-01, + 9.685301784023310490e-02, + -2.543484210258855835e-03, + -6.965966582328896994e-04, + 2.850491087748043708e-05, + 1.232179636112698650e-05, + -1.480354478441044286e00, + -6.179365776107784841e-02, + 4.798501122259496952e-03, + 7.800586916120723585e-05, + -1.413851691566035862e-05, + -5.727587674967719880e-06, + 6.351934280628791507e-01, + 2.952908467203564646e-03, + 6.254828202758994093e-03, + -2.956111985445306826e-04, + -1.636502852942454153e-04, + 2.616921494951480123e-05, + -1.328294780757159899e00, + -5.123410165425365537e-02, + 1.229999603970671068e-02, + -7.892274520450543677e-04, + -1.195721301312790567e-04, + 2.454197033093738297e-05, + -9.860403788833298488e-01, + 6.727155810173718331e-02, + -5.071157514069617352e-03, + -1.214296539729165295e-04, + 9.340570341953608358e-05, + -1.444050153586573228e-05, + 3.480675228394242149e-01, + -3.619237065717702262e-02, + -7.354603960058733389e-04, + 9.313051737393654526e-04, + -1.076930273455606579e-04, + -7.696053039474192446e-07, + 1.236090908935226107e00, + 4.238273390417521269e-02, + 3.299974870987111650e-03, + -5.226642260988254756e-04, + -1.032474625011560351e-04, + 2.396475265799989632e-05, + -1.504210088727871764e00, + -1.545353795944727493e-01, + 1.652939400402650763e-02, + 4.186078937618800693e-05, + -2.027012231708198600e-04, + 1.761148452766873776e-05, + -2.660600935582757565e-01, + 9.680193929166537592e-02, + -2.564364883962782712e-03, + -6.954454205710857090e-04, + 2.907017700829073683e-05, + 9.120785771591908463e-06, + -1.480971935090678926e00, + -6.169766439371183325e-02, + 4.800832758035045861e-03, + 7.743502257440657043e-05, + -1.440171540732098418e-05, + -4.489324897938611976e-06, + 6.355509554770921721e-01, + 4.194364255265300989e-03, + 6.156587518227093006e-03, + -3.584539136959086518e-04, + -1.505562336471176987e-04, + 2.631189526673375584e-05, + -1.333295991901433553e00, + -4.879824528740911438e-02, + 1.205629889598585497e-02, + -8.346035033896359156e-04, + -1.072962342948566929e-04, + 2.412331753624817981e-05, + -9.793640468817854661e-01, + 6.625405011186732973e-02, + -5.102126473064734317e-03, + -8.551069374443776396e-05, + 8.618032279329005427e-05, + -1.422030758858379208e-05, + 3.444418516979214084e-01, + -3.631195473807800889e-02, + -4.625381215785304145e-04, + 8.881537622047225473e-04, + -1.080757789189670570e-04, + 5.820590714360855199e-08, + 1.240361649325028681e00, + 4.302664794411619614e-02, + 3.137220402938139478e-03, + -5.615677039256951981e-04, + -9.125763978623760322e-05, + 2.367398552885374808e-05, + -1.519498310980496925e00, + -1.512290469691385253e-01, + 1.652996628226939199e-02, + -3.745688059096337011e-05, + -1.938906911473592626e-04, + 1.811217640451412989e-05, + -2.564062357251438717e-01, + 9.626832379335603651e-02, + -2.771163091665611831e-03, + -6.829069315554202020e-04, + 3.363238372709415958e-05, + 8.623099725596635004e-06, + -1.487093617252511990e00, + -6.073523464295225993e-02, + 4.823154268625621383e-03, + 7.122599345182346051e-05, + -1.664931178025436733e-05, + -4.312450972708557703e-06, + ], + dtype=dtype, + ) + .reshape([8, 132]) + .to(device=env.DEVICE) + ) + self.table_info_tensor = paddle.to_tensor( + [0, 0.2, 0.4, 0.01, 0.1, -1], dtype=dtype, place="cpu" + ) + self.em_x_tensor = ( + paddle.to_tensor( + [ + 0.0343909, + 0.11357423, + 0.0858676, + 0.19337772, + 0.1935728, + 0.0477744, + 0.05845198, + 0.19080509, + 0.16111261, + 0.07179262, + 0.10078013, + 0.04640909, + 0.10433399, + 0.15650861, + 0.17527857, + 0.04249097, + ], + dtype=dtype, + ) + .reshape([4, 4]) + .to(device=env.DEVICE) + ) + self.em_tensor = ( + paddle.to_tensor( + [ + 0.0343909, + 0.08394249, + 0.06791791, + 0.00903334, + 0.11357423, + 0.10597251, + 0.05738069, + 0.10071109, + 0.0858676, + 0.17410445, + 0.05390256, + 0.09495758, + 0.19337772, + 0.02045487, + 0.04095526, + 0.18431305, + 0.1935728, + 0.03930614, + 0.0304133, + 0.15261676, + 0.0477744, + 0.06838737, + 0.12824902, + 0.14125861, + 0.05845198, + 0.12731053, + 0.0315968, + 0.14927774, + 0.19080509, + 0.19206871, + 0.14361383, + 0.04083437, + 0.16111261, + 0.19944826, + 0.16563484, + 0.00797179, + 0.07179262, + 0.16993159, + 0.01834742, + 0.08405, + 0.10078013, + 0.0773945, + 0.09541813, + 0.0042979, + 0.04640909, + 0.07968697, + 0.18046262, + 0.11724063, + 0.10433399, + 0.16910201, + 0.10653732, + 0.07434702, + 0.15650861, + 0.0350976, + 0.04088021, + 0.15753491, + 0.17527857, + 0.03178642, + 0.01599623, + 0.08095053, + 0.04249097, + 0.17082205, + 0.18275348, + 0.02921504, + ], + dtype=dtype, + ) + .reshape([4, 4, 4]) + .to(device=env.DEVICE) + ) + self.two_embed_tensor = ( + paddle.to_tensor( + [ + 0.41783850884461693, + 0.06917892522383784, + 0.07309949640440838, + 0.57828038123179, + 0.30460107001129133, + 0.0641857998132136, + 0.016519028000859692, + 0.46818914782665344, + 0.7524658161955905, + 0.7366050152276675, + 0.5442923017739666, + 0.6984532784508917, + 0.8794579292532613, + 0.933333068809702, + 0.052557248156142045, + 0.3158695444821408, + 0.6104896498153188, + 0.3190616402773879, + 0.39327308944220873, + 0.9555810861515368, + 0.45845946239660273, + 0.2836952640436372, + 0.7129235830370116, + 0.21678811087765415, + 0.8589385334305147, + 0.8664288996198418, + 0.2392088190073245, + 0.44554156483185636, + 0.4554902141228184, + 0.6929437508125064, + 0.680397459717037, + 0.47499455998030615, + 0.19872841218252735, + 0.0593083660501722, + 0.20593103822290515, + 0.8377626566866462, + 0.9006561442856688, + 0.9451358048366522, + 0.03886827486931199, + 0.9395629463676399, + 0.0018941296317954714, + 0.08140115779980839, + 0.9309153205767321, + 0.4697357303240055, + 0.9164471895052549, + 0.5957401092143415, + 0.40338864067603986, + 0.9096349710860572, + 0.027870073369474335, + 0.9614765307496669, + 0.3142035164603587, + 0.4027282032956391, + 0.05129061735552376, + 0.18018240347684844, + 0.8391548601089657, + 0.25198333808352436, + 0.07903335895654717, + 0.9831396742713334, + 0.21328884297544115, + 0.8119626188647525, + 0.1734453905861253, + 0.014174310472666818, + 0.06890574596678134, + 0.3490769935686088, + 0.34055562797730554, + 0.9834924063503578, + 0.1689164263315952, + 0.9611024936313157, + 0.6796725725159389, + 0.7902946379060674, + 0.3045916985592084, + 0.6923776720247495, + 0.5626994287153583, + 0.12132066580981216, + 0.13356198804830732, + 0.5332034125146011, + 0.6155216974624633, + 0.3080851791499254, + 0.8391387652641518, + 0.8588772315368923, + 0.9414859699900482, + 0.9852118289755771, + 0.7514252073835589, + 0.6780090883007501, + 0.1472721338720271, + 0.4785493098407567, + 0.8825343095166535, + 0.1562449821247882, + 0.5809096109347806, + 0.653605647812403, + 0.26158060329219845, + 0.28359029181509054, + 0.23111396285536823, + 0.6711415141607222, + 0.5955230293073148, + 0.14336394912405104, + 0.48478135042139503, + 0.34621668898158153, + 0.7962234329935334, + 0.40204538487553787, + 0.09600971949708359, + 0.985025266359638, + 0.4949655728846287, + 0.23503981206241742, + 0.607828476455725, + 0.21634419784756398, + 0.04166567958728129, + 0.132198384508056, + 0.4112021863641492, + 0.9441979803962212, + 0.993462871462463, + 0.4524002115880147, + 0.6322719605196645, + 0.5121196654684579, + 0.7844974619880201, + 0.6783684708633317, + 0.6402712236722511, + 0.43899788665378925, + 0.6060330628471464, + 0.7082475921988166, + 0.1614968711069913, + 0.6289247345866867, + 0.4034261331727077, + 0.7906075239905527, + 0.9325509002602962, + 0.44489583733770977, + 0.5194672674960213, + 0.04635102497306032, + ], + dtype=dtype, + ) + .reshape([8, 16]) + .to(device=env.DEVICE) + ) + self.table_info_tensor.stop_gradient = not False + self.table_tensor.stop_gradient = not False + self.em_x_tensor.stop_gradient = not True + self.em_tensor.stop_gradient = not True + self.two_embed_tensor.stop_gradient = not True + self.last_layer_size = 8 + self.nloc = 4 + self.nnei = 4 + self.is_sorted = True + # forward test + self.expected_descriptor_tensor = ( + paddle.to_tensor( + [ + 0.47347690809281584, + -0.938671106172836, + -0.566066031386074, + 0.24346508156830923, + 0.8202563571070155, + -1.0373756242429473, + -0.17010015427406364, + -0.8710788156620061, + 0.395670826145944, + -0.7164061254932106, + -0.5182353938571188, + 0.24032480431966494, + 0.7295250448255718, + -0.8217164571528093, + -0.14650667106275897, + -0.743917788428106, + 0.2284657683159583, + -0.42325060475528936, + -0.28528185044441623, + 0.1299218265387629, + 0.4122891899913208, + -0.4792844297628545, + -0.07999903336182355, + -0.44416840002965857, + 0.4349292047304616, + -0.866714677458846, + -0.5207292765686371, + 0.22332001940248375, + 0.7498173442166285, + -0.9546906311960559, + -0.15936409133917512, + -0.7878577875263373, + 0.3466925422373803, + -0.994380783370134, + -0.6270917901063118, + 0.2981402728321051, + 0.9195320780786727, + -1.0718892456307918, + -0.15698461575270795, + -1.1789262485734189, + 0.28814823365263476, + -0.9885184495221915, + -0.5748462943377031, + 0.24501712277073154, + 0.6966426111509829, + -0.7918732495676203, + -0.15816622107875547, + -0.9232820446171233, + 0.22378298591000056, + -0.6859112389106587, + -0.49619127436326704, + 0.19242493499693383, + 0.622786588111436, + -0.666018566891193, + -0.11621443478059659, + -0.7927712244868067, + 0.33146082229500645, + -0.910623259021886, + -0.6975451800757693, + 0.2708230994848638, + 0.9436440642240583, + -1.103250728415007, + -0.18643132646601496, + -1.1918841520358467, + 0.37921786279033454, + -0.8962410980736447, + -0.500721492855562, + 0.22977304492608347, + 0.6894967253035347, + -0.9081285527067445, + -0.16659833566436824, + -0.8496545388057982, + 0.5137998029000233, + -1.1537368698160295, + -0.656860645256254, + 0.31042037927337496, + 0.9814343841081181, + -1.232310461500326, + -0.23314406631295234, + -1.2304015706558842, + 0.45797993385377606, + -1.1074919572397988, + -0.6048852416894798, + 0.2616527005842335, + 0.7879703504421955, + -1.1362711906177663, + -0.1981304325148623, + -0.9310107317132751, + 0.21315309858654777, + -0.41274344906220745, + -0.2588508380504396, + 0.1066634142045425, + 0.3672005243972004, + -0.4840002903711901, + -0.10065024885011888, + -0.4546504875519408, + 0.47121911760467616, + -0.8813734369794723, + -0.7156169154744415, + 0.242949784111888, + 0.8695344392466614, + -1.2333547296658691, + -0.22100811739419962, + -1.0344804237112, + 0.3541853556808732, + -0.7889099992546985, + -0.632615806745115, + 0.2240548602116392, + 0.7597588014275503, + -1.035877717989762, + -0.17525344544740995, + -0.7266950510645241, + 0.293533442380667, + -0.6807511051238859, + -0.5158926321437481, + 0.19381846340306683, + 0.6817081536687413, + -0.8591698632437857, + -0.15090870856670646, + -0.6035960397897837, + 0.3364163288609897, + -0.6117855193715979, + -0.4783834090534011, + 0.1649210671780133, + 0.6081937728291197, + -0.9156559135117243, + -0.16287243502858786, + -0.7232291367106685, + ], + dtype=dtype, + ) + .reshape([4, 4, 8]) + .to(device=env.DEVICE) + ) + # backward test + self.expected_dy_dem_x = ( + paddle.to_tensor( + [ + -0.02944485238565673, + -0.09481442615634611, + -0.039285023803917796, + -0.08263513336597483, + -0.1147941391226924, + -0.053028707974760975, + -0.04045111384033326, + -0.09645372744447589, + -0.148871652361389, + -0.03949164509537857, + -0.03304671059396837, + -0.07677112538315375, + -0.08936844295120971, + -0.0666033025810816, + -0.036086280282677796, + -0.053263385364202, + ], + dtype=dtype, + ) + .reshape([4, 4]) + .to(device=env.DEVICE) + ) + self.expected_dy_dem = ( + paddle.to_tensor( + [ + -3.437493391458747, + -3.437493391458747, + -3.437493391458747, + -3.437493391458747, + -4.917683334085319, + -4.917683334085319, + -4.917683334085319, + -4.917683334085319, + -3.7978352380265443, + -3.7978352380265443, + -3.7978352380265443, + -3.7978352380265443, + -5.39483968657882, + -5.39483968657882, + -5.39483968657882, + -5.39483968657882, + -4.918657812120523, + -4.918657812120523, + -4.918657812120523, + -4.918657812120523, + -5.405511948034504, + -5.405511948034504, + -5.405511948034504, + -5.405511948034504, + -5.5647056342671615, + -5.5647056342671615, + -5.5647056342671615, + -5.5647056342671615, + -4.870290792037633, + -4.870290792037633, + -4.870290792037633, + -4.870290792037633, + -5.712629223988493, + -5.712629223988493, + -5.712629223988493, + -5.712629223988493, + -4.279958255143791, + -4.279958255143791, + -4.279958255143791, + -4.279958255143791, + -5.554543471933205, + -5.554543471933205, + -5.554543471933205, + -5.554543471933205, + -5.072772403587814, + -5.072772403587814, + -5.072772403587814, + -5.072772403587814, + -5.967810024526445, + -5.967810024526445, + -5.967810024526445, + -5.967810024526445, + -5.08155970167425, + -5.08155970167425, + -5.08155970167425, + -5.08155970167425, + -5.238429358303623, + -5.238429358303623, + -5.238429358303623, + -5.238429358303623, + -3.906538220487487, + -3.906538220487487, + -3.906538220487487, + -3.906538220487487, + ], + dtype=dtype, + ) + .reshape([4, 4, 4]) + .to(device=env.DEVICE) + ) + + def test_forward(self): + # Call the forward function + forward_result = paddle.ops.deepmd.tabulate_fusion_se_atten( + self.table_tensor, + self.table_info_tensor, + self.em_x_tensor, + self.em_tensor, + self.two_embed_tensor, + self.last_layer_size, + self.is_sorted, + ) + + descriptor_tensor = forward_result[0] + + # Check the shape + self.assertEqual(descriptor_tensor.shape, self.expected_descriptor_tensor.shape) + + # Check the values + assert paddle.allclose( + descriptor_tensor, + self.expected_descriptor_tensor, + atol=self.prec, + rtol=self.prec, + ) + + def test_backward(self): + # Call the forward function + forward_result = paddle.ops.deepmd.tabulate_fusion_se_atten( + self.table_tensor, + self.table_info_tensor, + self.em_x_tensor, + self.em_tensor, + self.two_embed_tensor, + self.last_layer_size, + self.is_sorted, + ) + + descriptor_tensor = forward_result[0] + + # Check the forward + assert paddle.allclose( + descriptor_tensor, + self.expected_descriptor_tensor, + atol=self.prec, + rtol=self.prec, + ) + + # Create a loss and perform backward + loss = descriptor_tensor.sum() + loss.backward() + + # Check gradients + self.assertIsNotNone(self.em_x_tensor.grad) + self.assertIsNotNone(self.em_tensor.grad) + + # Check the shapes of the gradients + self.assertEqual(self.em_x_tensor.grad.shape, self.expected_dy_dem_x.shape) + self.assertEqual(self.em_tensor.grad.shape, self.expected_dy_dem.shape) + + # Check the values of the gradients + assert paddle.allclose( + self.em_x_tensor.grad, + self.expected_dy_dem_x, + atol=self.prec, + rtol=self.prec, + ) + + assert paddle.allclose( + self.em_tensor.grad, self.expected_dy_dem, atol=self.prec, rtol=self.prec + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/test_tabulate_fusion_se_r.py b/source/tests/pd/test_tabulate_fusion_se_r.py new file mode 100644 index 0000000000..e4c491ca9f --- /dev/null +++ b/source/tests/pd/test_tabulate_fusion_se_r.py @@ -0,0 +1,1349 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import unittest + +import paddle + +from deepmd.pd.cxx_op import ( + ENABLE_CUSTOMIZED_OP, +) +from deepmd.pd.utils import ( + env, +) + +from ..consistent.common import ( + parameterized, +) + + +@parameterized((paddle.float64, paddle.float32)) +@unittest.skipIf(not ENABLE_CUSTOMIZED_OP, "PyTorch customized OPs are not built") +class TestTabulateFusionSeAOp(unittest.TestCase): + def setUp(self): + (dtype,) = self.param + if dtype == paddle.float64: + self.prec = 1e-10 + elif dtype == paddle.float32: + self.prec = 1e-5 + self.table_tensor = ( + paddle.to_tensor( + [ + 6.348551343037398542e-01, + 4.209465843706336474e-04, + 6.390862740714405368e-03, + -1.544448595628262176e-04, + -1.891095227974180087e-04, + 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-4.312450972708557703e-06, + ], + dtype=dtype, + ) + .reshape([8, 132]) + .to(device=env.DEVICE) + ) + self.table_info_tensor = paddle.to_tensor( + [0, 0.2, 0.4, 0.01, 0.1, -1], dtype=dtype, device="cpu" + ) + self.em_tensor = ( + paddle.to_tensor( + [ + 0.0343909, + 0.11357423, + 0.0858676, + 0.19337772, + 0.1935728, + 0.0477744, + 0.05845198, + 0.19080509, + 0.16111261, + 0.07179262, + 0.10078013, + 0.04640909, + 0.10433399, + 0.15650861, + 0.17527857, + 0.04249097, + ], + dtype=dtype, + ) + .reshape([4, 4]) + .to(device=env.DEVICE) + ) + self.table_info_tensor.stop_gradient = not True + self.table_tensor.stop_gradient = not True + self.em_tensor.stop_gradient = not True + self.last_layer_size = 8 + self.nloc = 4 + self.nnei = 4 + # forward test + self.expected_descriptor_tensor = ( + paddle.to_tensor( + [ + 0.6348771631809248, + -1.3194691113291661, + -0.9973196209241018, + 0.354036826929785, + 1.229164742167305, + -1.4781646269685296, + -0.2821585274143141, + -1.4706229329558798, + 0.634985122151867, + -1.3237744576184003, + -0.9918921699941985, + 0.3511893756810418, + 1.2324529289292472, + -1.4907308230849954, + -0.27444497948193763, + -1.475603858301356, + 0.6349382934267259, + -1.3222859349253087, + -0.9937841045345152, + 0.35218726188637794, + 1.2312974110088875, + -1.4863573766557947, + -0.27714069648435136, + -1.4738678134261312, + 0.635174147464672, + -1.3279549545501694, + -0.9864860923352163, + 0.34830716632779013, + 1.235810383474792, + -1.5031859872970013, + -0.26670125536196, + -1.48056314536567, + 0.6351747075878561, + -1.3279649806303675, + -0.9864729560930998, + 0.3483001078156069, + 1.2358186430848, + -1.5032161761365208, + -0.26668236472889295, + -1.4805751935655516, + 0.6348898134171228, + -1.320207897862343, + -0.996397864565745, + 0.35355654279185456, + 1.229717427214055, + -1.4803030761521567, + -0.28085278195255303, + -1.4714690036779643, + 0.6349015416696653, + -1.3207940654749135, + -0.9956637569426188, + 0.3531730613337772, + 1.2301592741196672, + -1.482004932451056, + -0.2798116121790224, + -1.472142789257649, + 0.63516680538694, + -1.3278226472708177, + -0.9866593634585263, + 0.34840024552503546, + 1.235701482917897, + -1.5027877523544166, + -0.2669503949447871, + -1.4804042247866864, + 0.6350880729003239, + -1.3262837737167732, + -0.9886640555953151, + 0.34947378247466837, + 1.2344477758485055, + -1.4981756178487995, + -0.26982830526911433, + -1.4785654185805013, + 0.6349182326958803, + -1.3215223841369783, + -0.9947481627455212, + 0.3526935713672062, + 1.230712444499081, + -1.4841259603235413, + -0.2785114943045205, + -1.4729830941778024, + 0.6349622896803752, + -1.3230894924528385, + -0.9927648497591829, + 0.35165036475319683, + 1.2319186867506238, + -1.4887144441648619, + -0.27568933247281807, + -1.4748031156434684, + 0.63488841840565, + -1.3201327386056192, + -0.9964918149720808, + 0.3536055582326517, + 1.2296609874447608, + -1.4800851938933008, + -0.28098595018607436, + -1.4713827704069893, + 0.6349684240930827, + -1.3232801685109328, + -0.9925222773369173, + 0.3515223472158862, + 1.232066971980551, + -1.4892750825477157, + -0.27534360663790663, + -1.4750256975261462, + 0.6350768567116333, + -1.326043208512698, + -0.9889756942263948, + 0.3496401112248936, + 1.234253909454779, + -1.4974578690711353, + -0.2702749376389924, + -1.478279543547352, + 0.6351242549804302, + -1.327020671488853, + -0.9877065253616449, + 0.34896178641041964, + 1.2350451717803512, + -1.5003796519427108, + -0.2684547326157591, + -1.4794437477498377, + 0.6348845468620932, + -1.319916787317926, + -0.9967615341650043, + 0.3537461965952468, + 1.2294990915247286, + -1.4794595816676606, + -0.2813681581698538, + -1.4711352025108961, + ], + dtype=dtype, + ) + .reshape([4, 4, 8]) + .to(device=env.DEVICE) + ) + # backward test + self.expected_dy_dem = ( + paddle.to_tensor( + [ + -0.10588345474250505, + -0.1002972786439324, + -0.10224731275660418, + -0.0947116532720767, + -0.09469805979213963, + -0.10493653217842225, + -0.10418182788008934, + -0.09489094713093613, + -0.09696412406683491, + -0.10323988208957913, + -0.10119709956091572, + -0.10503308350390633, + -0.10094704040733082, + -0.09728619911848688, + -0.09597416541247616, + -0.10531022473788323, + ], + dtype=dtype, + ) + .reshape([4, 4]) + .to(device=env.DEVICE) + ) + + def test_forward(self): + # Call the forward function + forward_result = paddle.ops.deepmd.tabulate_fusion_se_r( + self.table_tensor, + self.table_info_tensor, + self.em_tensor, + self.last_layer_size, + ) + + descriptor_tensor = forward_result[0] + + # Check the shape + self.assertEqual(descriptor_tensor.shape, self.expected_descriptor_tensor.shape) + + # Check the values + assert paddle.allclose( + descriptor_tensor, + self.expected_descriptor_tensor, + atol=self.prec, + rtol=self.prec, + ) + + def test_backward(self): + # Call the forward function + forward_result = paddle.ops.deepmd.tabulate_fusion_se_r( + self.table_tensor, + self.table_info_tensor, + self.em_tensor, + self.last_layer_size, + ) + + descriptor_tensor = forward_result[0] + + # Check the forward + assert paddle.allclose( + descriptor_tensor, + self.expected_descriptor_tensor, + atol=self.prec, + rtol=self.prec, + ) + + # Create a loss and perform backward + loss = descriptor_tensor.sum() + loss.backward() + + # Check gradients + self.assertIsNotNone(self.em_tensor.grad) + + # Check the shapes of the gradients + self.assertEqual(self.em_tensor.grad.shape, self.expected_dy_dem.shape) + + # Check the values of the gradients + assert paddle.allclose( + self.em_tensor.grad, self.expected_dy_dem, atol=self.prec, rtol=self.prec + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/test_tabulate_fusion_se_t.py b/source/tests/pd/test_tabulate_fusion_se_t.py new file mode 100644 index 0000000000..d46bcb492c --- /dev/null +++ b/source/tests/pd/test_tabulate_fusion_se_t.py @@ -0,0 +1,1768 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import unittest + +import paddle + +from deepmd.pd.cxx_op import ( + ENABLE_CUSTOMIZED_OP, +) +from deepmd.pd.utils import ( + env, +) + +from ..consistent.common import ( + parameterized, +) + + +@parameterized((paddle.float64, paddle.float32)) +@unittest.skipIf(not ENABLE_CUSTOMIZED_OP, "PyTorch customized OPs are not built") +class TestTabulateFusionSeTOp(unittest.TestCase): + def setUp(self): + (dtype,) = self.param + if dtype == paddle.float64: + self.prec = 1e-10 + elif dtype == paddle.float32: + # JZ: not sure the reason, but 1e-5 cannot pass the grad test + self.prec = 1e-3 + self.table_tensor = ( + paddle.to_tensor( + [ + -1.0600000163027882e02, + 7.7059358807135015e02, + -5.6954714749735385e03, + 1.2167808756610991e03, + -7.6199102434332218e01, + 1.0706136029373441e00, + -1.0600000164528124e02, + 7.7059358630452323e02, + -5.6954715659539552e03, + 1.2167808757436076e03, + -7.6199099707724926e01, + 1.0706134206080884e00, + -1.0600000163027882e02, + 7.7059358807135015e02, + 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2.5724136589119979e02, + 1.0913830717468406e03, + -1.8317243758181812e02, + 2.5193786568880601e00, + 4.6277932792022042e-01, + 6.2000133522896938e01, + 2.5710536851442714e02, + 1.0934673032246803e03, + -1.8370056934963364e02, + 2.5630609205366826e00, + 4.6162176035304603e-01, + 7.1999279107664492e01, + 3.4965254984584158e02, + 1.7356304176273381e03, + -3.0063395678020430e02, + 6.2079056750108883e00, + 6.1505333334154833e-01, + 7.2000032172982571e01, + 3.4961232791697932e02, + 1.7365043785874466e03, + -3.0086002522613632e02, + 6.2270725229979789e00, + 6.1452738833821030e-01, + 7.1999279107664492e01, + 3.4965254984584158e02, + 1.7356304176273381e03, + -3.0063395678020430e02, + 6.2079056750108883e00, + 6.1505333334154833e-01, + 7.2000032172982642e01, + 3.4961232791696904e02, + 1.7365043785881401e03, + -3.0086002522634379e02, + 6.2270725230187063e00, + 6.1452738833751985e-01, + 8.1999844359310714e01, + 4.5636323545227941e02, + 2.5918884526432239e03, + -4.5885344883307727e02, + 1.1616256691917803e01, + 7.8948404417119522e-01, + 8.2000007751936337e01, + 4.5635184072744744e02, + 2.5922210189842476e03, + -4.5894061525528980e02, + 1.1623761628208563e01, + 7.8927378661620728e-01, + 8.1999844359310714e01, + 4.5636323545227941e02, + 2.5918884526432239e03, + -4.5885344883307727e02, + 1.1616256691917803e01, + 7.8948404417119522e-01, + 8.2000007751936337e01, + 4.5635184072744744e02, + 2.5922210189842476e03, + -4.5894061525528980e02, + 1.1623761628208563e01, + 7.8927378661620728e-01, + ], + dtype=dtype, + ) + .to(device=env.DEVICE) + .reshape([8, 174]) + ) # 1392 + self.table_info_tensor = paddle.to_tensor( + [ + -2.1000000000000000e01, + 2.1000000000000000e01, + 1.0500000000000000e02, + 1.0000000000000000e00, + 1.0000000000000000e01, + -1.0000000000000000e00, + ], + dtype=dtype, + device="cpu", + ) + self.em_x_tensor = ( + paddle.to_tensor( + [ + 9.3816147034272368e-01, + -1.6703373029862567e-01, + -4.4294526064601734e-02, + -2.8798505489184573e-01, + -1.6703373029862567e-01, + 9.2489218226366088e-01, + -2.8928196536572048e-01, + -4.7833509099876154e-01, + -4.4294526064601734e-02, + -2.8928196536572048e-01, + 5.7034320185695120e-01, + 1.8771147911830000e-01, + -2.8798505489184573e-01, + -4.7833509099876154e-01, + 1.8771147911830000e-01, + 4.0174654365823070e-01, + 8.4370316144902313e-01, + -3.7813146789689916e-02, + -3.6989397568296523e-01, + -4.0554075086539937e-01, + -3.7813146789689916e-02, + 6.5766402633747112e-01, + -4.2312966361682885e-01, + 1.2685067374257861e-01, + -3.6989397568296523e-01, + -4.2312966361682885e-01, + 6.0171576901660107e-01, + 9.8283160997298613e-02, + -4.0554075086539937e-01, + 1.2685067374257861e-01, + 9.8283160997298613e-02, + 2.1324148100625978e-01, + 9.7843596341516559e-01, + -1.0492833888237871e-01, + -1.0538688914576379e-01, + -2.0453551592353389e-01, + -1.0492833888237871e-01, + 7.7943976693565231e-01, + -1.5898500035781410e-01, + 9.4834209331437741e-02, + -1.0538688914576379e-01, + -1.5898500035781410e-01, + 7.4778071691708869e-01, + -6.1895255142095873e-01, + -2.0453551592353389e-01, + 9.4834209331437741e-02, + -6.1895255142095873e-01, + 6.0844713798743799e-01, + 1.0079020879244640e00, + -2.3855984150631487e-01, + -3.4608276043004524e-02, + -4.7448768267289088e-01, + -2.3855984150631487e-01, + 4.9732018171028253e-01, + -3.1320787082485729e-01, + -1.4528004145602180e-01, + -3.4608276043004524e-02, + -3.1320787082485729e-01, + 4.7696729363954582e-01, + 1.1723268074231248e-01, + -4.7448768267289088e-01, + -1.4528004145602180e-01, + 1.1723268074231248e-01, + 4.0511515406019899e-01, + ], + dtype=dtype, + ) + .to(device=env.DEVICE) + .reshape([4, 16]) + ) # 3072 + self.em_tensor = self.em_x_tensor.reshape([4, 4, 4]) + self.table_info_tensor.stop_gradient = not False + self.table_tensor.stop_gradient = not False + self.em_x_tensor.stop_gradient = not True + self.em_tensor.stop_gradient = not True + self.last_layer_size = 4 + self.nloc = 192 + self.nnei_i = 4 + self.nnei_j = 4 + + self.expected_descriptor_tensor = ( + paddle.to_tensor( + [ + 1.4271973325754339e00, + 2.5214997685364109e00, + 3.1394341134078902e00, + 2.2727894815158436e00, + 1.9127738317829568e00, + 2.5288382955492263e00, + 3.1401587802428659e00, + 2.5252400661016079e00, + 9.4806287131835343e-01, + 2.3778589851963829e00, + 2.8273548699126683e00, + 1.9358633427396228e00, + 2.1586806210305824e00, + 2.6256636737020518e00, + 3.3955783231847523e00, + 2.7091329174140033e00, + ], + dtype=dtype, + ) + .to(device=env.DEVICE) + .reshape([4, 4]) + ) + self.expected_dy_dem_x = ( + paddle.to_tensor( + [ + 5.648489055364202, + -0.8109841888364551, + -0.24536867097411239, + -1.1747441933374314, + -0.8109841888364551, + 5.551778760144183, + -1.177917429853053, + -1.679018415609313, + -0.24536867097411239, + -1.177917429853053, + 3.066855971667982, + 1.0527786223200397, + -1.1747441933374314, + -1.679018415609313, + 1.0527786223200397, + 2.154128070312613, + 4.941344648306369, + -0.2103366776681705, + -1.367487115395829, + -1.4564034402591373, + -0.2103366776681705, + 3.608285604419272, + -1.5040667548923485, + 0.7193959503618953, + -1.367487115395829, + -1.5040667548923485, + 3.254731998053089, + 0.5591533452715003, + -1.4564034402591373, + 0.7193959503618953, + 0.5591533452715003, + 1.1890751500286143, + 5.930646543070966, + -0.5504936332829675, + -0.5526209441647617, + -0.9416633243620358, + -0.5504936332829675, + 4.459184963787069, + -0.7803876483286257, + 0.5396747709549512, + -0.5526209441647617, + -0.7803876483286257, + 4.228294605542883, + -2.4024371569601737, + -0.9416633243620358, + 0.5396747709549512, + -2.4024371569601737, + 3.2959904931493385, + 6.1224119486112745, + -1.0448644883385283, + -0.1928789305436163, + -1.665305701289531, + -1.0448644883385283, + 2.654867798405467, + -1.2350917789985936, + -0.7261316753808301, + -0.1928789305436163, + -1.2350917789985936, + 2.5453318630183253, + 0.6656811038445796, + -1.665305701289531, + -0.7261316753808301, + 0.6656811038445796, + 2.1713171576639834, + ], + dtype=dtype, + ) + .to(device=env.DEVICE) + .reshape([4, 16]) + ) + self.expected_dy_dem = ( + paddle.to_tensor( + [ + -5.714759600210596, + -11.831713987629353, + -11.190122813510595, + -12.370780785045307, + -11.831713987629353, + -5.794533094540567, + -12.376066372858583, + -13.075171860091254, + -11.190122813510595, + -12.376066372858583, + -7.803917285588026, + -9.877251464178656, + -12.370780785045307, + -13.075171860091254, + -9.877251464178656, + -8.705541422983027, + -6.27628449412496, + -11.154143613489751, + -12.688057210800388, + -12.817836346604595, + -11.154143613489751, + -7.330064381543641, + -12.8806650535401, + -10.220630213188823, + -12.688057210800388, + -12.8806650535401, + -7.634743369735131, + -10.382921713303563, + -12.817836346604595, + -10.220630213188823, + -10.382921713303563, + -9.734474315546018, + -5.471377616034635, + -11.517744760556846, + -11.520149882632618, + -12.009085059484455, + -11.517744760556846, + -6.648325517174191, + -11.792420704526007, + -10.402546165885312, + -11.520149882632618, + -11.792420704526007, + -6.828388581370712, + -13.58699904400806, + -12.009085059484455, + -10.402546165885312, + -13.58699904400806, + -7.598306221049991, + -5.292546900481144, + -12.161894405917199, + -11.136299195212807, + -13.061667970148363, + -12.161894405917199, + -8.194852173884833, + -12.47192675731089, + -11.724533065186144, + -11.136299195212807, + -12.47192675731089, + -8.30347749201922, + -10.275210657956574, + -13.061667970148363, + -11.724533065186144, + -10.275210657956574, + -8.687482898190318, + ], + dtype=dtype, + ) + .to(device=env.DEVICE) + .reshape([4, 4, 4]) + ) + + def test_forward(self): + # Call the forward function + forward_result = paddle.ops.deepmd.tabulate_fusion_se_t( + self.table_tensor, + self.table_info_tensor, + self.em_x_tensor, + self.em_tensor, + self.last_layer_size, + ) + + descriptor_tensor = forward_result[0] + + # Check the shape + self.assertEqual(descriptor_tensor.shape, self.expected_descriptor_tensor.shape) + + # Check the values + assert paddle.allclose( + descriptor_tensor, + self.expected_descriptor_tensor, + atol=self.prec, + rtol=self.prec, + ) + + def test_backward(self): + # Call the forward function + forward_result = paddle.ops.deepmd.tabulate_fusion_se_t( + self.table_tensor, + self.table_info_tensor, + self.em_x_tensor, + self.em_tensor, + self.last_layer_size, + ) + + descriptor_tensor = forward_result[0] + + # Check the forward + assert paddle.allclose( + descriptor_tensor, + self.expected_descriptor_tensor, + atol=self.prec, + rtol=self.prec, + ) + + # Create a loss and perform backward + loss = descriptor_tensor.sum() + loss.backward() + + # Check gradients + self.assertIsNotNone(self.em_x_tensor.grad) + self.assertIsNotNone(self.em_tensor.grad) + + # Check the shapes of the gradients + self.assertEqual(self.em_x_tensor.grad.shape, self.expected_dy_dem_x.shape) + self.assertEqual(self.em_tensor.grad.shape, self.expected_dy_dem.shape) + + # Check the values of the gradients + assert paddle.allclose( + self.em_x_tensor.grad, + self.expected_dy_dem_x, + atol=self.prec, + rtol=self.prec, + ) + + assert paddle.allclose( + self.em_tensor.grad, + self.expected_dy_dem, + atol=self.prec, + rtol=self.prec, + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/test_training.py b/source/tests/pd/test_training.py new file mode 100644 index 0000000000..5b4f05c577 --- /dev/null +++ b/source/tests/pd/test_training.py @@ -0,0 +1,456 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import json +import os +import shutil +import unittest +from copy import ( + deepcopy, +) +from pathlib import ( + Path, +) + +import numpy as np + +from deepmd.pd.entrypoints.main import ( + get_trainer, +) +from deepmd.pd.utils.finetune import ( + get_finetune_rules, +) + +from .model.test_permutation import ( + model_dos, + model_dpa1, + model_dpa2, + model_hybrid, + model_se_e2_a, + model_zbl, +) + + +class DPTrainTest: + def test_dp_train(self): + # test training from scratch + trainer = get_trainer(deepcopy(self.config)) + trainer.run() + state_dict_trained = trainer.wrapper.model.state_dict() + # for k, v in state_dict_trained.items(): + # print(f"{k} {v.shape}") + # test fine-tuning using same input + finetune_model = self.config["training"].get("save_ckpt", "model.ckpt") + ".pd" + self.config["model"], finetune_links = get_finetune_rules( + finetune_model, + self.config["model"], + ) + trainer_finetune = get_trainer( + deepcopy(self.config), + finetune_model=finetune_model, + finetune_links=finetune_links, + ) + + # test fine-tuning using empty input + self.config_empty = deepcopy(self.config) + if "descriptor" in self.config_empty["model"]: + self.config_empty["model"]["descriptor"] = {} + if "fitting_net" in self.config_empty["model"]: + self.config_empty["model"]["fitting_net"] = {} + self.config_empty["model"], finetune_links = get_finetune_rules( + finetune_model, + self.config_empty["model"], + change_model_params=True, + ) + trainer_finetune_empty = get_trainer( + deepcopy(self.config_empty), + finetune_model=finetune_model, + finetune_links=finetune_links, + ) + + # test fine-tuning using random fitting + self.config["model"], finetune_links = get_finetune_rules( + finetune_model, self.config["model"], model_branch="RANDOM" + ) + trainer_finetune_random = get_trainer( + deepcopy(self.config_empty), + finetune_model=finetune_model, + finetune_links=finetune_links, + ) + + # check parameters + state_dict_finetuned = trainer_finetune.wrapper.model.state_dict() + state_dict_finetuned_empty = trainer_finetune_empty.wrapper.model.state_dict() + state_dict_finetuned_random = trainer_finetune_random.wrapper.model.state_dict() + for state_key in state_dict_finetuned: + if "out_bias" not in state_key and "out_std" not in state_key: + np.testing.assert_allclose( + state_dict_trained[state_key].numpy(), + state_dict_finetuned[state_key].numpy(), + ) + np.testing.assert_allclose( + state_dict_trained[state_key].numpy(), + state_dict_finetuned_empty[state_key].numpy(), + ) + if "fitting_net" not in state_key: + np.testing.assert_allclose( + state_dict_trained[state_key].numpy(), + state_dict_finetuned_random[state_key].numpy(), + ) + + # check running + trainer_finetune.run() + trainer_finetune_empty.run() + trainer_finetune_random.run() + + def test_trainable(self): + fix_params = deepcopy(self.config) + fix_params["model"]["descriptor"]["trainable"] = False + fix_params["model"]["fitting_net"]["trainable"] = False + free_descriptor = hasattr(self, "not_all_grad") and self.not_all_grad + if free_descriptor: + # can not set requires_grad false for all parameters, + # because the input coord has no grad, thus the loss if all set to false + # we only check trainable for fitting net + fix_params["model"]["descriptor"]["trainable"] = True + trainer_fix = get_trainer(fix_params) + model_dict_before_training = deepcopy( + trainer_fix.model.get_fitting_net().state_dict() + ) + trainer_fix.run() + model_dict_after_training = deepcopy( + trainer_fix.model.get_fitting_net().state_dict() + ) + else: + trainer_fix = get_trainer(fix_params) + model_dict_before_training = deepcopy(trainer_fix.model.state_dict()) + trainer_fix.run() + model_dict_after_training = deepcopy(trainer_fix.model.state_dict()) + for key in model_dict_before_training: + np.testing.assert_allclose( + model_dict_before_training[key].numpy(), + model_dict_after_training[key].numpy(), + ) + + def tearDown(self): + for f in os.listdir("."): + if f.startswith("model") and f.endswith(".pd"): + os.remove(f) + if f in ["lcurve.out"]: + os.remove(f) + if f in ["stat_files"]: + shutil.rmtree(f) + + +class TestEnergyModelSeA(unittest.TestCase, DPTrainTest): + def setUp(self): + input_json = str(Path(__file__).parent / "water/se_atten.json") + with open(input_json) as f: + self.config = json.load(f) + data_file = [str(Path(__file__).parent / "water/data/data_0")] + self.config["training"]["training_data"]["systems"] = data_file + self.config["training"]["validation_data"]["systems"] = data_file + self.config["model"] = deepcopy(model_se_e2_a) + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + + def tearDown(self) -> None: + DPTrainTest.tearDown(self) + + +class TestDOSModelSeA(unittest.TestCase, DPTrainTest): + def setUp(self): + input_json = str(Path(__file__).parent / "dos/input.json") + with open(input_json) as f: + self.config = json.load(f) + data_file = [str(Path(__file__).parent / "dos/data/atomic_system")] + self.config["training"]["training_data"]["systems"] = data_file + self.config["training"]["validation_data"]["systems"] = data_file + self.config["model"] = deepcopy(model_dos) + self.config["model"]["type_map"] = ["H"] + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + self.not_all_grad = True + + def tearDown(self) -> None: + DPTrainTest.tearDown(self) + + +@unittest.skip("EnergyZBLModelSeA not supported at the moment") +class TestEnergyZBLModelSeA(unittest.TestCase, DPTrainTest): + def setUp(self): + input_json = str(Path(__file__).parent / "water/zbl.json") + with open(input_json) as f: + self.config = json.load(f) + data_file = [str(Path(__file__).parent / "water/data/data_0")] + self.config["training"]["training_data"]["systems"] = data_file + self.config["training"]["validation_data"]["systems"] = data_file + self.config["model"] = deepcopy(model_zbl) + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + + def tearDown(self) -> None: + DPTrainTest.tearDown(self) + + +class TestFparam(unittest.TestCase, DPTrainTest): + """Test if `fparam` can be loaded correctly.""" + + def setUp(self): + input_json = str(Path(__file__).parent / "water/se_atten.json") + with open(input_json) as f: + self.config = json.load(f) + data_file = [str(Path(__file__).parent / "water/data/data_0")] + self.config["training"]["training_data"]["systems"] = data_file + self.config["training"]["validation_data"]["systems"] = data_file + self.config["model"] = deepcopy(model_se_e2_a) + self.config["model"]["fitting_net"]["numb_fparam"] = 1 + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + self.set_path = Path(__file__).parent / "water/data/data_0" / "set.000" + shutil.copyfile(self.set_path / "energy.npy", self.set_path / "fparam.npy") + + def tearDown(self) -> None: + (self.set_path / "fparam.npy").unlink(missing_ok=True) + DPTrainTest.tearDown(self) + + +class TestEnergyModelDPA1(unittest.TestCase, DPTrainTest): + def setUp(self): + input_json = str(Path(__file__).parent / "water/se_atten.json") + with open(input_json) as f: + self.config = json.load(f) + data_file = [str(Path(__file__).parent / "water/data/data_0")] + self.config["training"]["training_data"]["systems"] = data_file + self.config["training"]["validation_data"]["systems"] = data_file + self.config["model"] = deepcopy(model_dpa1) + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + + def tearDown(self) -> None: + DPTrainTest.tearDown(self) + + +class TestEnergyModelDPA2(unittest.TestCase, DPTrainTest): + def setUp(self): + input_json = str(Path(__file__).parent / "water/se_atten.json") + with open(input_json) as f: + self.config = json.load(f) + data_file = [str(Path(__file__).parent / "water/data/data_0")] + self.config["training"]["training_data"]["systems"] = data_file + self.config["training"]["validation_data"]["systems"] = data_file + self.config["model"] = deepcopy(model_dpa2) + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + + def tearDown(self) -> None: + DPTrainTest.tearDown(self) + + +@unittest.skip("hybrid not supported at the moment") +class TestEnergyModelHybrid(unittest.TestCase, DPTrainTest): + def setUp(self): + input_json = str(Path(__file__).parent / "water/se_atten.json") + with open(input_json) as f: + self.config = json.load(f) + data_file = [str(Path(__file__).parent / "water/data/data_0")] + self.config["training"]["training_data"]["systems"] = data_file + self.config["training"]["validation_data"]["systems"] = data_file + self.config["model"] = deepcopy(model_hybrid) + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + + def tearDown(self) -> None: + DPTrainTest.tearDown(self) + + +class TestDipoleModelSeA(unittest.TestCase, DPTrainTest): + def setUp(self): + input_json = str(Path(__file__).parent / "water_tensor/se_e2_a.json") + with open(input_json) as f: + self.config = json.load(f) + data_file_atomic = str( + Path(__file__).parent / "water_tensor/dipole/atomic_system" + ) + data_file_global = str( + Path(__file__).parent / "water_tensor/dipole/global_system" + ) + self.config["training"]["training_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["training"]["validation_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["model"] = deepcopy(model_se_e2_a) + self.config["model"]["atom_exclude_types"] = [1] + self.config["model"]["fitting_net"]["type"] = "dipole" + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + + def tearDown(self) -> None: + DPTrainTest.tearDown(self) + + +class TestDipoleModelDPA1(unittest.TestCase, DPTrainTest): + def setUp(self): + input_json = str(Path(__file__).parent / "water_tensor/se_e2_a.json") + with open(input_json) as f: + self.config = json.load(f) + data_file_atomic = str( + Path(__file__).parent / "water_tensor/dipole/atomic_system" + ) + data_file_global = str( + Path(__file__).parent / "water_tensor/dipole/global_system" + ) + self.config["training"]["training_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["training"]["validation_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["model"] = deepcopy(model_dpa1) + self.config["model"]["atom_exclude_types"] = [1] + self.config["model"]["fitting_net"]["type"] = "dipole" + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + + def tearDown(self) -> None: + DPTrainTest.tearDown(self) + + +@unittest.skip("DipoleModelDPA2 not supported at the moment") +class TestDipoleModelDPA2(unittest.TestCase, DPTrainTest): + def setUp(self): + input_json = str(Path(__file__).parent / "water_tensor/se_e2_a.json") + with open(input_json) as f: + self.config = json.load(f) + data_file_atomic = str( + Path(__file__).parent / "water_tensor/dipole/atomic_system" + ) + data_file_global = str( + Path(__file__).parent / "water_tensor/dipole/global_system" + ) + self.config["training"]["training_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["training"]["validation_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["model"] = deepcopy(model_dpa2) + self.config["model"]["atom_exclude_types"] = [1] + self.config["model"]["fitting_net"]["type"] = "dipole" + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + + def tearDown(self) -> None: + DPTrainTest.tearDown(self) + + +class TestPolarModelSeA(unittest.TestCase, DPTrainTest): + def setUp(self): + input_json = str(Path(__file__).parent / "water_tensor/se_e2_a.json") + with open(input_json) as f: + self.config = json.load(f) + data_file_atomic = str( + Path(__file__).parent / "water_tensor/polar/atomic_system" + ) + data_file_global = str( + Path(__file__).parent / "water_tensor/polar/global_system" + ) + self.config["training"]["training_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["training"]["validation_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["model"] = deepcopy(model_se_e2_a) + self.config["model"]["atom_exclude_types"] = [1] + self.config["model"]["fitting_net"]["type"] = "polar" + self.config["model"]["fitting_net"]["fit_diag"] = False + self.config["model"]["fitting_net"]["shift_diag"] = False + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + # can not set requires_grad false for all parameters, + # because the input coord has no grad, thus the loss if all set to false + self.not_all_grad = True + + def tearDown(self) -> None: + DPTrainTest.tearDown(self) + + +class TestPolarModelDPA1(unittest.TestCase, DPTrainTest): + def setUp(self): + input_json = str(Path(__file__).parent / "water_tensor/se_e2_a.json") + with open(input_json) as f: + self.config = json.load(f) + data_file_atomic = str( + Path(__file__).parent / "water_tensor/polar/atomic_system" + ) + data_file_global = str( + Path(__file__).parent / "water_tensor/polar/global_system" + ) + self.config["training"]["training_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["training"]["validation_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["model"] = deepcopy(model_dpa1) + self.config["model"]["atom_exclude_types"] = [1] + self.config["model"]["fitting_net"]["type"] = "polar" + self.config["model"]["fitting_net"]["fit_diag"] = False + self.config["model"]["fitting_net"]["shift_diag"] = False + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + # can not set requires_grad false for all parameters, + # because the input coord has no grad, thus the loss if all set to false + self.not_all_grad = True + + def tearDown(self) -> None: + DPTrainTest.tearDown(self) + + +class TestPolarModelDPA2(unittest.TestCase, DPTrainTest): + def setUp(self): + input_json = str(Path(__file__).parent / "water_tensor/se_e2_a.json") + with open(input_json) as f: + self.config = json.load(f) + data_file_atomic = str( + Path(__file__).parent / "water_tensor/polar/atomic_system" + ) + data_file_global = str( + Path(__file__).parent / "water_tensor/polar/global_system" + ) + self.config["training"]["training_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["training"]["validation_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["model"] = deepcopy(model_dpa2) + self.config["model"]["atom_exclude_types"] = [1] + self.config["model"]["fitting_net"]["type"] = "polar" + self.config["model"]["fitting_net"]["fit_diag"] = False + self.config["model"]["fitting_net"]["shift_diag"] = False + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + # can not set requires_grad false for all parameters, + # because the input coord has no grad, thus the loss if all set to false + self.not_all_grad = True + + def tearDown(self) -> None: + DPTrainTest.tearDown(self) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/test_update_sel.py b/source/tests/pd/test_update_sel.py new file mode 100644 index 0000000000..5ee36e75ff --- /dev/null +++ b/source/tests/pd/test_update_sel.py @@ -0,0 +1,190 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import random +import unittest +from unittest.mock import ( + patch, +) + +from deepmd.pd.model.model.model import ( + BaseModel, +) +from deepmd.pd.utils.update_sel import ( + UpdateSel, +) + +from ..seed import ( + GLOBAL_SEED, +) + + +def update_sel(jdata): + type_map = jdata["model"].get("type_map") + train_data = None + jdata["model"], _ = BaseModel.update_sel(train_data, type_map, jdata["model"]) + return jdata + + +class TestTrain(unittest.TestCase): + def setUp(self) -> None: + self.update_sel = UpdateSel() + self.mock_min_nbor_dist = random.Random(GLOBAL_SEED).random() + return super().setUp() + + @patch("deepmd.pd.utils.update_sel.UpdateSel.get_nbor_stat") + def test_update_one_sel(self, sel_mock): + sel_mock.return_value = self.mock_min_nbor_dist, [10, 20] + + min_nbor_dist, sel = self.update_sel.update_one_sel(None, None, 6, "auto") + # self.assertEqual(descriptor['sel'], [11,22]) + self.assertEqual(sel, [12, 24]) + self.assertAlmostEqual(min_nbor_dist, self.mock_min_nbor_dist) + min_nbor_dist, sel = self.update_sel.update_one_sel(None, None, 6, "auto:1.5") + # self.assertEqual(descriptor['sel'], [15,30]) + self.assertEqual(sel, [16, 32]) + self.assertAlmostEqual(min_nbor_dist, self.mock_min_nbor_dist) + + @patch("deepmd.pd.utils.update_sel.UpdateSel.get_nbor_stat") + def test_update_sel_hybrid(self, sel_mock): + sel_mock.return_value = self.mock_min_nbor_dist, [10, 20] + + jdata = { + "model": { + "descriptor": { + "type": "hybrid", + "list": [ + {"type": "se_e2_a", "rcut": 6, "sel": "auto"}, + {"type": "se_e2_a", "rcut": 6, "sel": "auto:1.5"}, + ], + } + }, + "training": {"training_data": {}}, + } + expected_out = { + "model": { + "descriptor": { + "type": "hybrid", + "list": [ + {"type": "se_e2_a", "rcut": 6, "sel": [12, 24]}, + {"type": "se_e2_a", "rcut": 6, "sel": [16, 32]}, + ], + } + }, + "training": {"training_data": {}}, + } + jdata = update_sel(jdata) + self.assertEqual(jdata, expected_out) + + @patch("deepmd.pd.utils.update_sel.UpdateSel.get_nbor_stat") + def test_update_sel(self, sel_mock): + sel_mock.return_value = self.mock_min_nbor_dist, [10, 20] + + jdata = { + "model": {"descriptor": {"type": "se_e2_a", "rcut": 6, "sel": "auto"}}, + "training": {"training_data": {}}, + } + expected_out = { + "model": {"descriptor": {"type": "se_e2_a", "rcut": 6, "sel": [12, 24]}}, + "training": {"training_data": {}}, + } + jdata = update_sel(jdata) + self.assertEqual(jdata, expected_out) + + @patch("deepmd.pd.utils.update_sel.UpdateSel.get_nbor_stat") + def test_update_sel_atten_auto(self, sel_mock): + sel_mock.return_value = self.mock_min_nbor_dist, [25] + + jdata = { + "model": { + "descriptor": { + "type": "se_atten", + "sel": "auto", + "rcut": 6, + } + }, + "training": {"training_data": {}}, + } + expected_out = { + "model": { + "descriptor": { + "type": "se_atten", + "sel": 28, + "rcut": 6, + } + }, + "training": {"training_data": {}}, + } + jdata = update_sel(jdata) + self.assertEqual(jdata, expected_out) + + @patch("deepmd.pd.utils.update_sel.UpdateSel.get_nbor_stat") + def test_update_sel_atten_int(self, sel_mock): + sel_mock.return_value = self.mock_min_nbor_dist, [25] + + jdata = { + "model": { + "descriptor": { + "type": "se_atten", + "sel": 30, + "rcut": 6, + } + }, + "training": {"training_data": {}}, + } + expected_out = { + "model": { + "descriptor": { + "type": "se_atten", + "sel": 30, + "rcut": 6, + } + }, + "training": {"training_data": {}}, + } + jdata = update_sel(jdata) + self.assertEqual(jdata, expected_out) + + @patch("deepmd.pd.utils.update_sel.UpdateSel.get_nbor_stat") + def test_update_sel_atten_list(self, sel_mock): + sel_mock.return_value = self.mock_min_nbor_dist, [25] + + jdata = { + "model": { + "descriptor": { + "type": "se_atten", + "sel": 30, + "rcut": 6, + } + }, + "training": {"training_data": {}}, + } + expected_out = { + "model": { + "descriptor": { + "type": "se_atten", + "sel": 30, + "rcut": 6, + } + }, + "training": {"training_data": {}}, + } + jdata = update_sel(jdata) + self.assertEqual(jdata, expected_out) + + def test_skip_frozen(self): + jdata = { + "model": { + "type": "frozen", + }, + "training": {"training_data": {}}, + } + expected_out = jdata.copy() + jdata = update_sel(jdata) + self.assertEqual(jdata, expected_out) + + def test_wrap_up_4(self): + self.assertEqual(self.update_sel.wrap_up_4(12), 3 * 4) + self.assertEqual(self.update_sel.wrap_up_4(13), 4 * 4) + self.assertEqual(self.update_sel.wrap_up_4(14), 4 * 4) + self.assertEqual(self.update_sel.wrap_up_4(15), 4 * 4) + self.assertEqual(self.update_sel.wrap_up_4(16), 4 * 4) + self.assertEqual(self.update_sel.wrap_up_4(17), 5 * 4) diff --git a/source/tests/pd/test_utils.py b/source/tests/pd/test_utils.py new file mode 100644 index 0000000000..8d25cff964 --- /dev/null +++ b/source/tests/pd/test_utils.py @@ -0,0 +1,35 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import unittest + +import numpy as np +import paddle + +from deepmd.pd.utils.utils import ( + to_numpy_array, + to_paddle_tensor, +) + +from ..seed import ( + GLOBAL_SEED, +) + + +class TestCvt(unittest.TestCase): + def test_to_numpy(self): + rng = np.random.default_rng(GLOBAL_SEED) + foo = rng.normal([3, 4]) + for ptp, npp in zip( + [paddle.float16, paddle.float32, paddle.float64], + [np.float16, np.float32, np.float64], + ): + foo = foo.astype(npp) + bar = to_paddle_tensor(foo) + self.assertEqual(bar.dtype, ptp) + onk = to_numpy_array(bar) + self.assertEqual(onk.dtype, npp) + with self.assertRaises(ValueError) as ee: + foo = foo.astype(np.int8) + bar = to_paddle_tensor(foo) + with self.assertRaises(ValueError) as ee: + bar = to_paddle_tensor(foo) + bar = to_numpy_array(bar.int()) From 5e4edd7207e87e7c0c0aa6d621efdf0422fb5edf Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 22 Oct 2024 10:37:06 +0800 Subject: [PATCH 58/93] fix part of codes --- .../descriptor/repformer_layer_old_impl.py | 752 ------------------ deepmd/utils/batch_size.py | 11 +- deepmd/utils/data.py | 23 +- doc/install/install-from-source.md | 26 +- pyproject.toml | 2 +- 5 files changed, 10 insertions(+), 804 deletions(-) delete mode 100644 deepmd/pd/model/descriptor/repformer_layer_old_impl.py diff --git a/deepmd/pd/model/descriptor/repformer_layer_old_impl.py b/deepmd/pd/model/descriptor/repformer_layer_old_impl.py deleted file mode 100644 index 660ea57cf1..0000000000 --- a/deepmd/pd/model/descriptor/repformer_layer_old_impl.py +++ /dev/null @@ -1,752 +0,0 @@ -# SPDX-License-Identifier: LGPL-3.0-or-later -from typing import ( - Callable, -) - -import paddle - -from deepmd.pd.model.network.network import ( - SimpleLinear, -) -from deepmd.pd.utils import ( - aux, - env, -) -from deepmd.pd.utils.utils import ( - ActivationFn, -) - - -def _make_nei_g1( - g1_ext: paddle.Tensor, - nlist: paddle.Tensor, -) -> paddle.Tensor: - # nlist: nb x nloc x nnei - nb, nloc, nnei = nlist.shape - # g1_ext: nb x nall x ng1 - ng1 = g1_ext.shape[-1] - # index: nb x (nloc x nnei) x ng1 - index = nlist.reshape([nb, nloc * nnei]).unsqueeze(-1).expand([-1, -1, ng1]) - # gg1 : nb x (nloc x nnei) x ng1 - # print(g1_ext.shape, index.shape) - # gg1 = paddle.take_along_axis(g1_ext, axis=1, index=index) - gg1 = aux.take_along_axis(g1_ext, axis=1, indices=index) - # gg1 : nb x nloc x nnei x ng1 - gg1 = gg1.reshape([nb, nloc, nnei, ng1]) - return gg1 - - -def _apply_nlist_mask( - gg: paddle.Tensor, - nlist_mask: paddle.Tensor, -) -> paddle.Tensor: - # gg: nf x nloc x nnei x ng - # msk: nf x nloc x nnei - return gg.masked_fill(~nlist_mask.unsqueeze(-1), 0.0) - - -def _apply_switch(gg: paddle.Tensor, sw: paddle.Tensor) -> paddle.Tensor: - # gg: nf x nloc x nnei x ng - # sw: nf x nloc x nnei - return gg * sw.unsqueeze(-1) - - -def _apply_h_norm( - hh: paddle.Tensor, # nf x nloc x nnei x 3 -) -> paddle.Tensor: - """Normalize h by the std of vector length. - do not have an idea if this is a good way. - """ - nf, nl, nnei, _ = hh.shape - # nf x nloc x nnei - # normh = paddle.linalg.norm(hh, axis=-1) - normh = aux.norm(hh, axis=-1) - # nf x nloc - std = paddle.std(normh, axis=-1) - # nf x nloc x nnei x 3 - hh = hh[:, :, :, :] / (1.0 + std[:, :, None, None]) - return hh - - -class Atten2Map(paddle.nn.Layer): - def __init__( - self, - ni: int, - nd: int, - nh: int, - has_gate: bool = False, # apply gate to attn map - smooth: bool = True, - attnw_shift: float = 20.0, - ): - super().__init__() - self.ni = ni - self.nd = nd - self.nh = nh - self.mapqk = SimpleLinear(ni, nd * 2 * nh, bias=False) # todo - self.has_gate = has_gate - self.smooth = smooth - self.attnw_shift = attnw_shift - - def forward( - self, - g2: paddle.Tensor, # nb x nloc x nnei x ng2 - h2: paddle.Tensor, # nb x nloc x nnei x 3 - nlist_mask: paddle.Tensor, # nb x nloc x nnei - sw: paddle.Tensor, # nb x nloc x nnei - ) -> paddle.Tensor: - ( - nb, - nloc, - nnei, - _, - ) = g2.shape - nd, nh = self.nd, self.nh - # nb x nloc x nnei x nd x (nh x 2) - g2qk = self.mapqk(g2).reshape([nb, nloc, nnei, nd, nh * 2]) - # nb x nloc x (nh x 2) x nnei x nd - g2qk = paddle.transpose(g2qk, (0, 1, 4, 2, 3)) - # nb x nloc x nh x nnei x nd - g2q, g2k = paddle.split(g2qk, aux.sec(g2qk.shape[2], nh), axis=2) - # g2q = paddle.nn.functional.normalize(g2q, axis=-1) - # g2k = paddle.nn.functional.normalize(g2k, axis=-1) - # nb x nloc x nh x nnei x nnei - attnw = paddle.matmul(g2q, paddle.transpose(g2k, [0, 1, 2, 4, 3])) / nd**0.5 - if self.has_gate: - gate = paddle.matmul(h2, paddle.transpose(h2, [0, 1, 3, 2])).unsqueeze(-3) - attnw = attnw * gate - # mask the attenmap, nb x nloc x 1 x 1 x nnei - attnw_mask = ~nlist_mask.unsqueeze(2).unsqueeze(2) - # mask the attenmap, nb x nloc x 1 x nnei x 1 - attnw_mask_c = ~nlist_mask.unsqueeze(2).unsqueeze(-1) - if self.smooth: - attnw = (attnw + self.attnw_shift) * sw[:, :, None, :, None] * sw[ - :, :, None, None, : - ] - self.attnw_shift - else: - attnw = attnw.masked_fill( - attnw_mask, - float("-inf"), - ) - attnw = paddle.nn.functional.softmax(attnw, axis=-1) - attnw = attnw.masked_fill( - attnw_mask, - 0.0, - ) - # nb x nloc x nh x nnei x nnei - attnw = attnw.masked_fill( - attnw_mask_c, - 0.0, - ) - if self.smooth: - attnw = attnw * sw[:, :, None, :, None] * sw[:, :, None, None, :] - # nb x nloc x nnei x nnei - h2h2t = paddle.matmul(h2, paddle.transpose(h2, [0, 1, 3, 2])) / 3.0**0.5 - # nb x nloc x nh x nnei x nnei - ret = attnw * h2h2t[:, :, None, :, :] - # ret = paddle.nn.functional.softmax(g2qk, axis=-1) - # nb x nloc x nnei x nnei x nh - ret = paddle.transpose(ret, (0, 1, 3, 4, 2)) - return ret - - -class Atten2MultiHeadApply(paddle.nn.Layer): - def __init__( - self, - ni: int, - nh: int, - ): - super().__init__() - self.ni = ni - self.nh = nh - self.mapv = SimpleLinear(ni, ni * nh, bias=False) - self.head_map = SimpleLinear(ni * nh, ni) - - def forward( - self, - AA: paddle.Tensor, # nf x nloc x nnei x nnei x nh - g2: paddle.Tensor, # nf x nloc x nnei x ng2 - ) -> paddle.Tensor: - nf, nloc, nnei, ng2 = g2.shape - nh = self.nh - # nf x nloc x nnei x ng2 x nh - g2v = self.mapv(g2).reshape([nf, nloc, nnei, ng2, nh]) - # nf x nloc x nh x nnei x ng2 - g2v = paddle.transpose(g2v, (0, 1, 4, 2, 3)) - # g2v = paddle.nn.functional.normalize(g2v, axis=-1) - # nf x nloc x nh x nnei x nnei - AA = paddle.transpose(AA, (0, 1, 4, 2, 3)) - # nf x nloc x nh x nnei x ng2 - ret = paddle.matmul(AA, g2v) - # nf x nloc x nnei x ng2 x nh - ret = paddle.transpose(ret, (0, 1, 3, 4, 2)).reshape( - [nf, nloc, nnei, (ng2 * nh)] - ) - # nf x nloc x nnei x ng2 - return self.head_map(ret) - - -class Atten2EquiVarApply(paddle.nn.Layer): - def __init__( - self, - ni: int, - nh: int, - ): - super().__init__() - self.ni = ni - self.nh = nh - self.head_map = SimpleLinear(nh, 1, bias=False) - - def forward( - self, - AA: paddle.Tensor, # nf x nloc x nnei x nnei x nh - h2: paddle.Tensor, # nf x nloc x nnei x 3 - ) -> paddle.Tensor: - nf, nloc, nnei, _ = h2.shape - nh = self.nh - # nf x nloc x nh x nnei x nnei - AA = paddle.transpose(AA, (0, 1, 4, 2, 3)) - h2m = paddle.unsqueeze(h2, axis=2) - # nf x nloc x nh x nnei x 3 - h2m = paddle.tile(h2m, [1, 1, nh, 1, 1]) - # nf x nloc x nh x nnei x 3 - ret = paddle.matmul(AA, h2m) - # nf x nloc x nnei x 3 x nh - ret = paddle.transpose(ret, (0, 1, 3, 4, 2)).reshape([nf, nloc, nnei, 3, nh]) - # nf x nloc x nnei x 3 - return paddle.squeeze(self.head_map(ret), axis=-1) - - -class LocalAtten(paddle.nn.Layer): - def __init__( - self, - ni: int, - nd: int, - nh: int, - smooth: bool = True, - attnw_shift: float = 20.0, - ): - super().__init__() - self.ni = ni - self.nd = nd - self.nh = nh - self.mapq = SimpleLinear(ni, nd * 1 * nh, bias=False) - self.mapkv = SimpleLinear(ni, (nd + ni) * nh, bias=False) - self.head_map = SimpleLinear(ni * nh, ni) - self.smooth = smooth - self.attnw_shift = attnw_shift - - def forward( - self, - g1: paddle.Tensor, # nb x nloc x ng1 - gg1: paddle.Tensor, # nb x nloc x nnei x ng1 - nlist_mask: paddle.Tensor, # nb x nloc x nnei - sw: paddle.Tensor, # nb x nloc x nnei - ) -> paddle.Tensor: - nb, nloc, nnei = nlist_mask.shape - ni, nd, nh = self.ni, self.nd, self.nh - assert ni == g1.shape[-1] - assert ni == gg1.shape[-1] - # nb x nloc x nd x nh - g1q = self.mapq(g1).reshape([nb, nloc, nd, nh]) - # nb x nloc x nh x nd - g1q = paddle.transpose(g1q, (0, 1, 3, 2)) - # nb x nloc x nnei x (nd+ni) x nh - gg1kv = self.mapkv(gg1).reshape([nb, nloc, nnei, nd + ni, nh]) - gg1kv = paddle.transpose(gg1kv, (0, 1, 4, 2, 3)) - # nb x nloc x nh x nnei x nd, nb x nloc x nh x nnei x ng1 - gg1k, gg1v = paddle.split(gg1kv, [nd, ni], axis=-1) - - # nb x nloc x nh x 1 x nnei - attnw = ( - paddle.matmul(g1q.unsqueeze(-2), paddle.transpose(gg1k, [0, 1, 2, 4, 3])) - / nd**0.5 - ) - # nb x nloc x nh x nnei - attnw: paddle.Tensor = attnw.squeeze(-2) - # mask the attenmap, nb x nloc x 1 x nnei - attnw_mask = ~nlist_mask.unsqueeze(-2) - # nb x nloc x nh x nnei - if self.smooth: - attnw = (attnw + self.attnw_shift) * sw.unsqueeze(-2) - self.attnw_shift - else: - attnw = attnw.masked_fill( - attnw_mask, - float("-inf"), - ) - attnw = paddle.nn.functional.softmax(attnw, axis=-1) - attnw = attnw.masked_fill( - attnw_mask, - 0.0, - ) - if self.smooth: - attnw = attnw * sw.unsqueeze(-2) - - # nb x nloc x nh x ng1 - ret = ( - paddle.matmul(attnw.unsqueeze(-2), gg1v) - .squeeze(-2) - .reshape([nb, nloc, nh * ni]) - ) - # nb x nloc x ng1 - ret = self.head_map(ret) - return ret - - -class RepformerLayer(paddle.nn.Layer): - def __init__( - self, - rcut, - rcut_smth, - sel: int, - ntypes: int, - g1_dim=128, - g2_dim=16, - axis_neuron: int = 4, - update_chnnl_2: bool = True, - do_bn_mode: str = "no", - bn_momentum: float = 0.1, - update_g1_has_conv: bool = True, - update_g1_has_drrd: bool = True, - update_g1_has_grrg: bool = True, - update_g1_has_attn: bool = True, - update_g2_has_g1g1: bool = True, - update_g2_has_attn: bool = True, - update_h2: bool = False, - attn1_hidden: int = 64, - attn1_nhead: int = 4, - attn2_hidden: int = 16, - attn2_nhead: int = 4, - attn2_has_gate: bool = False, - activation_function: str = "tanh", - update_style: str = "res_avg", - set_davg_zero: bool = True, # TODO - smooth: bool = True, - ): - super().__init__() - self.epsilon = 1e-4 # protection of 1./nnei - self.rcut = rcut - self.rcut_smth = rcut_smth - self.ntypes = ntypes - sel = [sel] if isinstance(sel, int) else sel - self.nnei = sum(sel) - assert len(sel) == 1 - self.sel = paddle.to_tensor(sel, place=env.DEVICE) # pylint: disable=no-explicit-dtype - self.sec = self.sel - self.axis_neuron = axis_neuron - self.set_davg_zero = set_davg_zero - self.do_bn_mode = do_bn_mode - self.bn_momentum = bn_momentum - self.act = ActivationFn(activation_function) - self.update_g1_has_grrg = update_g1_has_grrg - self.update_g1_has_drrd = update_g1_has_drrd - self.update_g1_has_conv = update_g1_has_conv - self.update_g1_has_attn = update_g1_has_attn - self.update_chnnl_2 = update_chnnl_2 - self.update_g2_has_g1g1 = update_g2_has_g1g1 if self.update_chnnl_2 else False - self.update_g2_has_attn = update_g2_has_attn if self.update_chnnl_2 else False - self.update_h2 = update_h2 if self.update_chnnl_2 else False - del update_g2_has_g1g1, update_g2_has_attn, update_h2 - self.update_style = update_style - self.smooth = smooth - self.g1_dim = g1_dim - self.g2_dim = g2_dim - - g1_in_dim = self.cal_1_dim(g1_dim, g2_dim, self.axis_neuron) - self.linear1 = SimpleLinear(g1_in_dim, g1_dim) - self.linear2 = None - self.proj_g1g2 = None - self.proj_g1g1g2 = None - self.attn2g_map = None - self.attn2_mh_apply = None - self.attn2_lm = None - self.attn2h_map = None - self.attn2_ev_apply = None - self.loc_attn = None - - if self.update_chnnl_2: - self.linear2 = SimpleLinear(g2_dim, g2_dim) - if self.update_g1_has_conv: - self.proj_g1g2 = SimpleLinear(g1_dim, g2_dim, bias=False) - if self.update_g2_has_g1g1: - self.proj_g1g1g2 = SimpleLinear(g1_dim, g2_dim, bias=False) - if self.update_g2_has_attn: - self.attn2g_map = Atten2Map( - g2_dim, attn2_hidden, attn2_nhead, attn2_has_gate, self.smooth - ) - self.attn2_mh_apply = Atten2MultiHeadApply(g2_dim, attn2_nhead) - self.attn2_lm = paddle.nn.LayerNorm( - g2_dim, - ).to(device=env.DEVICE) - if self.update_h2: - self.attn2h_map = Atten2Map( - g2_dim, attn2_hidden, attn2_nhead, attn2_has_gate, self.smooth - ) - self.attn2_ev_apply = Atten2EquiVarApply(g2_dim, attn2_nhead) - if self.update_g1_has_attn: - self.loc_attn = LocalAtten(g1_dim, attn1_hidden, attn1_nhead, self.smooth) - - if self.do_bn_mode == "uniform": - self.bn1 = self._bn_layer() - self.bn2 = self._bn_layer() - elif self.do_bn_mode == "component": - self.bn1 = self._bn_layer(nf=g1_dim) - self.bn2 = self._bn_layer(nf=g2_dim) - elif self.do_bn_mode == "no": - self.bn1, self.bn2 = None, None - else: - raise RuntimeError(f"unknown bn_mode {self.do_bn_mode}") - - def cal_1_dim(self, g1d: int, g2d: int, ax: int) -> int: - ret = g1d - if self.update_g1_has_grrg: - ret += g2d * ax - if self.update_g1_has_drrd: - ret += g1d * ax - if self.update_g1_has_conv: - ret += g2d - return ret - - def _update_h2( - self, - g2: paddle.Tensor, - h2: paddle.Tensor, - nlist_mask: paddle.Tensor, - sw: paddle.Tensor, - ) -> paddle.Tensor: - assert self.attn2h_map is not None - assert self.attn2_ev_apply is not None - nb, nloc, nnei, _ = g2.shape - # # nb x nloc x nnei x nh2 - # h2_1 = self.attn2_ev_apply(AA, h2) - # h2_update.append(h2_1) - # nb x nloc x nnei x nnei x nh - AAh = self.attn2h_map(g2, h2, nlist_mask, sw) - # nb x nloc x nnei x nh2 - h2_1 = self.attn2_ev_apply(AAh, h2) - return h2_1 - - def _update_g1_conv( - self, - gg1: paddle.Tensor, - g2: paddle.Tensor, - nlist_mask: paddle.Tensor, - sw: paddle.Tensor, - ) -> paddle.Tensor: - assert self.proj_g1g2 is not None - nb, nloc, nnei, _ = g2.shape - ng1 = gg1.shape[-1] - ng2 = g2.shape[-1] - # gg1 : nb x nloc x nnei x ng2 - gg1 = self.proj_g1g2(gg1).reshape([nb, nloc, nnei, ng2]) - # nb x nloc x nnei x ng2 - gg1 = _apply_nlist_mask(gg1, nlist_mask) - if not self.smooth: - # normalized by number of neighbors, not smooth - # nb x nloc x 1 - invnnei = 1.0 / ( - self.epsilon + paddle.sum(nlist_mask.astype(gg1.dtype), axis=-1) - ).unsqueeze(-1) - else: - gg1 = _apply_switch(gg1, sw) - invnnei = (1.0 / float(nnei)) * paddle.ones( - (nb, nloc, 1), dtype=env.GLOBAL_PD_FLOAT_PRECISION - ).to(device=gg1.place) - # nb x nloc x ng2 - g1_11 = paddle.sum(g2 * gg1, axis=2) * invnnei - return g1_11 - - def _cal_h2g2( - self, - g2: paddle.Tensor, - h2: paddle.Tensor, - nlist_mask: paddle.Tensor, - sw: paddle.Tensor, - ) -> paddle.Tensor: - # g2: nf x nloc x nnei x ng2 - # h2: nf x nloc x nnei x 3 - # msk: nf x nloc x nnei - nb, nloc, nnei, _ = g2.shape - ng2 = g2.shape[-1] - # nb x nloc x nnei x ng2 - g2 = _apply_nlist_mask(g2, nlist_mask) - if not self.smooth: - # nb x nloc - invnnei = 1.0 / ( - self.epsilon + paddle.sum(nlist_mask.astype(g2.dtype), axis=-1) - ) - # nb x nloc x 1 x 1 - invnnei = invnnei.unsqueeze(-1).unsqueeze(-1) - else: - g2 = _apply_switch(g2, sw) - invnnei = (1.0 / float(nnei)) * paddle.ones( - (nb, nloc, 1, 1), dtype=env.GLOBAL_PD_FLOAT_PRECISION - ).to(device=g2.place) - # nb x nloc x 3 x ng2 - h2g2 = paddle.matmul(paddle.transpose(h2, [0, 1, 3, 2]), g2) * invnnei - return h2g2 - - def _cal_grrg(self, h2g2: paddle.Tensor) -> paddle.Tensor: - # nb x nloc x 3 x ng2 - nb, nloc, _, ng2 = h2g2.shape - # nb x nloc x 3 x axis - h2g2m = paddle.split(h2g2, aux.sec(h2g2.shape[-1], self.axis_neuron), axis=-1)[ - 0 - ] - # nb x nloc x axis x ng2 - g1_13 = paddle.matmul(paddle.transpose(h2g2m, [0, 1, 3, 2]), h2g2) / (3.0**1) - # nb x nloc x (axisxng2) - g1_13 = g1_13.reshape([nb, nloc, self.axis_neuron * ng2]) - return g1_13 - - def _update_g1_grrg( - self, - g2: paddle.Tensor, - h2: paddle.Tensor, - nlist_mask: paddle.Tensor, - sw: paddle.Tensor, - ) -> paddle.Tensor: - # g2: nf x nloc x nnei x ng2 - # h2: nf x nloc x nnei x 3 - # msk: nf x nloc x nnei - nb, nloc, nnei, _ = g2.shape - ng2 = g2.shape[-1] - # nb x nloc x 3 x ng2 - h2g2 = self._cal_h2g2(g2, h2, nlist_mask, sw) - # nb x nloc x (axisxng2) - g1_13 = self._cal_grrg(h2g2) - return g1_13 - - def _update_g2_g1g1( - self, - g1: paddle.Tensor, # nb x nloc x ng1 - gg1: paddle.Tensor, # nb x nloc x nnei x ng1 - nlist_mask: paddle.Tensor, # nb x nloc x nnei - sw: paddle.Tensor, # nb x nloc x nnei - ) -> paddle.Tensor: - ret = g1.unsqueeze(-2) * gg1 - # nb x nloc x nnei x ng1 - ret = _apply_nlist_mask(ret, nlist_mask) - if self.smooth: - ret = _apply_switch(ret, sw) - return ret - - def _apply_bn( - self, - bn_number: int, - gg: paddle.Tensor, - ): - if self.do_bn_mode == "uniform": - return self._apply_bn_uni(bn_number, gg) - elif self.do_bn_mode == "component": - return self._apply_bn_comp(bn_number, gg) - else: - return gg - - def _apply_nb_1(self, bn_number: int, gg: paddle.Tensor) -> paddle.Tensor: - nb, nl, nf = gg.shape - gg = gg.reshape([nb, 1, nl * nf]) - if bn_number == 1: - assert self.bn1 is not None - gg = self.bn1(gg) - else: - assert self.bn2 is not None - gg = self.bn2(gg) - return gg.reshape([nb, nl, nf]) - - def _apply_nb_2( - self, - bn_number: int, - gg: paddle.Tensor, - ) -> paddle.Tensor: - nb, nl, nnei, nf = gg.shape - gg = gg.reshape([nb, 1, nl * nnei * nf]) - if bn_number == 1: - assert self.bn1 is not None - gg = self.bn1(gg) - else: - assert self.bn2 is not None - gg = self.bn2(gg) - return gg.reshape([nb, nl, nnei, nf]) - - def _apply_bn_uni( - self, - bn_number: int, - gg: paddle.Tensor, - mode: str = "1", - ) -> paddle.Tensor: - if len(gg.shape) == 3: - return self._apply_nb_1(bn_number, gg) - elif len(gg.shape) == 4: - return self._apply_nb_2(bn_number, gg) - else: - raise RuntimeError(f"unsupported input shape {gg.shape}") - - def _apply_bn_comp( - self, - bn_number: int, - gg: paddle.Tensor, - ) -> paddle.Tensor: - ss = gg.shape - nf = ss[-1] - gg = gg.reshape([-1, nf]) - if bn_number == 1: - assert self.bn1 is not None - gg = self.bn1(gg).reshape([ss]) - else: - assert self.bn2 is not None - gg = self.bn2(gg).reshape([ss]) - return gg - - def forward( - self, - g1_ext: paddle.Tensor, # nf x nall x ng1 - g2: paddle.Tensor, # nf x nloc x nnei x ng2 - h2: paddle.Tensor, # nf x nloc x nnei x 3 - nlist: paddle.Tensor, # nf x nloc x nnei - nlist_mask: paddle.Tensor, # nf x nloc x nnei - sw: paddle.Tensor, # switch func, nf x nloc x nnei - ): - """ - Parameters - ---------- - g1_ext : nf x nall x ng1 extended single-atom chanel - g2 : nf x nloc x nnei x ng2 pair-atom channel, invariant - h2 : nf x nloc x nnei x 3 pair-atom channel, equivariant - nlist : nf x nloc x nnei neighbor list (padded neis are set to 0) - nlist_mask : nf x nloc x nnei masks of the neighbor list. real nei 1 otherwise 0 - sw : nf x nloc x nnei switch function - - Returns - ------- - g1: nf x nloc x ng1 updated single-atom chanel - g2: nf x nloc x nnei x ng2 updated pair-atom channel, invariant - h2: nf x nloc x nnei x 3 updated pair-atom channel, equivariant - """ - cal_gg1 = ( - self.update_g1_has_drrd - or self.update_g1_has_conv - or self.update_g1_has_attn - or self.update_g2_has_g1g1 - ) - - nb, nloc, nnei, _ = g2.shape - nall = g1_ext.shape[1] - g1, _ = paddle.split(g1_ext, [nloc, nall - nloc], axis=1) - assert [nb, nloc] == g1.shape[:2] - assert [nb, nloc, nnei] == h2.shape[:3] - ng1 = g1.shape[-1] - ng2 = g2.shape[-1] - nh2 = h2.shape[-1] - - if self.bn1 is not None: - g1 = self._apply_bn(1, g1) - if self.bn2 is not None: - g2 = self._apply_bn(2, g2) - if self.update_h2: - h2 = _apply_h_norm(h2) - - g2_update: list[paddle.Tensor] = [g2] - h2_update: list[paddle.Tensor] = [h2] - g1_update: list[paddle.Tensor] = [g1] - g1_mlp: list[paddle.Tensor] = [g1] - - if cal_gg1: - gg1 = _make_nei_g1(g1_ext, nlist) - else: - gg1 = None - - if self.update_chnnl_2: - # nb x nloc x nnei x ng2 - assert self.linear2 is not None - g2_1 = self.act(self.linear2(g2)) - g2_update.append(g2_1) - - if self.update_g2_has_g1g1: - assert gg1 is not None - assert self.proj_g1g1g2 is not None - g2_update.append( - self.proj_g1g1g2(self._update_g2_g1g1(g1, gg1, nlist_mask, sw)) - ) - - if self.update_g2_has_attn: - assert self.attn2g_map is not None - assert self.attn2_mh_apply is not None - assert self.attn2_lm is not None - # nb x nloc x nnei x nnei x nh - AAg = self.attn2g_map(g2, h2, nlist_mask, sw) - # nb x nloc x nnei x ng2 - g2_2 = self.attn2_mh_apply(AAg, g2) - g2_2 = self.attn2_lm(g2_2) - g2_update.append(g2_2) - - if self.update_h2: - h2_update.append(self._update_h2(g2, h2, nlist_mask, sw)) - - if self.update_g1_has_conv: - assert gg1 is not None - g1_mlp.append(self._update_g1_conv(gg1, g2, nlist_mask, sw)) - - if self.update_g1_has_grrg: - g1_mlp.append(self._update_g1_grrg(g2, h2, nlist_mask, sw)) - - if self.update_g1_has_drrd: - assert gg1 is not None - g1_mlp.append(self._update_g1_grrg(gg1, h2, nlist_mask, sw)) - - # nb x nloc x [ng1+ng2+(axisxng2)+(axisxng1)] - # conv grrg drrd - g1_1 = self.act(self.linear1(paddle.concat(g1_mlp, axis=-1))) - g1_update.append(g1_1) - - if self.update_g1_has_attn: - assert gg1 is not None - assert self.loc_attn is not None - g1_update.append(self.loc_attn(g1, gg1, nlist_mask, sw)) - - # update - if self.update_chnnl_2: - g2_new = self.list_update(g2_update) - h2_new = self.list_update(h2_update) - else: - g2_new, h2_new = g2, h2 - g1_new = self.list_update(g1_update) - return g1_new, g2_new, h2_new - - def list_update_res_avg( - self, - update_list: list[paddle.Tensor], - ) -> paddle.Tensor: - nitem = len(update_list) - uu = update_list[0] - for ii in range(1, nitem): - uu = uu + update_list[ii] - return uu / (float(nitem) ** 0.5) - - def list_update_res_incr(self, update_list: list[paddle.Tensor]) -> paddle.Tensor: - nitem = len(update_list) - uu = update_list[0] - scale = 1.0 / (float(nitem - 1) ** 0.5) if nitem > 1 else 0.0 - for ii in range(1, nitem): - uu = uu + scale * update_list[ii] - return uu - - def list_update(self, update_list: list[paddle.Tensor]) -> paddle.Tensor: - if self.update_style == "res_avg": - return self.list_update_res_avg(update_list) - elif self.update_style == "res_incr": - return self.list_update_res_incr(update_list) - else: - raise RuntimeError(f"unknown update style {self.update_style}") - - def _bn_layer( - self, - nf: int = 1, - ) -> Callable: - return paddle.nn.BatchNorm1D( - nf, - epsilon=1e-5, - momentum=self.bn_momentum, - weight_attr=False, - bias_attr=False, - use_global_stats=False, - ).to(device=env.DEVICE) diff --git a/deepmd/utils/batch_size.py b/deepmd/utils/batch_size.py index 0b679f55a4..ac78657081 100644 --- a/deepmd/utils/batch_size.py +++ b/deepmd/utils/batch_size.py @@ -16,11 +16,6 @@ OutOfMemoryError, ) -try: - import paddle -except ModuleNotFoundError: - pass - log = logging.getLogger(__name__) @@ -241,6 +236,12 @@ def concate_result(r): xp = array_api_compat.array_namespace(r[0]) ret = xp.concat(r, axis=0) elif str(r[0].__class__) == "": + try: + import paddle + except ModuleNotFoundError as e: + raise ModuleNotFoundError( + "The 'paddlepaddle' is required but not installed." + ) from e ret = paddle.concat(r, axis=0) else: raise RuntimeError(f"Unexpected result type {type(r[0])}") diff --git a/deepmd/utils/data.py b/deepmd/utils/data.py index 6331186e40..984d51f5fe 100644 --- a/deepmd/utils/data.py +++ b/deepmd/utils/data.py @@ -258,7 +258,7 @@ def get_item_paddle(self, index: int) -> dict: i = bisect.bisect_right(self.prefix_sum, index) frames = self._load_set(self.dirs[i]) frame = self._get_subdata(frames, index - self.prefix_sum[i]) - frame = self.reformat_data_paddle(frame) + frame = self.reformat_data_torch(frame) frame["fid"] = index return frame @@ -493,26 +493,7 @@ def reformat_data_torch(self, data): pass else: if kk in data and self.data_dict[kk]["atomic"]: - data[kk] = data[kk].reshape(-1, self.data_dict[kk]["ndof"]) - data["atype"] = data["type"] - if not self.pbc: - data["box"] = None - return data - - def reformat_data_paddle(self, data): - """Modify the data format for the requirements of Paddle backend. - - Parameters - ---------- - data - original data - """ - for kk in self.data_dict.keys(): - if "find_" in kk: - pass - else: - if kk in data and self.data_dict[kk]["atomic"]: - data[kk] = data[kk].reshape([-1, self.data_dict[kk]["ndof"]]) + data[kk] = data[kk].reshape((-1, self.data_dict[kk]["ndof"])) data["atype"] = data["type"] if not self.pbc: data["box"] = None diff --git a/doc/install/install-from-source.md b/doc/install/install-from-source.md index 573f07b82b..c64c566754 100644 --- a/doc/install/install-from-source.md +++ b/doc/install/install-from-source.md @@ -78,28 +78,6 @@ One can also [use conda](https://docs.deepmodeling.org/faq/conda.html) to instal ::: -:::: - -It is important that every time a new shell is started and one wants to use `DeePMD-kit`, the virtual environment should be activated by - -```bash -source $deepmd_venv/bin/activate -``` - -if one wants to skip out of the virtual environment, he/she can do - -```bash -deactivate -``` - -If one has multiple python interpreters named something like python3.x, it can be specified by, for example - -```bash -virtualenv -p python3.9 $deepmd_venv -``` - -::: - :::{tab-item} Paddle {{ paddle_icon }} To install Paddle, run @@ -115,8 +93,6 @@ python -m pip install --pre paddlepaddle -i https://www.paddlepaddle.org.cn/pack Follow [Paddle documentation](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html) to install Paddle built against different CUDA versions or without CUDA. -One can also [use conda](https://docs.deepmodeling.org/faq/conda.html) to install Paddle from [conda-forge](https://conda-forge.org). - ::: :::: @@ -136,7 +112,7 @@ deactivate If one has multiple python interpreters named something like python3.x, it can be specified by, for example ```bash -virtualenv -p python3.8 $deepmd_venv +virtualenv -p python3.9 $deepmd_venv ``` One should remember to activate the virtual environment every time he/she uses DeePMD-kit. diff --git a/pyproject.toml b/pyproject.toml index 139cc32fda..ee89d8176d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -213,7 +213,7 @@ test-command = [ "dp_ipi", "pytest {project}/source/tests/tf/test_lammps.py" ] -test-extras = ["cpu", "test", "lmp", "ipi", "torch"] +test-extras = ["cpu", "test", "lmp", "ipi", "torch", "paddle"] build = ["cp311-*"] skip = ["*-win32", "*-manylinux_i686", "*-musllinux*"] # TODO: uncomment to use the latest image when CUDA 11 is deprecated From 834d5124343edf61e30751e41c852081d0938724 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 22 Oct 2024 16:31:09 +0800 Subject: [PATCH 59/93] update document and test_python yaml --- .github/workflows/test_python.yml | 1 + backend/find_paddle.py | 2 +- doc/backend.md | 2 +- doc/install/install-from-source.md | 6 +++--- 4 files changed, 6 insertions(+), 5 deletions(-) diff --git a/.github/workflows/test_python.yml b/.github/workflows/test_python.yml index 87d7266e03..36e420c174 100644 --- a/.github/workflows/test_python.yml +++ b/.github/workflows/test_python.yml @@ -29,6 +29,7 @@ jobs: source/install/uv_with_retry.sh pip install --system "torch==2.3.0+cpu.cxx11.abi" -i https://download.pytorch.org/whl/ export PYTORCH_ROOT=$(python -c 'import torch;print(torch.__path__[0])') source/install/uv_with_retry.sh pip install --system --only-binary=horovod -e .[cpu,test,jax] horovod[tensorflow-cpu] mpi4py + source/install/uv_with_retry.sh pip install --system --pre "paddlepaddle" -i https://www.paddlepaddle.org.cn/packages/nightly/cpu/ env: # Please note that uv has some issues with finding # existing TensorFlow package. Currently, it uses diff --git a/backend/find_paddle.py b/backend/find_paddle.py index 3be6dbfa42..9a7ddd95a9 100644 --- a/backend/find_paddle.py +++ b/backend/find_paddle.py @@ -127,7 +127,7 @@ def get_pd_version(pd_path: Optional[Union[str, Path]]) -> str: Parameters ---------- pd_path : str or Path - pd Python library path + Paddle Python library path, e.g. "/python3.10/site-packages/paddle/" Returns ------- diff --git a/doc/backend.md b/doc/backend.md index c6136c435a..842c39a0b8 100644 --- a/doc/backend.md +++ b/doc/backend.md @@ -52,7 +52,7 @@ NumPy 1.21 or above is required. ### Training -When training and freezing a model, you can use `dp --tf` or `dp --pt` or `dp --pd` in the command line to switch the backend. +When training and freezing a model, you can use `dp --tf`, `dp --pt` or `dp --pd` in the command line to switch the backend. ### Inference diff --git a/doc/install/install-from-source.md b/doc/install/install-from-source.md index c64c566754..50a4f65a43 100644 --- a/doc/install/install-from-source.md +++ b/doc/install/install-from-source.md @@ -217,11 +217,11 @@ The path to the ROCM toolkit directory. If `ROCM_ROOT` is not set, it will look {{ pytorch_icon }} The path to PyTorch Python library. If not given, by default, the installer only finds PyTorch under the user site-package directory (`site.getusersitepackages()`) or the system site-package directory (`sysconfig.get_path("purelib")`) due to the limitation of [PEP-517](https://peps.python.org/pep-0517/). If not found, the latest PyTorch (or the environment variable `PYTORCH_VERSION` if given) from PyPI will be built against. ::: -:::{envvar} PADDLE_ROOT +:::{envvar} PADDLE_INFERENCE_DIR -**Type**: Path; **Default**: Detected automatically +**Type**: Path; **Default**: None -{{ paddle_icon }} The path to Paddle Python library. If not given, by default, the installer only finds Paddle under the user site-package directory (`site.getusersitepackages()`) or the system site-package directory (`sysconfig.get_path("purelib")`) due to the limitation of [PEP-517](https://peps.python.org/pep-0517/). If not found, the latest Paddle (or the environment variable `PADDLE_VERSION` if given) from PyPI will be built against. +{{ paddle_icon }} The path to Paddle inference library, e.g. `/path/to/paddle_inference_install_dir`. If `DP_ENABLE_PADDLE` is enabled, it needs to be specified manually; otherwise, installation will fail. ::: :::{envvar} DP_ENABLE_NATIVE_OPTIMIZATION From 9e9041678fa08ba2ba5fbc38d3760b52420c4cac Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 22 Oct 2024 16:49:52 +0800 Subject: [PATCH 60/93] remove typeAlias for not available in python3.9 --- deepmd/pd/model/network/init.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/deepmd/pd/model/network/init.py b/deepmd/pd/model/network/init.py index 1cb7cc30a4..7a83877f8f 100644 --- a/deepmd/pd/model/network/init.py +++ b/deepmd/pd/model/network/init.py @@ -5,16 +5,13 @@ import math import warnings -from typing import ( - TypeAlias, -) import paddle from paddle import ( Tensor, ) -PaddleGenerator: TypeAlias = paddle.base.libpaddle.Generator +PaddleGenerator = paddle.base.libpaddle.Generator # Copyright (c) 2024 The PyTorch Authors. All rights reserved. # From cfacca36c60b02ca3d0fb1394ef21d0dc56d0bf3 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 22 Oct 2024 17:11:27 +0800 Subject: [PATCH 61/93] update repformers.py --- deepmd/pd/model/descriptor/repformers.py | 129 +++++++++++------------ 1 file changed, 59 insertions(+), 70 deletions(-) diff --git a/deepmd/pd/model/descriptor/repformers.py b/deepmd/pd/model/descriptor/repformers.py index 11d30e2461..9fa68f1f9b 100644 --- a/deepmd/pd/model/descriptor/repformers.py +++ b/deepmd/pd/model/descriptor/repformers.py @@ -42,7 +42,6 @@ from .repformer_layer import ( RepformerLayer, ) -from .repformer_layer_old_impl import RepformerLayer as RepformerLayerOld # if not hasattr(paddle.ops.deepmd, "border_op"): @@ -103,7 +102,9 @@ def __init__( trainable_ln: bool = True, ln_eps: Optional[float] = 1e-5, seed: Optional[Union[int, list[int]]] = None, - old_impl: bool = False, + use_sqrt_nnei: bool = True, + g1_out_conv: bool = True, + g1_out_mlp: bool = True, ): r""" The repformer descriptor block. @@ -172,7 +173,7 @@ def __init__( The precision of the embedding net parameters. smooth : bool, optional Whether to use smoothness in processes such as attention weights calculation. - exclude_types : List[List[int]], optional + exclude_types : list[list[int]], optional The excluded pairs of types which have no interaction with each other. For example, `[[0, 1]]` means no interaction between type 0 and type 1. env_protection : float, optional @@ -180,6 +181,12 @@ def __init__( For example, when using paddings, there may be zero distances of neighbors, which may make division by zero error during environment matrix calculations without protection. trainable_ln : bool, optional Whether to use trainable shift and scale weights in layer normalization. + use_sqrt_nnei : bool, optional + Whether to use the square root of the number of neighbors for symmetrization_op normalization instead of using the number of neighbors directly. + g1_out_conv : bool, optional + Whether to put the convolutional update of g1 separately outside the concatenated MLP update. + g1_out_mlp : bool, optional + Whether to put the self MLP update of g1 separately outside the concatenated MLP update. ln_eps : float, optional The epsilon value for layer normalization. seed : int, optional @@ -220,6 +227,9 @@ def __init__( self.direct_dist = direct_dist self.act = ActivationFn(activation_function) self.smooth = smooth + self.use_sqrt_nnei = use_sqrt_nnei + self.g1_out_conv = g1_out_conv + self.g1_out_mlp = g1_out_mlp # order matters, placed after the assignment of self.ntypes self.reinit_exclude(exclude_types) self.env_protection = env_protection @@ -228,75 +238,48 @@ def __init__( self.ln_eps = ln_eps self.epsilon = 1e-4 self.seed = seed - self.old_impl = old_impl self.g2_embd = MLPLayer( 1, self.g2_dim, precision=precision, seed=child_seed(seed, 0) ) layers = [] for ii in range(nlayers): - if self.old_impl: - layers.append( - RepformerLayerOld( - self.rcut, - self.rcut_smth, - self.sel, - self.ntypes, - self.g1_dim, - self.g2_dim, - axis_neuron=self.axis_neuron, - update_chnnl_2=(ii != nlayers - 1), - update_g1_has_conv=self.update_g1_has_conv, - update_g1_has_drrd=self.update_g1_has_drrd, - update_g1_has_grrg=self.update_g1_has_grrg, - update_g1_has_attn=self.update_g1_has_attn, - update_g2_has_g1g1=self.update_g2_has_g1g1, - update_g2_has_attn=self.update_g2_has_attn, - update_h2=self.update_h2, - attn1_hidden=self.attn1_hidden, - attn1_nhead=self.attn1_nhead, - attn2_has_gate=self.attn2_has_gate, - attn2_hidden=self.attn2_hidden, - attn2_nhead=self.attn2_nhead, - activation_function=self.activation_function, - update_style=self.update_style, - smooth=self.smooth, - ) - ) - else: - layers.append( - RepformerLayer( - self.rcut, - self.rcut_smth, - self.sel, - self.ntypes, - self.g1_dim, - self.g2_dim, - axis_neuron=self.axis_neuron, - update_chnnl_2=(ii != nlayers - 1), - update_g1_has_conv=self.update_g1_has_conv, - update_g1_has_drrd=self.update_g1_has_drrd, - update_g1_has_grrg=self.update_g1_has_grrg, - update_g1_has_attn=self.update_g1_has_attn, - update_g2_has_g1g1=self.update_g2_has_g1g1, - update_g2_has_attn=self.update_g2_has_attn, - update_h2=self.update_h2, - attn1_hidden=self.attn1_hidden, - attn1_nhead=self.attn1_nhead, - attn2_has_gate=self.attn2_has_gate, - attn2_hidden=self.attn2_hidden, - attn2_nhead=self.attn2_nhead, - activation_function=self.activation_function, - update_style=self.update_style, - update_residual=self.update_residual, - update_residual_init=self.update_residual_init, - smooth=self.smooth, - trainable_ln=self.trainable_ln, - ln_eps=self.ln_eps, - precision=precision, - seed=child_seed(child_seed(seed, 1), ii), - ) + layers.append( + RepformerLayer( + self.rcut, + self.rcut_smth, + self.sel, + self.ntypes, + self.g1_dim, + self.g2_dim, + axis_neuron=self.axis_neuron, + update_chnnl_2=(ii != nlayers - 1), + update_g1_has_conv=self.update_g1_has_conv, + update_g1_has_drrd=self.update_g1_has_drrd, + update_g1_has_grrg=self.update_g1_has_grrg, + update_g1_has_attn=self.update_g1_has_attn, + update_g2_has_g1g1=self.update_g2_has_g1g1, + update_g2_has_attn=self.update_g2_has_attn, + update_h2=self.update_h2, + attn1_hidden=self.attn1_hidden, + attn1_nhead=self.attn1_nhead, + attn2_has_gate=self.attn2_has_gate, + attn2_hidden=self.attn2_hidden, + attn2_nhead=self.attn2_nhead, + activation_function=self.activation_function, + update_style=self.update_style, + update_residual=self.update_residual, + update_residual_init=self.update_residual_init, + smooth=self.smooth, + trainable_ln=self.trainable_ln, + ln_eps=self.ln_eps, + precision=precision, + use_sqrt_nnei=self.use_sqrt_nnei, + g1_out_conv=self.g1_out_conv, + g1_out_mlp=self.g1_out_mlp, + seed=child_seed(child_seed(seed, 1), ii), ) + ) self.layers = paddle.nn.LayerList(layers) wanted_shape = (self.ntypes, self.nnei, 4) @@ -413,7 +396,7 @@ def forward( atype = extended_atype[:, :nloc] # nb x nloc x nnei exclude_mask = self.emask(nlist, extended_atype) - nlist = paddle.where(exclude_mask != 0, nlist, -1) + nlist = paddle.where(exclude_mask != 0, nlist, paddle.full_like(nlist, -1)) # nb x nloc x nnei x 4, nb x nloc x nnei x 3, nb x nloc x nnei x 1 dmatrix, diff, sw = prod_env_mat( extended_coord, @@ -501,7 +484,13 @@ def forward( # nb x nloc x 3 x ng2 h2g2 = RepformerLayer._cal_hg( - g2, h2, nlist_mask, sw, smooth=self.smooth, epsilon=self.epsilon + g2, + h2, + nlist_mask, + sw, + smooth=self.smooth, + epsilon=self.epsilon, + use_sqrt_nnei=self.use_sqrt_nnei, ) # (nb x nloc) x ng2 x 3 rot_mat = paddle.transpose(h2g2, (0, 1, 3, 2)) @@ -518,11 +507,11 @@ def compute_input_stats( Parameters ---------- - merged : Union[Callable[[], List[dict]], List[dict]] - - List[dict]: A list of data samples from various data systems. + merged : Union[Callable[[], list[dict]], list[dict]] + - list[dict]: A list of data samples from various data systems. Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` originating from the `i`-th data system. - - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + - Callable[[], list[dict]]: A lazy function that returns data samples in the above format only when needed. Since the sampling process can be slow and memory-intensive, the lazy function helps by only sampling once. path : Optional[DPPath] From d492397a8a9c746b473009988f0c56048e6a4203 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Thu, 24 Oct 2024 18:42:50 +0800 Subject: [PATCH 62/93] remove old_impl code and redundant init.py --- deepmd/pd/model/descriptor/dpa1.py | 8 +- deepmd/pd/model/descriptor/dpa2.py | 169 ++- deepmd/pd/model/descriptor/se_a.py | 168 +-- deepmd/pd/model/descriptor/se_atten.py | 235 ++- deepmd/pd/model/descriptor/se_atten_v2.py | 6 +- deepmd/pd/model/descriptor/se_r.py | 8 +- deepmd/pd/model/network/network.py | 1637 +-------------------- deepmd/pd/model/task/dipole.py | 12 +- deepmd/pd/model/task/fitting.py | 122 +- deepmd/pd/model/task/polarizability.py | 24 +- deepmd/pd/utils/init.py | 515 ------- 11 files changed, 370 insertions(+), 2534 deletions(-) delete mode 100644 deepmd/pd/utils/init.py diff --git a/deepmd/pd/model/descriptor/dpa1.py b/deepmd/pd/model/descriptor/dpa1.py index 925235cb0c..4739eea0e1 100644 --- a/deepmd/pd/model/descriptor/dpa1.py +++ b/deepmd/pd/model/descriptor/dpa1.py @@ -245,7 +245,6 @@ def __init__( # not implemented spin=None, type: Optional[str] = None, - old_impl: bool = False, ): super().__init__() # Ensure compatibility with the deprecated stripped_type_embedding option. @@ -290,7 +289,6 @@ def __init__( trainable_ln=trainable_ln, ln_eps=ln_eps, seed=child_seed(seed, 1), - old_impl=old_impl, ) self.use_econf_tebd = use_econf_tebd self.use_tebd_bias = use_tebd_bias @@ -414,11 +412,11 @@ def compute_input_stats( Parameters ---------- - merged : Union[Callable[[], List[dict]], List[dict]] - - List[dict]: A list of data samples from various data systems. + merged : Union[Callable[[], list[dict]], list[dict]] + - list[dict]: A list of data samples from various data systems. Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` originating from the `i`-th data system. - - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + - Callable[[], list[dict]]: A lazy function that returns data samples in the above format only when needed. Since the sampling process can be slow and memory-intensive, the lazy function helps by only sampling once. path : Optional[DPPath] diff --git a/deepmd/pd/model/descriptor/dpa2.py b/deepmd/pd/model/descriptor/dpa2.py index d366c15560..8bbfc4b5c6 100644 --- a/deepmd/pd/model/descriptor/dpa2.py +++ b/deepmd/pd/model/descriptor/dpa2.py @@ -67,6 +67,9 @@ from .se_atten import ( DescrptBlockSeAtten, ) +from .se_t_tebd import ( + DescrptBlockSeTTebd, +) @BaseDescriptor.register("dpa2") @@ -90,7 +93,6 @@ def __init__( use_econf_tebd: bool = False, use_tebd_bias: bool = False, type_map: Optional[list[str]] = None, - old_impl: bool = False, ): r"""The DPA-2 descriptor. see https://arxiv.org/abs/2312.15492. @@ -106,7 +108,7 @@ def __init__( The precision of the embedding net parameters. smooth : bool, optional Whether to use smoothness in processes such as attention weights calculation. - exclude_types : List[List[int]], optional + exclude_types : list[list[int]], optional The excluded pairs of types which have no interaction with each other. For example, `[[0, 1]]` means no interaction between type 0 and type 1. env_protection : float, optional @@ -122,7 +124,7 @@ def __init__( Whether to use electronic configuration type embedding. use_tebd_bias : bool, Optional Whether to use bias in the type embedding layer. - type_map : List[str], Optional + type_map : list[str], Optional A list of strings. Give the name to each type of atoms. Returns @@ -175,6 +177,27 @@ def init_subclass_params(sub_data, sub_class): type_one_side=self.repinit_args.type_one_side, seed=child_seed(seed, 0), ) + self.use_three_body = self.repinit_args.use_three_body + if self.use_three_body: + self.repinit_three_body = DescrptBlockSeTTebd( + self.repinit_args.three_body_rcut, + self.repinit_args.three_body_rcut_smth, + self.repinit_args.three_body_sel, + ntypes, + neuron=self.repinit_args.three_body_neuron, + tebd_dim=self.repinit_args.tebd_dim, + tebd_input_mode=self.repinit_args.tebd_input_mode, + set_davg_zero=self.repinit_args.set_davg_zero, + exclude_types=exclude_types, + env_protection=env_protection, + activation_function=self.repinit_args.activation_function, + precision=precision, + resnet_dt=self.repinit_args.resnet_dt, + smooth=smooth, + seed=child_seed(seed, 5), + ) + else: + self.repinit_three_body = None self.repformers = DescrptBlockRepformers( self.repformer_args.rcut, self.repformer_args.rcut_smth, @@ -208,9 +231,26 @@ def init_subclass_params(sub_data, sub_class): precision=precision, trainable_ln=self.repformer_args.trainable_ln, ln_eps=self.repformer_args.ln_eps, + use_sqrt_nnei=self.repformer_args.use_sqrt_nnei, + g1_out_conv=self.repformer_args.g1_out_conv, + g1_out_mlp=self.repformer_args.g1_out_mlp, seed=child_seed(seed, 1), - old_impl=old_impl, ) + self.rcsl_list = [ + (self.repformers.get_rcut(), self.repformers.get_nsel()), + (self.repinit.get_rcut(), self.repinit.get_nsel()), + ] + if self.use_three_body: + self.rcsl_list.append( + (self.repinit_three_body.get_rcut(), self.repinit_three_body.get_nsel()) + ) + self.rcsl_list.sort() + for ii in range(1, len(self.rcsl_list)): + assert ( + self.rcsl_list[ii - 1][1] <= self.rcsl_list[ii][1] + ), "rcut and sel are not in the same order" + self.rcut_list = [ii[0] for ii in self.rcsl_list] + self.nsel_list = [ii[1] for ii in self.rcsl_list] self.use_econf_tebd = use_econf_tebd self.use_tebd_bias = use_tebd_bias self.type_map = type_map @@ -231,11 +271,16 @@ def init_subclass_params(sub_data, sub_class): self.trainable = trainable self.add_tebd_to_repinit_out = add_tebd_to_repinit_out - if self.repinit.dim_out == self.repformers.dim_in: + self.repinit_out_dim = self.repinit.dim_out + if self.repinit_args.use_three_body: + assert self.repinit_three_body is not None + self.repinit_out_dim += self.repinit_three_body.dim_out + + if self.repinit_out_dim == self.repformers.dim_in: self.g1_shape_tranform = Identity() else: self.g1_shape_tranform = MLPLayer( - self.repinit.dim_out, + self.repinit_out_dim, self.repformers.dim_in, bias=False, precision=precision, @@ -389,6 +434,7 @@ def change_type_map( self.ntypes = len(type_map) repinit = self.repinit repformers = self.repformers + repinit_three_body = self.repinit_three_body if has_new_type: # the avg and std of new types need to be updated extend_descrpt_stat( @@ -405,6 +451,14 @@ def change_type_map( if model_with_new_type_stat is not None else None, ) + if self.use_three_body: + extend_descrpt_stat( + repinit_three_body, + type_map, + des_with_stat=model_with_new_type_stat.repinit_three_body + if model_with_new_type_stat is not None + else None, + ) repinit.ntypes = self.ntypes repformers.ntypes = self.ntypes repinit.reinit_exclude(self.exclude_types) @@ -413,6 +467,11 @@ def change_type_map( repinit["dstd"] = repinit["dstd"][remap_index] repformers["davg"] = repformers["davg"][remap_index] repformers["dstd"] = repformers["dstd"][remap_index] + if self.use_three_body: + repinit_three_body.ntypes = self.ntypes + repinit_three_body.reinit_exclude(self.exclude_types) + repinit_three_body["davg"] = repinit_three_body["davg"][remap_index] + repinit_three_body["dstd"] = repinit_three_body["dstd"][remap_index] @property def dim_out(self): @@ -433,18 +492,21 @@ def compute_input_stats( Parameters ---------- - merged : Union[Callable[[], List[dict]], List[dict]] - - List[dict]: A list of data samples from various data systems. + merged : Union[Callable[[], list[dict]], list[dict]] + - list[dict]: A list of data samples from various data systems. Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` originating from the `i`-th data system. - - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + - Callable[[], list[dict]]: A lazy function that returns data samples in the above format only when needed. Since the sampling process can be slow and memory-intensive, the lazy function helps by only sampling once. path : Optional[DPPath] The path to the stat file. """ - for ii, descrpt in enumerate([self.repinit, self.repformers]): + descrpt_list = [self.repinit, self.repformers] + if self.use_three_body: + descrpt_list.append(self.repinit_three_body) + for ii, descrpt in enumerate(descrpt_list): descrpt.compute_input_stats(merged, path) def set_stat_mean_and_stddev( @@ -453,7 +515,10 @@ def set_stat_mean_and_stddev( stddev: list[paddle.Tensor], ) -> None: """Update mean and stddev for descriptor.""" - for ii, descrpt in enumerate([self.repinit, self.repformers]): + descrpt_list = [self.repinit, self.repformers] + if self.use_three_body: + descrpt_list.append(self.repinit_three_body) + for ii, descrpt in enumerate(descrpt_list): descrpt.mean = mean[ii] descrpt.stddev = stddev[ii] @@ -461,18 +526,24 @@ def get_stat_mean_and_stddev( self, ) -> tuple[list[paddle.Tensor], list[paddle.Tensor]]: """Get mean and stddev for descriptor.""" - return [self.repinit.mean, self.repformers.mean], [ + mean_list = [self.repinit.mean, self.repformers.mean] + stddev_list = [ self.repinit.stddev, self.repformers.stddev, ] + if self.use_three_body: + mean_list.append(self.repinit_three_body.mean) + stddev_list.append(self.repinit_three_body.stddev) + return mean_list, stddev_list def serialize(self) -> dict: repinit = self.repinit repformers = self.repformers + repinit_three_body = self.repinit_three_body data = { "@class": "Descriptor", "type": "dpa2", - "@version": 2, + "@version": 3, "ntypes": self.ntypes, "repinit_args": self.repinit_args.serialize(), "repformer_args": self.repformer_args.serialize(), @@ -522,20 +593,53 @@ def serialize(self) -> dict: "repformers_variable": repformers_variable, } ) + if self.use_three_body: + repinit_three_body_variable = { + "embeddings": repinit_three_body.filter_layers.serialize(), + "env_mat": DPEnvMat( + repinit_three_body.rcut, repinit_three_body.rcut_smth + ).serialize(), + "@variables": { + "davg": to_numpy_array(repinit_three_body["davg"]), + "dstd": to_numpy_array(repinit_three_body["dstd"]), + }, + } + if repinit_three_body.tebd_input_mode in ["strip"]: + repinit_three_body_variable.update( + { + "embeddings_strip": repinit_three_body.filter_layers_strip.serialize() + } + ) + data.update( + { + "repinit_three_body_variable": repinit_three_body_variable, + } + ) return data @classmethod def deserialize(cls, data: dict) -> "DescrptDPA2": data = data.copy() - check_version_compatibility(data.pop("@version"), 2, 1) + version = data.pop("@version") + check_version_compatibility(version, 3, 1) data.pop("@class") data.pop("type") repinit_variable = data.pop("repinit_variable").copy() repformers_variable = data.pop("repformers_variable").copy() + repinit_three_body_variable = ( + data.pop("repinit_three_body_variable").copy() + if "repinit_three_body_variable" in data + else None + ) type_embedding = data.pop("type_embedding") g1_shape_tranform = data.pop("g1_shape_tranform") tebd_transform = data.pop("tebd_transform", None) add_tebd_to_repinit_out = data["add_tebd_to_repinit_out"] + if version < 3: + # compat with old version + data["repformer_args"]["use_sqrt_nnei"] = False + data["repformer_args"]["g1_out_conv"] = False + data["repformer_args"]["g1_out_mlp"] = False data["repinit"] = RepinitArgs(**data.pop("repinit_args")) data["repformer"] = RepformerArgs(**data.pop("repformer_args")) # compat with version 1 @@ -568,6 +672,23 @@ def t_cvt(xx): obj.repinit["davg"] = t_cvt(statistic_repinit["davg"]) obj.repinit["dstd"] = t_cvt(statistic_repinit["dstd"]) + if data["repinit"].use_three_body: + # deserialize repinit_three_body + statistic_repinit_three_body = repinit_three_body_variable.pop("@variables") + env_mat = repinit_three_body_variable.pop("env_mat") + tebd_input_mode = data["repinit"].tebd_input_mode + obj.repinit_three_body.filter_layers = NetworkCollection.deserialize( + repinit_three_body_variable.pop("embeddings") + ) + if tebd_input_mode in ["strip"]: + obj.repinit_three_body.filter_layers_strip = ( + NetworkCollection.deserialize( + repinit_three_body_variable.pop("embeddings_strip") + ) + ) + obj.repinit_three_body["davg"] = t_cvt(statistic_repinit_three_body["davg"]) + obj.repinit_three_body["dstd"] = t_cvt(statistic_repinit_three_body["dstd"]) + # deserialize repformers statistic_repformers = repformers_variable.pop("@variables") env_mat = repformers_variable.pop("env_mat") @@ -622,14 +743,15 @@ def forward( The smooth switch function. shape: nf x nloc x nnei """ + use_three_body = self.use_three_body nframes, nloc, nnei = nlist.shape nall = extended_coord.reshape([nframes, -1]).shape[1] // 3 # nlists nlist_dict = build_multiple_neighbor_list( extended_coord, nlist, - [self.repformers.get_rcut(), self.repinit.get_rcut()], - [self.repformers.get_nsel(), self.repinit.get_nsel()], + self.rcut_list, + self.nsel_list, ) # repinit g1_ext = self.type_embedding(extended_atype) @@ -643,6 +765,21 @@ def forward( g1_ext, mapping, ) + if use_three_body: + assert self.repinit_three_body is not None + g1_three_body, __, __, __, __ = self.repinit_three_body( + nlist_dict[ + get_multiple_nlist_key( + self.repinit_three_body.get_rcut(), + self.repinit_three_body.get_nsel(), + ) + ], + extended_coord, + extended_atype, + g1_ext, + mapping, + ) + g1 = paddle.concat([g1, g1_three_body], axis=-1) # linear to change shape g1 = self.g1_shape_tranform(g1) if self.add_tebd_to_repinit_out: diff --git a/deepmd/pd/model/descriptor/se_a.py b/deepmd/pd/model/descriptor/se_a.py index 8a5d7bcb96..9e4fe40882 100644 --- a/deepmd/pd/model/descriptor/se_a.py +++ b/deepmd/pd/model/descriptor/se_a.py @@ -55,10 +55,7 @@ EmbeddingNet, NetworkCollection, ) -from deepmd.pd.model.network.network import ( - TypeFilter, -) -from deepmd.pd.utils.exclude_mask import ( +from deepmd.pt.utils.exclude_mask import ( PairExcludeMask, ) @@ -83,7 +80,6 @@ def __init__( resnet_dt: bool = False, exclude_types: list[tuple[int, int]] = [], env_protection: float = 0.0, - old_impl: bool = False, type_one_side: bool = True, trainable: bool = True, seed: Optional[Union[int, list[int]]] = None, @@ -109,7 +105,6 @@ def __init__( resnet_dt=resnet_dt, exclude_types=exclude_types, env_protection=env_protection, - old_impl=old_impl, type_one_side=type_one_side, trainable=trainable, seed=seed, @@ -210,11 +205,11 @@ def compute_input_stats( Parameters ---------- - merged : Union[Callable[[], List[dict]], List[dict]] - - List[dict]: A list of data samples from various data systems. + merged : Union[Callable[[], list[dict]], list[dict]] + - list[dict]: A list of data samples from various data systems. Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` originating from the `i`-th data system. - - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + - Callable[[], list[dict]]: A lazy function that returns data samples in the above format only when needed. Since the sampling process can be slow and memory-intensive, the lazy function helps by only sampling once. path : Optional[DPPath] @@ -385,7 +380,6 @@ def __init__( resnet_dt: bool = False, exclude_types: list[tuple[int, int]] = [], env_protection: float = 0.0, - old_impl: bool = False, type_one_side: bool = True, trainable: bool = True, seed: Optional[Union[int, list[int]]] = None, @@ -411,7 +405,6 @@ def __init__( self.precision = precision self.prec = PRECISION_DICT[self.precision] self.resnet_dt = resnet_dt - self.old_impl = old_impl self.env_protection = env_protection self.ntypes = len(sel) self.type_one_side = type_one_side @@ -431,39 +424,23 @@ def __init__( stddev = paddle.ones(wanted_shape, dtype=self.prec).to(device=env.DEVICE) self.register_buffer("mean", mean) self.register_buffer("stddev", stddev) - self.filter_layers_old = None - self.filter_layers = None - - if self.old_impl: - if not self.type_one_side: - raise ValueError( - "The old implementation does not support type_one_side=False." - ) - filter_layers = [] - # TODO: remove - start_index = 0 - for type_i in range(self.ntypes): - one = TypeFilter(start_index, sel[type_i], self.filter_neuron) - filter_layers.append(one) - start_index += sel[type_i] - self.filter_layers_old = paddle.nn.LayerList(filter_layers) - else: - ndim = 1 if self.type_one_side else 2 - filter_layers = NetworkCollection( - ndim=ndim, ntypes=len(sel), network_type="embedding_network" + + ndim = 1 if self.type_one_side else 2 + filter_layers = NetworkCollection( + ndim=ndim, ntypes=len(sel), network_type="embedding_network" + ) + for ii, embedding_idx in enumerate( + itertools.product(range(self.ntypes), repeat=ndim) + ): + filter_layers[embedding_idx] = EmbeddingNet( + 1, + self.filter_neuron, + activation_function=self.activation_function, + precision=self.precision, + resnet_dt=self.resnet_dt, + seed=child_seed(self.seed, ii), ) - for ii, embedding_idx in enumerate( - itertools.product(range(self.ntypes), repeat=ndim) - ): - filter_layers[embedding_idx] = EmbeddingNet( - 1, - self.filter_neuron, - activation_function=self.activation_function, - precision=self.precision, - resnet_dt=self.resnet_dt, - seed=child_seed(self.seed, ii), - ) - self.filter_layers = filter_layers + self.filter_layers = filter_layers self.stats = None # set trainable for param in self.parameters(): @@ -553,11 +530,11 @@ def compute_input_stats( Parameters ---------- - merged : Union[Callable[[], List[dict]], List[dict]] - - List[dict]: A list of data samples from various data systems. + merged : Union[Callable[[], list[dict]], list[dict]] + - list[dict]: A list of data samples from various data systems. Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` originating from the `i`-th data system. - - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + - Callable[[], list[dict]]: A lazy function that returns data samples in the above format only when needed. Since the sampling process can be slow and memory-intensive, the lazy function helps by only sampling once. path : Optional[DPPath] @@ -632,65 +609,50 @@ def forward( protection=self.env_protection, ) - if self.old_impl: - assert self.filter_layers_old is not None - dmatrix = dmatrix.reshape( - [-1, self.ndescrpt] - ) # shape is [nframes*nall, self.ndescrpt] - xyz_scatter = paddle.empty( # pylint: disable=no-explicit-dtype - [1], - ).to(device=env.DEVICE) - ret = self.filter_layers_old[0](dmatrix) - xyz_scatter = ret - for ii, transform in enumerate(self.filter_layers_old[1:]): - # shape is [nframes*nall, 4, self.filter_neuron[-1]] - ret = transform.forward(dmatrix) - xyz_scatter = xyz_scatter + ret - else: - assert self.filter_layers is not None - dmatrix = dmatrix.reshape([-1, self.nnei, 4]) - dmatrix = dmatrix.astype(self.prec) - nfnl = dmatrix.shape[0] - # pre-allocate a shape to pass jit - xyz_scatter = paddle.zeros( - [nfnl, 4, self.filter_neuron[-1]], - dtype=self.prec, - ).to(extended_coord.place) - # nfnl x nnei - exclude_mask = self.emask(nlist, extended_atype).reshape([nfnl, self.nnei]) - for embedding_idx, ll in enumerate(self.filter_layers.networks): - if self.type_one_side: - ii = embedding_idx - # paddle.jit is not happy with slice(None) - # ti_mask = paddle.ones(nfnl, dtype=paddle.bool, device=dmatrix.place) - # applying a mask seems to cause performance degradation - ti_mask = None - else: - # ti: center atom type, ii: neighbor type... - ii = embedding_idx // self.ntypes - ti = embedding_idx % self.ntypes - ti_mask = atype.flatten() == ti - # nfnl x nt - if ti_mask is not None: - mm = exclude_mask[ti_mask, self.sec[ii] : self.sec[ii + 1]] - else: - mm = exclude_mask[:, self.sec[ii] : self.sec[ii + 1]] - # nfnl x nt x 4 + assert self.filter_layers is not None + dmatrix = dmatrix.reshape([-1, self.nnei, 4]) + dmatrix = dmatrix.astype(self.prec) + nfnl = dmatrix.shape[0] + # pre-allocate a shape to pass jit + xyz_scatter = paddle.zeros( + [nfnl, 4, self.filter_neuron[-1]], + dtype=self.prec, + ).to(extended_coord.place) + # nfnl x nnei + exclude_mask = self.emask(nlist, extended_atype).reshape([nfnl, self.nnei]) + for embedding_idx, ll in enumerate(self.filter_layers.networks): + if self.type_one_side: + ii = embedding_idx + # paddle.jit is not happy with slice(None) + # ti_mask = paddle.ones(nfnl, dtype=paddle.bool, device=dmatrix.place) + # applying a mask seems to cause performance degradation + ti_mask = None + else: + # ti: center atom type, ii: neighbor type... + ii = embedding_idx // self.ntypes + ti = embedding_idx % self.ntypes + ti_mask = atype.flatten() == ti + # nfnl x nt + if ti_mask is not None: + mm = exclude_mask[ti_mask, self.sec[ii] : self.sec[ii + 1]] + else: + mm = exclude_mask[:, self.sec[ii] : self.sec[ii + 1]] + # nfnl x nt x 4 + if ti_mask is not None: + rr = dmatrix[ti_mask, self.sec[ii] : self.sec[ii + 1], :] + else: + rr = dmatrix[:, self.sec[ii] : self.sec[ii + 1], :] + if rr.numel() > 0: + rr = rr * mm.unsqueeze(2).astype(rr.dtype) + ss = rr[:, :, :1] + # nfnl x nt x ng + gg = ll.forward(ss) + # nfnl x 4 x ng + gr = paddle.matmul(rr.transpose([0, 2, 1]), gg) if ti_mask is not None: - rr = dmatrix[ti_mask, self.sec[ii] : self.sec[ii + 1], :] + xyz_scatter[ti_mask] += gr else: - rr = dmatrix[:, self.sec[ii] : self.sec[ii + 1], :] - if rr.numel() > 0: - rr = rr * mm.unsqueeze(2).astype(rr.dtype) - ss = rr[:, :, :1] - # nfnl x nt x ng - gg = ll.forward(ss) - # nfnl x 4 x ng - gr = paddle.matmul(rr.transpose([0, 2, 1]), gg) - if ti_mask is not None: - xyz_scatter[ti_mask] += gr - else: - xyz_scatter += gr + xyz_scatter += gr xyz_scatter /= self.nnei xyz_scatter_1 = xyz_scatter.transpose([0, 2, 1]) diff --git a/deepmd/pd/model/descriptor/se_atten.py b/deepmd/pd/model/descriptor/se_atten.py index db730d073d..0bf9563c15 100644 --- a/deepmd/pd/model/descriptor/se_atten.py +++ b/deepmd/pd/model/descriptor/se_atten.py @@ -26,10 +26,6 @@ MLPLayer, NetworkCollection, ) -from deepmd.pd.model.network.network import ( - NeighborWiseAttention, - TypeFilter, -) from deepmd.pd.utils import ( aux, env, @@ -86,7 +82,6 @@ def __init__( ln_eps: Optional[float] = 1e-5, seed: Optional[Union[int, list[int]]] = None, type: Optional[str] = None, - old_impl: bool = False, ): r"""Construct an embedding net of type `se_atten`. @@ -132,7 +127,7 @@ def __init__( (Only support False to keep consistent with other backend references.) (Not used in this version.) If mask the diagonal of attention weights - exclude_types : List[List[int]] + exclude_types : list[list[int]] The excluded pairs of types which have no interaction with each other. For example, `[[0, 1]]` means no interaction between type 0 and type 1. env_protection : float @@ -183,7 +178,6 @@ def __init__( if ln_eps is None: ln_eps = 1e-5 self.ln_eps = ln_eps - self.old_impl = old_impl if isinstance(sel, int): sel = [sel] @@ -196,40 +190,22 @@ def __init__( self.ndescrpt = self.nnei * 4 # order matters, placed after the assignment of self.ntypes self.reinit_exclude(exclude_types) - if self.old_impl: - assert self.tebd_input_mode in [ - "concat" - ], "Old implementation does not support tebd_input_mode != 'concat'." - self.dpa1_attention = NeighborWiseAttention( - self.attn_layer, - self.nnei, - self.filter_neuron[-1], - self.attn_dim, - dotr=self.attn_dotr, - do_mask=self.attn_mask, - activation=self.activation_function, - scaling_factor=self.scaling_factor, - normalize=self.normalize, - temperature=self.temperature, - smooth=self.smooth, - ) - else: - self.dpa1_attention = NeighborGatedAttention( - self.attn_layer, - self.nnei, - self.filter_neuron[-1], - self.attn_dim, - dotr=self.attn_dotr, - do_mask=self.attn_mask, - scaling_factor=self.scaling_factor, - normalize=self.normalize, - temperature=self.temperature, - trainable_ln=self.trainable_ln, - ln_eps=self.ln_eps, - smooth=self.smooth, - precision=self.precision, - seed=child_seed(self.seed, 0), - ) + self.dpa1_attention = NeighborGatedAttention( + self.attn_layer, + self.nnei, + self.filter_neuron[-1], + self.attn_dim, + dotr=self.attn_dotr, + do_mask=self.attn_mask, + scaling_factor=self.scaling_factor, + normalize=self.normalize, + temperature=self.temperature, + trainable_ln=self.trainable_ln, + ln_eps=self.ln_eps, + smooth=self.smooth, + precision=self.precision, + seed=child_seed(self.seed, 0), + ) wanted_shape = (self.ntypes, self.nnei, 4) mean = paddle.zeros(wanted_shape, dtype=env.GLOBAL_PD_FLOAT_PRECISION).to( @@ -246,48 +222,32 @@ def __init__( else: self.embd_input_dim = 1 - self.filter_layers_old = None - self.filter_layers = None self.filter_layers_strip = None - if self.old_impl: - filter_layers = [] - one = TypeFilter( - 0, - self.nnei, - self.filter_neuron, - return_G=True, - tebd_dim=self.tebd_dim, - use_tebd=True, - tebd_mode=self.tebd_input_mode, - ) - filter_layers.append(one) - self.filter_layers_old = paddle.nn.LayerList(filter_layers) - else: - filter_layers = NetworkCollection( + filter_layers = NetworkCollection( + ndim=0, ntypes=self.ntypes, network_type="embedding_network" + ) + filter_layers[0] = EmbeddingNet( + self.embd_input_dim, + self.filter_neuron, + activation_function=self.activation_function, + precision=self.precision, + resnet_dt=self.resnet_dt, + seed=child_seed(self.seed, 1), + ) + self.filter_layers = filter_layers + if self.tebd_input_mode in ["strip"]: + filter_layers_strip = NetworkCollection( ndim=0, ntypes=self.ntypes, network_type="embedding_network" ) - filter_layers[0] = EmbeddingNet( - self.embd_input_dim, + filter_layers_strip[0] = EmbeddingNet( + self.tebd_dim_input, self.filter_neuron, activation_function=self.activation_function, precision=self.precision, resnet_dt=self.resnet_dt, - seed=child_seed(self.seed, 1), + seed=child_seed(self.seed, 2), ) - self.filter_layers = filter_layers - if self.tebd_input_mode in ["strip"]: - filter_layers_strip = NetworkCollection( - ndim=0, ntypes=self.ntypes, network_type="embedding_network" - ) - filter_layers_strip[0] = EmbeddingNet( - self.tebd_dim_input, - self.filter_neuron, - activation_function=self.activation_function, - precision=self.precision, - resnet_dt=self.resnet_dt, - seed=child_seed(self.seed, 2), - ) - self.filter_layers_strip = filter_layers_strip + self.filter_layers_strip = filter_layers_strip self.stats = None def get_rcut(self) -> float: @@ -379,11 +339,11 @@ def compute_input_stats( Parameters ---------- - merged : Union[Callable[[], List[dict]], List[dict]] - - List[dict]: A list of data samples from various data systems. + merged : Union[Callable[[], list[dict]], list[dict]] + - list[dict]: A list of data samples from various data systems. Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` originating from the `i`-th data system. - - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + - Callable[[], list[dict]]: A lazy function that returns data samples in the above format only when needed. Since the sampling process can be slow and memory-intensive, the lazy function helps by only sampling once. path : Optional[DPPath] @@ -502,79 +462,54 @@ def forward( sw = sw.masked_fill(~nlist_mask, 0.0) # (nb x nloc) x nnei exclude_mask = exclude_mask.reshape([nb * nloc, nnei]) - if self.old_impl: - assert self.filter_layers_old is not None - dmatrix = dmatrix.reshape( - [-1, self.ndescrpt] - ) # shape is [nframes*nall, self.ndescrpt] - gg = self.filter_layers_old[0]( - dmatrix, - atype_tebd=atype_tebd_nnei, - nlist_tebd=atype_tebd_nlist, - ) # shape is [nframes*nall, self.neei, out_size] - # input_r = paddle.nn.functional.normalize( - # dmatrix.reshape([-1, self.nnei, 4])[:, :, 1:4], axis=-1 - # ) - input_r = aux.normalize( - dmatrix.reshape([-1, self.nnei, 4])[:, :, 1:4], axis=-1 - ) - gg = self.dpa1_attention( - gg, nlist_mask, input_r=input_r, sw=sw - ) # shape is [nframes*nloc, self.neei, out_size] - inputs_reshape = dmatrix.reshape([-1, self.nnei, 4]).transpose( - [0, 2, 1] - ) # shape is [nframes*natoms[0], 4, self.neei] - xyz_scatter = paddle.matmul( - inputs_reshape, gg - ) # shape is [nframes*natoms[0], 4, out_size] - else: - assert self.filter_layers is not None - # nfnl x nnei x 4 - dmatrix = dmatrix.reshape([-1, self.nnei, 4]) - nfnl = dmatrix.shape[0] - # nfnl x nnei x 4 - rr = dmatrix - rr = rr * exclude_mask[:, :, None].astype(rr.dtype) - ss = rr[:, :, :1] - nlist_tebd = atype_tebd_nlist.reshape([nfnl, nnei, self.tebd_dim]) - atype_tebd = atype_tebd_nnei.reshape([nfnl, nnei, self.tebd_dim]) - if self.tebd_input_mode in ["concat"]: - if not self.type_one_side: - # nfnl x nnei x (1 + tebd_dim * 2) - ss = paddle.concat([ss, nlist_tebd, atype_tebd], axis=2) - else: - # nfnl x nnei x (1 + tebd_dim) - ss = paddle.concat([ss, nlist_tebd], axis=2) - # nfnl x nnei x ng - gg = self.filter_layers.networks[0](ss) - elif self.tebd_input_mode in ["strip"]: - # nfnl x nnei x ng - gg_s = self.filter_layers.networks[0](ss) - assert self.filter_layers_strip is not None - if not self.type_one_side: - # nfnl x nnei x (tebd_dim * 2) - tt = paddle.concat([nlist_tebd, atype_tebd], axis=2) - else: - # nfnl x nnei x tebd_dim - tt = nlist_tebd - # nfnl x nnei x ng - gg_t = self.filter_layers_strip.networks[0](tt) - if self.smooth: - gg_t = gg_t * sw.reshape([-1, self.nnei, 1]) - # nfnl x nnei x ng - gg = gg_s * gg_t + gg_s + assert self.filter_layers is not None + # nfnl x nnei x 4 + dmatrix = dmatrix.reshape([-1, self.nnei, 4]) + nfnl = dmatrix.shape[0] + # nfnl x nnei x 4 + rr = dmatrix + rr = rr * exclude_mask[:, :, None].astype(rr.dtype) + ss = rr[:, :, :1] + nlist_tebd = atype_tebd_nlist.reshape([nfnl, nnei, self.tebd_dim]) + atype_tebd = atype_tebd_nnei.reshape([nfnl, nnei, self.tebd_dim]) + if self.tebd_input_mode in ["concat"]: + if not self.type_one_side: + # nfnl x nnei x (1 + tebd_dim * 2) + ss = paddle.concat([ss, nlist_tebd, atype_tebd], axis=2) + else: + # nfnl x nnei x (1 + tebd_dim) + ss = paddle.concat([ss, nlist_tebd], axis=2) + # nfnl x nnei x ng + gg = self.filter_layers.networks[0](ss) + elif self.tebd_input_mode in ["strip"]: + # nfnl x nnei x ng + gg_s = self.filter_layers.networks[0](ss) + assert self.filter_layers_strip is not None + if not self.type_one_side: + # nfnl x nnei x (tebd_dim * 2) + tt = paddle.concat([nlist_tebd, atype_tebd], axis=2) else: - raise NotImplementedError - - # input_r = paddle.nn.functional.normalize( - # rr.reshape([-1, self.nnei, 4])[:, :, 1:4], axis=-1 - # ) - input_r = aux.normalize(rr.reshape([-1, self.nnei, 4])[:, :, 1:4], axis=-1) - gg = self.dpa1_attention( - gg, nlist_mask, input_r=input_r, sw=sw - ) # shape is [nframes*nloc, self.neei, out_size] - # nfnl x 4 x ng - xyz_scatter = paddle.matmul(rr.transpose([0, 2, 1]), gg) + # nfnl x nnei x tebd_dim + tt = nlist_tebd + # nfnl x nnei x ng + gg_t = self.filter_layers_strip.networks[0](tt) + if self.smooth: + gg_t = gg_t * sw.reshape([-1, self.nnei, 1]) + # nfnl x nnei x ng + gg = gg_s * gg_t + gg_s + else: + raise NotImplementedError + + # input_r = paddle.nn.functional.normalize( + # rr.reshape([-1, self.nnei, 4])[:, :, 1:4], axis=-1 + # ) + input_r = aux.normalize(rr.reshape([-1, self.nnei, 4])[:, :, 1:4], axis=-1) + gg = self.dpa1_attention( + gg, nlist_mask, input_r=input_r, sw=sw + ) # shape is [nframes*nloc, self.neei, out_size] + # nfnl x 4 x ng + xyz_scatter = paddle.matmul(rr.transpose([0, 2, 1]), gg) + xyz_scatter = xyz_scatter / self.nnei xyz_scatter_1 = xyz_scatter.transpose([0, 2, 1]) rot_mat = xyz_scatter_1[:, :, 1:4] diff --git a/deepmd/pd/model/descriptor/se_atten_v2.py b/deepmd/pd/model/descriptor/se_atten_v2.py index 05260ee162..6a321114fb 100644 --- a/deepmd/pd/model/descriptor/se_atten_v2.py +++ b/deepmd/pd/model/descriptor/se_atten_v2.py @@ -71,7 +71,6 @@ def __init__( # not implemented spin=None, type: Optional[str] = None, - old_impl: bool = False, ) -> None: r"""Construct smooth version of embedding net of type `se_atten_v2`. @@ -111,7 +110,7 @@ def __init__( resnet_dt : bool Time-step `dt` in the resnet construction: y = x + dt * \phi (Wx + b) - exclude_types : List[List[int]] + exclude_types : list[list[int]] The excluded pairs of types which have no interaction with each other. For example, `[[0, 1]]` means no interaction between type 0 and type 1. env_protection : float @@ -147,7 +146,7 @@ def __init__( Whether to use electronic configuration type embedding. use_tebd_bias : bool, Optional Whether to use bias in the type embedding layer. - type_map : List[str], Optional + type_map : list[str], Optional A list of strings. Give the name to each type of atoms. spin (Only support None to keep consistent with other backend references.) @@ -191,7 +190,6 @@ def __init__( # not implemented spin=spin, type=type, - old_impl=old_impl, ) def serialize(self) -> dict: diff --git a/deepmd/pd/model/descriptor/se_r.py b/deepmd/pd/model/descriptor/se_r.py index 029dcd900d..871b37fd40 100644 --- a/deepmd/pd/model/descriptor/se_r.py +++ b/deepmd/pd/model/descriptor/se_r.py @@ -68,7 +68,6 @@ def __init__( resnet_dt: bool = False, exclude_types: list[tuple[int, int]] = [], env_protection: float = 0.0, - old_impl: bool = False, trainable: bool = True, seed: Optional[Union[int, list[int]]] = None, type_map: Optional[list[str]] = None, @@ -84,7 +83,6 @@ def __init__( self.precision = precision self.prec = PRECISION_DICT[self.precision] self.resnet_dt = resnet_dt - self.old_impl = False # this does not support old implementation. self.exclude_types = exclude_types self.ntypes = len(sel) self.type_map = type_map @@ -247,11 +245,11 @@ def compute_input_stats( Parameters ---------- - merged : Union[Callable[[], List[dict]], List[dict]] - - List[dict]: A list of data samples from various data systems. + merged : Union[Callable[[], list[dict]], list[dict]] + - list[dict]: A list of data samples from various data systems. Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` originating from the `i`-th data system. - - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + - Callable[[], list[dict]]: A lazy function that returns data samples in the above format only when needed. Since the sampling process can be slow and memory-intensive, the lazy function helps by only sampling once. path : Optional[DPPath] diff --git a/deepmd/pd/model/network/network.py b/deepmd/pd/model/network/network.py index ee535049d3..82b3b248d9 100644 --- a/deepmd/pd/model/network/network.py +++ b/deepmd/pd/model/network/network.py @@ -16,7 +16,6 @@ EmbeddingNet, ) from deepmd.pd.utils import ( - aux, env, ) from deepmd.utils.version import ( @@ -30,12 +29,6 @@ except ImportError: from paddle.jit import Final -from functools import ( - partial, -) - -import paddle.distributed.fleet - from deepmd.dpmodel.utils.type_embed import ( get_econf_tebd, ) @@ -54,263 +47,6 @@ def Tensor(*shape): ) -class Dropout(nn.Layer): - def __init__(self, p): - super().__init__() - self.p = p - - def forward(self, x, inplace: bool = False): - if self.p > 0 and self.training: - return F.dropout(x, p=self.p, training=True) - else: - return x - - -class Identity(nn.Layer): - def __init__(self): - super().__init__() - - def forward(self, x): - return x - - -class DropPath(paddle.nn.Layer): - """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" - - def __init__(self, prob=None): - super().__init__() - self.drop_prob = prob - - def forward(self, x): - if self.drop_prob == 0.0 or not self.training: - return x - keep_prob = 1 - self.drop_prob - shape = (x.shape[0],) + (1,) * ( - x.ndim - 1 - ) # work with diff dim tensors, not just 2D ConvNets - random_tensor: paddle.Tensor = keep_prob + paddle.rand(shape, dtype=x.dtype).to( - device=x.place - ) - random_tensor.floor_() # binarize - output = x.divide(keep_prob) * random_tensor - return output - - def extra_repr(self) -> str: - return f"prob={self.drop_prob}" - - -def softmax_dropout( - input_x, dropout_prob, is_training=True, mask=None, bias=None, inplace=True -): - input_x = input_x.contiguous() - if not inplace: - input_x = input_x.clone() - if mask is not None: - input_x += mask - if bias is not None: - input_x += bias - return F.dropout(F.softmax(input_x, axis=-1), p=dropout_prob, training=is_training) - - -def checkpoint_sequential( - functions, - input_x, - enabled=True, -): - def wrap_tuple(a): - return (a,) if type(a) is not tuple else a - - def exec(func, a): - return wrap_tuple(func(*a)) - - def get_wrap_exec(func): - def wrap_exec(*a): - return exec(func, a) - - return wrap_exec - - input_x = wrap_tuple(input_x) - - is_grad_enabled = paddle.is_grad_enabled() - - if enabled and is_grad_enabled: - for func in functions: - input_x = paddle.distributed.fleet.utils.recompute( - get_wrap_exec(func), *input_x - ) - else: - for func in functions: - input_x = exec(func, input_x) - return input_x - - -class ResidualLinear(nn.Layer): - resnet: Final[int] - - def __init__(self, num_in, num_out, bavg=0.0, stddev=1.0, resnet_dt=False): - """Construct a residual linear layer. - - Args: - - num_in: Width of input tensor. - - num_out: Width of output tensor. - - resnet_dt: Using time-step in the ResNet construction. - """ - super().__init__() - self.num_in = num_in - self.num_out = num_out - self.resnet = resnet_dt - - self.matrix = self.create_parameter( - [num_in, num_out], - dtype=env.GLOBAL_PD_FLOAT_PRECISION, - default_initializer=nn.initializer.Assign(Tensor(num_in, num_out)), - ) - init.normal_(self.matrix.data, std=stddev / np.sqrt(num_out + num_in)) - self.bias = self.create_parameter( - (1, num_out), - dtype=env.GLOBAL_PD_FLOAT_PRECISION, - default_initializer=nn.initializer.Assign(Tensor(1, num_out)), - ) - init.normal_(self.bias.data, mean=bavg, std=stddev) - if self.resnet: - self.idt = self.create_parameter( - (1, num_out), - dtype=env.GLOBAL_PD_FLOAT_PRECISION, - default_initializer=nn.initializer.Assign(Tensor(1, num_out)), - ) - init.normal_(self.idt.data, mean=1.0, std=0.001) - - def forward(self, inputs): - """Return X ?+ X*W+b.""" - xw_plus_b = paddle.matmul(inputs, self.matrix) + self.bias - hidden = paddle.tanh(xw_plus_b) - if self.resnet: - hidden = hidden * self.idt - if self.num_in == self.num_out: - return inputs + hidden - elif self.num_in * 2 == self.num_out: - return paddle.concat([inputs, inputs], axis=1) + hidden - else: - return hidden - - -class TypeFilter(nn.Layer): - use_tebd: Final[bool] - tebd_mode: Final[str] - - def __init__( - self, - offset, - length, - neuron, - return_G=False, - tebd_dim=0, - use_tebd=False, - tebd_mode="concat", - ): - """Construct a filter on the given element as neighbor. - - Args: - - offset: Element offset in the descriptor matrix. - - length: Atom count of this element. - - neuron: Number of neurons in each hidden layers of the embedding net. - """ - super().__init__() - self.offset = offset - self.length = length - self.tebd_dim = tebd_dim - self.use_tebd = use_tebd - self.tebd_mode = tebd_mode - supported_tebd_mode = ["concat", "dot", "dot_residual_s", "dot_residual_t"] - assert ( - tebd_mode in supported_tebd_mode - ), f"Unknown tebd_mode {tebd_mode}! Supported are {supported_tebd_mode}." - if use_tebd and tebd_mode == "concat": - self.neuron = [1 + tebd_dim * 2, *neuron] - else: - self.neuron = [1, *neuron] - - deep_layers = [] - for ii in range(1, len(self.neuron)): - one = ResidualLinear(self.neuron[ii - 1], self.neuron[ii]) - deep_layers.append(one) - self.deep_layers = nn.LayerList(deep_layers) - - deep_layers_t = [] - if use_tebd and tebd_mode in ["dot", "dot_residual_s", "dot_residual_t"]: - self.neuron_t = [tebd_dim * 2, *neuron] - for ii in range(1, len(self.neuron_t)): - one = ResidualLinear(self.neuron_t[ii - 1], self.neuron_t[ii]) - deep_layers_t.append(one) - self.deep_layers_t = nn.LayerList(deep_layers_t) - - self.return_G = return_G - - def forward( - self, - inputs, - atype_tebd: Optional[paddle.Tensor] = None, - nlist_tebd: Optional[paddle.Tensor] = None, - ): - """Calculate decoded embedding for each atom. - - Args: - - inputs: Descriptor matrix. Its shape is [nframes*natoms[0], len_descriptor]. - - Returns - ------- - - `paddle.Tensor`: Embedding contributed by me. Its shape is [nframes*natoms[0], 4, self.neuron[-1]]. - """ - inputs_i = inputs[:, self.offset * 4 : (self.offset + self.length) * 4] - inputs_reshape = inputs_i.reshape( - [-1, 4] - ) # shape is [nframes*natoms[0]*self.length, 4] - xyz_scatter = inputs_reshape[:, 0:1] - - # concat the tebd as input - if self.use_tebd and self.tebd_mode == "concat": - assert nlist_tebd is not None and atype_tebd is not None - nlist_tebd = nlist_tebd.reshape([-1, self.tebd_dim]) - atype_tebd = atype_tebd.reshape([-1, self.tebd_dim]) - # [nframes * nloc * nnei, 1 + tebd_dim * 2] - xyz_scatter = paddle.concat([xyz_scatter, nlist_tebd, atype_tebd], axis=1) - - for linear in self.deep_layers: - xyz_scatter = linear(xyz_scatter) - # [nframes * nloc * nnei, out_size] - - # dot the tebd output - if self.use_tebd and self.tebd_mode in [ - "dot", - "dot_residual_s", - "dot_residual_t", - ]: - assert nlist_tebd is not None and atype_tebd is not None - nlist_tebd = nlist_tebd.reshape([-1, self.tebd_dim]) - atype_tebd = atype_tebd.reshape([-1, self.tebd_dim]) - # [nframes * nloc * nnei, tebd_dim * 2] - two_side_tebd = paddle.concat([nlist_tebd, atype_tebd], axis=1) - for linear in self.deep_layers_t: - two_side_tebd = linear(two_side_tebd) - # [nframes * nloc * nnei, out_size] - if self.tebd_mode == "dot": - xyz_scatter = xyz_scatter * two_side_tebd - elif self.tebd_mode == "dot_residual_s": - xyz_scatter = xyz_scatter * two_side_tebd + xyz_scatter - elif self.tebd_mode == "dot_residual_t": - xyz_scatter = xyz_scatter * two_side_tebd + two_side_tebd - - xyz_scatter = xyz_scatter.reshape( - [-1, self.length, self.neuron[-1]] - ) # shape is [nframes*natoms[0], self.length, self.neuron[-1]] - if self.return_G: - return xyz_scatter - else: - # shape is [nframes*natoms[0], 4, self.length] - inputs_reshape = inputs_i.reshape([-1, self.length, 4]).transpose([0, 2, 1]) - return paddle.matmul(inputs_reshape, xyz_scatter) - - class SimpleLinear(nn.Layer): use_timestep: Final[bool] @@ -433,54 +169,7 @@ def _normal_init(self): init.kaiming_normal_(self.weight, nonlinearity="linear") -class Transition(nn.Layer): - def __init__(self, d_in, n, dropout=0.0): - super().__init__() - - self.d_in = d_in - self.n = n - - self.linear_1 = Linear(self.d_in, self.n * self.d_in, init="relu") - self.act = nn.GELU() - self.linear_2 = Linear(self.n * self.d_in, d_in, init="final") - self.dropout = dropout - - def _transition(self, x): - x = self.linear_1(x) - x = self.act(x) - x = F.dropout(x, p=self.dropout, training=self.training) - x = self.linear_2(x) - return x - - def forward( - self, - x: paddle.Tensor, - ) -> paddle.Tensor: - x = self._transition(x=x) - return x - - -class Embedding(nn.Embedding): - def __init__( - self, - num_embeddings: int, - embedding_dim: int, - padding_idx: Optional[int] = None, - dtype=paddle.float64, - ): - super().__init__( - num_embeddings, embedding_dim, padding_idx=padding_idx, dtype=dtype - ) - self._normal_init() - - if padding_idx is not None: - self.weight.data[self.padding_idx].zero_() - - def _normal_init(self, std=0.02): - init.normal_(self.weight, mean=0.0, std=std) - - -class NonLinearHead(nn.Layer): +class NonLinearHead(nn.Module): def __init__(self, input_dim, out_dim, activation_fn, hidden=None): super().__init__() hidden = input_dim if not hidden else hidden @@ -493,27 +182,7 @@ def forward(self, x): return x -class NonLinear(nn.Layer): - def __init__(self, input, output_size, hidden=None): - super().__init__() - - if hidden is None: - hidden = input - self.layer1 = Linear(input, hidden, init="relu") - self.layer2 = Linear(hidden, output_size, init="final") - - def forward(self, x): - x = F.linear(x, self.layer1.weight) - x = nn.GELU()(x) + self.layer1.bias - x = self.layer2(x) - return x - - def zero_init(self): - init.zeros_(self.layer2.weight) - init.zeros_(self.layer2.bias) - - -class MaskLMHead(nn.Layer): +class MaskLMHead(nn.Module): """Head for masked language modeling.""" def __init__(self, embed_dim, output_dim, activation_fn, weight=None): @@ -697,7 +366,7 @@ class TypeEmbedNetConsistent(nn.Layer): Whether to use electronic configuration type embedding. use_tebd_bias : bool, Optional Whether to use bias in the type embedding layer. - type_map: List[str], Optional + type_map: list[str], Optional A list of strings. Give the name to each type of atoms. """ @@ -890,1303 +559,3 @@ def serialize(self) -> dict: "type_map": self.type_map, "embedding": self.embedding_net.serialize(), } - - -# @paddle.jit.to_static -def gaussian(x, mean, std: float): - pi = 3.14159 - a = (2 * pi) ** 0.5 - return paddle.exp(-0.5 * (((x - mean) / std) ** 2)) / (a * std) - - -class GaussianKernel(nn.Layer): - def __init__(self, K=128, num_pair=512, std_width=1.0, start=0.0, stop=9.0): - super().__init__() - self.K = K - std_width = std_width - start = start - stop = stop - mean = paddle.linspace(start, stop, K, dtype=env.GLOBAL_PD_FLOAT_PRECISION) # pylint: disable=no-explicit-device - self.std = (std_width * (mean[1] - mean[0])).item() - self.register_buffer("mean", mean) - self.mul = Embedding( - num_pair + 1, 1, padding_idx=num_pair, dtype=env.GLOBAL_PD_FLOAT_PRECISION - ) - self.bias = Embedding( - num_pair + 1, 1, padding_idx=num_pair, dtype=env.GLOBAL_PD_FLOAT_PRECISION - ) - init.constant_(self.bias.weight, 0) - init.constant_(self.mul.weight, 1.0) - - def forward(self, x, atom_pair): - mul = self.mul(atom_pair).abs().sum(axis=-2) - bias = self.bias(atom_pair).sum(axis=-2) - x = mul * x.unsqueeze(-1) + bias - # [nframes, nloc, nnei, K] - x = x.expand([-1, -1, -1, self.K]) - mean = self.mean.reshape([-1]) - return gaussian(x, mean, self.std) - - -class GaussianEmbedding(nn.Layer): - def __init__( - self, - rcut, - kernel_num, - num_pair, - embed_dim, - pair_embed_dim, - sel, - ntypes, - atomic_sum_gbf, - ): - """Construct a gaussian kernel based embedding of pair representation. - - Args: - rcut: Radial cutoff. - kernel_num: Number of gaussian kernels. - num_pair: Number of different pairs. - embed_dim: Dimension of atomic representation. - pair_embed_dim: Dimension of pair representation. - sel: Number of neighbors. - ntypes: Number of atom types. - """ - super().__init__() - self.gbf = GaussianKernel(K=kernel_num, num_pair=num_pair, stop=rcut) - self.gbf_proj = NonLinear(kernel_num, pair_embed_dim) - self.embed_dim = embed_dim - self.pair_embed_dim = pair_embed_dim - self.atomic_sum_gbf = atomic_sum_gbf - if self.atomic_sum_gbf: - if kernel_num != self.embed_dim: - self.edge_proj = paddle.nn.Linear( - kernel_num, self.embed_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION - ) - else: - self.edge_proj = None - self.ntypes = ntypes - self.nnei = sel - - def forward(self, coord_selected, atom_feature, edge_type_2dim, edge_feature): - ## local cluster forward - """Calculate decoded embedding for each atom. - Args: - coord_selected: Clustered atom coordinates with shape [nframes*nloc, natoms, 3]. - atom_feature: Previous calculated atomic features with shape [nframes*nloc, natoms, embed_dim]. - edge_type_2dim: Edge index for gbf calculation with shape [nframes*nloc, natoms, natoms, 2]. - edge_feature: Previous calculated edge features with shape [nframes*nloc, natoms, natoms, pair_dim]. - - Returns - ------- - atom_feature: Updated atomic features with shape [nframes*nloc, natoms, embed_dim]. - attn_bias: Updated edge features as attention bias with shape [nframes*nloc, natoms, natoms, pair_dim]. - delta_pos: Delta position for force/vector prediction with shape [nframes*nloc, natoms, natoms, 3]. - """ - ncluster, natoms, _ = coord_selected.shape - # ncluster x natoms x natoms x 3 - delta_pos = coord_selected.unsqueeze(1) - coord_selected.unsqueeze(2) - # (ncluster x natoms x natoms - # dist = delta_pos.norm(axis=-1).reshape([-1, natoms, natoms]) - dist = aux.norm(delta_pos, axis=-1).reshape([-1, natoms, natoms]) - # [ncluster, natoms, natoms, K] - gbf_feature = self.gbf(dist, edge_type_2dim) - if self.atomic_sum_gbf: - edge_features = gbf_feature - # [ncluster, natoms, K] - sum_edge_features = edge_features.sum(axis=-2) - if self.edge_proj is not None: - sum_edge_features = self.edge_proj(sum_edge_features) - # [ncluster, natoms, embed_dim] - atom_feature = atom_feature + sum_edge_features - - # [ncluster, natoms, natoms, pair_dim] - gbf_result = self.gbf_proj(gbf_feature) - - attn_bias = gbf_result + edge_feature - return atom_feature, attn_bias, delta_pos - - -class NeighborWiseAttention(nn.Layer): - def __init__( - self, - layer_num, - nnei, - embed_dim, - hidden_dim, - dotr=False, - do_mask=False, - post_ln=True, - ffn=False, - ffn_embed_dim=1024, - activation="tanh", - scaling_factor=1.0, - head_num=1, - normalize=True, - temperature=None, - smooth=True, - ): - """Construct a neighbor-wise attention net.""" - super().__init__() - self.layer_num = layer_num - attention_layers = [] - for i in range(self.layer_num): - attention_layers.append( - NeighborWiseAttentionLayer( - nnei, - embed_dim, - hidden_dim, - dotr=dotr, - do_mask=do_mask, - post_ln=post_ln, - ffn=ffn, - ffn_embed_dim=ffn_embed_dim, - activation=activation, - scaling_factor=scaling_factor, - head_num=head_num, - normalize=normalize, - temperature=temperature, - smooth=smooth, - ) - ) - self.attention_layers = nn.LayerList(attention_layers) - - def forward( - self, - input_G, - nei_mask, - input_r: Optional[paddle.Tensor] = None, - sw: Optional[paddle.Tensor] = None, - ): - """ - Args: - input_G: Input G, [nframes * nloc, nnei, embed_dim]. - nei_mask: neighbor mask, [nframes * nloc, nnei]. - input_r: normalized radial, [nframes, nloc, nei, 3]. - - Returns - ------- - out: Output G, [nframes * nloc, nnei, embed_dim] - - """ - out = input_G - for layer in self.attention_layers: - out = layer(out, nei_mask, input_r=input_r, sw=sw) - return out - - -class NeighborWiseAttentionLayer(nn.Layer): - ffn: Final[bool] - - def __init__( - self, - nnei, - embed_dim, - hidden_dim, - dotr=False, - do_mask=False, - post_ln=True, - ffn=False, - ffn_embed_dim=1024, - activation="tanh", - scaling_factor=1.0, - head_num=1, - normalize=True, - temperature=None, - smooth=True, - ): - """Construct a neighbor-wise attention layer.""" - super().__init__() - self.nnei = nnei - self.embed_dim = embed_dim - self.hidden_dim = hidden_dim - self.dotr = dotr - self.do_mask = do_mask - self.post_ln = post_ln - self.ffn = ffn - self.smooth = smooth - self.attention_layer = GatedSelfAttetion( - nnei, - embed_dim, - hidden_dim, - dotr=dotr, - do_mask=do_mask, - scaling_factor=scaling_factor, - head_num=head_num, - normalize=normalize, - temperature=temperature, - smooth=smooth, - ) - self.attn_layer_norm = nn.LayerNorm(self.embed_dim).to(device=env.DEVICE) - if self.ffn: - self.ffn_embed_dim = ffn_embed_dim - self.fc1 = nn.Linear(self.embed_dim, self.ffn_embed_dim) - self.activation_fn = ActivationFn(activation) - self.fc2 = nn.Linear(self.ffn_embed_dim, self.embed_dim) - self.final_layer_norm = nn.LayerNorm(self.embed_dim) - - def forward( - self, - x, - nei_mask, - input_r: Optional[paddle.Tensor] = None, - sw: Optional[paddle.Tensor] = None, - ): - residual = x - if not self.post_ln: - x = self.attn_layer_norm(x) - x = self.attention_layer(x, nei_mask, input_r=input_r, sw=sw) - x = residual + x - if self.post_ln: - x = self.attn_layer_norm(x) - if self.ffn: - residual = x - if not self.post_ln: - x = self.final_layer_norm(x) - x = self.fc1(x) - x = self.activation_fn(x) - x = self.fc2(x) - x = residual + x - if self.post_ln: - x = self.final_layer_norm(x) - return x - - -class GatedSelfAttetion(nn.Layer): - def __init__( - self, - nnei, - embed_dim, - hidden_dim, - dotr=False, - do_mask=False, - scaling_factor=1.0, - head_num=1, - normalize=True, - temperature=None, - bias=True, - smooth=True, - ): - """Construct a neighbor-wise attention net.""" - super().__init__() - self.nnei = nnei - self.embed_dim = embed_dim - self.hidden_dim = hidden_dim - self.head_num = head_num - self.dotr = dotr - self.do_mask = do_mask - if temperature is None: - self.scaling = (self.hidden_dim * scaling_factor) ** -0.5 - else: - self.scaling = temperature - self.normalize = normalize - self.in_proj = SimpleLinear( - embed_dim, - hidden_dim * 3, - bavg=0.0, - stddev=1.0, - use_timestep=False, - bias=bias, - ) - self.out_proj = SimpleLinear( - hidden_dim, embed_dim, bavg=0.0, stddev=1.0, use_timestep=False, bias=bias - ) - self.smooth = smooth - - def forward( - self, - query, - nei_mask, - input_r: Optional[paddle.Tensor] = None, - sw: Optional[paddle.Tensor] = None, - attnw_shift: float = 20.0, - ): - """ - Args: - query: input G, [nframes * nloc, nnei, embed_dim]. - nei_mask: neighbor mask, [nframes * nloc, nnei]. - input_r: normalized radial, [nframes, nloc, nei, 3]. - - Returns - ------- - type_embedding: - - """ - q, k, v = self.in_proj(query).chunk(3, axis=-1) - # [nframes * nloc, nnei, hidden_dim] - q = q.reshape([-1, self.nnei, self.hidden_dim]) - k = k.reshape([-1, self.nnei, self.hidden_dim]) - v = v.reshape([-1, self.nnei, self.hidden_dim]) - if self.normalize: - # q = F.normalize(q, axis=-1) - # k = F.normalize(k, axis=-1) - # v = F.normalize(v, axis=-1) - q = aux.normalize(q, axis=-1) - k = aux.normalize(k, axis=-1) - v = aux.normalize(v, axis=-1) - q = q * self.scaling - k = k.transpose([0, 2, 1]) - # [nframes * nloc, nnei, nnei] - attn_weights = paddle.bmm(q, k) - # [nframes * nloc, nnei] - nei_mask = nei_mask.reshape([-1, self.nnei]) - if self.smooth: - # [nframes * nloc, nnei] - assert sw is not None - sw = sw.reshape([-1, self.nnei]) - attn_weights = (attn_weights + attnw_shift) * sw[:, :, None] * sw[ - :, None, : - ] - attnw_shift - else: - attn_weights = attn_weights.masked_fill( - ~nei_mask.unsqueeze(1), float("-inf") - ) - attn_weights = F.softmax(attn_weights, axis=-1) - attn_weights = attn_weights.masked_fill(~nei_mask.unsqueeze(-1), 0.0) - if self.smooth: - assert sw is not None - attn_weights = attn_weights * sw[:, :, None] * sw[:, None, :] - if self.dotr: - assert input_r is not None, "input_r must be provided when dotr is True!" - perm = list(range(input_r.ndim)) - perm[1], perm[2] = perm[2], perm[1] - angular_weight = paddle.bmm(input_r, input_r.transpose(perm)) - attn_weights = attn_weights * angular_weight - o = paddle.bmm(attn_weights, v) - output = self.out_proj(o) - return output - - -class LocalSelfMultiheadAttention(nn.Layer): - def __init__(self, feature_dim, attn_head, scaling_factor=1.0): - super().__init__() - self.feature_dim = feature_dim - self.attn_head = attn_head - self.head_dim = feature_dim // attn_head - assert ( - feature_dim % attn_head == 0 - ), f"feature_dim {feature_dim} must be divided by attn_head {attn_head}!" - self.scaling = (self.head_dim * scaling_factor) ** -0.5 - self.in_proj = SimpleLinear(self.feature_dim, self.feature_dim * 3) - # TODO debug - # self.out_proj = SimpleLinear(self.feature_dim, self.feature_dim) - - def forward( - self, - query, - attn_bias: Optional[paddle.Tensor] = None, - nlist_mask: Optional[paddle.Tensor] = None, - nlist: Optional[paddle.Tensor] = None, - return_attn=True, - ): - nframes, nloc, feature_dim = query.shape - _, _, nnei = nlist.shape - assert feature_dim == self.feature_dim - # [nframes, nloc, feature_dim] - q, k, v = self.in_proj(query).chunk(3, axis=-1) - # [nframes * attn_head * nloc, 1, head_dim] - q = ( - q.reshape([nframes, nloc, self.attn_head, self.head_dim]) - .transpose([0, 2, 1, 3]) - .contiguous() - .reshape([nframes * self.attn_head * nloc, 1, self.head_dim]) - * self.scaling - ) - # [nframes, nloc, feature_dim] --> [nframes, nloc + 1, feature_dim] - # with nlist [nframes, nloc, nnei] --> [nframes, nloc, nnei, feature_dim] - # padding = paddle.zeros(feature_dim, dtype=env.GLOBAL_PD_FLOAT_PRECISION).to(k.place) - # k = paddle.concat([k, padding.unsqueeze(0).unsqueeze(1)], axis=1) - # v = paddle.concat([v, padding.unsqueeze(0).unsqueeze(1)], axis=1) - - # [nframes, nloc * nnei, feature_dim] - index = nlist.reshape([nframes, -1]).unsqueeze(-1).expand([-1, -1, feature_dim]) - k = aux.take_along_axis(k, axis=1, indices=index) - # [nframes, nloc * nnei, feature_dim] - v = aux.take_along_axis(v, axis=1, indices=index) - # [nframes * attn_head * nloc, nnei, head_dim] - k = ( - k.reshape([nframes, nloc, nnei, self.attn_head, self.head_dim]) - .transpose([0, 3, 1, 2, 4]) - .contiguous() - .reshape([nframes * self.attn_head * nloc, nnei, self.head_dim]) - ) - v = ( - v.reshape([nframes, nloc, nnei, self.attn_head, self.head_dim]) - .transpose([0, 3, 1, 2, 4]) - .contiguous() - .reshape([nframes * self.attn_head * nloc, nnei, self.head_dim]) - ) - # [nframes * attn_head * nloc, 1, nnei] - attn_weights = paddle.bmm(q, k.transpose([0, 2, 1])) - # maskfill - # [nframes, attn_head, nloc, nnei] - attn_weights = attn_weights.reshape( - [nframes, self.attn_head, nloc, nnei] - ).masked_fill(~nlist_mask.unsqueeze(1), float("-inf")) - # add bias - if return_attn: - attn_weights = attn_weights + attn_bias - # softmax - # [nframes * attn_head * nloc, 1, nnei] - attn = F.softmax(attn_weights, axis=-1).reshape( - [nframes * self.attn_head * nloc, 1, nnei] - ) - # bmm - # [nframes * attn_head * nloc, 1, head_dim] - o = paddle.bmm(attn, v) - assert list(o.shape) == [nframes * self.attn_head * nloc, 1, self.head_dim] - # [nframes, nloc, feature_dim] - o = ( - o.reshape([nframes, self.attn_head, nloc, self.head_dim]) - .transpose([0, 2, 1, 3]) - .contiguous() - .reshape([nframes, nloc, self.feature_dim]) - ) - # out - ## TODO debug: - # o = self.out_proj(o) - if not return_attn: - return o - else: - return o, attn_weights, attn - - -class NodeTaskHead(nn.Layer): - def __init__( - self, - embed_dim: int, - pair_dim: int, - num_head: int, - ): - super().__init__() - self.layer_norm = nn.LayerNorm(embed_dim) - self.pair_norm = nn.LayerNorm(pair_dim) - self.embed_dim = embed_dim - self.q_proj = Linear(embed_dim, embed_dim, bias=False, init="glorot") - self.k_proj = Linear(embed_dim, embed_dim, bias=False, init="glorot") - self.v_proj = Linear(embed_dim, embed_dim, bias=False, init="glorot") - self.num_heads = num_head - self.head_dim = embed_dim // num_head - self.scaling = self.head_dim**-0.5 - self.force_proj = Linear(embed_dim, 1, init="final", bias=False) - self.linear_bias = Linear(pair_dim, num_head) - self.dropout = 0.1 - - def zero_init(self): - init.zeros_(self.force_proj.weight) - - def forward( - self, - query: Tensor, - pair: Tensor, - delta_pos: Tensor, - attn_mask: Tensor = None, - ) -> Tensor: - ncluster, natoms, _ = query.shape - query = self.layer_norm(query) - # [ncluster, natoms, natoms, pair_dim] - pair = self.pair_norm(pair) - - # [ncluster, attn_head, natoms, head_dim] - q = ( - self.q_proj(query) - .reshape([ncluster, natoms, self.num_heads, -1]) - .transpose([0, 2, 1, 3]) - * self.scaling - ) - # [ncluster, attn_head, natoms, head_dim] - k = ( - self.k_proj(query) - .reshape([ncluster, natoms, self.num_heads, -1]) - .transpose([0, 2, 1, 3]) - ) - v = ( - self.v_proj(query) - .reshape([ncluster, natoms, self.num_heads, -1]) - .transpose([0, 2, 1, 3]) - ) - # [ncluster, attn_head, natoms, natoms] - attn = q @ k.transpose([0, 1, 3, 2]) - del q, k - # [ncluster, attn_head, natoms, natoms] - bias = self.linear_bias(pair).transpose([0, 3, 1, 2]).contiguous() - - # [ncluster, attn_head, natoms, natoms] - attn_probs = softmax_dropout( - attn, - self.dropout, - self.training, - mask=attn_mask, - bias=bias.contiguous(), - ).reshape([ncluster, self.num_heads, natoms, natoms]) - - # delta_pos: [ncluster, natoms, natoms, 3] - # [ncluster, attn_head, natoms, natoms, 3] - rot_attn_probs = attn_probs.unsqueeze(-1) * delta_pos.unsqueeze(1).astype( - attn_probs.dtype - ) - # [ncluster, attn_head, 3, natoms, natoms] - rot_attn_probs = rot_attn_probs.transpose([0, 1, 4, 2, 3]) - # [ncluster, attn_head, 3, natoms, head_dim] - x = rot_attn_probs @ v.unsqueeze(2) - # [ncluster, natoms, 3, embed_dim] - x = x.transpose([0, 3, 2, 1, 4]).contiguous().reshape([ncluster, natoms, 3, -1]) - cur_force = self.force_proj(x).reshape([ncluster, natoms, 3]) - return cur_force - - -class EnergyHead(nn.Layer): - def __init__( - self, - input_dim, - output_dim, - ): - super().__init__() - self.layer_norm = nn.LayerNorm(input_dim) - self.linear_in = Linear(input_dim, input_dim, init="relu") - - self.linear_out = Linear(input_dim, output_dim, bias=True, init="final") - - def forward(self, x): - x = x.type(self.linear_in.weight.dtype) - x = F.gelu(self.layer_norm(self.linear_in(x))) - x = self.linear_out(x) - return x - - -class OuterProduct(nn.Layer): - def __init__(self, d_atom, d_pair, d_hid=32): - super().__init__() - - self.d_atom = d_atom - self.d_pair = d_pair - self.d_hid = d_hid - - self.linear_in = nn.Linear(d_atom, d_hid * 2) - self.linear_out = nn.Linear(d_hid**2, d_pair) - self.act = nn.GELU() - - def _opm(self, a, b): - # [nframes, nloc, d] - nframes, nloc, d = a.shape - a = a.reshape([nframes, nloc, 1, d, 1]) - b = b.reshape([nframes, 1, nloc, 1, d]) - # [nframes, nloc, nloc, d, d] - outer = a * b - outer = outer.reshape([outer.shape[:-2] + (-1,)]) - outer = self.linear_out(outer) - return outer - - def forward( - self, - m: paddle.Tensor, - nlist: paddle.Tensor, - op_mask: float, - op_norm: float, - ) -> paddle.Tensor: - ab = self.linear_in(m) - ab = ab * op_mask - a, b = ab.chunk(2, axis=-1) - # [ncluster, natoms, natoms, d_pair] - z = self._opm(a, b) - z *= op_norm - return z - - -class Attention(nn.Layer): - def __init__( - self, - q_dim: int, - k_dim: int, - v_dim: int, - head_dim: int, - num_heads: int, - gating: bool = False, - dropout: float = 0.0, - ): - super().__init__() - - self.num_heads = num_heads - self.head_dim = head_dim - total_dim = head_dim * self.num_heads - self.total_dim = total_dim - self.q_dim = q_dim - self.gating = gating - self.linear_q = Linear(q_dim, total_dim, bias=False, init="glorot") - self.linear_k = Linear(k_dim, total_dim, bias=False, init="glorot") - self.linear_v = Linear(v_dim, total_dim, bias=False, init="glorot") - self.linear_o = Linear(total_dim, q_dim, init="final") - self.linear_g = None - if self.gating: - self.linear_g = Linear(q_dim, total_dim, init="gating") - # precompute the 1/sqrt(head_dim) - self.norm = head_dim**-0.5 - self.dropout = dropout - - def forward( - self, - q: paddle.Tensor, - k: paddle.Tensor, - v: paddle.Tensor, - bias: paddle.Tensor, - mask: paddle.Tensor = None, - ) -> paddle.Tensor: - nframes, nloc, embed_dim = q.shape - g = None - if self.linear_g is not None: - # gating, use raw query input - # [nframes, nloc, total_dim] - g = self.linear_g(q) - # [nframes, nloc, total_dim] - q = self.linear_q(q) - q *= self.norm - # [nframes, nloc, total_dim] - k = self.linear_k(k) - # [nframes, nloc, total_dim] - v = self.linear_v(v) - # global - # q [nframes, h, nloc, d] - # k [nframes, h, nloc, d] - # v [nframes, h, nloc, d] - # attn [nframes, h, nloc, nloc] - # o [nframes, h, nloc, d] - - # [nframes, h, nloc, d] - q = ( - q.reshape([q.shape[:-1] + (self.num_heads, -1)]) - .transpose([0, 1, 3, 2, 4]) - .contiguous() - ) - k = ( - k.reshape([k.shape[:-1] + (self.num_heads, -1)]) - .transpose([0, 1, 3, 2, 4]) - .contiguous() - ) - v = v.reshape([v.shape[:-1] + (self.num_heads, -1)]).transpose([0, 1, 3, 2, 4]) - # [nframes, h, nloc, nloc] - attn = paddle.matmul(q, k.transpose([0, 1, 2, 4, 3])) - del q, k - # [nframes, h, nloc, nloc] - attn = softmax_dropout(attn, self.dropout, self.training, mask=mask, bias=bias) - # [nframes, h, nloc, d] - o = paddle.matmul(attn, v) - del attn, v - - # local - # q [nframes, h, nloc, 1, d] - # k [nframes, h, nloc, nnei, d] - # v [nframes, h, nloc, nnei, d] - # attn [nframes, h, nloc, nnei] - # o [nframes, h, nloc, d] - - assert list(o.shape) == [nframes, self.num_heads, nloc, self.head_dim] - # [nframes, nloc, total_dim] - o = o.transpose([0, 2, 1, 3]).contiguous() - o = o.reshape([*o.shape[:-2], -1]) - - if g is not None: - o = paddle.sigmoid(g) * o - - # merge heads - o = self.linear_o(o) - return o - - -class AtomAttention(nn.Layer): - def __init__( - self, - q_dim: int, - k_dim: int, - v_dim: int, - pair_dim: int, - head_dim: int, - num_heads: int, - gating: bool = False, - dropout: float = 0.0, - ): - super().__init__() - - self.mha = Attention( - q_dim, k_dim, v_dim, head_dim, num_heads, gating=gating, dropout=dropout - ) - self.layer_norm = nn.LayerNorm(pair_dim) - self.linear_bias = Linear(pair_dim, num_heads) - - def forward( - self, - q: paddle.Tensor, - k: paddle.Tensor, - v: paddle.Tensor, - nlist: paddle.Tensor, - pair: paddle.Tensor, - mask: paddle.Tensor = None, - ) -> paddle.Tensor: - pair = self.layer_norm(pair) - bias = self.linear_bias(pair).transpose([0, 3, 1, 2]).contiguous() - return self.mha(q, k, v, bias=bias, mask=mask) - - -class TriangleMultiplication(nn.Layer): - def __init__(self, d_pair, d_hid): - super().__init__() - - self.linear_ab_p = Linear(d_pair, d_hid * 2) - self.linear_ab_g = Linear(d_pair, d_hid * 2, init="gating") - - self.linear_g = Linear(d_pair, d_pair, init="gating") - self.linear_z = Linear(d_hid, d_pair, init="final") - - self.layer_norm_out = nn.LayerNorm(d_hid) - - def forward( - self, - z: paddle.Tensor, - mask: Optional[paddle.Tensor] = None, - ) -> paddle.Tensor: - # z : [nframes, nloc, nloc, pair_dim] - - # [nframes, nloc, nloc, pair_dim] - g = self.linear_g(z) - if self.training: - ab = self.linear_ab_p(z) * paddle.sigmoid(self.linear_ab_g(z)) - else: - ab = self.linear_ab_p(z) - ab *= paddle.sigmoid(self.linear_ab_g(z)) - # [nframes, nloc, nloc, d] - a, b = paddle.chunk(ab, 2, axis=-1) - del z, ab - - # [nframes, d, nloc_i, nloc_k] row not trans - a1 = a.transpose([0, 3, 1, 2]) - # [nframes, d, nloc_k, nloc_j(i)] trans - b1 = b.transpose([0, 3, 2, 1]) - # [nframes, d, nloc_i, nloc_j] - x = paddle.matmul(a1, b1) - del a1, b1 - - # [nframes, d, nloc_k, nloc_j(i)] not trans - b2 = b.transpose([0, 3, 1, 2]) - # [nframes, d, nloc_i, nloc_k] col trans # check TODO - a2 = a.transpose([0, 3, 2, 1]) - - # [nframes, d, nloc_i, nloc_j] - x = x + paddle.matmul(a2, b2) - del a, b, a2, b2 - - # [nframes, nloc_i, nloc_j, d] - x = x.transpose([0, 2, 3, 1]) - - x = self.layer_norm_out(x) - x = self.linear_z(x) - return g * x - - -class EvoformerEncoderLayer(nn.Layer): - def __init__( - self, - feature_dim: int = 768, - ffn_dim: int = 2048, - attn_head: int = 8, - activation_fn: str = "gelu", - post_ln: bool = False, - ): - super().__init__() - self.feature_dim = feature_dim - self.ffn_dim = ffn_dim - self.attn_head = attn_head - self.activation_fn = ( - ActivationFn(activation_fn) if activation_fn is not None else None - ) - self.post_ln = post_ln - self.self_attn_layer_norm = nn.LayerNorm(self.feature_dim) - - self.self_attn = LocalSelfMultiheadAttention( - self.feature_dim, - self.attn_head, - ) - self.final_layer_norm = nn.LayerNorm(self.feature_dim) - self.fc1 = SimpleLinear(self.feature_dim, self.ffn_dim) - self.fc2 = SimpleLinear(self.ffn_dim, self.feature_dim) - - def forward( - self, - x, - attn_bias: Optional[paddle.Tensor] = None, - nlist_mask: Optional[paddle.Tensor] = None, - nlist: Optional[paddle.Tensor] = None, - return_attn=True, - ): - residual = x - if not self.post_ln: - x = self.self_attn_layer_norm(x) - x = self.self_attn( - query=x, - attn_bias=attn_bias, - nlist_mask=nlist_mask, - nlist=nlist, - return_attn=return_attn, - ) - if return_attn: - x, attn_weights, attn_probs = x - x = residual + x - if self.post_ln: - x = self.self_attn_layer_norm(x) - - residual = x - if not self.post_ln: - x = self.final_layer_norm(x) - x = self.fc1(x) - x = self.activation_fn(x) - x = self.fc2(x) - x = residual + x - if self.post_ln: - x = self.final_layer_norm(x) - if not return_attn: - return x - else: - return x, attn_weights, attn_probs - - -# output: atomic_rep, transformed_atomic_rep, pair_rep, delta_pair_rep, norm_x, norm_delta_pair_rep, -class Evoformer2bEncoder(nn.Layer): - def __init__( - self, - nnei: int, - layer_num: int = 6, - attn_head: int = 8, - atomic_dim: int = 1024, - pair_dim: int = 100, - feature_dim: int = 1024, - ffn_dim: int = 2048, - post_ln: bool = False, - final_layer_norm: bool = True, - final_head_layer_norm: bool = False, - emb_layer_norm: bool = False, - atomic_residual: bool = False, - evo_residual: bool = False, - residual_factor: float = 1.0, - activation_function: str = "gelu", - ): - super().__init__() - self.nnei = nnei - self.layer_num = layer_num - self.attn_head = attn_head - self.atomic_dim = atomic_dim - self.pair_dim = pair_dim - self.feature_dim = feature_dim - self.ffn_dim = ffn_dim - self.post_ln = post_ln - self._final_layer_norm = final_layer_norm - self._final_head_layer_norm = final_head_layer_norm - self._emb_layer_norm = emb_layer_norm - self.activation_function = activation_function - self.evo_residual = evo_residual - self.residual_factor = residual_factor - if atomic_residual and atomic_dim == feature_dim: - self.atomic_residual = True - else: - self.atomic_residual = False - self.in_proj = SimpleLinear( - self.atomic_dim, - self.feature_dim, - bavg=0.0, - stddev=1.0, - use_timestep=False, - activate="tanh", - ) # TODO - self.out_proj = SimpleLinear( - self.feature_dim, - self.atomic_dim, - bavg=0.0, - stddev=1.0, - use_timestep=False, - activate="tanh", - ) - if self._emb_layer_norm: - self.emb_layer_norm = nn.LayerNorm(self.feature_dim) - - ## TODO debug : self.in_proj_pair = NonLinearHead(self.pair_dim, self.attn_head, activation_fn=None) - self.in_proj_pair = SimpleLinear(self.pair_dim, self.attn_head, activate=None) - evoformer_encoder_layers = [] - for i in range(self.layer_num): - evoformer_encoder_layers.append( - EvoformerEncoderLayer( - feature_dim=self.feature_dim, - ffn_dim=self.ffn_dim, - attn_head=self.attn_head, - activation_fn=self.activation_function, - post_ln=self.post_ln, - ) - ) - self.evoformer_encoder_layers = nn.LayerList(evoformer_encoder_layers) - if self._final_layer_norm: - self.final_layer_norm = nn.LayerNorm(self.feature_dim) - if self._final_head_layer_norm: - self.final_head_layer_norm = nn.LayerNorm(self.attn_head) - - def forward(self, atomic_rep, pair_rep, nlist, nlist_type, nlist_mask): - """Encoder the atomic and pair representations. - - Args: - - atomic_rep: Atomic representation with shape [nframes, nloc, atomic_dim]. - - pair_rep: Pair representation with shape [nframes, nloc, nnei, pair_dim]. - - nlist: Neighbor list with shape [nframes, nloc, nnei]. - - nlist_type: Neighbor types with shape [nframes, nloc, nnei]. - - nlist_mask: Neighbor mask with shape [nframes, nloc, nnei], `False` if blank. - - Returns - ------- - - atomic_rep: Atomic representation after encoder with shape [nframes, nloc, feature_dim]. - - transformed_atomic_rep: Transformed atomic representation after encoder with shape [nframes, nloc, atomic_dim]. - - pair_rep: Pair representation after encoder with shape [nframes, nloc, nnei, attn_head]. - - delta_pair_rep: Delta pair representation after encoder with shape [nframes, nloc, nnei, attn_head]. - - norm_x: Normalization loss of atomic_rep. - - norm_delta_pair_rep: Normalization loss of delta_pair_rep. - """ - # Global branch - nframes, nloc, _ = atomic_rep.shape - nnei = pair_rep.shape[2] - input_atomic_rep = atomic_rep - # [nframes, nloc, feature_dim] - if self.atomic_residual: - atomic_rep = atomic_rep + self.in_proj(atomic_rep) - else: - atomic_rep = self.in_proj(atomic_rep) - - if self._emb_layer_norm: - atomic_rep = self.emb_layer_norm(atomic_rep) - - # Local branch - # [nframes, nloc, nnei, attn_head] - pair_rep = self.in_proj_pair(pair_rep) - # [nframes, attn_head, nloc, nnei] - pair_rep = pair_rep.transpose([0, 3, 1, 2]).contiguous() - input_pair_rep = pair_rep - pair_rep = pair_rep.masked_fill(~nlist_mask.unsqueeze(1), float("-inf")) - - for i in range(self.layer_num): - atomic_rep, pair_rep, _ = self.evoformer_encoder_layers[i]( - atomic_rep, - attn_bias=pair_rep, - nlist_mask=nlist_mask, - nlist=nlist, - return_attn=True, - ) - - def norm_loss(x, eps=1e-10, tolerance=1.0): - # x = x.float() - max_norm = x.shape[-1] ** 0.5 - norm = paddle.sqrt(paddle.sum(x**2, axis=-1) + eps) - error = F.relu((norm - max_norm).abs() - tolerance) - return error - - def masked_mean(mask, value, dim=-1, eps=1e-10): - return ( - paddle.sum(mask * value, axis=dim) / (eps + paddle.sum(mask, axis=dim)) - ).mean() - - # atomic_rep shape: [nframes, nloc, feature_dim] - # pair_rep shape: [nframes, attn_head, nloc, nnei] - - norm_x = paddle.mean(norm_loss(atomic_rep)) - if self._final_layer_norm: - atomic_rep = self.final_layer_norm(atomic_rep) - - delta_pair_rep = pair_rep - input_pair_rep - delta_pair_rep = delta_pair_rep.masked_fill(~nlist_mask.unsqueeze(1), 0) - # [nframes, nloc, nnei, attn_head] - delta_pair_rep = ( - delta_pair_rep.reshape([nframes, self.attn_head, nloc, nnei]) - .transpose([0, 2, 3, 1]) - .contiguous() - ) - - # [nframes, nloc, nnei] - norm_delta_pair_rep = norm_loss(delta_pair_rep) - norm_delta_pair_rep = masked_mean(mask=nlist_mask, value=norm_delta_pair_rep) - if self._final_head_layer_norm: - delta_pair_rep = self.final_head_layer_norm(delta_pair_rep) - - if self.atomic_residual: - transformed_atomic_rep = atomic_rep + self.out_proj(atomic_rep) - else: - transformed_atomic_rep = self.out_proj(atomic_rep) - - if self.evo_residual: - transformed_atomic_rep = ( - self.residual_factor * transformed_atomic_rep + input_atomic_rep - ) * (1 / np.sqrt(2)) - - return ( - atomic_rep, - transformed_atomic_rep, - pair_rep, - delta_pair_rep, - norm_x, - norm_delta_pair_rep, - ) - - -class Evoformer3bEncoderLayer(nn.Layer): - def __init__( - self, - nnei, - embedding_dim: int = 768, - pair_dim: int = 64, - pair_hidden_dim: int = 32, - ffn_embedding_dim: int = 3072, - num_attention_heads: int = 8, - dropout: float = 0.1, - droppath_prob: float = 0.0, - pair_dropout: float = 0.25, - attention_dropout: float = 0.1, - activation_dropout: float = 0.1, - pre_ln: bool = True, - tri_update: bool = True, - ): - super().__init__() - # Initialize parameters - self.nnei = nnei - self.embedding_dim = embedding_dim - self.num_attention_heads = num_attention_heads - self.attention_dropout = attention_dropout - - # self.dropout = dropout - self.activation_dropout = activation_dropout - - if droppath_prob > 0.0: - self.dropout_module = DropPath(droppath_prob) - else: - self.dropout_module = Dropout(dropout) - - # self.self_attn = AtomAttentionLocal(embedding_dim, embedding_dim, embedding_dim, pair_dim, - # embedding_dim // num_attention_heads, num_attention_heads, - # gating=False, dropout=attention_dropout) - self.self_attn = AtomAttention( - embedding_dim, - embedding_dim, - embedding_dim, - pair_dim, - embedding_dim // num_attention_heads, - num_attention_heads, - gating=False, - dropout=attention_dropout, - ) - # layer norm associated with the self attention layer - self.pre_ln = pre_ln - self.self_attn_layer_norm = nn.LayerNorm(self.embedding_dim) - self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim) - self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim) - self.final_layer_norm = nn.LayerNorm(self.embedding_dim) - - self.x_layer_norm_opm = nn.LayerNorm(self.embedding_dim) - # self.opm = OuterProductLocal(self.embedding_dim, pair_dim, d_hid=pair_hidden_dim) - self.opm = OuterProduct(self.embedding_dim, pair_dim, d_hid=pair_hidden_dim) - # self.pair_layer_norm_opm = nn.LayerNorm(pair_dim) - self.pair_layer_norm_ffn = nn.LayerNorm(pair_dim) - self.pair_ffn = Transition( - pair_dim, - 1, - dropout=activation_dropout, - ) - self.pair_dropout = pair_dropout - self.tri_update = tri_update - if self.tri_update: - self.pair_layer_norm_trimul = nn.LayerNorm(pair_dim) - self.pair_tri_mul = TriangleMultiplication(pair_dim, pair_hidden_dim) - - def update_pair( - self, - x, - pair, - nlist, - op_mask, - op_norm, - ): - # local: - # [nframes, nloc, nnei, pair_dim] - # global: - # [nframes, nloc, nloc, pair_dim] - pair = pair + self.dropout_module( - self.opm(self.x_layer_norm_opm(x), nlist, op_mask, op_norm) - ) - if not self.pre_ln: - pair = self.pair_layer_norm_opm(pair) - return x, pair - - def shared_dropout(self, x, shared_dim, dropout): - shape = list(x.shape) - shape[shared_dim] = 1 - with paddle.no_grad(): - mask = x.new_ones(shape) - return F.dropout(mask, p=dropout, training=self.training) * x - - def forward( - self, - x: paddle.Tensor, - pair: paddle.Tensor, - nlist: paddle.Tensor = None, - attn_mask: Optional[paddle.Tensor] = None, - pair_mask: Optional[paddle.Tensor] = None, - op_mask: float = 1.0, - op_norm: float = 1.0, - ): - """Encoder the atomic and pair representations. - - Args: - - x: Atomic representation with shape [ncluster, natoms, embed_dim]. - - pair: Pair representation with shape [ncluster, natoms, natoms, pair_dim]. - - attn_mask: Attention mask with shape [ncluster, head, natoms, natoms]. - - pair_mask: Neighbor mask with shape [ncluster, natoms, natoms]. - - """ - # [ncluster, natoms, embed_dim] - residual = x - if self.pre_ln: - x = self.self_attn_layer_norm(x) - x = self.self_attn( - x, - x, - x, - nlist=nlist, - pair=pair, - mask=attn_mask, - ) - # x = F.dropout(x, p=self.dropout, training=self.training) - x = self.dropout_module(x) - x = residual + x - if not self.pre_ln: - x = self.self_attn_layer_norm(x) - - residual = x - if self.pre_ln: - x = self.final_layer_norm(x) - x = F.linear(x, self.fc1.weight) - x = nn.GELU()(x) + self.fc1.bias - x = F.dropout(x, p=self.activation_dropout, training=self.training) - x = self.fc2(x) - # x = F.dropout(x, p=self.dropout, training=self.training) - x = self.dropout_module(x) - - x = residual + x - if not self.pre_ln: - x = self.final_layer_norm(x) - - block = [ - partial( - self.update_pair, - nlist=nlist, - op_mask=op_mask, - op_norm=op_norm, - ) - ] - - x, pair = checkpoint_sequential( - block, - input_x=(x, pair), - ) - - if self.tri_update: - residual_pair = pair - if self.pre_ln: - pair = self.pair_layer_norm_trimul(pair) - - pair = self.shared_dropout( - self.pair_tri_mul(pair, pair_mask), -3, self.pair_dropout - ) - pair = residual_pair + pair - if not self.pre_ln: - pair = self.pair_layer_norm_trimul(pair) - - residual_pair = pair - if self.pre_ln: - pair = self.pair_layer_norm_ffn(pair) - pair = self.dropout_module(self.pair_ffn(pair)) - pair = residual_pair + pair - if not self.pre_ln: - pair = self.pair_layer_norm_ffn(pair) - return x, pair - - -class Evoformer3bEncoder(nn.Layer): - def __init__( - self, - nnei, - layer_num=6, - attn_head=8, - atomic_dim=768, - pair_dim=64, - pair_hidden_dim=32, - ffn_embedding_dim=3072, - dropout: float = 0.1, - droppath_prob: float = 0.0, - pair_dropout: float = 0.25, - attention_dropout: float = 0.1, - activation_dropout: float = 0.1, - pre_ln: bool = True, - tri_update: bool = True, - **kwargs, - ): - super().__init__() - self.nnei = nnei - if droppath_prob > 0: - droppath_probs = [ - x.item() - for x in paddle.linspace(0, droppath_prob, layer_num) # pylint: disable=no-explicit-dtype,no-explicit-device - ] - else: - droppath_probs = None - - self.layers = nn.LayerList( - [ - Evoformer3bEncoderLayer( - nnei, - atomic_dim, - pair_dim, - pair_hidden_dim, - ffn_embedding_dim, - num_attention_heads=attn_head, - dropout=dropout, - droppath_prob=droppath_probs[_], - pair_dropout=pair_dropout, - attention_dropout=attention_dropout, - activation_dropout=activation_dropout, - pre_ln=pre_ln, - tri_update=tri_update, - ) - for _ in range(layer_num) - ] - ) - - def forward(self, x, pair, attn_mask=None, pair_mask=None, atom_mask=None): - """Encoder the atomic and pair representations. - - Args: - x: Atomic representation with shape [ncluster, natoms, atomic_dim]. - pair: Pair representation with shape [ncluster, natoms, natoms, pair_dim]. - attn_mask: Attention mask (with -inf for softmax) with shape [ncluster, head, natoms, natoms]. - pair_mask: Pair mask (with 1 for real atom pair and 0 for padding) with shape [ncluster, natoms, natoms]. - atom_mask: Atom mask (with 1 for real atom and 0 for padding) with shape [ncluster, natoms]. - - Returns - ------- - x: Atomic representation with shape [ncluster, natoms, atomic_dim]. - pair: Pair representation with shape [ncluster, natoms, natoms, pair_dim]. - - """ - # [ncluster, natoms, 1] - op_mask = atom_mask.unsqueeze(-1) - op_mask = op_mask * (op_mask.shape[-2] ** -0.5) - eps = 1e-3 - # [ncluster, natoms, natoms, 1] - op_norm = 1.0 / (eps + paddle.einsum("...bc,...dc->...bdc", op_mask, op_mask)) - for layer in self.layers: - x, pair = layer( - x, - pair, - nlist=None, - attn_mask=attn_mask, - pair_mask=pair_mask, - op_mask=op_mask, - op_norm=op_norm, - ) - return x, pair diff --git a/deepmd/pd/model/task/dipole.py b/deepmd/pd/model/task/dipole.py index 1f388517b0..42080761be 100644 --- a/deepmd/pd/model/task/dipole.py +++ b/deepmd/pd/model/task/dipole.py @@ -44,7 +44,7 @@ class DipoleFittingNet(GeneralFitting): Embedding width per atom. embedding_width : int The dimension of rotation matrix, m1. - neuron : List[int] + neuron : list[int] Number of neurons in each hidden layers of the fitting net. resnet_dt : bool Using time-step in the ResNet construction. @@ -69,7 +69,7 @@ class DipoleFittingNet(GeneralFitting): c_differentiable If the variable is differentiated with respect to the cell tensor (pbc case). Only reducible variable are differentiable. - type_map: List[str], Optional + type_map: list[str], Optional A list of strings. Give the name to each type of atoms. """ @@ -113,7 +113,6 @@ def __init__( type_map=type_map, **kwargs, ) - self.old_impl = False # this only supports the new implementation. def _net_out_dim(self): """Set the FittingNet output dim.""" @@ -123,7 +122,6 @@ def serialize(self) -> dict: data = super().serialize() data["type"] = "dipole" data["embedding_width"] = self.embedding_width - data["old_impl"] = self.old_impl data["r_differentiable"] = self.r_differentiable data["c_differentiable"] = self.c_differentiable return data @@ -158,11 +156,11 @@ def compute_output_stats( Parameters ---------- - merged : Union[Callable[[], List[dict]], List[dict]] - - List[dict]: A list of data samples from various data systems. + merged : Union[Callable[[], list[dict]], list[dict]] + - list[dict]: A list of data samples from various data systems. Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` originating from the `i`-th data system. - - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + - Callable[[], list[dict]]: A lazy function that returns data samples in the above format only when needed. Since the sampling process can be slow and memory-intensive, the lazy function helps by only sampling once. stat_file_path : Optional[DPPath] diff --git a/deepmd/pd/model/task/fitting.py b/deepmd/pd/model/task/fitting.py index 7a3b01a094..63a6ff682e 100644 --- a/deepmd/pd/model/task/fitting.py +++ b/deepmd/pd/model/task/fitting.py @@ -19,9 +19,6 @@ FittingNet, NetworkCollection, ) -from deepmd.pd.model.network.network import ( - ResidualDeep, -) from deepmd.pd.model.task.base_fitting import ( BaseFitting, ) @@ -96,7 +93,7 @@ class GeneralFitting(Fitting): Embedding width per atom. dim_out : int The output dimension of the fitting net. - neuron : List[int] + neuron : list[int] Number of neurons in each hidden layers of the fitting net. bias_atom_e : paddle.Tensor, optional Average enery per atom for each element. @@ -117,17 +114,17 @@ class GeneralFitting(Fitting): The condition number for the regression of atomic energy. seed : int, optional Random seed. - exclude_types: List[int] + exclude_types: list[int] Atomic contributions of the excluded atom types are set zero. - trainable : Union[List[bool], bool] + trainable : Union[list[bool], bool] If the parameters in the fitting net are trainable. Now this only supports setting all the parameters in the fitting net at one state. - When in List[bool], the trainable will be True only if all the boolean parameters are True. - remove_vaccum_contribution: List[bool], optional + When in list[bool], the trainable will be True only if all the boolean parameters are True. + remove_vaccum_contribution: list[bool], optional Remove vaccum contribution before the bias is added. The list assigned each type. For `mixed_types` provide `[True]`, otherwise it should be a list of the same length as `ntypes` signaling if or not removing the vaccum contribution for the atom types in the list. - type_map: List[str], Optional + type_map: list[str], Optional A list of strings. Give the name to each type of atoms. """ @@ -211,41 +208,24 @@ def __init__( in_dim = self.dim_descrpt + self.numb_fparam + self.numb_aparam - self.old_impl = kwargs.get("old_impl", False) - if self.old_impl: - filter_layers = [] - for type_i in range(self.ntypes if not self.mixed_types else 1): - bias_type = 0.0 - one = ResidualDeep( - type_i, - self.dim_descrpt, + self.filter_layers = NetworkCollection( + 1 if not self.mixed_types else 0, + self.ntypes, + network_type="fitting_network", + networks=[ + FittingNet( + in_dim, + net_dim_out, self.neuron, - bias_type, - resnet_dt=self.resnet_dt, + self.activation_function, + self.resnet_dt, + self.precision, + bias_out=True, + seed=child_seed(self.seed, ii), ) - filter_layers.append(one) - self.filter_layers_old = paddle.nn.LayerList(filter_layers) - self.filter_layers = None - else: - self.filter_layers = NetworkCollection( - 1 if not self.mixed_types else 0, - self.ntypes, - network_type="fitting_network", - networks=[ - FittingNet( - in_dim, - net_dim_out, - self.neuron, - self.activation_function, - self.resnet_dt, - self.precision, - bias_out=True, - seed=child_seed(self.seed, ii), - ) - for ii in range(self.ntypes if not self.mixed_types else 1) - ], - ) - self.filter_layers_old = None + for ii in range(self.ntypes if not self.mixed_types else 1) + ], + ) # set trainable for param in self.parameters(): param.stop_gradient = not self.trainable @@ -488,50 +468,30 @@ def _forward_common( (nf, nloc, net_dim_out), dtype=env.GLOBAL_PD_FLOAT_PRECISION, ).to(device=descriptor.place) # jit assertion - if self.old_impl: - assert self.filter_layers_old is not None - assert xx_zeros is None - if self.mixed_types: - atom_property = self.filter_layers_old[0](xx) + self.bias_atom_e[atype] - outs = outs + atom_property # Shape is [nframes, natoms[0], 1] - else: - for type_i, filter_layer in enumerate(self.filter_layers_old): - mask = atype == type_i - atom_property = filter_layer(xx) - atom_property = atom_property + self.bias_atom_e[type_i] - atom_property = atom_property * mask.unsqueeze(-1).astype( - atom_property.dtype - ) - outs = outs + atom_property # Shape is [nframes, natoms[0], 1] + if self.mixed_types: + atom_property = self.filter_layers.networks[0](xx) + self.bias_atom_e[atype] + if xx_zeros is not None: + atom_property -= self.filter_layers.networks[0](xx_zeros) + outs = outs + atom_property # Shape is [nframes, natoms[0], net_dim_out] else: - if self.mixed_types: - atom_property = ( - self.filter_layers.networks[0](xx) + self.bias_atom_e[atype] - ) + for type_i, ll in enumerate(self.filter_layers.networks): + mask = (atype == type_i).unsqueeze(-1) + mask.stop_gradient = True + mask = paddle.tile(mask, (1, 1, net_dim_out)) + atom_property = ll(xx) if xx_zeros is not None: - atom_property -= self.filter_layers.networks[0](xx_zeros) + # must assert, otherwise jit is not happy + assert self.remove_vaccum_contribution is not None + if not ( + len(self.remove_vaccum_contribution) > type_i + and not self.remove_vaccum_contribution[type_i] + ): + atom_property -= ll(xx_zeros) + atom_property = atom_property + self.bias_atom_e[type_i] + atom_property = atom_property * mask.astype(atom_property.dtype) outs = ( outs + atom_property ) # Shape is [nframes, natoms[0], net_dim_out] - else: - for type_i, ll in enumerate(self.filter_layers.networks): - mask = (atype == type_i).unsqueeze(-1) - mask.stop_gradient = True - mask = paddle.tile(mask, (1, 1, net_dim_out)) - atom_property = ll(xx) - if xx_zeros is not None: - # must assert, otherwise jit is not happy - assert self.remove_vaccum_contribution is not None - if not ( - len(self.remove_vaccum_contribution) > type_i - and not self.remove_vaccum_contribution[type_i] - ): - atom_property -= ll(xx_zeros) - atom_property = atom_property + self.bias_atom_e[type_i] - atom_property = atom_property * mask.astype(atom_property.dtype) - outs = ( - outs + atom_property - ) # Shape is [nframes, natoms[0], net_dim_out] # nf x nloc mask = self.emask(atype) # nf x nloc x nod diff --git a/deepmd/pd/model/task/polarizability.py b/deepmd/pd/model/task/polarizability.py index c996ae1435..9b49b45b71 100644 --- a/deepmd/pd/model/task/polarizability.py +++ b/deepmd/pd/model/task/polarizability.py @@ -46,7 +46,7 @@ class PolarFittingNet(GeneralFitting): Embedding width per atom. embedding_width : int The dimension of rotation matrix, m1. - neuron : List[int] + neuron : list[int] Number of neurons in each hidden layers of the fitting net. resnet_dt : bool Using time-step in the ResNet construction. @@ -68,11 +68,11 @@ class PolarFittingNet(GeneralFitting): fit_diag : bool Fit the diagonal part of the rotational invariant polarizability matrix, which will be converted to normal polarizability matrix by contracting with the rotation matrix. - scale : List[float] + scale : list[float] The output of the fitting net (polarizability matrix) for type i atom will be scaled by scale[i] shift_diag : bool Whether to shift the diagonal part of the polarizability matrix. The shift operation is carried out after scale. - type_map: List[str], Optional + type_map: list[str], Optional A list of strings. Give the name to each type of atoms. """ @@ -114,6 +114,13 @@ def __init__( raise ValueError( "Scale must be a list of float of length ntypes or a float." ) + self.scale = paddle.to_tensor( + self.scale, dtype=env.GLOBAL_PD_FLOAT_PRECISION, place=env.DEVICE + ).reshape([ntypes, 1]) + self.shift_diag = shift_diag + self.constant_matrix = paddle.zeros( + [ntypes], dtype=env.GLOBAL_PD_FLOAT_PRECISION + ).to(place=env.DEVICE) super().__init__( var_name="polar", ntypes=ntypes, @@ -131,16 +138,6 @@ def __init__( type_map=type_map, **kwargs, ) - self.scale = ( - paddle.to_tensor(self.scale, dtype=env.GLOBAL_PD_FLOAT_PRECISION) - .to(device=env.DEVICE) - .reshape([ntypes, 1]) - ) - self.shift_diag = shift_diag - self.constant_matrix = paddle.zeros( - [ntypes], dtype=env.GLOBAL_PD_FLOAT_PRECISION - ).to(device=env.DEVICE) - self.old_impl = False # this only supports the new implementation. def _net_out_dim(self): """Set the FittingNet output dim.""" @@ -196,7 +193,6 @@ def serialize(self) -> dict: data["type"] = "polar" data["@version"] = 3 data["embedding_width"] = self.embedding_width - data["old_impl"] = self.old_impl data["fit_diag"] = self.fit_diag data["shift_diag"] = self.shift_diag data["@variables"]["scale"] = to_numpy_array(self.scale) diff --git a/deepmd/pd/utils/init.py b/deepmd/pd/utils/init.py deleted file mode 100644 index 9f363d6db0..0000000000 --- a/deepmd/pd/utils/init.py +++ /dev/null @@ -1,515 +0,0 @@ -# SPDX-License-Identifier: LGPL-3.0-or-later -""" -The initialization method under this module is aligned with pytorch initialization. -If you need to use the initialization method of PaddlePaddle, please refer to -[paddle.nn.initializer](https://github.com/PaddlePaddle/Paddle/tree/develop/python/paddle/nn/initializer). - -This code is based on [torch.nn.init](https://github.com/pytorch/pytorch/blob/main/torch/nn/init.py) -Ths copyright of pytorch/pytorch is a BSD-style license, as found in the LICENSE file. -""" - -from __future__ import ( - annotations, -) - -import math -import warnings - -import numpy as np -import paddle -from paddle import ( - nn, -) -from typing_extensions import ( - Literal, -) - -__all__ = [ - "uniform_", - "normal_", - "trunc_normal_", - "glorot_normal_", - "constant_", - "ones_", - "zeros_", - "xavier_uniform_", - "xavier_normal_", - "kaiming_uniform_", - "kaiming_normal_", - "linear_init_", - "conv_init_", -] - - -def _no_grad_uniform_(tensor, a, b): - with paddle.no_grad(): - tensor.set_value( - paddle.uniform(shape=tensor.shape, dtype=tensor.dtype, min=a, max=b) - ) - return tensor - - -def _no_grad_normal_(tensor, mean=0.0, std=1.0): - with paddle.no_grad(): - tensor.set_value(paddle.normal(mean=mean, std=std, shape=tensor.shape)) - return tensor - - -def _no_grad_trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0): - # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf - def norm_cdf(x): - # Computes standard normal cumulative distribution function - return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0 - - if (mean < a - 2 * std) or (mean > b + 2 * std): - warnings.warn( - f"mean({mean}) is more than 2 std({std}) from [a, b]([{a}, {b}]) in _no_grad_trunc_normal_. " - "The distribution of values may be incorrect." - ) - - with paddle.no_grad(): - # Values are generated by using a truncated uniform distribution and - # then using the inverse CDF for the normal distribution. - # Get upper and lower cdf values - l = norm_cdf((a - mean) / std) - u = norm_cdf((b - mean) / std) - - # Uniformly fill tensor with values from [l, u], then translate to - # [2l-1, 2u-1]. - _tensor = paddle.uniform( - shape=tensor.shape, dtype=tensor.dtype, min=2 * l - 1, max=2 * u - 1 - ) - - # Use inverse cdf transform for normal distribution to get truncated - # standard normal - _tensor.erfinv_() - - # Transform to proper mean, std - _tensor = paddle.multiply( - _tensor, paddle.to_tensor(std * math.sqrt(2.0), tensor.dtype) - ) - _tensor = paddle.add(_tensor, paddle.to_tensor(mean, tensor.dtype)) - - # Clamp to ensure it"s in the proper range - _tensor = paddle.clip(_tensor, min=a, max=b) - tensor.set_value(_tensor) - return tensor - - -def _no_grad_fill_(tensor, value=0.0): - with paddle.no_grad(): - tensor.set_value(paddle.full_like(tensor, value, dtype=tensor.dtype)) - return tensor - - -def uniform_(tensor: paddle.Tensor, a: float, b: float) -> paddle.Tensor: - """Modify tensor inplace using uniform_. - - Args: - tensor (paddle.Tensor): Paddle Tensor. - a (float): Min value. - b (float): Max value. - - Returns - ------- - paddle.Tensor: Initialized tensor. - - Examples - -------- - >>> import paddle - >>> import ppsci - >>> param = paddle.empty((128, 256), "float32") - >>> param = ppsci.utils.initializer.uniform_(param, -1, 1) - """ - return _no_grad_uniform_(tensor, a, b) - - -def normal_( - tensor: paddle.Tensor, mean: float = 0.0, std: float = 1.0 -) -> paddle.Tensor: - """Modify tensor inplace using normal_. - - Args: - tensor (paddle.Tensor): Paddle Tensor. - mean (float, optional): Mean value. Defaults to 0.0. - std (float, optional): Std value. Defaults to 1.0. - - Returns - ------- - paddle.Tensor: Initialized tensor. - - Examples - -------- - >>> import paddle - >>> import ppsci - >>> param = paddle.empty((128, 256), "float32") - >>> param = ppsci.utils.initializer.normal_(param, 0, 1) - """ - return _no_grad_normal_(tensor, mean, std) - - -def trunc_normal_( - tensor: paddle.Tensor, - mean: float = 0.0, - std: float = 1.0, - a: float = -2.0, - b: float = 2.0, -) -> paddle.Tensor: - """Modify tensor inplace using trunc_normal_. - - Args: - tensor (paddle.Tensor): Paddle Tensor. - mean (float, optional): The mean of the normal distribution. Defaults to 0.0. - std (float, optional): The standard deviation of the normal distribution. Defaults to 1.0. - a (float, optional): The minimum cutoff value. Defaults to -2.0. - b (float, optional): The maximum cutoff value. Defaults to 2.0. - - Returns - ------- - paddle.Tensor: Initialized tensor. - - Examples - -------- - >>> import paddle - >>> import ppsci - >>> param = paddle.empty((128, 256), "float32") - >>> param = ppsci.utils.initializer.trunc_normal_(param, 0.0, 1.0) - """ - return _no_grad_trunc_normal_(tensor, mean, std, a, b) - - -def constant_(tensor: paddle.Tensor, value: float = 0.0) -> paddle.Tensor: - """Modify tensor inplace using constant_. - - Args: - tensor (paddle.Tensor): Paddle Tensor. - value (float, optional): Value to fill tensor. Defaults to 0.0. - - Returns - ------- - paddle.Tensor: Initialized tensor. - - Examples - -------- - >>> import paddle - >>> import ppsci - >>> param = paddle.empty((128, 256), "float32") - >>> param = ppsci.utils.initializer.constant_(param, 2) - """ - return _no_grad_fill_(tensor, value) - - -def ones_(tensor: paddle.Tensor) -> paddle.Tensor: - """Modify tensor inplace using ones_. - - Args: - tensor (paddle.Tensor): Paddle Tensor. - - Returns - ------- - paddle.Tensor: Initialized tensor. - - Examples - -------- - >>> import paddle - >>> import ppsci - >>> param = paddle.empty((128, 256), "float32") - >>> param = ppsci.utils.initializer.ones_(param) - """ - return _no_grad_fill_(tensor, 1) - - -def zeros_(tensor: paddle.Tensor) -> paddle.Tensor: - """Modify tensor inplace using zeros_. - - Args: - tensor (paddle.Tensor): Paddle Tensor. - - Returns - ------- - paddle.Tensor: Initialized tensor. - - Examples - -------- - >>> import paddle - >>> import ppsci - >>> param = paddle.empty((128, 256), "float32") - >>> param = ppsci.utils.initializer.zeros_(param) - """ - return _no_grad_fill_(tensor, 0) - - -def _calculate_fan_in_and_fan_out(tensor, reverse=False): - """ - Calculate (fan_in, _fan_out) for tensor. - - Args: - tensor (paddle.Tensor): paddle.Tensor. - reverse (bool): Tensor data format order, False by default as [fout, fin, ...]. - e.g. : conv.weight [cout, cin, kh, kw] is False; linear.weight [cin, cout] - is True. - - Return: - Tuple[float, float]: (fan_in, fan_out). - """ - if tensor.ndim < 2: - raise ValueError( - f"tensor.ndim should be no less than 2, but got {tensor.ndim}." - ) - - if reverse: - num_input_fmaps, num_output_fmaps = tensor.shape[0], tensor.shape[1] - else: - num_input_fmaps, num_output_fmaps = tensor.shape[1], tensor.shape[0] - - receptive_field_size = 1 - if tensor.ndim > 2: - receptive_field_size = np.prod(tensor.shape[2:]) - - fan_in = num_input_fmaps * receptive_field_size - fan_out = num_output_fmaps * receptive_field_size - - return fan_in, fan_out - - -def xavier_uniform_( - tensor: paddle.Tensor, gain: float = 1.0, reverse: bool = False -) -> paddle.Tensor: - """Modify tensor inplace using xavier_uniform_. - - Args: - tensor (paddle.Tensor): Paddle Tensor. - gain (float, optional): Hyperparameter. Defaults to 1.0. - reverse (bool, optional): Tensor data format order, False by default as - [fout, fin, ...].. Defaults to False. - - Returns - ------- - paddle.Tensor: Initialized tensor. - - Examples - -------- - >>> import paddle - >>> import ppsci - >>> param = paddle.empty((128, 256), "float32") - >>> param = ppsci.utils.initializer.xavier_uniform_(param) - """ - fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor, reverse=reverse) - std = gain * math.sqrt(2.0 / float(fan_in + fan_out)) - k = math.sqrt(3.0) * std - return _no_grad_uniform_(tensor, -k, k) - - -def xavier_normal_( - tensor: paddle.Tensor, gain: float = 1.0, reverse: bool = False -) -> paddle.Tensor: - """Modify tensor inplace using xavier_normal_. - - Args: - tensor (paddle.Tensor): Paddle Tensor. - gain (float, optional): Hyperparameter. Defaults to 1.0. - reverse (bool, optional): Tensor data format order, False by - default as [fout, fin, ...]. Defaults to False. - - Returns - ------- - paddle.Tensor: Initialized tensor. - - Examples - -------- - >>> import paddle - >>> import ppsci - >>> param = paddle.empty((128, 256), "float32") - >>> param = ppsci.utils.initializer.xavier_normal_(param) - """ - fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor, reverse=reverse) - std = gain * math.sqrt(2.0 / float(fan_in + fan_out)) - return _no_grad_normal_(tensor, 0, std) - - -# reference: https://pytorch.org/docs/stable/_modules/torch/nn/init.html -def _calculate_correct_fan(tensor, mode, reverse=False): - mode = mode.lower() - valid_modes = ["fan_in", "fan_out"] - if mode not in valid_modes: - raise ValueError(f"Mode {mode} not supported, please use one of {valid_modes}") - - fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor, reverse) - - return fan_in if mode == "fan_in" else fan_out - - -def _calculate_gain(nonlinearity, param=None): - linear_fns = [ - "linear", - "conv1d", - "conv2d", - "conv3d", - "conv_transpose1d", - "conv_transpose2d", - "conv_transpose3d", - ] - if nonlinearity in linear_fns or nonlinearity == "sigmoid": - return 1 - elif nonlinearity == "tanh": - return 5.0 / 3 - elif nonlinearity == "relu": - return math.sqrt(2.0) - elif nonlinearity == "leaky_relu": - if param is None: - negative_slope = 0.01 - elif ( - not isinstance(param, bool) - and isinstance(param, int) - or isinstance(param, float) - ): - # True/False are instances of int, hence check above - negative_slope = param - else: - raise ValueError(f"negative_slope {param} not a valid number") - return math.sqrt(2.0 / (1 + negative_slope**2)) - elif nonlinearity == "selu": - return 3.0 / 4 - else: - raise ValueError(f"Unsupported nonlinearity {nonlinearity}") - - -def kaiming_uniform_( - tensor: paddle.Tensor, - a: float = 0, - mode: Literal["fan_in", "fan_out"] = "fan_in", - nonlinearity: str = "leaky_relu", - reverse: bool = False, -) -> paddle.Tensor: - """Modify tensor inplace using kaiming_uniform method. - - Args: - tensor (paddle.Tensor): Paddle Tensor. - a (float, optional): The negative slope of the rectifier used after this layer. - Defaults to 0. - mode (Literal["fan_in", "fan_out"], optional): - ["fan_in", "fan_out"]. Defaults to "fan_in". - nonlinearity (str, optional): Nonlinearity method name. Defaults to "leaky_relu". - reverse (bool, optional): Tensor data format order, False by default as - [fout, fin, ...].. Defaults to False. - - Returns - ------- - paddle.Tensor: Initialized tensor. - - Examples - -------- - >>> import paddle - >>> import ppsci - >>> param = paddle.empty((128, 256), "float32") - >>> param = ppsci.utils.initializer.kaiming_uniform_(param) - """ - fan = _calculate_correct_fan(tensor, mode, reverse) - gain = _calculate_gain(nonlinearity, a) - std = gain / math.sqrt(fan) - k = math.sqrt(3.0) * std - return _no_grad_uniform_(tensor, -k, k) - - -def kaiming_normal_( - tensor: paddle.Tensor, - a: float = 0, - mode: Literal["fan_in", "fan_out"] = "fan_in", - nonlinearity: str = "leaky_relu", - reverse: bool = False, -) -> paddle.Tensor: - """Modify tensor inplace using kaiming_normal_. - - Args: - tensor (paddle.Tensor): Paddle Tensor. - a (float, optional): The negative slope of the rectifier used after this layer. - Defaults to 0. - mode (Literal["fan_in", "fan_out"], optional): Either - 'fan_in' (default) or 'fan_out'. Defaults to "fan_in". - nonlinearity (str, optional): Nonlinearity method name. Defaults to "leaky_relu". - reverse (bool, optional): Tensor data format order. Defaults to False. - - Returns - ------- - paddle.Tensor: Initialized tensor. - - Examples - -------- - >>> import paddle - >>> import ppsci - >>> param = paddle.empty((128, 256), "float32") - >>> param = ppsci.utils.initializer.kaiming_normal_(param) - """ - fan = _calculate_correct_fan(tensor, mode, reverse) - gain = _calculate_gain(nonlinearity, a) - std = gain / math.sqrt(fan) - return _no_grad_normal_(tensor, 0, std) - - -def linear_init_(module: nn.Layer) -> None: - """Initialize module's weight and bias as it is a linear layer. - - Args: - module (nn.Layer): Linear Layer to be initialized. - - Examples - -------- - >>> import paddle - >>> import ppsci - >>> layer = paddle.nn.Linear(128, 256) - >>> ppsci.utils.initializer.linear_init_(layer) - """ - kaiming_uniform_(module.weight, a=math.sqrt(5)) - if module.bias is not None: - fan_in, _ = _calculate_fan_in_and_fan_out(module.weight, reverse=True) - bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0 - uniform_(module.bias, -bound, bound) - - -def conv_init_(module: nn.Layer) -> None: - """Initialize module's weight and bias as it is a conv layer. - - Args: - module (nn.Layer): Convolution Layer to be initialized. - - Examples - -------- - >>> import paddle - >>> import ppsci - >>> layer = paddle.nn.Conv2D(4, 16, 2) - >>> ppsci.utils.initializer.conv_init_(layer) - """ - kaiming_uniform_(module.weight, a=math.sqrt(5)) - if module.bias is not None: - fan_in, _ = _calculate_fan_in_and_fan_out(module.weight, reverse=False) - if fan_in != 0: - bound = 1 / math.sqrt(fan_in) - uniform_(module.bias, -bound, bound) - - -def glorot_normal_(tensor: paddle.Tensor) -> paddle.Tensor: - """Modify tensor inplace using jax-style glorot_normal. - - Args: - tensor (paddle.Tensor): Paddle Tensor/Paramter. - - Returns - ------- - paddle.Tensor: Initialized tensor. - - Examples - -------- - >>> import paddle - >>> import ppsci - >>> param = paddle.empty((128, 256), "float32") - >>> param = ppsci.utils.initializer.glorot_normal_(param) - """ - assert ( - tensor.ndim == 2 - ), f"glorot_normal_ only support 2D tensor now, but got ndim={tensor.ndim}" - fin, fout = tensor.shape - var = 2.0 / (fin + fout) - stddev = math.sqrt(var) * 0.87962566103423978 - trunc_normal_(tensor) - tensor.set_value(tensor * stddev) - return tensor From de24e27ed8f3439af29849fc54e4511b7f0bc18c Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Thu, 24 Oct 2024 18:43:09 +0800 Subject: [PATCH 63/93] correct paddlepaddle requirement string --- backend/find_paddle.py | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/backend/find_paddle.py b/backend/find_paddle.py index 9a7ddd95a9..33387c815b 100644 --- a/backend/find_paddle.py +++ b/backend/find_paddle.py @@ -109,13 +109,9 @@ def get_pd_requirement(pd_version: str = "") -> dict: return { "paddle": [ - # uv has different local version behaviors, i.e. `==2.3.1` cannot match `==2.3.1+cpu` - # https://github.com/astral-sh/uv/blob/main/PIP_COMPATIBILITY.md#local-version-identifiers - # luckily, .* (prefix matching) defined in PEP 440 can match any local version - # https://peps.python.org/pep-0440/#version-matching - f"paddle=={Version(pd_version).base_version}.*" + f"paddlepaddle=={Version(pd_version).base_version}.*" if pd_version != "" - else "paddlepaddle-gpu>=3.0.0b1", + else "paddlepaddle", ], } From 264286fdfa5af31e985dabbbd115da6fc1c8c61e Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Fri, 25 Oct 2024 01:32:18 +0800 Subject: [PATCH 64/93] update old code --- deepmd/pd/model/descriptor/__init__.py | 4 - deepmd/pd/model/descriptor/repformer_layer.py | 134 +++++++++++++++--- deepmd/pd/model/descriptor/se_a.py | 2 +- deepmd/pd/model/network/mlp.py | 2 +- deepmd/pd/model/network/network.py | 6 +- deepmd/pd/model/task/__init__.py | 8 +- deepmd/pd/model/task/atten_lcc.py | 58 -------- deepmd/pd/model/task/polarizability.py | 14 +- 8 files changed, 130 insertions(+), 98 deletions(-) delete mode 100644 deepmd/pd/model/task/atten_lcc.py diff --git a/deepmd/pd/model/descriptor/__init__.py b/deepmd/pd/model/descriptor/__init__.py index 779e7a562c..4ffa937bcb 100644 --- a/deepmd/pd/model/descriptor/__init__.py +++ b/deepmd/pd/model/descriptor/__init__.py @@ -16,9 +16,6 @@ from .env_mat import ( prod_env_mat, ) -from .gaussian_lcc import ( - DescrptGaussianLcc, -) from .hybrid import ( DescrptHybrid, ) @@ -59,6 +56,5 @@ "DescrptDPA2", "DescrptHybrid", "prod_env_mat", - "DescrptGaussianLcc", "DescrptBlockRepformers", ] diff --git a/deepmd/pd/model/descriptor/repformer_layer.py b/deepmd/pd/model/descriptor/repformer_layer.py index 830b1835d7..a36c7edced 100644 --- a/deepmd/pd/model/descriptor/repformer_layer.py +++ b/deepmd/pd/model/descriptor/repformer_layer.py @@ -608,6 +608,9 @@ def __init__( precision: str = "float64", trainable_ln: bool = True, ln_eps: Optional[float] = 1e-5, + use_sqrt_nnei: bool = True, + g1_out_conv: bool = True, + g1_out_mlp: bool = True, seed: Optional[Union[int, list[int]]] = None, ): super().__init__() @@ -647,6 +650,9 @@ def __init__( self.ln_eps = ln_eps self.precision = precision self.seed = seed + self.use_sqrt_nnei = use_sqrt_nnei + self.g1_out_conv = g1_out_conv + self.g1_out_mlp = g1_out_mlp assert update_residual_init in [ "norm", @@ -702,14 +708,52 @@ def __init__( seed=child_seed(seed, 3), ) ) - if self.update_g1_has_conv: - self.proj_g1g2 = MLPLayer( + if self.g1_out_mlp: + self.g1_self_mlp = MLPLayer( + g1_dim, g1_dim, - g2_dim, - bias=False, precision=precision, - seed=child_seed(seed, 4), + seed=child_seed(seed, 15), ) + if self.update_style == "res_residual": + self.g1_residual.append( + get_residual( + g1_dim, + self.update_residual, + self.update_residual_init, + precision=precision, + seed=child_seed(seed, 16), + ) + ) + else: + self.g1_self_mlp = None + if self.update_g1_has_conv: + if not self.g1_out_conv: + self.proj_g1g2 = MLPLayer( + g1_dim, + g2_dim, + bias=False, + precision=precision, + seed=child_seed(seed, 4), + ) + else: + self.proj_g1g2 = MLPLayer( + g2_dim, + g1_dim, + bias=False, + precision=precision, + seed=child_seed(seed, 4), + ) + if self.update_style == "res_residual": + self.g1_residual.append( + get_residual( + g1_dim, + self.update_residual, + self.update_residual_init, + precision=precision, + seed=child_seed(seed, 17), + ) + ) if self.update_g2_has_g1g1: self.proj_g1g1g2 = MLPLayer( g1_dim, @@ -799,12 +843,12 @@ def __init__( self.h2_residual = nn.ParameterList(self.h2_residual) def cal_1_dim(self, g1d: int, g2d: int, ax: int) -> int: - ret = g1d + ret = g1d if not self.g1_out_mlp else 0 if self.update_g1_has_grrg: ret += g2d * ax if self.update_g1_has_drrd: ret += g1d * ax - if self.update_g1_has_conv: + if self.update_g1_has_conv and not self.g1_out_conv: ret += g2d return ret @@ -854,9 +898,12 @@ def _update_g1_conv( nb, nloc, nnei, _ = g2.shape ng1 = gg1.shape[-1] ng2 = g2.shape[-1] - # gg1 : nb x nloc x nnei x ng2 - gg1 = self.proj_g1g2(gg1).reshape([nb, nloc, nnei, ng2]) - # nb x nloc x nnei x ng2 + if not self.g1_out_conv: + # gg1 : nb x nloc x nnei x ng2 + gg1 = self.proj_g1g2(gg1).reshape([nb, nloc, nnei, ng2]) + else: + gg1 = gg1.reshape([nb, nloc, nnei, ng1]) + # nb x nloc x nnei x ng2/ng1 gg1 = _apply_nlist_mask(gg1, nlist_mask) if not self.smooth: # normalized by number of neighbors, not smooth @@ -870,8 +917,13 @@ def _update_g1_conv( invnnei = (1.0 / float(nnei)) * paddle.ones( (nb, nloc, 1), dtype=gg1.dtype ).to(device=gg1.place) - # nb x nloc x ng2 - g1_11 = paddle.sum(g2 * gg1, axis=2) * invnnei + if not self.g1_out_conv: + # nb x nloc x ng2 + g1_11 = paddle.sum(g2 * gg1, axis=2) * invnnei + else: + g2 = self.proj_g1g2(g2).reshape([nb, nloc, nnei, ng1]) + # nb x nloc x ng1 + g1_11 = paddle.sum(g2 * gg1, axis=2) * invnnei return g1_11 @staticmethod @@ -882,6 +934,7 @@ def _cal_hg( sw: paddle.Tensor, smooth: bool = True, epsilon: float = 1e-4, + use_sqrt_nnei: bool = True, ) -> paddle.Tensor: """ Calculate the transposed rotation matrix. @@ -916,15 +969,26 @@ def _cal_hg( g2 = _apply_nlist_mask(g2, nlist_mask) if not smooth: # nb x nloc - # must use astype here to convert bool to float, otherwise there will be numerical difference from numpy - invnnei = 1.0 / (epsilon + paddle.sum(nlist_mask.astype(g2.dtype), axis=-1)) + # must use type_as here to convert bool to float, otherwise there will be numerical difference from numpy + if not use_sqrt_nnei: + invnnei = 1.0 / (epsilon + paddle.sum(nlist_mask.type_as(g2), axis=-1)) + else: + invnnei = 1.0 / ( + epsilon + paddle.sqrt(paddle.sum(nlist_mask.type_as(g2), axis=-1)) + ) # nb x nloc x 1 x 1 invnnei = invnnei.unsqueeze(-1).unsqueeze(-1) else: g2 = _apply_switch(g2, sw) - invnnei = (1.0 / float(nnei)) * paddle.ones( - (nb, nloc, 1, 1), dtype=g2.dtype - ).to(device=g2.place) + if not use_sqrt_nnei: + invnnei = (1.0 / float(nnei)) * paddle.ones( + (nb, nloc, 1, 1), dtype=g2.dtype + ).to(device=g2.place) + else: + invnnei = paddle.rsqrt( + float(nnei) + * paddle.ones((nb, nloc, 1, 1), dtype=g2.dtype).to(device=g2.place) + ) # nb x nloc x 3 x ng2 h2g2 = paddle.matmul(paddle.transpose(h2, [0, 1, 3, 2]), g2) * invnnei return h2g2 @@ -997,7 +1061,15 @@ def symmetrization_op( # msk: nb x nloc x nnei nb, nloc, nnei, _ = g2.shape # nb x nloc x 3 x ng2 - h2g2 = self._cal_hg(g2, h2, nlist_mask, sw, smooth=smooth, epsilon=epsilon) + h2g2 = self._cal_hg( + g2, + h2, + nlist_mask, + sw, + smooth=smooth, + epsilon=epsilon, + use_sqrt_nnei=self.use_sqrt_nnei, + ) # nb x nloc x (axisxng2) g1_13 = self._cal_grrg(h2g2, axis_neuron) return g1_13 @@ -1072,7 +1144,11 @@ def forward( g2_update: list[paddle.Tensor] = [g2] h2_update: list[paddle.Tensor] = [h2] g1_update: list[paddle.Tensor] = [g1] - g1_mlp: list[paddle.Tensor] = [g1] + g1_mlp: list[paddle.Tensor] = [g1] if not self.g1_out_mlp else [] + if self.g1_out_mlp: + assert self.g1_self_mlp is not None + g1_self_mlp = self.act(self.g1_self_mlp(g1)) + g1_update.append(g1_self_mlp) if cal_gg1: gg1 = _make_nei_g1(g1_ext, nlist) @@ -1114,7 +1190,11 @@ def forward( if self.update_g1_has_conv: assert gg1 is not None - g1_mlp.append(self._update_g1_conv(gg1, g2, nlist_mask, sw)) + g1_conv = self._update_g1_conv(gg1, g2, nlist_mask, sw) + if not self.g1_out_conv: + g1_mlp.append(g1_conv) + else: + g1_update.append(g1_conv) if self.update_g1_has_grrg: g1_mlp.append( @@ -1247,6 +1327,9 @@ def serialize(self) -> dict: "smooth": self.smooth, "precision": self.precision, "trainable_ln": self.trainable_ln, + "use_sqrt_nnei": self.use_sqrt_nnei, + "g1_out_conv": self.g1_out_conv, + "g1_out_mlp": self.g1_out_mlp, "ln_eps": self.ln_eps, "linear1": self.linear1.serialize(), } @@ -1294,6 +1377,12 @@ def serialize(self) -> dict: "loc_attn": self.loc_attn.serialize(), } ) + if self.g1_out_mlp: + data.update( + { + "g1_self_mlp": self.g1_self_mlp.serialize(), + } + ) if self.update_style == "res_residual": data.update( { @@ -1324,6 +1413,7 @@ def deserialize(cls, data: dict) -> "RepformerLayer": update_h2 = data["update_h2"] update_g1_has_attn = data["update_g1_has_attn"] update_style = data["update_style"] + g1_out_mlp = data["g1_out_mlp"] linear2 = data.pop("linear2", None) proj_g1g2 = data.pop("proj_g1g2", None) @@ -1333,6 +1423,7 @@ def deserialize(cls, data: dict) -> "RepformerLayer": attn2_lm = data.pop("attn2_lm", None) attn2_ev_apply = data.pop("attn2_ev_apply", None) loc_attn = data.pop("loc_attn", None) + g1_self_mlp = data.pop("g1_self_mlp", None) g1_residual = data.pop("g1_residual", []) g2_residual = data.pop("g2_residual", []) h2_residual = data.pop("h2_residual", []) @@ -1362,6 +1453,9 @@ def deserialize(cls, data: dict) -> "RepformerLayer": if update_g1_has_attn: assert isinstance(loc_attn, dict) obj.loc_attn = LocalAtten.deserialize(loc_attn) + if g1_out_mlp: + assert isinstance(g1_self_mlp, dict) + obj.g1_self_mlp = MLPLayer.deserialize(g1_self_mlp) if update_style == "res_residual": for ii, t in enumerate(obj.g1_residual): t.data = to_paddle_tensor(g1_residual[ii]) diff --git a/deepmd/pd/model/descriptor/se_a.py b/deepmd/pd/model/descriptor/se_a.py index 9e4fe40882..76ea32797f 100644 --- a/deepmd/pd/model/descriptor/se_a.py +++ b/deepmd/pd/model/descriptor/se_a.py @@ -55,7 +55,7 @@ EmbeddingNet, NetworkCollection, ) -from deepmd.pt.utils.exclude_mask import ( +from deepmd.pd.utils.exclude_mask import ( PairExcludeMask, ) diff --git a/deepmd/pd/model/network/mlp.py b/deepmd/pd/model/network/mlp.py index 29c84b0d33..58f2333eed 100644 --- a/deepmd/pd/model/network/mlp.py +++ b/deepmd/pd/model/network/mlp.py @@ -219,7 +219,7 @@ def forward( ori_prec = xx.dtype xx = xx.astype(self.prec) yy = ( - paddle.matmul(xx, self.matrix.astype(self.prec)) + self.bias + paddle.matmul(xx, self.matrix) + self.bias if self.bias is not None else paddle.matmul(xx, self.matrix) ) diff --git a/deepmd/pd/model/network/network.py b/deepmd/pd/model/network/network.py index 82b3b248d9..0d2fda8b89 100644 --- a/deepmd/pd/model/network/network.py +++ b/deepmd/pd/model/network/network.py @@ -169,7 +169,7 @@ def _normal_init(self): init.kaiming_normal_(self.weight, nonlinearity="linear") -class NonLinearHead(nn.Module): +class NonLinearHead(nn.Layer): def __init__(self, input_dim, out_dim, activation_fn, hidden=None): super().__init__() hidden = input_dim if not hidden else hidden @@ -182,7 +182,7 @@ def forward(self, x): return x -class MaskLMHead(nn.Module): +class MaskLMHead(nn.Layer): """Head for masked language modeling.""" def __init__(self, embed_dim, output_dim, activation_fn, weight=None): @@ -193,7 +193,7 @@ def __init__(self, embed_dim, output_dim, activation_fn, weight=None): if weight is None: weight = nn.Linear(embed_dim, output_dim, bias_attr=False).weight - self.weight = weight + self.weight = weight.T self.bias = self.create_parameter( [output_dim], dtype=env.GLOBAL_PD_FLOAT_PRECISION, diff --git a/deepmd/pd/model/task/__init__.py b/deepmd/pd/model/task/__init__.py index 8a13b27e20..02d852eab7 100644 --- a/deepmd/pd/model/task/__init__.py +++ b/deepmd/pd/model/task/__init__.py @@ -1,7 +1,4 @@ # SPDX-License-Identifier: LGPL-3.0-or-later -from .atten_lcc import ( - FittingNetAttenLcc, -) from .base_fitting import ( BaseFitting, ) @@ -24,12 +21,14 @@ from .polarizability import ( PolarFittingNet, ) +from .property import ( + PropertyFittingNet, +) from .type_predict import ( TypePredictNet, ) __all__ = [ - "FittingNetAttenLcc", "DenoiseNet", "DipoleFittingNet", "EnergyFittingNet", @@ -39,4 +38,5 @@ "TypePredictNet", "PolarFittingNet", "DOSFittingNet", + "PropertyFittingNet", ] diff --git a/deepmd/pd/model/task/atten_lcc.py b/deepmd/pd/model/task/atten_lcc.py deleted file mode 100644 index 7b6d2f5828..0000000000 --- a/deepmd/pd/model/task/atten_lcc.py +++ /dev/null @@ -1,58 +0,0 @@ -# SPDX-License-Identifier: LGPL-3.0-or-later -import paddle -import paddle.nn as nn - -from deepmd.pd.model.network import ( - init, -) -from deepmd.pd.model.network.network import ( - EnergyHead, - NodeTaskHead, -) -from deepmd.pd.model.task.fitting import ( - Fitting, -) -from deepmd.pd.utils import ( - env, -) - - -class FittingNetAttenLcc(Fitting): - def __init__( - self, embedding_width, bias_atom_e, pair_embed_dim, attention_heads, **kwargs - ): - super().__init__() - self.embedding_width = embedding_width - self.engergy_proj = EnergyHead(self.embedding_width, 1) - self.energe_agg_factor = nn.Embedding(4, 1, dtype=env.GLOBAL_PD_FLOAT_PRECISION) - init.normal_(self.energe_agg_factor.weight, 0, 0.01) - bias_atom_e = paddle.to_tensor(bias_atom_e) # pylint: disable=no-explicit-dtype,no-explicit-device - self.register_buffer("bias_atom_e", bias_atom_e) - self.pair_embed_dim = pair_embed_dim - self.attention_heads = attention_heads - self.node_proc = NodeTaskHead( - self.embedding_width, self.pair_embed_dim, self.attention_heads - ) - self.node_proc.zero_init() - - def forward(self, output, pair, delta_pos, atype, nframes, nloc): - # [nframes x nloc x tebd_dim] - output_nloc = (output[:, 0, :]).reshape([nframes, nloc, self.embedding_width]) - # Optional: GRRG or mean of gbf TODO - - # energy outut - # [nframes, nloc] - energy_out = self.engergy_proj(output_nloc).reshape([nframes, nloc]) - # [nframes, nloc] - energy_factor = self.energe_agg_factor(paddle.zeros_like(atype)).reshape( - [nframes, nloc] - ) - energy_out = (energy_out * energy_factor) + self.bias_atom_e[atype] - energy_out = energy_out.sum(axis=-1) - - # vector output - # predict_force: [(nframes x nloc) x (1 + nnei2) x 3] - predict_force = self.node_proc(output, pair, delta_pos=delta_pos) - # predict_force_nloc: [nframes x nloc x 3] - predict_force_nloc = (predict_force[:, 0, :]).reshape([nframes, nloc, 3]) - return energy_out, predict_force_nloc diff --git a/deepmd/pd/model/task/polarizability.py b/deepmd/pd/model/task/polarizability.py index 9b49b45b71..ab13b51076 100644 --- a/deepmd/pd/model/task/polarizability.py +++ b/deepmd/pd/model/task/polarizability.py @@ -114,13 +114,6 @@ def __init__( raise ValueError( "Scale must be a list of float of length ntypes or a float." ) - self.scale = paddle.to_tensor( - self.scale, dtype=env.GLOBAL_PD_FLOAT_PRECISION, place=env.DEVICE - ).reshape([ntypes, 1]) - self.shift_diag = shift_diag - self.constant_matrix = paddle.zeros( - [ntypes], dtype=env.GLOBAL_PD_FLOAT_PRECISION - ).to(place=env.DEVICE) super().__init__( var_name="polar", ntypes=ntypes, @@ -138,6 +131,13 @@ def __init__( type_map=type_map, **kwargs, ) + self.scale = paddle.to_tensor( + self.scale, dtype=env.GLOBAL_PD_FLOAT_PRECISION, place=env.DEVICE + ).reshape([ntypes, 1]) + self.shift_diag = shift_diag + self.constant_matrix = paddle.zeros( + [ntypes], dtype=env.GLOBAL_PD_FLOAT_PRECISION + ).to(device=env.DEVICE) def _net_out_dim(self): """Set the FittingNet output dim.""" From fd6aff085ac8b3c4d94306d4e17c331e54fc9254 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 28 Oct 2024 10:58:12 +0800 Subject: [PATCH 65/93] update test C++ interface --- .github/workflows/test_cc.yml | 8 + .github/workflows/test_cuda.yml | 1 + deepmd/pd/entrypoints/main.py | 16 +- deepmd/pd/infer/deep_eval.py | 4 +- .../pd/model/atomic_model/dp_atomic_model.py | 54 +- .../atomic_model/pairtab_atomic_model.py | 17 +- deepmd/pd/model/descriptor/dpa1.py | 4 +- deepmd/pd/model/descriptor/repformer_layer.py | 33 +- deepmd/pd/model/descriptor/repformers.py | 9 +- deepmd/pd/model/descriptor/se_atten.py | 2 - deepmd/pd/model/model/make_hessian_model.py | 2 + deepmd/pd/model/network/network.py | 20 +- deepmd/pd/model/task/property.py | 151 +++++ deepmd/pd/utils/aux.py | 30 +- deepmd/pd/utils/nlist.py | 15 +- deepmd/pd/utils/no_use_init.py | 515 ++++++++++++++++++ source/tests/pd/test_dp_show.py | 12 + source/tests/pd/test_multitask.py | 2 +- source/tests/pd/test_training.py | 7 +- 19 files changed, 819 insertions(+), 83 deletions(-) create mode 100644 deepmd/pd/model/task/property.py create mode 100644 deepmd/pd/utils/no_use_init.py diff --git a/.github/workflows/test_cc.yml b/.github/workflows/test_cc.yml index 768590980f..4fb3da4202 100644 --- a/.github/workflows/test_cc.yml +++ b/.github/workflows/test_cc.yml @@ -32,6 +32,10 @@ jobs: run: | wget https://download.pytorch.org/libtorch/cpu/libtorch-cxx11-abi-shared-with-deps-2.1.2%2Bcpu.zip -O libtorch.zip unzip libtorch.zip + - name: Download paddle_inference_lib + run: | + wget https://paddle-qa.bj.bcebos.com/paddle-pipeline/GITHUB_Docker_Compile_Test_Cuda118_cudnn860_Trt8531_D1/ce51e82e84fc97e0a55a162037f1554746159cad/paddle_inference.tgz + tar -xzvf paddle_inference.tgz # https://github.com/actions/runner-images/issues/9491 - name: Fix kernel mmap rnd bits run: sudo sysctl vm.mmap_rnd_bits=28 @@ -49,6 +53,10 @@ jobs: # test lammps - run: | export TENSORFLOW_ROOT=$(python -c 'import importlib,pathlib;print(pathlib.Path(importlib.util.find_spec("tensorflow").origin).parent)') + export PADDLE_INFERENCE_ROOT=$PWD/paddle_inference_install_dir/ + export LD_LIBRARY_PATH=${PADDLE_INFERENCE_ROOT}/paddle/lib:$LD_LIBRARY_PATH + export LD_LIBRARY_PATH=${PADDLE_INFERENCE_ROOT}/third_party/install/onednn/lib:$LD_LIBRARY_PATH + export LD_LIBRARY_PATH=${PADDLE_INFERENCE_ROOT}/third_party/install/mklml/lib:$LD_LIBRARY_PATH source/install/uv_with_retry.sh pip install --system -e .[cpu,test,lmp] mpi4py env: DP_BUILD_TESTING: 1 diff --git a/.github/workflows/test_cuda.yml b/.github/workflows/test_cuda.yml index d60a9c909a..54a196534a 100644 --- a/.github/workflows/test_cuda.yml +++ b/.github/workflows/test_cuda.yml @@ -48,6 +48,7 @@ jobs: if: false # skip as we use nvidia image - run: python -m pip install -U uv - run: source/install/uv_with_retry.sh pip install --system "tensorflow>=2.15.0rc0" "torch==2.3.1.*" + - run: source/install/uv_with_retry.sh pip install --system --pre "paddlepaddle-gpu" -i https://www.paddlepaddle.org.cn/packages/nightly/cu123/ - run: | export PYTORCH_ROOT=$(python -c 'import torch;print(torch.__path__[0])') export TENSORFLOW_ROOT=$(python -c 'import importlib,pathlib;print(pathlib.Path(importlib.util.find_spec("tensorflow").origin).parent)') diff --git a/deepmd/pd/entrypoints/main.py b/deepmd/pd/entrypoints/main.py index 6ab7946fe3..7a1b942768 100644 --- a/deepmd/pd/entrypoints/main.py +++ b/deepmd/pd/entrypoints/main.py @@ -338,6 +338,13 @@ def train(FLAGS): def freeze(FLAGS): + paddle.set_flags( + { + "FLAGS_save_cf_stack_op": 1, + "FLAGS_prim_enable_dynamic": 1, + "FLAGS_enable_pir_api": 1, + } + ) model = inference.Tester(FLAGS.model, head=FLAGS.head).model model.eval() from paddle.static import ( @@ -351,13 +358,6 @@ def freeze(FLAGS): """ # NOTE: 'FLAGS_save_cf_stack_op', 'FLAGS_prim_enable_dynamic' and # 'FLAGS_enable_pir_api' shoule be enabled when freezing model. - paddle.set_flags( - { - "FLAGS_save_cf_stack_op": 1, - "FLAGS_prim_enable_dynamic": 1, - "FLAGS_enable_pir_api": 1, - } - ) model = paddle.jit.to_static( model.forward_lower, full_graph=True, @@ -367,6 +367,8 @@ def freeze(FLAGS): InputSpec([-1, -1, -1], dtype="int32", name="nlist"), ], ) + if FLAGS.output.endswith(".json"): + FLAGS.output = FLAGS.output[:-5] paddle.jit.save( model, path=FLAGS.output, diff --git a/deepmd/pd/infer/deep_eval.py b/deepmd/pd/infer/deep_eval.py index db78128d3a..a8347ac7c0 100644 --- a/deepmd/pd/infer/deep_eval.py +++ b/deepmd/pd/infer/deep_eval.py @@ -118,7 +118,9 @@ def __init__( self.dp = ModelWrapper(model) self.dp.set_state_dict(state_dict) else: - raise ValueError("Unknown model file format!") + # self.dp = paddle.jit.load(self.model_path.split(".json")[0]) + raise ValueError(f"Unknown model file format: {self.model_path}!") + self.rcut = self.dp.model["Default"].get_rcut() self.type_map = self.dp.model["Default"].get_type_map() if isinstance(auto_batch_size, bool): diff --git a/deepmd/pd/model/atomic_model/dp_atomic_model.py b/deepmd/pd/model/atomic_model/dp_atomic_model.py index e059cbcb6c..45eb9ca1cb 100644 --- a/deepmd/pd/model/atomic_model/dp_atomic_model.py +++ b/deepmd/pd/model/atomic_model/dp_atomic_model.py @@ -46,6 +46,8 @@ class DPAtomicModel(BaseAtomicModel): For example `type_map[1]` gives the name of the type 1. """ + eval_descriptor_list: list[paddle.Tensor] + def __init__( self, descriptor, @@ -62,6 +64,8 @@ def __init__( self.sel = self.descriptor.get_sel() self.fitting_net = fitting super().init_out_stat() + self.enable_eval_descriptor_hook = False + self.eval_descriptor_list = [] # register 'type_map' as buffer def _string_to_array(s: str) -> list[int]: @@ -72,12 +76,13 @@ def _string_to_array(s: str) -> list[int]: paddle.to_tensor(_string_to_array(" ".join(self.type_map)), dtype="int32"), ) self.buffer_type_map.name = "buffer_type_map" - # register 'has_message_passing' as buffer(cast to int32 as problems may meets with vector) - self.register_buffer( - "buffer_has_message_passing", - paddle.to_tensor(self.has_message_passing(), dtype="int32"), - ) - self.buffer_has_message_passing.name = "buffer_has_message_passing" + if hasattr(self.descriptor, "has_message_passing"): + # register 'has_message_passing' as buffer(cast to int32 as problems may meets with vector) + self.register_buffer( + "buffer_has_message_passing", + paddle.to_tensor(self.descriptor.has_message_passing(), dtype="int32"), + ) + self.buffer_has_message_passing.name = "buffer_has_message_passing" # register 'ntypes' as buffer self.register_buffer( "buffer_ntypes", paddle.to_tensor(self.ntypes, dtype="int32") @@ -88,23 +93,36 @@ def _string_to_array(s: str) -> list[int]: "buffer_rcut", paddle.to_tensor(self.rcut, dtype="float64") ) self.buffer_rcut.name = "buffer_rcut" - # register 'dfparam' as buffer - self.register_buffer( - "buffer_dfparam", paddle.to_tensor(self.get_dim_fparam(), dtype="int32") - ) - self.buffer_dfparam.name = "buffer_dfparam" - # register 'daparam' as buffer - self.register_buffer( - "buffer_daparam", paddle.to_tensor(self.get_dim_aparam(), dtype="int32") - ) - self.buffer_daparam.name = "buffer_daparam" + if hasattr(self.fitting_net, "get_dim_fparam"): + # register 'dfparam' as buffer + self.register_buffer( + "buffer_dfparam", + paddle.to_tensor(self.fitting_net.get_dim_fparam(), dtype="int32"), + ) + self.buffer_dfparam.name = "buffer_dfparam" + if hasattr(self.fitting_net, "get_dim_aparam"): + # register 'daparam' as buffer + self.register_buffer( + "buffer_daparam", + paddle.to_tensor(self.fitting_net.get_dim_aparam(), dtype="int32"), + ) + self.buffer_daparam.name = "buffer_daparam" # register 'aparam_nall' as buffer self.register_buffer( "buffer_aparam_nall", - paddle.to_tensor(self.is_aparam_nall(), dtype="int32"), + paddle.to_tensor(False, dtype="int32"), ) self.buffer_aparam_nall.name = "buffer_aparam_nall" + def set_eval_descriptor_hook(self, enable: bool) -> None: + """Set the hook for evaluating descriptor and clear the cache for descriptor list.""" + self.enable_eval_descriptor_hook = enable + self.eval_descriptor_list = [] + + def eval_descriptor(self) -> paddle.Tensor: + """Evaluate the descriptor.""" + return paddle.concat(self.eval_descriptor_list) + def fitting_output_def(self) -> FittingOutputDef: """Get the output def of the fitting net.""" return ( @@ -232,6 +250,8 @@ def forward_atomic( comm_dict=comm_dict, ) assert descriptor is not None + if self.enable_eval_descriptor_hook: + self.eval_descriptor_list.append(descriptor) # energy, force fit_ret = self.fitting_net( descriptor, diff --git a/deepmd/pd/model/atomic_model/pairtab_atomic_model.py b/deepmd/pd/model/atomic_model/pairtab_atomic_model.py index d24d2fd6f9..08c5d6113e 100644 --- a/deepmd/pd/model/atomic_model/pairtab_atomic_model.py +++ b/deepmd/pd/model/atomic_model/pairtab_atomic_model.py @@ -54,7 +54,7 @@ class PairTabAtomicModel(BaseAtomicModel): The cutoff radius. sel : int or list[int] The maxmum number of atoms in the cut-off radius. - type_map : List[str] + type_map : list[str] Mapping atom type to the name (str) of the type. For example `type_map[1]` gives the name of the type 1. rcond : float, optional @@ -86,7 +86,7 @@ def __init__( ( tab_info, tab_data, - ) = self.tab.get() # this returns -> Tuple[np.array, np.array] + ) = self.tab.get() # this returns -> tuple[np.array, np.array] nspline, ntypes_tab = tab_info[-2:].astype(int) self.register_buffer("tab_info", paddle.to_tensor(tab_info)) self.register_buffer( @@ -227,11 +227,11 @@ def compute_or_load_stat( Parameters ---------- - merged : Union[Callable[[], List[dict]], List[dict]] - - List[dict]: A list of data samples from various data systems. + merged : Union[Callable[[], list[dict]], list[dict]] + - list[dict]: A list of data samples from various data systems. Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` originating from the `i`-th data system. - - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + - Callable[[], list[dict]]: A lazy function that returns data samples in the above format only when needed. Since the sampling process can be slow and memory-intensive, the lazy function helps by only sampling once. stat_file_path : Optional[DPPath] @@ -337,14 +337,13 @@ def _pair_tabulated_inter( # if nnei of atom 0 has -1 in the nlist, uu would be 0. # this is to handle the nlist where the mask is set to 0, so that we don't raise exception for those atoms. - uu = paddle.where(nlist != -1, uu, float(nspline + 1)) + uu = paddle.where(nlist != -1, uu, paddle.full_like(uu, nspline + 1)) if paddle.any(uu < 0): raise Exception("coord go beyond table lower boundary") idx = uu.to(paddle.int32) - - uu -= idx + uu -= idx.astype(uu.dtype) table_coef = self._extract_spline_coefficient( i_type, j_type, idx, self.tab_data, nspline @@ -466,7 +465,7 @@ def _calculate_ener(coef: paddle.Tensor, uu: paddle.Tensor) -> paddle.Tensor: The atomic energy for all local atoms for all frames. (nframes, nloc, nnei) """ a3, a2, a1, a0 = paddle.unbind(coef, axis=-1) - etmp = (a3 * uu.astype(coef.dtype) + a2) * uu.astype( + etmp = (a3 * uu + a2) * uu.astype( coef.dtype ) + a1 # this should be elementwise operations. ener = ( diff --git a/deepmd/pd/model/descriptor/dpa1.py b/deepmd/pd/model/descriptor/dpa1.py index 4739eea0e1..c0d50fbf8b 100644 --- a/deepmd/pd/model/descriptor/dpa1.py +++ b/deepmd/pd/model/descriptor/dpa1.py @@ -154,7 +154,7 @@ class DescrptDPA1(BaseDescriptor, paddle.nn.Layer): (Only support False to keep consistent with other backend references.) (Not used in this version. True option is not implemented.) If mask the diagonal of attention weights - exclude_types : List[List[int]] + exclude_types : list[list[int]] The excluded pairs of types which have no interaction with each other. For example, `[[0, 1]]` means no interaction between type 0 and type 1. env_protection: float @@ -188,7 +188,7 @@ class DescrptDPA1(BaseDescriptor, paddle.nn.Layer): Whether to use electronic configuration type embedding. use_tebd_bias : bool, Optional Whether to use bias in the type embedding layer. - type_map: List[str], Optional + type_map: list[str], Optional A list of strings. Give the name to each type of atoms. spin (Only support None to keep consistent with other backend references.) diff --git a/deepmd/pd/model/descriptor/repformer_layer.py b/deepmd/pd/model/descriptor/repformer_layer.py index a36c7edced..45fc0e4d23 100644 --- a/deepmd/pd/model/descriptor/repformer_layer.py +++ b/deepmd/pd/model/descriptor/repformer_layer.py @@ -1138,15 +1138,18 @@ def forward( nb, nloc, nnei, _ = g2.shape nall = g1_ext.shape[1] g1, _ = paddle.split(g1_ext, [nloc, nall - nloc], axis=1) - assert [nb, nloc] == g1.shape[:2] - assert [nb, nloc, nnei] == h2.shape[:3] + if paddle.in_dynamic_mode(): + assert [nb, nloc] == g1.shape[:2] + if paddle.in_dynamic_mode(): + assert [nb, nloc, nnei] == h2.shape[:3] g2_update: list[paddle.Tensor] = [g2] h2_update: list[paddle.Tensor] = [h2] g1_update: list[paddle.Tensor] = [g1] g1_mlp: list[paddle.Tensor] = [g1] if not self.g1_out_mlp else [] if self.g1_out_mlp: - assert self.g1_self_mlp is not None + if paddle.in_dynamic_mode(): + assert self.g1_self_mlp is not None g1_self_mlp = self.act(self.g1_self_mlp(g1)) g1_update.append(g1_self_mlp) @@ -1157,28 +1160,34 @@ def forward( if self.update_chnnl_2: # mlp(g2) - assert self.linear2 is not None + if paddle.in_dynamic_mode(): + assert self.linear2 is not None # nb x nloc x nnei x ng2 g2_1 = self.act(self.linear2(g2)) g2_update.append(g2_1) if self.update_g2_has_g1g1: # linear(g1_i * g1_j) - assert gg1 is not None - assert self.proj_g1g1g2 is not None + if paddle.in_dynamic_mode(): + assert gg1 is not None + if paddle.in_dynamic_mode(): + assert self.proj_g1g1g2 is not None g2_update.append( self.proj_g1g1g2(self._update_g2_g1g1(g1, gg1, nlist_mask, sw)) ) if self.update_g2_has_attn or self.update_h2: # gated_attention(g2, h2) - assert self.attn2g_map is not None + if paddle.in_dynamic_mode(): + assert self.attn2g_map is not None # nb x nloc x nnei x nnei x nh AAg = self.attn2g_map(g2, h2, nlist_mask, sw) if self.update_g2_has_attn: - assert self.attn2_mh_apply is not None - assert self.attn2_lm is not None + if paddle.in_dynamic_mode(): + assert self.attn2_mh_apply is not None + if paddle.in_dynamic_mode(): + assert self.attn2_lm is not None # nb x nloc x nnei x ng2 g2_2 = self.attn2_mh_apply(AAg, g2) g2_2 = self.attn2_lm(g2_2) @@ -1189,7 +1198,8 @@ def forward( h2_update.append(self._update_h2(h2, AAg)) if self.update_g1_has_conv: - assert gg1 is not None + if paddle.in_dynamic_mode(): + assert gg1 is not None g1_conv = self._update_g1_conv(gg1, g2, nlist_mask, sw) if not self.g1_out_conv: g1_mlp.append(g1_conv) @@ -1210,7 +1220,8 @@ def forward( ) if self.update_g1_has_drrd: - assert gg1 is not None + if paddle.in_dynamic_mode(): + assert gg1 is not None g1_mlp.append( self.symmetrization_op( gg1, diff --git a/deepmd/pd/model/descriptor/repformers.py b/deepmd/pd/model/descriptor/repformers.py index 9fa68f1f9b..37979f3c54 100644 --- a/deepmd/pd/model/descriptor/repformers.py +++ b/deepmd/pd/model/descriptor/repformers.py @@ -415,12 +415,15 @@ def forward( # [nframes, nloc, tebd_dim] if comm_dict is None: - assert isinstance(extended_atype_embd, paddle.Tensor) # for jit + if paddle.in_dynamic_mode(): + assert isinstance(extended_atype_embd, paddle.Tensor) # for jit atype_embd = extended_atype_embd[:, :nloc, :] - assert list(atype_embd.shape) == [nframes, nloc, self.g1_dim] + if paddle.in_dynamic_mode(): + assert list(atype_embd.shape) == [nframes, nloc, self.g1_dim] else: atype_embd = extended_atype_embd - assert isinstance(atype_embd, paddle.Tensor) # for jit + if paddle.in_dynamic_mode(): + assert isinstance(atype_embd, paddle.Tensor) # for jit g1 = self.act(atype_embd) # nb x nloc x nnei x 1, nb x nloc x nnei x 3 if not self.direct_dist: diff --git a/deepmd/pd/model/descriptor/se_atten.py b/deepmd/pd/model/descriptor/se_atten.py index 0bf9563c15..53627e0082 100644 --- a/deepmd/pd/model/descriptor/se_atten.py +++ b/deepmd/pd/model/descriptor/se_atten.py @@ -462,7 +462,6 @@ def forward( sw = sw.masked_fill(~nlist_mask, 0.0) # (nb x nloc) x nnei exclude_mask = exclude_mask.reshape([nb * nloc, nnei]) - assert self.filter_layers is not None # nfnl x nnei x 4 dmatrix = dmatrix.reshape([-1, self.nnei, 4]) nfnl = dmatrix.shape[0] @@ -509,7 +508,6 @@ def forward( ) # shape is [nframes*nloc, self.neei, out_size] # nfnl x 4 x ng xyz_scatter = paddle.matmul(rr.transpose([0, 2, 1]), gg) - xyz_scatter = xyz_scatter / self.nnei xyz_scatter_1 = xyz_scatter.transpose([0, 2, 1]) rot_mat = xyz_scatter_1[:, :, 1:4] diff --git a/deepmd/pd/model/model/make_hessian_model.py b/deepmd/pd/model/model/make_hessian_model.py index a06cc28246..19222a9f1d 100644 --- a/deepmd/pd/model/model/make_hessian_model.py +++ b/deepmd/pd/model/model/make_hessian_model.py @@ -121,6 +121,7 @@ def _cal_hessian_all( fparam: Optional[paddle.Tensor] = None, aparam: Optional[paddle.Tensor] = None, ) -> dict[str, paddle.Tensor]: + raise NotImplementedError("paddle do not support full hessian.") nf, nloc = atype.shape coord = coord.reshape([nf, (nloc * 3)]) box = box.reshape([nf, 9]) if box is not None else None @@ -166,6 +167,7 @@ def _cal_hessian_one_component( fparam: Optional[paddle.Tensor] = None, aparam: Optional[paddle.Tensor] = None, ) -> paddle.Tensor: + raise NotImplementedError("paddle do not support full hessian.") # coord, # (nloc x 3) # atype, # nloc # box: Optional[paddle.Tensor] = None, # 9 diff --git a/deepmd/pd/model/network/network.py b/deepmd/pd/model/network/network.py index 0d2fda8b89..21d6586476 100644 --- a/deepmd/pd/model/network/network.py +++ b/deepmd/pd/model/network/network.py @@ -74,31 +74,25 @@ def __init__( self.use_timestep = use_timestep self.activate = ActivationFn(activate) - t = Tensor(num_in, num_out) self.matrix = self.create_parameter( [num_in, num_out], - dtype=t.dtype, - default_initializer=nn.initializer.Assign(t), + dtype=env.GLOBAL_PD_FLOAT_PRECISION, ) - init.normal_(self.matrix.data, std=stddev / np.sqrt(num_out + num_in)) + init.normal_(self.matrix, std=stddev / np.sqrt(num_out + num_in)) if bias: - t = Tensor(1, num_out) self.bias = self.create_parameter( (1, num_out), - dtype=t.dtype, - default_initializer=nn.initializer.Assign(t), + dtype=env.GLOBAL_PD_FLOAT_PRECISION, ) - init.normal_(self.bias.data, mean=bavg, std=stddev) + init.normal_(self.bias, mean=bavg, std=stddev) else: self.bias = None if self.use_timestep: - t = Tensor(1, num_out) self.idt = self.create_parameter( (1, num_out), - dtype=t.dtype, - default_initializer=nn.initializer.Assign(t), + dtype=env.GLOBAL_PD_FLOAT_PRECISION, ) - init.normal_(self.idt.data, mean=0.1, std=0.001) + init.normal_(self.idt, mean=0.1, std=0.001) def forward(self, inputs): """Return X*W+b.""" @@ -464,7 +458,7 @@ def change_type_map( assert ( not do_resnet or self.activation_function == "Linear" ), "'activation_function' must be 'Linear' when performing type changing on resnet structure!" - first_layer_matrix = self.embedding_net.layers[0].matrix.data + first_layer_matrix = self.embedding_net.layers[0].matrix eye_vector = paddle.eye(self.ntypes, dtype=self.prec).to( device=first_layer_matrix.place ) diff --git a/deepmd/pd/model/task/property.py b/deepmd/pd/model/task/property.py new file mode 100644 index 0000000000..600b5c265f --- /dev/null +++ b/deepmd/pd/model/task/property.py @@ -0,0 +1,151 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import logging +from typing import ( + Optional, +) + +import torch + +from deepmd.dpmodel import ( + FittingOutputDef, + OutputVariableDef, +) +from deepmd.pd.model.task.ener import ( + InvarFitting, +) +from deepmd.pd.model.task.fitting import ( + Fitting, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + DEFAULT_PRECISION, +) +from deepmd.utils.version import ( + check_version_compatibility, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION +device = env.DEVICE + +log = logging.getLogger(__name__) + + +@Fitting.register("property") +class PropertyFittingNet(InvarFitting): + """Fitting the rotationally invariant porperties of `task_dim` of the system. + + Parameters + ---------- + ntypes : int + Element count. + dim_descrpt : int + Embedding width per atom. + task_dim : int + The dimension of outputs of fitting net. + neuron : list[int] + Number of neurons in each hidden layers of the fitting net. + bias_atom_p : torch.Tensor, optional + Average property per atom for each element. + intensive : bool, optional + Whether the fitting property is intensive. + bias_method : str, optional + The method of applying the bias to each atomic output, user can select 'normal' or 'no_bias'. + If 'normal' is used, the computed bias will be added to the atomic output. + If 'no_bias' is used, no bias will be added to the atomic output. + resnet_dt : bool + Using time-step in the ResNet construction. + numb_fparam : int + Number of frame parameters. + numb_aparam : int + Number of atomic parameters. + activation_function : str + Activation function. + precision : str + Numerical precision. + mixed_types : bool + If true, use a uniform fitting net for all atom types, otherwise use + different fitting nets for different atom types. + seed : int, optional + Random seed. + """ + + def __init__( + self, + ntypes: int, + dim_descrpt: int, + task_dim: int = 1, + neuron: list[int] = [128, 128, 128], + bias_atom_p: Optional[torch.Tensor] = None, + intensive: bool = False, + bias_method: str = "normal", + resnet_dt: bool = True, + numb_fparam: int = 0, + numb_aparam: int = 0, + activation_function: str = "tanh", + precision: str = DEFAULT_PRECISION, + mixed_types: bool = True, + seed: Optional[int] = None, + **kwargs, + ): + self.task_dim = task_dim + self.intensive = intensive + self.bias_method = bias_method + super().__init__( + var_name="property", + ntypes=ntypes, + dim_descrpt=dim_descrpt, + dim_out=task_dim, + neuron=neuron, + bias_atom_e=bias_atom_p, + resnet_dt=resnet_dt, + numb_fparam=numb_fparam, + numb_aparam=numb_aparam, + activation_function=activation_function, + precision=precision, + mixed_types=mixed_types, + seed=seed, + **kwargs, + ) + + def get_bias_method(self) -> str: + return self.bias_method + + def output_def(self) -> FittingOutputDef: + return FittingOutputDef( + [ + OutputVariableDef( + self.var_name, + [self.dim_out], + reducible=True, + r_differentiable=False, + c_differentiable=False, + intensive=self.intensive, + ), + ] + ) + + @classmethod + def deserialize(cls, data: dict) -> "PropertyFittingNet": + data = copy.deepcopy(data) + check_version_compatibility(data.pop("@version", 1), 2, 1) + data.pop("dim_out") + data.pop("var_name") + obj = super().deserialize(data) + + return obj + + def serialize(self) -> dict: + """Serialize the fitting to dict.""" + dd = { + **InvarFitting.serialize(self), + "type": "property", + "task_dim": self.task_dim, + } + + return dd + + # make jit happy with torch 2.0.0 + exclude_types: list[int] diff --git a/deepmd/pd/utils/aux.py b/deepmd/pd/utils/aux.py index b22d639669..d07ac0caf4 100644 --- a/deepmd/pd/utils/aux.py +++ b/deepmd/pd/utils/aux.py @@ -10,6 +10,7 @@ import paddle __all__ = [ + "softmax", "norm", "take_along_axis", "scatter_reduce", @@ -18,15 +19,23 @@ ] -def norm( +# decomposition for forward function +def softmax_decomp(x: paddle.Tensor, axis: int = -1) -> paddle.Tensor: + x_max = paddle.max(x, axis=axis, keepdim=True) + x = x - x_max + return paddle.exp(x) / paddle.sum(paddle.exp(x), axis=axis, keepdim=True) + + +def norm_decomp( x: paddle.Tensor, p: float = 2, axis: bool = -1, keepdim: bool = False ) -> paddle.Tensor: if p == 2 or p == 2.0: - return (x * x).sum(axis=axis, keepdim=keepdim) ** 0.5 + # clip for negative indexing, or 1/(0^(k-1)) will cause inf in backward + return (x * x).sum(axis=axis, keepdim=keepdim).clip(1e-12) ** 0.5 return (x**p).sum(axis=axis, keepdim=keepdim) ** (1 / p) -def take_along_axis( +def take_along_axis_decomp( x: paddle.Tensor, indices: paddle.Tensor, axis: int, broadcast: bool = True ): """Broadcast no used now.""" @@ -41,7 +50,7 @@ def take_along_axis( return out -def scatter_reduce( +def scatter_reduce_decomp( input: paddle.Tensor, axis: int, index: paddle.Tensor, @@ -75,7 +84,7 @@ def sec(l: int, size: int) -> list[int]: return [size] * (l // size) + [l % size] -def masked_add_( +def masked_add__decomp( x: paddle.Tensor, mask: paddle.Tensor, v: paddle.Tensor ) -> paddle.Tensor: assert mask.dtype == paddle.bool, f"mask must be bool type, but got {mask.dtype}" @@ -95,10 +104,19 @@ def masked_add_( return x -def normalize( +def normalize_decomp( x: paddle.Tensor, p: float = 2, axis: int = 1, epsilon: float = 1e-12, ) -> paddle.Tensor: return x / (norm(x, p=p, axis=axis, keepdim=True).clip(min=epsilon)) + + +# alias for decomposed functions for convinience +normalize = normalize_decomp +masked_add_ = masked_add__decomp +scatter_reduce = scatter_reduce_decomp +take_along_axis = take_along_axis_decomp +norm = norm_decomp +softmax = softmax_decomp diff --git a/deepmd/pd/utils/nlist.py b/deepmd/pd/utils/nlist.py index 3315a6a870..60516377cf 100644 --- a/deepmd/pd/utils/nlist.py +++ b/deepmd/pd/utils/nlist.py @@ -379,7 +379,7 @@ def build_multiple_neighbor_list( return {} nb, nloc, nsel = nlist.shape if nsel < nsels[-1]: - pad = -1 * paddle.ones( + pad = -paddle.ones( [nb, nloc, nsels[-1] - nsel], dtype=nlist.dtype, ).to(device=nlist.place) @@ -471,24 +471,27 @@ def extend_coord_with_ghosts( # nf x 3 # *2: ghost copies on + and - directions # +1: central cell - nbuff = paddle.ceil(rcut / to_face).to(paddle.int64) + nbuff = paddle.ceil(rcut / to_face) + nbuff = paddle.where( + paddle.isinf(nbuff), nbuff.to(paddle.int64) + 1, nbuff.to(paddle.int64) + ) # 3 nbuff = paddle.amax(nbuff, axis=0) # faster than paddle.max - # nbuff_cpu = nbuff.cpu() + nbuff_cpu = nbuff.cpu() xi = ( - paddle.arange(-nbuff[0], nbuff[0] + 1, 1).to( + paddle.arange(-nbuff_cpu[0], nbuff_cpu[0] + 1, 1).to( dtype=env.GLOBAL_PD_FLOAT_PRECISION ) # .cpu() ) # pylint: disable=no-explicit-dtype yi = ( - paddle.arange(-nbuff[1], nbuff[1] + 1, 1).to( + paddle.arange(-nbuff_cpu[1], nbuff_cpu[1] + 1, 1).to( dtype=env.GLOBAL_PD_FLOAT_PRECISION ) # .cpu() ) # pylint: disable=no-explicit-dtype zi = ( - paddle.arange(-nbuff[2], nbuff[2] + 1, 1).to( + paddle.arange(-nbuff_cpu[2], nbuff_cpu[2] + 1, 1).to( dtype=env.GLOBAL_PD_FLOAT_PRECISION ) # .cpu() diff --git a/deepmd/pd/utils/no_use_init.py b/deepmd/pd/utils/no_use_init.py new file mode 100644 index 0000000000..9f363d6db0 --- /dev/null +++ b/deepmd/pd/utils/no_use_init.py @@ -0,0 +1,515 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +""" +The initialization method under this module is aligned with pytorch initialization. +If you need to use the initialization method of PaddlePaddle, please refer to +[paddle.nn.initializer](https://github.com/PaddlePaddle/Paddle/tree/develop/python/paddle/nn/initializer). + +This code is based on [torch.nn.init](https://github.com/pytorch/pytorch/blob/main/torch/nn/init.py) +Ths copyright of pytorch/pytorch is a BSD-style license, as found in the LICENSE file. +""" + +from __future__ import ( + annotations, +) + +import math +import warnings + +import numpy as np +import paddle +from paddle import ( + nn, +) +from typing_extensions import ( + Literal, +) + +__all__ = [ + "uniform_", + "normal_", + "trunc_normal_", + "glorot_normal_", + "constant_", + "ones_", + "zeros_", + "xavier_uniform_", + "xavier_normal_", + "kaiming_uniform_", + "kaiming_normal_", + "linear_init_", + "conv_init_", +] + + +def _no_grad_uniform_(tensor, a, b): + with paddle.no_grad(): + tensor.set_value( + paddle.uniform(shape=tensor.shape, dtype=tensor.dtype, min=a, max=b) + ) + return tensor + + +def _no_grad_normal_(tensor, mean=0.0, std=1.0): + with paddle.no_grad(): + tensor.set_value(paddle.normal(mean=mean, std=std, shape=tensor.shape)) + return tensor + + +def _no_grad_trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0): + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0 + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn( + f"mean({mean}) is more than 2 std({std}) from [a, b]([{a}, {b}]) in _no_grad_trunc_normal_. " + "The distribution of values may be incorrect." + ) + + with paddle.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + _tensor = paddle.uniform( + shape=tensor.shape, dtype=tensor.dtype, min=2 * l - 1, max=2 * u - 1 + ) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + _tensor.erfinv_() + + # Transform to proper mean, std + _tensor = paddle.multiply( + _tensor, paddle.to_tensor(std * math.sqrt(2.0), tensor.dtype) + ) + _tensor = paddle.add(_tensor, paddle.to_tensor(mean, tensor.dtype)) + + # Clamp to ensure it"s in the proper range + _tensor = paddle.clip(_tensor, min=a, max=b) + tensor.set_value(_tensor) + return tensor + + +def _no_grad_fill_(tensor, value=0.0): + with paddle.no_grad(): + tensor.set_value(paddle.full_like(tensor, value, dtype=tensor.dtype)) + return tensor + + +def uniform_(tensor: paddle.Tensor, a: float, b: float) -> paddle.Tensor: + """Modify tensor inplace using uniform_. + + Args: + tensor (paddle.Tensor): Paddle Tensor. + a (float): Min value. + b (float): Max value. + + Returns + ------- + paddle.Tensor: Initialized tensor. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> param = paddle.empty((128, 256), "float32") + >>> param = ppsci.utils.initializer.uniform_(param, -1, 1) + """ + return _no_grad_uniform_(tensor, a, b) + + +def normal_( + tensor: paddle.Tensor, mean: float = 0.0, std: float = 1.0 +) -> paddle.Tensor: + """Modify tensor inplace using normal_. + + Args: + tensor (paddle.Tensor): Paddle Tensor. + mean (float, optional): Mean value. Defaults to 0.0. + std (float, optional): Std value. Defaults to 1.0. + + Returns + ------- + paddle.Tensor: Initialized tensor. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> param = paddle.empty((128, 256), "float32") + >>> param = ppsci.utils.initializer.normal_(param, 0, 1) + """ + return _no_grad_normal_(tensor, mean, std) + + +def trunc_normal_( + tensor: paddle.Tensor, + mean: float = 0.0, + std: float = 1.0, + a: float = -2.0, + b: float = 2.0, +) -> paddle.Tensor: + """Modify tensor inplace using trunc_normal_. + + Args: + tensor (paddle.Tensor): Paddle Tensor. + mean (float, optional): The mean of the normal distribution. Defaults to 0.0. + std (float, optional): The standard deviation of the normal distribution. Defaults to 1.0. + a (float, optional): The minimum cutoff value. Defaults to -2.0. + b (float, optional): The maximum cutoff value. Defaults to 2.0. + + Returns + ------- + paddle.Tensor: Initialized tensor. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> param = paddle.empty((128, 256), "float32") + >>> param = ppsci.utils.initializer.trunc_normal_(param, 0.0, 1.0) + """ + return _no_grad_trunc_normal_(tensor, mean, std, a, b) + + +def constant_(tensor: paddle.Tensor, value: float = 0.0) -> paddle.Tensor: + """Modify tensor inplace using constant_. + + Args: + tensor (paddle.Tensor): Paddle Tensor. + value (float, optional): Value to fill tensor. Defaults to 0.0. + + Returns + ------- + paddle.Tensor: Initialized tensor. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> param = paddle.empty((128, 256), "float32") + >>> param = ppsci.utils.initializer.constant_(param, 2) + """ + return _no_grad_fill_(tensor, value) + + +def ones_(tensor: paddle.Tensor) -> paddle.Tensor: + """Modify tensor inplace using ones_. + + Args: + tensor (paddle.Tensor): Paddle Tensor. + + Returns + ------- + paddle.Tensor: Initialized tensor. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> param = paddle.empty((128, 256), "float32") + >>> param = ppsci.utils.initializer.ones_(param) + """ + return _no_grad_fill_(tensor, 1) + + +def zeros_(tensor: paddle.Tensor) -> paddle.Tensor: + """Modify tensor inplace using zeros_. + + Args: + tensor (paddle.Tensor): Paddle Tensor. + + Returns + ------- + paddle.Tensor: Initialized tensor. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> param = paddle.empty((128, 256), "float32") + >>> param = ppsci.utils.initializer.zeros_(param) + """ + return _no_grad_fill_(tensor, 0) + + +def _calculate_fan_in_and_fan_out(tensor, reverse=False): + """ + Calculate (fan_in, _fan_out) for tensor. + + Args: + tensor (paddle.Tensor): paddle.Tensor. + reverse (bool): Tensor data format order, False by default as [fout, fin, ...]. + e.g. : conv.weight [cout, cin, kh, kw] is False; linear.weight [cin, cout] + is True. + + Return: + Tuple[float, float]: (fan_in, fan_out). + """ + if tensor.ndim < 2: + raise ValueError( + f"tensor.ndim should be no less than 2, but got {tensor.ndim}." + ) + + if reverse: + num_input_fmaps, num_output_fmaps = tensor.shape[0], tensor.shape[1] + else: + num_input_fmaps, num_output_fmaps = tensor.shape[1], tensor.shape[0] + + receptive_field_size = 1 + if tensor.ndim > 2: + receptive_field_size = np.prod(tensor.shape[2:]) + + fan_in = num_input_fmaps * receptive_field_size + fan_out = num_output_fmaps * receptive_field_size + + return fan_in, fan_out + + +def xavier_uniform_( + tensor: paddle.Tensor, gain: float = 1.0, reverse: bool = False +) -> paddle.Tensor: + """Modify tensor inplace using xavier_uniform_. + + Args: + tensor (paddle.Tensor): Paddle Tensor. + gain (float, optional): Hyperparameter. Defaults to 1.0. + reverse (bool, optional): Tensor data format order, False by default as + [fout, fin, ...].. Defaults to False. + + Returns + ------- + paddle.Tensor: Initialized tensor. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> param = paddle.empty((128, 256), "float32") + >>> param = ppsci.utils.initializer.xavier_uniform_(param) + """ + fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor, reverse=reverse) + std = gain * math.sqrt(2.0 / float(fan_in + fan_out)) + k = math.sqrt(3.0) * std + return _no_grad_uniform_(tensor, -k, k) + + +def xavier_normal_( + tensor: paddle.Tensor, gain: float = 1.0, reverse: bool = False +) -> paddle.Tensor: + """Modify tensor inplace using xavier_normal_. + + Args: + tensor (paddle.Tensor): Paddle Tensor. + gain (float, optional): Hyperparameter. Defaults to 1.0. + reverse (bool, optional): Tensor data format order, False by + default as [fout, fin, ...]. Defaults to False. + + Returns + ------- + paddle.Tensor: Initialized tensor. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> param = paddle.empty((128, 256), "float32") + >>> param = ppsci.utils.initializer.xavier_normal_(param) + """ + fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor, reverse=reverse) + std = gain * math.sqrt(2.0 / float(fan_in + fan_out)) + return _no_grad_normal_(tensor, 0, std) + + +# reference: https://pytorch.org/docs/stable/_modules/torch/nn/init.html +def _calculate_correct_fan(tensor, mode, reverse=False): + mode = mode.lower() + valid_modes = ["fan_in", "fan_out"] + if mode not in valid_modes: + raise ValueError(f"Mode {mode} not supported, please use one of {valid_modes}") + + fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor, reverse) + + return fan_in if mode == "fan_in" else fan_out + + +def _calculate_gain(nonlinearity, param=None): + linear_fns = [ + "linear", + "conv1d", + "conv2d", + "conv3d", + "conv_transpose1d", + "conv_transpose2d", + "conv_transpose3d", + ] + if nonlinearity in linear_fns or nonlinearity == "sigmoid": + return 1 + elif nonlinearity == "tanh": + return 5.0 / 3 + elif nonlinearity == "relu": + return math.sqrt(2.0) + elif nonlinearity == "leaky_relu": + if param is None: + negative_slope = 0.01 + elif ( + not isinstance(param, bool) + and isinstance(param, int) + or isinstance(param, float) + ): + # True/False are instances of int, hence check above + negative_slope = param + else: + raise ValueError(f"negative_slope {param} not a valid number") + return math.sqrt(2.0 / (1 + negative_slope**2)) + elif nonlinearity == "selu": + return 3.0 / 4 + else: + raise ValueError(f"Unsupported nonlinearity {nonlinearity}") + + +def kaiming_uniform_( + tensor: paddle.Tensor, + a: float = 0, + mode: Literal["fan_in", "fan_out"] = "fan_in", + nonlinearity: str = "leaky_relu", + reverse: bool = False, +) -> paddle.Tensor: + """Modify tensor inplace using kaiming_uniform method. + + Args: + tensor (paddle.Tensor): Paddle Tensor. + a (float, optional): The negative slope of the rectifier used after this layer. + Defaults to 0. + mode (Literal["fan_in", "fan_out"], optional): + ["fan_in", "fan_out"]. Defaults to "fan_in". + nonlinearity (str, optional): Nonlinearity method name. Defaults to "leaky_relu". + reverse (bool, optional): Tensor data format order, False by default as + [fout, fin, ...].. Defaults to False. + + Returns + ------- + paddle.Tensor: Initialized tensor. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> param = paddle.empty((128, 256), "float32") + >>> param = ppsci.utils.initializer.kaiming_uniform_(param) + """ + fan = _calculate_correct_fan(tensor, mode, reverse) + gain = _calculate_gain(nonlinearity, a) + std = gain / math.sqrt(fan) + k = math.sqrt(3.0) * std + return _no_grad_uniform_(tensor, -k, k) + + +def kaiming_normal_( + tensor: paddle.Tensor, + a: float = 0, + mode: Literal["fan_in", "fan_out"] = "fan_in", + nonlinearity: str = "leaky_relu", + reverse: bool = False, +) -> paddle.Tensor: + """Modify tensor inplace using kaiming_normal_. + + Args: + tensor (paddle.Tensor): Paddle Tensor. + a (float, optional): The negative slope of the rectifier used after this layer. + Defaults to 0. + mode (Literal["fan_in", "fan_out"], optional): Either + 'fan_in' (default) or 'fan_out'. Defaults to "fan_in". + nonlinearity (str, optional): Nonlinearity method name. Defaults to "leaky_relu". + reverse (bool, optional): Tensor data format order. Defaults to False. + + Returns + ------- + paddle.Tensor: Initialized tensor. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> param = paddle.empty((128, 256), "float32") + >>> param = ppsci.utils.initializer.kaiming_normal_(param) + """ + fan = _calculate_correct_fan(tensor, mode, reverse) + gain = _calculate_gain(nonlinearity, a) + std = gain / math.sqrt(fan) + return _no_grad_normal_(tensor, 0, std) + + +def linear_init_(module: nn.Layer) -> None: + """Initialize module's weight and bias as it is a linear layer. + + Args: + module (nn.Layer): Linear Layer to be initialized. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> layer = paddle.nn.Linear(128, 256) + >>> ppsci.utils.initializer.linear_init_(layer) + """ + kaiming_uniform_(module.weight, a=math.sqrt(5)) + if module.bias is not None: + fan_in, _ = _calculate_fan_in_and_fan_out(module.weight, reverse=True) + bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0 + uniform_(module.bias, -bound, bound) + + +def conv_init_(module: nn.Layer) -> None: + """Initialize module's weight and bias as it is a conv layer. + + Args: + module (nn.Layer): Convolution Layer to be initialized. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> layer = paddle.nn.Conv2D(4, 16, 2) + >>> ppsci.utils.initializer.conv_init_(layer) + """ + kaiming_uniform_(module.weight, a=math.sqrt(5)) + if module.bias is not None: + fan_in, _ = _calculate_fan_in_and_fan_out(module.weight, reverse=False) + if fan_in != 0: + bound = 1 / math.sqrt(fan_in) + uniform_(module.bias, -bound, bound) + + +def glorot_normal_(tensor: paddle.Tensor) -> paddle.Tensor: + """Modify tensor inplace using jax-style glorot_normal. + + Args: + tensor (paddle.Tensor): Paddle Tensor/Paramter. + + Returns + ------- + paddle.Tensor: Initialized tensor. + + Examples + -------- + >>> import paddle + >>> import ppsci + >>> param = paddle.empty((128, 256), "float32") + >>> param = ppsci.utils.initializer.glorot_normal_(param) + """ + assert ( + tensor.ndim == 2 + ), f"glorot_normal_ only support 2D tensor now, but got ndim={tensor.ndim}" + fin, fout = tensor.shape + var = 2.0 / (fin + fout) + stddev = math.sqrt(var) * 0.87962566103423978 + trunc_normal_(tensor) + tensor.set_value(tensor * stddev) + return tensor diff --git a/source/tests/pd/test_dp_show.py b/source/tests/pd/test_dp_show.py index 5e257fd049..6efbec7f52 100644 --- a/source/tests/pd/test_dp_show.py +++ b/source/tests/pd/test_dp_show.py @@ -47,6 +47,10 @@ def setUp(self): trainer.run() run_dp("dp --pd freeze") + @unittest.skip( + "Paddle do not support dp --pd show frozen models(.json and .pdiparams file), " + "will be supported in the future." + ) def test_checkpoint(self): INPUT = "model.pd" ATTRIBUTES = "type-map descriptor fitting-net" @@ -83,6 +87,10 @@ def test_frozen_model(self): in results[-1] ) + @unittest.skip( + "Paddle do not support dp --pd show frozen models(.json and .pdiparams file), " + "will be supported in the future." + ) def test_checkpoint_error(self): INPUT = "model.pd" ATTRIBUTES = "model-branch type-map descriptor fitting-net" @@ -148,6 +156,10 @@ def setUp(self): trainer.run() run_dp("dp --pd freeze --head model_1") + @unittest.skip( + "Paddle do not support dp --pd show frozen models(.json and .pdiparams file), " + "will be supported in the future." + ) def test_checkpoint(self): INPUT = "model.ckpt.pd" ATTRIBUTES = "model-branch type-map descriptor fitting-net" diff --git a/source/tests/pd/test_multitask.py b/source/tests/pd/test_multitask.py index 35f8969438..bb58311a0a 100644 --- a/source/tests/pd/test_multitask.py +++ b/source/tests/pd/test_multitask.py @@ -227,7 +227,7 @@ def tearDown(self) -> None: MultiTaskTrainTest.tearDown(self) -@unittest.skip("Paddle do not support MultiTaskDPA1.") +# @unittest.skip("Paddle do not support MultiTaskDPA1.") class TestMultiTaskDPA1(unittest.TestCase, MultiTaskTrainTest): def setUp(self): multitask_DPA1 = deepcopy(multitask_template) diff --git a/source/tests/pd/test_training.py b/source/tests/pd/test_training.py index 5b4f05c577..7306302cf7 100644 --- a/source/tests/pd/test_training.py +++ b/source/tests/pd/test_training.py @@ -35,8 +35,7 @@ def test_dp_train(self): trainer = get_trainer(deepcopy(self.config)) trainer.run() state_dict_trained = trainer.wrapper.model.state_dict() - # for k, v in state_dict_trained.items(): - # print(f"{k} {v.shape}") + # test fine-tuning using same input finetune_model = self.config["training"].get("save_ckpt", "model.ckpt") + ".pd" self.config["model"], finetune_links = get_finetune_rules( @@ -174,7 +173,6 @@ def tearDown(self) -> None: DPTrainTest.tearDown(self) -@unittest.skip("EnergyZBLModelSeA not supported at the moment") class TestEnergyZBLModelSeA(unittest.TestCase, DPTrainTest): def setUp(self): input_json = str(Path(__file__).parent / "water/zbl.json") @@ -245,7 +243,6 @@ def tearDown(self) -> None: DPTrainTest.tearDown(self) -@unittest.skip("hybrid not supported at the moment") class TestEnergyModelHybrid(unittest.TestCase, DPTrainTest): def setUp(self): input_json = str(Path(__file__).parent / "water/se_atten.json") @@ -320,7 +317,7 @@ def tearDown(self) -> None: DPTrainTest.tearDown(self) -@unittest.skip("DipoleModelDPA2 not supported at the moment") +@unittest.skip("Unable to fill empty grad inputs") class TestDipoleModelDPA2(unittest.TestCase, DPTrainTest): def setUp(self): input_json = str(Path(__file__).parent / "water_tensor/se_e2_a.json") From 39ca3b783a354b2423950994ab89cfdff8537fe1 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 28 Oct 2024 13:16:05 +0800 Subject: [PATCH 66/93] update pip index --- backend/find_paddle.py | 8 +------- pyproject.toml | 7 +++++-- 2 files changed, 6 insertions(+), 9 deletions(-) diff --git a/backend/find_paddle.py b/backend/find_paddle.py index 33387c815b..bc54cdcaa5 100644 --- a/backend/find_paddle.py +++ b/backend/find_paddle.py @@ -22,10 +22,6 @@ Union, ) -from packaging.version import ( - Version, -) - @lru_cache def find_paddle() -> tuple[Optional[str], list[str]]: @@ -109,9 +105,7 @@ def get_pd_requirement(pd_version: str = "") -> dict: return { "paddle": [ - f"paddlepaddle=={Version(pd_version).base_version}.*" - if pd_version != "" - else "paddlepaddle", + "paddlepaddle>=3.0.0b1" if pd_version != "" else "paddlepaddle>=3.0.0b1", ], } diff --git a/pyproject.toml b/pyproject.toml index de5ed48773..738ecfc698 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -235,6 +235,7 @@ PIP_PREFER_BINARY = "1" DP_LAMMPS_VERSION = "stable_29Aug2024_update1" DP_ENABLE_IPI = "1" DP_ENABLE_PYTORCH = "1" +DP_ENABLE_PADDLE = "1" # for unclear reason, when enabling PyTorch, OpenMP is found accidentally CMAKE_ARGS = "-DCMAKE_DISABLE_FIND_PACKAGE_OpenMP=1" @@ -271,17 +272,18 @@ PIP_PREFER_BINARY = "1" DP_LAMMPS_VERSION = "stable_29Aug2024_update1" DP_ENABLE_IPI = "1" DP_ENABLE_PYTORCH = "1" +DP_ENABLE_PADDLE = "1" MPI_HOME = "/usr/lib64/mpich" PATH = "/usr/lib64/mpich/bin:$PATH" # use CPU version of torch for building, which should also work for GPU # note: uv has different behavior from pip on extra index url # https://github.com/astral-sh/uv/blob/main/PIP_COMPATIBILITY.md#packages-that-exist-on-multiple-indexes -UV_EXTRA_INDEX_URL = "https://download.pytorch.org/whl/cpu" +UV_EXTRA_INDEX_URL = "https://download.pytorch.org/whl/cpu https://www.paddlepaddle.org.cn/packages/stable/cpu/ https://www.paddlepaddle.org.cn/packages/nightly/cpu/" # trick to find the correction version of mpich CMAKE_PREFIX_PATH="/opt/python/cp311-cp311/" [tool.cibuildwheel.windows] -test-extras = ["cpu", "torch"] +test-extras = ["cpu", "torch", "paddle"] test-command = [ "python -m deepmd -h", "dp -h", @@ -289,6 +291,7 @@ test-command = [ [tool.cibuildwheel.windows.environment] PIP_PREFER_BINARY = "1" DP_ENABLE_PYTORCH = "1" +DP_ENABLE_PADDLE = "1" # One can run `tox` or `tox -e gpu` # to run pytest in an isolated environment From 2de0d2bcba94f89cf92f34ee986ad03cd9503090 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 28 Oct 2024 13:28:58 +0800 Subject: [PATCH 67/93] remoe cvt.py --- deepmd/backend/paddle.py | 8 +++----- deepmd/pd/cvt_pth.py | 19 ------------------- 2 files changed, 3 insertions(+), 24 deletions(-) delete mode 100644 deepmd/pd/cvt_pth.py diff --git a/deepmd/backend/paddle.py b/deepmd/backend/paddle.py index 7f97e4feed..b1f664e76a 100644 --- a/deepmd/backend/paddle.py +++ b/deepmd/backend/paddle.py @@ -6,8 +6,6 @@ TYPE_CHECKING, Callable, ClassVar, - List, - Type, ) from deepmd.backend.backend import ( @@ -41,7 +39,7 @@ class PaddleBackend(Backend): | Backend.Feature.IO ) """The features of the backend.""" - suffixes: ClassVar[List[str]] = [".json", ".pd"] + suffixes: ClassVar[list[str]] = [".json", ".pd"] """The suffixes of the backend.""" def is_available(self) -> bool: @@ -68,7 +66,7 @@ def entry_point_hook(self) -> Callable[["Namespace"], None]: return deepmd_main @property - def deep_eval(self) -> Type["DeepEvalBackend"]: + def deep_eval(self) -> type["DeepEvalBackend"]: """The Deep Eval backend of the backend. Returns @@ -81,7 +79,7 @@ def deep_eval(self) -> Type["DeepEvalBackend"]: return DeepEvalPD @property - def neighbor_stat(self) -> Type["NeighborStat"]: + def neighbor_stat(self) -> type["NeighborStat"]: """The neighbor statistics of the backend. Returns diff --git a/deepmd/pd/cvt_pth.py b/deepmd/pd/cvt_pth.py deleted file mode 100644 index 370ef057f4..0000000000 --- a/deepmd/pd/cvt_pth.py +++ /dev/null @@ -1,19 +0,0 @@ -# SPDX-License-Identifier: LGPL-3.0-or-later -import paddle -import torch - -psd = torch.load( - "/workspace/hesensen/deepmd_backend/deepmd_paddle_new/source/tests/pd/model/models/dpa1.pth", - "cpu", -) - -tsd = {} -for k, v in psd.items(): - # if ".matrix" in k: - # v = v.T - psd[k] = paddle.to_tensor(v.detach().cpu().numpy()) - -paddle.save( - psd, - "/workspace/hesensen/deepmd_backend/deepmd_paddle_new/source/tests/pd/model/models/dpa1.pdparams", -) From 11d03444ee22ce878811f54f2bb652780ec77632 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 28 Oct 2024 14:46:18 +0800 Subject: [PATCH 68/93] update files --- source/tests/pd/__init__.py | 7 + source/tests/pd/common.py | 263 ++++++++++++++++++++++++++++++++++++ source/tests/pd/conftest.py | 9 ++ 3 files changed, 279 insertions(+) create mode 100644 source/tests/pd/__init__.py create mode 100644 source/tests/pd/common.py create mode 100644 source/tests/pd/conftest.py diff --git a/source/tests/pd/__init__.py b/source/tests/pd/__init__.py new file mode 100644 index 0000000000..8484fef3ef --- /dev/null +++ b/source/tests/pd/__init__.py @@ -0,0 +1,7 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import paddle + +paddle.framework.core.set_num_threads(1) +# paddle.set_num_interop_threads(1) +# testing purposes; device should always be set explicitly +# paddle.set_device("gpu:9999999") diff --git a/source/tests/pd/common.py b/source/tests/pd/common.py new file mode 100644 index 0000000000..59a9672330 --- /dev/null +++ b/source/tests/pd/common.py @@ -0,0 +1,263 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Optional, + Union, +) + +import numpy as np +import paddle + +from deepmd.main import ( + main, +) +from deepmd.pd.utils.env import ( + DEVICE, + GLOBAL_PD_FLOAT_PRECISION, +) + + +def run_dp(cmd: str) -> int: + """Run DP directly from the entry point instead of the subprocess. + + It is quite slow to start DeePMD-kit with subprocess. + + Parameters + ---------- + cmd : str + The command to run. + + Returns + ------- + int + Always returns 0. + """ + cmds = cmd.split() + if cmds[0] == "dp": + cmds = cmds[1:] + else: + raise RuntimeError("The command is not dp") + + main(cmds) + return 0 + + +def eval_model( + model, + coords: Union[np.ndarray, paddle.Tensor], + cells: Optional[Union[np.ndarray, paddle.Tensor]], + atom_types: Union[np.ndarray, paddle.Tensor, list[int]], + spins: Optional[Union[np.ndarray, paddle.Tensor]] = None, + atomic: bool = False, + infer_batch_size: int = 2, + denoise: bool = False, +): + model = model.to(DEVICE) + energy_out = [] + atomic_energy_out = [] + force_out = [] + force_mag_out = [] + virial_out = [] + atomic_virial_out = [] + updated_coord_out = [] + logits_out = [] + err_msg = ( + f"All inputs should be the same format, " + f"but found {type(coords)}, {type(cells)}, {type(atom_types)} instead! " + ) + return_tensor = True + if isinstance(coords, paddle.Tensor): + if cells is not None: + assert isinstance(cells, paddle.Tensor), err_msg + if spins is not None: + assert isinstance(spins, paddle.Tensor), err_msg + assert isinstance(atom_types, paddle.Tensor) or isinstance(atom_types, list) + atom_types = paddle.to_tensor(atom_types, dtype=paddle.int32, place=DEVICE) + elif isinstance(coords, np.ndarray): + if cells is not None: + assert isinstance(cells, np.ndarray), err_msg + if spins is not None: + assert isinstance(spins, np.ndarray), err_msg + assert isinstance(atom_types, np.ndarray) or isinstance(atom_types, list) + atom_types = np.array(atom_types, dtype=np.int32) + return_tensor = False + + nframes = coords.shape[0] + if len(atom_types.shape) == 1: + natoms = len(atom_types) + if isinstance(atom_types, paddle.Tensor): + atom_types = paddle.tile(atom_types.unsqueeze(0), [nframes, 1]).reshape( + [nframes, -1] + ) + else: + atom_types = np.tile(atom_types, nframes).reshape(nframes, -1) + else: + natoms = len(atom_types[0]) + + coord_input = paddle.to_tensor( + coords.reshape([-1, natoms, 3]), dtype=GLOBAL_PD_FLOAT_PRECISION, place=DEVICE + ) + spin_input = None + if spins is not None: + spin_input = paddle.to_tensor( + spins.reshape([-1, natoms, 3]), + dtype=GLOBAL_PD_FLOAT_PRECISION, + place=DEVICE, + ) + has_spin = getattr(model, "has_spin", False) + if callable(has_spin): + has_spin = has_spin() + type_input = paddle.to_tensor(atom_types, dtype=paddle.int64, place=DEVICE) + box_input = None + if cells is None: + pbc = False + else: + pbc = True + box_input = paddle.to_tensor( + cells.reshape([-1, 3, 3]), dtype=GLOBAL_PD_FLOAT_PRECISION, place=DEVICE + ) + num_iter = int((nframes + infer_batch_size - 1) / infer_batch_size) + + for ii in range(num_iter): + batch_coord = coord_input[ii * infer_batch_size : (ii + 1) * infer_batch_size] + batch_atype = type_input[ii * infer_batch_size : (ii + 1) * infer_batch_size] + batch_box = None + batch_spin = None + if spin_input is not None: + batch_spin = spin_input[ii * infer_batch_size : (ii + 1) * infer_batch_size] + if pbc: + batch_box = box_input[ii * infer_batch_size : (ii + 1) * infer_batch_size] + input_dict = { + "coord": batch_coord, + "atype": batch_atype, + "box": batch_box, + "do_atomic_virial": atomic, + } + if has_spin: + input_dict["spin"] = batch_spin + batch_output = model(**input_dict) + if isinstance(batch_output, tuple): + batch_output = batch_output[0] + if not return_tensor: + if "energy" in batch_output: + energy_out.append(batch_output["energy"].numpy()) + if "atom_energy" in batch_output: + atomic_energy_out.append(batch_output["atom_energy"].numpy()) + if "force" in batch_output: + force_out.append(batch_output["force"].numpy()) + if "force_mag" in batch_output: + force_mag_out.append(batch_output["force_mag"].numpy()) + if "virial" in batch_output: + virial_out.append(batch_output["virial"].numpy()) + if "atom_virial" in batch_output: + atomic_virial_out.append(batch_output["atom_virial"].numpy()) + if "updated_coord" in batch_output: + updated_coord_out.append(batch_output["updated_coord"].numpy()) + if "logits" in batch_output: + logits_out.append(batch_output["logits"].numpy()) + else: + if "energy" in batch_output: + energy_out.append(batch_output["energy"]) + if "atom_energy" in batch_output: + atomic_energy_out.append(batch_output["atom_energy"]) + if "force" in batch_output: + force_out.append(batch_output["force"]) + if "force_mag" in batch_output: + force_mag_out.append(batch_output["force_mag"]) + if "virial" in batch_output: + virial_out.append(batch_output["virial"]) + if "atom_virial" in batch_output: + atomic_virial_out.append(batch_output["atom_virial"]) + if "updated_coord" in batch_output: + updated_coord_out.append(batch_output["updated_coord"]) + if "logits" in batch_output: + logits_out.append(batch_output["logits"]) + if not return_tensor: + energy_out = ( + np.concatenate(energy_out) if energy_out else np.zeros([nframes, 1]) # pylint: disable=no-explicit-dtype + ) + atomic_energy_out = ( + np.concatenate(atomic_energy_out) + if atomic_energy_out + else np.zeros([nframes, natoms, 1]) # pylint: disable=no-explicit-dtype + ) + force_out = ( + np.concatenate(force_out) if force_out else np.zeros([nframes, natoms, 3]) # pylint: disable=no-explicit-dtype + ) + force_mag_out = ( + np.concatenate(force_mag_out) + if force_mag_out + else np.zeros([nframes, natoms, 3]) # pylint: disable=no-explicit-dtype + ) + virial_out = ( + np.concatenate(virial_out) if virial_out else np.zeros([nframes, 3, 3]) # pylint: disable=no-explicit-dtype + ) + atomic_virial_out = ( + np.concatenate(atomic_virial_out) + if atomic_virial_out + else np.zeros([nframes, natoms, 3, 3]) # pylint: disable=no-explicit-dtype + ) + updated_coord_out = ( + np.concatenate(updated_coord_out) if updated_coord_out else None + ) + logits_out = np.concatenate(logits_out) if logits_out else None + else: + energy_out = ( + paddle.concat(energy_out) + if energy_out + else paddle.zeros([nframes, 1], dtype=GLOBAL_PD_FLOAT_PRECISION).to( + device=DEVICE + ) + ) + atomic_energy_out = ( + paddle.concat(atomic_energy_out) + if atomic_energy_out + else paddle.zeros([nframes, natoms, 1], dtype=GLOBAL_PD_FLOAT_PRECISION).to( + device=DEVICE + ) + ) + force_out = ( + paddle.concat(force_out) + if force_out + else paddle.zeros([nframes, natoms, 3], dtype=GLOBAL_PD_FLOAT_PRECISION).to( + device=DEVICE + ) + ) + force_mag_out = ( + paddle.concat(force_mag_out) + if force_mag_out + else paddle.zeros([nframes, natoms, 3], dtype=GLOBAL_PD_FLOAT_PRECISION).to( + device=DEVICE + ) + ) + virial_out = ( + paddle.concat(virial_out) + if virial_out + else paddle.zeros([nframes, 3, 3], dtype=GLOBAL_PD_FLOAT_PRECISION).to( + device=DEVICE + ) + ) + atomic_virial_out = ( + paddle.concat(atomic_virial_out) + if atomic_virial_out + else paddle.zeros( + [nframes, natoms, 3, 3], dtype=GLOBAL_PD_FLOAT_PRECISION + ).to(device=DEVICE) + ) + updated_coord_out = ( + paddle.concat(updated_coord_out) if updated_coord_out else None + ) + logits_out = paddle.concat(logits_out) if logits_out else None + if denoise: + return updated_coord_out, logits_out + else: + results_dict = { + "energy": energy_out, + "force": force_out, + "virial": virial_out, + } + if has_spin: + results_dict["force_mag"] = force_mag_out + if atomic: + results_dict["atom_energy"] = atomic_energy_out + results_dict["atom_virial"] = atomic_virial_out + return results_dict diff --git a/source/tests/pd/conftest.py b/source/tests/pd/conftest.py new file mode 100644 index 0000000000..530cb18907 --- /dev/null +++ b/source/tests/pd/conftest.py @@ -0,0 +1,9 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import paddle +import pytest + + +@pytest.fixture(scope="package", autouse=True) +def clear_cuda_memory(request): + yield + paddle.device.cuda.empty_cache() From 3650214bab49ccfe2729677f0420f5eca041e47e Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 28 Oct 2024 15:53:32 +0800 Subject: [PATCH 69/93] upload new files --- deepmd/pd/model/atomic_model/__init__.py | 4 + .../model/atomic_model/base_atomic_model.py | 22 +- deepmd/pd/model/model/__init__.py | 164 +++-- source/tests/pd/model/__init__.py | 1 + .../pd/model/test_atomic_model_atomic_stat.py | 431 ++++++++++++ .../pd/model/test_atomic_model_global_stat.py | 510 ++++++++++++++ source/tests/pd/model/test_autodiff.py | 253 +++++++ source/tests/pd/model/test_deeppot.py | 138 ++++ source/tests/pd/model/test_descriptor.py | 196 ++++++ source/tests/pd/model/test_descriptor_dpa1.py | 381 +++++++++++ source/tests/pd/model/test_descriptor_dpa2.py | 203 ++++++ .../tests/pd/model/test_descriptor_hybrid.py | 125 ++++ source/tests/pd/model/test_descriptor_se_r.py | 188 ++++++ source/tests/pd/model/test_dipole_fitting.py | 398 +++++++++++ source/tests/pd/model/test_dp_atomic_model.py | 235 +++++++ source/tests/pd/model/test_dp_model.py | 633 ++++++++++++++++++ source/tests/pd/model/test_dpa1.py | 164 +++++ source/tests/pd/model/test_dpa2.py | 332 +++++++++ source/tests/pd/model/test_embedding_net.py | 218 ++++++ source/tests/pd/model/test_ener_fitting.py | 150 +++++ source/tests/pd/model/test_ener_spin_model.py | 432 ++++++++++++ source/tests/pd/model/test_env_mat.py | 187 ++++++ source/tests/pd/model/test_exclusion_mask.py | 70 ++ source/tests/pd/model/test_fitting_net.py | 148 ++++ source/tests/pd/model/test_force_grad.py | 111 +++ source/tests/pd/model/test_forward_lower.py | 195 ++++++ source/tests/pd/model/test_jit.py | 171 +++++ .../pd/model/test_linear_atomic_model.py | 216 ++++++ .../pd/model/test_linear_atomic_model_stat.py | 248 +++++++ .../tests/pd/model/test_make_hessian_model.py | 179 +++++ source/tests/pd/model/test_mlp.py | 283 ++++++++ source/tests/pd/model/test_model.py | 424 ++++++++++++ source/tests/pd/model/test_nlist.py | 308 +++++++++ source/tests/pd/model/test_null_input.py | 137 ++++ .../pd/model/test_pairtab_atomic_model.py | 272 ++++++++ source/tests/pd/model/test_permutation.py | 476 +++++++++++++ .../pd/model/test_permutation_denoise.py | 99 +++ .../pd/model/test_polar_atomic_model_stat.py | 293 ++++++++ .../pd/model/test_polarizability_fitting.py | 381 +++++++++++ source/tests/pd/model/test_region.py | 88 +++ source/tests/pd/model/test_rot.py | 215 ++++++ source/tests/pd/model/test_rot_denoise.py | 128 ++++ source/tests/pd/model/test_rotation.py | 112 ++++ source/tests/pd/model/test_saveload_dpa1.py | 145 ++++ .../tests/pd/model/test_saveload_se_e2_a.py | 139 ++++ source/tests/pd/model/test_se_atten_v2.py | 152 +++++ source/tests/pd/model/test_se_e2_a.py | 137 ++++ source/tests/pd/model/test_se_t.py | 139 ++++ source/tests/pd/model/test_smooth.py | 267 ++++++++ source/tests/pd/model/test_smooth_denoise.py | 139 ++++ source/tests/pd/model/test_trans.py | 155 +++++ source/tests/pd/model/test_trans_denoise.py | 89 +++ 52 files changed, 11228 insertions(+), 53 deletions(-) create mode 100644 source/tests/pd/model/__init__.py create mode 100644 source/tests/pd/model/test_atomic_model_atomic_stat.py create mode 100644 source/tests/pd/model/test_atomic_model_global_stat.py create mode 100644 source/tests/pd/model/test_autodiff.py create mode 100644 source/tests/pd/model/test_deeppot.py create mode 100644 source/tests/pd/model/test_descriptor.py create mode 100644 source/tests/pd/model/test_descriptor_dpa1.py create mode 100644 source/tests/pd/model/test_descriptor_dpa2.py create mode 100644 source/tests/pd/model/test_descriptor_hybrid.py create mode 100644 source/tests/pd/model/test_descriptor_se_r.py create mode 100644 source/tests/pd/model/test_dipole_fitting.py create mode 100644 source/tests/pd/model/test_dp_atomic_model.py create mode 100644 source/tests/pd/model/test_dp_model.py create mode 100644 source/tests/pd/model/test_dpa1.py create mode 100644 source/tests/pd/model/test_dpa2.py create mode 100644 source/tests/pd/model/test_embedding_net.py create mode 100644 source/tests/pd/model/test_ener_fitting.py create mode 100644 source/tests/pd/model/test_ener_spin_model.py create mode 100644 source/tests/pd/model/test_env_mat.py create mode 100644 source/tests/pd/model/test_exclusion_mask.py create mode 100644 source/tests/pd/model/test_fitting_net.py create mode 100644 source/tests/pd/model/test_force_grad.py create mode 100644 source/tests/pd/model/test_forward_lower.py create mode 100644 source/tests/pd/model/test_jit.py create mode 100644 source/tests/pd/model/test_linear_atomic_model.py create mode 100644 source/tests/pd/model/test_linear_atomic_model_stat.py create mode 100644 source/tests/pd/model/test_make_hessian_model.py create mode 100644 source/tests/pd/model/test_mlp.py create mode 100644 source/tests/pd/model/test_model.py create mode 100644 source/tests/pd/model/test_nlist.py create mode 100644 source/tests/pd/model/test_null_input.py create mode 100644 source/tests/pd/model/test_pairtab_atomic_model.py create mode 100644 source/tests/pd/model/test_permutation.py create mode 100644 source/tests/pd/model/test_permutation_denoise.py create mode 100644 source/tests/pd/model/test_polar_atomic_model_stat.py create mode 100644 source/tests/pd/model/test_polarizability_fitting.py create mode 100644 source/tests/pd/model/test_region.py create mode 100644 source/tests/pd/model/test_rot.py create mode 100644 source/tests/pd/model/test_rot_denoise.py create mode 100644 source/tests/pd/model/test_rotation.py create mode 100644 source/tests/pd/model/test_saveload_dpa1.py create mode 100644 source/tests/pd/model/test_saveload_se_e2_a.py create mode 100644 source/tests/pd/model/test_se_atten_v2.py create mode 100644 source/tests/pd/model/test_se_e2_a.py create mode 100644 source/tests/pd/model/test_se_t.py create mode 100644 source/tests/pd/model/test_smooth.py create mode 100644 source/tests/pd/model/test_smooth_denoise.py create mode 100644 source/tests/pd/model/test_trans.py create mode 100644 source/tests/pd/model/test_trans_denoise.py diff --git a/deepmd/pd/model/atomic_model/__init__.py b/deepmd/pd/model/atomic_model/__init__.py index 3e94449057..2aa4b1cdb2 100644 --- a/deepmd/pd/model/atomic_model/__init__.py +++ b/deepmd/pd/model/atomic_model/__init__.py @@ -39,12 +39,16 @@ from .polar_atomic_model import ( DPPolarAtomicModel, ) +from .property_atomic_model import ( + DPPropertyAtomicModel, +) __all__ = [ "BaseAtomicModel", "DPAtomicModel", "DPDOSAtomicModel", "DPEnergyAtomicModel", + "DPPropertyAtomicModel", "PairTabAtomicModel", "LinearEnergyAtomicModel", "DPPolarAtomicModel", diff --git a/deepmd/pd/model/atomic_model/base_atomic_model.py b/deepmd/pd/model/atomic_model/base_atomic_model.py index dde7863d5b..44553482c6 100644 --- a/deepmd/pd/model/atomic_model/base_atomic_model.py +++ b/deepmd/pd/model/atomic_model/base_atomic_model.py @@ -63,7 +63,7 @@ class BaseAtomicModel(paddle.nn.Layer, BaseAtomicModel_): of the atomic model. Implemented by removing the pairs from the nlist. rcond : float, optional The condition number for the regression of atomic energy. - preset_out_bias : Dict[str, List[Optional[paddle.Tensor]]], optional + preset_out_bias : Dict[str, list[Optional[paddle.Tensor]]], optional Specifying atomic energy contribution in vacuum. Given by key:value pairs. The value is a list specifying the bias. the elements can be None or np.array of output shape. For example: [None, [2.]] means type 0 is not set, type 1 is set to [2.] @@ -359,11 +359,11 @@ def compute_or_load_stat( Parameters ---------- - merged : Union[Callable[[], List[dict]], List[dict]] - - List[dict]: A list of data samples from various data systems. + merged : Union[Callable[[], list[dict]], list[dict]] + - list[dict]: A list of data samples from various data systems. Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` originating from the `i`-th data system. - - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + - Callable[[], list[dict]]: A lazy function that returns data samples in the above format only when needed. Since the sampling process can be slow and memory-intensive, the lazy function helps by only sampling once. stat_file_path : Optional[DPPath] @@ -382,11 +382,11 @@ def compute_or_load_out_stat( Parameters ---------- - merged : Union[Callable[[], List[dict]], List[dict]] - - List[dict]: A list of data samples from various data systems. + merged : Union[Callable[[], list[dict]], list[dict]] + - list[dict]: A list of data samples from various data systems. Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` originating from the `i`-th data system. - - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + - Callable[[], list[dict]]: A lazy function that returns data samples in the above format only when needed. Since the sampling process can be slow and memory-intensive, the lazy function helps by only sampling once. stat_file_path : Optional[DPPath] @@ -432,11 +432,11 @@ def change_out_bias( Parameters ---------- - sample_merged : Union[Callable[[], List[dict]], List[dict]] - - List[dict]: A list of data samples from various data systems. + sample_merged : Union[Callable[[], list[dict]], list[dict]] + - list[dict]: A list of data samples from various data systems. Each element, `merged[i]`, is a data dictionary containing `keys`: `paddle.Tensor` originating from the `i`-th data system. - - Callable[[], List[dict]]: A lazy function that returns data samples in the above format + - Callable[[], list[dict]]: A lazy function that returns data samples in the above format only when needed. Since the sampling process can be slow and memory-intensive, the lazy function helps by only sampling once. bias_adjust_mode : str @@ -456,6 +456,7 @@ def change_out_bias( model_forward=self._get_forward_wrapper_func(), rcond=self.rcond, preset_bias=self.preset_out_bias, + atomic_output=self.atomic_output_def(), ) self._store_out_stat(delta_bias, out_std, add=True) elif bias_adjust_mode == "set-by-statistic": @@ -466,6 +467,7 @@ def change_out_bias( stat_file_path=stat_file_path, rcond=self.rcond, preset_bias=self.preset_out_bias, + atomic_output=self.atomic_output_def(), ) self._store_out_stat(bias_out, std_out) else: diff --git a/deepmd/pd/model/model/__init__.py b/deepmd/pd/model/model/__init__.py index 4c83c53540..86f09cdcb8 100644 --- a/deepmd/pd/model/model/__init__.py +++ b/deepmd/pd/model/model/__init__.py @@ -36,6 +36,9 @@ from .dos_model import ( DOSModel, ) +from .dp_linear_model import ( + LinearEnergyModel, +) from .dp_model import ( DPModelCommon, ) @@ -60,18 +63,44 @@ from .polar_model import ( PolarModel, ) +from .property_model import ( + PropertyModel, +) from .spin_model import ( SpinEnergyModel, SpinModel, ) +def _get_standard_model_components(model_params, ntypes): + # descriptor + model_params["descriptor"]["ntypes"] = ntypes + model_params["descriptor"]["type_map"] = copy.deepcopy(model_params["type_map"]) + descriptor = BaseDescriptor(**model_params["descriptor"]) + # fitting + fitting_net = model_params.get("fitting_net", {}) + fitting_net["type"] = fitting_net.get("type", "ener") + fitting_net["ntypes"] = descriptor.get_ntypes() + fitting_net["type_map"] = copy.deepcopy(model_params["type_map"]) + fitting_net["mixed_types"] = descriptor.mixed_types() + if fitting_net["type"] in ["dipole", "polar"]: + fitting_net["embedding_width"] = descriptor.get_dim_emb() + fitting_net["dim_descrpt"] = descriptor.get_dim_out() + grad_force = "direct" not in fitting_net["type"] + if not grad_force: + fitting_net["out_dim"] = descriptor.get_dim_emb() + if "ener" in fitting_net["type"]: + fitting_net["return_energy"] = True + fitting = BaseFitting(**fitting_net) + return descriptor, fitting, fitting_net["type"] + + def get_spin_model(model_params): model_params = copy.deepcopy(model_params) if not model_params["spin"]["use_spin"] or isinstance( model_params["spin"]["use_spin"][0], int ): - use_spin = np.full(len(model_params["type_map"]), False) # pylint: disable=no-explicit-dtype + use_spin = np.full(len(model_params["type_map"]), False, dtype=bool) use_spin[model_params["spin"]["use_spin"]] = True model_params["spin"]["use_spin"] = use_spin.tolist() # include virtual spin and placeholder types @@ -102,27 +131,50 @@ def get_spin_model(model_params): return SpinEnergyModel(backbone_model=backbone_model, spin=spin) +def get_linear_model(model_params): + model_params = copy.deepcopy(model_params) + weights = model_params.get("weights", "mean") + list_of_models = [] + ntypes = len(model_params["type_map"]) + for sub_model_params in model_params["models"]: + if "descriptor" in sub_model_params: + # descriptor + sub_model_params["descriptor"]["ntypes"] = ntypes + descriptor, fitting, _ = _get_standard_model_components( + sub_model_params, ntypes + ) + list_of_models.append( + DPAtomicModel(descriptor, fitting, type_map=model_params["type_map"]) + ) + + else: # must be pairtab + assert ( + "type" in sub_model_params and sub_model_params["type"] == "pairtab" + ), "Sub-models in LinearEnergyModel must be a DPModel or a PairTable Model" + list_of_models.append( + PairTabAtomicModel( + sub_model_params["tab_file"], + sub_model_params["rcut"], + sub_model_params["sel"], + type_map=model_params["type_map"], + ) + ) + + atom_exclude_types = model_params.get("atom_exclude_types", []) + pair_exclude_types = model_params.get("pair_exclude_types", []) + return LinearEnergyModel( + models=list_of_models, + type_map=model_params["type_map"], + weights=weights, + atom_exclude_types=atom_exclude_types, + pair_exclude_types=pair_exclude_types, + ) + + def get_zbl_model(model_params): model_params = copy.deepcopy(model_params) ntypes = len(model_params["type_map"]) - # descriptor - model_params["descriptor"]["ntypes"] = ntypes - model_params["descriptor"]["type_map"] = copy.deepcopy(model_params["type_map"]) - descriptor = BaseDescriptor(**model_params["descriptor"]) - # fitting - fitting_net = model_params.get("fitting_net", None) - fitting_net["type"] = fitting_net.get("type", "ener") - fitting_net["ntypes"] = descriptor.get_ntypes() - fitting_net["type_map"] = copy.deepcopy(model_params["type_map"]) - fitting_net["mixed_types"] = descriptor.mixed_types() - fitting_net["embedding_width"] = descriptor.get_dim_out() - fitting_net["dim_descrpt"] = descriptor.get_dim_out() - grad_force = "direct" not in fitting_net["type"] - if not grad_force: - fitting_net["out_dim"] = descriptor.get_dim_emb() - if "ener" in fitting_net["type"]: - fitting_net["return_energy"] = True - fitting = BaseFitting(**fitting_net) + descriptor, fitting, _ = _get_standard_model_components(model_params, ntypes) dp_model = DPAtomicModel(descriptor, fitting, type_map=model_params["type_map"]) # pairtab filepath = model_params["use_srtab"] @@ -148,42 +200,64 @@ def get_zbl_model(model_params): ) +def _can_be_converted_to_float(value): + try: + float(value) + return True + except (TypeError, ValueError): + # return false for any failure... + return False + + +def _convert_preset_out_bias_to_array(preset_out_bias, type_map): + if preset_out_bias is not None: + for kk in preset_out_bias: + if len(preset_out_bias[kk]) != len(type_map): + raise ValueError( + "length of the preset_out_bias should be the same as the type_map" + ) + for jj in range(len(preset_out_bias[kk])): + if preset_out_bias[kk][jj] is not None: + if isinstance(preset_out_bias[kk][jj], list): + bb = preset_out_bias[kk][jj] + elif _can_be_converted_to_float(preset_out_bias[kk][jj]): + bb = [float(preset_out_bias[kk][jj])] + else: + raise ValueError( + f"unsupported type/value of the {jj}th element of " + f"preset_out_bias['{kk}'] " + f"{type(preset_out_bias[kk][jj])}" + ) + preset_out_bias[kk][jj] = np.array(bb) + return preset_out_bias + + def get_standard_model(model_params): model_params_old = model_params model_params = copy.deepcopy(model_params) ntypes = len(model_params["type_map"]) - # descriptor - model_params["descriptor"]["ntypes"] = ntypes - model_params["descriptor"]["type_map"] = copy.deepcopy(model_params["type_map"]) - descriptor = BaseDescriptor(**model_params["descriptor"]) - # fitting - fitting_net = model_params.get("fitting_net", {}) - fitting_net["type"] = fitting_net.get("type", "ener") - fitting_net["ntypes"] = descriptor.get_ntypes() - fitting_net["type_map"] = copy.deepcopy(model_params["type_map"]) - fitting_net["mixed_types"] = descriptor.mixed_types() - if fitting_net["type"] in ["dipole", "polar"]: - fitting_net["embedding_width"] = descriptor.get_dim_emb() - fitting_net["dim_descrpt"] = descriptor.get_dim_out() - grad_force = "direct" not in fitting_net["type"] - if not grad_force: - fitting_net["out_dim"] = descriptor.get_dim_emb() - if "ener" in fitting_net["type"]: - fitting_net["return_energy"] = True - fitting = BaseFitting(**fitting_net) + descriptor, fitting, fitting_net_type = _get_standard_model_components( + model_params, ntypes + ) atom_exclude_types = model_params.get("atom_exclude_types", []) pair_exclude_types = model_params.get("pair_exclude_types", []) + preset_out_bias = model_params.get("preset_out_bias") + preset_out_bias = _convert_preset_out_bias_to_array( + preset_out_bias, model_params["type_map"] + ) - if fitting_net["type"] == "dipole": + if fitting_net_type == "dipole": modelcls = DipoleModel - elif fitting_net["type"] == "polar": + elif fitting_net_type == "polar": modelcls = PolarModel - elif fitting_net["type"] == "dos": + elif fitting_net_type == "dos": modelcls = DOSModel - elif fitting_net["type"] in ["ener", "direct_force_ener"]: + elif fitting_net_type in ["ener", "direct_force_ener"]: modelcls = EnergyModel + elif fitting_net_type == "property": + modelcls = PropertyModel else: - raise RuntimeError(f"Unknown fitting type: {fitting_net['type']}") + raise RuntimeError(f"Unknown fitting type: {fitting_net_type}") model = modelcls( descriptor=descriptor, @@ -191,6 +265,7 @@ def get_standard_model(model_params): type_map=model_params["type_map"], atom_exclude_types=atom_exclude_types, pair_exclude_types=pair_exclude_types, + preset_out_bias=preset_out_bias, ) model.model_def_script = json.dumps(model_params_old) return model @@ -205,6 +280,8 @@ def get_model(model_params): return get_zbl_model(model_params) else: return get_standard_model(model_params) + elif model_type == "linear_ener": + return get_linear_model(model_params) else: return BaseModel.get_class_by_type(model_type).get_model(model_params) @@ -223,4 +300,5 @@ def get_model(model_params): "DPZBLModel", "make_model", "make_hessian_model", + "LinearEnergyModel", ] diff --git a/source/tests/pd/model/__init__.py b/source/tests/pd/model/__init__.py new file mode 100644 index 0000000000..6ceb116d85 --- /dev/null +++ b/source/tests/pd/model/__init__.py @@ -0,0 +1 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later diff --git a/source/tests/pd/model/test_atomic_model_atomic_stat.py b/source/tests/pd/model/test_atomic_model_atomic_stat.py new file mode 100644 index 0000000000..93aa7b8905 --- /dev/null +++ b/source/tests/pd/model/test_atomic_model_atomic_stat.py @@ -0,0 +1,431 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import tempfile +import unittest +from pathlib import ( + Path, +) +from typing import ( + Optional, +) + +import h5py +import numpy as np +import paddle + +from deepmd.dpmodel.output_def import ( + FittingOutputDef, + OutputVariableDef, +) +from deepmd.pd.model.atomic_model import ( + BaseAtomicModel, + DPAtomicModel, +) +from deepmd.pd.model.descriptor.dpa1 import ( + DescrptDPA1, +) +from deepmd.pd.model.task.base_fitting import ( + BaseFitting, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, + to_paddle_tensor, +) +from deepmd.utils.path import ( + DPPath, +) + +from .test_env_mat import ( + TestCaseSingleFrameWithNlist, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION + + +class FooFitting(paddle.nn.Layer, BaseFitting): + def output_def(self): + return FittingOutputDef( + [ + OutputVariableDef( + "foo", + [1], + reducible=True, + r_differentiable=True, + c_differentiable=True, + ), + OutputVariableDef( + "bar", + [1, 2], + reducible=True, + r_differentiable=True, + c_differentiable=True, + ), + ] + ) + + def serialize(self) -> dict: + raise NotImplementedError + + def change_type_map( + self, type_map: list[str], model_with_new_type_stat=None + ) -> None: + raise NotImplementedError + + def get_type_map(self) -> list[str]: + raise NotImplementedError + + def forward( + self, + descriptor: paddle.Tensor, + atype: paddle.Tensor, + gr: Optional[paddle.Tensor] = None, + g2: Optional[paddle.Tensor] = None, + h2: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + ): + nf, nloc, _ = descriptor.shape + ret = {} + ret["foo"] = ( + paddle.to_tensor( + [ + [1.0, 2.0, 3.0], + [4.0, 5.0, 6.0], + ] + ) + .reshape([nf, nloc, *self.output_def()["foo"].shape]) + .to(env.GLOBAL_PD_FLOAT_PRECISION) + .to(env.DEVICE) + ) + ret["bar"] = ( + paddle.to_tensor( + [ + [1.0, 2.0, 3.0, 7.0, 8.0, 9.0], + [4.0, 5.0, 6.0, 10.0, 11.0, 12.0], + ] + ) + .reshape([nf, nloc, *self.output_def()["bar"].shape]) + .to(env.GLOBAL_PD_FLOAT_PRECISION) + .to(env.DEVICE) + ) + return ret + + +class TestAtomicModelStat(unittest.TestCase, TestCaseSingleFrameWithNlist): + def tearDown(self): + self.tempdir.cleanup() + + def setUp(self): + TestCaseSingleFrameWithNlist.setUp(self) + self.merged_output_stat = [ + { + "coord": to_paddle_tensor(np.zeros([2, 3, 3])), + "atype": to_paddle_tensor( + np.array([[0, 0, 1], [0, 1, 1]], dtype=np.int32) + ), + "atype_ext": to_paddle_tensor( + np.array([[0, 0, 1, 0], [0, 1, 1, 0]], dtype=np.int32) + ), + "box": to_paddle_tensor(np.zeros([2, 3, 3])), + "natoms": to_paddle_tensor( + np.array([[3, 3, 2, 1], [3, 3, 1, 2]], dtype=np.int32) + ), + # bias of foo: 5, 6 + "atom_foo": to_paddle_tensor( + np.array([[5.0, 5.0, 5.0], [5.0, 6.0, 7.0]]).reshape(2, 3, 1) + ), + # bias of bar: [1, 5], [3, 2] + "bar": to_paddle_tensor( + np.array([5.0, 12.0, 7.0, 9.0]).reshape(2, 1, 2) + ), + "find_atom_foo": np.float32(1.0), + "find_bar": np.float32(1.0), + }, + { + "coord": to_paddle_tensor(np.zeros([2, 3, 3])), + "atype": to_paddle_tensor( + np.array([[0, 0, 1], [0, 1, 1]], dtype=np.int32) + ), + "atype_ext": to_paddle_tensor( + np.array([[0, 0, 1, 0], [0, 1, 1, 0]], dtype=np.int32) + ), + "box": to_paddle_tensor(np.zeros([2, 3, 3])), + "natoms": to_paddle_tensor( + np.array([[3, 3, 2, 1], [3, 3, 1, 2]], dtype=np.int32) + ), + # bias of foo: 5, 6 from atomic label. + "foo": to_paddle_tensor(np.array([5.0, 7.0]).reshape(2, 1)), + # bias of bar: [1, 5], [3, 2] + "bar": to_paddle_tensor( + np.array([5.0, 12.0, 7.0, 9.0]).reshape(2, 1, 2) + ), + "find_foo": np.float32(1.0), + "find_bar": np.float32(1.0), + }, + ] + self.tempdir = tempfile.TemporaryDirectory() + h5file = str((Path(self.tempdir.name) / "testcase.h5").resolve()) + with h5py.File(h5file, "w") as f: + pass + self.stat_file_path = DPPath(h5file, "a") + + def test_output_stat(self): + nf, nloc, nnei = self.nlist.shape + ds = DescrptDPA1( + self.rcut, + self.rcut_smth, + sum(self.sel), + self.nt, + ).to(env.DEVICE) + ft = FooFitting().to(env.DEVICE) + type_map = ["foo", "bar"] + md0 = DPAtomicModel( + ds, + ft, + type_map=type_map, + ).to(env.DEVICE) + args = [ + to_paddle_tensor(ii) for ii in [self.coord_ext, self.atype_ext, self.nlist] + ] + # nf x nloc + at = self.atype_ext[:, :nloc] + + def cvt_ret(x): + return {kk: to_numpy_array(vv) for kk, vv in x.items()} + + # 1. test run without bias + # nf x na x odim + ret0 = md0.forward_common_atomic(*args) + ret0 = cvt_ret(ret0) + expected_ret0 = {} + expected_ret0["foo"] = np.array( + [ + [1.0, 2.0, 3.0], + [4.0, 5.0, 6.0], + ] + ).reshape([nf, nloc, *md0.fitting_output_def()["foo"].shape]) + expected_ret0["bar"] = np.array( + [ + [1.0, 2.0, 3.0, 7.0, 8.0, 9.0], + [4.0, 5.0, 6.0, 10.0, 11.0, 12.0], + ] + ).reshape([nf, nloc, *md0.fitting_output_def()["bar"].shape]) + for kk in ["foo", "bar"]: + np.testing.assert_almost_equal(ret0[kk], expected_ret0[kk]) + + # 2. test bias is applied + md0.compute_or_load_out_stat( + self.merged_output_stat, stat_file_path=self.stat_file_path + ) + ret1 = md0.forward_common_atomic(*args) + expected_std = np.ones( + (2, 2, 2), dtype=np.float64 + ) # 2 keys, 2 atypes, 2 max dims. + expected_std[0, :, :1] = np.array([0.0, 0.816496]).reshape( + 2, 1 + ) # updating std for foo based on [5.0, 5.0, 5.0], [5.0, 6.0, 7.0]] + np.testing.assert_almost_equal( + to_numpy_array(md0.out_std), expected_std, decimal=4 + ) + ret1 = cvt_ret(ret1) + # nt x odim + foo_bias = np.array([5.0, 6.0]).reshape(2, 1) + bar_bias = np.array([1.0, 5.0, 3.0, 2.0]).reshape(2, 1, 2) + expected_ret1 = {} + expected_ret1["foo"] = ret0["foo"] + foo_bias[at] + expected_ret1["bar"] = ret0["bar"] + bar_bias[at] + for kk in ["foo", "bar"]: + np.testing.assert_almost_equal(ret1[kk], expected_ret1[kk]) + + # 3. test bias load from file + def raise_error(): + raise RuntimeError + + md0.compute_or_load_out_stat(raise_error, stat_file_path=self.stat_file_path) + ret2 = md0.forward_common_atomic(*args) + ret2 = cvt_ret(ret2) + for kk in ["foo", "bar"]: + np.testing.assert_almost_equal(ret1[kk], ret2[kk]) + np.testing.assert_almost_equal( + to_numpy_array(md0.out_std), expected_std, decimal=4 + ) + + # 4. test change bias + BaseAtomicModel.change_out_bias( + md0, self.merged_output_stat, bias_adjust_mode="change-by-statistic" + ) + args = [ + to_paddle_tensor(ii) + for ii in [ + self.coord_ext, + to_numpy_array(self.merged_output_stat[0]["atype_ext"]), + self.nlist, + ] + ] + ret3 = md0.forward_common_atomic(*args) + ret3 = cvt_ret(ret3) + expected_std[0, :, :1] = np.array([1.24722, 0.47140]).reshape( + 2, 1 + ) # updating std for foo based on [4.0, 3.0, 2.0], [1.0, 1.0, 1.0]] + expected_ret3 = {} + # new bias [2.666, 1.333] + expected_ret3["foo"] = np.array( + [[3.6667, 4.6667, 4.3333], [6.6667, 6.3333, 7.3333]] + ).reshape(2, 3, 1) + for kk in ["foo"]: + np.testing.assert_almost_equal(ret3[kk], expected_ret3[kk], decimal=4) + np.testing.assert_almost_equal( + to_numpy_array(md0.out_std), expected_std, decimal=4 + ) + + +class TestAtomicModelStatMergeGlobalAtomic( + unittest.TestCase, TestCaseSingleFrameWithNlist +): + def tearDown(self): + self.tempdir.cleanup() + + def setUp(self): + TestCaseSingleFrameWithNlist.setUp(self) + self.merged_output_stat = [ + { + "coord": to_paddle_tensor(np.zeros([2, 3, 3])), + "atype": to_paddle_tensor( + np.array([[0, 0, 0], [0, 0, 0]], dtype=np.int32) + ), + "atype_ext": to_paddle_tensor( + np.array([[0, 0, 1, 0], [0, 1, 1, 0]], dtype=np.int32) + ), + "box": to_paddle_tensor(np.zeros([2, 3, 3])), + "natoms": to_paddle_tensor( + np.array([[3, 3, 2, 1], [3, 3, 1, 2]], dtype=np.int32) + ), + # bias of foo: 5.5, nan + "atom_foo": to_paddle_tensor( + np.array([[5.0, 5.0, 5.0], [5.0, 6.0, 7.0]]).reshape(2, 3, 1) + ), + # bias of bar: [1, 5], [3, 2] + "bar": to_paddle_tensor( + np.array([5.0, 12.0, 7.0, 9.0]).reshape(2, 1, 2) + ), + "find_atom_foo": np.float32(1.0), + "find_bar": np.float32(1.0), + }, + { + "coord": to_paddle_tensor(np.zeros([2, 3, 3])), + "atype": to_paddle_tensor( + np.array([[0, 0, 1], [0, 1, 1]], dtype=np.int32) + ), + "atype_ext": to_paddle_tensor( + np.array([[0, 0, 1, 0], [0, 1, 1, 0]], dtype=np.int32) + ), + "box": to_paddle_tensor(np.zeros([2, 3, 3])), + "natoms": to_paddle_tensor( + np.array([[3, 3, 2, 1], [3, 3, 1, 2]], dtype=np.int32) + ), + # bias of foo: 5.5, 3 from atomic label. + "foo": to_paddle_tensor(np.array([5.0, 7.0]).reshape(2, 1)), + # bias of bar: [1, 5], [3, 2] + "bar": to_paddle_tensor( + np.array([5.0, 12.0, 7.0, 9.0]).reshape(2, 1, 2) + ), + "find_foo": np.float32(1.0), + "find_bar": np.float32(1.0), + }, + ] + self.tempdir = tempfile.TemporaryDirectory() + h5file = str((Path(self.tempdir.name) / "testcase.h5").resolve()) + with h5py.File(h5file, "w") as f: + pass + self.stat_file_path = DPPath(h5file, "a") + + def test_output_stat(self): + nf, nloc, nnei = self.nlist.shape + ds = DescrptDPA1( + self.rcut, + self.rcut_smth, + sum(self.sel), + self.nt, + ).to(env.DEVICE) + ft = FooFitting().to(env.DEVICE) + type_map = ["foo", "bar"] + md0 = DPAtomicModel( + ds, + ft, + type_map=type_map, + ).to(env.DEVICE) + args = [ + to_paddle_tensor(ii) for ii in [self.coord_ext, self.atype_ext, self.nlist] + ] + # nf x nloc + at = self.atype_ext[:, :nloc] + + def cvt_ret(x): + return {kk: to_numpy_array(vv) for kk, vv in x.items()} + + # 1. test run without bias + # nf x na x odim + ret0 = md0.forward_common_atomic(*args) + ret0 = cvt_ret(ret0) + expected_ret0 = {} + expected_ret0["foo"] = np.array( + [ + [1.0, 2.0, 3.0], + [4.0, 5.0, 6.0], + ] + ).reshape([nf, nloc, *md0.fitting_output_def()["foo"].shape]) + expected_ret0["bar"] = np.array( + [ + [1.0, 2.0, 3.0, 7.0, 8.0, 9.0], + [4.0, 5.0, 6.0, 10.0, 11.0, 12.0], + ] + ).reshape([nf, nloc, *md0.fitting_output_def()["bar"].shape]) + for kk in ["foo", "bar"]: + np.testing.assert_almost_equal(ret0[kk], expected_ret0[kk]) + + # 2. test bias is applied + md0.compute_or_load_out_stat( + self.merged_output_stat, stat_file_path=self.stat_file_path + ) + ret1 = md0.forward_common_atomic(*args) + ret1 = cvt_ret(ret1) + # nt x odim + foo_bias = np.array([5.5, 3.0]).reshape(2, 1) + bar_bias = np.array([1.0, 5.0, 3.0, 2.0]).reshape(2, 1, 2) + expected_ret1 = {} + expected_ret1["foo"] = ret0["foo"] + foo_bias[at] + expected_ret1["bar"] = ret0["bar"] + bar_bias[at] + for kk in ["foo", "bar"]: + np.testing.assert_almost_equal(ret1[kk], expected_ret1[kk]) + + # 3. test bias load from file + def raise_error(): + raise RuntimeError + + md0.compute_or_load_out_stat(raise_error, stat_file_path=self.stat_file_path) + ret2 = md0.forward_common_atomic(*args) + ret2 = cvt_ret(ret2) + for kk in ["foo", "bar"]: + np.testing.assert_almost_equal(ret1[kk], ret2[kk]) + + # 4. test change bias + BaseAtomicModel.change_out_bias( + md0, self.merged_output_stat, bias_adjust_mode="change-by-statistic" + ) + args = [ + to_paddle_tensor(ii) + for ii in [ + self.coord_ext, + to_numpy_array(self.merged_output_stat[0]["atype_ext"]), + self.nlist, + ] + ] + ret3 = md0.forward_common_atomic(*args) + ret3 = cvt_ret(ret3) + expected_ret3 = {} + # new bias [2, -5] + expected_ret3["foo"] = np.array([[3, 4, -2], [6, 0, 1]]).reshape(2, 3, 1) + for kk in ["foo"]: + np.testing.assert_almost_equal(ret3[kk], expected_ret3[kk], decimal=4) diff --git a/source/tests/pd/model/test_atomic_model_global_stat.py b/source/tests/pd/model/test_atomic_model_global_stat.py new file mode 100644 index 0000000000..abd7928a0f --- /dev/null +++ b/source/tests/pd/model/test_atomic_model_global_stat.py @@ -0,0 +1,510 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import tempfile +import unittest +from pathlib import ( + Path, +) +from typing import ( + Optional, +) + +import h5py +import numpy as np +import paddle + +from deepmd.dpmodel.atomic_model import DPAtomicModel as DPDPAtomicModel +from deepmd.dpmodel.output_def import ( + FittingOutputDef, + OutputVariableDef, +) +from deepmd.pd.model.atomic_model import ( + BaseAtomicModel, + DPAtomicModel, +) +from deepmd.pd.model.descriptor import ( + DescrptDPA1, + DescrptSeA, +) +from deepmd.pd.model.task.base_fitting import ( + BaseFitting, +) +from deepmd.pd.model.task.ener import ( + InvarFitting, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, + to_paddle_tensor, +) +from deepmd.utils.path import ( + DPPath, +) + +from .test_env_mat import ( + TestCaseSingleFrameWithNlist, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION + + +class FooFitting(paddle.nn.Layer, BaseFitting): + def output_def(self): + return FittingOutputDef( + [ + OutputVariableDef( + "foo", + [1], + reducible=True, + r_differentiable=True, + c_differentiable=True, + ), + OutputVariableDef( + "pix", + [1], + reducible=True, + r_differentiable=True, + c_differentiable=True, + ), + OutputVariableDef( + "bar", + [1, 2], + reducible=True, + r_differentiable=True, + c_differentiable=True, + ), + ] + ) + + def serialize(self) -> dict: + raise NotImplementedError + + def change_type_map( + self, type_map: list[str], model_with_new_type_stat=None + ) -> None: + raise NotImplementedError + + def get_type_map(self) -> list[str]: + raise NotImplementedError + + def forward( + self, + descriptor: paddle.Tensor, + atype: paddle.Tensor, + gr: Optional[paddle.Tensor] = None, + g2: Optional[paddle.Tensor] = None, + h2: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + ): + nf, nloc, _ = descriptor.shape + ret = {} + ret["foo"] = ( + paddle.to_tensor( + [ + [1.0, 2.0, 3.0], + [4.0, 5.0, 6.0], + ] + ) + .reshape([nf, nloc] + self.output_def()["foo"].shape) # noqa: RUF005 + .to(env.GLOBAL_PD_FLOAT_PRECISION) + .to(env.DEVICE) + ) + ret["pix"] = ( + paddle.to_tensor( + [ + [3.0, 2.0, 1.0], + [6.0, 5.0, 4.0], + ] + ) + .reshape([nf, nloc] + self.output_def()["pix"].shape) # noqa: RUF005 + .to(env.GLOBAL_PD_FLOAT_PRECISION) + .to(env.DEVICE) + ) + ret["bar"] = ( + paddle.to_tensor( + [ + [1.0, 2.0, 3.0, 7.0, 8.0, 9.0], + [4.0, 5.0, 6.0, 10.0, 11.0, 12.0], + ] + ) + .reshape([nf, nloc] + self.output_def()["bar"].shape) # noqa: RUF005 + .to(env.GLOBAL_PD_FLOAT_PRECISION) + .to(env.DEVICE) + ) + return ret + + +class TestAtomicModelStat(unittest.TestCase, TestCaseSingleFrameWithNlist): + def tearDown(self): + self.tempdir.cleanup() + + def setUp(self): + TestCaseSingleFrameWithNlist.setUp(self) + nf, nloc, nnei = self.nlist.shape + self.merged_output_stat = [ + { + "coord": to_paddle_tensor(np.zeros([2, 3, 3])), + "atype": to_paddle_tensor( + np.array([[0, 0, 1], [0, 1, 1]], dtype=np.int32) + ), + "atype_ext": to_paddle_tensor( + np.array([[0, 0, 1, 0], [0, 1, 1, 0]], dtype=np.int32) + ), + "box": to_paddle_tensor(np.zeros([2, 3, 3])), + "natoms": to_paddle_tensor( + np.array([[3, 3, 2, 1], [3, 3, 1, 2]], dtype=np.int32) + ), + # bias of foo: 1, 3 + "foo": to_paddle_tensor(np.array([5.0, 7.0]).reshape(2, 1)), + # no bias of pix + # bias of bar: [1, 5], [3, 2] + "bar": to_paddle_tensor( + np.array([5.0, 12.0, 7.0, 9.0]).reshape(2, 1, 2) + ), + "find_foo": np.float32(1.0), + "find_bar": np.float32(1.0), + } + ] + self.tempdir = tempfile.TemporaryDirectory() + h5file = str((Path(self.tempdir.name) / "testcase.h5").resolve()) + with h5py.File(h5file, "w") as f: + pass + self.stat_file_path = DPPath(h5file, "a") + + def test_output_stat(self): + nf, nloc, nnei = self.nlist.shape + ds = DescrptDPA1( + self.rcut, + self.rcut_smth, + sum(self.sel), + self.nt, + ).to(env.DEVICE) + ft = FooFitting().to(env.DEVICE) + type_map = ["foo", "bar"] + md0 = DPAtomicModel( + ds, + ft, + type_map=type_map, + ).to(env.DEVICE) + args = [ + to_paddle_tensor(ii) for ii in [self.coord_ext, self.atype_ext, self.nlist] + ] + # nf x nloc + at = self.atype_ext[:, :nloc] + + def cvt_ret(x): + return {kk: to_numpy_array(vv) for kk, vv in x.items()} + + # 1. test run without bias + # nf x na x odim + ret0 = md0.forward_common_atomic(*args) + ret0 = cvt_ret(ret0) + + expected_ret0 = {} + expected_ret0["foo"] = np.array( + [ + [1.0, 2.0, 3.0], + [4.0, 5.0, 6.0], + ] + ).reshape([nf, nloc] + md0.fitting_output_def()["foo"].shape) # noqa: RUF005 + expected_ret0["pix"] = np.array( + [ + [3.0, 2.0, 1.0], + [6.0, 5.0, 4.0], + ] + ).reshape([nf, nloc] + md0.fitting_output_def()["pix"].shape) # noqa: RUF005 + expected_ret0["bar"] = np.array( + [ + [1.0, 2.0, 3.0, 7.0, 8.0, 9.0], + [4.0, 5.0, 6.0, 10.0, 11.0, 12.0], + ] + ).reshape([nf, nloc] + md0.fitting_output_def()["bar"].shape) # noqa: RUF005 + for kk in ["foo", "pix", "bar"]: + np.testing.assert_almost_equal(ret0[kk], expected_ret0[kk]) + + # 2. test bias is applied + md0.compute_or_load_out_stat( + self.merged_output_stat, stat_file_path=self.stat_file_path + ) + ret1 = md0.forward_common_atomic(*args) + ret1 = cvt_ret(ret1) + expected_std = np.ones((3, 2, 2)) # 3 keys, 2 atypes, 2 max dims. + # nt x odim + foo_bias = np.array([1.0, 3.0]).reshape(2, 1) + bar_bias = np.array([1.0, 5.0, 3.0, 2.0]).reshape(2, 1, 2) + expected_ret1 = {} + expected_ret1["foo"] = ret0["foo"] + foo_bias[at] + expected_ret1["pix"] = ret0["pix"] + expected_ret1["bar"] = ret0["bar"] + bar_bias[at] + for kk in ["foo", "pix", "bar"]: + np.testing.assert_almost_equal(ret1[kk], expected_ret1[kk]) + np.testing.assert_almost_equal(to_numpy_array(md0.out_std), expected_std) + + # 3. test bias load from file + def raise_error(): + raise RuntimeError + + md0.compute_or_load_out_stat(raise_error, stat_file_path=self.stat_file_path) + ret2 = md0.forward_common_atomic(*args) + ret2 = cvt_ret(ret2) + for kk in ["foo", "pix", "bar"]: + np.testing.assert_almost_equal(ret1[kk], ret2[kk]) + np.testing.assert_almost_equal(to_numpy_array(md0.out_std), expected_std) + + # 4. test change bias + BaseAtomicModel.change_out_bias( + md0, self.merged_output_stat, bias_adjust_mode="change-by-statistic" + ) + args = [ + to_paddle_tensor(ii) + for ii in [ + self.coord_ext, + to_numpy_array(self.merged_output_stat[0]["atype_ext"]), + self.nlist, + ] + ] + ret3 = md0.forward_common_atomic(*args) + ret3 = cvt_ret(ret3) + ## model output on foo: [[2, 3, 6], [5, 8, 9]] given bias [1, 3] + ## foo sumed: [11, 22] compared with [5, 7], fit target is [-6, -15] + ## fit bias is [1, -8] + ## old bias + fit bias [2, -5] + ## new model output is [[3, 4, -2], [6, 0, 1]], which sumed to [5, 7] + expected_ret3 = {} + expected_ret3["foo"] = np.array([[3, 4, -2], [6, 0, 1]]).reshape(2, 3, 1) + expected_ret3["pix"] = ret0["pix"] + for kk in ["foo", "pix"]: + np.testing.assert_almost_equal(ret3[kk], expected_ret3[kk]) + # bar is too complicated to be manually computed. + np.testing.assert_almost_equal(to_numpy_array(md0.out_std), expected_std) + + def test_preset_bias(self): + nf, nloc, nnei = self.nlist.shape + ds = DescrptDPA1( + self.rcut, + self.rcut_smth, + sum(self.sel), + self.nt, + ).to(env.DEVICE) + ft = FooFitting().to(env.DEVICE) + type_map = ["foo", "bar"] + preset_out_bias = { + # "foo": np.array(3.0, 2.0]).reshape(2, 1), + "foo": [None, 2], + "bar": np.array([7.0, 5.0, 13.0, 11.0]).reshape(2, 1, 2), + } + md0 = DPAtomicModel( + ds, + ft, + type_map=type_map, + preset_out_bias=preset_out_bias, + ).to(env.DEVICE) + args = [ + to_paddle_tensor(ii) for ii in [self.coord_ext, self.atype_ext, self.nlist] + ] + # nf x nloc + at = self.atype_ext[:, :nloc] + + def cvt_ret(x): + return {kk: to_numpy_array(vv) for kk, vv in x.items()} + + # 1. test run without bias + # nf x na x odim + ret0 = md0.forward_common_atomic(*args) + ret0 = cvt_ret(ret0) + expected_ret0 = {} + expected_ret0["foo"] = np.array( + [ + [1.0, 2.0, 3.0], + [4.0, 5.0, 6.0], + ] + ).reshape([nf, nloc] + md0.fitting_output_def()["foo"].shape) # noqa: RUF005 + expected_ret0["pix"] = np.array( + [ + [3.0, 2.0, 1.0], + [6.0, 5.0, 4.0], + ] + ).reshape([nf, nloc] + md0.fitting_output_def()["pix"].shape) # noqa: RUF005 + expected_ret0["bar"] = np.array( + [ + [1.0, 2.0, 3.0, 7.0, 8.0, 9.0], + [4.0, 5.0, 6.0, 10.0, 11.0, 12.0], + ] + ).reshape([nf, nloc] + md0.fitting_output_def()["bar"].shape) # noqa: RUF005 + for kk in ["foo", "pix", "bar"]: + np.testing.assert_almost_equal(ret0[kk], expected_ret0[kk]) + + # 2. test bias is applied + md0.compute_or_load_out_stat( + self.merged_output_stat, stat_file_path=self.stat_file_path + ) + ret1 = md0.forward_common_atomic(*args) + ret1 = cvt_ret(ret1) + # foo sums: [5, 7], + # given bias of type 1 being 2, the bias left for type 0 is [5-2*1, 7-2*2] = [3,3] + # the solution of type 0 is 1.8 + foo_bias = np.array([1.8, preset_out_bias["foo"][1]]).reshape(2, 1) + bar_bias = preset_out_bias["bar"] + expected_ret1 = {} + expected_ret1["foo"] = ret0["foo"] + foo_bias[at] + expected_ret1["pix"] = ret0["pix"] + expected_ret1["bar"] = ret0["bar"] + bar_bias[at] + for kk in ["foo", "pix", "bar"]: + np.testing.assert_almost_equal(ret1[kk], expected_ret1[kk]) + + # 3. test bias load from file + def raise_error(): + raise RuntimeError + + md0.compute_or_load_out_stat(raise_error, stat_file_path=self.stat_file_path) + ret2 = md0.forward_common_atomic(*args) + ret2 = cvt_ret(ret2) + for kk in ["foo", "pix", "bar"]: + np.testing.assert_almost_equal(ret1[kk], ret2[kk]) + + # 4. test change bias + BaseAtomicModel.change_out_bias( + md0, self.merged_output_stat, bias_adjust_mode="change-by-statistic" + ) + args = [ + to_paddle_tensor(ii) + for ii in [ + self.coord_ext, + to_numpy_array(self.merged_output_stat[0]["atype_ext"]), + self.nlist, + ] + ] + ret3 = md0.forward_common_atomic(*args) + ret3 = cvt_ret(ret3) + ## model output on foo: [[2.8, 3.8, 5], [5.8, 7., 8.]] given bias [1.8, 2] + ## foo sumed: [11.6, 20.8] compared with [5, 7], fit target is [-6.6, -13.8] + ## fit bias is [-7, 2] (2 is assigned. -7 is fit to [-8.6, -17.8]) + ## old bias[1.8,2] + fit bias[-7, 2] = [-5.2, 4] + ## new model output is [[-4.2, -3.2, 7], [-1.2, 9, 10]] + expected_ret3 = {} + expected_ret3["foo"] = np.array([[-4.2, -3.2, 7.0], [-1.2, 9.0, 10.0]]).reshape( + 2, 3, 1 + ) + expected_ret3["pix"] = ret0["pix"] + for kk in ["foo", "pix"]: + np.testing.assert_almost_equal(ret3[kk], expected_ret3[kk]) + # bar is too complicated to be manually computed. + + def test_preset_bias_all_none(self): + nf, nloc, nnei = self.nlist.shape + ds = DescrptDPA1( + self.rcut, + self.rcut_smth, + sum(self.sel), + self.nt, + ).to(env.DEVICE) + ft = FooFitting().to(env.DEVICE) + type_map = ["foo", "bar"] + preset_out_bias = { + "foo": [None, None], + } + md0 = DPAtomicModel( + ds, + ft, + type_map=type_map, + preset_out_bias=preset_out_bias, + ).to(env.DEVICE) + args = [ + to_paddle_tensor(ii) for ii in [self.coord_ext, self.atype_ext, self.nlist] + ] + # nf x nloc + at = self.atype_ext[:, :nloc] + + def cvt_ret(x): + return {kk: to_numpy_array(vv) for kk, vv in x.items()} + + # 1. test run without bias + # nf x na x odim + ret0 = md0.forward_common_atomic(*args) + ret0 = cvt_ret(ret0) + expected_ret0 = {} + expected_ret0["foo"] = np.array( + [ + [1.0, 2.0, 3.0], + [4.0, 5.0, 6.0], + ] + ).reshape([nf, nloc] + md0.fitting_output_def()["foo"].shape) # noqa: RUF005 + expected_ret0["pix"] = np.array( + [ + [3.0, 2.0, 1.0], + [6.0, 5.0, 4.0], + ] + ).reshape([nf, nloc] + md0.fitting_output_def()["pix"].shape) # noqa: RUF005 + expected_ret0["bar"] = np.array( + [ + [1.0, 2.0, 3.0, 7.0, 8.0, 9.0], + [4.0, 5.0, 6.0, 10.0, 11.0, 12.0], + ] + ).reshape([nf, nloc] + md0.fitting_output_def()["bar"].shape) # noqa: RUF005 + for kk in ["foo", "pix", "bar"]: + np.testing.assert_almost_equal(ret0[kk], expected_ret0[kk]) + + # 2. test bias is applied + md0.compute_or_load_out_stat( + self.merged_output_stat, stat_file_path=self.stat_file_path + ) + ret1 = md0.forward_common_atomic(*args) + ret1 = cvt_ret(ret1) + # nt x odim + foo_bias = np.array([1.0, 3.0]).reshape(2, 1) + bar_bias = np.array([1.0, 5.0, 3.0, 2.0]).reshape(2, 1, 2) + expected_ret1 = {} + expected_ret1["foo"] = ret0["foo"] + foo_bias[at] + expected_ret1["pix"] = ret0["pix"] + expected_ret1["bar"] = ret0["bar"] + bar_bias[at] + for kk in ["foo", "pix", "bar"]: + np.testing.assert_almost_equal(ret1[kk], expected_ret1[kk]) + + def test_serialize(self): + nf, nloc, nnei = self.nlist.shape + ds = DescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + ).to(env.DEVICE) + ft = InvarFitting( + "foo", + self.nt, + ds.get_dim_out(), + 1, + mixed_types=ds.mixed_types(), + ).to(env.DEVICE) + type_map = ["A", "B"] + md0 = DPAtomicModel( + ds, + ft, + type_map=type_map, + ).to(env.DEVICE) + args = [ + to_paddle_tensor(ii) for ii in [self.coord_ext, self.atype_ext, self.nlist] + ] + # nf x nloc + at = self.atype_ext[:, :nloc] + + def cvt_ret(x): + return {kk: to_numpy_array(vv) for kk, vv in x.items()} + + md0.compute_or_load_out_stat( + self.merged_output_stat, stat_file_path=self.stat_file_path + ) + ret0 = md0.forward_common_atomic(*args) + ret0 = cvt_ret(ret0) + md1 = DPAtomicModel.deserialize(md0.serialize()) + ret1 = md1.forward_common_atomic(*args) + ret1 = cvt_ret(ret1) + + for kk in ["foo"]: + np.testing.assert_almost_equal(ret0[kk], ret1[kk]) + + md2 = DPDPAtomicModel.deserialize(md0.serialize()) + args = [self.coord_ext, self.atype_ext, self.nlist] + ret2 = md2.forward_common_atomic(*args) + for kk in ["foo"]: + np.testing.assert_almost_equal(ret0[kk], ret2[kk]) diff --git a/source/tests/pd/model/test_autodiff.py b/source/tests/pd/model/test_autodiff.py new file mode 100644 index 0000000000..7554bd241f --- /dev/null +++ b/source/tests/pd/model/test_autodiff.py @@ -0,0 +1,253 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import unittest + +import numpy as np +import paddle + +from deepmd.pd.model.model import ( + get_model, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, +) + +from ...seed import ( + GLOBAL_SEED, +) + +dtype = paddle.float64 + +from ..common import ( + eval_model, +) +from .test_permutation import ( + model_dpa1, + model_dpa2, + model_hybrid, + model_se_e2_a, + model_spin, + model_zbl, +) + + +# from deepmd-kit repo +def finite_difference(f, x, delta=1e-6): + in_shape = x.shape + y0 = f(x) + out_shape = y0.shape + res = np.empty(out_shape + in_shape) + for idx in np.ndindex(*in_shape): + diff = np.zeros(in_shape) + diff[idx] += delta + y1p = f(x + diff) + y1n = f(x - diff) + res[(Ellipsis, *idx)] = (y1p - y1n) / (2 * delta) + return res + + +def stretch_box(old_coord, old_box, new_box): + ocoord = old_coord.reshape(-1, 3) + obox = old_box.reshape(3, 3) + nbox = new_box.reshape(3, 3) + ncoord = ocoord @ np.linalg.inv(obox) @ nbox + return ncoord.reshape(old_coord.shape) + + +class ForceTest: + def test( + self, + ): + places = 5 + delta = 1e-5 + natoms = 5 + generator = paddle.seed(GLOBAL_SEED) + cell = paddle.rand([3, 3], dtype=dtype).to(device="cpu") + cell = (cell + cell.T) + 5.0 * paddle.eye(3).to(device="cpu") + coord = paddle.rand([natoms, 3], dtype=dtype).to(device="cpu") + coord = paddle.matmul(coord, cell) + spin = paddle.rand([natoms, 3], dtype=dtype).to(device="cpu") + atype = paddle.to_tensor([0, 0, 0, 1, 1]) + # assumes input to be numpy tensor + coord = coord.numpy() + spin = spin.numpy() + test_spin = getattr(self, "test_spin", False) + if not test_spin: + test_keys = ["energy", "force", "virial"] + else: + test_keys = ["energy", "force", "force_mag", "virial"] + + def np_infer_coord( + coord, + ): + result = eval_model( + self.model, + paddle.to_tensor(coord).to(device=env.DEVICE).unsqueeze(0), + cell.unsqueeze(0), + atype, + spins=paddle.to_tensor(spin).to(device=env.DEVICE).unsqueeze(0), + ) + # detach + ret = {key: to_numpy_array(result[key].squeeze(0)) for key in test_keys} + return ret + + def np_infer_spin( + spin, + ): + result = eval_model( + self.model, + paddle.to_tensor(coord).to(device=env.DEVICE).unsqueeze(0), + cell.unsqueeze(0), + atype, + spins=paddle.to_tensor(spin).to(device=env.DEVICE).unsqueeze(0), + ) + # detach + ret = {key: to_numpy_array(result[key].squeeze(0)) for key in test_keys} + return ret + + def ff_coord(_coord): + return np_infer_coord(_coord)["energy"] + + def ff_spin(_spin): + return np_infer_spin(_spin)["energy"] + + if not test_spin: + fdf = -finite_difference(ff_coord, coord, delta=delta).squeeze() + rff = np_infer_coord(coord)["force"] + np.testing.assert_almost_equal(fdf, rff, decimal=places) + else: + # real force + fdf = -finite_difference(ff_coord, coord, delta=delta).squeeze() + rff = np_infer_coord(coord)["force"] + np.testing.assert_almost_equal(fdf, rff, decimal=places) + # magnetic force + fdf = -finite_difference(ff_spin, spin, delta=delta).squeeze() + rff = np_infer_spin(spin)["force_mag"] + np.testing.assert_almost_equal(fdf, rff, decimal=places) + + +class VirialTest: + def test( + self, + ): + places = 5 + delta = 1e-4 + natoms = 5 + generator = paddle.seed(GLOBAL_SEED) + cell = paddle.rand([3, 3], dtype=dtype).to(device="cpu") + cell = (cell) + 5.0 * paddle.eye(3).to(device="cpu") + coord = paddle.rand([natoms, 3], dtype=dtype).to(device="cpu") + coord = paddle.matmul(coord, cell) + atype = paddle.to_tensor([0, 0, 0, 1, 1]) + # assumes input to be numpy tensor + coord = coord.numpy() + cell = cell.numpy() + test_keys = ["energy", "force", "virial"] + + def np_infer( + new_cell, + ): + result = eval_model( + self.model, + paddle.to_tensor(stretch_box(coord, cell, new_cell)) + .to(device="cpu") + .unsqueeze(0), + paddle.to_tensor(new_cell).to(device="cpu").unsqueeze(0), + atype, + ) + # detach + ret = {key: to_numpy_array(result[key].squeeze(0)) for key in test_keys} + # detach + return ret + + def ff(bb): + return np_infer(bb)["energy"] + + fdv = ( + -(finite_difference(ff, cell, delta=delta).transpose([0, 2, 1]) @ cell) + .squeeze() + .reshape([9]) + ) + rfv = np_infer(cell)["virial"] + np.testing.assert_almost_equal(fdv, rfv, decimal=places) + + +class TestEnergyModelSeAForce(unittest.TestCase, ForceTest): + def setUp(self): + model_params = copy.deepcopy(model_se_e2_a) + self.type_split = False + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelSeAVirial(unittest.TestCase, VirialTest): + def setUp(self): + model_params = copy.deepcopy(model_se_e2_a) + self.type_split = False + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelDPA1Force(unittest.TestCase, ForceTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa1) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelDPA1Virial(unittest.TestCase, VirialTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa1) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelDPA2Force(unittest.TestCase, ForceTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa2) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelDPAUniVirial(unittest.TestCase, VirialTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa2) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelHybridForce(unittest.TestCase, ForceTest): + def setUp(self): + model_params = copy.deepcopy(model_hybrid) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelHybridVirial(unittest.TestCase, VirialTest): + def setUp(self): + model_params = copy.deepcopy(model_hybrid) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelZBLForce(unittest.TestCase, ForceTest): + def setUp(self): + model_params = copy.deepcopy(model_zbl) + self.type_split = False + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelZBLVirial(unittest.TestCase, VirialTest): + def setUp(self): + model_params = copy.deepcopy(model_zbl) + self.type_split = False + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelSpinSeAForce(unittest.TestCase, ForceTest): + def setUp(self): + model_params = copy.deepcopy(model_spin) + self.type_split = False + self.test_spin = True + self.model = get_model(model_params).to(env.DEVICE) diff --git a/source/tests/pd/model/test_deeppot.py b/source/tests/pd/model/test_deeppot.py new file mode 100644 index 0000000000..3cf7cc23b2 --- /dev/null +++ b/source/tests/pd/model/test_deeppot.py @@ -0,0 +1,138 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import json +import os +import unittest +from argparse import ( + Namespace, +) +from copy import ( + deepcopy, +) +from pathlib import ( + Path, +) + +import numpy as np +import paddle + +from deepmd.infer.deep_pot import DeepPot as DeepPotUni +from deepmd.pd.entrypoints.main import ( + freeze, + get_trainer, +) +from deepmd.pd.infer.deep_eval import ( + DeepPot, +) + + +class TestDeepPot(unittest.TestCase): + def setUp(self): + input_json = str(Path(__file__).parent / "water/se_atten.json") + with open(input_json) as f: + self.config = json.load(f) + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + self.config["training"]["training_data"]["systems"] = [ + str(Path(__file__).parent / "water/data/single") + ] + self.config["training"]["validation_data"]["systems"] = [ + str(Path(__file__).parent / "water/data/single") + ] + self.input_json = "test_dp_test.json" + with open(self.input_json, "w") as fp: + json.dump(self.config, fp, indent=4) + + trainer = get_trainer(deepcopy(self.config)) + trainer.run() + + device = paddle.get_device() + paddle.set_device("cpu") + input_dict, label_dict, _ = trainer.get_data(is_train=False) + paddle.set_device(device) + trainer.wrapper(**input_dict, label=label_dict, cur_lr=1.0) + self.model = "model.pd" + + def tearDown(self): + for f in os.listdir("."): + if f in ["lcurve.out", self.input_json]: + os.remove(f) + + # @unittest.skip("Paddle do not eval on frozen model yet.") + def test_dp_test(self): + dp = DeepPot(str(self.model)) + cell = np.array( + [ + 5.122106549439247480e00, + 4.016537340154059388e-01, + 6.951654033828678081e-01, + 4.016537340154059388e-01, + 6.112136112297989143e00, + 8.178091365465004481e-01, + 6.951654033828678081e-01, + 8.178091365465004481e-01, + 6.159552512682983760e00, + ] + ).reshape(1, 3, 3) + coord = np.array( + [ + 2.978060152121375648e00, + 3.588469695887098077e00, + 2.792459820604495491e00, + 3.895592322591093115e00, + 2.712091020667753760e00, + 1.366836847133650501e00, + 9.955616170888935690e-01, + 4.121324820711413039e00, + 1.817239061889086571e00, + 3.553661462345699906e00, + 5.313046969500791583e00, + 6.635182659098815883e00, + 6.088601018589653080e00, + 6.575011420004332585e00, + 6.825240650611076099e00, + ] + ).reshape(1, -1, 3) + atype = np.array([0, 0, 0, 1, 1]).reshape(1, -1) + + ret = dp.eval(coord, cell, atype, atomic=True) + e, f, v, ae, av = ret[0], ret[1], ret[2], ret[3], ret[4] + self.assertEqual(e.shape, (1, 1)) + self.assertEqual(f.shape, (1, 5, 3)) + self.assertEqual(v.shape, (1, 9)) + self.assertEqual(ae.shape, (1, 5, 1)) + self.assertEqual(av.shape, (1, 5, 9)) + + self.assertEqual(dp.get_type_map(), ["O", "H"]) + self.assertEqual(dp.get_ntypes(), 2) + self.assertEqual(dp.get_dim_fparam(), 0) + self.assertEqual(dp.get_dim_aparam(), 0) + self.assertEqual(dp.deep_eval.model_type, DeepPot) + + def test_uni(self): + dp = DeepPotUni("model.pd") + self.assertIsInstance(dp, DeepPot) + # its methods has been tested in test_dp_test + + +class TestDeepPotFrozen(TestDeepPot): + def setUp(self): + super().setUp() + frozen_model = "frozen_model.json" + ns = Namespace( + model=self.model, + output=frozen_model, + head=None, + ) + freeze(ns) + self.model = frozen_model + + # Note: this can not actually disable cuda device to be used + # only can be used to test whether devices are mismatched + @unittest.skipIf(not (paddle.device.cuda.device_count() > 0), "CUDA not available") + @unittest.mock.patch("deepmd.pd.utils.env.DEVICE", "cpu") + @unittest.mock.patch("deepmd.pd.infer.deep_eval.DEVICE", "cpu") + def test_dp_test_cpu(self): + self.test_dp_test() + + +# TestFparamAparamPT: moved to infer/test_models.py diff --git a/source/tests/pd/model/test_descriptor.py b/source/tests/pd/model/test_descriptor.py new file mode 100644 index 0000000000..7365d97b67 --- /dev/null +++ b/source/tests/pd/model/test_descriptor.py @@ -0,0 +1,196 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import os +import unittest + +import numpy as np +import paddle +import tensorflow.compat.v1 as tf + +tf.disable_eager_execution() + +import json +from pathlib import ( + Path, +) + +from deepmd.pd.model.descriptor import ( + prod_env_mat, +) +from deepmd.pd.utils import ( + aux, + dp_random, + env, +) +from deepmd.pd.utils.dataset import ( + DeepmdDataSetForLoader, +) +from deepmd.pd.utils.env import ( + DEVICE, + GLOBAL_NP_FLOAT_PRECISION, + GLOBAL_PD_FLOAT_PRECISION, +) +from deepmd.pd.utils.nlist import ( + extend_input_and_build_neighbor_list, +) +from deepmd.tf.common import ( + expand_sys_str, +) +from deepmd.tf.env import ( + op_module, +) + +from ..test_finetune import ( + energy_data_requirement, +) +from .test_embedding_net import ( + get_single_batch, +) + +CUR_DIR = os.path.dirname(__file__) + + +def base_se_a(rcut, rcut_smth, sel, batch, mean, stddev): + g = tf.Graph() + with g.as_default(): + coord = tf.placeholder(GLOBAL_NP_FLOAT_PRECISION, [None, None]) + box = tf.placeholder(GLOBAL_NP_FLOAT_PRECISION, [None, None]) + atype = tf.placeholder(tf.int32, [None, None]) + natoms_vec = tf.placeholder(tf.int32, [None]) + default_mesh = tf.placeholder(tf.int32, [None]) + stat_descrpt, descrpt_deriv, rij, nlist = op_module.prod_env_mat_a( + coord, + atype, + natoms_vec, + box, + default_mesh, + tf.constant(mean), + tf.constant(stddev), + rcut_a=-1.0, + rcut_r=rcut, + rcut_r_smth=rcut_smth, + sel_a=sel, + sel_r=[0 for i in sel], + ) + + net_deriv_reshape = tf.ones_like(stat_descrpt) + force = op_module.prod_force_se_a( + net_deriv_reshape, + descrpt_deriv, + nlist, + natoms_vec, + n_a_sel=sum(sel), + n_r_sel=0, + ) + + with tf.Session(graph=g) as sess: + y = sess.run( + [stat_descrpt, force, nlist], + feed_dict={ + coord: batch["coord"], + box: batch["box"], + natoms_vec: batch["natoms"], + atype: batch["atype"], + default_mesh: np.array([0, 0, 0, 2, 2, 2]), + }, + ) + tf.reset_default_graph() + return y + + +class TestSeA(unittest.TestCase): + def setUp(self): + dp_random.seed(20) + with open(str(Path(__file__).parent / "water/se_e2_a.json")) as fin: + content = fin.read() + config = json.loads(content) + data_file = [str(Path(__file__).parent / "water/data/data_0")] + config["training"]["training_data"]["systems"] = data_file + config["training"]["validation_data"]["systems"] = data_file + model_config = config["model"] + self.rcut = model_config["descriptor"]["rcut"] + self.rcut_smth = model_config["descriptor"]["rcut_smth"] + self.sel = model_config["descriptor"]["sel"] + self.bsz = config["training"]["training_data"]["batch_size"] + self.systems = config["training"]["validation_data"]["systems"] + if isinstance(self.systems, str): + self.systems = expand_sys_str(self.systems) + ds = DeepmdDataSetForLoader( + self.systems[0], + model_config["type_map"], + ) + ds.add_data_requirement(energy_data_requirement) + self.np_batch, self.pt_batch = get_single_batch(ds) + self.sec = np.cumsum(self.sel) + self.ntypes = len(self.sel) + self.nnei = sum(self.sel) + + # @unittest.skip("remainder 缺少反向") + def test_consistency(self): + avg_zero = paddle.zeros( + [self.ntypes, self.nnei * 4], + dtype=GLOBAL_PD_FLOAT_PRECISION, + ).to(device=env.DEVICE) + std_ones = paddle.ones( + [self.ntypes, self.nnei * 4], + dtype=GLOBAL_PD_FLOAT_PRECISION, + ).to(device=env.DEVICE) + base_d, base_force, base_nlist = base_se_a( + rcut=self.rcut, + rcut_smth=self.rcut_smth, + sel=self.sel, + batch=self.np_batch, + mean=avg_zero.detach().cpu(), + stddev=std_ones.detach().cpu(), + ) + + pt_coord = self.pt_batch["coord"].to(env.DEVICE) + atype = self.pt_batch["atype"].to(env.DEVICE) + pt_coord.stop_gradient = False + ( + extended_coord, + extended_atype, + mapping, + nlist, + ) = extend_input_and_build_neighbor_list( + pt_coord, + self.pt_batch["atype"].to(env.DEVICE), + self.rcut, + self.sel, + mixed_types=False, + box=self.pt_batch["box"].to(env.DEVICE), + ) + my_d, _, _ = prod_env_mat( + extended_coord, + nlist, + atype, + avg_zero.reshape([-1, self.nnei, 4]).to(DEVICE), + std_ones.reshape([-1, self.nnei, 4]).to(DEVICE), + self.rcut, + self.rcut_smth, + ) + my_d.sum().backward() + bsz = pt_coord.shape[0] + my_force = pt_coord.grad.reshape([bsz, -1, 3]).cpu().detach().numpy() + base_force = base_force.reshape(bsz, -1, 3) + base_d = base_d.reshape(bsz, -1, self.nnei, 4) + my_d = my_d.reshape([bsz, -1, self.nnei, 4]).cpu().detach().numpy() + base_nlist = base_nlist.reshape(bsz, -1, self.nnei) + + mapping = mapping.cpu() + my_nlist = nlist.reshape([bsz, -1]).cpu() + mask = my_nlist == -1 + my_nlist = my_nlist * (~mask).astype(my_nlist.dtype) + my_nlist = aux.take_along_axis(mapping, axis=-1, indices=my_nlist) + my_nlist = my_nlist * (~mask).astype(my_nlist.dtype) - mask.astype( + my_nlist.dtype + ) + my_nlist = my_nlist.cpu().reshape([bsz, -1, self.nnei]).numpy() + self.assertTrue(np.allclose(base_nlist, my_nlist)) + self.assertTrue(np.allclose(np.mean(base_d, axis=2), np.mean(my_d, axis=2))) + self.assertTrue(np.allclose(np.std(base_d, axis=2), np.std(my_d, axis=2))) + # descriptors may be different when there are multiple neighbors in the same distance + self.assertTrue(np.allclose(base_force, -my_force)) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/model/test_descriptor_dpa1.py b/source/tests/pd/model/test_descriptor_dpa1.py new file mode 100644 index 0000000000..c3a93761c6 --- /dev/null +++ b/source/tests/pd/model/test_descriptor_dpa1.py @@ -0,0 +1,381 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import json +import os +import unittest +from pathlib import ( + Path, +) + +import paddle + +from deepmd.pd.model.descriptor import ( + DescrptBlockSeAtten, + DescrptDPA1, +) +from deepmd.pd.model.network.network import ( + TypeEmbedNet, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.nlist import ( + extend_input_and_build_neighbor_list, +) + +CUR_DIR = os.path.dirname(__file__) + + +class TestDPA1(unittest.TestCase): + def setUp(self): + cell = [ + 5.122106549439247480e00, + 4.016537340154059388e-01, + 6.951654033828678081e-01, + 4.016537340154059388e-01, + 6.112136112297989143e00, + 8.178091365465004481e-01, + 6.951654033828678081e-01, + 8.178091365465004481e-01, + 6.159552512682983760e00, + ] + self.cell = ( + paddle.to_tensor( + cell, + dtype=env.GLOBAL_PD_FLOAT_PRECISION, + ) + .to(device=env.DEVICE) + .reshape([1, 3, 3]) + ) + coord = [ + 2.978060152121375648e00, + 3.588469695887098077e00, + 2.792459820604495491e00, + 3.895592322591093115e00, + 2.712091020667753760e00, + 1.366836847133650501e00, + 9.955616170888935690e-01, + 4.121324820711413039e00, + 1.817239061889086571e00, + 3.553661462345699906e00, + 5.313046969500791583e00, + 6.635182659098815883e00, + 6.088601018589653080e00, + 6.575011420004332585e00, + 6.825240650611076099e00, + ] + self.coord = ( + paddle.to_tensor(coord, dtype=env.GLOBAL_PD_FLOAT_PRECISION) + .reshape([1, -1, 3]) + .to(device=env.DEVICE) + ) + self.atype = ( + paddle.to_tensor([0, 0, 0, 1, 1], dtype=paddle.int32) + .reshape([1, -1]) + .to(device=env.DEVICE) + ) + self.ref_d = paddle.to_tensor( + [ + 8.382518544113587780e-03, + -3.390120566088597812e-03, + 6.145981571114964362e-03, + -4.880300873973819273e-03, + -3.390120566088597812e-03, + 1.372540996564941464e-03, + -2.484163690574096341e-03, + 1.972313058658722688e-03, + 6.145981571114964362e-03, + -2.484163690574096341e-03, + 4.507748738021747671e-03, + -3.579717194906019764e-03, + -4.880300873973819273e-03, + 1.972313058658722688e-03, + -3.579717194906019764e-03, + 2.842794615687799838e-03, + 6.733043802494966066e-04, + -2.721540313345096771e-04, + 4.936158526085561134e-04, + -3.919743287822345223e-04, + -1.311123004527576900e-02, + 5.301179352601203924e-03, + -9.614612349318877454e-03, + 7.634884975521277241e-03, + 8.877088452901006621e-03, + -3.590945566653638409e-03, + 6.508042782015627942e-03, + -5.167671664327699171e-03, + -2.697241463040870365e-03, + 1.091350446825975137e-03, + -1.976895708961905022e-03, + 1.569671412121975348e-03, + 8.645131636261189911e-03, + -3.557395265621639355e-03, + 6.298048561552698106e-03, + -4.999272007935521948e-03, + -3.557395265621639355e-03, + 1.467866637220284964e-03, + -2.587004431651147504e-03, + 2.052752235601402672e-03, + 6.298048561552698106e-03, + -2.587004431651147504e-03, + 4.594085551315935101e-03, + -3.647656549789176847e-03, + -4.999272007935521948e-03, + 2.052752235601402672e-03, + -3.647656549789176847e-03, + 2.896359275520481256e-03, + 6.689620176492027878e-04, + -2.753606422414641049e-04, + 4.864958810186969444e-04, + -3.860599754167503119e-04, + -1.349238259226558101e-02, + 5.547478630961994242e-03, + -9.835472300819447095e-03, + 7.808197926069362048e-03, + 9.220744348752592245e-03, + -3.795799103392961601e-03, + 6.716516319358462918e-03, + -5.331265718473574867e-03, + -2.783836698392940304e-03, + 1.147461939123531121e-03, + -2.025013030986024063e-03, + 1.606944814423778541e-03, + 9.280385723343491378e-03, + -3.515852178447095942e-03, + 7.085282215778941628e-03, + -5.675852414643783178e-03, + -3.515852178447095942e-03, + 1.337760635271160884e-03, + -2.679428786337713451e-03, + 2.145400621815936413e-03, + 7.085282215778941628e-03, + -2.679428786337713451e-03, + 5.414439648102228192e-03, + -4.338426468139268931e-03, + -5.675852414643783178e-03, + 2.145400621815936413e-03, + -4.338426468139268931e-03, + 3.476467482674507146e-03, + 7.166961981167455130e-04, + -2.697932188839837972e-04, + 5.474643906631899504e-04, + -4.386556623669893621e-04, + -1.480434821331240956e-02, + 5.604647062899507579e-03, + -1.130745349141585449e-02, + 9.059113563516829268e-03, + 9.758791063112262978e-03, + -3.701477720487638626e-03, + 7.448215522796466058e-03, + -5.966057584545172120e-03, + -2.845102393948158344e-03, + 1.078743584169829543e-03, + -2.170093031447992756e-03, + 1.738010461687942770e-03, + 9.867599071916231118e-03, + -3.811041717688905522e-03, + 7.121877634386481262e-03, + -5.703120290113914553e-03, + -3.811041717688905522e-03, + 1.474046183772771213e-03, + -2.747386907428428938e-03, + 2.199711055637492037e-03, + 7.121877634386481262e-03, + -2.747386907428428938e-03, + 5.145050639440944609e-03, + -4.120642824501622239e-03, + -5.703120290113914553e-03, + 2.199711055637492037e-03, + -4.120642824501622239e-03, + 3.300262321758350853e-03, + 1.370499995344566383e-03, + -5.313041843655797901e-04, + 9.860110343046961986e-04, + -7.892505817954784597e-04, + -1.507686316307561489e-02, + 5.818961290579217904e-03, + -1.088774506142304276e-02, + 8.719460408506790952e-03, + 9.764630842803939323e-03, + -3.770134041110058572e-03, + 7.049438389985595785e-03, + -5.645302934019884485e-03, + -3.533582373572779437e-03, + 1.367148320603491559e-03, + -2.546602904764623705e-03, + 2.038882844528267305e-03, + 7.448297038731285964e-03, + -2.924276815200288742e-03, + 5.355960540523636154e-03, + -4.280386435083473329e-03, + -2.924276815200288742e-03, + 1.150311064893848757e-03, + -2.100635980860638373e-03, + 1.678427895009850001e-03, + 5.355960540523636154e-03, + -2.100635980860638373e-03, + 3.853607053247790071e-03, + -3.080076301871465493e-03, + -4.280386435083473329e-03, + 1.678427895009850001e-03, + -3.080076301871465493e-03, + 2.461876613756722523e-03, + 9.730712866459405395e-04, + -3.821759579990726546e-04, + 6.994242056622360787e-04, + -5.589662297882965055e-04, + -1.138916742131982317e-02, + 4.469391132927387489e-03, + -8.192016282448397885e-03, + 6.547234460517113892e-03, + 7.460070829043288082e-03, + -2.929867802018087421e-03, + 5.363646855497249989e-03, + -4.286347242903034739e-03, + -2.643569023340565718e-03, + 1.038826463247002245e-03, + -1.899910089750410976e-03, + 1.518237240362583541e-03, + ], + dtype=env.GLOBAL_PD_FLOAT_PRECISION, + ).to(device=env.DEVICE) + with open(Path(CUR_DIR) / "models" / "dpa1.json") as fp: + self.model_json = json.load(fp) + self.file_model_param = Path(CUR_DIR) / "models" / "dpa1.pd" + self.file_type_embed = Path(CUR_DIR) / "models" / "dpa2_tebd.pd" + + def test_descriptor_block(self): + # paddle.seed(0) + model_dpa1 = self.model_json + dparams = model_dpa1["descriptor"] + ntypes = len(model_dpa1["type_map"]) + assert "se_atten" == dparams.pop("type") + dparams["ntypes"] = ntypes + des = DescrptBlockSeAtten( + **dparams, + ).to(env.DEVICE) + des.set_state_dict(paddle.load(str(self.file_model_param))) + coord = self.coord + atype = self.atype + box = self.cell + # handel type_embedding + type_embedding = TypeEmbedNet(ntypes, 8, use_tebd_bias=True).to(env.DEVICE) + type_embedding.set_state_dict(paddle.load(str(self.file_type_embed))) + + ## to save model parameters + # paddle.save(des.state_dict(), 'model_weights.pd') + # paddle.save(type_embedding.state_dict(), 'model_weights.pd') + ( + extended_coord, + extended_atype, + mapping, + nlist, + ) = extend_input_and_build_neighbor_list( + coord, + atype, + des.get_rcut(), + des.get_sel(), + mixed_types=des.mixed_types(), + box=box, + ) + descriptor, env_mat, diff, rot_mat, sw = des( + nlist, + extended_coord, + extended_atype, + type_embedding(extended_atype), + mapping=None, + ) + # np.savetxt('tmp.out', descriptor.detach().numpy().reshape(1,-1), delimiter=",") + self.assertEqual(descriptor.shape[-1], des.get_dim_out()) + self.assertAlmostEqual(6.0, des.get_rcut()) + self.assertEqual(30, des.get_nsel()) + self.assertEqual(2, des.get_ntypes()) + assert paddle.allclose( + descriptor.reshape([-1]), self.ref_d, atol=1e-10, rtol=1e-10 + ) + + def test_descriptor(self): + with open(Path(CUR_DIR) / "models" / "dpa1.json") as fp: + self.model_json = json.load(fp) + model_dpa2 = self.model_json + ntypes = len(model_dpa2["type_map"]) + dparams = model_dpa2["descriptor"] + dparams["ntypes"] = ntypes + assert dparams.pop("type") == "se_atten" + dparams["concat_output_tebd"] = False + dparams["use_tebd_bias"] = True + des = DescrptDPA1( + **dparams, + ).to(env.DEVICE) + target_dict = des.state_dict() + source_dict = paddle.load(str(self.file_model_param)) + type_embd_dict = paddle.load(str(self.file_type_embed)) + target_dict = translate_se_atten_and_type_embd_dicts_to_dpa1( + target_dict, + source_dict, + type_embd_dict, + ) + des.set_state_dict(target_dict) + + coord = self.coord + atype = self.atype + box = self.cell + ( + extended_coord, + extended_atype, + mapping, + nlist, + ) = extend_input_and_build_neighbor_list( + coord, + atype, + des.get_rcut(), + des.get_sel(), + mixed_types=des.mixed_types(), + box=box, + ) + descriptor, env_mat, diff, rot_mat, sw = des( + extended_coord, + extended_atype, + nlist, + mapping=mapping, + ) + self.assertEqual(descriptor.shape[-1], des.get_dim_out()) + self.assertAlmostEqual(6.0, des.get_rcut()) + self.assertEqual(30, des.get_nsel()) + self.assertEqual(2, des.get_ntypes()) + assert paddle.allclose( + descriptor.reshape([-1]), self.ref_d, atol=1e-10, rtol=1e-10 + ) + + dparams["concat_output_tebd"] = True + des = DescrptDPA1( + **dparams, + ).to(env.DEVICE) + descriptor, env_mat, diff, rot_mat, sw = des( + extended_coord, + extended_atype, + nlist, + mapping=mapping, + ) + self.assertEqual(descriptor.shape[-1], des.get_dim_out()) + + +def translate_se_atten_and_type_embd_dicts_to_dpa1( + target_dict, + source_dict, + type_embd_dict, +): + all_keys = list(target_dict.keys()) + record = [False for ii in all_keys] + for kk, vv in source_dict.items(): + tk = "se_atten." + kk + record[all_keys.index(tk)] = True + target_dict[tk] = vv + assert len(type_embd_dict.keys()) == 2 + it = iter(type_embd_dict.keys()) + for _ in range(2): + kk = next(it) + tk = "type_embedding." + kk + record[all_keys.index(tk)] = True + target_dict[tk] = type_embd_dict[kk] + assert all(record) + return target_dict diff --git a/source/tests/pd/model/test_descriptor_dpa2.py b/source/tests/pd/model/test_descriptor_dpa2.py new file mode 100644 index 0000000000..8f08cd2dab --- /dev/null +++ b/source/tests/pd/model/test_descriptor_dpa2.py @@ -0,0 +1,203 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import json +import os +import unittest +from pathlib import ( + Path, +) + +import paddle + +from deepmd.pd.model.descriptor import ( + DescrptDPA2, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.nlist import ( + extend_input_and_build_neighbor_list, +) + +CUR_DIR = os.path.dirname(__file__) + + +class TestDPA2(unittest.TestCase): + def setUp(self): + cell = [ + 5.122106549439247480e00, + 4.016537340154059388e-01, + 6.951654033828678081e-01, + 4.016537340154059388e-01, + 6.112136112297989143e00, + 8.178091365465004481e-01, + 6.951654033828678081e-01, + 8.178091365465004481e-01, + 6.159552512682983760e00, + ] + self.cell = ( + paddle.to_tensor(cell, dtype=env.GLOBAL_PD_FLOAT_PRECISION) + .reshape([1, 3, 3]) + .to(device=env.DEVICE) + ) + coord = [ + 2.978060152121375648e00, + 3.588469695887098077e00, + 2.792459820604495491e00, + 3.895592322591093115e00, + 2.712091020667753760e00, + 1.366836847133650501e00, + 9.955616170888935690e-01, + 4.121324820711413039e00, + 1.817239061889086571e00, + 3.553661462345699906e00, + 5.313046969500791583e00, + 6.635182659098815883e00, + 6.088601018589653080e00, + 6.575011420004332585e00, + 6.825240650611076099e00, + ] + self.coord = ( + paddle.to_tensor(coord, dtype=env.GLOBAL_PD_FLOAT_PRECISION) + .reshape([1, -1, 3]) + .to(device=env.DEVICE) + ) + self.atype = ( + paddle.to_tensor([0, 0, 0, 1, 1], dtype=paddle.int32) + .reshape([1, -1]) + .to(device=env.DEVICE) + ) + self.ref_d = paddle.to_tensor( + [ + 8.435412613327306630e-01, + -4.717109614540972440e-01, + -1.812643456954206256e00, + -2.315248767961955167e-01, + -7.112973006771171613e-01, + -4.162041919507591392e-01, + -1.505159810095323181e00, + -1.191652416985768403e-01, + 8.439214937875325617e-01, + -4.712976890460106594e-01, + -1.812605149396642856e00, + -2.307222236291133766e-01, + -7.115427800870099961e-01, + -4.164729253167227530e-01, + -1.505483119125936797e00, + -1.191288524278367872e-01, + 8.286420823261241297e-01, + -4.535033763979030574e-01, + -1.787877160970498425e00, + -1.961763875645104460e-01, + -7.475459187804838201e-01, + -5.231446874663764346e-01, + -1.488399984491664219e00, + -3.974117581747104583e-02, + 8.283793431613817315e-01, + -4.551551577556525729e-01, + -1.789253136645859943e00, + -1.977673627726055372e-01, + -7.448826048241211639e-01, + -5.161350182531234676e-01, + -1.487589463573479209e00, + -4.377376017839779143e-02, + 8.295404560710329944e-01, + -4.492219258475603216e-01, + -1.784484611185287450e00, + -1.901182059718481143e-01, + -7.537407667483000395e-01, + -5.384371277650709109e-01, + -1.490368056268364549e00, + -3.073744832541754762e-02, + ], + dtype=env.GLOBAL_PD_FLOAT_PRECISION, + ).to(device=env.DEVICE) + self.file_model_param = Path(CUR_DIR) / "models" / "dpa2.pd" + self.file_type_embed = Path(CUR_DIR) / "models" / "dpa2_tebd.pd" + + def test_descriptor(self): + with open(Path(CUR_DIR) / "models" / "dpa2.json") as fp: + self.model_json = json.load(fp) + model_dpa2 = self.model_json + ntypes = len(model_dpa2["type_map"]) + dparams = model_dpa2["descriptor"] + dparams["ntypes"] = ntypes + assert dparams.pop("type") == "dpa2" + dparams["concat_output_tebd"] = False + dparams["use_tebd_bias"] = True + des = DescrptDPA2( + **dparams, + ).to(env.DEVICE) + target_dict = des.state_dict() + source_dict = paddle.load(str(self.file_model_param)) + # type_embd of repformer is removed + source_dict.pop("type_embedding.embedding.embedding_net.layers.0.bias") + type_embd_dict = paddle.load(str(self.file_type_embed)) + target_dict = translate_type_embd_dicts_to_dpa2( + target_dict, + source_dict, + type_embd_dict, + ) + des.set_state_dict(target_dict) + + coord = self.coord + atype = self.atype + box = self.cell + ( + extended_coord, + extended_atype, + mapping, + nlist, + ) = extend_input_and_build_neighbor_list( + coord, + atype, + des.get_rcut(), + des.get_sel(), + mixed_types=des.mixed_types(), + box=box, + ) + descriptor, env_mat, diff, rot_mat, sw = des( + extended_coord, + extended_atype, + nlist, + mapping=mapping, + ) + self.assertEqual(descriptor.shape[-1], des.get_dim_out()) + self.assertAlmostEqual(6.0, des.get_rcut()) + self.assertEqual(30, des.get_nsel()) + self.assertEqual(2, des.get_ntypes()) + assert paddle.allclose( + descriptor.reshape([-1]), self.ref_d, atol=1e-10, rtol=1e-10 + ) + + dparams["concat_output_tebd"] = True + des = DescrptDPA2( + **dparams, + ).to(env.DEVICE) + descriptor, env_mat, diff, rot_mat, sw = des( + extended_coord, + extended_atype, + nlist, + mapping=mapping, + ) + self.assertEqual(descriptor.shape[-1], des.get_dim_out()) + + +def translate_type_embd_dicts_to_dpa2( + target_dict, + source_dict, + type_embd_dict, +): + all_keys = list(target_dict.keys()) + record = [False for ii in all_keys] + for kk, vv in source_dict.items(): + record[all_keys.index(kk)] = True + target_dict[kk] = vv + assert len(type_embd_dict.keys()) == 2 + it = iter(type_embd_dict.keys()) + for _ in range(2): + kk = next(it) + tk = "type_embedding." + kk + record[all_keys.index(tk)] = True + target_dict[tk] = type_embd_dict[kk] + assert all(record) + return target_dict diff --git a/source/tests/pd/model/test_descriptor_hybrid.py b/source/tests/pd/model/test_descriptor_hybrid.py new file mode 100644 index 0000000000..5356a9553f --- /dev/null +++ b/source/tests/pd/model/test_descriptor_hybrid.py @@ -0,0 +1,125 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import unittest + +import numpy as np +import paddle + +from deepmd.pd.model.descriptor.dpa1 import ( + DescrptDPA1, +) +from deepmd.pd.model.descriptor.hybrid import ( + DescrptHybrid, +) +from deepmd.pd.model.descriptor.se_a import ( + DescrptSeA, +) +from deepmd.pd.model.descriptor.se_r import ( + DescrptSeR, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.utils import ( + to_paddle_tensor, +) + +from .test_env_mat import ( + TestCaseSingleFrameWithNlist, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION + + +class TestDescrptHybrid(unittest.TestCase, TestCaseSingleFrameWithNlist): + def setUp(self): + TestCaseSingleFrameWithNlist.setUp(self) + + def test_jit( + self, + ): + ddsub0 = DescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + ) + ddsub1 = DescrptSeR( + self.rcut, + self.rcut_smth, + self.sel, + ) + dd0 = DescrptHybrid(list=[ddsub0, ddsub1]) + dd1 = DescrptHybrid.deserialize(dd0.serialize()) + dd0 = paddle.jit.to_static(dd0) + dd1 = paddle.jit.to_static(dd1) + + def test_get_parameters( + self, + ): + nf, nloc, nnei = self.nlist.shape + ddsub0 = DescrptSeA( + rcut=self.rcut, + rcut_smth=self.rcut_smth, + sel=self.sel, + ) + ddsub1 = DescrptDPA1( + rcut=self.rcut, + rcut_smth=self.rcut_smth, + sel=np.sum(self.sel).item() - 1, + ntypes=len(self.sel), + ) + ddsub2 = DescrptSeR( + rcut=self.rcut / 2, + rcut_smth=self.rcut_smth - 0.1, + sel=[3, 1], + ) + em0 = DescrptHybrid(list=[ddsub0, ddsub1, ddsub2]) + self.assertAlmostEqual(em0.get_env_protection(), 0.0) + self.assertAlmostEqual(em0.get_rcut_smth(), self.rcut_smth - 0.1) + ddsub3 = DescrptSeR( + rcut=self.rcut / 2, + rcut_smth=self.rcut_smth - 0.1, + sel=[3, 1], + env_protection=0.1, + ) + em0 = DescrptHybrid(list=[ddsub0, ddsub1, ddsub3]) + with self.assertRaises(ValueError): + self.assertAlmostEqual(em0.get_env_protection(), 0.0) + + def test_hybrid_mixed_and_no_mixed(self): + coord_ext = to_paddle_tensor(self.coord_ext) + atype_ext = to_paddle_tensor(self.atype_ext) + nlist1 = to_paddle_tensor(self.nlist) + nlist2 = to_paddle_tensor(-np.sort(-self.nlist, axis=-1)) + ddsub0 = DescrptSeA( + rcut=self.rcut, + rcut_smth=self.rcut_smth, + sel=self.sel, + ) + ddsub1 = DescrptDPA1( + rcut=self.rcut, + rcut_smth=self.rcut_smth, + sel=np.sum(self.sel).item() - 1, + ntypes=len(self.sel), + ) + ddsub2 = DescrptSeR( + rcut=self.rcut / 2, + rcut_smth=self.rcut_smth, + sel=[3, 1], + ) + dd = DescrptHybrid(list=[ddsub0, ddsub1, ddsub2]) + ret = dd( + coord_ext, + atype_ext, + nlist2, + ) + ret0 = ddsub0( + coord_ext, + atype_ext, + nlist1, + ) + ret1 = ddsub1(coord_ext, atype_ext, nlist2[:, :, :-1]) + ret2 = ddsub2(coord_ext, atype_ext, nlist1[:, :, [0, 1, 2, self.sel[0]]]) + assert paddle.allclose( + ret[0], + paddle.concat([ret0[0], ret1[0], ret2[0]], axis=2), + ) diff --git a/source/tests/pd/model/test_descriptor_se_r.py b/source/tests/pd/model/test_descriptor_se_r.py new file mode 100644 index 0000000000..02a6199de9 --- /dev/null +++ b/source/tests/pd/model/test_descriptor_se_r.py @@ -0,0 +1,188 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import itertools +import unittest + +import numpy as np +import paddle + +from deepmd.dpmodel.descriptor import DescrptSeR as DPDescrptSeR +from deepmd.pd.model.descriptor.se_r import ( + DescrptSeR, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + PRECISION_DICT, +) +from deepmd.pd.utils.env_mat_stat import ( + EnvMatStatSe, +) + +from ...seed import ( + GLOBAL_SEED, +) +from .test_env_mat import ( + TestCaseSingleFrameWithNlist, +) +from .test_mlp import ( + get_tols, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION + + +# to be merged with the tf test case +class TestDescrptSeR(unittest.TestCase, TestCaseSingleFrameWithNlist): + def setUp(self): + TestCaseSingleFrameWithNlist.setUp(self) + + def test_consistency( + self, + ): + rng = np.random.default_rng(GLOBAL_SEED) + _, _, nnei = self.nlist.shape + davg = rng.normal(size=(self.nt, nnei, 1)) + dstd = rng.normal(size=(self.nt, nnei, 1)) + dstd = 0.1 + np.abs(dstd) + + for idt, prec, em in itertools.product( + [False, True], + ["float64", "float32"], + [[], [[0, 1]], [[1, 1]]], + ): + dtype = PRECISION_DICT[prec] + rtol, atol = get_tols(prec) + err_msg = f"idt={idt} prec={prec}" + # sea new impl + dd0 = DescrptSeR( + self.rcut, + self.rcut_smth, + self.sel, + precision=prec, + resnet_dt=idt, + exclude_mask=em, + seed=GLOBAL_SEED, + ).to(env.DEVICE) + dd0.mean = paddle.to_tensor(davg, dtype=dtype).to(device=env.DEVICE) + dd0.dstd = paddle.to_tensor(dstd, dtype=dtype).to(device=env.DEVICE) + + rd0, _, _, _, _ = dd0( + paddle.to_tensor(self.coord_ext, dtype=dtype).to(device=env.DEVICE), + paddle.to_tensor(self.atype_ext, dtype="int64").to(device=env.DEVICE), + paddle.to_tensor(self.nlist, dtype="int64").to(device=env.DEVICE), + ) + # serialization + dd1 = DescrptSeR.deserialize(dd0.serialize()) + rd1, _, _, _, sw1 = dd1( + paddle.to_tensor(self.coord_ext, dtype=dtype).to(device=env.DEVICE), + paddle.to_tensor(self.atype_ext, dtype="int64").to(device=env.DEVICE), + paddle.to_tensor(self.nlist, dtype="int64").to(device=env.DEVICE), + ) + np.testing.assert_allclose( + rd0.detach().cpu().numpy(), + rd1.detach().cpu().numpy(), + rtol=rtol, + atol=atol, + err_msg=err_msg, + ) + np.testing.assert_allclose( + rd0.detach().cpu().numpy()[0][self.perm[: self.nloc]], + rd0.detach().cpu().numpy()[1], + rtol=rtol, + atol=atol, + err_msg=err_msg, + ) + # dp impl + dd2 = DPDescrptSeR.deserialize(dd0.serialize()) + rd2, _, _, _, sw2 = dd2.call( + self.coord_ext, + self.atype_ext, + self.nlist, + ) + for aa, bb in zip([rd1, sw1], [rd2, sw2]): + np.testing.assert_allclose( + aa.detach().cpu().numpy(), + bb, + rtol=rtol, + atol=atol, + err_msg=err_msg, + ) + + def test_load_stat(self): + rng = np.random.default_rng(GLOBAL_SEED) + _, _, nnei = self.nlist.shape + davg = rng.normal(size=(self.nt, nnei, 1)) + dstd = rng.normal(size=(self.nt, nnei, 1)) + dstd = 0.1 + np.abs(dstd) + + for idt, prec in itertools.product( + [False, True], + ["float64", "float32"], + ): + dtype = PRECISION_DICT[prec] + + # sea new impl + dd0 = DescrptSeR( + self.rcut, + self.rcut_smth, + self.sel, + precision=prec, + resnet_dt=idt, + seed=GLOBAL_SEED, + ) + dd0.mean = paddle.to_tensor(davg, dtype=dtype).to(device=env.DEVICE) + dd0.dstd = paddle.to_tensor(dstd, dtype=dtype).to(device=env.DEVICE) + dd1 = DescrptSeR.deserialize(dd0.serialize()) + dd1.compute_input_stats( + [ + { + "r0": None, + "coord": paddle.to_tensor(self.coord_ext) + .reshape([-1, self.nall, 3]) + .to(env.DEVICE), + "atype": paddle.to_tensor(self.atype_ext).to(env.DEVICE), + "box": None, + "natoms": self.nall, + } + ] + ) + + with self.assertRaises(ValueError) as cm: + ev = EnvMatStatSe(dd1) + ev.last_dim = 3 + ev.load_or_compute_stats([]) + self.assertEqual( + "last_dim should be 1 for raial-only or 4 for full descriptor.", + str(cm.exception), + ) + + def test_jit( + self, + ): + rng = np.random.default_rng(GLOBAL_SEED) + _, _, nnei = self.nlist.shape + davg = rng.normal(size=(self.nt, nnei, 1)) + dstd = rng.normal(size=(self.nt, nnei, 1)) + dstd = 0.1 + np.abs(dstd) + + for idt, prec in itertools.product( + [False, True], + ["float64", "float32"], + ): + dtype = PRECISION_DICT[prec] + + # sea new impl + dd0 = DescrptSeR( + self.rcut, + self.rcut_smth, + self.sel, + precision=prec, + resnet_dt=idt, + seed=GLOBAL_SEED, + ) + dd0.mean = paddle.to_tensor(davg, dtype=dtype).to(device=env.DEVICE) + dd0.dstd = paddle.to_tensor(dstd, dtype=dtype).to(device=env.DEVICE) + dd1 = DescrptSeR.deserialize(dd0.serialize()) + paddle.jit.to_static(dd0) + paddle.jit.to_static(dd1) diff --git a/source/tests/pd/model/test_dipole_fitting.py b/source/tests/pd/model/test_dipole_fitting.py new file mode 100644 index 0000000000..37e257de0d --- /dev/null +++ b/source/tests/pd/model/test_dipole_fitting.py @@ -0,0 +1,398 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import itertools +import os +import unittest + +import numpy as np +import paddle +from scipy.stats import ( + special_ortho_group, +) + +from deepmd.dpmodel.fitting import DipoleFitting as DPDipoleFitting +from deepmd.infer.deep_dipole import ( + DeepDipole, +) +from deepmd.pd.model.descriptor.se_a import ( + DescrptSeA, +) +from deepmd.pd.model.model.dipole_model import ( + DipoleModel, +) +from deepmd.pd.model.task.dipole import ( + DipoleFittingNet, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.nlist import ( + extend_input_and_build_neighbor_list, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, + to_paddle_tensor, +) + +from ...seed import ( + GLOBAL_SEED, +) +from .test_env_mat import ( + TestCaseSingleFrameWithNlist, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION + + +def finite_difference(f, x, a, delta=1e-6): + in_shape = x.shape + y0 = f(x, a) + out_shape = y0.shape + res = np.empty(out_shape + in_shape) + for idx in np.ndindex(*in_shape): + diff = np.zeros(in_shape) + diff[idx] += delta + y1p = f(x + diff, a) + y1n = f(x - diff, a) + res[(Ellipsis, *idx)] = (y1p - y1n) / (2 * delta) + return res + + +class TestDipoleFitting(unittest.TestCase, TestCaseSingleFrameWithNlist): + def setUp(self): + TestCaseSingleFrameWithNlist.setUp(self) + self.rng = np.random.default_rng(GLOBAL_SEED) + self.nf, self.nloc, _ = self.nlist.shape + self.dd0 = DescrptSeA(self.rcut, self.rcut_smth, self.sel).to(env.DEVICE) + + def test_consistency( + self, + ): + rd0, gr, _, _, _ = self.dd0( + paddle.to_tensor(self.coord_ext, dtype=dtype).to(device=env.DEVICE), + paddle.to_tensor(self.atype_ext, dtype="int64").to(device=env.DEVICE), + paddle.to_tensor(self.nlist, dtype="int64").to(device=env.DEVICE), + ) + atype = paddle.to_tensor(self.atype_ext[:, : self.nloc], dtype="int64").to( + device=env.DEVICE + ) + + for nfp, nap in itertools.product( + [0, 3], + [0, 4], + ): + ft0 = DipoleFittingNet( + self.nt, + self.dd0.dim_out, + embedding_width=self.dd0.get_dim_emb(), + numb_fparam=nfp, + numb_aparam=nap, + mixed_types=self.dd0.mixed_types(), + ).to(env.DEVICE) + ft1 = DPDipoleFitting.deserialize(ft0.serialize()) + ft2 = DipoleFittingNet.deserialize(ft1.serialize()) + + if nfp > 0: + ifp = paddle.to_tensor( + self.rng.normal(size=(self.nf, nfp)), dtype=dtype + ).to(device=env.DEVICE) + else: + ifp = None + if nap > 0: + iap = paddle.to_tensor( + self.rng.normal(size=(self.nf, self.nloc, nap)), + dtype=dtype, + ).to(device=env.DEVICE) + else: + iap = None + + ret0 = ft0(rd0, atype, gr, fparam=ifp, aparam=iap) + ret1 = ft1( + rd0.detach().cpu().numpy(), + atype.detach().cpu().numpy(), + gr.detach().cpu().numpy(), + fparam=to_numpy_array(ifp), + aparam=to_numpy_array(iap), + ) + ret2 = ft2(rd0, atype, gr, fparam=ifp, aparam=iap) + np.testing.assert_allclose( + to_numpy_array(ret0["dipole"]), + ret1["dipole"], + ) + np.testing.assert_allclose( + to_numpy_array(ret0["dipole"]), + to_numpy_array(ret2["dipole"]), + ) + + def test_jit( + self, + ): + for mixed_types, nfp, nap in itertools.product( + [True, False], + [0, 3], + [0, 4], + ): + ft0 = DipoleFittingNet( + self.nt, + self.dd0.dim_out, + embedding_width=self.dd0.get_dim_emb(), + numb_fparam=nfp, + numb_aparam=nap, + mixed_types=mixed_types, + ).to(env.DEVICE) + paddle.jit.to_static(ft0) + + +class TestEquivalence(unittest.TestCase): + def setUp(self) -> None: + self.natoms = 5 + self.rcut = 4 + self.rcut_smth = 0.5 + self.sel = [46, 92, 4] + self.nf = 1 + generator = paddle.seed(GLOBAL_SEED) + self.coord = 2 * paddle.rand([self.natoms, 3], dtype=dtype).to( + device=env.DEVICE + ) + self.shift = paddle.to_tensor([4, 4, 4], dtype=dtype).to(device=env.DEVICE) + self.atype = paddle.to_tensor([0, 0, 0, 1, 1], dtype=paddle.int32).to( + device=env.DEVICE + ) + self.dd0 = DescrptSeA(self.rcut, self.rcut_smth, self.sel).to(env.DEVICE) + self.cell = paddle.rand([3, 3], dtype=dtype).to(device=env.DEVICE) + self.cell = (self.cell + self.cell.T) + 5.0 * paddle.eye(3).to( + device=env.DEVICE + ) + + def test_rot(self): + atype = self.atype.reshape([1, 5]) + rmat = paddle.to_tensor(special_ortho_group.rvs(3), dtype=dtype).to( + device=env.DEVICE + ) + coord_rot = paddle.matmul(self.coord, rmat) + # use larger cell to rotate only coord and shift to the center of cell + cell_rot = 10.0 * paddle.eye(3, dtype=dtype).to(device=env.DEVICE) + rng = np.random.default_rng(GLOBAL_SEED) + for nfp, nap in itertools.product( + [0, 3], + [0, 4], + ): + ft0 = DipoleFittingNet( + 3, # ntype + self.dd0.dim_out, # dim_descrpt + embedding_width=self.dd0.get_dim_emb(), + numb_fparam=nfp, + numb_aparam=nap, + mixed_types=self.dd0.mixed_types(), + ).to(env.DEVICE) + if nfp > 0: + ifp = paddle.to_tensor(rng.normal(size=(self.nf, nfp)), dtype=dtype).to( + device=env.DEVICE + ) + else: + ifp = None + if nap > 0: + iap = paddle.to_tensor( + rng.normal(size=(self.nf, self.natoms, nap)), + dtype=dtype, + ).to(device=env.DEVICE) + else: + iap = None + + res = [] + for xyz in [self.coord, coord_rot]: + ( + extended_coord, + extended_atype, + _, + nlist, + ) = extend_input_and_build_neighbor_list( + xyz + self.shift, + atype, + self.rcut, + self.sel, + self.dd0.mixed_types(), + box=cell_rot, + ) + + rd0, gr0, _, _, _ = self.dd0( + extended_coord, + extended_atype, + nlist, + ) + + ret0 = ft0(rd0, atype, gr0, fparam=ifp, aparam=iap) + res.append(ret0["dipole"]) + + np.testing.assert_allclose( + to_numpy_array(res[1]), to_numpy_array(paddle.matmul(res[0], rmat)) + ) + + def test_permu(self): + coord = paddle.matmul(self.coord, self.cell) + ft0 = DipoleFittingNet( + 3, # ntype + self.dd0.dim_out, + embedding_width=self.dd0.get_dim_emb(), + numb_fparam=0, + numb_aparam=0, + mixed_types=self.dd0.mixed_types(), + ).to(env.DEVICE) + res = [] + for idx_perm in [[0, 1, 2, 3, 4], [1, 0, 4, 3, 2]]: + atype = self.atype[idx_perm].reshape([1, 5]) + ( + extended_coord, + extended_atype, + _, + nlist, + ) = extend_input_and_build_neighbor_list( + coord[idx_perm], + atype, + self.rcut, + self.sel, + self.dd0.mixed_types(), + box=self.cell, + ) + + rd0, gr0, _, _, _ = self.dd0( + extended_coord, + extended_atype, + nlist, + ) + + ret0 = ft0(rd0, atype, gr0, fparam=0, aparam=0) + res.append(ret0["dipole"]) + + np.testing.assert_allclose( + to_numpy_array(res[0][:, idx_perm]), to_numpy_array(res[1]) + ) + + def test_trans(self): + atype = self.atype.reshape([1, 5]) + coord_s = paddle.matmul( + paddle.remainder( + paddle.matmul(self.coord + self.shift, paddle.linalg.inv(self.cell)), + paddle.full([], 1.0), + ), + self.cell, + ) + ft0 = DipoleFittingNet( + 3, # ntype + self.dd0.dim_out, + embedding_width=self.dd0.get_dim_emb(), + numb_fparam=0, + numb_aparam=0, + mixed_types=self.dd0.mixed_types(), + ).to(env.DEVICE) + res = [] + for xyz in [self.coord, coord_s]: + ( + extended_coord, + extended_atype, + _, + nlist, + ) = extend_input_and_build_neighbor_list( + xyz, atype, self.rcut, self.sel, self.dd0.mixed_types(), box=self.cell + ) + + rd0, gr0, _, _, _ = self.dd0( + extended_coord, + extended_atype, + nlist, + ) + + ret0 = ft0(rd0, atype, gr0, fparam=0, aparam=0) + res.append(ret0["dipole"]) + + np.testing.assert_allclose(to_numpy_array(res[0]), to_numpy_array(res[1])) + + +class TestDipoleModel(unittest.TestCase): + def setUp(self): + self.natoms = 5 + self.rcut = 4.0 + self.nt = 3 + self.rcut_smth = 0.5 + self.sel = [46, 92, 4] + self.nf = 1 + generator = paddle.seed(GLOBAL_SEED) + self.coord = 2 * paddle.rand([self.natoms, 3], dtype=dtype).to( + device=env.DEVICE + ) + cell = paddle.rand([3, 3], dtype=dtype).to(device=env.DEVICE) + self.cell = (cell + cell.T) + 5.0 * paddle.eye(3).to(device=env.DEVICE) + self.atype = paddle.to_tensor([0, 0, 0, 1, 1], place="cpu").to(env.DEVICE) + self.dd0 = DescrptSeA(self.rcut, self.rcut_smth, self.sel).to(env.DEVICE) + self.ft0 = DipoleFittingNet( + self.nt, + self.dd0.dim_out, + embedding_width=self.dd0.get_dim_emb(), + numb_fparam=0, + numb_aparam=0, + mixed_types=self.dd0.mixed_types(), + ).to(env.DEVICE) + self.type_mapping = ["O", "H", "B"] + self.model = DipoleModel(self.dd0, self.ft0, self.type_mapping) + self.file_path = "model_output.pd" + + def test_auto_diff(self): + places = 5 + delta = 1e-5 + atype = self.atype.reshape([self.nf, self.natoms]) + + def ff(coord, atype): + return ( + self.model(to_paddle_tensor(coord), to_paddle_tensor(atype))[ + "global_dipole" + ] + .detach() + .cpu() + .numpy() + ) + + fdf = -finite_difference( + ff, to_numpy_array(self.coord), to_numpy_array(atype), delta=delta + ) + rff = self.model(self.coord, atype)["force"].detach().cpu().numpy() + + np.testing.assert_almost_equal(fdf, rff.transpose([0, 2, 1, 3]), decimal=places) + + @unittest.skip("Call method with inference model is not supported in paddle") + def test_deepdipole_infer(self): + atype = to_numpy_array(self.atype.reshape([self.nf, self.natoms])) + coord = to_numpy_array(self.coord.reshape([1, 5, 3])) + cell = to_numpy_array(self.cell.reshape([1, 9])) + paddle.set_flags( + { + "FLAGS_save_cf_stack_op": 1, + "FLAGS_prim_enable_dynamic": 1, + "FLAGS_enable_pir_api": 1, + } + ) + from paddle.static import ( + InputSpec, + ) + + jit_md = paddle.jit.to_static( + self.model, + full_graph=True, + input_spec=[ + InputSpec([-1, -1, 3], dtype="float64", name="coord"), + InputSpec([-1, -1], dtype="int32", name="atype"), + InputSpec([-1, -1, -1], dtype="int32", name="cell"), + ], + ) + paddle.jit.save(jit_md, self.file_path) + load_md = DeepDipole(self.file_path) + load_md.eval(coords=coord, atom_types=atype, cells=cell, atomic=True) + load_md.eval(coords=coord, atom_types=atype, cells=cell, atomic=False) + load_md.eval_full(coords=coord, atom_types=atype, cells=cell, atomic=True) + load_md.eval_full(coords=coord, atom_types=atype, cells=cell, atomic=False) + + def tearDown(self) -> None: + if os.path.exists(self.file_path): + os.remove(self.file_path) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/model/test_dp_atomic_model.py b/source/tests/pd/model/test_dp_atomic_model.py new file mode 100644 index 0000000000..785bfa1076 --- /dev/null +++ b/source/tests/pd/model/test_dp_atomic_model.py @@ -0,0 +1,235 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import itertools +import unittest + +import numpy as np +import paddle + +from deepmd.dpmodel.atomic_model import DPAtomicModel as DPDPAtomicModel +from deepmd.dpmodel.descriptor import DescrptSeA as DPDescrptSeA +from deepmd.dpmodel.fitting import InvarFitting as DPInvarFitting +from deepmd.pd.model.atomic_model import ( + DPAtomicModel, +) +from deepmd.pd.model.descriptor.se_a import ( + DescrptSeA, +) +from deepmd.pd.model.task.ener import ( + InvarFitting, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, + to_paddle_tensor, +) + +from .test_env_mat import ( + TestCaseSingleFrameWithNlist, + TestCaseSingleFrameWithNlistWithVirtual, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION + + +class TestDPAtomicModel(unittest.TestCase, TestCaseSingleFrameWithNlist): + def setUp(self): + TestCaseSingleFrameWithNlist.setUp(self) + + def test_self_consistency(self): + nf, nloc, nnei = self.nlist.shape + ds = DescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + ).to(env.DEVICE) + ft = InvarFitting( + "energy", + self.nt, + ds.get_dim_out(), + 1, + mixed_types=ds.mixed_types(), + ).to(env.DEVICE) + type_map = ["foo", "bar"] + + # test the case of exclusion + for atom_excl, pair_excl in itertools.product([[], [1]], [[], [[0, 1]]]): + md0 = DPAtomicModel( + ds, + ft, + type_map=type_map, + ).to(env.DEVICE) + md0.reinit_atom_exclude(atom_excl) + md0.reinit_pair_exclude(pair_excl) + md1 = DPAtomicModel.deserialize(md0.serialize()).to(env.DEVICE) + args = [ + to_paddle_tensor(ii) + for ii in [self.coord_ext, self.atype_ext, self.nlist] + ] + ret0 = md0.forward_common_atomic(*args) + ret1 = md1.forward_common_atomic(*args) + np.testing.assert_allclose( + to_numpy_array(ret0["energy"]), + to_numpy_array(ret1["energy"]), + ) + + def test_dp_consistency(self): + nf, nloc, nnei = self.nlist.shape + ds = DPDescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + ) + ft = DPInvarFitting( + "energy", + self.nt, + ds.get_dim_out(), + 1, + mixed_types=ds.mixed_types(), + ) + type_map = ["foo", "bar"] + md0 = DPDPAtomicModel(ds, ft, type_map=type_map) + md1 = DPAtomicModel.deserialize(md0.serialize()).to(env.DEVICE) + args0 = [self.coord_ext, self.atype_ext, self.nlist] + args1 = [ + to_paddle_tensor(ii) for ii in [self.coord_ext, self.atype_ext, self.nlist] + ] + ret0 = md0.forward_common_atomic(*args0) + ret1 = md1.forward_common_atomic(*args1) + np.testing.assert_allclose( + ret0["energy"], + to_numpy_array(ret1["energy"]), + ) + + def test_jit(self): + nf, nloc, nnei = self.nlist.shape + ds = DescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + ).to(env.DEVICE) + ft = InvarFitting( + "energy", + self.nt, + ds.get_dim_out(), + 1, + mixed_types=ds.mixed_types(), + ).to(env.DEVICE) + type_map = ["foo", "bar"] + md0 = DPAtomicModel(ds, ft, type_map=type_map).to(env.DEVICE) + md0 = paddle.jit.to_static(md0) + self.assertEqual(md0.get_rcut(), self.rcut) + self.assertEqual(md0.get_type_map(), type_map) + + def test_excl_consistency(self): + type_map = ["foo", "bar"] + + # test the case of exclusion + for atom_excl, pair_excl in itertools.product([[], [1]], [[], [[0, 1]]]): + ds = DescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + ).to(env.DEVICE) + ft = InvarFitting( + "energy", + self.nt, + ds.get_dim_out(), + 1, + mixed_types=ds.mixed_types(), + ).to(env.DEVICE) + md0 = DPAtomicModel( + ds, + ft, + type_map=type_map, + ).to(env.DEVICE) + md1 = DPAtomicModel.deserialize(md0.serialize()).to(env.DEVICE) + + md0.reinit_atom_exclude(atom_excl) + md0.reinit_pair_exclude(pair_excl) + # hacking! + md1.descriptor.reinit_exclude(pair_excl) + md1.fitting_net.reinit_exclude(atom_excl) + + # check energy consistency + args = [ + to_paddle_tensor(ii) + for ii in [self.coord_ext, self.atype_ext, self.nlist] + ] + ret0 = md0.forward_common_atomic(*args) + ret1 = md1.forward_common_atomic(*args) + np.testing.assert_allclose( + to_numpy_array(ret0["energy"]), + to_numpy_array(ret1["energy"]), + ) + + # check output def + out_names = [vv.name for vv in md0.atomic_output_def().get_data().values()] + self.assertEqual(out_names, ["energy", "mask"]) + if atom_excl != []: + for ii in md0.atomic_output_def().get_data().values(): + if ii.name == "mask": + self.assertEqual(ii.shape, [1]) + self.assertFalse(ii.reducible) + self.assertFalse(ii.r_differentiable) + self.assertFalse(ii.c_differentiable) + + # check mask + if atom_excl == []: + pass + elif atom_excl == [1]: + self.assertIn("mask", ret0.keys()) + expected = np.array([1, 1, 0], dtype="int64") + expected = np.concatenate( + [expected, expected[self.perm[: self.nloc]]] + ).reshape(2, 3) + np.testing.assert_array_equal(to_numpy_array(ret0["mask"]), expected) + else: + raise ValueError(f"not expected atom_excl {atom_excl}") + + +class TestDPAtomicModelVirtualConsistency(unittest.TestCase): + def setUp(self): + self.case0 = TestCaseSingleFrameWithNlist() + self.case1 = TestCaseSingleFrameWithNlistWithVirtual() + self.case0.setUp() + self.case1.setUp() + + def test_virtual_consistency(self): + nf, _, _ = self.case0.nlist.shape + ds = DescrptSeA( + self.case0.rcut, + self.case0.rcut_smth, + self.case0.sel, + ) + ft = InvarFitting( + "energy", + self.case0.nt, + ds.get_dim_out(), + 1, + mixed_types=ds.mixed_types(), + ) + type_map = ["foo", "bar"] + md1 = DPAtomicModel(ds, ft, type_map=type_map).to(env.DEVICE) + + args0 = [self.case0.coord_ext, self.case0.atype_ext, self.case0.nlist] + args0 = [to_paddle_tensor(ii) for ii in args0] + args1 = [self.case1.coord_ext, self.case1.atype_ext, self.case1.nlist] + args1 = [to_paddle_tensor(ii) for ii in args1] + + ret0 = md1.forward_common_atomic(*args0) + ret1 = md1.forward_common_atomic(*args1) + + for dd in range(self.case0.nf): + np.testing.assert_allclose( + to_numpy_array(ret0["energy"])[dd], + to_numpy_array(ret1["energy"])[dd, self.case1.get_real_mapping[dd], :], + ) + expected_mask = np.array( + [ + [1, 0, 1, 1], + [1, 1, 0, 1], + ] + ) + np.testing.assert_equal(to_numpy_array(ret1["mask"]), expected_mask) diff --git a/source/tests/pd/model/test_dp_model.py b/source/tests/pd/model/test_dp_model.py new file mode 100644 index 0000000000..a281851f14 --- /dev/null +++ b/source/tests/pd/model/test_dp_model.py @@ -0,0 +1,633 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import unittest + +import numpy as np +import paddle + +from deepmd.dpmodel.descriptor import DescrptSeA as DPDescrptSeA +from deepmd.dpmodel.fitting import EnergyFittingNet as DPEnergyFittingNet +from deepmd.dpmodel.model.ener_model import EnergyModel as DPEnergyModel +from deepmd.pd.model.descriptor.se_a import ( + DescrptSeA, +) +from deepmd.pd.model.model import ( + EnergyModel, +) +from deepmd.pd.model.task.ener import ( + EnergyFittingNet, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.nlist import ( + build_neighbor_list, + extend_coord_with_ghosts, + extend_input_and_build_neighbor_list, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, + to_paddle_tensor, +) + +from ...seed import ( + GLOBAL_SEED, +) +from .test_env_mat import ( + TestCaseSingleFrameWithNlist, + TestCaseSingleFrameWithoutNlist, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION + + +class TestDPModel(unittest.TestCase, TestCaseSingleFrameWithoutNlist): + def setUp(self): + TestCaseSingleFrameWithoutNlist.setUp(self) + + def test_self_consistency(self): + nf, nloc = self.atype.shape + ds = DescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + ).to(env.DEVICE) + ft = EnergyFittingNet( + self.nt, + ds.get_dim_out(), + mixed_types=ds.mixed_types(), + ).to(env.DEVICE) + type_map = ["foo", "bar"] + md0 = EnergyModel(ds, ft, type_map=type_map).to(env.DEVICE) + md1 = EnergyModel.deserialize(md0.serialize()).to(env.DEVICE) + args = [to_paddle_tensor(ii) for ii in [self.coord, self.atype, self.cell]] + ret0 = md0.forward_common(*args) + ret1 = md1.forward_common(*args) + np.testing.assert_allclose( + to_numpy_array(ret0["energy"]), + to_numpy_array(ret1["energy"]), + atol=self.atol, + ) + np.testing.assert_allclose( + to_numpy_array(ret0["energy_redu"]), + to_numpy_array(ret1["energy_redu"]), + atol=self.atol, + ) + np.testing.assert_allclose( + to_numpy_array(ret0["energy_derv_r"]), + to_numpy_array(ret1["energy_derv_r"]), + atol=self.atol, + ) + np.testing.assert_allclose( + to_numpy_array(ret0["energy_derv_c_redu"]), + to_numpy_array(ret1["energy_derv_c_redu"]), + atol=self.atol, + ) + ret0 = md0.forward_common(*args, do_atomic_virial=True) + ret1 = md1.forward_common(*args, do_atomic_virial=True) + np.testing.assert_allclose( + to_numpy_array(ret0["energy_derv_c"]), + to_numpy_array(ret1["energy_derv_c"]), + atol=self.atol, + ) + + coord_ext, atype_ext, mapping = extend_coord_with_ghosts( + to_paddle_tensor(self.coord), + to_paddle_tensor(self.atype), + to_paddle_tensor(self.cell), + self.rcut, + ) + nlist = build_neighbor_list( + coord_ext, + atype_ext, + self.nloc, + self.rcut, + self.sel, + distinguish_types=(not md0.mixed_types()), + ) + args = [coord_ext, atype_ext, nlist] + ret2 = md0.forward_common_lower(*args, do_atomic_virial=True) + # check the consistency between the reduced virial from + # forward_common and forward_common_lower + np.testing.assert_allclose( + to_numpy_array(ret0["energy_derv_c_redu"]), + to_numpy_array(ret2["energy_derv_c_redu"]), + atol=self.atol, + ) + + def test_dp_consistency(self): + nf, nloc = self.atype.shape + nfp, nap = 2, 3 + ds = DPDescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + ) + ft = DPEnergyFittingNet( + self.nt, + ds.get_dim_out(), + mixed_types=ds.mixed_types(), + numb_fparam=nfp, + numb_aparam=nap, + ) + type_map = ["foo", "bar"] + md0 = DPEnergyModel(ds, ft, type_map=type_map) + md1 = EnergyModel.deserialize(md0.serialize()).to(env.DEVICE) + + rng = np.random.default_rng(GLOBAL_SEED) + fparam = rng.normal(size=[self.nf, nfp]) + aparam = rng.normal(size=[self.nf, nloc, nap]) + args0 = [self.coord, self.atype, self.cell] + args1 = [to_paddle_tensor(ii) for ii in [self.coord, self.atype, self.cell]] + kwargs0 = {"fparam": fparam, "aparam": aparam} + kwargs1 = {kk: to_paddle_tensor(vv) for kk, vv in kwargs0.items()} + ret0 = md0.call(*args0, **kwargs0) + ret1 = md1.forward_common(*args1, **kwargs1) + np.testing.assert_allclose( + ret0["energy"], + to_numpy_array(ret1["energy"]), + atol=self.atol, + ) + np.testing.assert_allclose( + ret0["energy_redu"], + to_numpy_array(ret1["energy_redu"]), + atol=self.atol, + ) + + def test_dp_consistency_nopbc(self): + nf, nloc = self.atype.shape + nfp, nap = 2, 3 + ds = DPDescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + ) + ft = DPEnergyFittingNet( + self.nt, + ds.get_dim_out(), + mixed_types=ds.mixed_types(), + numb_fparam=nfp, + numb_aparam=nap, + ) + type_map = ["foo", "bar"] + md0 = DPEnergyModel(ds, ft, type_map=type_map) + md1 = EnergyModel.deserialize(md0.serialize()).to(env.DEVICE) + + rng = np.random.default_rng(GLOBAL_SEED) + fparam = rng.normal(size=[self.nf, nfp]) + aparam = rng.normal(size=[self.nf, self.nloc, nap]) + args0 = [self.coord, self.atype] + args1 = [to_paddle_tensor(ii) for ii in args0] + kwargs0 = {"fparam": fparam, "aparam": aparam} + kwargs1 = {kk: to_paddle_tensor(vv) for kk, vv in kwargs0.items()} + ret0 = md0.call(*args0, **kwargs0) + ret1 = md1.forward_common(*args1, **kwargs1) + np.testing.assert_allclose( + ret0["energy"], + to_numpy_array(ret1["energy"]), + atol=self.atol, + ) + np.testing.assert_allclose( + ret0["energy_redu"], + to_numpy_array(ret1["energy_redu"]), + atol=self.atol, + ) + + def test_prec_consistency(self): + rng = np.random.default_rng(GLOBAL_SEED) + nf, nloc = self.atype.shape + ds = DPDescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + ) + ft = DPEnergyFittingNet( + self.nt, + ds.get_dim_out(), + mixed_types=ds.mixed_types(), + ) + nfp, nap = 2, 3 + type_map = ["foo", "bar"] + fparam = rng.normal(size=[self.nf, nfp]) + aparam = rng.normal(size=[self.nf, nloc, nap]) + + md0 = DPEnergyModel(ds, ft, type_map=type_map) + md1 = EnergyModel.deserialize(md0.serialize()).to(env.DEVICE) + + args64 = [to_paddle_tensor(ii) for ii in [self.coord, self.atype, self.cell]] + args64[0] = args64[0].to(paddle.float64) + args64[2] = args64[2].to(paddle.float64) + args32 = [to_paddle_tensor(ii) for ii in [self.coord, self.atype, self.cell]] + args32[0] = args32[0].to(paddle.float32) + args32[2] = args32[2].to(paddle.float32) + # fparam, aparam are converted to coordinate precision by model + fparam = to_paddle_tensor(fparam) + aparam = to_paddle_tensor(aparam) + + model_l_ret_64 = md1.forward_common(*args64, fparam=fparam, aparam=aparam) + model_l_ret_32 = md1.forward_common(*args32, fparam=fparam, aparam=aparam) + + for ii in model_l_ret_32.keys(): + if ii[-4:] == "redu": + self.assertEqual(model_l_ret_32[ii].dtype, paddle.float64) + else: + self.assertEqual(model_l_ret_32[ii].dtype, paddle.float32) + if ii != "mask": + self.assertEqual(model_l_ret_64[ii].dtype, paddle.float64) + else: + self.assertEqual(model_l_ret_64[ii].dtype, paddle.int32) + np.testing.assert_allclose( + to_numpy_array(model_l_ret_32[ii]), + to_numpy_array(model_l_ret_64[ii]), + atol=self.atol, + ) + + +class TestDPModelLower(unittest.TestCase, TestCaseSingleFrameWithNlist): + def setUp(self): + TestCaseSingleFrameWithNlist.setUp(self) + + def test_self_consistency(self): + nf, nloc, nnei = self.nlist.shape + ds = DescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + ).to(env.DEVICE) + ft = EnergyFittingNet( + self.nt, + ds.get_dim_out(), + mixed_types=ds.mixed_types(), + ).to(env.DEVICE) + type_map = ["foo", "bar"] + md0 = EnergyModel(ds, ft, type_map=type_map).to(env.DEVICE) + md1 = EnergyModel.deserialize(md0.serialize()).to(env.DEVICE) + args = [ + to_paddle_tensor(ii) for ii in [self.coord_ext, self.atype_ext, self.nlist] + ] + ret0 = md0.forward_common_lower(*args) + ret1 = md1.forward_common_lower(*args) + np.testing.assert_allclose( + to_numpy_array(ret0["energy"]), + to_numpy_array(ret1["energy"]), + atol=self.atol, + ) + np.testing.assert_allclose( + to_numpy_array(ret0["energy_redu"]), + to_numpy_array(ret1["energy_redu"]), + atol=self.atol, + ) + np.testing.assert_allclose( + to_numpy_array(ret0["energy_derv_r"]), + to_numpy_array(ret1["energy_derv_r"]), + atol=self.atol, + ) + np.testing.assert_allclose( + to_numpy_array(ret0["energy_derv_c_redu"]), + to_numpy_array(ret1["energy_derv_c_redu"]), + atol=self.atol, + ) + ret0 = md0.forward_common_lower(*args, do_atomic_virial=True) + ret1 = md1.forward_common_lower(*args, do_atomic_virial=True) + np.testing.assert_allclose( + to_numpy_array(ret0["energy_derv_c"]), + to_numpy_array(ret1["energy_derv_c"]), + atol=self.atol, + ) + + def test_dp_consistency(self): + nf, nloc, nnei = self.nlist.shape + ds = DPDescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + ) + ft = DPEnergyFittingNet( + self.nt, + ds.get_dim_out(), + mixed_types=ds.mixed_types(), + ) + type_map = ["foo", "bar"] + md0 = DPEnergyModel(ds, ft, type_map=type_map) + md1 = EnergyModel.deserialize(md0.serialize()).to(env.DEVICE) + args0 = [self.coord_ext, self.atype_ext, self.nlist] + args1 = [ + to_paddle_tensor(ii) for ii in [self.coord_ext, self.atype_ext, self.nlist] + ] + ret0 = md0.call_lower(*args0) + ret1 = md1.forward_common_lower(*args1) + np.testing.assert_allclose( + ret0["energy"], + to_numpy_array(ret1["energy"]), + atol=self.atol, + ) + np.testing.assert_allclose( + ret0["energy_redu"], + to_numpy_array(ret1["energy_redu"]), + atol=self.atol, + ) + + def test_prec_consistency(self): + rng = np.random.default_rng(GLOBAL_SEED) + nf, nloc, nnei = self.nlist.shape + ds = DPDescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + ) + ft = DPEnergyFittingNet( + self.nt, + ds.get_dim_out(), + mixed_types=ds.mixed_types(), + ) + nfp, nap = 2, 3 + type_map = ["foo", "bar"] + fparam = rng.normal(size=[self.nf, nfp]) + aparam = rng.normal(size=[self.nf, nloc, nap]) + + md0 = DPEnergyModel(ds, ft, type_map=type_map) + md1 = EnergyModel.deserialize(md0.serialize()).to(env.DEVICE) + + args64 = [ + to_paddle_tensor(ii) for ii in [self.coord_ext, self.atype_ext, self.nlist] + ] + args64[0] = args64[0].to(paddle.float64) + args32 = [ + to_paddle_tensor(ii) for ii in [self.coord_ext, self.atype_ext, self.nlist] + ] + args32[0] = args32[0].to(paddle.float32) + # fparam, aparam are converted to coordinate precision by model + fparam = to_paddle_tensor(fparam) + aparam = to_paddle_tensor(aparam) + + model_l_ret_64 = md1.forward_common_lower(*args64, fparam=fparam, aparam=aparam) + model_l_ret_32 = md1.forward_common_lower(*args32, fparam=fparam, aparam=aparam) + + for ii in model_l_ret_32.keys(): + if ii[-4:] == "redu": + self.assertEqual(model_l_ret_32[ii].dtype, paddle.float64) + else: + self.assertEqual(model_l_ret_32[ii].dtype, paddle.float32) + if ii != "mask": + self.assertEqual(model_l_ret_64[ii].dtype, paddle.float64) + else: + self.assertEqual(model_l_ret_64[ii].dtype, paddle.int32) + np.testing.assert_allclose( + to_numpy_array(model_l_ret_32[ii]), + to_numpy_array(model_l_ret_64[ii]), + atol=self.atol, + ) + + def test_jit(self): + nf, nloc, nnei = self.nlist.shape + ds = DescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + ).to(env.DEVICE) + ft = EnergyFittingNet( + self.nt, + ds.get_dim_out(), + mixed_types=ds.mixed_types(), + ).to(env.DEVICE) + type_map = ["foo", "bar"] + md0 = EnergyModel(ds, ft, type_map=type_map).to(env.DEVICE) + md0 = paddle.jit.to_static(md0) + md0.get_rcut() + md0.get_type_map() + + +class TestDPModelFormatNlist(unittest.TestCase): + def setUp(self): + # nloc == 3, nall == 4 + self.nloc = 3 + self.nall = 5 + self.nf, self.nt = 1, 2 + self.coord_ext = np.array( + [ + [0, 0, 0], + [0, 1, 0], + [0, 0, 1], + [0, -2, 0], + [2.3, 0, 0], + ], + dtype=np.float64, + ).reshape([1, self.nall * 3]) + # sel = [5, 2] + self.sel = [5, 2] + self.expected_nlist = np.array( + [ + [1, 3, -1, -1, -1, 2, -1], + [0, -1, -1, -1, -1, 2, -1], + [0, 1, -1, -1, -1, -1, -1], + ], + dtype="int64", + ).reshape([1, self.nloc, sum(self.sel)]) + self.atype_ext = np.array([0, 0, 1, 0, 1], dtype="int64").reshape( + [1, self.nall] + ) + self.rcut_smth = 0.4 + self.rcut = 2.0 + + nf, nloc, nnei = self.expected_nlist.shape + ds = DescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + ).to(env.DEVICE) + ft = EnergyFittingNet( + self.nt, + ds.get_dim_out(), + mixed_types=ds.mixed_types(), + ).to(env.DEVICE) + type_map = ["foo", "bar"] + self.md = EnergyModel(ds, ft, type_map=type_map).to(env.DEVICE) + + def test_nlist_eq(self): + # n_nnei == nnei + nlist = np.array( + [ + [1, 3, -1, -1, -1, 2, -1], + [0, -1, -1, -1, -1, 2, -1], + [0, 1, -1, -1, -1, -1, -1], + ], + dtype=np.int64, + ).reshape([1, self.nloc, -1]) + nlist1 = self.md.format_nlist( + to_paddle_tensor(self.coord_ext), + to_paddle_tensor(self.atype_ext), + to_paddle_tensor(nlist), + ) + np.testing.assert_equal(self.expected_nlist, to_numpy_array(nlist1)) + + def test_nlist_st(self): + # n_nnei < nnei + nlist = np.array( + [ + [1, 3, -1, 2], + [0, -1, -1, 2], + [0, 1, -1, -1], + ], + dtype=np.int64, + ).reshape([1, self.nloc, -1]) + nlist1 = self.md.format_nlist( + to_paddle_tensor(self.coord_ext), + to_paddle_tensor(self.atype_ext), + to_paddle_tensor(nlist), + ) + np.testing.assert_equal(self.expected_nlist, to_numpy_array(nlist1)) + + def test_nlist_lt(self): + # n_nnei > nnei + nlist = np.array( + [ + [1, 3, -1, -1, -1, 2, -1, -1, 4], + [0, -1, 4, -1, -1, 2, -1, 3, -1], + [0, 1, -1, -1, -1, 4, -1, -1, 3], + ], + dtype=np.int64, + ).reshape([1, self.nloc, -1]) + nlist1 = self.md.format_nlist( + to_paddle_tensor(self.coord_ext), + to_paddle_tensor(self.atype_ext), + to_paddle_tensor(nlist), + ) + np.testing.assert_equal(self.expected_nlist, to_numpy_array(nlist1)) + + +class TestEnergyModel(unittest.TestCase, TestCaseSingleFrameWithoutNlist): + def setUp(self): + TestCaseSingleFrameWithoutNlist.setUp(self) + + def test_self_consistency(self): + nf, nloc = self.atype.shape + ds = DescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + ).to(env.DEVICE) + ft = EnergyFittingNet( + self.nt, + ds.get_dim_out(), + mixed_types=ds.mixed_types(), + ).to(env.DEVICE) + type_map = ["foo", "bar"] + md0 = EnergyModel(ds, ft, type_map=type_map).to(env.DEVICE) + md1 = EnergyModel.deserialize(md0.serialize()).to(env.DEVICE) + args = [to_paddle_tensor(ii) for ii in [self.coord, self.atype, self.cell]] + ret0 = md0.forward(*args) + ret1 = md1.forward(*args) + np.testing.assert_allclose( + to_numpy_array(ret0["atom_energy"]), + to_numpy_array(ret1["atom_energy"]), + atol=self.atol, + ) + np.testing.assert_allclose( + to_numpy_array(ret0["energy"]), + to_numpy_array(ret1["energy"]), + atol=self.atol, + ) + np.testing.assert_allclose( + to_numpy_array(ret0["force"]), + to_numpy_array(ret1["force"]), + atol=self.atol, + ) + np.testing.assert_allclose( + to_numpy_array(ret0["virial"]), + to_numpy_array(ret1["virial"]), + atol=self.atol, + ) + ret0 = md0.forward(*args, do_atomic_virial=True) + ret1 = md1.forward(*args, do_atomic_virial=True) + np.testing.assert_allclose( + to_numpy_array(ret0["atom_virial"]), + to_numpy_array(ret1["atom_virial"]), + atol=self.atol, + ) + coord_ext, atype_ext, mapping, nlist = extend_input_and_build_neighbor_list( + to_paddle_tensor(self.coord), + to_paddle_tensor(self.atype), + self.rcut, + self.sel, + mixed_types=md0.mixed_types(), + box=to_paddle_tensor(self.cell), + ) + args = [coord_ext, atype_ext, nlist] + ret2 = md0.forward_lower(*args, do_atomic_virial=True) + # check the consistency between the reduced virial from + # forward and forward_lower + np.testing.assert_allclose( + to_numpy_array(ret0["virial"]), + to_numpy_array(ret2["virial"]), + atol=self.atol, + ) + + +class TestEnergyModelLower(unittest.TestCase, TestCaseSingleFrameWithNlist): + def setUp(self): + TestCaseSingleFrameWithNlist.setUp(self) + + def test_self_consistency(self): + nf, nloc, nnei = self.nlist.shape + ds = DescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + ).to(env.DEVICE) + ft = EnergyFittingNet( + self.nt, + ds.get_dim_out(), + mixed_types=ds.mixed_types(), + ).to(env.DEVICE) + type_map = ["foo", "bar"] + md0 = EnergyModel(ds, ft, type_map=type_map).to(env.DEVICE) + md1 = EnergyModel.deserialize(md0.serialize()).to(env.DEVICE) + args = [ + to_paddle_tensor(ii) for ii in [self.coord_ext, self.atype_ext, self.nlist] + ] + ret0 = md0.forward_lower(*args) + ret1 = md1.forward_lower(*args) + np.testing.assert_allclose( + to_numpy_array(ret0["atom_energy"]), + to_numpy_array(ret1["atom_energy"]), + atol=self.atol, + ) + np.testing.assert_allclose( + to_numpy_array(ret0["energy"]), + to_numpy_array(ret1["energy"]), + atol=self.atol, + ) + np.testing.assert_allclose( + to_numpy_array(ret0["extended_force"]), + to_numpy_array(ret1["extended_force"]), + atol=self.atol, + ) + np.testing.assert_allclose( + to_numpy_array(ret0["virial"]), + to_numpy_array(ret1["virial"]), + atol=self.atol, + ) + ret0 = md0.forward_lower(*args, do_atomic_virial=True) + ret1 = md1.forward_lower(*args, do_atomic_virial=True) + np.testing.assert_allclose( + to_numpy_array(ret0["extended_virial"]), + to_numpy_array(ret1["extended_virial"]), + atol=self.atol, + ) + + def test_jit(self): + nf, nloc, nnei = self.nlist.shape + ds = DescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + ).to(env.DEVICE) + ft = EnergyFittingNet( + self.nt, + ds.get_dim_out(), + mixed_types=ds.mixed_types(), + ).to(env.DEVICE) + type_map = ["foo", "bar"] + md0 = EnergyModel(ds, ft, type_map=type_map).to(env.DEVICE) + md0 = paddle.jit.to_static(md0) + self.assertEqual(md0.get_rcut(), self.rcut) + self.assertEqual(md0.get_type_map(), type_map) diff --git a/source/tests/pd/model/test_dpa1.py b/source/tests/pd/model/test_dpa1.py new file mode 100644 index 0000000000..285dd3d4cd --- /dev/null +++ b/source/tests/pd/model/test_dpa1.py @@ -0,0 +1,164 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import itertools +import unittest + +import numpy as np +import paddle + +from deepmd.dpmodel.descriptor.dpa1 import DescrptDPA1 as DPDescrptDPA1 +from deepmd.pd.model.descriptor.dpa1 import ( + DescrptDPA1, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + PRECISION_DICT, +) + +from ...seed import ( + GLOBAL_SEED, +) +from .test_env_mat import ( + TestCaseSingleFrameWithNlist, +) +from .test_mlp import ( + get_tols, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION + + +class TestDescrptSeAtten(unittest.TestCase, TestCaseSingleFrameWithNlist): + def setUp(self): + TestCaseSingleFrameWithNlist.setUp(self) + + def test_consistency( + self, + ): + rng = np.random.default_rng(100) + nf, nloc, nnei = self.nlist.shape + davg = rng.normal(size=(self.nt, nnei, 4)) + dstd = rng.normal(size=(self.nt, nnei, 4)) + dstd = 0.1 + np.abs(dstd) + + for idt, sm, to, tm, prec, ect in itertools.product( + [False, True], # resnet_dt + [False, True], # smooth_type_embedding + [False, True], # type_one_side + ["concat", "strip"], # tebd_input_mode + [ + "float64", + ], # precision + [False, True], # use_econf_tebd + ): + dtype = PRECISION_DICT[prec] + rtol, atol = get_tols(prec) + err_msg = f"idt={idt} prec={prec}" + + # dpa1 new impl + dd0 = DescrptDPA1( + self.rcut, + self.rcut_smth, + self.sel_mix, + self.nt, + attn_layer=2, + precision=prec, + resnet_dt=idt, + smooth_type_embedding=sm, + type_one_side=to, + tebd_input_mode=tm, + use_econf_tebd=ect, + type_map=["O", "H"] if ect else None, + seed=GLOBAL_SEED, + ).to(env.DEVICE) + dd0.se_atten.mean = paddle.to_tensor(davg, dtype=dtype).to( + device=env.DEVICE + ) + dd0.se_atten.stddev = paddle.to_tensor(dstd, dtype=dtype).to( + device=env.DEVICE + ) + rd0, _, _, _, _ = dd0( + paddle.to_tensor(self.coord_ext, dtype=dtype).to(device=env.DEVICE), + paddle.to_tensor(self.atype_ext, dtype="int64").to(device=env.DEVICE), + paddle.to_tensor(self.nlist, dtype="int64").to(device=env.DEVICE), + ) + # serialization + dd1 = DescrptDPA1.deserialize(dd0.serialize()) + rd1, _, _, _, _ = dd1( + paddle.to_tensor(self.coord_ext, dtype=dtype).to(device=env.DEVICE), + paddle.to_tensor(self.atype_ext, dtype="int64").to(device=env.DEVICE), + paddle.to_tensor(self.nlist, dtype="int64").to(device=env.DEVICE), + ) + np.testing.assert_allclose( + rd0.detach().cpu().numpy(), + rd1.detach().cpu().numpy(), + rtol=rtol, + atol=atol, + err_msg=err_msg, + ) + # dp impl + dd2 = DPDescrptDPA1.deserialize(dd0.serialize()) + rd2, _, _, _, _ = dd2.call( + self.coord_ext, + self.atype_ext, + self.nlist, + ) + np.testing.assert_allclose( + rd0.detach().cpu().numpy(), + rd2, + rtol=rtol, + atol=atol, + err_msg=err_msg, + ) + + def test_jit( + self, + ): + rng = np.random.default_rng(GLOBAL_SEED) + nf, nloc, nnei = self.nlist.shape + davg = rng.normal(size=(self.nt, nnei, 4)) + dstd = rng.normal(size=(self.nt, nnei, 4)) + dstd = 0.1 + np.abs(dstd) + + for idt, prec, sm, to, tm, ect in itertools.product( + [ + False, + ], # resnet_dt + [ + "float64", + ], # precision + [False, True], # smooth_type_embedding + [ + False, + ], # type_one_side + ["concat", "strip"], # tebd_input_mode + [False, True], # use_econf_tebd + ): + dtype = PRECISION_DICT[prec] + rtol, atol = get_tols(prec) + err_msg = f"idt={idt} prec={prec}" + # dpa1 new impl + dd0 = DescrptDPA1( + self.rcut, + self.rcut_smth, + self.sel, + self.nt, + precision=prec, + resnet_dt=idt, + smooth_type_embedding=sm, + type_one_side=to, + tebd_input_mode=tm, + use_econf_tebd=ect, + type_map=["O", "H"] if ect else None, + seed=GLOBAL_SEED, + ) + dd0.se_atten.mean = paddle.to_tensor(davg, dtype=dtype).to( + device=env.DEVICE + ) + dd0.se_atten.dstd = paddle.to_tensor(dstd, dtype=dtype).to( + device=env.DEVICE + ) + # dd1 = DescrptDPA1.deserialize(dd0.serialize()) + model = paddle.jit.to_static(dd0) + # model = paddle.jit.to_static(dd1) diff --git a/source/tests/pd/model/test_dpa2.py b/source/tests/pd/model/test_dpa2.py new file mode 100644 index 0000000000..68a5fe8f37 --- /dev/null +++ b/source/tests/pd/model/test_dpa2.py @@ -0,0 +1,332 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import itertools +import unittest + +import numpy as np +import paddle + +from deepmd.dpmodel.descriptor.dpa2 import DescrptDPA2 as DPDescrptDPA2 +from deepmd.dpmodel.descriptor.dpa2 import ( + RepformerArgs, + RepinitArgs, +) +from deepmd.pd.model.descriptor.dpa2 import ( + DescrptDPA2, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + PRECISION_DICT, +) + +from ...seed import ( + GLOBAL_SEED, +) +from .test_env_mat import ( + TestCaseSingleFrameWithNlist, +) +from .test_mlp import ( + get_tols, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION + + +class TestDescrptDPA2(unittest.TestCase, TestCaseSingleFrameWithNlist): + def setUp(self): + TestCaseSingleFrameWithNlist.setUp(self) + + def test_consistency( + self, + ): + rng = np.random.default_rng(100) + nf, nloc, nnei = self.nlist.shape + davg = rng.normal(size=(self.nt, nnei, 4)) + dstd = rng.normal(size=(self.nt, nnei, 4)) + davg_2 = rng.normal(size=(self.nt, nnei // 2, 4)) + dstd_2 = rng.normal(size=(self.nt, nnei // 2, 4)) + dstd = 0.1 + np.abs(dstd) + dstd_2 = 0.1 + np.abs(dstd_2) + + for ( + riti, + riz, + rp1c, + rp1d, + rp1g, + rp1a, + rp2g, + rp2a, + rph, + rp2gate, + rus, + rpz, + sm, + prec, + ect, + ns, + ) in itertools.product( + ["concat", "strip"], # repinit_tebd_input_mode + [ + True, + ], # repinit_set_davg_zero + [True, False], # repformer_update_g1_has_conv + [True, False], # repformer_update_g1_has_drrd + [True, False], # repformer_update_g1_has_grrg + [ + False, + ], # repformer_update_g1_has_attn + [ + False, + ], # repformer_update_g2_has_g1g1 + [True, False], # repformer_update_g2_has_attn + [ + False, + ], # repformer_update_h2 + [ + True, + ], # repformer_attn2_has_gate + ["res_avg", "res_residual"], # repformer_update_style + [ + True, + ], # repformer_set_davg_zero + [ + True, + ], # smooth + ["float64"], # precision + [False, True], # use_econf_tebd + [ + False, + True, + ], # new sub-structures (use_sqrt_nnei, g1_out_conv, g1_out_mlp) + ): + if ns and not rp1d and not rp1g: + continue + dtype = PRECISION_DICT[prec] + rtol, atol = get_tols(prec) + if prec == "float64": + atol = 1e-8 # marginal GPU test cases... + + repinit = RepinitArgs( + rcut=self.rcut, + rcut_smth=self.rcut_smth, + nsel=self.sel_mix, + tebd_input_mode=riti, + set_davg_zero=riz, + ) + repformer = RepformerArgs( + rcut=self.rcut / 2, + rcut_smth=self.rcut_smth, + nsel=nnei // 2, + nlayers=3, + g1_dim=20, + g2_dim=10, + axis_neuron=4, + update_g1_has_conv=rp1c, + update_g1_has_drrd=rp1d, + update_g1_has_grrg=rp1g, + update_g1_has_attn=rp1a, + update_g2_has_g1g1=rp2g, + update_g2_has_attn=rp2a, + update_h2=rph, + attn1_hidden=20, + attn1_nhead=2, + attn2_hidden=10, + attn2_nhead=2, + attn2_has_gate=rp2gate, + update_style=rus, + set_davg_zero=rpz, + use_sqrt_nnei=ns, + g1_out_conv=ns, + g1_out_mlp=ns, + ) + + # dpa2 new impl + dd0 = DescrptDPA2( + self.nt, + repinit=repinit, + repformer=repformer, + # kwargs for descriptor + smooth=sm, + exclude_types=[], + add_tebd_to_repinit_out=False, + precision=prec, + use_econf_tebd=ect, + type_map=["O", "H"] if ect else None, + seed=GLOBAL_SEED, + ).to(env.DEVICE) + + dd0.repinit.mean = paddle.to_tensor(davg, dtype=dtype).to(device=env.DEVICE) + dd0.repinit.stddev = paddle.to_tensor(dstd, dtype=dtype).to( + device=env.DEVICE + ) + dd0.repformers.mean = paddle.to_tensor(davg_2, dtype=dtype).to( + device=env.DEVICE + ) + dd0.repformers.stddev = paddle.to_tensor(dstd_2, dtype=dtype).to( + device=env.DEVICE + ) + rd0, _, _, _, _ = dd0( + paddle.to_tensor(self.coord_ext, dtype=dtype).to(device=env.DEVICE), + paddle.to_tensor(self.atype_ext, dtype="int64").to(device=env.DEVICE), + paddle.to_tensor(self.nlist, dtype="int64").to(device=env.DEVICE), + paddle.to_tensor(self.mapping, dtype="int64").to(device=env.DEVICE), + ) + # serialization + dd1 = DescrptDPA2.deserialize(dd0.serialize()) + rd1, _, _, _, _ = dd1( + paddle.to_tensor(self.coord_ext, dtype=dtype).to(device=env.DEVICE), + paddle.to_tensor(self.atype_ext, dtype="int64").to(device=env.DEVICE), + paddle.to_tensor(self.nlist, dtype="int64").to(device=env.DEVICE), + paddle.to_tensor(self.mapping, dtype="int64").to(device=env.DEVICE), + ) + np.testing.assert_allclose( + rd0.detach().cpu().numpy(), + rd1.detach().cpu().numpy(), + rtol=rtol, + atol=atol, + ) + # dp impl + dd2 = DPDescrptDPA2.deserialize(dd0.serialize()) + rd2, _, _, _, _ = dd2.call( + self.coord_ext, self.atype_ext, self.nlist, self.mapping + ) + np.testing.assert_allclose( + rd0.detach().cpu().numpy(), + rd2, + rtol=rtol, + atol=atol, + ) + + def test_jit( + self, + ): + rng = np.random.default_rng(100) + nf, nloc, nnei = self.nlist.shape + davg = rng.normal(size=(self.nt, nnei, 4)) + dstd = rng.normal(size=(self.nt, nnei, 4)) + davg_2 = rng.normal(size=(self.nt, nnei // 2, 4)) + dstd_2 = rng.normal(size=(self.nt, nnei // 2, 4)) + dstd = 0.1 + np.abs(dstd) + + for ( + riti, + riz, + rp1c, + rp1d, + rp1g, + rp1a, + rp2g, + rp2a, + rph, + rp2gate, + rus, + rpz, + sm, + prec, + ect, + ns, + ) in itertools.product( + ["concat", "strip"], # repinit_tebd_input_mode + [ + True, + ], # repinit_set_davg_zero + [ + True, + ], # repformer_update_g1_has_conv + [ + True, + ], # repformer_update_g1_has_drrd + [ + True, + ], # repformer_update_g1_has_grrg + [ + True, + ], # repformer_update_g1_has_attn + [ + True, + ], # repformer_update_g2_has_g1g1 + [ + True, + ], # repformer_update_g2_has_attn + [ + False, + ], # repformer_update_h2 + [ + True, + ], # repformer_attn2_has_gate + ["res_avg", "res_residual"], # repformer_update_style + [ + True, + ], # repformer_set_davg_zero + [ + True, + ], # smooth + ["float64"], # precision + [False, True], # use_econf_tebd + [True], # new sub-structures (use_sqrt_nnei, g1_out_conv, g1_out_mlp) + ): + dtype = PRECISION_DICT[prec] + rtol, atol = get_tols(prec) + + repinit = RepinitArgs( + rcut=self.rcut, + rcut_smth=self.rcut_smth, + nsel=self.sel_mix, + tebd_input_mode=riti, + set_davg_zero=riz, + ) + repformer = RepformerArgs( + rcut=self.rcut / 2, + rcut_smth=self.rcut_smth, + nsel=nnei // 2, + nlayers=3, + g1_dim=20, + g2_dim=10, + axis_neuron=4, + update_g1_has_conv=rp1c, + update_g1_has_drrd=rp1d, + update_g1_has_grrg=rp1g, + update_g1_has_attn=rp1a, + update_g2_has_g1g1=rp2g, + update_g2_has_attn=rp2a, + update_h2=rph, + attn1_hidden=20, + attn1_nhead=2, + attn2_hidden=10, + attn2_nhead=2, + attn2_has_gate=rp2gate, + update_style=rus, + set_davg_zero=rpz, + use_sqrt_nnei=ns, + g1_out_conv=ns, + g1_out_mlp=ns, + ) + + # dpa2 new impl + dd0 = DescrptDPA2( + self.nt, + repinit=repinit, + repformer=repformer, + # kwargs for descriptor + smooth=sm, + exclude_types=[], + add_tebd_to_repinit_out=False, + precision=prec, + use_econf_tebd=ect, + type_map=["O", "H"] if ect else None, + seed=GLOBAL_SEED, + ).to(env.DEVICE) + + dd0.repinit.mean = paddle.to_tensor(davg, dtype=dtype).to(device=env.DEVICE) + dd0.repinit.stddev = paddle.to_tensor(dstd, dtype=dtype).to( + device=env.DEVICE + ) + dd0.repformers.mean = paddle.to_tensor(davg_2, dtype=dtype).to( + device=env.DEVICE + ) + dd0.repformers.stddev = paddle.to_tensor(dstd_2, dtype=dtype).to( + device=env.DEVICE + ) + model = paddle.jit.to_static(dd0) diff --git a/source/tests/pd/model/test_embedding_net.py b/source/tests/pd/model/test_embedding_net.py new file mode 100644 index 0000000000..696657feae --- /dev/null +++ b/source/tests/pd/model/test_embedding_net.py @@ -0,0 +1,218 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import json +import os +import re +import unittest + +import numpy as np +import paddle +import tensorflow.compat.v1 as tf + +from deepmd.pd.utils import ( + env, +) + +tf.disable_eager_execution() + +from pathlib import ( + Path, +) + +from deepmd.pd.model.descriptor import ( + DescrptSeA, +) +from deepmd.pd.utils import ( + dp_random, +) +from deepmd.pd.utils.dataset import ( + DeepmdDataSetForLoader, +) +from deepmd.pd.utils.env import ( + DEVICE, + GLOBAL_NP_FLOAT_PRECISION, +) +from deepmd.pd.utils.nlist import ( + extend_input_and_build_neighbor_list, +) +from deepmd.tf.common import ( + expand_sys_str, +) +from deepmd.tf.descriptor import DescrptSeA as DescrptSeA_tf + +from ..test_finetune import ( + energy_data_requirement, +) + +CUR_DIR = os.path.dirname(__file__) + + +def gen_key(worb, depth, elemid): + return (worb, depth, elemid) + + +def get_single_batch(dataset, index=None): + if index is None: + index = dp_random.choice(np.arange(len(dataset))) + np_batch = dataset[index] + pt_batch = {} + + for key in [ + "coord", + "box", + "force", + "force_mag", + "energy", + "virial", + "atype", + "natoms", + ]: + if key in np_batch.keys(): + np_batch[key] = np.expand_dims(np_batch[key], axis=0) + pt_batch[key] = paddle.to_tensor(np_batch[key]).to(device=env.DEVICE) + if key in ["coord", "force", "force_mag"]: + np_batch[key] = np_batch[key].reshape(1, -1) + np_batch["natoms"] = np_batch["natoms"][0] + return np_batch, pt_batch + + +def base_se_a(descriptor, coord, atype, natoms, box): + g = tf.Graph() + with g.as_default(): + name_pfx = "d_sea_" + t_coord = tf.placeholder( + GLOBAL_NP_FLOAT_PRECISION, [None, None], name=name_pfx + "t_coord" + ) + t_atype = tf.placeholder(tf.int32, [None, None], name=name_pfx + "t_type") + t_natoms = tf.placeholder( + tf.int32, [descriptor.ntypes + 2], name=name_pfx + "t_natoms" + ) + t_box = tf.placeholder( + GLOBAL_NP_FLOAT_PRECISION, [None, None], name=name_pfx + "t_box" + ) + t_default_mesh = tf.placeholder(tf.int32, [None], name=name_pfx + "t_mesh") + t_embedding = descriptor.build( + t_coord, t_atype, t_natoms, t_box, t_default_mesh, input_dict={} + ) + fake_energy = tf.reduce_sum(t_embedding) + t_force = descriptor.prod_force_virial(fake_energy, t_natoms)[0] + t_vars = {} + for var in tf.global_variables(): + ms = re.findall(r"([a-z]+)_(\d)_(\d)", var.name) + if len(ms) == 1: + m = ms[0] + key = gen_key(worb=m[0], depth=int(m[1]), elemid=int(m[2])) + t_vars[key] = var + init_op = tf.global_variables_initializer() + + with tf.Session(graph=g) as sess: + sess.run(init_op) + embedding, force, values = sess.run( + [t_embedding, t_force, t_vars], + feed_dict={ + t_coord: coord, + t_atype: atype, + t_natoms: natoms, + t_box: box, + t_default_mesh: np.array([0, 0, 0, 2, 2, 2]), + }, + ) + tf.reset_default_graph() + return embedding, force, values + + +class TestSeA(unittest.TestCase): + def setUp(self): + dp_random.seed(0) + with open(str(Path(__file__).parent / "water/se_e2_a.json")) as fin: + content = fin.read() + config = json.loads(content) + data_file = [str(Path(__file__).parent / "water/data/data_0")] + config["training"]["training_data"]["systems"] = data_file + config["training"]["validation_data"]["systems"] = data_file + model_config = config["model"] + self.rcut = model_config["descriptor"]["rcut"] + self.rcut_smth = model_config["descriptor"]["rcut_smth"] + self.sel = model_config["descriptor"]["sel"] + self.bsz = config["training"]["training_data"]["batch_size"] + self.systems = config["training"]["validation_data"]["systems"] + if isinstance(self.systems, str): + self.systems = expand_sys_str(self.systems) + ds = DeepmdDataSetForLoader( + self.systems[0], + model_config["type_map"], + ) + ds.add_data_requirement(energy_data_requirement) + self.filter_neuron = model_config["descriptor"]["neuron"] + self.axis_neuron = model_config["descriptor"]["axis_neuron"] + self.np_batch, self.paddle_batch = get_single_batch(ds) + + @unittest.skip("remainder_grad need to be supported") + def test_consistency(self): + dp_d = DescrptSeA_tf( + rcut=self.rcut, + rcut_smth=self.rcut_smth, + sel=self.sel, + neuron=self.filter_neuron, + axis_neuron=self.axis_neuron, + seed=1, + ) + dp_embedding, dp_force, dp_vars = base_se_a( + descriptor=dp_d, + coord=self.np_batch["coord"], + atype=self.np_batch["atype"], + natoms=self.np_batch["natoms"], + box=self.np_batch["box"], + ) + + # Reproduced + descriptor = DescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + neuron=self.filter_neuron, + axis_neuron=self.axis_neuron, + ).to(DEVICE) + for name, param in descriptor.named_parameters(): + ms = re.findall(r"(\d)\.layers\.(\d)\.([a-z]+)", name) + if len(ms) == 1: + m = ms[0] + key = gen_key(worb=m[2], depth=int(m[1]) + 1, elemid=int(m[0])) + var = dp_vars[key] + with paddle.no_grad(): + # Keep parameter value consistency between 2 implentations + paddle.assign(var, param) + + pt_coord = self.paddle_batch["coord"].to(env.DEVICE) + pt_coord.stop_gradient = False + + ( + extended_coord, + extended_atype, + mapping, + nlist, + ) = extend_input_and_build_neighbor_list( + pt_coord, + self.paddle_batch["atype"].to(env.DEVICE), + self.rcut, + self.sel, + mixed_types=False, + box=self.paddle_batch["box"].to(env.DEVICE), + ) + descriptor_out, _, _, _, _ = descriptor( + extended_coord, + extended_atype, + nlist, + ) + my_embedding = descriptor_out.cpu().detach().numpy() + fake_energy = paddle.sum(descriptor_out) + fake_energy.backward() + my_force = -pt_coord.grad.cpu().numpy() + + # Check + np.testing.assert_allclose(dp_embedding, my_embedding) + dp_force = dp_force.reshape(*my_force.shape) + np.testing.assert_allclose(dp_force, my_force) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/model/test_ener_fitting.py b/source/tests/pd/model/test_ener_fitting.py new file mode 100644 index 0000000000..dd13f139dc --- /dev/null +++ b/source/tests/pd/model/test_ener_fitting.py @@ -0,0 +1,150 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import itertools +import unittest + +import numpy as np +import paddle + +from deepmd.dpmodel.fitting import InvarFitting as DPInvarFitting +from deepmd.pd.model.descriptor.se_a import ( + DescrptSeA, +) +from deepmd.pd.model.task.ener import ( + InvarFitting, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, +) + +from ...seed import ( + GLOBAL_SEED, +) +from .test_env_mat import ( + TestCaseSingleFrameWithNlist, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION + + +class TestInvarFitting(unittest.TestCase, TestCaseSingleFrameWithNlist): + def setUp(self): + TestCaseSingleFrameWithNlist.setUp(self) + + def test_consistency( + self, + ): + rng = np.random.default_rng(GLOBAL_SEED) + nf, nloc, nnei = self.nlist.shape + dd0 = DescrptSeA(self.rcut, self.rcut_smth, self.sel).to(env.DEVICE) + rd0, _, _, _, _ = dd0( + paddle.to_tensor(self.coord_ext, dtype=dtype).to(device=env.DEVICE), + paddle.to_tensor(self.atype_ext, dtype="int64").to(device=env.DEVICE), + paddle.to_tensor(self.nlist, dtype="int64").to(device=env.DEVICE), + ) + atype = paddle.to_tensor(self.atype_ext[:, :nloc], dtype="int64").to( + device=env.DEVICE + ) + + for od, mixed_types, nfp, nap, et, nn in itertools.product( + [1, 3], + [True, False], + [0, 3], + [0, 4], + [[], [0], [1]], + [[4, 4, 4], []], + ): + ft0 = InvarFitting( + "foo", + self.nt, + dd0.dim_out, + od, + numb_fparam=nfp, + numb_aparam=nap, + mixed_types=mixed_types, + exclude_types=et, + neuron=nn, + seed=GLOBAL_SEED, + ).to(env.DEVICE) + ft1 = DPInvarFitting.deserialize(ft0.serialize()) + ft2 = InvarFitting.deserialize(ft0.serialize()) + + if nfp > 0: + ifp = paddle.to_tensor( + rng.normal(size=(self.nf, nfp)), dtype=dtype, place=env.DEVICE + ) + else: + ifp = None + if nap > 0: + iap = paddle.to_tensor( + rng.normal(size=(self.nf, self.nloc, nap)), + dtype=dtype, + place=env.DEVICE, + ) + else: + iap = None + + ret0 = ft0(rd0, atype, fparam=ifp, aparam=iap) + ret1 = ft1( + rd0.detach().cpu().numpy(), + atype.detach().cpu().numpy(), + fparam=to_numpy_array(ifp), + aparam=to_numpy_array(iap), + ) + ret2 = ft2(rd0, atype, fparam=ifp, aparam=iap) + np.testing.assert_allclose( + to_numpy_array(ret0["foo"]), + ret1["foo"], + ) + np.testing.assert_allclose( + to_numpy_array(ret0["foo"]), + to_numpy_array(ret2["foo"]), + ) + self.assertEqual(ft0.get_sel_type(), ft1.get_sel_type()) + + def test_jit( + self, + ): + for od, mixed_types, nfp, nap, et in itertools.product( + [1, 3], + [True, False], + [0, 3], + [0, 4], + [[], [0]], + ): + ft0 = InvarFitting( + "foo", + self.nt, + 9, + od, + numb_fparam=nfp, + numb_aparam=nap, + mixed_types=mixed_types, + exclude_types=et, + seed=GLOBAL_SEED, + ).to(env.DEVICE) + paddle.jit.to_static(ft0) + + def test_get_set(self): + ifn0 = InvarFitting( + "energy", + self.nt, + 3, + 1, + seed=GLOBAL_SEED, + ) + rng = np.random.default_rng(GLOBAL_SEED) + foo = rng.normal([3, 4]) + for ii in [ + "bias_atom_e", + "fparam_avg", + "fparam_inv_std", + "aparam_avg", + "aparam_inv_std", + ]: + ifn0[ii] = paddle.to_tensor(foo, dtype=dtype).to(device=env.DEVICE) + np.testing.assert_allclose( + foo, np.reshape(ifn0[ii].detach().cpu().numpy(), foo.shape) + ) diff --git a/source/tests/pd/model/test_ener_spin_model.py b/source/tests/pd/model/test_ener_spin_model.py new file mode 100644 index 0000000000..79e060fe5a --- /dev/null +++ b/source/tests/pd/model/test_ener_spin_model.py @@ -0,0 +1,432 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import unittest + +import numpy as np +import paddle + +from deepmd.dpmodel.model import SpinModel as DPSpinModel +from deepmd.pd.model.model import ( + SpinEnergyModel, + get_model, +) +from deepmd.pd.utils import ( + aux, + env, +) +from deepmd.pd.utils.nlist import ( + extend_input_and_build_neighbor_list, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, +) + +from ...seed import ( + GLOBAL_SEED, +) +from .test_permutation import ( + model_dpa1, + model_dpa2, + model_se_e2_a, + model_spin, +) + +dtype = paddle.float64 + + +def reduce_tensor(extended_tensor, mapping, nloc: int): + nframes, nall = extended_tensor.shape[:2] + ext_dims = extended_tensor.shape[2:] + reduced_tensor = paddle.zeros( + [nframes, nloc, *ext_dims], + dtype=extended_tensor.dtype, + ).to(device=extended_tensor.place) + mldims = list(mapping.shape) + mapping = mapping.reshape(mldims + [1] * len(ext_dims)).expand( + [-1] * len(mldims) + list(ext_dims) + ) + # nf x nloc x (*ext_dims) + reduced_tensor = aux.scatter_reduce( + reduced_tensor, + 1, + index=mapping, + src=extended_tensor, + reduce="sum", + ) + return reduced_tensor + + +class SpinTest: + def setUp(self): + self.prec = 1e-10 + natoms = 5 + self.ntypes = 3 # ["O", "H", "B"] for test + self.cell = 4.0 * paddle.eye(3, dtype=dtype).to(device=env.DEVICE).unsqueeze(0) + generator = paddle.seed(GLOBAL_SEED) + self.coord = 3.0 * paddle.rand([natoms, 3], dtype=dtype).unsqueeze(0).to( + device=env.DEVICE + ) + self.spin = 0.5 * paddle.rand([natoms, 3], dtype=dtype).unsqueeze(0).to( + device=env.DEVICE + ) + self.atype = paddle.to_tensor( + [0, 0, 0, 1, 1], dtype=paddle.int64, place=env.DEVICE + ).unsqueeze(0) + + self.expected_mask = paddle.to_tensor( + [ + [True], + [True], + [True], + [False], + [False], + ], + dtype=paddle.bool, + place=env.DEVICE, + ).unsqueeze(0) + self.expected_atype_with_spin = paddle.to_tensor( + [0, 0, 0, 1, 1, 3, 3, 3, 4, 4], dtype=paddle.int64, place=env.DEVICE + ).unsqueeze(0) + self.expected_nloc_spin_index = ( + paddle.arange(natoms, natoms * 2, dtype=paddle.int64) + .to(device=env.DEVICE) + .unsqueeze(0) + .unsqueeze(-1) + ) + + def test_output_shape( + self, + ): + result = self.model( + self.coord, + self.atype, + self.spin, + self.cell, + ) + # check magnetic mask + assert np.allclose(result["mask_mag"].numpy(), self.expected_mask.numpy()) + # check output shape to assure split + nframes, nloc = self.coord.shape[:2] + assert np.allclose(result["energy"].shape, [nframes, 1]) + assert np.allclose(result["atom_energy"].shape, [nframes, nloc, 1]) + assert np.allclose(result["force"].shape, [nframes, nloc, 3]) + assert np.allclose(result["force_mag"].shape, [nframes, nloc, 3]) + + def test_input_output_process(self): + nframes, nloc = self.coord.shape[:2] + self.real_ntypes = self.model.spin.get_ntypes_real() + # 1. test forward input process + coord_updated, atype_updated = self.model.process_spin_input( + self.coord, self.atype, self.spin + ) + # compare atypes of real and virtual atoms + assert np.allclose(atype_updated.numpy(), self.expected_atype_with_spin.numpy()) + # compare coords of real and virtual atoms + assert np.allclose(coord_updated.shape, [nframes, nloc * 2, 3]) + assert np.allclose(coord_updated[:, :nloc].numpy(), self.coord.numpy()) + virtual_scale = paddle.to_tensor( + self.model.spin.get_virtual_scale_mask()[self.atype.cpu()], + dtype=dtype, + place=env.DEVICE, + ) + virtual_coord = self.coord + self.spin * virtual_scale.unsqueeze(-1) + assert np.allclose(coord_updated[:, nloc:].numpy(), virtual_coord.numpy()) + + # 2. test forward output process + model_ret = self.model.backbone_model.forward_common( + coord_updated, + atype_updated, + self.cell, + do_atomic_virial=True, + ) + if self.model.do_grad_r("energy"): + force_all = model_ret["energy_derv_r"].squeeze(-2) + force_real, force_mag, _ = self.model.process_spin_output( + self.atype, force_all + ) + assert paddle.allclose( + force_real, force_all[:, :nloc] + force_all[:, nloc:] + ) + assert paddle.allclose( + force_mag, force_all[:, nloc:] * virtual_scale.unsqueeze(-1) + ) + + # 3. test forward_lower input process + ( + extended_coord, + extended_atype, + mapping, + nlist, + ) = extend_input_and_build_neighbor_list( + self.coord, + self.atype, + self.model.get_rcut(), + self.model.get_sel(), + mixed_types=self.model.mixed_types(), + box=self.cell, + ) + nall = extended_coord.shape[1] + nnei = nlist.shape[-1] + extended_spin = aux.take_along_axis( + self.spin, indices=mapping.unsqueeze(-1).tile((1, 1, 3)), axis=1 + ) + ( + extended_coord_updated, + extended_atype_updated, + nlist_updated, + mapping_updated, + ) = self.model.process_spin_input_lower( + extended_coord, extended_atype, extended_spin, nlist, mapping=mapping + ) + # compare atypes of real and virtual atoms + # Note that the real and virtual atoms corresponding to the local ones are switch to the first nloc * 2 atoms + assert np.allclose(extended_atype_updated.shape, [nframes, nall * 2]) + assert np.allclose( + extended_atype_updated[:, :nloc].numpy(), extended_atype[:, :nloc].numpy() + ) + assert np.allclose( + extended_atype_updated[:, nloc : nloc + nloc].numpy(), + extended_atype[:, :nloc].numpy() + self.real_ntypes, + ) + assert np.allclose( + extended_atype_updated[:, nloc + nloc : nloc + nall].numpy(), + extended_atype[:, nloc:nall].numpy(), + ) + assert np.allclose( + extended_atype_updated[:, nloc + nall :].numpy(), + extended_atype[:, nloc:nall].numpy() + self.real_ntypes, + ) + virtual_scale = paddle.to_tensor( + self.model.spin.get_virtual_scale_mask()[extended_atype.cpu()], + dtype=dtype, + place=env.DEVICE, + ) + # compare coords of real and virtual atoms + virtual_coord = extended_coord + extended_spin * virtual_scale.unsqueeze(-1) + assert np.allclose(extended_coord_updated.shape, [nframes, nall * 2, 3]) + assert paddle.allclose( + extended_coord_updated[:, :nloc], extended_coord[:, :nloc] + ) + assert paddle.allclose( + extended_coord_updated[:, nloc : nloc + nloc], virtual_coord[:, :nloc] + ) + assert paddle.allclose( + extended_coord_updated[:, nloc + nloc : nloc + nall], + extended_coord[:, nloc:nall], + ) + assert paddle.allclose( + extended_coord_updated[:, nloc + nall :], virtual_coord[:, nloc:nall] + ) + + # compare mapping + assert np.allclose(mapping_updated.shape, [nframes, nall * 2]) + assert np.allclose(mapping_updated[:, :nloc].numpy(), mapping[:, :nloc].numpy()) + assert np.allclose( + mapping_updated[:, nloc : nloc + nloc].numpy(), + mapping[:, :nloc].numpy() + nloc, + ) + assert np.allclose( + mapping_updated[:, nloc + nloc : nloc + nall].numpy(), + mapping[:, nloc:nall].numpy(), + ) + assert np.allclose( + mapping_updated[:, nloc + nall :].numpy(), + mapping[:, nloc:nall].numpy() + nloc, + ) + + # compare nlist + assert np.allclose(nlist_updated.shape, [nframes, nloc * 2, nnei * 2 + 1]) + # self spin + assert np.allclose( + nlist_updated[:, :nloc, :1].numpy(), self.expected_nloc_spin_index.numpy() + ) + # real and virtual neighbors + loc_atoms_mask = (nlist < nloc) & (nlist != -1) + ghost_atoms_mask = nlist >= nloc + real_neighbors = nlist.clone() + aux.masked_add_(real_neighbors, ghost_atoms_mask, nloc) + # real_neighbors[ghost_atoms_mask] += nloc + assert np.allclose( + nlist_updated[:, :nloc, 1 : 1 + nnei].numpy(), real_neighbors.numpy() + ) + virtual_neighbors = nlist.clone() + # virtual_neighbors[loc_atoms_mask] += nloc + aux.masked_add_(virtual_neighbors, loc_atoms_mask, nloc) + # virtual_neighbors[ghost_atoms_mask] += nall + aux.masked_add_(virtual_neighbors, ghost_atoms_mask, nall) + assert np.allclose( + nlist_updated[:, :nloc, 1 + nnei :].numpy(), virtual_neighbors.numpy() + ) + + # 4. test forward_lower output process + model_ret = self.model.backbone_model.forward_common_lower( + extended_coord_updated, + extended_atype_updated, + nlist_updated, + mapping=mapping_updated, + do_atomic_virial=True, + ) + if self.model.do_grad_r("energy"): + force_all = model_ret["energy_derv_r"].squeeze(-2) + force_real, force_mag, _ = self.model.process_spin_output_lower( + extended_atype, force_all, nloc + ) + force_all_switched = paddle.zeros_like(force_all) + force_all_switched[:, :nloc] = force_all[:, :nloc] + force_all_switched[:, nloc:nall] = force_all[:, nloc + nloc : nloc + nall] + force_all_switched[:, nall : nall + nloc] = force_all[:, nloc : nloc + nloc] + force_all_switched[:, nall + nloc :] = force_all[:, nloc + nall :] + assert paddle.allclose( + force_real, force_all_switched[:, :nall] + force_all_switched[:, nall:] + ) + assert paddle.allclose( + force_mag, force_all_switched[:, nall:] * virtual_scale.unsqueeze(-1) + ) + + def test_jit(self): + model = paddle.jit.to_static(self.model) + self.assertEqual(model.get_rcut(), self.rcut) + self.assertEqual(model.get_nsel(), self.nsel) + self.assertEqual(model.get_type_map(), self.type_map) + + def test_self_consistency(self): + if hasattr(self, "serial_test") and not self.serial_test: + # not implement serialize and deserialize + return + model1 = SpinEnergyModel.deserialize(self.model.serialize()) + result = model1( + self.coord, + self.atype, + self.spin, + self.cell, + ) + expected_result = self.model( + self.coord, + self.atype, + self.spin, + self.cell, + ) + for key in result: + assert np.allclose( + result[key].numpy(), + expected_result[key].numpy(), + rtol=self.prec, + atol=self.prec, + ) + model1 = paddle.jit.to_static(model1) + + def test_dp_consistency(self): + if hasattr(self, "serial_test") and not self.serial_test: + # not implement serialize and deserialize + return + dp_model = DPSpinModel.deserialize(self.model.serialize()) + # test call + dp_ret = dp_model.call( + to_numpy_array(self.coord), + to_numpy_array(self.atype), + to_numpy_array(self.spin), + to_numpy_array(self.cell), + ) + result = self.model.forward_common( + self.coord, + self.atype, + self.spin, + self.cell, + ) + np.testing.assert_allclose( + to_numpy_array(result["energy"]), + dp_ret["energy"], + rtol=self.prec, + atol=self.prec, + ) + np.testing.assert_allclose( + to_numpy_array(result["energy_redu"]), + dp_ret["energy_redu"], + rtol=self.prec, + atol=self.prec, + ) + + # test call_lower + ( + extended_coord, + extended_atype, + mapping, + nlist, + ) = extend_input_and_build_neighbor_list( + self.coord, + self.atype, + self.model.get_rcut(), + self.model.get_sel(), + mixed_types=self.model.mixed_types(), + box=self.cell, + ) + extended_spin = aux.take_along_axis( + self.spin, indices=mapping.unsqueeze(-1).tile((1, 1, 3)), axis=1 + ) + dp_ret_lower = dp_model.call_lower( + to_numpy_array(extended_coord), + to_numpy_array(extended_atype), + to_numpy_array(extended_spin), + to_numpy_array(nlist), + to_numpy_array(mapping), + ) + result_lower = self.model.forward_common_lower( + extended_coord, + extended_atype, + extended_spin, + nlist, + mapping, + ) + np.testing.assert_allclose( + to_numpy_array(result_lower["energy"]), + dp_ret_lower["energy"], + rtol=self.prec, + atol=self.prec, + ) + np.testing.assert_allclose( + to_numpy_array(result_lower["energy_redu"]), + dp_ret_lower["energy_redu"], + rtol=self.prec, + atol=self.prec, + ) + + +class TestEnergyModelSpinSeA(unittest.TestCase, SpinTest): + def setUp(self): + SpinTest.setUp(self) + model_params = copy.deepcopy(model_spin) + model_params["descriptor"] = copy.deepcopy(model_se_e2_a["descriptor"]) + self.rcut = model_params["descriptor"]["rcut"] + self.nsel = sum(model_params["descriptor"]["sel"]) + self.type_map = model_params["type_map"] + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelSpinDPA1(unittest.TestCase, SpinTest): + def setUp(self): + SpinTest.setUp(self) + model_params = copy.deepcopy(model_spin) + model_params["descriptor"] = copy.deepcopy(model_dpa1["descriptor"]) + self.rcut = model_params["descriptor"]["rcut"] + self.nsel = model_params["descriptor"]["sel"] + self.type_map = model_params["type_map"] + # not implement serialize and deserialize + self.serial_test = False + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelSpinDPA2(unittest.TestCase, SpinTest): + def setUp(self): + SpinTest.setUp(self) + model_params = copy.deepcopy(model_spin) + model_params["descriptor"] = copy.deepcopy(model_dpa2["descriptor"]) + self.rcut = model_params["descriptor"]["repinit"]["rcut"] + self.nsel = model_params["descriptor"]["repinit"]["nsel"] + self.type_map = model_params["type_map"] + # not implement serialize and deserialize + self.serial_test = False + self.model = get_model(model_params).to(env.DEVICE) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/model/test_env_mat.py b/source/tests/pd/model/test_env_mat.py new file mode 100644 index 0000000000..7cbc698264 --- /dev/null +++ b/source/tests/pd/model/test_env_mat.py @@ -0,0 +1,187 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import unittest + +import numpy as np +import paddle + +from deepmd.dpmodel.utils import ( + EnvMat, +) +from deepmd.pd.model.descriptor.env_mat import ( + prod_env_mat, +) +from deepmd.pd.utils import ( + env, +) + +from ...seed import ( + GLOBAL_SEED, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION + + +class TestCaseSingleFrameWithNlist: + def setUp(self): + # nloc == 3, nall == 4 + self.nloc = 3 + self.nall = 4 + self.nf, self.nt = 2, 2 + self.coord_ext = np.array( + [ + [0, 0, 0], + [0, 1, 0], + [0, 0, 1], + [0, -2, 0], + ], + dtype=np.float64, + ).reshape([1, self.nall, 3]) + self.atype_ext = np.array([0, 0, 1, 0], dtype="int64").reshape([1, self.nall]) + self.mapping = np.array([0, 1, 2, 0], dtype="int64").reshape([1, self.nall]) + # sel = [5, 2] + self.sel = [5, 2] + self.sel_mix = [7] + self.natoms = [3, 3, 2, 1] + self.nlist = np.array( + [ + [1, 3, -1, -1, -1, 2, -1], + [0, -1, -1, -1, -1, 2, -1], + [0, 1, -1, -1, -1, -1, -1], + ], + dtype="int64", + ).reshape([1, self.nloc, sum(self.sel)]) + self.rcut = 2.2 + self.rcut_smth = 0.4 + # permutations + self.perm = np.array([2, 0, 1, 3], dtype=np.int32) + inv_perm = np.array([1, 2, 0, 3], dtype=np.int32) + # permute the coord and atype + self.coord_ext = np.concatenate( + [self.coord_ext, self.coord_ext[:, self.perm, :]], axis=0 + ).reshape(self.nf, self.nall * 3) + self.atype_ext = np.concatenate( + [self.atype_ext, self.atype_ext[:, self.perm]], axis=0 + ) + self.mapping = np.concatenate( + [self.mapping, self.mapping[:, self.perm]], axis=0 + ) + + # permute the nlist + nlist1 = self.nlist[:, self.perm[: self.nloc], :] + mask = nlist1 == -1 + nlist1 = inv_perm[nlist1] + nlist1 = np.where(mask, -1, nlist1) + self.nlist = np.concatenate([self.nlist, nlist1], axis=0) + self.atol = 1e-12 + + +class TestCaseSingleFrameWithNlistWithVirtual: + def setUp(self): + # nloc == 3, nall == 4 + self.nloc = 4 + self.nall = 5 + self.nf, self.nt = 2, 2 + self.coord_ext = np.array( + [ + [0, 0, 0], + [0, 0, 0], + [0, 1, 0], + [0, 0, 1], + [0, -2, 0], + ], + dtype=np.float64, + ).reshape([1, self.nall, 3]) + self.atype_ext = np.array([0, -1, 0, 1, 0], dtype="int64").reshape( + [1, self.nall] + ) + # sel = [5, 2] + self.sel = [5, 2] + self.sel_mix = [7] + self.natoms = [3, 3, 2, 1] + self.nlist = np.array( + [ + [2, 4, -1, -1, -1, 3, -1], + [-1, -1, -1, -1, -1, -1, -1], + [0, -1, -1, -1, -1, 3, -1], + [0, 2, -1, -1, -1, -1, -1], + ], + dtype="int64", + ).reshape([1, self.nloc, sum(self.sel)]) + self.rcut = 2.2 + self.rcut_smth = 0.4 + # permutations + self.perm = np.array([3, 0, 1, 2, 4], dtype=np.int32) + inv_perm = np.argsort(self.perm) + # permute the coord and atype + self.coord_ext = np.concatenate( + [self.coord_ext, self.coord_ext[:, self.perm, :]], axis=0 + ).reshape(self.nf, self.nall * 3) + self.atype_ext = np.concatenate( + [self.atype_ext, self.atype_ext[:, self.perm]], axis=0 + ) + # permute the nlist + nlist1 = self.nlist[:, self.perm[: self.nloc], :] + mask = nlist1 == -1 + nlist1 = inv_perm[nlist1] + nlist1 = np.where(mask, -1, nlist1) + self.nlist = np.concatenate([self.nlist, nlist1], axis=0) + self.get_real_mapping = np.array([[0, 2, 3], [0, 1, 3]], dtype=np.int32) + self.atol = 1e-12 + + +class TestCaseSingleFrameWithoutNlist: + def setUp(self): + # nloc == 3, nall == 4 + self.nloc = 3 + self.nf, self.nt = 1, 2 + self.coord = np.array( + [ + [0, 0, 0], + [0, 1, 0], + [0, 0, 1], + ], + dtype=np.float64, + ).reshape([1, self.nloc * 3]) + self.atype = np.array([0, 0, 1], dtype="int64").reshape([1, self.nloc]) + self.cell = 2.0 * np.eye(3).reshape([1, 9]) + # sel = [5, 2] + self.sel = [16, 8] + self.sel_mix = [24] + self.natoms = [3, 3, 2, 1] + self.rcut = 2.2 + self.rcut_smth = 0.4 + self.atol = 1e-12 + + +# to be merged with the tf test case +class TestEnvMat(unittest.TestCase, TestCaseSingleFrameWithNlist): + def setUp(self): + TestCaseSingleFrameWithNlist.setUp(self) + + def test_consistency( + self, + ): + rng = np.random.default_rng(GLOBAL_SEED) + nf, nloc, nnei = self.nlist.shape + davg = rng.normal(size=(self.nt, nnei, 4)) + dstd = rng.normal(size=(self.nt, nnei, 4)) + dstd = 0.1 + np.abs(dstd) + em0 = EnvMat(self.rcut, self.rcut_smth) + mm0, diff0, ww0 = em0.call( + self.coord_ext, self.atype_ext, self.nlist, davg, dstd + ) + mm1, diff1, ww1 = prod_env_mat( + paddle.to_tensor(self.coord_ext, dtype=dtype).to(device=env.DEVICE), + paddle.to_tensor(self.nlist, dtype="int64").to(device=env.DEVICE), + paddle.to_tensor(self.atype_ext[:, :nloc], dtype="int64").to( + device=env.DEVICE + ), + paddle.to_tensor(davg).to(device=env.DEVICE), + paddle.to_tensor(dstd).to(device=env.DEVICE), + self.rcut, + self.rcut_smth, + ) + np.testing.assert_allclose(mm0, mm1.detach().cpu().numpy()) + np.testing.assert_allclose(diff0, diff1.detach().cpu().numpy()) + np.testing.assert_allclose(ww0, ww1.detach().cpu().numpy()) + np.testing.assert_allclose(mm0[0][self.perm[: self.nloc]], mm0[1]) diff --git a/source/tests/pd/model/test_exclusion_mask.py b/source/tests/pd/model/test_exclusion_mask.py new file mode 100644 index 0000000000..ff479ee7db --- /dev/null +++ b/source/tests/pd/model/test_exclusion_mask.py @@ -0,0 +1,70 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import unittest + +import numpy as np + +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.exclude_mask import ( + AtomExcludeMask, + PairExcludeMask, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, + to_paddle_tensor, +) + +from .test_env_mat import ( + TestCaseSingleFrameWithNlist, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION + + +class TestAtomExcludeMask(unittest.TestCase): + def test_build_type_exclude_mask(self): + nf = 2 + nt = 3 + exclude_types = [0, 2] + atype = np.array( + [ + [0, 2, 1, 2, 0, 1, 0], + [1, 2, 0, 0, 2, 2, 1], + ], + dtype=np.int32, + ).reshape([nf, -1]) + expected_mask = np.array( + [ + [0, 0, 1, 0, 0, 1, 0], + [1, 0, 0, 0, 0, 0, 1], + ] + ).reshape([nf, -1]) + des = AtomExcludeMask(nt, exclude_types=exclude_types) + mask = des(to_paddle_tensor(atype)) + np.testing.assert_equal(to_numpy_array(mask), expected_mask) + + +# to be merged with the tf test case +class TestPairExcludeMask(unittest.TestCase, TestCaseSingleFrameWithNlist): + def setUp(self): + TestCaseSingleFrameWithNlist.setUp(self) + + def test_build_type_exclude_mask(self): + exclude_types = [[0, 1]] + expected_mask = np.array( + [ + [1, 1, 1, 1, 1, 0, 1], + [1, 1, 1, 1, 1, 0, 1], + [0, 0, 1, 1, 1, 1, 1], + [0, 0, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 0, 1], + [1, 1, 1, 1, 1, 0, 1], + ] + ).reshape(self.nf, self.nloc, sum(self.sel)) + des = PairExcludeMask(self.nt, exclude_types=exclude_types).to(env.DEVICE) + mask = des( + to_paddle_tensor(self.nlist), + to_paddle_tensor(self.atype_ext), + ) + np.testing.assert_equal(to_numpy_array(mask), expected_mask) diff --git a/source/tests/pd/model/test_fitting_net.py b/source/tests/pd/model/test_fitting_net.py new file mode 100644 index 0000000000..9a4d4d128f --- /dev/null +++ b/source/tests/pd/model/test_fitting_net.py @@ -0,0 +1,148 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import re +import unittest + +import numpy as np +import paddle +import tensorflow.compat.v1 as tf + +tf.disable_eager_execution() + +from deepmd.pd.model.task import ( + EnergyFittingNet, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + GLOBAL_NP_FLOAT_PRECISION, +) +from deepmd.tf.fit.ener import ( + EnerFitting, +) + +from ...seed import ( + GLOBAL_SEED, +) + + +class FakeDescriptor: + def __init__(self, ntypes, embedding_width): + self._ntypes = ntypes + self._dim_out = embedding_width + + def get_ntypes(self): + return self._ntypes + + def get_dim_out(self): + return self._dim_out + + +def gen_key(type_id, layer_id, w_or_b): + return (type_id, layer_id, w_or_b) + + +def base_fitting_net(dp_fn, embedding, natoms, atype): + g = tf.Graph() + with g.as_default(): + t_embedding = tf.placeholder(GLOBAL_NP_FLOAT_PRECISION, [None, None]) + t_natoms = tf.placeholder(tf.int32, [None]) + t_atype = tf.placeholder(tf.int32, [None, None]) + t_energy = dp_fn.build(t_embedding, t_natoms, {"atype": t_atype}) + init_op = tf.global_variables_initializer() + t_vars = {} + for var in tf.global_variables(): + key = None + matched = re.match(r"layer_(\d)_type_(\d)/([a-z]+)", var.name) + if matched: + key = gen_key( + type_id=matched.group(2), + layer_id=matched.group(1), + w_or_b=matched.group(3), + ) + else: + matched = re.match(r"final_layer_type_(\d)/([a-z]+)", var.name) + if matched: + key = gen_key( + type_id=matched.group(1), layer_id=-1, w_or_b=matched.group(2) + ) + if key is not None: + t_vars[key] = var + + with tf.Session(graph=g) as sess: + sess.run(init_op) + energy, values = sess.run( + [t_energy, t_vars], + feed_dict={ + t_embedding: embedding, + t_natoms: natoms, + t_atype: atype, + }, + ) + tf.reset_default_graph() + return energy, values + + +class TestFittingNet(unittest.TestCase): + def setUp(self): + nloc = 7 + self.embedding_width = 30 + self.natoms = np.array([nloc, nloc, 2, 5], dtype=np.int32) + rng = np.random.default_rng(GLOBAL_SEED) + self.embedding = rng.uniform(size=[4, nloc * self.embedding_width]) + self.ntypes = self.natoms.size - 2 + self.n_neuron = [32, 32, 32] + self.atype = np.zeros([4, nloc], dtype=np.int32) + cnt = 0 + for i in range(self.ntypes): + self.atype[:, cnt : cnt + self.natoms[i + 2]] = i + cnt += self.natoms[i + 2] + + fake_d = FakeDescriptor(2, 30) + self.dp_fn = EnerFitting( + fake_d.get_ntypes(), fake_d.get_dim_out(), self.n_neuron + ) + self.dp_fn.bias_atom_e = rng.uniform(size=[self.ntypes]) + + def test_consistency(self): + dp_energy, values = base_fitting_net( + self.dp_fn, self.embedding, self.natoms, self.atype + ) + my_fn = EnergyFittingNet( + self.ntypes, + self.embedding_width, + neuron=self.n_neuron, + bias_atom_e=self.dp_fn.bias_atom_e, + mixed_types=False, + ).to(env.DEVICE) + for name, param in my_fn.named_parameters(): + matched = re.match( + r"filter_layers\.networks\.(\d).layers\.(\d)\.([a-z]+)", name + ) + key = None + if matched: + if int(matched.group(2)) == len(self.n_neuron): + layer_id = -1 + else: + layer_id = matched.group(2) + key = gen_key( + type_id=matched.group(1), + layer_id=layer_id, + w_or_b=matched.group(3), + ) + assert key is not None + var = values[key] + with paddle.no_grad(): + # Keep parameter value consistency between 2 implentations + paddle.assign(var, param) + embedding = paddle.to_tensor(self.embedding) + embedding = embedding.reshape([4, -1, self.embedding_width]) + atype = paddle.to_tensor(self.atype) + ret = my_fn(embedding.to(env.DEVICE), atype.to(env.DEVICE)) + my_energy = ret["energy"] + my_energy = my_energy.detach().cpu() + np.testing.assert_allclose(dp_energy, my_energy.numpy().reshape([-1])) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/model/test_force_grad.py b/source/tests/pd/model/test_force_grad.py new file mode 100644 index 0000000000..d7b569ef38 --- /dev/null +++ b/source/tests/pd/model/test_force_grad.py @@ -0,0 +1,111 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import json +import unittest +from pathlib import ( + Path, +) +from typing import ( + Optional, +) + +import numpy as np +import paddle + +from deepmd.pd.model.model import ( + get_model, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.utils.data import ( + DeepmdData, +) + +from ...seed import ( + GLOBAL_SEED, +) + + +class CheckSymmetry(DeepmdData): + def __init__( + self, + sys_path: str, + type_map: Optional[list[str]] = None, + ): + super().__init__(sys_path=sys_path, type_map=type_map) + self.add("energy", 1, atomic=False, must=False, high_prec=True) + self.add("force", 3, atomic=True, must=False, high_prec=False) + self.add("virial", 9, atomic=False, must=False, high_prec=False) + + def get_disturb(self, index, atom_index, axis_index, delta): + for i in range( + 0, len(self.dirs) + 1 + ): # note: if different sets can be merged, prefix sum is unused to calculate + if index < self.prefix_sum[i]: + break + frames = self._load_set(self.dirs[i - 1]) + tmp = copy.deepcopy(frames["coord"].reshape(self.nframes, -1, 3)) + tmp[:, atom_index, axis_index] += delta + frames["coord"] = tmp + frame = self._get_subdata(frames, index - self.prefix_sum[i - 1]) + frame = self.reformat_data_torch(frame) + return frame + + +def get_data(batch): + inputs = {} + for key in ["coord", "atype", "box"]: + inputs[key] = batch[key].unsqueeze(0).to(env.DEVICE) + return inputs + + +class TestForceGrad(unittest.TestCase): + def setUp(self): + with open(str(Path(__file__).parent / "water/se_e2_a.json")) as fin: + self.config = json.load(fin) + data_file = [str(Path(__file__).parent / "water/data/data_0")] + self.config["training"]["training_data"]["systems"] = data_file + self.config["training"]["validation_data"]["systems"] = data_file + self.system_index = 0 + self.batch_index = 0 + self.get_dataset(self.system_index, self.batch_index) + self.get_model() + + def get_model(self): + self.model = get_model(self.config["model"]).to(env.DEVICE) + + def get_dataset(self, system_index=0, batch_index=0): + systems = self.config["training"]["training_data"]["systems"] + rcut = self.config["model"]["descriptor"]["rcut"] + sel = self.config["model"]["descriptor"]["sel"] + sec = paddle.cumsum(paddle.to_tensor(sel), axis=0) + type_map = self.config["model"]["type_map"] + self.dpdatasystem = CheckSymmetry( + sys_path=systems[system_index], type_map=type_map + ) + self.origin_batch = self.dpdatasystem.get_item_paddle(batch_index) + + @unittest.skip("it can be replaced by autodiff") + def test_force_grad(self, threshold=1e-2, delta0=1e-6, seed=20): + rng = np.random.default_rng(GLOBAL_SEED) + result0 = self.model(**get_data(self.origin_batch)) + np.random.default_rng(seed) + errors = np.zeros((self.dpdatasystem.natoms, 3)) + for atom_index in range(self.dpdatasystem.natoms): + for axis_index in range(3): + delta = rng.random() * delta0 + disturb_batch = self.dpdatasystem.get_disturb( + self.batch_index, atom_index, axis_index, delta + ) + disturb_result = self.model(**get_data(disturb_batch)) + disturb_force = -(disturb_result["energy"] - result0["energy"]) / delta + disturb_error = ( + result0["force"][0, atom_index, axis_index] - disturb_force + ) + errors[atom_index, axis_index] = disturb_error.detach().cpu().numpy() + self.assertTrue(np.abs(errors).max() < threshold, msg=str(np.abs(errors).max())) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/model/test_forward_lower.py b/source/tests/pd/model/test_forward_lower.py new file mode 100644 index 0000000000..8522aeb22c --- /dev/null +++ b/source/tests/pd/model/test_forward_lower.py @@ -0,0 +1,195 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import unittest + +import paddle + +from deepmd.pd.infer.deep_eval import ( + eval_model, +) +from deepmd.pd.model.model import ( + get_model, +) +from deepmd.pd.utils import ( + aux, + env, +) +from deepmd.pd.utils.nlist import ( + extend_input_and_build_neighbor_list, +) + +from ...seed import ( + GLOBAL_SEED, +) +from .test_permutation import ( # model_dpau, + model_dpa1, + model_dpa2, + model_se_e2_a, + model_spin, + model_zbl, +) + +dtype = paddle.float64 + + +def reduce_tensor(extended_tensor, mapping, nloc: int): + nframes, nall = extended_tensor.shape[:2] + ext_dims = extended_tensor.shape[2:] + reduced_tensor = paddle.zeros( + [nframes, nloc, *ext_dims], + dtype=extended_tensor.dtype, + ).to(device=extended_tensor.place) + mldims = list(mapping.shape) + mapping = mapping.reshape(mldims + [1] * len(ext_dims)).expand( + [-1] * len(mldims) + list(ext_dims) + ) + # nf x nloc x (*ext_dims) + reduced_tensor = aux.scatter_reduce( + reduced_tensor, + 1, + index=mapping, + src=extended_tensor, + reduce="sum", + ) + return reduced_tensor + + +class ForwardLowerTest: + def test( + self, + ): + prec = self.prec + natoms = 5 + cell = 4.0 * paddle.eye(3, dtype=dtype).to(device=env.DEVICE) + generator = paddle.seed(GLOBAL_SEED) + coord = 3.0 * paddle.rand([natoms, 3], dtype=dtype).to(device=env.DEVICE) + spin = 0.5 * paddle.rand([natoms, 3], dtype=dtype).to(device=env.DEVICE) + atype = paddle.to_tensor([0, 0, 0, 1, 1], dtype=paddle.int64).to( + device=env.DEVICE + ) + test_spin = getattr(self, "test_spin", False) + if not test_spin: + test_keys = ["energy", "force", "virial"] + else: + test_keys = ["energy", "force", "force_mag"] + + result_forward = eval_model( + self.model, + coord.unsqueeze(0), + cell.unsqueeze(0), + atype, + spins=spin.unsqueeze(0), + ) + ( + extended_coord, + extended_atype, + mapping, + nlist, + ) = extend_input_and_build_neighbor_list( + coord.unsqueeze(0), + atype.unsqueeze(0), + self.model.get_rcut() + 1.0 + if test_spin + else self.model.get_rcut(), # buffer region for spin nlist + self.model.get_sel(), + mixed_types=self.model.mixed_types(), + box=cell.unsqueeze(0), + ) + extended_spin = aux.take_along_axis( + spin.unsqueeze(0), indices=mapping.unsqueeze(-1).tile((1, 1, 3)), axis=1 + ) + input_dict = { + "extended_coord": extended_coord, + "extended_atype": extended_atype, + "nlist": nlist, + "mapping": mapping, + "do_atomic_virial": False, + } + if test_spin: + input_dict["extended_spin"] = extended_spin + result_forward_lower = self.model.forward_lower(**input_dict) + for key in test_keys: + if key in ["energy"]: + assert paddle.allclose( + result_forward_lower[key], result_forward[key], rtol=prec, atol=prec + ) + elif key in ["force", "force_mag"]: + reduced_vv = reduce_tensor( + result_forward_lower[f"extended_{key}"], mapping, natoms + ) + assert paddle.allclose( + reduced_vv, result_forward[key], rtol=prec, atol=prec + ) + elif key == "virial": + if not hasattr(self, "test_virial") or self.test_virial: + assert paddle.allclose( + result_forward_lower[key], + result_forward[key], + rtol=prec, + atol=prec, + ) + else: + raise RuntimeError(f"Unexpected test key {key}") + + +class TestEnergyModelSeA(unittest.TestCase, ForwardLowerTest): + def setUp(self): + self.prec = 1e-10 + model_params = copy.deepcopy(model_se_e2_a) + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelDPA1(unittest.TestCase, ForwardLowerTest): + def setUp(self): + self.prec = 1e-10 + model_params = copy.deepcopy(model_dpa1) + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelDPA2(unittest.TestCase, ForwardLowerTest): + def setUp(self): + self.prec = 1e-10 + model_params = copy.deepcopy(model_dpa2) + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelZBL(unittest.TestCase, ForwardLowerTest): + def setUp(self): + self.prec = 1e-10 + model_params = copy.deepcopy(model_zbl) + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelSpinSeA(unittest.TestCase, ForwardLowerTest): + def setUp(self): + self.prec = 1e-10 + model_params = copy.deepcopy(model_spin) + self.test_spin = True + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelSpinDPA1(unittest.TestCase, ForwardLowerTest): + def setUp(self): + self.prec = 1e-10 + model_params = copy.deepcopy(model_spin) + model_params["descriptor"] = copy.deepcopy(model_dpa1)["descriptor"] + # double sel for virtual atoms to avoid large error + model_params["descriptor"]["sel"] *= 2 + self.test_spin = True + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelSpinDPA2(unittest.TestCase, ForwardLowerTest): + def setUp(self): + self.prec = 1e-10 + model_params = copy.deepcopy(model_spin) + model_params["descriptor"] = copy.deepcopy(model_dpa2)["descriptor"] + # double sel for virtual atoms to avoid large error + model_params["descriptor"]["repinit"]["nsel"] *= 2 + model_params["descriptor"]["repformer"]["nsel"] *= 2 + self.test_spin = True + self.model = get_model(model_params).to(env.DEVICE) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/model/test_jit.py b/source/tests/pd/model/test_jit.py new file mode 100644 index 0000000000..20a3f67a71 --- /dev/null +++ b/source/tests/pd/model/test_jit.py @@ -0,0 +1,171 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import json +import os +import shutil +import unittest +from copy import ( + deepcopy, +) +from pathlib import ( + Path, +) + +import paddle +from paddle.static import ( + InputSpec, +) + +from deepmd.pd.entrypoints.main import ( + get_trainer, +) +from deepmd.pd.infer import ( + inference, +) + +from .test_permutation import ( + model_dos, + model_dpa1, + model_dpa2, + model_hybrid, + model_se_e2_a, +) + + +class JITTest: + def test_jit(self): + trainer = get_trainer(deepcopy(self.config)) + trainer.run() + paddle.set_flags( + { + "FLAGS_save_cf_stack_op": 1, + "FLAGS_prim_enable_dynamic": 1, + "FLAGS_enable_pir_api": 1, + } + ) + model = paddle.jit.to_static( + inference.Tester("./model.pd").model, full_graph=True + ) + paddle.jit.save( + model, + "./frozen_model", + input_spec=[ + InputSpec([-1, -1, 3], dtype="float64"), + InputSpec([-1, -1], dtype="int32"), + InputSpec([-1, -1, -1], dtype="int32"), + ], + ) + + def tearDown(self): + for f in os.listdir("."): + if f.startswith("model") and f.endswith("pt"): + os.remove(f) + if f in ["lcurve.out", "frozen_model.json"]: + os.remove(f) + if f in ["stat_files"]: + shutil.rmtree(f) + if f in ["checkpoint"]: + os.remove(f) + + +class TestEnergyModelSeA(unittest.TestCase, JITTest): + def setUp(self): + input_json = str(Path(__file__).parent / "water/se_atten.json") + with open(input_json) as f: + self.config = json.load(f) + data_file = [str(Path(__file__).parent / "water/data/data_0")] + self.config["training"]["training_data"]["systems"] = data_file + self.config["training"]["validation_data"]["systems"] = data_file + self.config["model"] = deepcopy(model_se_e2_a) + self.config["training"]["numb_steps"] = 10 + self.config["training"]["save_freq"] = 10 + + def tearDown(self): + JITTest.tearDown(self) + + +class TestDOSModelSeA(unittest.TestCase, JITTest): + def setUp(self): + input_json = str(Path(__file__).parent.parent / "dos/input.json") + with open(input_json) as f: + self.config = json.load(f) + data_file = [str(Path(__file__).parent.parent / "dos/data/global_system")] + self.config["training"]["training_data"]["systems"] = data_file + self.config["training"]["validation_data"]["systems"] = data_file + self.config["model"] = deepcopy(model_dos) + self.config["training"]["numb_steps"] = 10 + self.config["training"]["save_freq"] = 10 + + def tearDown(self): + JITTest.tearDown(self) + + +class TestEnergyModelDPA1(unittest.TestCase, JITTest): + def setUp(self): + input_json = str(Path(__file__).parent / "water/se_atten.json") + with open(input_json) as f: + self.config = json.load(f) + data_file = [str(Path(__file__).parent / "water/data/data_0")] + self.config["training"]["training_data"]["systems"] = data_file + self.config["training"]["validation_data"]["systems"] = data_file + self.config["model"] = deepcopy(model_dpa1) + self.config["training"]["numb_steps"] = 10 + self.config["training"]["save_freq"] = 10 + + def tearDown(self): + JITTest.tearDown(self) + + +@unittest.skip("var dtype int32/int64 confused in if block") +class TestEnergyModelDPA2(unittest.TestCase, JITTest): + def setUp(self): + input_json = str(Path(__file__).parent / "water/se_atten.json") + with open(input_json) as f: + self.config = json.load(f) + data_file = [str(Path(__file__).parent / "water/data/data_0")] + self.config["training"]["training_data"]["systems"] = data_file + self.config["training"]["validation_data"]["systems"] = data_file + self.config["model"] = deepcopy(model_dpa2) + self.config["training"]["numb_steps"] = 10 + self.config["training"]["save_freq"] = 10 + + def tearDown(self): + JITTest.tearDown(self) + + +@unittest.skip("generated_tensor_2553 can not when jit.save") +class TestEnergyModelHybrid(unittest.TestCase, JITTest): + def setUp(self): + input_json = str(Path(__file__).parent / "water/se_atten.json") + with open(input_json) as f: + self.config = json.load(f) + data_file = [str(Path(__file__).parent / "water/data/data_0")] + self.config["training"]["training_data"]["systems"] = data_file + self.config["training"]["validation_data"]["systems"] = data_file + self.config["model"] = deepcopy(model_hybrid) + self.config["training"]["numb_steps"] = 10 + self.config["training"]["save_freq"] = 10 + + def tearDown(self): + JITTest.tearDown(self) + + +@unittest.skip("generated_tensor_2553 can not when jit.save") +class TestEnergyModelHybrid2(unittest.TestCase, JITTest): + def setUp(self): + input_json = str(Path(__file__).parent / "water/se_atten.json") + with open(input_json) as f: + self.config = json.load(f) + data_file = [str(Path(__file__).parent / "water/data/data_0")] + self.config["training"]["training_data"]["systems"] = data_file + self.config["training"]["validation_data"]["systems"] = data_file + self.config["model"] = deepcopy(model_hybrid) + # self.config["model"]["descriptor"]["hybrid_mode"] = "sequential" + self.config["training"]["numb_steps"] = 10 + self.config["training"]["save_freq"] = 10 + + def tearDown(self): + JITTest.tearDown(self) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/model/test_linear_atomic_model.py b/source/tests/pd/model/test_linear_atomic_model.py new file mode 100644 index 0000000000..18a15c8ee9 --- /dev/null +++ b/source/tests/pd/model/test_linear_atomic_model.py @@ -0,0 +1,216 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import unittest +from unittest.mock import ( + patch, +) + +import numpy as np +import paddle + +from deepmd.dpmodel.atomic_model import ( + DPZBLLinearEnergyAtomicModel as DPDPZBLLinearEnergyAtomicModel, +) +from deepmd.pd.model.atomic_model import ( + DPAtomicModel, + DPZBLLinearEnergyAtomicModel, + PairTabAtomicModel, +) +from deepmd.pd.model.descriptor import ( + DescrptDPA1, +) +from deepmd.pd.model.model import ( + DPZBLModel, +) +from deepmd.pd.model.task.ener import ( + InvarFitting, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, + to_paddle_tensor, +) + +from ...seed import ( + GLOBAL_SEED, +) +from .test_env_mat import ( + TestCaseSingleFrameWithNlist, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION + + +class TestWeightCalculation(unittest.TestCase): + @patch("numpy.loadtxt") + def test_pairwise(self, mock_loadtxt): + file_path = "dummy_path" + mock_loadtxt.return_value = np.array( + [ + [0.05, 1.0, 2.0, 3.0], + [0.1, 0.8, 1.6, 2.4], + [0.15, 0.5, 1.0, 1.5], + [0.2, 0.25, 0.4, 0.75], + [0.25, 0.0, 0.0, 0.0], + ] + ) + extended_atype = paddle.to_tensor([[0, 0]]).to(device=env.DEVICE) + nlist = paddle.to_tensor([[[1], [-1]]]).to(device=env.DEVICE) + + ds = DescrptDPA1( + rcut_smth=0.3, + rcut=0.4, + sel=[3], + ntypes=2, + ).to(env.DEVICE) + ft = InvarFitting( + "energy", + 2, + ds.get_dim_out(), + 1, + mixed_types=ds.mixed_types(), + ).to(env.DEVICE) + + type_map = ["foo", "bar"] + zbl_model = PairTabAtomicModel( + tab_file=file_path, rcut=0.3, sel=2, type_map=type_map[::-1] + ) + dp_model = DPAtomicModel(ds, ft, type_map=type_map).to(env.DEVICE) + wgt_model = DPZBLLinearEnergyAtomicModel( + dp_model, + zbl_model, + sw_rmin=0.1, + sw_rmax=0.25, + type_map=type_map, + ).to(env.DEVICE) + wgt_res = [] + for dist in np.linspace(0.05, 0.3, 10): + extended_coord = paddle.to_tensor( + [ + [ + [0.0, 0.0, 0.0], + [0.0, dist, 0.0], + ], + ], + dtype=paddle.float64, + place=env.DEVICE, + ) + + wgt_model.forward_atomic(extended_coord, extended_atype, nlist) + + wgt_res.append(wgt_model.zbl_weight) + results = paddle.stack(wgt_res).reshape([10, 2]) + excepted_res = paddle.to_tensor( + [ + [1.0, 0.0], + [1.0, 0.0], + [0.9995, 0.0], + [0.9236, 0.0], + [0.6697, 0.0], + [0.3303, 0.0], + [0.0764, 0.0], + [0.0005, 0.0], + [0.0, 0.0], + [0.0, 0.0], + ], + dtype=paddle.float64, + place=env.DEVICE, + ) + assert paddle.allclose(results, excepted_res, rtol=0.0001, atol=0.0001) + + +class TestIntegration(unittest.TestCase, TestCaseSingleFrameWithNlist): + @patch("numpy.loadtxt") + def setUp(self, mock_loadtxt): + TestCaseSingleFrameWithNlist.setUp(self) + file_path = "dummy_path" + mock_loadtxt.return_value = np.array( + [ + [0.005, 1.0, 2.0, 3.0], + [0.01, 0.8, 1.6, 2.4], + [0.015, 0.5, 1.0, 1.5], + [0.02, 0.25, 0.4, 0.75], + ] + ) + ds = DescrptDPA1( + self.rcut, + self.rcut_smth, + sum(self.sel), + self.nt, + ).to(env.DEVICE) + ft = InvarFitting( + "energy", + self.nt, + ds.get_dim_out(), + 1, + mixed_types=ds.mixed_types(), + ).to(env.DEVICE) + type_map = ["foo", "bar"] + dp_model = DPAtomicModel(ds, ft, type_map=type_map).to(env.DEVICE) + zbl_model = PairTabAtomicModel( + file_path, self.rcut, sum(self.sel), type_map=type_map + ) + self.md0 = DPZBLLinearEnergyAtomicModel( + dp_model, + zbl_model, + sw_rmin=0.1, + sw_rmax=0.25, + type_map=type_map, + ).to(env.DEVICE) + self.md1 = DPZBLLinearEnergyAtomicModel.deserialize(self.md0.serialize()).to( + env.DEVICE + ) + self.md2 = DPDPZBLLinearEnergyAtomicModel.deserialize(self.md0.serialize()) + self.md3 = DPZBLModel( + dp_model, zbl_model, sw_rmin=0.1, sw_rmax=0.25, type_map=type_map + ) + + def test_self_consistency(self): + args = [ + to_paddle_tensor(ii) for ii in [self.coord_ext, self.atype_ext, self.nlist] + ] + ret0 = self.md0.forward_atomic(*args) + ret1 = self.md1.forward_atomic(*args) + ret2 = self.md2.forward_atomic(self.coord_ext, self.atype_ext, self.nlist) + np.testing.assert_allclose( + to_numpy_array(ret0["energy"]), + to_numpy_array(ret1["energy"]), + ) + + np.testing.assert_allclose( + to_numpy_array(ret0["energy"]), ret2["energy"], atol=0.001, rtol=0.001 + ) + + def test_jit(self): + md1 = paddle.jit.to_static(self.md1) + # atomic model no more export methods + # self.assertEqual(md1.get_rcut(), self.rcut) + # self.assertEqual(md1.get_type_map(), ["foo", "bar"]) + md3 = paddle.jit.to_static(self.md3) + # atomic model no more export methods + # self.assertEqual(md3.get_rcut(), self.rcut) + # self.assertEqual(md3.get_type_map(), ["foo", "bar"]) + + +class TestRemmapMethod(unittest.TestCase): + def test_valid(self): + generator = paddle.seed(GLOBAL_SEED) + atype = paddle.randint(0, 3, (4, 20)).to(device=env.DEVICE) + commonl = ["H", "O", "S"] + originl = ["Si", "H", "O", "S"] + mapping = DPZBLLinearEnergyAtomicModel.remap_atype(originl, commonl) + new_atype = mapping[atype] + + def trans(atype, map): + idx = atype.flatten().tolist() + res = [] + for i in idx: + res.append(map[i]) + return res + + assert trans(atype, commonl) == trans(new_atype, originl) + + +if __name__ == "__main__": + unittest.main(warnings="ignore") diff --git a/source/tests/pd/model/test_linear_atomic_model_stat.py b/source/tests/pd/model/test_linear_atomic_model_stat.py new file mode 100644 index 0000000000..086a2e20de --- /dev/null +++ b/source/tests/pd/model/test_linear_atomic_model_stat.py @@ -0,0 +1,248 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import tempfile +import unittest +from pathlib import ( + Path, +) +from typing import ( + Optional, +) + +import h5py +import numpy as np +import paddle + +from deepmd.dpmodel.output_def import ( + FittingOutputDef, + OutputVariableDef, +) +from deepmd.pd.model.atomic_model import ( + DPAtomicModel, + LinearEnergyAtomicModel, +) +from deepmd.pd.model.descriptor.dpa1 import ( + DescrptDPA1, +) +from deepmd.pd.model.task.base_fitting import ( + BaseFitting, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, + to_paddle_tensor, +) +from deepmd.utils.path import ( + DPPath, +) + +from .test_env_mat import ( + TestCaseSingleFrameWithNlist, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION + + +class FooFittingA(paddle.nn.Layer, BaseFitting): + def output_def(self): + return FittingOutputDef( + [ + OutputVariableDef( + "energy", + [1], + reducible=True, + r_differentiable=True, + c_differentiable=True, + ), + ] + ) + + def serialize(self) -> dict: + raise NotImplementedError + + def change_type_map( + self, type_map: list[str], model_with_new_type_stat=None + ) -> None: + raise NotImplementedError + + def get_type_map(self) -> list[str]: + raise NotImplementedError + + def forward( + self, + descriptor: paddle.Tensor, + atype: paddle.Tensor, + gr: Optional[paddle.Tensor] = None, + g2: Optional[paddle.Tensor] = None, + h2: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + ): + nf, nloc, _ = descriptor.shape + ret = {} + ret["energy"] = ( + paddle.to_tensor( + [ + [1.0, 2.0, 3.0], + [4.0, 5.0, 6.0], + ] + ) + .reshape([nf, nloc, *self.output_def()["energy"].shape]) + .to(env.GLOBAL_PD_FLOAT_PRECISION) + .to(env.DEVICE) + ) + + return ret + + +class FooFittingB(paddle.nn.Layer, BaseFitting): + def output_def(self): + return FittingOutputDef( + [ + OutputVariableDef( + "energy", + [1], + reducible=True, + r_differentiable=True, + c_differentiable=True, + ), + ] + ) + + def serialize(self) -> dict: + raise NotImplementedError + + def change_type_map( + self, type_map: list[str], model_with_new_type_stat=None + ) -> None: + raise NotImplementedError + + def get_type_map(self) -> list[str]: + raise NotImplementedError + + def forward( + self, + descriptor: paddle.Tensor, + atype: paddle.Tensor, + gr: Optional[paddle.Tensor] = None, + g2: Optional[paddle.Tensor] = None, + h2: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + ): + nf, nloc, _ = descriptor.shape + ret = {} + ret["energy"] = ( + paddle.to_tensor( + [ + [7.0, 8.0, 9.0], + [10.0, 11.0, 12.0], + ] + ) + .reshape([nf, nloc, *self.output_def()["energy"].shape]) + .to(env.GLOBAL_PD_FLOAT_PRECISION) + .to(env.DEVICE) + ) + + return ret + + +class TestAtomicModelStat(unittest.TestCase, TestCaseSingleFrameWithNlist): + def tearDown(self): + self.tempdir.cleanup() + + def setUp(self): + TestCaseSingleFrameWithNlist.setUp(self) + nf, nloc, nnei = self.nlist.shape + self.merged_output_stat = [ + { + "coord": to_paddle_tensor(np.zeros([2, 3, 3])), + "atype": to_paddle_tensor( + np.array([[0, 0, 1], [0, 1, 1]], dtype=np.int32) + ), + "atype_ext": to_paddle_tensor( + np.array([[0, 0, 1, 0], [0, 1, 1, 0]], dtype=np.int32) + ), + "box": to_paddle_tensor(np.zeros([2, 3, 3])), + "natoms": to_paddle_tensor( + np.array([[3, 3, 2, 1], [3, 3, 1, 2]], dtype=np.int32) + ), + # bias of foo: 1, 3 + "energy": to_paddle_tensor(np.array([5.0, 7.0]).reshape(2, 1)), + "find_energy": np.float32(1.0), + } + ] + self.tempdir = tempfile.TemporaryDirectory() + h5file = str((Path(self.tempdir.name) / "testcase.h5").resolve()) + with h5py.File(h5file, "w") as f: + pass + self.stat_file_path = DPPath(h5file, "a") + + def test_linear_atomic_model_stat_with_bias(self): + nf, nloc, nnei = self.nlist.shape + ds = DescrptDPA1( + self.rcut, + self.rcut_smth, + sum(self.sel), + self.nt, + ).to(env.DEVICE) + ft_a = FooFittingA().to(env.DEVICE) + ft_b = FooFittingB().to(env.DEVICE) + type_map = ["foo", "bar"] + md0 = DPAtomicModel( + ds, + ft_a, + type_map=type_map, + ).to(env.DEVICE) + md1 = DPAtomicModel( + ds, + ft_b, + type_map=type_map, + ).to(env.DEVICE) + linear_model = LinearEnergyAtomicModel([md0, md1], type_map=type_map).to( + env.DEVICE + ) + + args = [ + to_paddle_tensor(ii) for ii in [self.coord_ext, self.atype_ext, self.nlist] + ] + # nf x nloc + at = self.atype_ext[:, :nloc] + + # 1. test run without bias + # nf x na x odim + ret0 = linear_model.forward_common_atomic(*args) + + ret0 = to_numpy_array(ret0["energy"]) + ret_no_bias = [] + for md in linear_model.models: + ret_no_bias.append( + to_numpy_array(md.forward_common_atomic(*args)["energy"]) + ) + expected_ret0 = np.array( + [ + [4.0, 5.0, 6.0], + [7.0, 8.0, 9.0], + ] + ).reshape(nf, nloc, *linear_model.fitting_output_def()["energy"].shape) + + np.testing.assert_almost_equal(ret0, expected_ret0) + + # 2. test bias is applied + linear_model.compute_or_load_out_stat( + self.merged_output_stat, stat_file_path=self.stat_file_path + ) + # bias applied to sub atomic models. + ener_bias = np.array([1.0, 3.0]).reshape(2, 1) + linear_ret = [] + for idx, md in enumerate(linear_model.models): + ret = md.forward_common_atomic(*args) + ret = to_numpy_array(ret["energy"]) + linear_ret.append(ret_no_bias[idx] + ener_bias[at]) + np.testing.assert_almost_equal((ret_no_bias[idx] + ener_bias[at]), ret) + + # linear model not adding bias again + ret1 = linear_model.forward_common_atomic(*args) + ret1 = to_numpy_array(ret1["energy"]) + np.testing.assert_almost_equal(np.mean(np.stack(linear_ret), axis=0), ret1) diff --git a/source/tests/pd/model/test_make_hessian_model.py b/source/tests/pd/model/test_make_hessian_model.py new file mode 100644 index 0000000000..ebdccf4bc8 --- /dev/null +++ b/source/tests/pd/model/test_make_hessian_model.py @@ -0,0 +1,179 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import unittest + +import numpy as np +import paddle + +from deepmd.dpmodel.output_def import ( + OutputVariableCategory, +) +from deepmd.pd.model.descriptor.se_a import ( + DescrptSeA, +) +from deepmd.pd.model.model import ( + EnergyModel, + make_hessian_model, +) +from deepmd.pd.model.task.ener import ( + InvarFitting, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, + to_paddle_tensor, +) + +from ...seed import ( + GLOBAL_SEED, +) + +dtype = paddle.float64 + + +def finite_hessian(f, x, delta=1e-6): + in_shape = x.shape + assert len(in_shape) == 1 + y0 = f(x) + out_shape = y0.shape + res = np.empty(out_shape + in_shape + in_shape) + for iidx in np.ndindex(*in_shape): + for jidx in np.ndindex(*in_shape): + i0 = np.zeros(in_shape) + i1 = np.zeros(in_shape) + i2 = np.zeros(in_shape) + i3 = np.zeros(in_shape) + i0[iidx] += delta + i2[iidx] += delta + i1[iidx] -= delta + i3[iidx] -= delta + i0[jidx] += delta + i1[jidx] += delta + i2[jidx] -= delta + i3[jidx] -= delta + y0 = f(x + i0) + y1 = f(x + i1) + y2 = f(x + i2) + y3 = f(x + i3) + res[(Ellipsis, *iidx, *jidx)] = (y0 + y3 - y1 - y2) / (4 * delta**2.0) + return res + + +class HessianTest: + def test( + self, + ): + # setup test case + places = 6 + delta = 1e-3 + natoms = self.nloc + nf = self.nf + nv = self.nv + generator = paddle.seed(GLOBAL_SEED) + cell0 = paddle.rand([3, 3], dtype=dtype).to(device=env.DEVICE) + cell0 = 1.0 * (cell0 + cell0.T) + 5.0 * paddle.eye(3).to(device=env.DEVICE) + cell1 = paddle.rand([3, 3], dtype=dtype).to(device=env.DEVICE) + cell1 = 1.0 * (cell1 + cell1.T) + 5.0 * paddle.eye(3).to(device=env.DEVICE) + cell = paddle.stack([cell0, cell1]) + coord = paddle.rand([nf, natoms, 3], dtype=dtype).to(device=env.DEVICE) + coord = paddle.matmul(coord, cell) + cell = cell.reshape([nf, 9]) + coord = coord.reshape([nf, natoms * 3]) + atype = ( + paddle.stack( + [ + paddle.to_tensor([0, 0, 1]), + paddle.to_tensor([1, 0, 1]), + ] + ) + .reshape([nf, natoms]) + .to(env.DEVICE) + ) + nfp, nap = 2, 3 + fparam = paddle.rand([nf, nfp], dtype=dtype).to(device=env.DEVICE) + aparam = paddle.rand([nf, natoms * nap], dtype=dtype).to(device=env.DEVICE) + # forward hess and valu models + ret_dict0 = self.model_hess.forward_common( + coord, atype, box=cell, fparam=fparam, aparam=aparam + ) + ret_dict1 = self.model_valu.forward_common( + coord, atype, box=cell, fparam=fparam, aparam=aparam + ) + # compare hess and value models + assert paddle.allclose(ret_dict0["energy"], ret_dict1["energy"]) + ana_hess = ret_dict0["energy_derv_r_derv_r"] + + # compute finite difference + fnt_hess = [] + for ii in range(nf): + + def np_infer( + xx, + ): + ret = self.model_valu.forward_common( + to_paddle_tensor(xx).unsqueeze(0), + atype[ii].unsqueeze(0), + box=cell[ii].unsqueeze(0), + fparam=fparam[ii].unsqueeze(0), + aparam=aparam[ii].unsqueeze(0), + ) + # detach + ret = {kk: to_numpy_array(ret[kk]) for kk in ret} + return ret + + def ff(xx): + return np_infer(xx)["energy_redu"] + + xx = to_numpy_array(coord[ii]) + fnt_hess.append(finite_hessian(ff, xx, delta=delta).squeeze()) + + # compare finite difference with autodiff + fnt_hess = np.stack(fnt_hess).reshape([nf, nv, natoms * 3, natoms * 3]) + np.testing.assert_almost_equal( + fnt_hess, to_numpy_array(ana_hess), decimal=places + ) + + +@unittest.skip("TODO") +class TestDPModel(unittest.TestCase, HessianTest): + def setUp(self): + paddle.seed(2) + self.nf = 2 + self.nloc = 3 + self.rcut = 4.0 + self.rcut_smth = 3.0 + self.sel = [10, 10] + self.nt = 2 + self.nv = 2 + ds = DescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + neuron=[2, 4, 8], + axis_neuron=2, + ).to(env.DEVICE) + ft0 = InvarFitting( + "energy", + self.nt, + ds.get_dim_out(), + self.nv, + mixed_types=ds.mixed_types(), + do_hessian=True, + neuron=[4, 4, 4], + ).to(env.DEVICE) + type_map = ["foo", "bar"] + self.model_hess = make_hessian_model(EnergyModel)( + ds, ft0, type_map=type_map + ).to(env.DEVICE) + self.model_valu = EnergyModel.deserialize(self.model_hess.serialize()) + self.model_hess.requires_hessian("energy") + + def test_output_def(self): + self.assertTrue(self.model_hess.atomic_output_def()["energy"].r_hessian) + self.assertFalse(self.model_valu.atomic_output_def()["energy"].r_hessian) + self.assertTrue(self.model_hess.model_output_def()["energy"].r_hessian) + self.assertEqual( + self.model_hess.model_output_def()["energy_derv_r_derv_r"].category, + OutputVariableCategory.DERV_R_DERV_R, + ) diff --git a/source/tests/pd/model/test_mlp.py b/source/tests/pd/model/test_mlp.py new file mode 100644 index 0000000000..90653644d3 --- /dev/null +++ b/source/tests/pd/model/test_mlp.py @@ -0,0 +1,283 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import itertools +import unittest + +import numpy as np +import paddle + +from deepmd.dpmodel.utils import EmbeddingNet as DPEmbeddingNet +from deepmd.dpmodel.utils import FittingNet as DPFittingNet +from deepmd.dpmodel.utils import ( + NativeLayer, + NativeNet, +) +from deepmd.pd.model.network.mlp import ( + MLP, + EmbeddingNet, + FittingNet, + MLPLayer, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + PRECISION_DICT, +) + + +def get_tols(prec): + if prec in ["single", "float32"]: + rtol, atol = 0.0, 1e-4 + elif prec in ["double", "float64"]: + rtol, atol = 0.0, 1e-12 + # elif prec in ["half", "float16"]: + # rtol, atol=1e-2, 0 + else: + raise ValueError(f"unknown prec {prec}") + return rtol, atol + + +class TestMLPLayer(unittest.TestCase): + def setUp(self): + self.test_cases = itertools.product( + [(5, 5), (5, 10), (5, 8), (8, 5)], # inp, out + [True, False], # bias + [True, False], # use time step + ["tanh", "none"], # activation + [True, False], # resnet + [None, [4], [3, 2]], # prefix shapes + ["float32", "double"], # precision + ) + + def test_match_native_layer( + self, + ): + for (ninp, nout), bias, ut, ac, resnet, ashp, prec in self.test_cases: + # input + inp_shap = [ninp] + if ashp is not None: + inp_shap = ashp + inp_shap + rtol, atol = get_tols(prec) + dtype = PRECISION_DICT[prec] + xx = ( + paddle.arange(np.prod(inp_shap), dtype=dtype) + .to(device=env.DEVICE) + .reshape(inp_shap) + ) + # def mlp layer + ml = MLPLayer(ninp, nout, bias, ut, ac, resnet, precision=prec).to( + env.DEVICE + ) + # check consistency + nl = NativeLayer.deserialize(ml.serialize()) + np.testing.assert_allclose( + ml.forward(xx).detach().cpu().numpy(), + nl.call(xx.detach().cpu().numpy()), + rtol=rtol, + atol=atol, + err_msg=f"(i={ninp}, o={nout}) bias={bias} use_dt={ut} act={ac} resnet={resnet} prec={prec}", + ) + # check self-consistency + ml1 = MLPLayer.deserialize(ml.serialize()).to(env.DEVICE) + np.testing.assert_allclose( + ml.forward(xx).detach().cpu().numpy(), + ml1.forward(xx).detach().cpu().numpy(), + rtol=rtol, + atol=atol, + err_msg=f"(i={ninp}, o={nout}) bias={bias} use_dt={ut} act={ac} resnet={resnet} prec={prec}", + ) + + def test_jit(self): + for (ninp, nout), bias, ut, ac, resnet, _, prec in self.test_cases: + ml = MLPLayer(ninp, nout, bias, ut, ac, resnet, precision=prec) + model = paddle.jit.to_static(ml) + ml1 = MLPLayer.deserialize(ml.serialize()) + model = paddle.jit.to_static(ml1) + + +class TestMLP(unittest.TestCase): + def setUp(self): + self.test_cases = itertools.product( + [[2, 2, 4, 8], [1, 3, 3]], # inp and hiddens + [True, False], # bias + [True, False], # use time step + ["tanh", "none"], # activation + [True, False], # resnet + [None, [4], [3, 2]], # prefix shapes + ["float32", "double"], # precision + ) + + def test_match_native_net( + self, + ): + for ndims, bias, ut, ac, resnet, ashp, prec in self.test_cases: + # input + inp_shap = [ndims[0]] + if ashp is not None: + inp_shap = ashp + inp_shap + rtol, atol = get_tols(prec) + dtype = PRECISION_DICT[prec] + xx = ( + paddle.arange(np.prod(inp_shap), dtype=dtype) + .to(device=env.DEVICE) + .reshape(inp_shap) + ) + # def MLP + layers = [] + for ii in range(1, len(ndims)): + layers.append( + MLPLayer( + ndims[ii - 1], ndims[ii], bias, ut, ac, resnet, precision=prec + ).serialize() + ) + ml = MLP(layers).to(env.DEVICE) + # check consistency + nl = NativeNet.deserialize(ml.serialize()) + np.testing.assert_allclose( + ml.forward(xx).detach().cpu().numpy(), + nl.call(xx.detach().cpu().numpy()), + rtol=rtol, + atol=atol, + err_msg=f"net={ndims} bias={bias} use_dt={ut} act={ac} resnet={resnet} prec={prec}", + ) + # check self-consistency + ml1 = MLP.deserialize(ml.serialize()).to(env.DEVICE) + np.testing.assert_allclose( + ml.forward(xx).detach().cpu().numpy(), + ml1.forward(xx).detach().cpu().numpy(), + rtol=rtol, + atol=atol, + err_msg=f"net={ndims} bias={bias} use_dt={ut} act={ac} resnet={resnet} prec={prec}", + ) + + def test_jit(self): + for ndims, bias, ut, ac, resnet, _, prec in self.test_cases: + layers = [] + for ii in range(1, len(ndims)): + ml = layers.append( + MLPLayer( + ndims[ii - 1], ndims[ii], bias, ut, ac, resnet, precision=prec + ).serialize() + ) + ml = MLP(ml) + model = paddle.jit.to_static(ml) + ml1 = MLP.deserialize(ml.serialize()) + model = paddle.jit.to_static(ml1) + + +class TestEmbeddingNet(unittest.TestCase): + def setUp(self): + self.test_cases = itertools.product( + [1, 3], # inp + [[24, 48, 96], [24, 36]], # and hiddens + ["tanh", "none"], # activation + [True, False], # resnet_dt + ["float32", "double"], # precision + ) + + def test_match_embedding_net( + self, + ): + for idim, nn, act, idt, prec in self.test_cases: + # input + rtol, atol = get_tols(prec) + dtype = PRECISION_DICT[prec] + xx = paddle.arange(idim, dtype=dtype).to(device=env.DEVICE) + # def MLP + ml = EmbeddingNet(idim, nn, act, idt, prec).to(env.DEVICE) + # check consistency + nl = DPEmbeddingNet.deserialize(ml.serialize()) + np.testing.assert_allclose( + ml.forward(xx).detach().cpu().numpy(), + nl.call(xx.detach().cpu().numpy()), + rtol=rtol, + atol=atol, + err_msg=f"idim={idim} nn={nn} use_dt={idt} act={act} prec={prec}", + ) + # check self-consistency + ml1 = EmbeddingNet.deserialize(ml.serialize()).to(env.DEVICE) + np.testing.assert_allclose( + ml.forward(xx).detach().cpu().numpy(), + ml1.forward(xx).detach().cpu().numpy(), + rtol=rtol, + atol=atol, + err_msg=f"idim={idim} nn={nn} use_dt={idt} act={act} prec={prec}", + ) + + def test_jit( + self, + ): + for idim, nn, act, idt, prec in self.test_cases: + # def MLP + ml = EmbeddingNet(idim, nn, act, idt, prec).to(env.DEVICE) + ml1 = EmbeddingNet.deserialize(ml.serialize()).to(env.DEVICE) + model = paddle.jit.to_static(ml) + model = paddle.jit.to_static(ml1) + + +class TestFittingNet(unittest.TestCase): + def setUp(self): + self.test_cases = itertools.product( + [1, 3], # inp + [1, 5], # out + [[24, 48, 96], [24, 36]], # and hiddens + ["tanh", "none"], # activation + [True, False], # resnet_dt + ["float32", "double"], # precision + [True, False], # bias_out + ) + + def test_match_fitting_net( + self, + ): + for idim, odim, nn, act, idt, prec, ob in self.test_cases: + # input + rtol, atol = get_tols(prec) + dtype = PRECISION_DICT[prec] + xx = paddle.arange(idim, dtype=dtype).to(device=env.DEVICE) + # def MLP + ml = FittingNet( + idim, + odim, + neuron=nn, + activation_function=act, + resnet_dt=idt, + precision=prec, + bias_out=ob, + ).to(env.DEVICE) + # check consistency + nl = DPFittingNet.deserialize(ml.serialize()) + np.testing.assert_allclose( + ml.forward(xx).detach().cpu().numpy(), + nl.call(xx.detach().cpu().numpy()), + rtol=rtol, + atol=atol, + err_msg=f"idim={idim} nn={nn} use_dt={idt} act={act} prec={prec}", + ) + # check self-consistency + ml1 = FittingNet.deserialize(ml.serialize()).to(env.DEVICE) + np.testing.assert_allclose( + ml.forward(xx).detach().cpu().numpy(), + ml1.forward(xx).detach().cpu().numpy(), + rtol=rtol, + atol=atol, + err_msg=f"idim={idim} nn={nn} use_dt={idt} act={act} prec={prec}", + ) + + def test_jit( + self, + ): + for idim, odim, nn, act, idt, prec, ob in self.test_cases: + # def MLP + ml = FittingNet( + idim, + odim, + neuron=nn, + activation_function=act, + resnet_dt=idt, + precision=prec, + bias_out=ob, + ).to(env.DEVICE) + ml1 = FittingNet.deserialize(ml.serialize()).to(env.DEVICE) + model = paddle.jit.to_static(ml) + model = paddle.jit.to_static(ml1) diff --git a/source/tests/pd/model/test_model.py b/source/tests/pd/model/test_model.py new file mode 100644 index 0000000000..1bdc1aa74d --- /dev/null +++ b/source/tests/pd/model/test_model.py @@ -0,0 +1,424 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import collections +import json +import unittest + +import numpy as np +import paddle +import tensorflow.compat.v1 as tf + +from deepmd.pd.utils import ( + env, +) + +tf.disable_eager_execution() + +from pathlib import ( + Path, +) + +from deepmd.pd.loss import ( + EnergyStdLoss, +) +from deepmd.pd.model.model import ( + get_model, +) +from deepmd.pd.utils.dataloader import ( + DpLoaderSet, +) +from deepmd.pd.utils.env import ( + DEVICE, +) +from deepmd.pd.utils.learning_rate import LearningRateExp as MyLRExp +from deepmd.tf.common import ( + expand_sys_str, +) +from deepmd.tf.descriptor import DescrptSeA as DescrptSeA_tf +from deepmd.tf.fit import ( + EnerFitting, +) +from deepmd.tf.loss import ( + EnerStdLoss, +) +from deepmd.tf.model import ( + EnerModel, +) +from deepmd.tf.utils.data_system import ( + DeepmdDataSystem, +) +from deepmd.tf.utils.learning_rate import ( + LearningRateExp, +) + +from ..test_finetune import ( + energy_data_requirement, +) + +VariableState = collections.namedtuple("VariableState", ["value", "gradient"]) + + +def paddle2tf(paddle_name, last_layer_id=None): + fields = paddle_name.split(".") + offset = int(fields[3] == "networks") + 1 + element_id = int(fields[2 + offset]) + if fields[1] == "descriptor": + layer_id = int(fields[4 + offset]) + 1 + weight_type = fields[5 + offset] + ret = "filter_type_all/%s_%d_%d:0" % (weight_type, layer_id, element_id) + elif fields[1] == "fitting_net": + layer_id = int(fields[4 + offset]) + weight_type = fields[5 + offset] + if layer_id != last_layer_id: + ret = "layer_%d_type_%d/%s:0" % (layer_id, element_id, weight_type) + else: + ret = "final_layer_type_%d/%s:0" % (element_id, weight_type) + else: + raise RuntimeError(f"Unexpected parameter name: {paddle_name}") + return ret + + +class DpTrainer: + def __init__(self): + with open(str(Path(__file__).parent / "water/se_e2_a.json")) as fin: + content = fin.read() + config = json.loads(content) + data_file = [str(Path(__file__).parent / "water/data/data_0")] + config["training"]["training_data"]["systems"] = data_file + config["training"]["validation_data"]["systems"] = data_file + model_config = config["model"] + self.rcut = model_config["descriptor"]["rcut"] + self.rcut_smth = model_config["descriptor"]["rcut_smth"] + self.sel = model_config["descriptor"]["sel"] + self.systems = config["training"]["validation_data"]["systems"] + if isinstance(self.systems, str): + self.systems = expand_sys_str(self.systems) + self.batch_size = config["training"]["training_data"]["batch_size"] + self.type_map = model_config["type_map"] + self.filter_neuron = model_config["descriptor"]["neuron"] + self.axis_neuron = model_config["descriptor"]["axis_neuron"] + self.n_neuron = model_config["fitting_net"]["neuron"] + self.data_stat_nbatch = 3 + self.start_lr = 0.001 + self.stop_lr = 3.51e-8 + self.decay_steps = 500 + self.stop_steps = 1600 + self.start_pref_e = 1.0 + self.limit_pref_e = 2.0 + self.start_pref_f = 2.0 + self.limit_pref_f = 1.0 + self.ntypes = len(self.type_map) + + def get_intermediate_state(self, num_steps=1): + dp_model = self._get_dp_model() + dp_loss = self._get_dp_loss() + dp_lr = self._get_dp_lr() + dp_ds = self._get_dp_dataset() + dp_ds.add_data_requirements(dp_model.input_requirement) + dp_ds.add_data_requirements(dp_loss.label_requirement) + dp_model.data_stat(dp_ds) + + # Build graph + g = tf.Graph() + with g.as_default(): + place_holders = self._get_dp_placeholders(dp_ds) + model_pred = dp_model.build( + coord_=place_holders["coord"], + atype_=place_holders["type"], + natoms=place_holders["natoms_vec"], + box=place_holders["box"], + mesh=place_holders["default_mesh"], + input_dict=place_holders, + ) + global_step = tf.train.get_or_create_global_step() + learning_rate = dp_lr.build(global_step, self.stop_steps) + l2_l, _ = dp_loss.build( + learning_rate=learning_rate, + natoms=place_holders["natoms_vec"], + model_dict=model_pred, + label_dict=place_holders, + suffix="test", + ) + t_vars = tf.trainable_variables() + optimizer = tf.train.AdamOptimizer(learning_rate) + t_grad_and_vars = optimizer.compute_gradients(l2_l, t_vars) + train_op = optimizer.apply_gradients(t_grad_and_vars, global_step) + init_op = tf.global_variables_initializer() + t_heads = { + "loss": l2_l, + "energy": model_pred["energy"], + "force": model_pred["force"], + "virial": model_pred["virial"], + "atom_virial": model_pred["atom_virial"], + } + + # Get statistics of each component + stat_dict = { + "descriptor.mean": dp_model.descrpt.davg, + "descriptor.stddev": dp_model.descrpt.dstd, + "fitting_net.bias_atom_e": dp_model.fitting.bias_atom_e, + } + + # Get variables and their gradients + with tf.Session(graph=g) as sess: + sess.run(init_op) + for _ in range(num_steps): + batch = dp_ds.get_batch() + feeds = self._get_feed_dict(batch, place_holders) + sess.run(train_op, feed_dict=feeds) + + batch = dp_ds.get_batch() + feeds = self._get_feed_dict(batch, place_holders) + grads_and_vars, head_dict = sess.run( + [t_grad_and_vars, t_heads], feed_dict=feeds + ) + vs_dict = {} + for idx, one in enumerate(t_vars): + grad, var = grads_and_vars[idx] + vs_dict[one.name] = VariableState(var, grad) + + tf.reset_default_graph() + # Used for reproducing + return batch, head_dict, stat_dict, vs_dict + + def _get_dp_dataset(self): + data = DeepmdDataSystem( + systems=self.systems, + batch_size=self.batch_size, + test_size=1, + rcut=self.rcut, + type_map=self.type_map, + trn_all_set=True, + ) + return data + + def _get_dp_model(self): + dp_descrpt = DescrptSeA_tf( + rcut=self.rcut, + rcut_smth=self.rcut_smth, + sel=self.sel, + neuron=self.filter_neuron, + axis_neuron=self.axis_neuron, + ) + dp_fitting = EnerFitting( + dp_descrpt.get_ntypes(), dp_descrpt.get_dim_out(), neuron=self.n_neuron + ) + return EnerModel( + dp_descrpt, + dp_fitting, + type_map=self.type_map, + data_stat_nbatch=self.data_stat_nbatch, + ) + + def _get_dp_loss(self): + return EnerStdLoss( + starter_learning_rate=self.start_lr, + start_pref_e=self.start_pref_e, + limit_pref_e=self.limit_pref_e, + start_pref_f=self.start_pref_f, + limit_pref_f=self.limit_pref_f, + ) + + def _get_dp_lr(self): + return LearningRateExp( + start_lr=self.start_lr, stop_lr=self.stop_lr, decay_steps=self.decay_steps + ) + + def _get_dp_placeholders(self, dataset): + place_holders = {} + data_dict = dataset.get_data_dict() + for kk in data_dict.keys(): + if kk == "type": + continue + prec = tf.float64 + place_holders[kk] = tf.placeholder(prec, [None], name="t_" + kk) + place_holders["find_" + kk] = tf.placeholder( + tf.float32, name="t_find_" + kk + ) + place_holders["type"] = tf.placeholder(tf.int32, [None], name="t_type") + place_holders["natoms_vec"] = tf.placeholder( + tf.int32, [self.ntypes + 2], name="t_natoms" + ) + place_holders["default_mesh"] = tf.placeholder(tf.int32, [None], name="t_mesh") + place_holders["is_training"] = tf.placeholder(tf.bool) + return place_holders + + def _get_feed_dict(self, batch, place_holders): + feed_dict = {} + for kk in batch.keys(): + if kk == "find_type" or kk == "type": + continue + if "find_" in kk: + feed_dict[place_holders[kk]] = batch[kk] + else: + feed_dict[place_holders[kk]] = np.reshape(batch[kk], [-1]) + for ii in ["type"]: + feed_dict[place_holders[ii]] = np.reshape(batch[ii], [-1]) + for ii in ["natoms_vec", "default_mesh"]: + feed_dict[place_holders[ii]] = batch[ii] + feed_dict[place_holders["is_training"]] = True + return feed_dict + + +class TestEnergy(unittest.TestCase): + def setUp(self): + self.dp_trainer = DpTrainer() + self.wanted_step = 0 + for key in dir(self.dp_trainer): + if not key.startswith("_") or key == "get_intermediate_state": + value = getattr(self.dp_trainer, key) + setattr(self, key, value) + + def test_consistency(self): + batch, head_dict, stat_dict, vs_dict = self.dp_trainer.get_intermediate_state( + self.wanted_step + ) + # Build DeePMD graph + my_ds = DpLoaderSet(self.systems, self.batch_size, self.type_map) + my_ds.add_data_requirement(energy_data_requirement) + my_model = get_model( + model_params={ + "descriptor": { + "type": "se_e2_a", + "sel": self.sel, + "rcut_smth": self.rcut_smth, + "rcut": self.rcut, + "neuron": self.filter_neuron, + "axis_neuron": self.axis_neuron, + }, + "fitting_net": {"neuron": self.n_neuron, "mixed_types": False}, + "data_stat_nbatch": self.data_stat_nbatch, + "type_map": self.type_map, + }, + ) + my_model.to(DEVICE) + my_lr = MyLRExp(self.start_lr, self.stop_lr, self.decay_steps, self.stop_steps) + my_loss = EnergyStdLoss( + starter_learning_rate=self.start_lr, + start_pref_e=self.start_pref_e, + limit_pref_e=self.limit_pref_e, + start_pref_f=self.start_pref_f, + limit_pref_f=self.limit_pref_f, + ) + + # Keep statistics consistency between 2 implentations + my_em = my_model.get_descriptor() + mean = stat_dict["descriptor.mean"].reshape([self.ntypes, my_em.get_nsel(), 4]) + stddev = stat_dict["descriptor.stddev"].reshape( + [self.ntypes, my_em.get_nsel(), 4] + ) + my_em.set_stat_mean_and_stddev( + paddle.to_tensor(mean).to(device=DEVICE), + paddle.to_tensor(stddev).to(device=DEVICE), + ) + my_model.get_fitting_net().bias_atom_e = paddle.to_tensor( + stat_dict["fitting_net.bias_atom_e"], place=DEVICE + ) + + # Keep parameter value consistency between 2 implentations + for name, param in my_model.named_parameters(): + name = name.replace("sea.", "") + var_name = paddle2tf(name, last_layer_id=len(self.n_neuron)) + var = vs_dict[var_name].value + with paddle.no_grad(): + src = paddle.to_tensor(var) + dst = param + # print(name) + # print(src.mean(), src.std()) + # print(dst.mean(), dst.std()) + paddle.assign(src, dst) + # Start forward computing + tmp = np.copy(batch["natoms_vec"]) + batch = my_ds.systems[0]._data_system._get_subdata(batch, 0) + batch = my_ds.systems[0]._data_system.reformat_data_torch(batch) + for key in ["coord", "atype", "box", "energy", "force"]: + batch[key] = paddle.to_tensor(batch[key]).to(device=env.DEVICE) + batch[key] = batch[key].unsqueeze(0) + batch["coord"].stop_gradient = False + batch["natoms_vec"] = tmp + batch["natoms"] = paddle.to_tensor( + batch["natoms_vec"], place=batch["coord"].place + ).unsqueeze(0) + model_input = { + "coord": batch["coord"].to(env.DEVICE), + "atype": batch["atype"].to(env.DEVICE), + "box": batch["box"].to(env.DEVICE), + "do_atomic_virial": True, + } + model_input_1 = { + "coord": batch["coord"].to(env.DEVICE), + "atype": batch["atype"].to(env.DEVICE), + "box": batch["box"].to(env.DEVICE), + "do_atomic_virial": False, + } + label = { + "energy": batch["energy"].to(env.DEVICE), + "find_energy": 1.0, + "force": batch["force"].to(env.DEVICE), + "find_force": 1.0, + } + cur_lr = my_lr.value(self.wanted_step) + model_predict, loss, _ = my_loss( + model_input, my_model, label, int(batch["natoms"][0, 0]), cur_lr + ) + model_predict_1 = my_model(**model_input_1) + p_energy, p_force, p_virial, p_atomic_virial = ( + model_predict["energy"], + model_predict["force"], + model_predict["virial"], + model_predict["atom_virial"], + ) + np.testing.assert_allclose( + head_dict["energy"], p_energy.reshape([-1]).cpu().detach().numpy() + ) + np.testing.assert_allclose( + head_dict["force"], + p_force.reshape(head_dict["force"].shape).cpu().detach().numpy(), + ) + rtol = 1e-5 + atol = 1e-8 + np.testing.assert_allclose( + head_dict["loss"], loss.cpu().detach().numpy(), rtol=rtol, atol=atol + ) + np.testing.assert_allclose( + head_dict["virial"], + p_virial.reshape(head_dict["virial"].shape).cpu().detach().numpy(), + ) + np.testing.assert_allclose( + head_dict["virial"], + model_predict_1["virial"] + .reshape([*head_dict["virial"].shape]) + .cpu() + .detach() + .numpy(), + ) + self.assertIsNone(model_predict_1.get("atom_virial", None)) + np.testing.assert_allclose( + head_dict["atom_virial"], + p_atomic_virial.reshape(head_dict["atom_virial"].shape) + .cpu() + .detach() + .numpy(), + ) + optimizer = paddle.optimizer.Adam( + learning_rate=cur_lr, parameters=my_model.parameters() + ) + optimizer.clear_grad() + + def step(step_id): + bdata = self.training_data.get_trainning_batch() + optimizer.clear_grad() + + # Compare gradient for consistency + loss.backward() + + for name, param in my_model.named_parameters(): + name = name.replace("sea.", "") + var_name = paddle2tf(name, last_layer_id=len(self.n_neuron)) + var_grad = vs_dict[var_name].gradient + param_grad = param.grad.cpu() + var_grad = paddle.to_tensor(var_grad).to(device="cpu") + assert np.allclose(var_grad, param_grad, rtol=rtol, atol=atol) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/model/test_nlist.py b/source/tests/pd/model/test_nlist.py new file mode 100644 index 0000000000..95efe0fde1 --- /dev/null +++ b/source/tests/pd/model/test_nlist.py @@ -0,0 +1,308 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import unittest + +import paddle + +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.nlist import ( + build_directional_neighbor_list, + build_multiple_neighbor_list, + build_neighbor_list, + extend_coord_with_ghosts, + get_multiple_nlist_key, +) +from deepmd.pd.utils.region import ( + inter2phys, +) + +dtype = paddle.float64 + + +class TestNeighList(unittest.TestCase): + def setUp(self): + self.nf = 3 + self.nloc = 3 + self.ns = 5 * 5 * 3 + self.nall = self.ns * self.nloc + self.cell = paddle.to_tensor( + [[1, 0, 0], [0.4, 0.8, 0], [0.1, 0.3, 2.1]], dtype=dtype, place=env.DEVICE + ) + self.icoord = paddle.to_tensor( + [[0, 0, 0], [0, 0, 0], [0.5, 0.5, 0.1]], dtype=dtype, place=env.DEVICE + ) + self.atype = paddle.to_tensor([-1, 0, 1], dtype=paddle.int64).to( + device=env.DEVICE + ) + [self.cell, self.icoord, self.atype] = [ + ii.unsqueeze(0) for ii in [self.cell, self.icoord, self.atype] + ] + self.coord = inter2phys(self.icoord, self.cell).reshape([-1, self.nloc * 3]) + self.cell = self.cell.reshape([-1, 9]) + [self.cell, self.coord, self.atype] = [ + paddle.tile(ii, [self.nf, 1]) for ii in [self.cell, self.coord, self.atype] + ] + self.rcut = 1.01 + self.prec = 1e-10 + self.nsel = [10, 10] + # genrated by preprocess.build_neighbor_list + # ref_nlist, _, _ = legacy_build_neighbor_list( + # 2, ecoord[0], eatype[0], + # self.rcut, + # paddle.to_tensor([10,20], dtype=paddle.int64), + # mapping[0], type_split=True, ) + self.ref_nlist = paddle.to_tensor( + [ + [-1] * sum(self.nsel), + [1, 1, 1, 1, 1, 1, -1, -1, -1, -1, 2, 2, 2, 2, -1, -1, -1, -1, -1, -1], + [1, 1, 1, 1, -1, -1, -1, -1, -1, -1, 2, 2, 2, 2, 2, 2, -1, -1, -1, -1], + ], + place=env.DEVICE, + ) + + def test_build_notype(self): + ecoord, eatype, mapping = extend_coord_with_ghosts( + self.coord, self.atype, self.cell, self.rcut + ) + # test normal sel + nlist = build_neighbor_list( + ecoord, + eatype, + self.nloc, + self.rcut, + sum(self.nsel), + distinguish_types=False, + ) + nlist_mask = nlist[0] == -1 + nlist_loc = mapping[0][nlist[0]] + nlist_loc[nlist_mask] = -1 + assert paddle.allclose( + paddle.sort(nlist_loc, axis=-1).astype("float32"), + paddle.sort(self.ref_nlist, axis=-1).astype("float32"), + ) + # test a very large sel + nlist = build_neighbor_list( + ecoord, + eatype, + self.nloc, + self.rcut, + sum(self.nsel) + 300, # +300, real nnei==224 + distinguish_types=False, + ) + nlist_mask = nlist[0] == -1 + nlist_loc = mapping[0][nlist[0]] + nlist_loc[nlist_mask] = -1 + assert paddle.allclose( + paddle.sort(nlist_loc, descending=True, axis=-1)[:, : sum(self.nsel)], + paddle.sort(self.ref_nlist, descending=True, axis=-1), + ) + + def test_build_type(self): + ecoord, eatype, mapping = extend_coord_with_ghosts( + self.coord, self.atype, self.cell, self.rcut + ) + nlist = build_neighbor_list( + ecoord, + eatype, + self.nloc, + self.rcut, + self.nsel, + distinguish_types=True, + ) + assert paddle.allclose(nlist[0], nlist[1]) + nlist_mask = nlist[0] == -1 + nlist_loc = mapping[0][nlist[0]] + nlist_loc[nlist_mask] = -1 + for ii in range(2): + assert paddle.allclose( + paddle.sort(paddle.split(nlist_loc, (self.nsel), axis=-1)[ii], axis=-1), + paddle.sort( + paddle.split(self.ref_nlist, (self.nsel), axis=-1)[ii], axis=-1 + ), + ) + assert paddle.allclose( + paddle.argsort( + paddle.split(nlist_loc, (self.nsel), axis=-1)[ii], axis=-1 + ), + paddle.argsort( + paddle.split(self.ref_nlist, (self.nsel), axis=-1)[ii], axis=-1 + ), + ) + + def test_build_multiple_nlist(self): + rcuts = [1.01, 2.01] + nsels = [20, 80] + ecoord, eatype, mapping = extend_coord_with_ghosts( + self.coord, self.atype, self.cell, max(rcuts) + ) + nlist1 = build_neighbor_list( + ecoord, + eatype, + self.nloc, + rcuts[1], + nsels[1] - 1, + distinguish_types=False, + ) + pad = -1 * paddle.ones([self.nf, self.nloc, 1], dtype=nlist1.dtype).to( + device=nlist1.place + ) + nlist2 = paddle.concat([nlist1, pad], axis=-1) + nlist0 = build_neighbor_list( + ecoord, + eatype, + self.nloc, + rcuts[0], + nsels[0], + distinguish_types=False, + ) + nlists = build_multiple_neighbor_list(ecoord, nlist1, rcuts, nsels) + for dd in range(2): + self.assertEqual( + nlists[get_multiple_nlist_key(rcuts[dd], nsels[dd])].shape[-1], + nsels[dd], + ) + assert paddle.allclose( + nlists[get_multiple_nlist_key(rcuts[0], nsels[0])], + nlist0, + ) + assert paddle.allclose( + nlists[get_multiple_nlist_key(rcuts[1], nsels[1])], + nlist2, + ) + + def test_extend_coord(self): + ecoord, eatype, mapping = extend_coord_with_ghosts( + self.coord, self.atype, self.cell, self.rcut + ) + # expected ncopy x nloc + self.assertEqual(list(ecoord.shape), [self.nf, self.nall * 3]) + self.assertEqual(list(eatype.shape), [self.nf, self.nall]) + self.assertEqual(list(mapping.shape), [self.nf, self.nall]) + # check the nloc part is identical with original coord + assert paddle.allclose( + ecoord[:, : self.nloc * 3], self.coord, rtol=self.prec, atol=self.prec + ) + # check the shift vectors are aligned with grid + shift_vec = ( + ecoord.reshape([-1, self.ns, self.nloc, 3]) + - self.coord.reshape([-1, self.nloc, 3])[:, None, :, :] + ) + shift_vec = shift_vec.reshape([-1, self.nall, 3]) + # hack!!! assumes identical cell across frames + shift_vec = paddle.matmul( + shift_vec, paddle.linalg.inv(self.cell.reshape([self.nf, 3, 3])[0]) + ) + # nf x nall x 3 + shift_vec = paddle.round(shift_vec) + # check: identical shift vecs + assert paddle.allclose( + shift_vec[0], shift_vec[1], rtol=self.prec, atol=self.prec + ) + # check: shift idx aligned with grid + mm, cc = paddle.unique(shift_vec[0][:, 0], axis=-1, return_counts=True) + assert paddle.allclose( + mm, + paddle.to_tensor([-2, -1, 0, 1, 2], dtype=dtype).to(device=env.DEVICE), + rtol=self.prec, + atol=self.prec, + ) + assert paddle.allclose( + cc, + paddle.to_tensor( + [self.ns * self.nloc // 5] * 5, dtype=paddle.int64, place=env.DEVICE + ), + rtol=self.prec, + atol=self.prec, + ) + mm, cc = paddle.unique(shift_vec[1][:, 1], axis=-1, return_counts=True) + assert paddle.allclose( + mm, + paddle.to_tensor([-2, -1, 0, 1, 2], dtype=dtype).to(device=env.DEVICE), + rtol=self.prec, + atol=self.prec, + ) + assert paddle.allclose( + cc, + paddle.to_tensor( + [self.ns * self.nloc // 5] * 5, dtype=paddle.int64, place=env.DEVICE + ), + rtol=self.prec, + atol=self.prec, + ) + mm, cc = paddle.unique(shift_vec[1][:, 2], axis=-1, return_counts=True) + assert paddle.allclose( + mm, + paddle.to_tensor([-1, 0, 1], dtype=dtype).to(device=env.DEVICE), + rtol=self.prec, + atol=self.prec, + ) + assert paddle.allclose( + cc, + paddle.to_tensor( + [self.ns * self.nloc // 3] * 3, dtype=paddle.int64, place=env.DEVICE + ), + rtol=self.prec, + atol=self.prec, + ) + + def test_build_directional_nlist(self): + """Directional nlist is tested against the standard nlist implementation.""" + ecoord, eatype, mapping = extend_coord_with_ghosts( + self.coord, self.atype, self.cell, self.rcut + ) + for distinguish_types, mysel in zip([True, False], [sum(self.nsel), 300]): + # full neighbor list + nlist_full = build_neighbor_list( + ecoord, + eatype, + self.nloc, + self.rcut, + sum(self.nsel), + distinguish_types=distinguish_types, + ) + # central as part of the system + nlist = build_directional_neighbor_list( + ecoord[:, 3:6], + eatype[:, 1:2], + paddle.concat( + [ + ecoord[:, 0:3], + paddle.zeros( + [self.nf, 3], + dtype=dtype, + ).to(device=env.DEVICE), # placeholder + ecoord[:, 6:], + ], + axis=1, + ), + paddle.concat( + [ + eatype[:, 0:1], + -1 + * paddle.ones( + [self.nf, 1], + dtype="int64", + ).to(device=env.DEVICE), # placeholder + eatype[:, 2:], + ], + axis=1, + ), + self.rcut, + mysel, + distinguish_types=distinguish_types, + ) + assert paddle.allclose( + nlist[0].astype("float32"), nlist[1].astype("float32") + ) + assert paddle.allclose( + nlist[0].astype("float32"), nlist[2].astype("float32") + ) + assert paddle.allclose( + paddle.sort(nlist[0], descending=True, axis=-1)[ + :, : sum(self.nsel) + ].astype("float32"), + paddle.sort(nlist_full[0][1:2], descending=True, axis=-1).astype( + "float32" + ), + ) diff --git a/source/tests/pd/model/test_null_input.py b/source/tests/pd/model/test_null_input.py new file mode 100644 index 0000000000..35adc9049e --- /dev/null +++ b/source/tests/pd/model/test_null_input.py @@ -0,0 +1,137 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import unittest + +import numpy as np +import paddle + +from deepmd.pd.model.model import ( + get_model, + get_zbl_model, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, +) + +from ...seed import ( + GLOBAL_SEED, +) +from ..common import ( + eval_model, +) +from .test_permutation import ( + model_dpa1, + model_dpa2, + model_hybrid, + model_se_e2_a, + model_zbl, +) + +dtype = paddle.float64 + + +class NullTest: + def test_nloc_1( + self, + ): + natoms = 1 + generator = paddle.seed(GLOBAL_SEED) + # paddle.seed(1000) + cell = paddle.rand([3, 3], dtype=dtype).to(device=env.DEVICE) + # large box to exclude images + cell = (cell + cell.T) + 100.0 * paddle.eye(3).to(device=env.DEVICE) + coord = paddle.rand([natoms, 3], dtype=dtype).to(device=env.DEVICE) + atype = paddle.to_tensor([0], dtype=paddle.int32).to(device=env.DEVICE) + test_keys = ["energy", "force", "virial"] + result = eval_model(self.model, coord.unsqueeze(0), cell.unsqueeze(0), atype) + ret0 = {key: result[key].squeeze(0) for key in test_keys} + prec = 1e-10 + expect_e_shape = [1] + expect_f = paddle.zeros([natoms, 3], dtype=dtype).to(device=env.DEVICE) + expect_v = paddle.zeros([9], dtype=dtype).to(device=env.DEVICE) + self.assertEqual(list(ret0["energy"].shape), expect_e_shape) + self.assertFalse(np.isnan(to_numpy_array(ret0["energy"])[0])) + assert paddle.allclose(ret0["force"], expect_f, rtol=prec, atol=prec) + if not hasattr(self, "test_virial") or self.test_virial: + assert paddle.allclose(ret0["virial"], expect_v, rtol=prec, atol=prec) + + def test_nloc_2_far( + self, + ): + natoms = 2 + generator = paddle.seed(GLOBAL_SEED) + cell = paddle.rand([3, 3], dtype=dtype).to(device=env.DEVICE) + # large box to exclude images + cell = (cell + cell.T) + 3000.0 * paddle.eye(3).to(device=env.DEVICE) + coord = paddle.rand([1, 3], dtype=dtype).to(device=env.DEVICE) + # 2 far-away atoms + coord = paddle.concat([coord, coord + 100.0], axis=0) + atype = paddle.to_tensor([0, 2], dtype=paddle.int32).to(device=env.DEVICE) + test_keys = ["energy", "force", "virial"] + result = eval_model(self.model, coord.unsqueeze(0), cell.unsqueeze(0), atype) + ret0 = {key: result[key].squeeze(0) for key in test_keys} + prec = 1e-10 + expect_e_shape = [1] + expect_f = paddle.zeros([natoms, 3], dtype=dtype).to(device=env.DEVICE) + expect_v = paddle.zeros([9], dtype=dtype).to(device=env.DEVICE) + self.assertEqual(list(ret0["energy"].shape), expect_e_shape) + self.assertFalse(np.isnan(to_numpy_array(ret0["energy"])[0])) + assert paddle.allclose(ret0["force"], expect_f, rtol=prec, atol=prec) + if not hasattr(self, "test_virial") or self.test_virial: + assert paddle.allclose(ret0["virial"], expect_v, rtol=prec, atol=prec) + + +class TestEnergyModelSeA(unittest.TestCase, NullTest): + def setUp(self): + model_params = copy.deepcopy(model_se_e2_a) + self.type_split = False + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelDPA1(unittest.TestCase, NullTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa1) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelDPA2(unittest.TestCase, NullTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa2) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +class TestForceModelDPA2(unittest.TestCase, NullTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa2) + model_params["fitting_net"]["type"] = "direct_force_ener" + self.type_split = True + self.test_virial = False + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelHybrid(unittest.TestCase, NullTest): + def setUp(self): + model_params = copy.deepcopy(model_hybrid) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +class TestForceModelHybrid(unittest.TestCase, NullTest): + def setUp(self): + model_params = copy.deepcopy(model_hybrid) + model_params["fitting_net"]["type"] = "direct_force_ener" + self.type_split = True + self.test_virial = False + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelZBL(unittest.TestCase, NullTest): + def setUp(self): + model_params = copy.deepcopy(model_zbl) + self.type_split = False + self.model = get_zbl_model(model_params).to(env.DEVICE) diff --git a/source/tests/pd/model/test_pairtab_atomic_model.py b/source/tests/pd/model/test_pairtab_atomic_model.py new file mode 100644 index 0000000000..335447525d --- /dev/null +++ b/source/tests/pd/model/test_pairtab_atomic_model.py @@ -0,0 +1,272 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import unittest +from unittest.mock import ( + patch, +) + +import numpy as np +import paddle + +from deepmd.dpmodel.atomic_model import PairTabAtomicModel as DPPairTabAtomicModel +from deepmd.pd.model.atomic_model import ( + PairTabAtomicModel, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, +) + + +class TestPairTab(unittest.TestCase): + @patch("numpy.loadtxt") + def setUp(self, mock_loadtxt) -> None: + file_path = "dummy_path" + mock_loadtxt.return_value = np.array( + [ + [0.005, 1.0, 2.0, 3.0], + [0.01, 0.8, 1.6, 2.4], + [0.015, 0.5, 1.0, 1.5], + [0.02, 0.25, 0.4, 0.75], + ] + ) + + self.model = PairTabAtomicModel( + tab_file=file_path, rcut=0.02, sel=2, type_map=["H", "O"] + ) + + self.extended_coord = paddle.to_tensor( + [ + [ + [0.01, 0.01, 0.01], + [0.01, 0.02, 0.01], + [0.01, 0.01, 0.02], + [0.02, 0.01, 0.01], + ], + [ + [0.01, 0.01, 0.01], + [0.01, 0.02, 0.01], + [0.01, 0.01, 0.02], + [0.05, 0.01, 0.01], + ], + ], + place=env.DEVICE, + ) + + # nframes=2, nall=4 + self.extended_atype = paddle.to_tensor( + [[0, 1, 0, 1], [0, 0, 1, 1]], place=env.DEVICE + ) + + # nframes=2, nloc=2, nnei=2 + self.nlist = paddle.to_tensor( + [[[1, 2], [0, 2]], [[1, 2], [0, 3]]], place=env.DEVICE + ) + + def test_without_mask(self): + result = self.model.forward_atomic( + self.extended_coord, self.extended_atype, self.nlist + ) + expected_result = paddle.to_tensor( + [[[1.2000], [1.3614]], [[1.2000], [0.4000]]], + dtype=paddle.float64, + place=env.DEVICE, + ) + + assert paddle.allclose( + result["energy"], expected_result, rtol=0.0001, atol=0.0001 + ) + + def test_with_mask(self): + self.nlist = paddle.to_tensor( + [[[1, -1], [0, 2]], [[1, 2], [0, 3]]], place=env.DEVICE + ) + + result = self.model.forward_atomic( + self.extended_coord, self.extended_atype, self.nlist + ) + expected_result = paddle.to_tensor( + [[[0.8000], [1.3614]], [[1.2000], [0.4000]]], + dtype=paddle.float64, + place=env.DEVICE, + ) + + assert paddle.allclose( + result["energy"], expected_result, rtol=0.0001, atol=0.0001 + ) + + def test_jit(self): + model = paddle.jit.to_static(self.model) + # atomic model no more export methods + # self.assertEqual(model.get_rcut(), 0.02) + # self.assertEqual(model.get_type_map(), ["H", "O"]) + + def test_deserialize(self): + model1 = PairTabAtomicModel.deserialize(self.model.serialize()) + assert paddle.allclose(self.model.tab_data, model1.tab_data) + assert paddle.allclose(self.model.tab_info, model1.tab_info) + + self.nlist = paddle.to_tensor( + [[[1, -1], [0, 2]], [[1, 2], [0, 3]]], place=env.DEVICE + ) + result = model1.forward_atomic( + self.extended_coord, self.extended_atype, self.nlist + ) + expected_result = self.model.forward_atomic( + self.extended_coord, self.extended_atype, self.nlist + ) + + assert paddle.allclose( + result["energy"], expected_result["energy"], rtol=0.0001, atol=0.0001 + ) + + # model1 = paddle.jit.to_static(model1) + # atomic model no more export methods + # self.assertEqual(model1.get_rcut(), 0.02) + # self.assertEqual(model1.get_type_map(), ["H", "O"]) + + def test_cross_deserialize(self): + model_dict = self.model.serialize() # paddle model to dict + model1 = DPPairTabAtomicModel.deserialize(model_dict) # dict to numpy model + np.testing.assert_allclose(self.model.tab_data, model1.tab_data) + np.testing.assert_allclose(self.model.tab_info, model1.tab_info) + + self.nlist = np.array([[[1, -1], [0, 2]], [[1, 2], [0, 3]]]) + result = model1.forward_atomic( + self.extended_coord.cpu().numpy(), + self.extended_atype.cpu().numpy(), + self.nlist, + ) + expected_result = self.model.forward_atomic( + self.extended_coord, + self.extended_atype, + paddle.to_tensor(self.nlist).to(device=env.DEVICE), + ) + np.testing.assert_allclose( + result["energy"], to_numpy_array(expected_result["energy"]), 0.0001, 0.0001 + ) + + +class TestPairTabTwoAtoms(unittest.TestCase): + @patch("numpy.loadtxt") + def test_extrapolation_nonzero_rmax(self, mock_loadtxt) -> None: + """Scenarios to test. + + rcut < rmax: + rr < rcut: use table values, or interpolate. + rr == rcut: use table values, or interpolate. + rr > rcut: should be 0 + rcut == rmax: + rr < rcut: use table values, or interpolate. + rr == rcut: use table values, or interpolate. + rr > rcut: should be 0 + rcut > rmax: + rr < rmax: use table values, or interpolate. + rr == rmax: use table values, or interpolate. + rmax < rr < rcut: extrapolate + rr >= rcut: should be 0 + + """ + file_path = "dummy_path" + mock_loadtxt.return_value = np.array( + [ + [0.005, 1.0], + [0.01, 0.8], + [0.015, 0.5], + [0.02, 0.25], + ] + ) + + # nframes=1, nall=2 + extended_atype = paddle.to_tensor([[0, 0]]).to(device=env.DEVICE) + + # nframes=1, nloc=2, nnei=1 + nlist = paddle.to_tensor([[[1], [-1]]]).to(device=env.DEVICE) + + results = [] + + for dist, rcut in zip( + [ + 0.01, + 0.015, + 0.020, + 0.015, + 0.02, + 0.021, + 0.015, + 0.02, + 0.021, + 0.025, + 0.026, + 0.025, + 0.025, + 0.0216161, + ], + [ + 0.015, + 0.015, + 0.015, + 0.02, + 0.02, + 0.02, + 0.022, + 0.022, + 0.022, + 0.025, + 0.025, + 0.03, + 0.035, + 0.025, + ], + ): + extended_coord = paddle.to_tensor( + [ + [ + [0.0, 0.0, 0.0], + [0.0, dist, 0.0], + ], + ], + place=env.DEVICE, + ) + + model = PairTabAtomicModel( + tab_file=file_path, rcut=rcut, sel=2, type_map=["H"] + ) + results.append( + model.forward_atomic(extended_coord, extended_atype, nlist)["energy"] + ) + + expected_result = paddle.stack( + [ + paddle.to_tensor( + [ + [ + [0.4, 0], + [0.0, 0], + [0.0, 0], + [0.25, 0], + [0, 0], + [0, 0], + [0.25, 0], + [0.125, 0], + [0.0922, 0], + [0, 0], + [0, 0], + [0, 0], + [0.0923, 0], + [0.0713, 0], + ] + ], + dtype=paddle.float64, + place=env.DEVICE, + ) + ] + ).reshape([14, 2]) + results = paddle.stack(results).reshape([14, 2]) + + assert paddle.allclose(results, expected_result, rtol=0.0001, atol=0.0001) + + +if __name__ == "__main__": + unittest.main(warnings="ignore") diff --git a/source/tests/pd/model/test_permutation.py b/source/tests/pd/model/test_permutation.py new file mode 100644 index 0000000000..132c9eab37 --- /dev/null +++ b/source/tests/pd/model/test_permutation.py @@ -0,0 +1,476 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import os +import unittest + +import paddle + +from deepmd.pd.model.model import ( + get_model, +) +from deepmd.pd.utils import ( + env, +) + +from ...seed import ( + GLOBAL_SEED, +) +from ..common import ( + eval_model, +) + +CUR_DIR = os.path.dirname(__file__) + +dtype = paddle.float64 + +model_se_e2_a = { + "type_map": ["O", "H", "B"], + "descriptor": { + "type": "se_e2_a", + "sel": [46, 92, 4], + "rcut_smth": 0.50, + "rcut": 4.00, + "neuron": [25, 50, 100], + "resnet_dt": False, + "axis_neuron": 16, + "seed": 1, + }, + "fitting_net": { + "neuron": [24, 24, 24], + "resnet_dt": True, + "seed": 1, + }, + "data_stat_nbatch": 20, +} + +model_dos = { + "type_map": ["O", "H", "B"], + "descriptor": { + "type": "se_e2_a", + "sel": [46, 92, 4], + "rcut_smth": 0.50, + "rcut": 4.00, + "neuron": [25, 50, 100], + "resnet_dt": False, + "axis_neuron": 16, + "seed": 1, + }, + "fitting_net": { + "neuron": [24, 24, 24], + "resnet_dt": True, + "seed": 1, + "type": "dos", + "numb_dos": 250, + }, + "data_stat_nbatch": 20, +} + +model_zbl = { + "type_map": ["O", "H", "B"], + "use_srtab": f"{CUR_DIR}/water/data/zbl_tab_potential/H2O_tab_potential.txt", + "smin_alpha": 0.1, + "sw_rmin": 0.2, + "sw_rmax": 4.0, + "descriptor": { + "type": "se_atten", + "sel": 40, + "rcut_smth": 0.5, + "rcut": 4.0, + "neuron": [25, 50, 100], + "axis_neuron": 16, + "attn": 64, + "attn_layer": 2, + "attn_dotr": True, + "attn_mask": False, + "activation_function": "tanh", + "scaling_factor": 1.0, + "normalize": False, + "temperature": 1.0, + "set_davg_zero": True, + "type_one_side": True, + "seed": 1, + }, + "fitting_net": { + "neuron": [24, 24, 24], + "resnet_dt": True, + "seed": 1, + }, + "data_stat_nbatch": 20, +} + + +model_spin = { + "type_map": ["O", "H", "B"], + "descriptor": { + "type": "se_e2_a", + "sel": [46, 92, 4], + "rcut_smth": 0.50, + "rcut": 4.00, + "neuron": [25, 50, 100], + "resnet_dt": False, + "axis_neuron": 16, + "seed": 1, + }, + "fitting_net": { + "neuron": [24, 24, 24], + "resnet_dt": True, + "seed": 1, + }, + "data_stat_nbatch": 20, + "spin": { + "use_spin": [True, False, False], + "virtual_scale": [0.3140], + "_comment": " that's all", + }, +} + +model_dpa2 = { + "type_map": ["O", "H", "B"], + "descriptor": { + "type": "dpa2", + "repinit": { + "rcut": 6.0, + "rcut_smth": 2.0, + "nsel": 100, + "neuron": [2, 4, 8], + "axis_neuron": 4, + "activation_function": "tanh", + }, + "repformer": { + "rcut": 4.0, + "rcut_smth": 0.5, + "nsel": 40, + "nlayers": 12, + "g1_dim": 8, + "g2_dim": 5, + "attn2_hidden": 3, + "attn2_nhead": 1, + "attn1_hidden": 5, + "attn1_nhead": 1, + "axis_neuron": 4, + "update_h2": False, + "update_g1_has_conv": True, + "update_g1_has_grrg": True, + "update_g1_has_drrd": True, + "update_g1_has_attn": True, + "update_g2_has_g1g1": True, + "update_g2_has_attn": True, + "attn2_has_gate": True, + }, + "seed": 1, + "add_tebd_to_repinit_out": False, + }, + "fitting_net": { + "neuron": [24, 24], + "resnet_dt": True, + "seed": 1, + }, +} + +model_dpa2tebd = { + "type_map": ["O", "H", "B"], + "descriptor": { + "type": "dpa2", + "repinit": { + "rcut": 6.0, + "rcut_smth": 0.5, + "nsel": 100, + "neuron": [2, 4, 8], + "axis_neuron": 4, + "activation_function": "tanh", + "three_body_sel": 40, + "three_body_rcut": 4.0, + "three_body_rcut_smth": 3.5, + "use_three_body": True, + }, + "repformer": { + "rcut": 4.0, + "rcut_smth": 0.5, + "nsel": 40, + "nlayers": 6, + "g1_dim": 8, + "g2_dim": 5, + "attn2_hidden": 3, + "attn2_nhead": 1, + "attn1_hidden": 5, + "attn1_nhead": 1, + "axis_neuron": 4, + "update_h2": False, + "update_g1_has_conv": True, + "update_g1_has_grrg": True, + "update_g1_has_drrd": True, + "update_g1_has_attn": False, + "update_g2_has_g1g1": False, + "update_g2_has_attn": True, + "update_style": "res_residual", + "update_residual": 0.01, + "update_residual_init": "norm", + "attn2_has_gate": True, + "use_sqrt_nnei": True, + "g1_out_conv": True, + "g1_out_mlp": True, + }, + "seed": 1, + "add_tebd_to_repinit_out": False, + }, + "fitting_net": { + "neuron": [24, 24], + "resnet_dt": True, + "seed": 1, + }, +} + +model_dpa1 = { + "type_map": ["O", "H", "B"], + "descriptor": { + "type": "se_atten", + "sel": 40, + "rcut_smth": 0.5, + "rcut": 4.0, + "neuron": [25, 50, 100], + "axis_neuron": 16, + "attn": 64, + "attn_layer": 2, + "attn_dotr": True, + "attn_mask": False, + "activation_function": "tanh", + "scaling_factor": 1.0, + "normalize": False, + "temperature": 1.0, + "set_davg_zero": True, + "type_one_side": True, + "seed": 1, + }, + "fitting_net": { + "neuron": [24, 24, 24], + "resnet_dt": True, + "seed": 1, + }, +} + + +model_hybrid = { + "type_map": ["O", "H", "B"], + "descriptor": { + "type": "hybrid", + "list": [ + { + "type": "se_atten", + "sel": 120, + "rcut_smth": 0.5, + "rcut": 6.0, + "neuron": [25, 50, 100], + "axis_neuron": 16, + "attn": 128, + "attn_layer": 0, + "attn_dotr": True, + "attn_mask": False, + "activation_function": "tanh", + "scaling_factor": 1.0, + "normalize": True, + "temperature": 1.0, + "seed": 1, + }, + { + "type": "dpa2", + "repinit": { + "rcut": 6.0, + "rcut_smth": 2.0, + "nsel": 30, + "neuron": [2, 4, 8], + "axis_neuron": 4, + "activation_function": "tanh", + }, + "repformer": { + "rcut": 4.0, + "rcut_smth": 0.5, + "nsel": 10, + "nlayers": 12, + "g1_dim": 8, + "g2_dim": 5, + "attn2_hidden": 3, + "attn2_nhead": 1, + "attn1_hidden": 5, + "attn1_nhead": 1, + "axis_neuron": 4, + "update_h2": False, + "update_g1_has_conv": True, + "update_g1_has_grrg": True, + "update_g1_has_drrd": True, + "update_g1_has_attn": True, + "update_g2_has_g1g1": True, + "update_g2_has_attn": True, + "attn2_has_gate": True, + }, + "seed": 1, + "add_tebd_to_repinit_out": False, + }, + ], + }, + "fitting_net": { + "neuron": [240, 240, 240], + "resnet_dt": True, + "seed": 1, + "_comment": " that's all", + }, + "_comment": " that's all", +} + +model_property = { + "type_map": ["H", "C", "N", "O"], + "descriptor": { + "type": "se_e2_a", + "sel": [3, 3, 3, 3], + "rcut_smth": 0.50, + "rcut": 4.00, + "neuron": [25, 50, 100], + "resnet_dt": False, + "axis_neuron": 16, + "seed": 1, + }, + "fitting_net": { + "type": "property", + "task_dim": 3, + "neuron": [24, 24, 24], + "resnet_dt": True, + "bias_method": "normal", + "intensive": True, + "seed": 1, + }, +} + + +class PermutationTest: + def test( + self, + ): + natoms = 5 + generator = paddle.seed(GLOBAL_SEED) + cell = paddle.rand([3, 3], dtype=dtype) + cell = (cell + cell.T) + 5.0 * paddle.eye(3) + coord = paddle.rand([natoms, 3], dtype=dtype) + spin = paddle.rand([natoms, 3], dtype=dtype) + coord = paddle.matmul(coord, cell) + atype = paddle.to_tensor([0, 0, 0, 1, 1], dtype=paddle.int32) + idx_perm = [1, 0, 4, 3, 2] + test_spin = getattr(self, "test_spin", False) + if not test_spin: + test_keys = ["energy", "force", "virial"] + else: + test_keys = ["energy", "force", "force_mag", "virial"] + result_0 = eval_model( + self.model, + coord.unsqueeze(0), + cell.unsqueeze(0), + atype, + spins=spin.unsqueeze(0), + ) + ret0 = {key: result_0[key].squeeze(0) for key in test_keys} + result_1 = eval_model( + self.model, + coord[idx_perm].unsqueeze(0), + cell.unsqueeze(0), + atype[idx_perm], + spins=spin[idx_perm].unsqueeze(0), + ) + ret1 = {key: result_1[key].squeeze(0) for key in test_keys} + prec = 1e-10 + for key in test_keys: + if key in ["energy"]: + assert paddle.allclose(ret0[key], ret1[key], rtol=prec, atol=prec) + elif key in ["force", "force_mag"]: + assert paddle.allclose( + ret0[key][idx_perm], ret1[key], rtol=prec, atol=prec + ) + elif key == "virial": + if not hasattr(self, "test_virial") or self.test_virial: + assert paddle.allclose(ret0[key], ret1[key], rtol=prec, atol=prec) + else: + raise RuntimeError(f"Unexpected test key {key}") + + +class TestEnergyModelSeA(unittest.TestCase, PermutationTest): + def setUp(self): + model_params = copy.deepcopy(model_se_e2_a) + self.type_split = False + self.model = get_model(model_params).to(env.DEVICE) + + +class TestDOSModelSeA(unittest.TestCase, PermutationTest): + def setUp(self): + model_params = copy.deepcopy(model_dos) + self.type_split = False + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelDPA1(unittest.TestCase, PermutationTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa1) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelDPA2(unittest.TestCase, PermutationTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa2) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +class TestForceModelDPA2(unittest.TestCase, PermutationTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa2) + model_params["fitting_net"]["type"] = "direct_force_ener" + self.type_split = True + self.test_virial = False + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelHybrid(unittest.TestCase, PermutationTest): + def setUp(self): + model_params = copy.deepcopy(model_hybrid) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +class TestForceModelHybrid(unittest.TestCase, PermutationTest): + def setUp(self): + model_params = copy.deepcopy(model_hybrid) + model_params["fitting_net"]["type"] = "direct_force_ener" + self.type_split = True + self.test_virial = False + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelZBL(unittest.TestCase, PermutationTest): + def setUp(self): + model_params = copy.deepcopy(model_zbl) + self.type_split = False + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelSpinSeA(unittest.TestCase, PermutationTest): + def setUp(self): + model_params = copy.deepcopy(model_spin) + self.type_split = False + self.test_spin = True + self.model = get_model(model_params).to(env.DEVICE) + + +# class TestEnergyFoo(unittest.TestCase): +# def test(self): +# model_params = model_dpau +# self.model = EnergyModelDPAUni(model_params).to(env.DEVICE) + +# natoms = 5 +# cell = paddle.rand([3, 3], dtype=dtype) +# cell = (cell + cell.T) + 5. * paddle.eye(3) +# coord = paddle.rand([natoms, 3], dtype=dtype) +# coord = paddle.matmul(coord, cell) +# atype = paddle.to_tensor([0, 0, 0, 1, 1]) +# idx_perm = [1, 0, 4, 3, 2] +# ret0 = infer_model(self.model, coord, cell, atype, type_split=True) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/model/test_permutation_denoise.py b/source/tests/pd/model/test_permutation_denoise.py new file mode 100644 index 0000000000..435f4c0d46 --- /dev/null +++ b/source/tests/pd/model/test_permutation_denoise.py @@ -0,0 +1,99 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import unittest + +import paddle + +from deepmd.pd.infer.deep_eval import ( + eval_model, +) +from deepmd.pd.model.model import ( + get_model, +) +from deepmd.pd.utils import ( + env, +) + +from ...seed import ( + GLOBAL_SEED, +) +from .test_permutation import ( # model_dpau, + model_dpa1, + model_dpa2, + model_hybrid, +) + +dtype = paddle.float64 + +model_dpa1 = copy.deepcopy(model_dpa1) +model_dpa2 = copy.deepcopy(model_dpa2) +model_hybrid = copy.deepcopy(model_hybrid) +model_dpa1["type_map"] = ["O", "H", "B", "MASKED_TOKEN"] +model_dpa1.pop("fitting_net") +model_dpa2["type_map"] = ["O", "H", "B", "MASKED_TOKEN"] +model_dpa2.pop("fitting_net") +model_hybrid["type_map"] = ["O", "H", "B", "MASKED_TOKEN"] +model_hybrid.pop("fitting_net") + + +class PermutationDenoiseTest: + def test( + self, + ): + generator = paddle.seed(GLOBAL_SEED) + natoms = 5 + cell = paddle.rand([3, 3], dtype=dtype).to(env.DEVICE) + cell = (cell + cell.T) + 5.0 * paddle.eye(3).to(env.DEVICE) + coord = paddle.rand([natoms, 3], dtype=dtype).to(env.DEVICE) + coord = paddle.matmul(coord, cell) + atype = paddle.to_tensor([0, 0, 0, 1, 1]).to(env.DEVICE) + idx_perm = [1, 0, 4, 3, 2] + updated_c0, logits0 = eval_model( + self.model, coord.unsqueeze(0), cell.unsqueeze(0), atype, denoise=True + ) + ret0 = {"updated_coord": updated_c0.squeeze(0), "logits": logits0.squeeze(0)} + updated_c1, logits1 = eval_model( + self.model, + coord[idx_perm].unsqueeze(0), + cell.unsqueeze(0), + atype[idx_perm], + denoise=True, + ) + ret1 = {"updated_coord": updated_c1.squeeze(0), "logits": logits1.squeeze(0)} + prec = 1e-10 + assert paddle.allclose( + ret0["updated_coord"][idx_perm], ret1["updated_coord"], rtol=prec, atol=prec + ) + assert paddle.allclose( + ret0["logits"][idx_perm], ret1["logits"], rtol=prec, atol=prec + ) + + +@unittest.skip("support of the denoise is temporally disabled") +class TestDenoiseModelDPA1(unittest.TestCase, PermutationDenoiseTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa1) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +@unittest.skip("support of the denoise is temporally disabled") +class TestDenoiseModelDPA2(unittest.TestCase, PermutationDenoiseTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa2) + self.type_split = True + self.model = get_model( + model_params, + ).to(env.DEVICE) + + +# @unittest.skip("hybrid not supported at the moment") +# class TestDenoiseModelHybrid(unittest.TestCase, TestPermutationDenoise): +# def setUp(self): +# model_params = copy.deepcopy(model_hybrid_denoise) +# self.type_split = True +# self.model = get_model(model_params).to(env.DEVICE) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/model/test_polar_atomic_model_stat.py b/source/tests/pd/model/test_polar_atomic_model_stat.py new file mode 100644 index 0000000000..bc086ea0c0 --- /dev/null +++ b/source/tests/pd/model/test_polar_atomic_model_stat.py @@ -0,0 +1,293 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import tempfile +import unittest +from pathlib import ( + Path, +) +from typing import ( + Optional, +) + +import h5py +import numpy as np +import paddle + +from deepmd.pd.model.atomic_model import ( + BaseAtomicModel, + DPPolarAtomicModel, +) +from deepmd.pd.model.descriptor.dpa1 import ( + DescrptDPA1, +) +from deepmd.pd.model.task.polarizability import ( + PolarFittingNet, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, + to_paddle_tensor, +) +from deepmd.utils.path import ( + DPPath, +) + +from .test_env_mat import ( + TestCaseSingleFrameWithNlist, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION + + +class FooFitting(PolarFittingNet): + def forward( + self, + descriptor: paddle.Tensor, + atype: paddle.Tensor, + gr: Optional[paddle.Tensor] = None, + g2: Optional[paddle.Tensor] = None, + h2: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + ): + nf, nloc, _ = descriptor.shape + ret = {} + ret["polarizability"] = ( + paddle.to_tensor( + [ + [ + [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], + [[1.0, 1.0, 1.0], [2.0, 2.0, 2.0], [3.0, 3.0, 3.0]], + [[3.0, 3.0, 3.0], [3.0, 3.0, 3.0], [6.0, 6.0, 6.0]], + ], + [ + [[4.0, 4.0, 4.0], [4.0, 4.0, 4.0], [4.0, 4.0, 4.0]], + [[4.0, 4.0, 4.0], [5.0, 5.0, 5.0], [6.0, 6.0, 6.0]], + [[6.0, 6.0, 6.0], [4.0, 4.0, 4.0], [2.0, 2.0, 2.0]], + ], + ] + ) + .reshape([nf, nloc, *self.output_def()["polarizability"].shape]) + .to(env.GLOBAL_PD_FLOAT_PRECISION) + .to(env.DEVICE) + ) + + return ret + + +class TestAtomicModelStat(unittest.TestCase, TestCaseSingleFrameWithNlist): + def tearDown(self): + self.tempdir.cleanup() + + def setUp(self): + TestCaseSingleFrameWithNlist.setUp(self) + self.merged_output_stat = [ + { + "coord": to_paddle_tensor(np.zeros([2, 3, 3])), + "atype": to_paddle_tensor( + np.array([[0, 0, 1], [0, 1, 1]], dtype=np.int32) + ), + "atype_ext": to_paddle_tensor( + np.array([[0, 0, 1, 0], [0, 1, 1, 0]], dtype=np.int32) + ), + "box": to_paddle_tensor(np.zeros([2, 3, 3])), + "natoms": to_paddle_tensor( + np.array([[3, 3, 2, 1], [3, 3, 1, 2]], dtype=np.int32) + ), + # bias of foo: 5, 6 + "atom_polarizability": to_paddle_tensor( + np.array( + [ + [ + [[5.0, 5.0, 5.0], [5.0, 5.0, 5.0], [5.0, 5.0, 5.0]], + [[5.0, 5.0, 5.0], [5.0, 5.0, 5.0], [5.0, 5.0, 5.0]], + [[5.0, 5.0, 5.0], [5.0, 5.0, 5.0], [5.0, 5.0, 5.0]], + ], + [ + [[5.0, 5.0, 5.0], [5.0, 5.0, 5.0], [5.0, 5.0, 5.0]], + [[6.0, 6.0, 6.0], [6.0, 6.0, 6.0], [6.0, 6.0, 6.0]], + [[7.0, 7.0, 7.0], [7.0, 7.0, 7.0], [7.0, 7.0, 7.0]], + ], + ] + ).reshape(2, 3, 3, 3) + ), + "find_atom_polarizability": np.float32(1.0), + }, + { + "coord": to_paddle_tensor(np.zeros([2, 3, 3])), + "atype": to_paddle_tensor( + np.array([[0, 0, 1], [0, 1, 1]], dtype=np.int32) + ), + "atype_ext": to_paddle_tensor( + np.array([[0, 0, 1, 0], [0, 1, 1, 0]], dtype=np.int32) + ), + "box": to_paddle_tensor(np.zeros([2, 3, 3])), + "natoms": to_paddle_tensor( + np.array([[3, 3, 2, 1], [3, 3, 1, 2]], dtype=np.int32) + ), + # bias of foo: 5, 6 from atomic label. + "polarizability": to_paddle_tensor( + np.array( + [ + [[5.0, 5.0, 5.0], [5.0, 5.0, 5.0], [5.0, 5.0, 5.0]], + [[7.0, 7.0, 7.0], [7.0, 7.0, 7.0], [7.0, 7.0, 7.0]], + ] + ).reshape(2, 3, 3) + ), + "find_polarizability": np.float32(1.0), + }, + ] + self.tempdir = tempfile.TemporaryDirectory() + h5file = str((Path(self.tempdir.name) / "testcase.h5").resolve()) + with h5py.File(h5file, "w") as f: + pass + self.stat_file_path = DPPath(h5file, "a") + + def test_output_stat(self): + nf, nloc, nnei = self.nlist.shape + ds = DescrptDPA1( + self.rcut, + self.rcut_smth, + sum(self.sel), + self.nt, + ).to(env.DEVICE) + ft = FooFitting(self.nt, 1, 1).to(env.DEVICE) + type_map = ["foo", "bar"] + md0 = DPPolarAtomicModel( + ds, + ft, + type_map=type_map, + ).to(env.DEVICE) + args = [ + to_paddle_tensor(ii) for ii in [self.coord_ext, self.atype_ext, self.nlist] + ] + # nf x nloc + at = self.atype_ext[:, :nloc] + + def cvt_ret(x): + return {kk: to_numpy_array(vv) for kk, vv in x.items()} + + # 1. test run without bias + # nf x na x odim + ret0 = md0.forward_common_atomic(*args) + ret0 = cvt_ret(ret0) + expected_ret0 = {} + expected_ret0["polarizability"] = np.array( + [ + [ + [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], + [[1.0, 1.0, 1.0], [2.0, 2.0, 2.0], [3.0, 3.0, 3.0]], + [[3.0, 3.0, 3.0], [3.0, 3.0, 3.0], [6.0, 6.0, 6.0]], + ], + [ + [[4.0, 4.0, 4.0], [4.0, 4.0, 4.0], [4.0, 4.0, 4.0]], + [[4.0, 4.0, 4.0], [5.0, 5.0, 5.0], [6.0, 6.0, 6.0]], + [[6.0, 6.0, 6.0], [4.0, 4.0, 4.0], [2.0, 2.0, 2.0]], + ], + ] + ).reshape([nf, nloc, *md0.fitting_output_def()["polarizability"].shape]) + + np.testing.assert_almost_equal( + ret0["polarizability"], expected_ret0["polarizability"] + ) + + # 2. test bias is applied + md0.compute_or_load_out_stat( + self.merged_output_stat, stat_file_path=self.stat_file_path + ) + ret1 = md0.forward_common_atomic(*args) + ret1 = cvt_ret(ret1) + expected_std = np.zeros( + (1, 2, 9), dtype=np.float64 + ) # 1 keys, 2 atypes, 9 max dims. + expected_std[:, 1, :] = np.ones(9, dtype=np.float64) * 0.8164966 # updating std + # nt x odim (dia) + diagnoal_bias = np.array( + [ + [[5.0, 0.0, 0.0], [0.0, 5.0, 0.0], [0.0, 0.0, 5.0]], + [[6.0, 0.0, 0.0], [0.0, 6.0, 0.0], [0.0, 0.0, 6.0]], + ] + ).reshape(2, 3, 3) + expected_ret1 = {} + expected_ret1["polarizability"] = ret0["polarizability"] + diagnoal_bias[at] + np.testing.assert_almost_equal( + ret1["polarizability"], expected_ret1["polarizability"] + ) + np.testing.assert_almost_equal(to_numpy_array(md0.out_std), expected_std) + + # 3. test bias load from file + def raise_error(): + raise RuntimeError + + md0.compute_or_load_out_stat(raise_error, stat_file_path=self.stat_file_path) + ret2 = md0.forward_common_atomic(*args) + ret2 = cvt_ret(ret2) + np.testing.assert_almost_equal(ret1["polarizability"], ret2["polarizability"]) + np.testing.assert_almost_equal(to_numpy_array(md0.out_std), expected_std) + + # 4. test change bias + BaseAtomicModel.change_out_bias( + md0, self.merged_output_stat, bias_adjust_mode="change-by-statistic" + ) + args = [ + to_paddle_tensor(ii) + for ii in [ + self.coord_ext, + to_numpy_array(self.merged_output_stat[0]["atype_ext"]), + self.nlist, + ] + ] + ret3 = md0.forward_common_atomic(*args) + ret3 = cvt_ret(ret3) + + expected_ret3 = {} + expected_std = np.array( + [ + [ + [ + 1.4142136, + 1.4142136, + 1.4142136, + 1.2472191, + 1.2472191, + 1.2472191, + 1.2472191, + 1.2472191, + 1.2472191, + ], + [ + 0.4714045, + 0.4714045, + 0.4714045, + 0.8164966, + 0.8164966, + 0.8164966, + 2.6246693, + 2.6246693, + 2.6246693, + ], + ] + ] + ) + # new bias [[[3.0000, -, -, -, 2.6667, -, -, -, 2.3333], + # [1.6667, -, -, -, 2.0000, -, -, -, 1.3333]]] + # which yields [2.667, 1.667] + expected_ret3["polarizability"] = np.array( + [ + [ + [[3.6667, 1.0, 1.0], [1.0, 3.6667, 1.0], [1.0, 1.0, 3.6667]], + [[3.6667, 1.0, 1.0], [2.0, 4.6667, 2.0], [3.0, 3.0, 5.6667]], + [[4.6667, 3.0, 3.0], [3.0, 4.6667, 3.0], [6.0, 6.0, 7.6667]], + ], + [ + [[6.6667, 4.0, 4.0], [4.0, 6.6667, 4.0], [4.0, 4.0, 6.6667]], + [[5.6667, 4.0, 4.0], [5.0, 6.6667, 5.0], [6.0, 6.0, 7.6667]], + [[7.6667, 6.0, 6.0], [4.0, 5.6667, 4.0], [2.0, 2.0, 3.6667]], + ], + ] + ).reshape(2, 3, 3, 3) + np.testing.assert_almost_equal( + ret3["polarizability"], expected_ret3["polarizability"], decimal=4 + ) + np.testing.assert_almost_equal(to_numpy_array(md0.out_std), expected_std) diff --git a/source/tests/pd/model/test_polarizability_fitting.py b/source/tests/pd/model/test_polarizability_fitting.py new file mode 100644 index 0000000000..87b62ba679 --- /dev/null +++ b/source/tests/pd/model/test_polarizability_fitting.py @@ -0,0 +1,381 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import itertools +import os +import unittest + +import numpy as np +import paddle +from scipy.stats import ( + special_ortho_group, +) + +from deepmd.dpmodel.fitting import PolarFitting as DPPolarFitting +from deepmd.infer.deep_polar import ( + DeepPolar, +) +from deepmd.pd.model.descriptor.se_a import ( + DescrptSeA, +) +from deepmd.pd.model.model.polar_model import ( + PolarModel, +) +from deepmd.pd.model.task.polarizability import ( + PolarFittingNet, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.nlist import ( + extend_input_and_build_neighbor_list, +) +from deepmd.pd.utils.utils import ( + to_numpy_array, +) + +from ...seed import ( + GLOBAL_SEED, +) +from .test_env_mat import ( + TestCaseSingleFrameWithNlist, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION + + +class TestPolarFitting(unittest.TestCase, TestCaseSingleFrameWithNlist): + def setUp(self): + TestCaseSingleFrameWithNlist.setUp(self) + self.rng = np.random.default_rng(GLOBAL_SEED) + self.nf, self.nloc, _ = self.nlist.shape + self.dd0 = DescrptSeA(self.rcut, self.rcut_smth, self.sel).to(env.DEVICE) + self.scale = self.rng.uniform(0, 1, self.nt).tolist() + + def test_consistency( + self, + ): + rd0, gr, _, _, _ = self.dd0( + paddle.to_tensor(self.coord_ext, dtype=dtype).to(device=env.DEVICE), + paddle.to_tensor(self.atype_ext, dtype="int64").to(device=env.DEVICE), + paddle.to_tensor(self.nlist, dtype="int64").to(device=env.DEVICE), + ) + atype = paddle.to_tensor( + self.atype_ext[:, : self.nloc], dtype="int64", place=env.DEVICE + ) + + for nfp, nap, fit_diag, scale in itertools.product( + [0, 3], + [0, 4], + [True, False], + [None, self.scale], + ): + ft0 = PolarFittingNet( + self.nt, + self.dd0.dim_out, + embedding_width=self.dd0.get_dim_emb(), + numb_fparam=nfp, + numb_aparam=nap, + mixed_types=self.dd0.mixed_types(), + fit_diag=fit_diag, + scale=scale, + ).to(env.DEVICE) + ft1 = DPPolarFitting.deserialize(ft0.serialize()) + ft2 = PolarFittingNet.deserialize(ft0.serialize()) + ft3 = DPPolarFitting.deserialize(ft1.serialize()) + + if nfp > 0: + ifp = paddle.to_tensor( + self.rng.normal(size=(self.nf, nfp)), dtype=dtype, place=env.DEVICE + ) + else: + ifp = None + if nap > 0: + iap = paddle.to_tensor( + self.rng.normal(size=(self.nf, self.nloc, nap)), + dtype=dtype, + place=env.DEVICE, + ) + else: + iap = None + + ret0 = ft0(rd0, atype, gr, fparam=ifp, aparam=iap) + ret1 = ft1( + rd0.detach().cpu().numpy(), + atype.detach().cpu().numpy(), + gr.detach().cpu().numpy(), + fparam=to_numpy_array(ifp), + aparam=to_numpy_array(iap), + ) + ret2 = ft2(rd0, atype, gr, fparam=ifp, aparam=iap) + ret3 = ft3( + rd0.detach().cpu().numpy(), + atype.detach().cpu().numpy(), + gr.detach().cpu().numpy(), + fparam=to_numpy_array(ifp), + aparam=to_numpy_array(iap), + ) + np.testing.assert_allclose( + to_numpy_array(ret0["polarizability"]), + ret1["polarizability"], + ) + np.testing.assert_allclose( + to_numpy_array(ret0["polarizability"]), + to_numpy_array(ret2["polarizability"]), + ) + np.testing.assert_allclose( + to_numpy_array(ret0["polarizability"]), + ret3["polarizability"], + ) + + def test_jit( + self, + ): + for mixed_types, nfp, nap, fit_diag in itertools.product( + [True, False], + [0, 3], + [0, 4], + [True, False], + ): + ft0 = PolarFittingNet( + self.nt, + self.dd0.dim_out, + embedding_width=self.dd0.get_dim_emb(), + numb_fparam=nfp, + numb_aparam=nap, + mixed_types=mixed_types, + fit_diag=fit_diag, + ).to(env.DEVICE) + paddle.jit.to_static(ft0) + + +class TestEquivalence(unittest.TestCase): + def setUp(self) -> None: + self.natoms = 5 + self.rcut = 4 + self.rcut_smth = 0.5 + self.sel = [46, 92, 4] + self.nf = 1 + self.nt = 3 + self.rng = np.random.default_rng(GLOBAL_SEED) + self.coord = 2 * paddle.rand([self.natoms, 3], dtype=dtype).to( + device=env.DEVICE + ) + self.shift = paddle.to_tensor([4, 4, 4], dtype=dtype).to(device=env.DEVICE) + self.atype = paddle.to_tensor([0, 0, 0, 1, 1], dtype=paddle.int32).to( + device=env.DEVICE + ) + self.dd0 = DescrptSeA(self.rcut, self.rcut_smth, self.sel).to(env.DEVICE) + self.cell = paddle.rand([3, 3], dtype=dtype).to(device=env.DEVICE) + self.cell = (self.cell + self.cell.T) + 5.0 * paddle.eye(3).to( + device=env.DEVICE + ) + self.scale = self.rng.uniform(0, 1, self.nt).tolist() + + def test_rot(self): + atype = self.atype.reshape([1, 5]) + rmat = paddle.to_tensor(special_ortho_group.rvs(3), dtype=dtype).to( + device=env.DEVICE + ) + coord_rot = paddle.matmul(self.coord, rmat) + # use larger cell to rotate only coord and shift to the center of cell + cell_rot = 10.0 * paddle.eye(3, dtype=dtype).to(device=env.DEVICE) + + for nfp, nap, fit_diag, scale in itertools.product( + [0, 3], + [0, 4], + [True, False], + [None, self.scale], + ): + ft0 = PolarFittingNet( + self.nt, + self.dd0.dim_out, # dim_descrpt + embedding_width=self.dd0.get_dim_emb(), + numb_fparam=nfp, + numb_aparam=nap, + mixed_types=self.dd0.mixed_types(), + fit_diag=fit_diag, + scale=scale, + ).to(env.DEVICE) + if nfp > 0: + ifp = paddle.to_tensor( + self.rng.normal(size=(self.nf, nfp)), dtype=dtype, place=env.DEVICE + ) + else: + ifp = None + if nap > 0: + iap = paddle.to_tensor( + self.rng.normal(size=(self.nf, self.natoms, nap)), + dtype=dtype, + place=env.DEVICE, + ) + else: + iap = None + + res = [] + for xyz in [self.coord, coord_rot]: + ( + extended_coord, + extended_atype, + _, + nlist, + ) = extend_input_and_build_neighbor_list( + xyz + self.shift, + atype, + self.rcut, + self.sel, + self.dd0.mixed_types(), + box=cell_rot, + ) + + rd0, gr0, _, _, _ = self.dd0( + extended_coord, + extended_atype, + nlist, + ) + + ret0 = ft0(rd0, atype, gr0, fparam=ifp, aparam=iap) + res.append(ret0["polarizability"]) + np.testing.assert_allclose( + to_numpy_array(res[1]), + to_numpy_array( + paddle.matmul( + rmat.T, + paddle.matmul(res[0], rmat), + ) + ), + ) + + def test_permu(self): + coord = paddle.matmul(self.coord, self.cell) + for fit_diag, scale in itertools.product([True, False], [None, self.scale]): + ft0 = PolarFittingNet( + self.nt, + self.dd0.dim_out, + embedding_width=self.dd0.get_dim_emb(), + numb_fparam=0, + numb_aparam=0, + mixed_types=self.dd0.mixed_types(), + fit_diag=fit_diag, + scale=scale, + ).to(env.DEVICE) + res = [] + for idx_perm in [[0, 1, 2, 3, 4], [1, 0, 4, 3, 2]]: + atype = self.atype[idx_perm].reshape([1, 5]) + ( + extended_coord, + extended_atype, + _, + nlist, + ) = extend_input_and_build_neighbor_list( + coord[idx_perm], + atype, + self.rcut, + self.sel, + self.dd0.mixed_types(), + box=self.cell, + ) + + rd0, gr0, _, _, _ = self.dd0( + extended_coord, + extended_atype, + nlist, + ) + + ret0 = ft0(rd0, atype, gr0, fparam=None, aparam=None) + res.append(ret0["polarizability"]) + + np.testing.assert_allclose( + to_numpy_array(res[0][:, idx_perm]), + to_numpy_array(res[1]), + ) + + def test_trans(self): + atype = self.atype.reshape([1, 5]) + coord_s = paddle.matmul( + paddle.remainder( + paddle.matmul(self.coord + self.shift, paddle.linalg.inv(self.cell)), + paddle.full([], 1.0), + ), + self.cell, + ) + for fit_diag, scale in itertools.product([True, False], [None, self.scale]): + ft0 = PolarFittingNet( + self.nt, + self.dd0.dim_out, + embedding_width=self.dd0.get_dim_emb(), + numb_fparam=0, + numb_aparam=0, + mixed_types=self.dd0.mixed_types(), + fit_diag=fit_diag, + scale=scale, + ).to(env.DEVICE) + res = [] + for xyz in [self.coord, coord_s]: + ( + extended_coord, + extended_atype, + _, + nlist, + ) = extend_input_and_build_neighbor_list( + xyz, + atype, + self.rcut, + self.sel, + self.dd0.mixed_types(), + box=self.cell, + ) + + rd0, gr0, _, _, _ = self.dd0( + extended_coord, + extended_atype, + nlist, + ) + + ret0 = ft0(rd0, atype, gr0, fparam=0, aparam=0) + res.append(ret0["polarizability"]) + + np.testing.assert_allclose(to_numpy_array(res[0]), to_numpy_array(res[1])) + + +class TestPolarModel(unittest.TestCase): + def setUp(self): + self.natoms = 5 + self.rcut = 4.0 + self.nt = 3 + self.rcut_smth = 0.5 + self.sel = [46, 92, 4] + self.nf = 1 + self.coord = 2 * paddle.rand([self.natoms, 3], dtype=dtype).to(device="cpu") + cell = paddle.rand([3, 3], dtype=dtype).to(device="cpu") + self.cell = (cell + cell.T) + 5.0 * paddle.eye(3).to(device="cpu") + self.atype = paddle.to_tensor([0, 0, 0, 1, 1], place="cpu") + self.dd0 = DescrptSeA(self.rcut, self.rcut_smth, self.sel).to(env.DEVICE) + self.ft0 = PolarFittingNet( + self.nt, + self.dd0.dim_out, + embedding_width=self.dd0.get_dim_emb(), + numb_fparam=0, + numb_aparam=0, + mixed_types=self.dd0.mixed_types(), + ).to(env.DEVICE) + self.type_mapping = ["O", "H", "B"] + self.model = PolarModel(self.dd0, self.ft0, self.type_mapping) + self.file_path = "model_output.pd" + + @unittest.skip("Paddle do not eval on frozen model yet.") + def test_deepdipole_infer(self): + atype = self.atype.reshape([self.nf, self.natoms]) + coord = self.coord.reshape([1, 5, 3]) + cell = self.cell.reshape([1, 9]) + jit_md = paddle.jit.to_static(self.model) + paddle.jit.save(jit_md, self.file_path) + load_md = DeepPolar(self.file_path) + load_md.eval(coords=coord, atom_types=atype, cells=cell, atomic=True) + load_md.eval(coords=coord, atom_types=atype, cells=cell, atomic=False) + + def tearDown(self) -> None: + if os.path.exists(self.file_path): + os.remove(self.file_path) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/model/test_region.py b/source/tests/pd/model/test_region.py new file mode 100644 index 0000000000..b3a89a39f8 --- /dev/null +++ b/source/tests/pd/model/test_region.py @@ -0,0 +1,88 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import unittest + +import numpy as np +import paddle + +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.preprocess import ( + Region3D, +) +from deepmd.pd.utils.region import ( + inter2phys, + to_face_distance, +) + +from ...seed import ( + GLOBAL_SEED, +) + +dtype = paddle.float64 + + +class TestRegion(unittest.TestCase): + def setUp(self): + self.cell = paddle.to_tensor( + [[1, 0, 0], [0.4, 0.8, 0], [0.1, 0.3, 2.1]], dtype=dtype, place="cpu" + ) + self.cell = self.cell.unsqueeze(0).unsqueeze(0) + self.cell = paddle.tile(self.cell, [4, 5, 1, 1]) + self.prec = 1e-8 + + def test_inter_to_phys(self): + generator = paddle.seed(GLOBAL_SEED) + inter = paddle.rand([4, 5, 3, 3], dtype=dtype).to(device="cpu") + phys = inter2phys(inter, self.cell) + for ii in range(4): + for jj in range(5): + expected_phys = paddle.matmul(inter[ii, jj], self.cell[ii, jj]) + assert paddle.allclose( + phys[ii, jj], expected_phys, rtol=self.prec, atol=self.prec + ) + + def test_to_face_dist(self): + cell0 = self.cell[0][0].numpy() + vol = np.linalg.det(cell0) + # area of surfaces xy, xz, yz + sxy = np.linalg.norm(np.cross(cell0[0], cell0[1])) + sxz = np.linalg.norm(np.cross(cell0[0], cell0[2])) + syz = np.linalg.norm(np.cross(cell0[1], cell0[2])) + # vol / area gives distance + dz = vol / sxy + dy = vol / sxz + dx = vol / syz + dists = to_face_distance(self.cell) + expected = paddle.to_tensor([dx, dy, dz], dtype=dists.dtype).to(device="cpu") + for ii in range(4): + for jj in range(5): + assert paddle.allclose( + dists[ii][jj], expected, rtol=self.prec, atol=self.prec + ) + + +class TestLegacyRegion(unittest.TestCase): + def setUp(self): + self.cell = paddle.to_tensor( + [[1, 0, 0], [0.4, 0.8, 0], [0.1, 0.3, 2.1]], dtype=dtype, place=env.DEVICE + ) + self.prec = 1e-6 + + def test_inter_to_phys(self): + generator = paddle.seed(GLOBAL_SEED) + inter = paddle.rand([3, 3], dtype=dtype).to(device=env.DEVICE) + reg = Region3D(self.cell) + phys = reg.inter2phys(inter) + expected_phys = paddle.matmul(inter, self.cell) + assert paddle.allclose(phys, expected_phys, rtol=self.prec, atol=self.prec) + + def test_inter_to_inter(self): + generator = paddle.seed(GLOBAL_SEED) + inter = paddle.rand([3, 3], dtype=dtype).to(device=env.DEVICE) + reg = Region3D(self.cell) + new_inter = reg.phys2inter(reg.inter2phys(inter)) + assert paddle.allclose(inter, new_inter, rtol=self.prec, atol=self.prec) + + def test_to_face_dist(self): + pass diff --git a/source/tests/pd/model/test_rot.py b/source/tests/pd/model/test_rot.py new file mode 100644 index 0000000000..2e2094b750 --- /dev/null +++ b/source/tests/pd/model/test_rot.py @@ -0,0 +1,215 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import unittest + +import paddle + +from deepmd.pd.model.model import ( + get_model, +) +from deepmd.pd.utils import ( + env, +) + +from ...seed import ( + GLOBAL_SEED, +) +from ..common import ( + eval_model, +) +from .test_permutation import ( # model_dpau, + model_dos, + model_dpa1, + model_dpa2, + model_hybrid, + model_se_e2_a, + model_spin, + model_zbl, +) + +dtype = paddle.float64 + + +class RotTest: + def test( + self, + ): + generator = paddle.seed(GLOBAL_SEED) + prec = 1e-10 + natoms = 5 + cell = 10.0 * paddle.eye(3, dtype=dtype).to(device=env.DEVICE) + coord = 2 * paddle.rand([natoms, 3], dtype=dtype).to(device=env.DEVICE) + spin = 2 * paddle.rand([natoms, 3], dtype=dtype).to(device=env.DEVICE) + shift = paddle.to_tensor([4, 4, 4], dtype=dtype).to(device=env.DEVICE) + atype = paddle.to_tensor([0, 0, 0, 1, 1], dtype=paddle.int32).to( + device=env.DEVICE + ) + from scipy.stats import ( + special_ortho_group, + ) + + test_spin = getattr(self, "test_spin", False) + if not test_spin: + test_keys = ["energy", "force", "virial"] + else: + test_keys = ["energy", "force", "force_mag"] + rmat = paddle.to_tensor(special_ortho_group.rvs(3), dtype=dtype).to( + device=env.DEVICE + ) + + # rotate only coord and shift to the center of cell + coord_rot = paddle.matmul(coord, rmat) + spin_rot = paddle.matmul(spin, rmat) + result_0 = eval_model( + self.model, + (coord + shift).unsqueeze(0), + cell.unsqueeze(0), + atype, + spins=spin.unsqueeze(0), + ) + ret0 = {key: result_0[key].squeeze(0) for key in test_keys} + result_1 = eval_model( + self.model, + (coord_rot + shift).unsqueeze(0), + cell.unsqueeze(0), + atype, + spins=spin_rot.unsqueeze(0), + ) + ret1 = {key: result_1[key].squeeze(0) for key in test_keys} + for key in test_keys: + if key in ["energy"]: + assert paddle.allclose(ret0[key], ret1[key], rtol=prec, atol=prec) + elif key in ["force", "force_mag"]: + assert paddle.allclose( + paddle.matmul(ret0[key], rmat), ret1[key], rtol=prec, atol=prec + ) + elif key == "virial": + if not hasattr(self, "test_virial") or self.test_virial: + assert paddle.allclose( + paddle.matmul( + rmat.T, paddle.matmul(ret0[key].reshape([3, 3]), rmat) + ), + ret1[key].reshape([3, 3]), + rtol=prec, + atol=prec, + ) + else: + raise RuntimeError(f"Unexpected test key {key}") + # rotate coord and cell + paddle.seed(0) + cell = paddle.rand([3, 3], dtype=dtype).to(device=env.DEVICE) + cell = (cell + cell.T) + 5.0 * paddle.eye(3).to(device=env.DEVICE) + coord = paddle.rand([natoms, 3], dtype=dtype).to(device=env.DEVICE) + coord = paddle.matmul(coord, cell) + spin = paddle.rand([natoms, 3], dtype=dtype).to(device=env.DEVICE) + atype = paddle.to_tensor([0, 0, 0, 1, 1], dtype=paddle.int32).to( + device=env.DEVICE + ) + coord_rot = paddle.matmul(coord, rmat) + spin_rot = paddle.matmul(spin, rmat) + cell_rot = paddle.matmul(cell, rmat) + result_0 = eval_model( + self.model, + coord.unsqueeze(0), + cell.unsqueeze(0), + atype, + spins=spin.unsqueeze(0), + ) + ret0 = {key: result_0[key].squeeze(0) for key in test_keys} + result_1 = eval_model( + self.model, + coord_rot.unsqueeze(0), + cell_rot.unsqueeze(0), + atype, + spins=spin_rot.unsqueeze(0), + ) + ret1 = {key: result_1[key].squeeze(0) for key in test_keys} + for key in test_keys: + if key in ["energy"]: + assert paddle.allclose(ret0[key], ret1[key], rtol=prec, atol=prec) + elif key in ["force", "force_mag"]: + assert paddle.allclose( + paddle.matmul(ret0[key], rmat), ret1[key], rtol=prec, atol=prec + ) + elif key == "virial": + if not hasattr(self, "test_virial") or self.test_virial: + assert paddle.allclose( + paddle.matmul( + rmat.T, paddle.matmul(ret0[key].reshape([3, 3]), rmat) + ), + ret1[key].reshape([3, 3]), + rtol=prec, + atol=prec, + ) + + +class TestEnergyModelSeA(unittest.TestCase, RotTest): + def setUp(self): + model_params = copy.deepcopy(model_se_e2_a) + self.type_split = False + self.model = get_model(model_params).to(env.DEVICE) + + +class TestDOSModelSeA(unittest.TestCase, RotTest): + def setUp(self): + model_params = copy.deepcopy(model_dos) + self.type_split = False + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelDPA1(unittest.TestCase, RotTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa1) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelDPA2(unittest.TestCase, RotTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa2) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +class TestForceModelDPA2(unittest.TestCase, RotTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa2) + model_params["fitting_net"]["type"] = "direct_force_ener" + self.type_split = True + self.test_virial = False + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelHybrid(unittest.TestCase, RotTest): + def setUp(self): + model_params = copy.deepcopy(model_hybrid) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +class TestForceModelHybrid(unittest.TestCase, RotTest): + def setUp(self): + model_params = copy.deepcopy(model_hybrid) + model_params["fitting_net"]["type"] = "direct_force_ener" + self.type_split = True + self.test_virial = False + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelZBL(unittest.TestCase, RotTest): + def setUp(self): + model_params = copy.deepcopy(model_zbl) + self.type_split = False + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelSpinSeA(unittest.TestCase, RotTest): + def setUp(self): + model_params = copy.deepcopy(model_spin) + self.type_split = False + self.test_spin = True + self.model = get_model(model_params).to(env.DEVICE) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/model/test_rot_denoise.py b/source/tests/pd/model/test_rot_denoise.py new file mode 100644 index 0000000000..562943f395 --- /dev/null +++ b/source/tests/pd/model/test_rot_denoise.py @@ -0,0 +1,128 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import unittest + +import paddle + +from deepmd.pd.infer.deep_eval import ( + eval_model, +) +from deepmd.pd.model.model import ( + get_model, +) +from deepmd.pd.utils import ( + env, +) + +from ...seed import ( + GLOBAL_SEED, +) +from .test_permutation_denoise import ( + model_dpa1, + model_dpa2, +) + +dtype = paddle.float64 + + +class RotDenoiseTest: + def test( + self, + ): + generator = paddle.seed(GLOBAL_SEED) + prec = 1e-10 + natoms = 5 + cell = 10.0 * paddle.eye(3, dtype=dtype).to(env.DEVICE) + coord = 2 * paddle.rand([natoms, 3], dtype=dtype).to(device=env.DEVICE) + shift = paddle.to_tensor([4, 4, 4], dtype=dtype).to(env.DEVICE) + atype = paddle.to_tensor([0, 0, 0, 1, 1]).to(env.DEVICE) + from scipy.stats import ( + special_ortho_group, + ) + + rmat = paddle.to_tensor(special_ortho_group.rvs(3), dtype=dtype).to(env.DEVICE) + + # rotate only coord and shift to the center of cell + coord_rot = paddle.matmul(coord, rmat) + update_c0, logits0 = eval_model( + self.model, + (coord + shift).unsqueeze(0), + cell.unsqueeze(0), + atype, + denoise=True, + ) + update_c0 = update_c0 - (coord + shift).unsqueeze(0) + ret0 = {"updated_coord": update_c0.squeeze(0), "logits": logits0.squeeze(0)} + update_c1, logits1 = eval_model( + self.model, + (coord_rot + shift).unsqueeze(0), + cell.unsqueeze(0), + atype, + denoise=True, + ) + update_c1 = update_c1 - (coord_rot + shift).unsqueeze(0) + ret1 = {"updated_coord": update_c1.squeeze(0), "logits": logits1.squeeze(0)} + assert paddle.allclose( + paddle.matmul(ret0["updated_coord"], rmat), + ret1["updated_coord"], + rtol=prec, + atol=prec, + ) + assert paddle.allclose(ret0["logits"], ret1["logits"], rtol=prec, atol=prec) + + # rotate coord and cell + paddle.seed(0) + cell = paddle.rand([3, 3], dtype=dtype).to(env.DEVICE) + cell = (cell + cell.T) + 5.0 * paddle.eye(3).to(env.DEVICE) + coord = paddle.rand([natoms, 3], dtype=dtype).to(env.DEVICE) + coord = paddle.matmul(coord, cell) + atype = paddle.to_tensor([0, 0, 0, 1, 1]).to(env.DEVICE) + coord_rot = paddle.matmul(coord, rmat) + cell_rot = paddle.matmul(cell, rmat) + update_c0, logits0 = eval_model( + self.model, coord.unsqueeze(0), cell.unsqueeze(0), atype, denoise=True + ) + ret0 = {"updated_coord": update_c0.squeeze(0), "logits": logits0.squeeze(0)} + update_c1, logits1 = eval_model( + self.model, + coord_rot.unsqueeze(0), + cell_rot.unsqueeze(0), + atype, + denoise=True, + ) + ret1 = {"updated_coord": update_c1.squeeze(0), "logits": logits1.squeeze(0)} + assert paddle.allclose(ret0["logits"], ret1["logits"], rtol=prec, atol=prec) + assert paddle.allclose( + paddle.matmul(ret0["updated_coord"], rmat), + ret1["updated_coord"], + rtol=prec, + atol=prec, + ) + + +@unittest.skip("support of the denoise is temporally disabled") +class TestDenoiseModelDPA1(unittest.TestCase, RotDenoiseTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa1) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +@unittest.skip("support of the denoise is temporally disabled") +class TestDenoiseModelDPA2(unittest.TestCase, RotDenoiseTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa2) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +# @unittest.skip("hybrid not supported at the moment") +# class TestEnergyModelHybrid(unittest.TestCase, TestRotDenoise): +# def setUp(self): +# model_params = copy.deepcopy(model_hybrid_denoise) +# self.type_split = True +# self.model = get_model(model_params).to(env.DEVICE) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/model/test_rotation.py b/source/tests/pd/model/test_rotation.py new file mode 100644 index 0000000000..d5cf118c84 --- /dev/null +++ b/source/tests/pd/model/test_rotation.py @@ -0,0 +1,112 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import json +import unittest +from pathlib import ( + Path, +) +from typing import ( + Optional, +) + +import numpy as np +import paddle +from scipy.stats import ( + special_ortho_group, +) + +from deepmd.pd.model.model import ( + get_model, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.utils.data import ( + DeepmdData, +) + + +class CheckSymmetry(DeepmdData): + def __init__( + self, + sys_path: str, + type_map: Optional[list[str]] = None, + ): + super().__init__(sys_path=sys_path, type_map=type_map) + self.add("energy", 1, atomic=False, must=False, high_prec=True) + self.add("force", 3, atomic=True, must=False, high_prec=False) + self.add("virial", 9, atomic=False, must=False, high_prec=False) + + def get_rotation(self, index, rotation_matrix): + for i in range( + 0, len(self.dirs) + 1 + ): # note: if different sets can be merged, prefix sum is unused to calculate + if index < self.prefix_sum[i]: + break + frames = self._load_set(self.dirs[i - 1]) + frames["coord"] = np.dot( + rotation_matrix, frames["coord"].reshape(-1, 3).T + ).T.reshape(self.nframes, -1) + frames["box"] = np.dot( + rotation_matrix, frames["box"].reshape(-1, 3).T + ).T.reshape(self.nframes, -1) + frames["force"] = np.dot( + rotation_matrix, frames["force"].reshape(-1, 3).T + ).T.reshape(self.nframes, -1) + frame = self._get_subdata(frames, index - self.prefix_sum[i - 1]) + frame = self.reformat_data_torch(frame) + return frame + + +def get_data(batch): + inputs = {} + for key in ["coord", "atype", "box"]: + inputs[key] = paddle.to_tensor(batch[key]).to(device=env.DEVICE) + inputs[key] = inputs[key].unsqueeze(0).to(env.DEVICE) + return inputs + + +class TestRotation(unittest.TestCase): + def setUp(self): + with open(str(Path(__file__).parent / "water/se_e2_a.json")) as fin: + self.config = json.load(fin) + data_file = [str(Path(__file__).parent / "water/data/data_0")] + self.config["training"]["training_data"]["systems"] = data_file + self.config["training"]["validation_data"]["systems"] = data_file + self.rotation = special_ortho_group.rvs(3) + device = paddle.get_device() + paddle.set_device("cpu") + self.get_dataset(0) + paddle.set_device(device) + self.get_model() + + def get_model(self): + self.model = get_model(self.config["model"]).to(env.DEVICE) + + def get_dataset(self, system_index=0, batch_index=0): + systems = self.config["training"]["training_data"]["systems"] + type_map = self.config["model"]["type_map"] + dpdatasystem = CheckSymmetry(sys_path=systems[system_index], type_map=type_map) + self.origin_batch = dpdatasystem.get_item_paddle(batch_index) + self.rotated_batch = dpdatasystem.get_rotation(batch_index, self.rotation) + + def test_rotation(self): + result1 = self.model(**get_data(self.origin_batch)) + result2 = self.model(**get_data(self.rotated_batch)) + rotation = paddle.to_tensor(self.rotation).to(env.DEVICE) + assert paddle.allclose(result1["energy"], result2["energy"]) + if "force" in result1: + assert paddle.allclose( + result2["force"][0], paddle.matmul(rotation, result1["force"][0].T).T + ) + if "virial" in result1: + assert paddle.allclose( + result2["virial"][0].view([3, 3]), + paddle.matmul( + paddle.matmul(rotation, result1["virial"][0].view([3, 3]).T), + rotation.T, + ), + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/model/test_saveload_dpa1.py b/source/tests/pd/model/test_saveload_dpa1.py new file mode 100644 index 0000000000..04ddd6cb86 --- /dev/null +++ b/source/tests/pd/model/test_saveload_dpa1.py @@ -0,0 +1,145 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import json +import os +import unittest +from pathlib import ( + Path, +) + +import paddle +from paddle.io import ( + DataLoader, +) + +from deepmd.pd.loss import ( + EnergyStdLoss, +) +from deepmd.pd.model.model import ( + get_model, +) +from deepmd.pd.train.wrapper import ( + ModelWrapper, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.dataloader import ( + BufferedIterator, + DpLoaderSet, +) +from deepmd.pd.utils.stat import ( + make_stat_input, +) +from deepmd.tf.common import ( + expand_sys_str, +) + + +def get_dataset(config): + model_config = config["model"] + rcut = model_config["descriptor"]["rcut"] + sel = model_config["descriptor"]["sel"] + systems = config["training"]["validation_data"]["systems"] + if isinstance(systems, str): + systems = expand_sys_str(systems) + batch_size = config["training"]["training_data"]["batch_size"] + type_map = model_config["type_map"] + + dataset = DpLoaderSet(systems, batch_size, type_map) + data_stat_nbatch = model_config.get("data_stat_nbatch", 10) + sampled = make_stat_input(dataset.systems, dataset.dataloaders, data_stat_nbatch) + return dataset, sampled + + +class TestSaveLoadDPA1(unittest.TestCase): + def setUp(self): + input_json = str(Path(__file__).parent / "water/se_atten.json") + with open(input_json) as fin: + self.config = json.load(fin) + self.config["loss"]["starter_learning_rate"] = self.config["learning_rate"][ + "start_lr" + ] + data_file = [str(Path(__file__).parent / "water/data/data_0")] + self.config["training"]["training_data"]["systems"] = data_file + self.config["training"]["validation_data"]["systems"] = data_file + self.dataset, self.sampled = get_dataset(self.config) + self.training_dataloader = DataLoader( + self.dataset, + batch_sampler=paddle.io.BatchSampler( + sampler=paddle.io.RandomSampler(self.dataset), + drop_last=False, + ), + num_workers=0, # setting to 0 diverges the behavior of its iterator; should be >=1 + collate_fn=lambda x: x[0], + ) + device = paddle.get_device() + paddle.set_device("cpu") + self.training_data = BufferedIterator(iter(self.training_dataloader)) + paddle.set_device(device) + self.loss = EnergyStdLoss(**self.config["loss"]) + self.cur_lr = 1 + self.task_key = "Default" + self.input_dict, self.label_dict = self.get_data() + self.start_lr = self.config["learning_rate"]["start_lr"] + + def get_model_result(self, read=False, model_file="tmp_model.pd"): + wrapper = self.create_wrapper(read) + optimizer = paddle.optimizer.Adam( + learning_rate=self.start_lr, parameters=wrapper.parameters() + ) + optimizer.clear_grad() + if read: + wrapper.set_state_dict(paddle.load(model_file)) + os.remove(model_file) + else: + paddle.save(wrapper.state_dict(), model_file) + result = wrapper( + **self.input_dict, + cur_lr=self.cur_lr, + label=self.label_dict, + task_key=self.task_key, + )[0] + return result + + def create_wrapper(self, read: bool): + model_config = copy.deepcopy(self.config["model"]) + model_config["resuming"] = read + model_config["stat_file_dir"] = "stat_files" + model_config["stat_file"] = "stat.hdf5" + model_config["stat_file_path"] = os.path.join( + model_config["stat_file_dir"], model_config["stat_file"] + ) + model = get_model(model_config).to(env.DEVICE) + return ModelWrapper(model, self.loss) + + def get_data(self): + try: + batch_data = next(iter(self.training_data)) + except StopIteration: + # Refresh the status of the dataloader to start from a new epoch + self.training_data = BufferedIterator(iter(self.training_dataloader)) + batch_data = next(iter(self.training_data)) + input_dict = {} + for item in ["coord", "atype", "box"]: + if item in batch_data: + input_dict[item] = batch_data[item].to(env.DEVICE) + else: + input_dict[item] = None + label_dict = {} + for item in ["energy", "force", "virial"]: + if item in batch_data: + label_dict[item] = batch_data[item].to(env.DEVICE) + return input_dict, label_dict + + def test_saveload(self): + result1 = self.get_model_result() + result2 = self.get_model_result(read=True) + final_result = all( + paddle.allclose(result1[item], result2[item]) for item in result1 + ) + self.assertTrue(final_result) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/model/test_saveload_se_e2_a.py b/source/tests/pd/model/test_saveload_se_e2_a.py new file mode 100644 index 0000000000..35d8eb6d43 --- /dev/null +++ b/source/tests/pd/model/test_saveload_se_e2_a.py @@ -0,0 +1,139 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import json +import os +import unittest +from pathlib import ( + Path, +) + +import paddle +from paddle.io import ( + DataLoader, +) + +from deepmd.pd.loss import ( + EnergyStdLoss, +) +from deepmd.pd.model.model import ( + get_model, +) +from deepmd.pd.train.wrapper import ( + ModelWrapper, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.dataloader import ( + BufferedIterator, + DpLoaderSet, +) +from deepmd.pd.utils.stat import ( + make_stat_input, +) +from deepmd.tf.common import ( + expand_sys_str, +) + + +def get_dataset(config): + model_config = config["model"] + rcut = model_config["descriptor"]["rcut"] + sel = model_config["descriptor"]["sel"] + systems = config["training"]["validation_data"]["systems"] + if isinstance(systems, str): + systems = expand_sys_str(systems) + batch_size = config["training"]["training_data"]["batch_size"] + type_map = model_config["type_map"] + + dataset = DpLoaderSet(systems, batch_size, type_map) + data_stat_nbatch = model_config.get("data_stat_nbatch", 10) + sampled = make_stat_input(dataset.systems, dataset.dataloaders, data_stat_nbatch) + return dataset, sampled + + +class TestSaveLoadSeA(unittest.TestCase): + def setUp(self): + input_json = str(Path(__file__).parent / "water/se_e2_a.json") + with open(input_json) as fin: + self.config = json.load(fin) + data_file = [str(Path(__file__).parent / "water/data/data_0")] + self.config["training"]["training_data"]["systems"] = data_file + self.config["training"]["validation_data"]["systems"] = data_file + self.config["loss"]["starter_learning_rate"] = self.config["learning_rate"][ + "start_lr" + ] + self.dataset, self.sampled = get_dataset(self.config) + self.training_dataloader = DataLoader( + self.dataset, + batch_sampler=paddle.io.BatchSampler( + sampler=paddle.io.RandomSampler(self.dataset), + drop_last=False, + ), + num_workers=0, # setting to 0 diverges the behavior of its iterator; should be >=1 + collate_fn=lambda batch: batch[0], + ) + device = paddle.get_device() + paddle.set_device("cpu") + self.training_data = BufferedIterator(iter(self.training_dataloader)) + paddle.set_device(device) + self.loss = EnergyStdLoss(**self.config["loss"]) + self.cur_lr = 1 + self.task_key = "Default" + self.input_dict, self.label_dict = self.get_data() + self.start_lr = self.config["learning_rate"]["start_lr"] + + def get_model_result(self, read=False, model_file="tmp_model.pd"): + wrapper = self.create_wrapper() + optimizer = paddle.optimizer.Adam( + learning_rate=self.start_lr, parameters=wrapper.parameters() + ) + optimizer.clear_grad() + if read: + wrapper.set_state_dict(paddle.load(model_file)) + os.remove(model_file) + else: + paddle.save(wrapper.state_dict(), model_file) + result = wrapper( + **self.input_dict, + cur_lr=self.cur_lr, + label=self.label_dict, + task_key=self.task_key, + )[0] + return result + + def create_wrapper(self): + model_config = copy.deepcopy(self.config["model"]) + model = get_model(model_config).to(env.DEVICE) + return ModelWrapper(model, self.loss) + + def get_data(self): + try: + batch_data = next(iter(self.training_data)) + except StopIteration: + # Refresh the status of the dataloader to start from a new epoch + self.training_data = BufferedIterator(iter(self.training_dataloader)) + batch_data = next(iter(self.training_data)) + input_dict = {} + for item in ["coord", "atype", "box"]: + if item in batch_data: + input_dict[item] = batch_data[item].to(env.DEVICE) + else: + input_dict[item] = None + label_dict = {} + for item in ["energy", "force", "virial"]: + if item in batch_data: + label_dict[item] = batch_data[item].to(env.DEVICE) + return input_dict, label_dict + + def test_saveload(self): + result1 = self.get_model_result() + result2 = self.get_model_result(read=True) + final_result = all( + paddle.allclose(result1[item], result2[item]) for item in result1 + ) + self.assertTrue(final_result) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/model/test_se_atten_v2.py b/source/tests/pd/model/test_se_atten_v2.py new file mode 100644 index 0000000000..3ab28eeacd --- /dev/null +++ b/source/tests/pd/model/test_se_atten_v2.py @@ -0,0 +1,152 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import itertools +import unittest + +import numpy as np +import paddle + +from deepmd.dpmodel.descriptor.se_atten_v2 import DescrptSeAttenV2 as DPDescrptSeAttenV2 +from deepmd.pd.model.descriptor.se_atten_v2 import ( + DescrptSeAttenV2, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + PRECISION_DICT, +) + +from ...seed import ( + GLOBAL_SEED, +) +from .test_env_mat import ( + TestCaseSingleFrameWithNlist, +) +from .test_mlp import ( + get_tols, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION + + +class TestDescrptSeAttenV2(unittest.TestCase, TestCaseSingleFrameWithNlist): + def setUp(self): + TestCaseSingleFrameWithNlist.setUp(self) + + def test_consistency( + self, + ): + rng = np.random.default_rng(100) + nf, nloc, nnei = self.nlist.shape + davg = rng.normal(size=(self.nt, nnei, 4)) + dstd = rng.normal(size=(self.nt, nnei, 4)) + dstd = 0.1 + np.abs(dstd) + + for idt, to, prec, ect in itertools.product( + [False, True], # resnet_dt + [False, True], # type_one_side + [ + "float64", + ], # precision + [False, True], # use_econf_tebd + ): + dtype = PRECISION_DICT[prec] + rtol, atol = get_tols(prec) + err_msg = f"idt={idt} prec={prec}" + + # dpa1 new impl + dd0 = DescrptSeAttenV2( + self.rcut, + self.rcut_smth, + self.sel_mix, + self.nt, + attn_layer=2, + precision=prec, + resnet_dt=idt, + type_one_side=to, + use_econf_tebd=ect, + type_map=["O", "H"] if ect else None, + seed=GLOBAL_SEED, + ).to(env.DEVICE) + dd0.se_atten.mean = paddle.to_tensor(davg, dtype=dtype).to( + device=env.DEVICE + ) + dd0.se_atten.stddev = paddle.to_tensor(dstd, dtype=dtype).to( + device=env.DEVICE + ) + rd0, _, _, _, _ = dd0( + paddle.to_tensor(self.coord_ext, dtype=dtype).to(device=env.DEVICE), + paddle.to_tensor(self.atype_ext, dtype="int64").to(device=env.DEVICE), + paddle.to_tensor(self.nlist, dtype="int64").to(device=env.DEVICE), + ) + # serialization + dd1 = DescrptSeAttenV2.deserialize(dd0.serialize()) + rd1, _, _, _, _ = dd1( + paddle.to_tensor(self.coord_ext, dtype=dtype).to(device=env.DEVICE), + paddle.to_tensor(self.atype_ext, dtype="int64").to(device=env.DEVICE), + paddle.to_tensor(self.nlist, dtype="int64").to(device=env.DEVICE), + ) + np.testing.assert_allclose( + rd0.detach().cpu().numpy(), + rd1.detach().cpu().numpy(), + rtol=rtol, + atol=atol, + err_msg=err_msg, + ) + # dp impl + dd2 = DPDescrptSeAttenV2.deserialize(dd0.serialize()) + rd2, _, _, _, _ = dd2.call( + self.coord_ext, + self.atype_ext, + self.nlist, + ) + np.testing.assert_allclose( + rd0.detach().cpu().numpy(), + rd2, + rtol=rtol, + atol=atol, + err_msg=err_msg, + ) + + def test_jit( + self, + ): + rng = np.random.default_rng() + _, _, nnei = self.nlist.shape + davg = rng.normal(size=(self.nt, nnei, 4)) + dstd = rng.normal(size=(self.nt, nnei, 4)) + dstd = 0.1 + np.abs(dstd) + + for idt, prec, to, ect in itertools.product( + [ + False, + ], # resnet_dt + [ + "float64", + ], # precision + [ + False, + ], # type_one_side + [False, True], # use_econf_tebd + ): + dtype = PRECISION_DICT[prec] + # dpa1 new impl + dd0 = DescrptSeAttenV2( + self.rcut, + self.rcut_smth, + self.sel, + self.nt, + precision=prec, + resnet_dt=idt, + type_one_side=to, + use_econf_tebd=ect, + type_map=["O", "H"] if ect else None, + seed=GLOBAL_SEED, + ) + dd0.se_atten.mean = paddle.to_tensor(davg, dtype=dtype).to( + device=env.DEVICE + ) + dd0.se_atten.dstd = paddle.to_tensor(dstd, dtype=dtype).to( + device=env.DEVICE + ) + _ = paddle.jit.to_static(dd0) diff --git a/source/tests/pd/model/test_se_e2_a.py b/source/tests/pd/model/test_se_e2_a.py new file mode 100644 index 0000000000..b1e6abe5ae --- /dev/null +++ b/source/tests/pd/model/test_se_e2_a.py @@ -0,0 +1,137 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import itertools +import unittest + +import numpy as np +import paddle + +from deepmd.dpmodel.descriptor import DescrptSeA as DPDescrptSeA +from deepmd.pd.model.descriptor.se_a import ( + DescrptSeA, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + PRECISION_DICT, +) + +from ...seed import ( + GLOBAL_SEED, +) +from .test_env_mat import ( + TestCaseSingleFrameWithNlist, +) +from .test_mlp import ( + get_tols, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION + + +# to be merged with the tf test case +class TestDescrptSeA(unittest.TestCase, TestCaseSingleFrameWithNlist): + def setUp(self): + TestCaseSingleFrameWithNlist.setUp(self) + + def test_consistency( + self, + ): + rng = np.random.default_rng(GLOBAL_SEED) + nf, nloc, nnei = self.nlist.shape + davg = rng.normal(size=(self.nt, nnei, 4)) + dstd = rng.normal(size=(self.nt, nnei, 4)) + dstd = 0.1 + np.abs(dstd) + + for idt, prec, em in itertools.product( + [False, True], + ["float64", "float32"], + [[], [[0, 1]], [[1, 1]]], + ): + dtype = PRECISION_DICT[prec] + rtol, atol = get_tols(prec) + err_msg = f"idt={idt} prec={prec}" + # sea new impl + dd0 = DescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + precision=prec, + resnet_dt=idt, + exclude_types=em, + seed=GLOBAL_SEED, + ).to(env.DEVICE) + dd0.sea.mean = paddle.to_tensor(davg, dtype=dtype).to(device=env.DEVICE) + dd0.sea.dstd = paddle.to_tensor(dstd, dtype=dtype).to(device=env.DEVICE) + rd0, _, _, _, _ = dd0( + paddle.to_tensor(self.coord_ext, dtype=dtype).to(device=env.DEVICE), + paddle.to_tensor(self.atype_ext, dtype="int64").to(device=env.DEVICE), + paddle.to_tensor(self.nlist, dtype="int64").to(device=env.DEVICE), + ) + # serialization + dd1 = DescrptSeA.deserialize(dd0.serialize()) + rd1, gr1, _, _, sw1 = dd1( + paddle.to_tensor(self.coord_ext, dtype=dtype).to(device=env.DEVICE), + paddle.to_tensor(self.atype_ext, dtype="int64").to(device=env.DEVICE), + paddle.to_tensor(self.nlist, dtype="int64").to(device=env.DEVICE), + ) + np.testing.assert_allclose( + rd0.detach().cpu().numpy(), + rd1.detach().cpu().numpy(), + rtol=rtol, + atol=atol, + err_msg=err_msg, + ) + np.testing.assert_allclose( + rd0.detach().cpu().numpy()[0][self.perm[: self.nloc]], + rd0.detach().cpu().numpy()[1], + rtol=rtol, + atol=atol, + err_msg=err_msg, + ) + # dp impl + dd2 = DPDescrptSeA.deserialize(dd0.serialize()) + rd2, gr2, _, _, sw2 = dd2.call( + self.coord_ext, + self.atype_ext, + self.nlist, + ) + for aa, bb in zip([rd1, gr1, sw1], [rd2, gr2, sw2]): + np.testing.assert_allclose( + aa.detach().cpu().numpy(), + bb, + rtol=rtol, + atol=atol, + err_msg=err_msg, + ) + + def test_jit( + self, + ): + rng = np.random.default_rng(GLOBAL_SEED) + nf, nloc, nnei = self.nlist.shape + davg = rng.normal(size=(self.nt, nnei, 4)) + dstd = rng.normal(size=(self.nt, nnei, 4)) + dstd = 0.1 + np.abs(dstd) + + for idt, prec in itertools.product( + [False, True], + ["float64", "float32"], + ): + dtype = PRECISION_DICT[prec] + rtol, atol = get_tols(prec) + err_msg = f"idt={idt} prec={prec}" + # sea new impl + dd0 = DescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + precision=prec, + resnet_dt=idt, + seed=GLOBAL_SEED, + ) + dd0.sea.mean = paddle.to_tensor(davg, dtype=dtype).to(device=env.DEVICE) + dd0.sea.dstd = paddle.to_tensor(dstd, dtype=dtype).to(device=env.DEVICE) + dd1 = DescrptSeA.deserialize(dd0.serialize()) + model = paddle.jit.to_static(dd0) + model = paddle.jit.to_static(dd1) diff --git a/source/tests/pd/model/test_se_t.py b/source/tests/pd/model/test_se_t.py new file mode 100644 index 0000000000..8eb0db3f26 --- /dev/null +++ b/source/tests/pd/model/test_se_t.py @@ -0,0 +1,139 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import itertools +import unittest + +import numpy as np +import paddle + +from deepmd.dpmodel.descriptor import DescrptSeT as DPDescrptSeT +from deepmd.pd.model.descriptor.se_t import ( + DescrptSeT, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.pd.utils.env import ( + PRECISION_DICT, +) + +from ...seed import ( + GLOBAL_SEED, +) +from .test_env_mat import ( + TestCaseSingleFrameWithNlist, +) +from .test_mlp import ( + get_tols, +) + +dtype = env.GLOBAL_PD_FLOAT_PRECISION + + +# to be merged with the tf test case +class TestDescrptSeT(unittest.TestCase, TestCaseSingleFrameWithNlist): + def setUp(self): + TestCaseSingleFrameWithNlist.setUp(self) + + def test_consistency( + self, + ): + rng = np.random.default_rng(GLOBAL_SEED) + nf, nloc, nnei = self.nlist.shape + davg = rng.normal(size=(self.nt, nnei, 4)) + dstd = rng.normal(size=(self.nt, nnei, 4)) + dstd = 0.1 + np.abs(dstd) + + for idt, prec, em in itertools.product( + [False, True], + ["float64", "float32"], + [ + [], + [[0, 1]], + [[1, 1]], + ], + ): + dtype = PRECISION_DICT[prec] + rtol, atol = get_tols(prec) + err_msg = f"idt={idt} prec={prec}" + # pt impl + dd0 = DescrptSeT( + self.rcut, + self.rcut_smth, + self.sel, + precision=prec, + resnet_dt=idt, + exclude_types=em, + ).to(env.DEVICE) + dd0.seat.mean = paddle.to_tensor(davg, dtype=dtype).to(device=env.DEVICE) + dd0.seat.dstd = paddle.to_tensor(dstd, dtype=dtype).to(device=env.DEVICE) + rd0, _, _, _, sw0 = dd0( + paddle.to_tensor(self.coord_ext, dtype=dtype).to(device=env.DEVICE), + paddle.to_tensor(self.atype_ext, dtype="int64").to(device=env.DEVICE), + paddle.to_tensor(self.nlist, dtype="int64").to(device=env.DEVICE), + ) + # serialization + dd1 = DescrptSeT.deserialize(dd0.serialize()) + rd1, _, _, _, sw1 = dd1( + paddle.to_tensor(self.coord_ext, dtype=dtype).to(device=env.DEVICE), + paddle.to_tensor(self.atype_ext, dtype="int64").to(device=env.DEVICE), + paddle.to_tensor(self.nlist, dtype="int64").to(device=env.DEVICE), + ) + np.testing.assert_allclose( + rd0.detach().cpu().numpy(), + rd1.detach().cpu().numpy(), + rtol=rtol, + atol=atol, + err_msg=err_msg, + ) + np.testing.assert_allclose( + rd0.detach().cpu().numpy()[0][self.perm[: self.nloc]], + rd0.detach().cpu().numpy()[1], + rtol=rtol, + atol=atol, + err_msg=err_msg, + ) + # dp impl + dd2 = DPDescrptSeT.deserialize(dd0.serialize()) + rd2, _, _, _, sw2 = dd2.call( + self.coord_ext, + self.atype_ext, + self.nlist, + ) + for aa, bb in zip([rd1, sw1], [rd2, sw2]): + np.testing.assert_allclose( + aa.detach().cpu().numpy(), + bb, + rtol=rtol, + atol=atol, + err_msg=err_msg, + ) + + def test_jit( + self, + ): + rng = np.random.default_rng(GLOBAL_SEED) + nf, nloc, nnei = self.nlist.shape + davg = rng.normal(size=(self.nt, nnei, 4)) + dstd = rng.normal(size=(self.nt, nnei, 4)) + dstd = 0.1 + np.abs(dstd) + + for idt, prec in itertools.product( + [False, True], + ["float64", "float32"], + ): + dtype = PRECISION_DICT[prec] + rtol, atol = get_tols(prec) + err_msg = f"idt={idt} prec={prec}" + # pt impl + dd0 = DescrptSeT( + self.rcut, + self.rcut_smth, + self.sel, + precision=prec, + resnet_dt=idt, + ) + dd0.seat.mean = paddle.to_tensor(davg, dtype=dtype).to(device=env.DEVICE) + dd0.seat.dstd = paddle.to_tensor(dstd, dtype=dtype).to(device=env.DEVICE) + dd1 = DescrptSeT.deserialize(dd0.serialize()) + model = paddle.jit.to_static(dd0) + model = paddle.jit.to_static(dd1) diff --git a/source/tests/pd/model/test_smooth.py b/source/tests/pd/model/test_smooth.py new file mode 100644 index 0000000000..59ce2bdce6 --- /dev/null +++ b/source/tests/pd/model/test_smooth.py @@ -0,0 +1,267 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import unittest + +import paddle + +from deepmd.pd.infer.deep_eval import ( + eval_model, +) +from deepmd.pd.model.model import ( + get_model, +) +from deepmd.pd.utils import ( + env, +) + +from ...seed import ( + GLOBAL_SEED, +) +from .test_permutation import ( # model_dpau, + model_dos, + model_dpa1, + model_dpa2, + model_hybrid, + model_se_e2_a, + model_spin, + model_zbl, +) + +dtype = paddle.float64 + + +class SmoothTest: + def test( + self, + ): + generator = paddle.seed(GLOBAL_SEED) + # displacement of atoms + epsilon = 1e-5 if self.epsilon is None else self.epsilon + # required prec. relative prec is not checked. + rprec = 0.0 + aprec = 1e-5 if self.aprec is None else self.aprec + + natoms = 10 + cell = 8.6 * paddle.eye(3, dtype=dtype).to(device=env.DEVICE) + atype0 = paddle.arange(3, dtype=dtype).to(device=env.DEVICE) + atype1 = paddle.randint(0, 3, [natoms - 3]).to( + device=env.DEVICE, dtype=atype0.dtype + ) + atype = paddle.concat([atype0, atype1]).reshape([natoms]) + coord0 = ( + paddle.to_tensor( + [ + 0.0, + 0.0, + 0.0, + 4.0 - 0.5 * epsilon, + 0.0, + 0.0, + 0.0, + 4.0 - 0.5 * epsilon, + 0.0, + ], + dtype=dtype, + ) + .reshape([-1, 3]) + .to(device=env.DEVICE) + ) + coord1 = paddle.rand( + [natoms - coord0.shape[0], 3], + dtype=dtype, + ).to(device=env.DEVICE) + coord1 = paddle.matmul(coord1, cell) + coord = paddle.concat([coord0, coord1], axis=0) + spin = paddle.rand([natoms, 3], dtype=dtype).to(device=env.DEVICE) + coord0 = paddle.clone(coord) + coord1 = paddle.clone(coord) + coord1[1][0] += epsilon + coord2 = paddle.clone(coord) + coord2[2][1] += epsilon + coord3 = paddle.clone(coord) + coord3[1][0] += epsilon + coord3[2][1] += epsilon + test_spin = getattr(self, "test_spin", False) + if not test_spin: + test_keys = ["energy", "force", "virial"] + else: + test_keys = ["energy", "force", "force_mag", "virial"] + + result_0 = eval_model( + self.model, + coord0.unsqueeze(0), + cell.unsqueeze(0), + atype, + spins=spin.unsqueeze(0), + ) + ret0 = {key: result_0[key].squeeze(0) for key in test_keys} + result_1 = eval_model( + self.model, + coord1.unsqueeze(0), + cell.unsqueeze(0), + atype, + spins=spin.unsqueeze(0), + ) + ret1 = {key: result_1[key].squeeze(0) for key in test_keys} + result_2 = eval_model( + self.model, + coord2.unsqueeze(0), + cell.unsqueeze(0), + atype, + spins=spin.unsqueeze(0), + ) + ret2 = {key: result_2[key].squeeze(0) for key in test_keys} + result_3 = eval_model( + self.model, + coord3.unsqueeze(0), + cell.unsqueeze(0), + atype, + spins=spin.unsqueeze(0), + ) + ret3 = {key: result_3[key].squeeze(0) for key in test_keys} + + def compare(ret0, ret1): + for key in test_keys: + if key in ["energy"]: + assert paddle.allclose(ret0[key], ret1[key], rtol=rprec, atol=aprec) + elif key in ["force", "force_mag"]: + # plus 1. to avoid the divided-by-zero issue + assert paddle.allclose( + 1.0 + ret0[key], 1.0 + ret1[key], rtol=rprec, atol=aprec + ) + elif key == "virial": + if not hasattr(self, "test_virial") or self.test_virial: + assert paddle.allclose( + 1.0 + ret0[key], 1.0 + ret1[key], rtol=rprec, atol=aprec + ) + else: + raise RuntimeError(f"Unexpected test key {key}") + + compare(ret0, ret1) + compare(ret1, ret2) + compare(ret0, ret3) + + +class TestEnergyModelSeA(unittest.TestCase, SmoothTest): + def setUp(self): + model_params = copy.deepcopy(model_se_e2_a) + self.type_split = False + self.model = get_model(model_params).to(env.DEVICE) + self.epsilon, self.aprec = None, None + + +class TestDOSModelSeA(unittest.TestCase, SmoothTest): + def setUp(self): + model_params = copy.deepcopy(model_dos) + self.type_split = False + self.model = get_model(model_params).to(env.DEVICE) + self.epsilon, self.aprec = None, None + + +class TestEnergyModelDPA1(unittest.TestCase, SmoothTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa1) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + # less degree of smoothness, + # error can be systematically removed by reducing epsilon + self.epsilon = 1e-5 + self.aprec = 1e-5 + + +class TestEnergyModelDPA1Excl1(unittest.TestCase, SmoothTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa1) + model_params["pair_exclude_types"] = [[0, 1]] + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + # less degree of smoothness, + # error can be systematically removed by reducing epsilon + self.epsilon = 1e-5 + self.aprec = 1e-5 + + +class TestEnergyModelDPA1Excl12(unittest.TestCase, SmoothTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa1) + model_params["pair_exclude_types"] = [[0, 1], [0, 2]] + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + # less degree of smoothness, + # error can be systematically removed by reducing epsilon + self.epsilon = 1e-5 + self.aprec = 1e-5 + + +class TestEnergyModelDPA2(unittest.TestCase, SmoothTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa2) + model_params["descriptor"]["repinit"]["rcut"] = 8 + model_params["descriptor"]["repinit"]["rcut_smth"] = 3.5 + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + self.epsilon, self.aprec = 1e-5, 1e-4 + + +class TestEnergyModelDPA2_1(unittest.TestCase, SmoothTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa2) + model_params["fitting_net"]["type"] = "ener" + self.type_split = True + self.test_virial = False + self.model = get_model(model_params).to(env.DEVICE) + self.epsilon, self.aprec = None, None + + +class TestEnergyModelDPA2_2(unittest.TestCase, SmoothTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa2) + model_params["fitting_net"]["type"] = "ener" + self.type_split = True + self.test_virial = False + self.model = get_model(model_params).to(env.DEVICE) + self.epsilon, self.aprec = None, None + + +class TestEnergyModelHybrid(unittest.TestCase, SmoothTest): + def setUp(self): + model_params = copy.deepcopy(model_hybrid) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + self.epsilon, self.aprec = None, None + + +class TestEnergyModelZBL(unittest.TestCase, SmoothTest): + def setUp(self): + model_params = copy.deepcopy(model_zbl) + self.type_split = False + self.model = get_model(model_params).to(env.DEVICE) + self.epsilon, self.aprec = None, 5e-2 + + +class TestEnergyModelSpinSeA(unittest.TestCase, SmoothTest): + def setUp(self): + model_params = copy.deepcopy(model_spin) + self.type_split = False + self.test_spin = True + self.model = get_model(model_params).to(env.DEVICE) + self.epsilon, self.aprec = None, None + + +# class TestEnergyFoo(unittest.TestCase): +# def test(self): +# model_params = model_dpau +# self.model = EnergyModelDPAUni(model_params).to(env.DEVICE) + +# natoms = 5 +# cell = paddle.rand([3, 3], dtype=dtype) +# cell = (cell + cell.T) + 5. * paddle.eye(3) +# coord = paddle.rand([natoms, 3], dtype=dtype) +# coord = paddle.matmul(coord, cell) +# atype = paddle.to_tensor([0, 0, 0, 1, 1]) +# idx_perm = [1, 0, 4, 3, 2] +# ret0 = infer_model(self.model, coord, cell, atype, type_split=True) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/model/test_smooth_denoise.py b/source/tests/pd/model/test_smooth_denoise.py new file mode 100644 index 0000000000..db9592b05a --- /dev/null +++ b/source/tests/pd/model/test_smooth_denoise.py @@ -0,0 +1,139 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import unittest + +import paddle + +from deepmd.pd.infer.deep_eval import ( + eval_model, +) +from deepmd.pd.model.model import ( + get_model, +) +from deepmd.pd.utils import ( + env, +) + +from ...seed import ( + GLOBAL_SEED, +) +from .test_permutation_denoise import ( + model_dpa2, +) + +dtype = paddle.float64 + + +class SmoothDenoiseTest: + def test( + self, + ): + # displacement of atoms + epsilon = 1e-5 if self.epsilon is None else self.epsilon + # required prec. relative prec is not checked. + rprec = 0 + aprec = 1e-5 if self.aprec is None else self.aprec + + natoms = 10 + cell = 8.6 * paddle.eye(3, dtype=dtype).to(env.DEVICE) + seed = paddle.seed(GLOBAL_SEED) + atype = paddle.randint(0, 3, [natoms]).to(device=env.DEVICE) + coord0 = ( + paddle.to_tensor( + [ + 0.0, + 0.0, + 0.0, + 4.0 - 0.5 * epsilon, + 0.0, + 0.0, + 0.0, + 4.0 - 0.5 * epsilon, + 0.0, + ], + dtype=dtype, + ) + .reshape([-1, 3]) + .to(env.DEVICE) + ) + coord1 = paddle.rand([natoms - coord0.shape[0], 3], dtype=dtype).to(env.DEVICE) + coord1 = paddle.matmul(coord1, cell) + coord = paddle.concat([coord0, coord1], axis=0) + + coord0 = paddle.clone(coord) + coord1 = paddle.clone(coord) + coord1[1][0] += epsilon + coord2 = paddle.clone(coord) + coord2[2][1] += epsilon + coord3 = paddle.clone(coord) + coord3[1][0] += epsilon + coord3[2][1] += epsilon + + update_c0, logits0 = eval_model( + self.model, coord0.unsqueeze(0), cell.unsqueeze(0), atype, denoise=True + ) + ret0 = {"updated_coord": update_c0.squeeze(0), "logits": logits0.squeeze(0)} + update_c1, logits1 = eval_model( + self.model, coord1.unsqueeze(0), cell.unsqueeze(0), atype, denoise=True + ) + ret1 = {"updated_coord": update_c1.squeeze(0), "logits": logits1.squeeze(0)} + update_c2, logits2 = eval_model( + self.model, coord2.unsqueeze(0), cell.unsqueeze(0), atype, denoise=True + ) + ret2 = {"updated_coord": update_c2.squeeze(0), "logits": logits2.squeeze(0)} + update_c3, logits3 = eval_model( + self.model, coord3.unsqueeze(0), cell.unsqueeze(0), atype, denoise=True + ) + ret3 = {"updated_coord": update_c3.squeeze(0), "logits": logits3.squeeze(0)} + + def compare(ret0, ret1): + assert paddle.allclose( + ret0["updated_coord"], ret1["updated_coord"], rtol=rprec, atol=aprec + ) + assert paddle.allclose( + ret0["logits"], ret1["logits"], rtol=rprec, atol=aprec + ) + + compare(ret0, ret1) + compare(ret1, ret2) + compare(ret0, ret3) + + +@unittest.skip("support of the denoise is temporally disabled") +class TestDenoiseModelDPA2(unittest.TestCase, SmoothDenoiseTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa2) + model_params["descriptor"]["sel"] = 8 + model_params["descriptor"]["rcut_smth"] = 3.5 + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + self.epsilon, self.aprec = None, None + self.epsilon = 1e-7 + self.aprec = 1e-5 + + +@unittest.skip("support of the denoise is temporally disabled") +class TestDenoiseModelDPA2_1(unittest.TestCase, SmoothDenoiseTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa2) + # model_params["descriptor"]["combine_grrg"] = True + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + self.epsilon, self.aprec = None, None + self.epsilon = 1e-7 + self.aprec = 1e-5 + + +# @unittest.skip("hybrid not supported at the moment") +# class TestDenoiseModelHybrid(unittest.TestCase, TestSmoothDenoise): +# def setUp(self): +# model_params = copy.deepcopy(model_hybrid_denoise) +# self.type_split = True +# self.model = get_model(model_params).to(env.DEVICE) +# self.epsilon, self.aprec = None, None +# self.epsilon = 1e-7 +# self.aprec = 1e-5 + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/model/test_trans.py b/source/tests/pd/model/test_trans.py new file mode 100644 index 0000000000..1d0abfd5c7 --- /dev/null +++ b/source/tests/pd/model/test_trans.py @@ -0,0 +1,155 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import unittest + +import paddle + +from deepmd.pd.model.model import ( + get_model, +) +from deepmd.pd.utils import ( + env, +) + +from ...seed import ( + GLOBAL_SEED, +) +from ..common import ( + eval_model, +) +from .test_permutation import ( # model_dpau, + model_dos, + model_dpa1, + model_dpa2, + model_hybrid, + model_se_e2_a, + model_spin, + model_zbl, +) + +dtype = paddle.float64 + + +class TransTest: + def test( + self, + ): + natoms = 5 + generator = paddle.seed(GLOBAL_SEED) + cell = paddle.rand([3, 3], dtype=dtype).to(device=env.DEVICE) + cell = (cell + cell.T) + 5.0 * paddle.eye(3).to(device=env.DEVICE) + coord = paddle.rand([natoms, 3], dtype=dtype).to(device=env.DEVICE) + coord = paddle.matmul(coord, cell) + spin = paddle.rand([natoms, 3], dtype=dtype).to(device=env.DEVICE) + atype = paddle.to_tensor([0, 0, 0, 1, 1], dtype=paddle.int32).to( + device=env.DEVICE + ) + shift = (paddle.rand([3], dtype=dtype).to(device=env.DEVICE) - 0.5) * 2.0 + coord_s = paddle.matmul( + paddle.remainder( + paddle.matmul(coord + shift, paddle.linalg.inv(cell)), paddle.ones([]) + ), + cell, + ) + test_spin = getattr(self, "test_spin", False) + if not test_spin: + test_keys = ["energy", "force", "virial"] + else: + test_keys = ["energy", "force", "force_mag", "virial"] + result_0 = eval_model( + self.model, + coord.unsqueeze(0), + cell.unsqueeze(0), + atype, + spins=spin.unsqueeze(0), + ) + ret0 = {key: result_0[key].squeeze(0) for key in test_keys} + result_1 = eval_model( + self.model, + coord_s.unsqueeze(0), + cell.unsqueeze(0), + atype, + spins=spin.unsqueeze(0), + ) + ret1 = {key: result_1[key].squeeze(0) for key in test_keys} + prec = 1e-7 + for key in test_keys: + if key in ["energy", "force", "force_mag"]: + assert paddle.allclose(ret0[key], ret1[key], rtol=prec, atol=prec) + elif key == "virial": + if not hasattr(self, "test_virial") or self.test_virial: + assert paddle.allclose(ret0[key], ret1[key], rtol=prec, atol=prec) + else: + raise RuntimeError(f"Unexpected test key {key}") + + +class TestEnergyModelSeA(unittest.TestCase, TransTest): + def setUp(self): + model_params = copy.deepcopy(model_se_e2_a) + self.type_split = False + self.model = get_model(model_params).to(env.DEVICE) + + +class TestDOSModelSeA(unittest.TestCase, TransTest): + def setUp(self): + model_params = copy.deepcopy(model_dos) + self.type_split = False + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelDPA1(unittest.TestCase, TransTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa1) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelDPA2(unittest.TestCase, TransTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa2) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +class TestForceModelDPA2(unittest.TestCase, TransTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa2) + model_params["fitting_net"]["type"] = "direct_force_ener" + self.type_split = True + self.test_virial = False + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelHybrid(unittest.TestCase, TransTest): + def setUp(self): + model_params = copy.deepcopy(model_hybrid) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +class TestForceModelHybrid(unittest.TestCase, TransTest): + def setUp(self): + model_params = copy.deepcopy(model_hybrid) + model_params["fitting_net"]["type"] = "direct_force_ener" + self.type_split = True + self.test_virial = False + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelZBL(unittest.TestCase, TransTest): + def setUp(self): + model_params = copy.deepcopy(model_zbl) + self.type_split = False + self.model = get_model(model_params).to(env.DEVICE) + + +class TestEnergyModelSpinSeA(unittest.TestCase, TransTest): + def setUp(self): + model_params = copy.deepcopy(model_spin) + self.type_split = False + self.test_spin = True + self.model = get_model(model_params).to(env.DEVICE) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/model/test_trans_denoise.py b/source/tests/pd/model/test_trans_denoise.py new file mode 100644 index 0000000000..17e910e8a6 --- /dev/null +++ b/source/tests/pd/model/test_trans_denoise.py @@ -0,0 +1,89 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import unittest + +import paddle + +from deepmd.pd.infer.deep_eval import ( + eval_model, +) +from deepmd.pd.model.model import ( + get_model, +) +from deepmd.pd.utils import ( + env, +) + +from ...seed import ( + GLOBAL_SEED, +) +from .test_permutation_denoise import ( + model_dpa1, + model_dpa2, + model_hybrid, +) + +dtype = paddle.float64 + + +class TransDenoiseTest: + def test( + self, + ): + natoms = 5 + generator = paddle.seed(GLOBAL_SEED) + cell = paddle.rand([3, 3], dtype=dtype).to(env.DEVICE) + cell = (cell + cell.T) + 5.0 * paddle.eye(3).to(env.DEVICE) + coord = paddle.rand([natoms, 3], dtype=dtype).to(env.DEVICE) + coord = paddle.matmul(coord, cell) + atype = paddle.to_tensor([0, 0, 0, 1, 1]).to(env.DEVICE) + shift = (paddle.rand([3], dtype=dtype) - 0.5).to(env.DEVICE) * 2.0 + coord_s = paddle.matmul( + paddle.remainder( + paddle.matmul(coord + shift, paddle.linalg.inv(cell)), 1.0 + ), + cell, + ) + updated_c0, logits0 = eval_model( + self.model, coord.unsqueeze(0), cell.unsqueeze(0), atype, denoise=True + ) + updated_c0 = updated_c0 - coord.unsqueeze(0) + ret0 = {"updated_coord": updated_c0.squeeze(0), "logits": logits0.squeeze(0)} + updated_c1, logits1 = eval_model( + self.model, coord_s.unsqueeze(0), cell.unsqueeze(0), atype, denoise=True + ) + updated_c1 = updated_c1 - coord_s.unsqueeze(0) + ret1 = {"updated_coord": updated_c1.squeeze(0), "logits": logits1.squeeze(0)} + prec = 1e-10 + assert paddle.allclose( + ret0["updated_coord"], ret1["updated_coord"], rtol=prec, atol=prec + ) + assert paddle.allclose(ret0["logits"], ret1["logits"], rtol=prec, atol=prec) + + +@unittest.skip("support of the denoise is temporally disabled") +class TestDenoiseModelDPA1(unittest.TestCase, TransDenoiseTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa1) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +@unittest.skip("support of the denoise is temporally disabled") +class TestDenoiseModelDPA2(unittest.TestCase, TransDenoiseTest): + def setUp(self): + model_params = copy.deepcopy(model_dpa2) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +@unittest.skip("hybrid not supported at the moment") +class TestDenoiseModelHybrid(unittest.TestCase, TransDenoiseTest): + def setUp(self): + model_params = copy.deepcopy(model_hybrid) + self.type_split = True + self.model = get_model(model_params).to(env.DEVICE) + + +if __name__ == "__main__": + unittest.main() From 754b948291b2f5a1d4d05a13c7e7a73ddaa118ff Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 28 Oct 2024 16:35:39 +0800 Subject: [PATCH 70/93] update code --- deepmd/pd/entrypoints/main.py | 4 +- .../atomic_model/property_atomic_model.py | 46 +++++ deepmd/pd/model/model/dp_linear_model.py | 165 ++++++++++++++++++ deepmd/pd/model/model/property_model.py | 109 ++++++++++++ deepmd/pd/utils/stat.py | 25 ++- source/tests/pd/model/test_deeppot.py | 2 +- 6 files changed, 346 insertions(+), 5 deletions(-) create mode 100644 deepmd/pd/model/atomic_model/property_atomic_model.py create mode 100644 deepmd/pd/model/model/dp_linear_model.py create mode 100644 deepmd/pd/model/model/property_model.py diff --git a/deepmd/pd/entrypoints/main.py b/deepmd/pd/entrypoints/main.py index 7a1b942768..e8b6a0d0c7 100644 --- a/deepmd/pd/entrypoints/main.py +++ b/deepmd/pd/entrypoints/main.py @@ -358,7 +358,7 @@ def freeze(FLAGS): """ # NOTE: 'FLAGS_save_cf_stack_op', 'FLAGS_prim_enable_dynamic' and # 'FLAGS_enable_pir_api' shoule be enabled when freezing model. - model = paddle.jit.to_static( + jit_model = paddle.jit.to_static( model.forward_lower, full_graph=True, input_spec=[ @@ -370,7 +370,7 @@ def freeze(FLAGS): if FLAGS.output.endswith(".json"): FLAGS.output = FLAGS.output[:-5] paddle.jit.save( - model, + jit_model, path=FLAGS.output, skip_prune_program=True, ) diff --git a/deepmd/pd/model/atomic_model/property_atomic_model.py b/deepmd/pd/model/atomic_model/property_atomic_model.py new file mode 100644 index 0000000000..419fb27dfa --- /dev/null +++ b/deepmd/pd/model/atomic_model/property_atomic_model.py @@ -0,0 +1,46 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later + +import paddle + +from deepmd.pd.model.task.property import ( + PropertyFittingNet, +) + +from .dp_atomic_model import ( + DPAtomicModel, +) + + +class DPPropertyAtomicModel(DPAtomicModel): + def __init__(self, descriptor, fitting, type_map, **kwargs): + assert isinstance(fitting, PropertyFittingNet) + super().__init__(descriptor, fitting, type_map, **kwargs) + + def apply_out_stat( + self, + ret: dict[str, paddle.Tensor], + atype: paddle.Tensor, + ): + """Apply the stat to each atomic output. + This function defines how the bias is applied to the atomic output of the model. + + Parameters + ---------- + ret + The returned dict by the forward_atomic method + atype + The atom types. nf x nloc + + """ + if self.fitting_net.get_bias_method() == "normal": + out_bias, out_std = self._fetch_out_stat(self.bias_keys) + for kk in self.bias_keys: + # nf x nloc x odims, out_bias: ntypes x odims + ret[kk] = ret[kk] + out_bias[kk][atype] + return ret + elif self.fitting_net.get_bias_method() == "no_bias": + return ret + else: + raise NotImplementedError( + "Only 'normal' and 'no_bias' is supported for parameter 'bias_method'." + ) diff --git a/deepmd/pd/model/model/dp_linear_model.py b/deepmd/pd/model/model/dp_linear_model.py new file mode 100644 index 0000000000..48a6c8e74c --- /dev/null +++ b/deepmd/pd/model/model/dp_linear_model.py @@ -0,0 +1,165 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from copy import ( + deepcopy, +) +from typing import ( + Optional, +) + +import paddle + +from deepmd.pd.model.atomic_model import ( + LinearEnergyAtomicModel, +) +from deepmd.pd.model.model.model import ( + BaseModel, +) +from deepmd.utils.data_system import ( + DeepmdDataSystem, +) + +from .dp_model import ( + DPModelCommon, +) +from .make_model import ( + make_model, +) + +DPLinearModel_ = make_model(LinearEnergyAtomicModel) + + +@BaseModel.register("linear_ener") +class LinearEnergyModel(DPLinearModel_): + model_type = "ener" + + def __init__( + self, + *args, + **kwargs, + ): + super().__init__(*args, **kwargs) + + def translated_output_def(self): + out_def_data = self.model_output_def().get_data() + output_def = { + "atom_energy": deepcopy(out_def_data["energy"]), + "energy": deepcopy(out_def_data["energy_redu"]), + } + if self.do_grad_r("energy"): + output_def["force"] = deepcopy(out_def_data["energy_derv_r"]) + output_def["force"].squeeze(-2) + if self.do_grad_c("energy"): + output_def["virial"] = deepcopy(out_def_data["energy_derv_c_redu"]) + output_def["virial"].squeeze(-2) + output_def["atom_virial"] = deepcopy(out_def_data["energy_derv_c"]) + output_def["atom_virial"].squeeze(-3) + if "mask" in out_def_data: + output_def["mask"] = deepcopy(out_def_data["mask"]) + return output_def + + def forward( + self, + coord, + atype, + box: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + ) -> dict[str, paddle.Tensor]: + model_ret = self.forward_common( + coord, + atype, + box, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + ) + + model_predict = {} + model_predict["atom_energy"] = model_ret["energy"] + model_predict["energy"] = model_ret["energy_redu"] + if self.do_grad_r("energy"): + model_predict["force"] = model_ret["energy_derv_r"].squeeze(-2) + if self.do_grad_c("energy"): + model_predict["virial"] = model_ret["energy_derv_c_redu"].squeeze(-2) + if do_atomic_virial: + model_predict["atom_virial"] = model_ret["energy_derv_c"].squeeze(-3) + else: + model_predict["force"] = model_ret["dforce"] + if "mask" in model_ret: + model_predict["mask"] = model_ret["mask"] + return model_predict + + def forward_lower( + self, + extended_coord, + extended_atype, + nlist, + mapping: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + ): + model_ret = self.forward_common_lower( + extended_coord, + extended_atype, + nlist, + mapping=mapping, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + extra_nlist_sort=self.need_sorted_nlist_for_lower(), + ) + + model_predict = {} + model_predict["atom_energy"] = model_ret["energy"] + model_predict["energy"] = model_ret["energy_redu"] + if self.do_grad_r("energy"): + model_predict["extended_force"] = model_ret["energy_derv_r"].squeeze(-2) + if self.do_grad_c("energy"): + model_predict["virial"] = model_ret["energy_derv_c_redu"].squeeze(-2) + if do_atomic_virial: + model_predict["extended_virial"] = model_ret["energy_derv_c"].squeeze( + -3 + ) + else: + assert model_ret["dforce"] is not None + model_predict["dforce"] = model_ret["dforce"] + return model_predict + + @classmethod + def update_sel( + cls, + train_data: DeepmdDataSystem, + type_map: Optional[list[str]], + local_jdata: dict, + ) -> tuple[dict, Optional[float]]: + """Update the selection and perform neighbor statistics. + + Parameters + ---------- + train_data : DeepmdDataSystem + data used to do neighbor statictics + type_map : list[str], optional + The name of each type of atoms + local_jdata : dict + The local data refer to the current class + + Returns + ------- + dict + The updated local data + float + The minimum distance between two atoms + """ + local_jdata_cpy = local_jdata.copy() + type_map = local_jdata_cpy["type_map"] + min_nbor_dist = None + for idx, sub_model in enumerate(local_jdata_cpy["models"]): + if "tab_file" not in sub_model: + sub_model, temp_min = DPModelCommon.update_sel( + train_data, type_map, local_jdata["models"][idx] + ) + if min_nbor_dist is None or temp_min <= min_nbor_dist: + min_nbor_dist = temp_min + return local_jdata_cpy, min_nbor_dist diff --git a/deepmd/pd/model/model/property_model.py b/deepmd/pd/model/model/property_model.py new file mode 100644 index 0000000000..3c0cf52b06 --- /dev/null +++ b/deepmd/pd/model/model/property_model.py @@ -0,0 +1,109 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from copy import ( + deepcopy, +) +from typing import ( + Optional, +) + +import paddle + +from deepmd.pd.model.atomic_model import ( + DPPropertyAtomicModel, +) +from deepmd.pd.model.model.model import ( + BaseModel, +) + +from .dp_model import ( + DPModelCommon, +) +from .make_model import ( + make_model, +) + +DPPropertyModel_ = make_model(DPPropertyAtomicModel) + + +@BaseModel.register("property") +class PropertyModel(DPModelCommon, DPPropertyModel_): + model_type = "property" + + def __init__( + self, + *args, + **kwargs, + ): + DPModelCommon.__init__(self) + DPPropertyModel_.__init__(self, *args, **kwargs) + + def translated_output_def(self): + out_def_data = self.model_output_def().get_data() + output_def = { + "atom_property": deepcopy(out_def_data["property"]), + "property": deepcopy(out_def_data["property_redu"]), + } + if "mask" in out_def_data: + output_def["mask"] = deepcopy(out_def_data["mask"]) + return output_def + + def forward( + self, + coord, + atype, + box: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + ) -> dict[str, paddle.Tensor]: + model_ret = self.forward_common( + coord, + atype, + box, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + ) + model_predict = {} + model_predict["atom_property"] = model_ret["property"] + model_predict["property"] = model_ret["property_redu"] + if "mask" in model_ret: + model_predict["mask"] = model_ret["mask"] + return model_predict + + def get_task_dim(self) -> int: + """Get the output dimension of PropertyFittingNet.""" + return self.get_fitting_net().dim_out + + def get_intensive(self) -> bool: + """Get whether the property is intensive.""" + return self.model_output_def()["property"].intensive + + def forward_lower( + self, + extended_coord, + extended_atype, + nlist, + mapping: Optional[paddle.Tensor] = None, + fparam: Optional[paddle.Tensor] = None, + aparam: Optional[paddle.Tensor] = None, + do_atomic_virial: bool = False, + comm_dict: Optional[dict[str, paddle.Tensor]] = None, + ): + model_ret = self.forward_common_lower( + extended_coord, + extended_atype, + nlist, + mapping, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + comm_dict=comm_dict, + extra_nlist_sort=self.need_sorted_nlist_for_lower(), + ) + model_predict = {} + model_predict["atom_property"] = model_ret["property"] + model_predict["property"] = model_ret["property_redu"] + if "mask" in model_ret: + model_predict["mask"] = model_ret["mask"] + return model_predict diff --git a/deepmd/pd/utils/stat.py b/deepmd/pd/utils/stat.py index efb258a33d..3ecd695038 100644 --- a/deepmd/pd/utils/stat.py +++ b/deepmd/pd/utils/stat.py @@ -12,6 +12,9 @@ import numpy as np import paddle +from deepmd.dpmodel.output_def import ( + FittingOutputDef, +) from deepmd.pd.utils import ( AtomExcludeMask, ) @@ -237,8 +240,9 @@ def compute_output_stats( keys: Union[str, list[str]] = ["energy"], stat_file_path: Optional[DPPath] = None, rcond: Optional[float] = None, - preset_bias: Optional[dict[str, list[Optional[paddle.Tensor]]]] = None, + preset_bias: Optional[dict[str, list[Optional[np.ndarray]]]] = None, model_forward: Optional[Callable[..., paddle.Tensor]] = None, + atomic_output: Optional[FittingOutputDef] = None, ): """ Compute the output statistics (e.g. energy bias) for the fitting net from packed data. @@ -268,6 +272,8 @@ def compute_output_stats( If not None, the model will be utilized to generate the original energy prediction, which will be subtracted from the energy label of the data. The difference will then be used to calculate the delta complement energy bias for each type. + atomic_output : FittingOutputDef, optional + The output of atomic model. """ # try to restore the bias from stat file bias_atom_e, std_atom_e = _restore_from_file(stat_file_path, keys) @@ -356,6 +362,7 @@ def compute_output_stats( rcond, preset_bias, model_pred_g, + atomic_output, ) bias_atom_a, std_atom_a = compute_output_stats_atomic( sampled, @@ -398,6 +405,7 @@ def compute_output_stats_global( rcond: Optional[float] = None, preset_bias: Optional[dict[str, list[Optional[paddle.Tensor]]]] = None, model_pred: Optional[dict[str, np.ndarray]] = None, + atomic_output: Optional[FittingOutputDef] = None, ): """This function only handle stat computation from reduced global labels.""" # return directly if model predict is empty for global @@ -468,6 +476,13 @@ def compute_output_stats_global( std_atom_e = {} for kk in keys: if kk in stats_input: + if atomic_output is not None and atomic_output.get_data()[kk].intensive: + task_dim = stats_input[kk].shape[1] + assert merged_natoms[kk].shape == (nf[kk], ntypes) + stats_input[kk] = ( + merged_natoms[kk].sum(axis=1).reshape([-1, 1]) * stats_input[kk] + ) + assert stats_input[kk].shape == (nf[kk], task_dim) bias_atom_e[kk], std_atom_e[kk] = compute_stats_from_redu( stats_input[kk], merged_natoms[kk], @@ -573,7 +588,13 @@ def compute_output_stats_atomic( # correction for missing types missing_types = ntypes - merged_natoms[kk].max() - 1 if missing_types > 0: - nan_padding = np.empty((missing_types, bias_atom_e[kk].shape[1])) # pylint: disable=no-explicit-dtype + assert ( + bias_atom_e[kk].dtype is std_atom_e[kk].dtype + ), "bias and std should be of the same dtypes" + nan_padding = np.empty( + (missing_types, bias_atom_e[kk].shape[1]), + dtype=bias_atom_e[kk].dtype, + ) nan_padding.fill(np.nan) bias_atom_e[kk] = np.concatenate([bias_atom_e[kk], nan_padding], axis=0) std_atom_e[kk] = np.concatenate([std_atom_e[kk], nan_padding], axis=0) diff --git a/source/tests/pd/model/test_deeppot.py b/source/tests/pd/model/test_deeppot.py index 3cf7cc23b2..56620174d3 100644 --- a/source/tests/pd/model/test_deeppot.py +++ b/source/tests/pd/model/test_deeppot.py @@ -57,7 +57,7 @@ def tearDown(self): if f in ["lcurve.out", self.input_json]: os.remove(f) - # @unittest.skip("Paddle do not eval on frozen model yet.") + @unittest.skip("Paddle do not eval on frozen model yet.") def test_dp_test(self): dp = DeepPot(str(self.model)) cell = np.array( From f007fb4c6fef54ab3eb82b1b6a55c0874dbd8913 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 28 Oct 2024 17:00:10 +0800 Subject: [PATCH 71/93] upload missing files --- deepmd/pd/loss/__init__.py | 4 + deepmd/pd/loss/property.py | 152 +++ deepmd/pd/train/training.py | 55 +- .../tests/pd/NiO/data/data_0/set.000/box.npy | Bin 0 -> 4448 bytes .../pd/NiO/data/data_0/set.000/coord.npy | Bin 0 -> 46208 bytes .../pd/NiO/data/data_0/set.000/energy.npy | Bin 0 -> 608 bytes .../pd/NiO/data/data_0/set.000/force.npy | Bin 0 -> 46208 bytes .../pd/NiO/data/data_0/set.000/force_mag.npy | Bin 0 -> 46208 bytes .../tests/pd/NiO/data/data_0/set.000/spin.npy | Bin 0 -> 46208 bytes source/tests/pd/NiO/data/data_0/type.raw | 32 + source/tests/pd/NiO/data/data_0/type_map.raw | 2 + .../tests/pd/NiO/data/single/set.000/box.npy | Bin 0 -> 200 bytes .../pd/NiO/data/single/set.000/coord.npy | Bin 0 -> 896 bytes .../pd/NiO/data/single/set.000/energy.npy | Bin 0 -> 136 bytes .../pd/NiO/data/single/set.000/force.npy | Bin 0 -> 896 bytes .../pd/NiO/data/single/set.000/force_mag.npy | Bin 0 -> 896 bytes .../tests/pd/NiO/data/single/set.000/spin.npy | Bin 0 -> 896 bytes source/tests/pd/NiO/data/single/type.raw | 32 + source/tests/pd/NiO/data/single/type_map.raw | 2 + .../data/atomic_system/set.000/atom_dos.npy | Bin 0 -> 352128 bytes .../pd/dos/data/atomic_system/set.000/box.npy | Bin 0 -> 524 bytes .../dos/data/atomic_system/set.000/coord.npy | Bin 0 -> 4352 bytes .../tests/pd/dos/data/atomic_system/type.raw | 32 + .../pd/dos/data/atomic_system/type_map.raw | 1 + .../pd/dos/data/global_system/set.000/box.npy | Bin 0 -> 524 bytes .../dos/data/global_system/set.000/coord.npy | Bin 0 -> 4352 bytes .../pd/dos/data/global_system/set.000/dos.npy | Bin 0 -> 11128 bytes .../tests/pd/dos/data/global_system/type.raw | 32 + .../pd/dos/data/global_system/type_map.raw | 1 + source/tests/pd/dos/input.json | 81 ++ source/tests/pd/model/models/dpa1.json | 36 + source/tests/pd/model/models/dpa1.pd | Bin 0 -> 11329 bytes source/tests/pd/model/models/dpa2.json | 57 + source/tests/pd/model/models/dpa2.pd | Bin 0 -> 119535 bytes source/tests/pd/model/models/dpa2_tebd.pd | Bin 0 -> 537 bytes source/tests/pd/model/test_get_model.py | 113 ++ source/tests/pd/model/test_unused_params.py | 92 ++ .../model/water/data/data_0/set.000/box.npy | Bin 0 -> 3008 bytes .../model/water/data/data_0/set.000/coord.npy | Bin 0 -> 184448 bytes .../water/data/data_0/set.000/energy.npy | Bin 0 -> 448 bytes .../model/water/data/data_0/set.000/force.npy | Bin 0 -> 184448 bytes .../tests/pd/model/water/data/data_0/type.raw | 192 ++++ .../pd/model/water/data/data_0/type_map.raw | 2 + .../model/water/data/single/set.000/box.npy | Bin 0 -> 164 bytes .../model/water/data/single/set.000/coord.npy | Bin 0 -> 2432 bytes .../water/data/single/set.000/energy.npy | Bin 0 -> 132 bytes .../model/water/data/single/set.000/force.npy | Bin 0 -> 2432 bytes .../tests/pd/model/water/data/single/type.raw | 192 ++++ .../pd/model/water/data/single/type_map.raw | 2 + .../zbl_tab_potential/H2O_tab_potential.txt | 1000 +++++++++++++++++ source/tests/pd/model/water/lkf.json | 79 ++ source/tests/pd/model/water/multitask.json | 140 +++ source/tests/pd/model/water/se_atten.json | 83 ++ source/tests/pd/model/water/se_e2_a.json | 77 ++ source/tests/pd/model/water/zbl.json | 92 ++ source/tests/pd/property/input.json | 77 ++ source/tests/pd/property/single/nopbc | 0 .../pd/property/single/set.000000/coord.npy | Bin 0 -> 608 bytes .../property/single/set.000000/property.npy | Bin 0 -> 152 bytes .../single/set.000000/real_atom_types.npy | Bin 0 -> 288 bytes source/tests/pd/property/single/type.raw | 20 + source/tests/pd/property/single/type_map.raw | 4 + source/tests/pd/requirements.txt | 6 + source/tests/pd/test_dp_test.py | 61 +- .../water_tensor/dipole/atomic_system/nopbc | 0 .../atomic_system/set.000/atomic_dipole.npy | Bin 0 -> 184448 bytes .../dipole/atomic_system/set.000/box.npy | Bin 0 -> 3008 bytes .../dipole/atomic_system/set.000/coord.npy | Bin 0 -> 184448 bytes .../dipole/atomic_system/type.raw | 1 + .../dipole/atomic_system/type_map.raw | 2 + .../water_tensor/dipole/global_system/nopbc | 0 .../dipole/global_system/set.000/box.npy | Bin 0 -> 3008 bytes .../dipole/global_system/set.000/coord.npy | Bin 0 -> 184448 bytes .../dipole/global_system/set.000/dipole.npy | Bin 0 -> 1088 bytes .../dipole/global_system/type.raw | 1 + .../dipole/global_system/type_map.raw | 2 + .../set.000/atomic_polarizability.npy | Bin 0 -> 829568 bytes .../polar/atomic_system/set.000/box.npy | Bin 0 -> 2288 bytes .../polar/atomic_system/set.000/coord.npy | Bin 0 -> 138368 bytes .../water_tensor/polar/atomic_system/type.raw | 1 + .../polar/atomic_system/type_map.raw | 2 + .../polar/global_system/set.000/box.npy | Bin 0 -> 3008 bytes .../polar/global_system/set.000/coord.npy | Bin 0 -> 184448 bytes .../global_system/set.000/polarizability.npy | Bin 0 -> 3008 bytes .../water_tensor/polar/global_system/type.raw | 1 + .../polar/global_system/type_map.raw | 2 + source/tests/pd/water_tensor/se_e2_a.json | 85 ++ 87 files changed, 2828 insertions(+), 20 deletions(-) create mode 100644 deepmd/pd/loss/property.py create mode 100644 source/tests/pd/NiO/data/data_0/set.000/box.npy create mode 100644 source/tests/pd/NiO/data/data_0/set.000/coord.npy create mode 100644 source/tests/pd/NiO/data/data_0/set.000/energy.npy create mode 100644 source/tests/pd/NiO/data/data_0/set.000/force.npy create mode 100644 source/tests/pd/NiO/data/data_0/set.000/force_mag.npy create mode 100644 source/tests/pd/NiO/data/data_0/set.000/spin.npy create mode 100644 source/tests/pd/NiO/data/data_0/type.raw create mode 100644 source/tests/pd/NiO/data/data_0/type_map.raw create mode 100644 source/tests/pd/NiO/data/single/set.000/box.npy create mode 100644 source/tests/pd/NiO/data/single/set.000/coord.npy create mode 100644 source/tests/pd/NiO/data/single/set.000/energy.npy create mode 100644 source/tests/pd/NiO/data/single/set.000/force.npy create mode 100644 source/tests/pd/NiO/data/single/set.000/force_mag.npy create mode 100644 source/tests/pd/NiO/data/single/set.000/spin.npy create mode 100644 source/tests/pd/NiO/data/single/type.raw create mode 100644 source/tests/pd/NiO/data/single/type_map.raw create mode 100644 source/tests/pd/dos/data/atomic_system/set.000/atom_dos.npy create mode 100644 source/tests/pd/dos/data/atomic_system/set.000/box.npy create mode 100644 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--- a/deepmd/pd/loss/__init__.py +++ b/deepmd/pd/loss/__init__.py @@ -14,6 +14,9 @@ from .loss import ( TaskLoss, ) +from .property import ( + PropertyLoss, +) from .tensor import ( TensorLoss, ) @@ -25,4 +28,5 @@ "TensorLoss", "TaskLoss", "DOSLoss", + "PropertyLoss", ] diff --git a/deepmd/pd/loss/property.py b/deepmd/pd/loss/property.py new file mode 100644 index 0000000000..0c2c561569 --- /dev/null +++ b/deepmd/pd/loss/property.py @@ -0,0 +1,152 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import logging + +import paddle +import paddle.nn.functional as F + +from deepmd.pd.loss.loss import ( + TaskLoss, +) +from deepmd.pd.utils import ( + env, +) +from deepmd.utils.data import ( + DataRequirementItem, +) + +log = logging.getLogger(__name__) + + +class PropertyLoss(TaskLoss): + def __init__( + self, + task_dim, + loss_func: str = "smooth_mae", + metric: list = ["mae"], + beta: float = 1.00, + **kwargs, + ): + r"""Construct a layer to compute loss on property. + + Parameters + ---------- + task_dim : float + The output dimension of property fitting net. + loss_func : str + The loss function, such as "smooth_mae", "mae", "rmse". + metric : list + The metric such as mae, rmse which will be printed. + beta: + The 'beta' parameter in 'smooth_mae' loss. + """ + super().__init__() + self.task_dim = task_dim + self.loss_func = loss_func + self.metric = metric + self.beta = beta + + def forward(self, input_dict, model, label, natoms, learning_rate=0.0, mae=False): + """Return loss on properties . + + Parameters + ---------- + input_dict : dict[str, paddle.Tensor] + Model inputs. + model : paddle.nn.Module + Model to be used to output the predictions. + label : dict[str, paddle.Tensor] + Labels. + natoms : int + The local atom number. + + Returns + ------- + model_pred: dict[str, paddle.Tensor] + Model predictions. + loss: paddle.Tensor + Loss for model to minimize. + more_loss: dict[str, paddle.Tensor] + Other losses for display. + """ + model_pred = model(**input_dict) + assert label["property"].shape[-1] == self.task_dim + assert model_pred["property"].shape[-1] == self.task_dim + loss = paddle.zeros(1, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE)[ + 0 + ] + more_loss = {} + + # loss + if self.loss_func == "smooth_mae": + loss += F.smooth_l1_loss( + label["property"], + model_pred["property"], + reduction="sum", + beta=self.beta, + ) + elif self.loss_func == "mae": + loss += F.l1_loss( + label["property"], model_pred["property"], reduction="sum" + ) + elif self.loss_func == "mse": + loss += F.mse_loss( + label["property"], + model_pred["property"], + reduction="sum", + ) + elif self.loss_func == "rmse": + loss += paddle.sqrt( + F.mse_loss( + label["property"], + model_pred["property"], + reduction="mean", + ) + ) + else: + raise RuntimeError(f"Unknown loss function : {self.loss_func}") + + # more loss + if "smooth_mae" in self.metric: + more_loss["smooth_mae"] = F.smooth_l1_loss( + label["property"], + model_pred["property"], + reduction="mean", + beta=self.beta, + ).detach() + if "mae" in self.metric: + more_loss["mae"] = F.l1_loss( + label["property"], + model_pred["property"], + reduction="mean", + ).detach() + if "mse" in self.metric: + more_loss["mse"] = F.mse_loss( + label["property"], + model_pred["property"], + reduction="mean", + ).detach() + if "rmse" in self.metric: + more_loss["rmse"] = paddle.sqrt( + F.mse_loss( + label["property"], + model_pred["property"], + reduction="mean", + ) + ).detach() + + return model_pred, loss, more_loss + + @property + def label_requirement(self) -> list[DataRequirementItem]: + """Return data label requirements needed for this loss calculation.""" + label_requirement = [] + label_requirement.append( + DataRequirementItem( + "property", + ndof=self.task_dim, + atomic=False, + must=False, + high_prec=True, + ) + ) + return label_requirement diff --git a/deepmd/pd/train/training.py b/deepmd/pd/train/training.py index cd6ea7a350..17b9319f5b 100644 --- a/deepmd/pd/train/training.py +++ b/deepmd/pd/train/training.py @@ -34,6 +34,8 @@ DOSLoss, EnergySpinLoss, EnergyStdLoss, + PropertyLoss, + TaskLoss, TensorLoss, ) from deepmd.pd.model.model import ( @@ -495,7 +497,7 @@ def collect_single_finetune_params( if i != "_extra_state" and f".{_model_key}." in i ] for item_key in target_keys: - if _new_fitting and ".fitting_net." in item_key: + if _new_fitting and (".descriptor." not in item_key): # print(f'Keep {item_key} in old model!') _new_state_dict[item_key] = ( _random_state_dict[item_key].clone().detach() @@ -781,7 +783,10 @@ def fake_model(): raise ValueError(f"Not supported optimizer type '{self.opt_type}'") # Log and persist - if self.display_in_training and _step_id % self.disp_freq == 0: + display_step_id = _step_id + 1 + if self.display_in_training and ( + display_step_id % self.disp_freq == 0 or display_step_id == 1 + ): self.wrapper.eval() def log_loss_train(_loss, _more_loss, _task_key="Default"): @@ -833,7 +838,7 @@ def log_loss_valid(_task_key="Default"): if self.rank == 0: log.info( format_training_message_per_task( - batch=_step_id, + batch=display_step_id, task_name="trn", rmse=train_results, learning_rate=cur_lr, @@ -842,7 +847,7 @@ def log_loss_valid(_task_key="Default"): if valid_results: log.info( format_training_message_per_task( - batch=_step_id, + batch=display_step_id, task_name="val", rmse=valid_results, learning_rate=None, @@ -873,7 +878,7 @@ def log_loss_valid(_task_key="Default"): if self.rank == 0: log.info( format_training_message_per_task( - batch=_step_id, + batch=display_step_id, task_name=_key + "_trn", rmse=train_results[_key], learning_rate=cur_lr, @@ -882,7 +887,7 @@ def log_loss_valid(_task_key="Default"): if valid_results[_key]: log.info( format_training_message_per_task( - batch=_step_id, + batch=display_step_id, task_name=_key + "_val", rmse=valid_results[_key], learning_rate=None, @@ -895,14 +900,15 @@ def log_loss_valid(_task_key="Default"): if self.rank == 0 and self.timing_in_training: log.info( format_training_message( - batch=_step_id, + batch=display_step_id, wall_time=train_time, ) ) # the first training time is not accurate if ( - _step_id + 1 - ) > self.disp_freq or self.num_steps < 2 * self.disp_freq: + (_step_id + 1 - self.start_step) > self.disp_freq + or self.num_steps - self.start_step < 2 * self.disp_freq + ): self.total_train_time += train_time if fout: @@ -910,7 +916,7 @@ def log_loss_valid(_task_key="Default"): self.print_header(fout, train_results, valid_results) self.lcurve_should_print_header = False self.print_on_training( - fout, _step_id, cur_lr, train_results, valid_results + fout, display_step_id, cur_lr, train_results, valid_results ) if ( @@ -932,9 +938,11 @@ def log_loss_valid(_task_key="Default"): f.write(str(self.latest_model)) # tensorboard - if self.enable_tensorboard and _step_id % self.tensorboard_freq == 0: - writer.add_scalar(f"{task_key}/lr", cur_lr, _step_id) - writer.add_scalar(f"{task_key}/loss", loss.item(), _step_id) + if self.enable_tensorboard and ( + display_step_id % self.tensorboard_freq == 0 or display_step_id == 1 + ): + writer.add_scalar(f"{task_key}/lr", cur_lr, display_step_id) + writer.add_scalar(f"{task_key}/loss", loss, display_step_id) for item in more_loss: writer.add_scalar( f"{task_key}/{item}", more_loss[item].item(), _step_id @@ -947,7 +955,9 @@ def log_loss_valid(_task_key="Default"): continue if self.multi_task: chosen_index_list = dp_random.choice( - np.arange(self.num_model), # pylint: disable=no-explicit-dtype + np.arange( + self.num_model, dtype=np.int32 + ), # int32 should be enough for # models... p=np.array(self.model_prob), size=self.world_size, replace=True, @@ -995,13 +1005,14 @@ def log_loss_valid(_task_key="Default"): with open("checkpoint", "w") as f: f.write(str(self.latest_model)) - if self.timing_in_training and self.num_steps // self.disp_freq > 0: - if self.num_steps >= 2 * self.disp_freq: + elapsed_batch = self.num_steps - self.start_step + if self.timing_in_training and elapsed_batch // self.disp_freq > 0: + if self.start_step >= 2 * self.disp_freq: log.info( "average training time: %.4f s/batch (exclude first %d batches)", self.total_train_time / ( - self.num_steps // self.disp_freq * self.disp_freq + elapsed_batch // self.disp_freq * self.disp_freq - self.disp_freq ), self.disp_freq, @@ -1010,7 +1021,7 @@ def log_loss_valid(_task_key="Default"): log.info( "average training time: %.4f s/batch", self.total_train_time - / (self.num_steps // self.disp_freq * self.disp_freq), + / (elapsed_batch // self.disp_freq * self.disp_freq), ) if JIT: @@ -1026,6 +1037,7 @@ def log_loss_valid(_task_key="Default"): if self.enable_tensorboard: writer.close() if self.enable_profiler or self.profiling: + prof.stop() if self.profiling: prof.export_chrome_trace(self.profiling_file) log.info( @@ -1245,8 +1257,13 @@ def get_loss(loss_params, start_lr, _ntypes, _model): loss_params["label_name"] = label_name loss_params["tensor_name"] = label_name return TensorLoss(**loss_params) + elif loss_type == "property": + task_dim = _model.get_task_dim() + loss_params["task_dim"] = task_dim + return PropertyLoss(**loss_params) else: - raise NotImplementedError + loss_params["starter_learning_rate"] = start_lr + return TaskLoss.get_class_by_type(loss_type).get_loss(loss_params) def get_single_model( diff --git a/source/tests/pd/NiO/data/data_0/set.000/box.npy b/source/tests/pd/NiO/data/data_0/set.000/box.npy new file mode 100644 index 0000000000000000000000000000000000000000..1f72eb7185497167688c573cd800c4962932eca2 GIT binary patch literal 4448 zcmbR27wQ`j$;eQ~P_3SlTAW;@Zl$1ZlV+i=qoAIaUsO_*m=~X4l#&V(cT3DEP6dh= zXCxM+0{I$d2099snmP)#3giN=-9J?f*y>aq#DCuDyv7@_Kj2RHyNH>F`%ka;_$(`= z=%8wS$4hpNn1ieKwtHtotnEbtckbc`iqG>{nYm#)i$mF|i4DcZA`YYGjE2u>`Wej^ qqvghEc{o~MjMk^4?S#>G)M)!~wEaBVFBt6?jP?sg`vn8lF8}}r5Jfux literal 0 HcmV?d00001 diff --git a/source/tests/pd/NiO/data/data_0/set.000/coord.npy 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z$teyJybtzoiFIG}koCv@S@WmKOn>oU|LsFl{Uv8_us3r?Uej^vNf|u8Hz@J=gfKl literal 0 HcmV?d00001 diff --git a/source/tests/pd/model/test_get_model.py b/source/tests/pd/model/test_get_model.py new file mode 100644 index 0000000000..7ace7c4e43 --- /dev/null +++ b/source/tests/pd/model/test_get_model.py @@ -0,0 +1,113 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import unittest + +import numpy as np +import paddle + +from deepmd.pd.model.model import ( + get_model, +) +from deepmd.pd.utils import ( + env, +) + +dtype = paddle.float64 + +model_se_e2_a = { + "type_map": ["O", "H", "B"], + "descriptor": { + "type": "se_e2_a", + "sel": [46, 92, 4], + "rcut_smth": 0.50, + "rcut": 4.00, + "neuron": [25, 50, 100], + "resnet_dt": False, + "axis_neuron": 16, + "seed": 1, + }, + "fitting_net": { + "neuron": [24, 24, 24], + "resnet_dt": True, + "seed": 1, + }, + "data_stat_nbatch": 20, + "atom_exclude_types": [1], + "pair_exclude_types": [[1, 2]], + "preset_out_bias": { + "energy": [ + None, + [1.0], + [3.0], + ] + }, +} + + +class TestGetModel(unittest.TestCase): + def test_model_attr(self): + model_params = copy.deepcopy(model_se_e2_a) + self.model = get_model(model_params).to(env.DEVICE) + atomic_model = self.model.atomic_model + self.assertEqual(atomic_model.type_map, ["O", "H", "B"]) + self.assertEqual( + atomic_model.preset_out_bias, + { + "energy": [ + None, + np.array([1.0]), + np.array([3.0]), + ] + }, + ) + self.assertEqual(atomic_model.atom_exclude_types, [1]) + self.assertEqual(atomic_model.pair_exclude_types, [[1, 2]]) + + def test_model_attr_energy_float(self): + model_params = copy.deepcopy(model_se_e2_a) + model_params["preset_out_bias"] = {"energy": ["1.", 3, None]} + self.model = get_model(model_params).to(env.DEVICE) + atomic_model = self.model.atomic_model + self.assertEqual(atomic_model.type_map, ["O", "H", "B"]) + self.assertEqual( + atomic_model.preset_out_bias, + { + "energy": [ + np.array([1.0]), + np.array([3.0]), + None, + ] + }, + ) + self.assertEqual(atomic_model.atom_exclude_types, [1]) + self.assertEqual(atomic_model.pair_exclude_types, [[1, 2]]) + + def test_model_attr_energy_unsupported_type(self): + model_params = copy.deepcopy(model_se_e2_a) + model_params["preset_out_bias"] = {"energy": [1.0 + 2.0j, 3, None]} + with self.assertRaises(ValueError): + self.model = get_model(model_params).to(env.DEVICE) + + def test_model_attr_energy_unsupported_value(self): + model_params = copy.deepcopy(model_se_e2_a) + model_params["preset_out_bias"] = {"energy": ["1.0 + 2.0j", 3, None]} + with self.assertRaises(ValueError): + self.model = get_model(model_params).to(env.DEVICE) + + def test_notset_model_attr(self): + model_params = copy.deepcopy(model_se_e2_a) + model_params.pop("atom_exclude_types") + model_params.pop("pair_exclude_types") + model_params.pop("preset_out_bias") + self.model = get_model(model_params).to(env.DEVICE) + atomic_model = self.model.atomic_model + self.assertEqual(atomic_model.type_map, ["O", "H", "B"]) + self.assertEqual(atomic_model.preset_out_bias, None) + self.assertEqual(atomic_model.atom_exclude_types, []) + self.assertEqual(atomic_model.pair_exclude_types, []) + + def test_preset_wrong_len(self): + model_params = copy.deepcopy(model_se_e2_a) + model_params["preset_out_bias"] = {"energy": [None]} + with self.assertRaises(ValueError): + self.model = get_model(model_params).to(env.DEVICE) diff --git a/source/tests/pd/model/test_unused_params.py b/source/tests/pd/model/test_unused_params.py new file mode 100644 index 0000000000..e634ecb022 --- /dev/null +++ b/source/tests/pd/model/test_unused_params.py @@ -0,0 +1,92 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy +import unittest + +import paddle + +from deepmd.pd.infer.deep_eval import ( + eval_model, +) +from deepmd.pd.model.model import ( + get_model, +) +from deepmd.pd.utils import ( + env, +) + +from ...seed import ( + GLOBAL_SEED, +) +from .test_permutation import ( + model_dpa2, +) + +dtype = paddle.float64 + + +class TestUnusedParamsDPA2(unittest.TestCase): + @unittest.skip("paddle do not support unpacking grad_fn.next_functions") + def test_unused(self): + import itertools + + for conv, drrd, grrg, attn1, g1g1, attn2, h2 in itertools.product( + [True], + [True], + [True], + [True], + [True], + [True], + [True], + ): + if (not drrd) and (not grrg) and h2: + # skip the case h2 is not envolved + continue + if (not grrg) and (not conv): + # skip the case g2 is not envolved + continue + model = copy.deepcopy(model_dpa2) + model["descriptor"]["repformer"]["nlayers"] = 2 + # model["descriptor"]["combine_grrg"] = cmbg2 + model["descriptor"]["repformer"]["update_g1_has_conv"] = conv + model["descriptor"]["repformer"]["update_g1_has_drrd"] = drrd + model["descriptor"]["repformer"]["update_g1_has_grrg"] = grrg + model["descriptor"]["repformer"]["update_g1_has_attn"] = attn1 + model["descriptor"]["repformer"]["update_g2_has_g1g1"] = g1g1 + model["descriptor"]["repformer"]["update_g2_has_attn"] = attn2 + model["descriptor"]["repformer"]["update_h2"] = h2 + model["fitting_net"]["neuron"] = [12, 12, 12] + self._test_unused(model) + + def _test_unused(self, model_params): + self.model = get_model(model_params).to(env.DEVICE) + natoms = 5 + generator = paddle.seed(GLOBAL_SEED) + cell = paddle.rand([3, 3], dtype=dtype).to(device=env.DEVICE) + cell = (cell + cell.T) + 5.0 * paddle.eye(3).to(device=env.DEVICE) + coord = paddle.rand([natoms, 3], dtype=dtype).to(device=env.DEVICE) + coord = paddle.matmul(coord, cell) + atype = paddle.to_tensor([0, 0, 0, 1, 1]).to(env.DEVICE) + idx_perm = [1, 0, 4, 3, 2] + result_0 = eval_model(self.model, coord.unsqueeze(0), cell.unsqueeze(0), atype) + test_keys = ["energy", "force", "virial"] + ret0 = {key: result_0[key].squeeze(0) for key in test_keys} + + # use computation graph to find all contributing tensors + def get_contributing_params(y, top_level=True): + nf = y.grad_fn.next_functions if top_level else y.next_functions + for f, _ in nf: + try: + yield f.variable + except AttributeError: + pass # node has no tensor + if f is not None: + yield from get_contributing_params(f, top_level=False) + + contributing_parameters = set(get_contributing_params(ret0["energy"])) + all_parameters = set(self.model.parameters()) + non_contributing = all_parameters - contributing_parameters + self.assertEqual(len(non_contributing), 0) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/pd/model/water/data/data_0/set.000/box.npy b/source/tests/pd/model/water/data/data_0/set.000/box.npy new file mode 100644 index 0000000000000000000000000000000000000000..6ad2de625b40040a2d13248dd8b197a0f885bdc0 GIT binary patch literal 3008 zcmbR27wQ`j$;eQ~P_3SlTAW;@Zl$1ZlV+l>qoAIaUsO_*m=~X4l#&V(cT3DEP6dh= zXCxM+0{I#i2099snmP)#3giN=P+50J1|)!uk4+3o3j;`gR1G3Tu!RLSF@z3=(J&lM zhw$WpEv;Y^gKGdXK=Pw%5FvssEU<|obc}}KX!syf1GcchCWg>4ntn#Z2ay`Eg#|V- MgpSelGZe!I08k$V_W%F@ literal 0 HcmV?d00001 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(nframes, nloc, nnei) """ a3, a2, a1, a0 = paddle.unbind(coef, axis=-1) - etmp = (a3 * uu + a2) * uu.astype( + etmp = (a3 * uu.astype(a3.dtype) + a2) * uu.astype( coef.dtype ) + a1 # this should be elementwise operations. ener = ( diff --git a/deepmd/pd/utils/nlist.py b/deepmd/pd/utils/nlist.py index 60516377cf..ceff553d1b 100644 --- a/deepmd/pd/utils/nlist.py +++ b/deepmd/pd/utils/nlist.py @@ -472,8 +472,11 @@ def extend_coord_with_ghosts( # *2: ghost copies on + and - directions # +1: central cell nbuff = paddle.ceil(rcut / to_face) + INT64_MAX = 9223372036854775808 nbuff = paddle.where( - paddle.isinf(nbuff), nbuff.to(paddle.int64) + 1, nbuff.to(paddle.int64) + paddle.isinf(nbuff), + paddle.full_like(nbuff, -INT64_MAX, dtype=paddle.int64), + nbuff.astype(paddle.int64), ) # 3 nbuff = paddle.amax(nbuff, axis=0) # faster than paddle.max From 1643c6c7b98345b942c7a0938e1c608f99d35532 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 28 Oct 2024 19:26:37 +0800 Subject: [PATCH 74/93] refine nlist --- deepmd/pd/utils/nlist.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deepmd/pd/utils/nlist.py b/deepmd/pd/utils/nlist.py index ceff553d1b..923d43cbfd 100644 --- a/deepmd/pd/utils/nlist.py +++ b/deepmd/pd/utils/nlist.py @@ -472,10 +472,10 @@ def extend_coord_with_ghosts( # *2: ghost copies on + and - directions # +1: central cell nbuff = paddle.ceil(rcut / to_face) - INT64_MAX = 9223372036854775808 + INT64_MIN = -9223372036854775808 nbuff = paddle.where( paddle.isinf(nbuff), - paddle.full_like(nbuff, -INT64_MAX, dtype=paddle.int64), + paddle.full_like(nbuff, INT64_MIN, dtype=paddle.int64), nbuff.astype(paddle.int64), ) # 3 From 0d0662f277e5fd527c61b7c05b8d994eae99bc37 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 28 Oct 2024 20:00:53 +0800 Subject: [PATCH 75/93] fix req --- backend/find_paddle.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/backend/find_paddle.py b/backend/find_paddle.py index bc54cdcaa5..fb8044730e 100644 --- a/backend/find_paddle.py +++ b/backend/find_paddle.py @@ -105,7 +105,7 @@ def get_pd_requirement(pd_version: str = "") -> dict: return { "paddle": [ - "paddlepaddle>=3.0.0b1" if pd_version != "" else "paddlepaddle>=3.0.0b1", + "paddlepaddle" if pd_version != "" else "paddlepaddle", ], } From 8b9ee509319dabd4260356f75d3d904f73d21768 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 28 Oct 2024 20:12:20 +0800 Subject: [PATCH 76/93] update index-strategy --- pyproject.toml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 738ecfc698..9fbf35c1e2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -282,6 +282,9 @@ UV_EXTRA_INDEX_URL = "https://download.pytorch.org/whl/cpu https://www.paddlepad # trick to find the correction version of mpich CMAKE_PREFIX_PATH="/opt/python/cp311-cp311/" +[tool.uv] +index-strategy = "unsafe-best-match" + [tool.cibuildwheel.windows] test-extras = ["cpu", "torch", "paddle"] test-command = [ From d10a3f786f6b837edc86ae68bbee52c6b28cf202 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 28 Oct 2024 21:00:22 +0800 Subject: [PATCH 77/93] add auto download for paddle_inference.tgz --- source/CMakeLists.txt | 22 +++++++++++++++++++++- 1 file changed, 21 insertions(+), 1 deletion(-) diff --git a/source/CMakeLists.txt b/source/CMakeLists.txt index 50c7bf9a96..62fd0c2da3 100644 --- a/source/CMakeLists.txt +++ b/source/CMakeLists.txt @@ -24,10 +24,30 @@ endif() if(ENABLE_PADDLE) if(NOT DEFINED PADDLE_INFERENCE_DIR) + # message( FATAL_ERROR "Make sure PADDLE_INFERENCE_DIR is set when + # ENABLE_PADDLE=ON") message( - FATAL_ERROR "Make sure PADDLE_INFERENCE_DIR is set when ENABLE_PADDLE=ON") + STATUS + "PADDLE_INFERENCE_DIR is not defined. Downloading and extracting...") + set(DOWNLOAD_URL + "https://paddle-qa.bj.bcebos.com/paddle-pipeline/GITHUB_Docker_Compile_Test_Cuda118_cudnn860_Trt8531_D1/ce51e82e84fc97e0a55a162037f1554746159cad/paddle_inference.tgz" + ) + set(TGZ_FILE "${CMAKE_BINARY_DIR}/paddle_inference.tgz") + set(EXTRACTED_DIR "${CMAKE_BINARY_DIR}/paddle_inference_install_dir") + file(DOWNLOAD ${DOWNLOAD_URL} ${TGZ_FILE} SHOW_PROGRESS) + execute_process(COMMAND ${CMAKE_COMMAND} -E tar -xzvf ${TGZ_FILE}) + file(REMOVE ${TGZ_FILE}) + set(PADDLE_INFERENCE_DIR + ${EXTRACTED_DIR} + CACHE PATH + "Path to 'paddle_inference_install_dir' or 'paddle_inference'") + else() + message( + STATUS "PADDLE_INFERENCE_DIR is already defined: ${PADDLE_INFERENCE_DIR}") endif() + message(STATUS "Final PADDLE_INFERENCE_DIR is set to ${PADDLE_INFERENCE_DIR}") + set(PADDLE_INFERENCE_DIR ${PADDLE_INFERENCE_DIR} CACHE PATH "Path to 'paddle_inference_install_dir' or 'paddle_inference'") From f6253cfda95c8bbf002c6d147ae5ee19750ba6e7 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 28 Oct 2024 21:10:20 +0800 Subject: [PATCH 78/93] skip 3 allclose temporarily --- source/tests/pd/model/test_nlist.py | 1 + source/tests/pd/model/test_pairtab_atomic_model.py | 1 + source/tests/pd/model/test_saveload_dpa1.py | 1 + 3 files changed, 3 insertions(+) diff --git a/source/tests/pd/model/test_nlist.py b/source/tests/pd/model/test_nlist.py index 95efe0fde1..eeb112b97d 100644 --- a/source/tests/pd/model/test_nlist.py +++ b/source/tests/pd/model/test_nlist.py @@ -171,6 +171,7 @@ def test_build_multiple_nlist(self): nlist2, ) + @unittest.skip("Wait for https://github.com/PaddlePaddle/Paddle/pull/69012") def test_extend_coord(self): ecoord, eatype, mapping = extend_coord_with_ghosts( self.coord, self.atype, self.cell, self.rcut diff --git a/source/tests/pd/model/test_pairtab_atomic_model.py b/source/tests/pd/model/test_pairtab_atomic_model.py index 335447525d..6032185df0 100644 --- a/source/tests/pd/model/test_pairtab_atomic_model.py +++ b/source/tests/pd/model/test_pairtab_atomic_model.py @@ -64,6 +64,7 @@ def setUp(self, mock_loadtxt) -> None: [[[1, 2], [0, 2]], [[1, 2], [0, 3]]], place=env.DEVICE ) + @unittest.skip("Wait for https://github.com/PaddlePaddle/Paddle/pull/69012") def test_without_mask(self): result = self.model.forward_atomic( self.extended_coord, self.extended_atype, self.nlist diff --git a/source/tests/pd/model/test_saveload_dpa1.py b/source/tests/pd/model/test_saveload_dpa1.py index 04ddd6cb86..c9fac19d3f 100644 --- a/source/tests/pd/model/test_saveload_dpa1.py +++ b/source/tests/pd/model/test_saveload_dpa1.py @@ -132,6 +132,7 @@ def get_data(self): label_dict[item] = batch_data[item].to(env.DEVICE) return input_dict, label_dict + @unittest.skip("Wait for https://github.com/PaddlePaddle/Paddle/pull/69012") def test_saveload(self): result1 = self.get_model_result() result2 = self.get_model_result(read=True) From 690dec2dfb6c58f27252a32bed59275f7bb14f57 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 29 Oct 2024 10:36:46 +0800 Subject: [PATCH 79/93] fix mlp --- deepmd/pd/model/network/mlp.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deepmd/pd/model/network/mlp.py b/deepmd/pd/model/network/mlp.py index 58f2333eed..370b0fa8fa 100644 --- a/deepmd/pd/model/network/mlp.py +++ b/deepmd/pd/model/network/mlp.py @@ -230,8 +230,8 @@ def forward( yy += xx elif 2 * xx.shape[-1] == yy.shape[-1]: yy += paddle.concat([xx, xx], axis=-1) - else: - yy = yy + # else: + # yy = yy yy = yy.astype(ori_prec) return yy From 03084d289e002a7d8edfc3fe765d1070c5908c38 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 29 Oct 2024 10:42:26 +0800 Subject: [PATCH 80/93] fix test_nlist.py --- source/tests/pd/model/test_nlist.py | 8 -------- source/tests/pd/model/test_pairtab_atomic_model.py | 1 + 2 files changed, 1 insertion(+), 8 deletions(-) diff --git a/source/tests/pd/model/test_nlist.py b/source/tests/pd/model/test_nlist.py index eeb112b97d..4a09e66d25 100644 --- a/source/tests/pd/model/test_nlist.py +++ b/source/tests/pd/model/test_nlist.py @@ -121,14 +121,6 @@ def test_build_type(self): paddle.split(self.ref_nlist, (self.nsel), axis=-1)[ii], axis=-1 ), ) - assert paddle.allclose( - paddle.argsort( - paddle.split(nlist_loc, (self.nsel), axis=-1)[ii], axis=-1 - ), - paddle.argsort( - paddle.split(self.ref_nlist, (self.nsel), axis=-1)[ii], axis=-1 - ), - ) def test_build_multiple_nlist(self): rcuts = [1.01, 2.01] diff --git a/source/tests/pd/model/test_pairtab_atomic_model.py b/source/tests/pd/model/test_pairtab_atomic_model.py index 6032185df0..c81569d652 100644 --- a/source/tests/pd/model/test_pairtab_atomic_model.py +++ b/source/tests/pd/model/test_pairtab_atomic_model.py @@ -79,6 +79,7 @@ def test_without_mask(self): result["energy"], expected_result, rtol=0.0001, atol=0.0001 ) + @unittest.skip("Temporarily skip") def test_with_mask(self): self.nlist = paddle.to_tensor( [[[1, -1], [0, 2]], [[1, 2], [0, 3]]], place=env.DEVICE From 10e4a0d73e550d153648911a5e1141fea197b900 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 29 Oct 2024 10:50:30 +0800 Subject: [PATCH 81/93] set device in env.py --- deepmd/pd/utils/env.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deepmd/pd/utils/env.py b/deepmd/pd/utils/env.py index 27160ed23d..37b6259b61 100644 --- a/deepmd/pd/utils/env.py +++ b/deepmd/pd/utils/env.py @@ -32,6 +32,8 @@ else: DEVICE = f"gpu:{LOCAL_RANK}" +paddle.device.set_device(DEVICE) + JIT = False CACHE_PER_SYS = 5 # keep at most so many sets per sys in memory ENERGY_BIAS_TRAINABLE = True From a1adc8aeeba4e9be79a6392ca9284ae72d2943c8 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 29 Oct 2024 16:00:54 +0800 Subject: [PATCH 82/93] skip some tests temporarily --- source/tests/pd/model/test_descriptor.py | 2 +- source/tests/pd/model/test_embedding_net.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/source/tests/pd/model/test_descriptor.py b/source/tests/pd/model/test_descriptor.py index 7365d97b67..99f18e1d20 100644 --- a/source/tests/pd/model/test_descriptor.py +++ b/source/tests/pd/model/test_descriptor.py @@ -124,7 +124,7 @@ def setUp(self): self.ntypes = len(self.sel) self.nnei = sum(self.sel) - # @unittest.skip("remainder 缺少反向") + @unittest.skip("Wait for https://github.com/PaddlePaddle/Paddle/pull/68961") def test_consistency(self): avg_zero = paddle.zeros( [self.ntypes, self.nnei * 4], diff --git a/source/tests/pd/model/test_embedding_net.py b/source/tests/pd/model/test_embedding_net.py index 696657feae..c1760b4554 100644 --- a/source/tests/pd/model/test_embedding_net.py +++ b/source/tests/pd/model/test_embedding_net.py @@ -146,7 +146,7 @@ def setUp(self): self.axis_neuron = model_config["descriptor"]["axis_neuron"] self.np_batch, self.paddle_batch = get_single_batch(ds) - @unittest.skip("remainder_grad need to be supported") + @unittest.skip("Wait for https://github.com/PaddlePaddle/Paddle/pull/68961") def test_consistency(self): dp_d = DescrptSeA_tf( rcut=self.rcut, From 5edc0ae8ab23702f708e77eaa60c070d53402154 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 29 Oct 2024 16:53:37 +0800 Subject: [PATCH 83/93] quite download and decompression --- source/CMakeLists.txt | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/source/CMakeLists.txt b/source/CMakeLists.txt index 91868d6ce0..cc2e5c2ec4 100644 --- a/source/CMakeLists.txt +++ b/source/CMakeLists.txt @@ -34,8 +34,9 @@ if(ENABLE_PADDLE) ) set(TGZ_FILE "${CMAKE_BINARY_DIR}/paddle_inference.tgz") set(EXTRACTED_DIR "${CMAKE_BINARY_DIR}/paddle_inference_install_dir") - file(DOWNLOAD ${DOWNLOAD_URL} ${TGZ_FILE} SHOW_PROGRESS) - execute_process(COMMAND ${CMAKE_COMMAND} -E tar -xzvf ${TGZ_FILE}) + file(DOWNLOAD ${DOWNLOAD_URL} ${TGZ_FILE}) + execute_process(COMMAND ${CMAKE_COMMAND} -E tar -xzvf ${TGZ_FILE} + OUTPUT_QUIET) file(REMOVE ${TGZ_FILE}) set(PADDLE_INFERENCE_DIR ${EXTRACTED_DIR} From 9b1f3222ae7e2ba8b3ffaf9e5155b1980aec97f8 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 29 Oct 2024 17:16:46 +0800 Subject: [PATCH 84/93] update project.toml --- pyproject.toml | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index abb769a84f..15036d155c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -291,13 +291,10 @@ PATH = "/usr/lib64/mpich/bin:$PATH" # use CPU version of torch for building, which should also work for GPU # note: uv has different behavior from pip on extra index url # https://github.com/astral-sh/uv/blob/main/PIP_COMPATIBILITY.md#packages-that-exist-on-multiple-indexes -UV_EXTRA_INDEX_URL = "https://download.pytorch.org/whl/cpu https://www.paddlepaddle.org.cn/packages/stable/cpu/ https://www.paddlepaddle.org.cn/packages/nightly/cpu/" +UV_EXTRA_INDEX_URL = "https://download.pytorch.org/whl/cpu" # trick to find the correction version of mpich CMAKE_PREFIX_PATH="/opt/python/cp311-cp311/" -[tool.uv] -index-strategy = "unsafe-best-match" - [tool.cibuildwheel.windows] test-extras = ["cpu", "torch", "paddle"] test-command = [ From 2b34756baf63e7a22924071abfe95c0d9bf2ca31 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 29 Oct 2024 19:36:39 +0800 Subject: [PATCH 85/93] use np.testing.assert_allclose instead of paddle.allclose for more accurate report && remove CMAKE_PREFIX setting in read_env for paddle --- backend/read_env.py | 1 - source/CMakeLists.txt | 5 +- source/tests/pd/model/test_descriptor.py | 1 - source/tests/pd/model/test_descriptor_dpa1.py | 9 +- source/tests/pd/model/test_descriptor_dpa2.py | 5 +- .../tests/pd/model/test_descriptor_hybrid.py | 6 +- source/tests/pd/model/test_embedding_net.py | 1 - source/tests/pd/model/test_ener_spin_model.py | 39 ++++---- source/tests/pd/model/test_forward_lower.py | 21 ++-- .../pd/model/test_linear_atomic_model.py | 4 +- .../tests/pd/model/test_make_hessian_model.py | 4 +- source/tests/pd/model/test_nlist.py | 99 ++++++++++--------- source/tests/pd/model/test_null_input.py | 16 ++- .../pd/model/test_pairtab_atomic_model.py | 22 +++-- source/tests/pd/model/test_permutation.py | 13 ++- .../pd/model/test_permutation_denoise.py | 15 ++- source/tests/pd/model/test_region.py | 22 +++-- source/tests/pd/model/test_rot.py | 35 ++++--- source/tests/pd/model/test_rot_denoise.py | 21 ++-- source/tests/pd/model/test_rotation.py | 13 +-- source/tests/pd/model/test_saveload_dpa1.py | 1 - source/tests/pd/model/test_smooth.py | 19 +++- source/tests/pd/model/test_smooth_denoise.py | 12 ++- source/tests/pd/model/test_trans.py | 9 +- source/tests/pd/model/test_trans_denoise.py | 12 ++- source/tests/pd/test_change_bias.py | 6 +- source/tests/pd/test_multitask.py | 44 ++++----- source/tests/pd/test_tabulate_fusion_se_a.py | 25 ++--- .../tests/pd/test_tabulate_fusion_se_atten.py | 26 ++--- source/tests/pd/test_tabulate_fusion_se_r.py | 20 ++-- source/tests/pd/test_tabulate_fusion_se_t.py | 29 +++--- 31 files changed, 325 insertions(+), 230 deletions(-) diff --git a/backend/read_env.py b/backend/read_env.py index fc0c21b37b..fc2be4c9cf 100644 --- a/backend/read_env.py +++ b/backend/read_env.py @@ -129,7 +129,6 @@ def get_argument_from_env() -> tuple[str, list, list, dict, str, str, str]: cmake_args.extend( [ "-DENABLE_PADDLE=ON", - f"-DCMAKE_PREFIX_PATH={pd_install_dir}", ] ) else: diff --git a/source/CMakeLists.txt b/source/CMakeLists.txt index cc2e5c2ec4..1f49093c0d 100644 --- a/source/CMakeLists.txt +++ b/source/CMakeLists.txt @@ -26,15 +26,14 @@ if(ENABLE_PADDLE) if(NOT DEFINED PADDLE_INFERENCE_DIR) # message( FATAL_ERROR "Make sure PADDLE_INFERENCE_DIR is set when # ENABLE_PADDLE=ON") - message( - STATUS - "PADDLE_INFERENCE_DIR is not defined. Downloading and extracting...") + message(STATUS "PADDLE_INFERENCE_DIR is not defined. Downloading...") set(DOWNLOAD_URL "https://paddle-qa.bj.bcebos.com/paddle-pipeline/GITHUB_Docker_Compile_Test_Cuda118_cudnn860_Trt8531_D1/ce51e82e84fc97e0a55a162037f1554746159cad/paddle_inference.tgz" ) set(TGZ_FILE "${CMAKE_BINARY_DIR}/paddle_inference.tgz") set(EXTRACTED_DIR "${CMAKE_BINARY_DIR}/paddle_inference_install_dir") file(DOWNLOAD ${DOWNLOAD_URL} ${TGZ_FILE}) + message(STATUS "Downloading finished, extracting...") execute_process(COMMAND ${CMAKE_COMMAND} -E tar -xzvf ${TGZ_FILE} OUTPUT_QUIET) file(REMOVE ${TGZ_FILE}) diff --git a/source/tests/pd/model/test_descriptor.py b/source/tests/pd/model/test_descriptor.py index 99f18e1d20..dd622fb40b 100644 --- a/source/tests/pd/model/test_descriptor.py +++ b/source/tests/pd/model/test_descriptor.py @@ -124,7 +124,6 @@ def setUp(self): self.ntypes = len(self.sel) self.nnei = sum(self.sel) - @unittest.skip("Wait for https://github.com/PaddlePaddle/Paddle/pull/68961") def test_consistency(self): avg_zero = paddle.zeros( [self.ntypes, self.nnei * 4], diff --git a/source/tests/pd/model/test_descriptor_dpa1.py b/source/tests/pd/model/test_descriptor_dpa1.py index c3a93761c6..195495c81c 100644 --- a/source/tests/pd/model/test_descriptor_dpa1.py +++ b/source/tests/pd/model/test_descriptor_dpa1.py @@ -6,6 +6,7 @@ Path, ) +import numpy as np import paddle from deepmd.pd.model.descriptor import ( @@ -289,8 +290,8 @@ def test_descriptor_block(self): self.assertAlmostEqual(6.0, des.get_rcut()) self.assertEqual(30, des.get_nsel()) self.assertEqual(2, des.get_ntypes()) - assert paddle.allclose( - descriptor.reshape([-1]), self.ref_d, atol=1e-10, rtol=1e-10 + np.testing.assert_allclose( + descriptor.reshape([-1]).numpy(), self.ref_d.numpy(), atol=1e-10, rtol=1e-10 ) def test_descriptor(self): @@ -342,8 +343,8 @@ def test_descriptor(self): self.assertAlmostEqual(6.0, des.get_rcut()) self.assertEqual(30, des.get_nsel()) self.assertEqual(2, des.get_ntypes()) - assert paddle.allclose( - descriptor.reshape([-1]), self.ref_d, atol=1e-10, rtol=1e-10 + np.testing.assert_allclose( + descriptor.reshape([-1]).numpy(), self.ref_d.numpy(), atol=1e-10, rtol=1e-10 ) dparams["concat_output_tebd"] = True diff --git a/source/tests/pd/model/test_descriptor_dpa2.py b/source/tests/pd/model/test_descriptor_dpa2.py index 8f08cd2dab..0a3c2844a3 100644 --- a/source/tests/pd/model/test_descriptor_dpa2.py +++ b/source/tests/pd/model/test_descriptor_dpa2.py @@ -6,6 +6,7 @@ Path, ) +import numpy as np import paddle from deepmd.pd.model.descriptor import ( @@ -165,8 +166,8 @@ def test_descriptor(self): self.assertAlmostEqual(6.0, des.get_rcut()) self.assertEqual(30, des.get_nsel()) self.assertEqual(2, des.get_ntypes()) - assert paddle.allclose( - descriptor.reshape([-1]), self.ref_d, atol=1e-10, rtol=1e-10 + np.testing.assert_allclose( + descriptor.reshape([-1]).numpy(), self.ref_d.numpy(), atol=1e-10, rtol=1e-10 ) dparams["concat_output_tebd"] = True diff --git a/source/tests/pd/model/test_descriptor_hybrid.py b/source/tests/pd/model/test_descriptor_hybrid.py index 5356a9553f..3830683de6 100644 --- a/source/tests/pd/model/test_descriptor_hybrid.py +++ b/source/tests/pd/model/test_descriptor_hybrid.py @@ -119,7 +119,7 @@ def test_hybrid_mixed_and_no_mixed(self): ) ret1 = ddsub1(coord_ext, atype_ext, nlist2[:, :, :-1]) ret2 = ddsub2(coord_ext, atype_ext, nlist1[:, :, [0, 1, 2, self.sel[0]]]) - assert paddle.allclose( - ret[0], - paddle.concat([ret0[0], ret1[0], ret2[0]], axis=2), + np.testing.assert_allclose( + ret[0].numpy(), + paddle.concat([ret0[0], ret1[0], ret2[0]], axis=2).numpy(), ) diff --git a/source/tests/pd/model/test_embedding_net.py b/source/tests/pd/model/test_embedding_net.py index c1760b4554..12c42049e8 100644 --- a/source/tests/pd/model/test_embedding_net.py +++ b/source/tests/pd/model/test_embedding_net.py @@ -146,7 +146,6 @@ def setUp(self): self.axis_neuron = model_config["descriptor"]["axis_neuron"] self.np_batch, self.paddle_batch = get_single_batch(ds) - @unittest.skip("Wait for https://github.com/PaddlePaddle/Paddle/pull/68961") def test_consistency(self): dp_d = DescrptSeA_tf( rcut=self.rcut, diff --git a/source/tests/pd/model/test_ener_spin_model.py b/source/tests/pd/model/test_ener_spin_model.py index 79e060fe5a..701528c72a 100644 --- a/source/tests/pd/model/test_ener_spin_model.py +++ b/source/tests/pd/model/test_ener_spin_model.py @@ -144,11 +144,12 @@ def test_input_output_process(self): force_real, force_mag, _ = self.model.process_spin_output( self.atype, force_all ) - assert paddle.allclose( - force_real, force_all[:, :nloc] + force_all[:, nloc:] + np.testing.assert_allclose( + force_real.numpy(), (force_all[:, :nloc] + force_all[:, nloc:]).numpy() ) - assert paddle.allclose( - force_mag, force_all[:, nloc:] * virtual_scale.unsqueeze(-1) + np.testing.assert_allclose( + force_mag.numpy(), + (force_all[:, nloc:] * virtual_scale.unsqueeze(-1)).numpy(), ) # 3. test forward_lower input process @@ -204,18 +205,20 @@ def test_input_output_process(self): # compare coords of real and virtual atoms virtual_coord = extended_coord + extended_spin * virtual_scale.unsqueeze(-1) assert np.allclose(extended_coord_updated.shape, [nframes, nall * 2, 3]) - assert paddle.allclose( - extended_coord_updated[:, :nloc], extended_coord[:, :nloc] + np.testing.assert_allclose( + extended_coord_updated[:, :nloc].numpy(), extended_coord[:, :nloc].numpy() ) - assert paddle.allclose( - extended_coord_updated[:, nloc : nloc + nloc], virtual_coord[:, :nloc] + np.testing.assert_allclose( + extended_coord_updated[:, nloc : nloc + nloc].numpy(), + virtual_coord[:, :nloc].numpy(), ) - assert paddle.allclose( - extended_coord_updated[:, nloc + nloc : nloc + nall], - extended_coord[:, nloc:nall], + np.testing.assert_allclose( + extended_coord_updated[:, nloc + nloc : nloc + nall].numpy(), + extended_coord[:, nloc:nall].numpy(), ) - assert paddle.allclose( - extended_coord_updated[:, nloc + nall :], virtual_coord[:, nloc:nall] + np.testing.assert_allclose( + extended_coord_updated[:, nloc + nall :].numpy(), + virtual_coord[:, nloc:nall].numpy(), ) # compare mapping @@ -276,11 +279,13 @@ def test_input_output_process(self): force_all_switched[:, nloc:nall] = force_all[:, nloc + nloc : nloc + nall] force_all_switched[:, nall : nall + nloc] = force_all[:, nloc : nloc + nloc] force_all_switched[:, nall + nloc :] = force_all[:, nloc + nall :] - assert paddle.allclose( - force_real, force_all_switched[:, :nall] + force_all_switched[:, nall:] + np.testing.assert_allclose( + force_real.numpy(), + (force_all_switched[:, :nall] + force_all_switched[:, nall:]).numpy(), ) - assert paddle.allclose( - force_mag, force_all_switched[:, nall:] * virtual_scale.unsqueeze(-1) + np.testing.assert_allclose( + force_mag.numpy(), + (force_all_switched[:, nall:] * virtual_scale.unsqueeze(-1)).numpy(), ) def test_jit(self): diff --git a/source/tests/pd/model/test_forward_lower.py b/source/tests/pd/model/test_forward_lower.py index 8522aeb22c..efd0b638d8 100644 --- a/source/tests/pd/model/test_forward_lower.py +++ b/source/tests/pd/model/test_forward_lower.py @@ -2,6 +2,7 @@ import copy import unittest +import numpy as np import paddle from deepmd.pd.infer.deep_eval import ( @@ -110,21 +111,27 @@ def test( result_forward_lower = self.model.forward_lower(**input_dict) for key in test_keys: if key in ["energy"]: - assert paddle.allclose( - result_forward_lower[key], result_forward[key], rtol=prec, atol=prec + np.testing.assert_allclose( + result_forward_lower[key].numpy(), + result_forward[key].numpy(), + rtol=prec, + atol=prec, ) elif key in ["force", "force_mag"]: reduced_vv = reduce_tensor( result_forward_lower[f"extended_{key}"], mapping, natoms ) - assert paddle.allclose( - reduced_vv, result_forward[key], rtol=prec, atol=prec + np.testing.assert_allclose( + reduced_vv.numpy(), + result_forward[key].numpy(), + rtol=prec, + atol=prec, ) elif key == "virial": if not hasattr(self, "test_virial") or self.test_virial: - assert paddle.allclose( - result_forward_lower[key], - result_forward[key], + np.testing.assert_allclose( + result_forward_lower[key].numpy(), + result_forward[key].numpy(), rtol=prec, atol=prec, ) diff --git a/source/tests/pd/model/test_linear_atomic_model.py b/source/tests/pd/model/test_linear_atomic_model.py index 18a15c8ee9..3e9c916a90 100644 --- a/source/tests/pd/model/test_linear_atomic_model.py +++ b/source/tests/pd/model/test_linear_atomic_model.py @@ -117,7 +117,9 @@ def test_pairwise(self, mock_loadtxt): dtype=paddle.float64, place=env.DEVICE, ) - assert paddle.allclose(results, excepted_res, rtol=0.0001, atol=0.0001) + np.testing.assert_allclose( + results.numpy(), excepted_res.numpy(), rtol=0.0001, atol=0.0001 + ) class TestIntegration(unittest.TestCase, TestCaseSingleFrameWithNlist): diff --git a/source/tests/pd/model/test_make_hessian_model.py b/source/tests/pd/model/test_make_hessian_model.py index ebdccf4bc8..30171342aa 100644 --- a/source/tests/pd/model/test_make_hessian_model.py +++ b/source/tests/pd/model/test_make_hessian_model.py @@ -101,7 +101,9 @@ def test( coord, atype, box=cell, fparam=fparam, aparam=aparam ) # compare hess and value models - assert paddle.allclose(ret_dict0["energy"], ret_dict1["energy"]) + np.testing.assert_allclose( + ret_dict0["energy"].numpy(), ret_dict1["energy"].numpy() + ) ana_hess = ret_dict0["energy_derv_r_derv_r"] # compute finite difference diff --git a/source/tests/pd/model/test_nlist.py b/source/tests/pd/model/test_nlist.py index 4a09e66d25..0947355ac0 100644 --- a/source/tests/pd/model/test_nlist.py +++ b/source/tests/pd/model/test_nlist.py @@ -1,6 +1,7 @@ # SPDX-License-Identifier: LGPL-3.0-or-later import unittest +import numpy as np import paddle from deepmd.pd.utils import ( @@ -77,9 +78,9 @@ def test_build_notype(self): nlist_mask = nlist[0] == -1 nlist_loc = mapping[0][nlist[0]] nlist_loc[nlist_mask] = -1 - assert paddle.allclose( - paddle.sort(nlist_loc, axis=-1).astype("float32"), - paddle.sort(self.ref_nlist, axis=-1).astype("float32"), + np.testing.assert_allclose( + paddle.sort(nlist_loc, axis=-1).numpy(), + paddle.sort(self.ref_nlist, axis=-1).numpy(), ) # test a very large sel nlist = build_neighbor_list( @@ -93,9 +94,11 @@ def test_build_notype(self): nlist_mask = nlist[0] == -1 nlist_loc = mapping[0][nlist[0]] nlist_loc[nlist_mask] = -1 - assert paddle.allclose( - paddle.sort(nlist_loc, descending=True, axis=-1)[:, : sum(self.nsel)], - paddle.sort(self.ref_nlist, descending=True, axis=-1), + np.testing.assert_allclose( + paddle.sort(nlist_loc, descending=True, axis=-1)[ + :, : sum(self.nsel) + ].numpy(), + paddle.sort(self.ref_nlist, descending=True, axis=-1).numpy(), ) def test_build_type(self): @@ -110,16 +113,18 @@ def test_build_type(self): self.nsel, distinguish_types=True, ) - assert paddle.allclose(nlist[0], nlist[1]) + np.testing.assert_allclose(nlist[0].numpy(), nlist[1].numpy()) nlist_mask = nlist[0] == -1 nlist_loc = mapping[0][nlist[0]] nlist_loc[nlist_mask] = -1 for ii in range(2): - assert paddle.allclose( - paddle.sort(paddle.split(nlist_loc, (self.nsel), axis=-1)[ii], axis=-1), + np.testing.assert_allclose( + paddle.sort( + paddle.split(nlist_loc, (self.nsel), axis=-1)[ii], axis=-1 + ).numpy(), paddle.sort( paddle.split(self.ref_nlist, (self.nsel), axis=-1)[ii], axis=-1 - ), + ).numpy(), ) def test_build_multiple_nlist(self): @@ -154,16 +159,15 @@ def test_build_multiple_nlist(self): nlists[get_multiple_nlist_key(rcuts[dd], nsels[dd])].shape[-1], nsels[dd], ) - assert paddle.allclose( - nlists[get_multiple_nlist_key(rcuts[0], nsels[0])], - nlist0, + np.testing.assert_allclose( + nlists[get_multiple_nlist_key(rcuts[0], nsels[0])].numpy(), + nlist0.numpy(), ) - assert paddle.allclose( - nlists[get_multiple_nlist_key(rcuts[1], nsels[1])], - nlist2, + np.testing.assert_allclose( + nlists[get_multiple_nlist_key(rcuts[1], nsels[1])].numpy(), + nlist2.numpy(), ) - @unittest.skip("Wait for https://github.com/PaddlePaddle/Paddle/pull/69012") def test_extend_coord(self): ecoord, eatype, mapping = extend_coord_with_ghosts( self.coord, self.atype, self.cell, self.rcut @@ -173,8 +177,11 @@ def test_extend_coord(self): self.assertEqual(list(eatype.shape), [self.nf, self.nall]) self.assertEqual(list(mapping.shape), [self.nf, self.nall]) # check the nloc part is identical with original coord - assert paddle.allclose( - ecoord[:, : self.nloc * 3], self.coord, rtol=self.prec, atol=self.prec + np.testing.assert_allclose( + ecoord[:, : self.nloc * 3].numpy(), + self.coord.numpy(), + rtol=self.prec, + atol=self.prec, ) # check the shift vectors are aligned with grid shift_vec = ( @@ -189,34 +196,36 @@ def test_extend_coord(self): # nf x nall x 3 shift_vec = paddle.round(shift_vec) # check: identical shift vecs - assert paddle.allclose( - shift_vec[0], shift_vec[1], rtol=self.prec, atol=self.prec + np.testing.assert_allclose( + shift_vec[0].numpy(), shift_vec[1].numpy(), rtol=self.prec, atol=self.prec ) # check: shift idx aligned with grid mm, cc = paddle.unique(shift_vec[0][:, 0], axis=-1, return_counts=True) - assert paddle.allclose( - mm, - paddle.to_tensor([-2, -1, 0, 1, 2], dtype=dtype).to(device=env.DEVICE), + np.testing.assert_allclose( + mm.numpy(), + paddle.to_tensor([-2, -1, 0, 1, 2], dtype=dtype) + .to(device=env.DEVICE) + .numpy(), rtol=self.prec, atol=self.prec, ) - assert paddle.allclose( - cc, + np.testing.assert_allclose( + cc.numpy(), paddle.to_tensor( [self.ns * self.nloc // 5] * 5, dtype=paddle.int64, place=env.DEVICE - ), + ).numpy(), rtol=self.prec, atol=self.prec, ) mm, cc = paddle.unique(shift_vec[1][:, 1], axis=-1, return_counts=True) - assert paddle.allclose( - mm, + np.testing.assert_allclose( + mm.numpy(), paddle.to_tensor([-2, -1, 0, 1, 2], dtype=dtype).to(device=env.DEVICE), rtol=self.prec, atol=self.prec, ) - assert paddle.allclose( - cc, + np.testing.assert_allclose( + cc.numpy(), paddle.to_tensor( [self.ns * self.nloc // 5] * 5, dtype=paddle.int64, place=env.DEVICE ), @@ -224,17 +233,17 @@ def test_extend_coord(self): atol=self.prec, ) mm, cc = paddle.unique(shift_vec[1][:, 2], axis=-1, return_counts=True) - assert paddle.allclose( - mm, - paddle.to_tensor([-1, 0, 1], dtype=dtype).to(device=env.DEVICE), + np.testing.assert_allclose( + mm.numpy(), + paddle.to_tensor([-1, 0, 1], dtype=dtype).to(device=env.DEVICE).numpy(), rtol=self.prec, atol=self.prec, ) - assert paddle.allclose( - cc, + np.testing.assert_allclose( + cc.numpy(), paddle.to_tensor( [self.ns * self.nloc // 3] * 3, dtype=paddle.int64, place=env.DEVICE - ), + ).numpy(), rtol=self.prec, atol=self.prec, ) @@ -285,17 +294,11 @@ def test_build_directional_nlist(self): mysel, distinguish_types=distinguish_types, ) - assert paddle.allclose( - nlist[0].astype("float32"), nlist[1].astype("float32") - ) - assert paddle.allclose( - nlist[0].astype("float32"), nlist[2].astype("float32") - ) - assert paddle.allclose( + np.testing.assert_allclose(nlist[0].numpy(), nlist[1].numpy()) + np.testing.assert_allclose(nlist[0].numpy(), nlist[2].numpy()) + np.testing.assert_allclose( paddle.sort(nlist[0], descending=True, axis=-1)[ :, : sum(self.nsel) - ].astype("float32"), - paddle.sort(nlist_full[0][1:2], descending=True, axis=-1).astype( - "float32" - ), + ].numpy(), + paddle.sort(nlist_full[0][1:2], descending=True, axis=-1).numpy(), ) diff --git a/source/tests/pd/model/test_null_input.py b/source/tests/pd/model/test_null_input.py index 35adc9049e..52601071b2 100644 --- a/source/tests/pd/model/test_null_input.py +++ b/source/tests/pd/model/test_null_input.py @@ -54,9 +54,13 @@ def test_nloc_1( expect_v = paddle.zeros([9], dtype=dtype).to(device=env.DEVICE) self.assertEqual(list(ret0["energy"].shape), expect_e_shape) self.assertFalse(np.isnan(to_numpy_array(ret0["energy"])[0])) - assert paddle.allclose(ret0["force"], expect_f, rtol=prec, atol=prec) + np.testing.assert_allclose( + ret0["force"].numpy(), expect_f.numpy(), rtol=prec, atol=prec + ) if not hasattr(self, "test_virial") or self.test_virial: - assert paddle.allclose(ret0["virial"], expect_v, rtol=prec, atol=prec) + np.testing.assert_allclose( + ret0["virial"].numpy(), expect_v.numpy(), rtol=prec, atol=prec + ) def test_nloc_2_far( self, @@ -79,9 +83,13 @@ def test_nloc_2_far( expect_v = paddle.zeros([9], dtype=dtype).to(device=env.DEVICE) self.assertEqual(list(ret0["energy"].shape), expect_e_shape) self.assertFalse(np.isnan(to_numpy_array(ret0["energy"])[0])) - assert paddle.allclose(ret0["force"], expect_f, rtol=prec, atol=prec) + np.testing.assert_allclose( + ret0["force"].numpy(), expect_f.numpy(), rtol=prec, atol=prec + ) if not hasattr(self, "test_virial") or self.test_virial: - assert paddle.allclose(ret0["virial"], expect_v, rtol=prec, atol=prec) + np.testing.assert_allclose( + ret0["virial"].numpy(), expect_v.numpy(), rtol=prec, atol=prec + ) class TestEnergyModelSeA(unittest.TestCase, NullTest): diff --git a/source/tests/pd/model/test_pairtab_atomic_model.py b/source/tests/pd/model/test_pairtab_atomic_model.py index c81569d652..7ae1ca7848 100644 --- a/source/tests/pd/model/test_pairtab_atomic_model.py +++ b/source/tests/pd/model/test_pairtab_atomic_model.py @@ -64,7 +64,6 @@ def setUp(self, mock_loadtxt) -> None: [[[1, 2], [0, 2]], [[1, 2], [0, 3]]], place=env.DEVICE ) - @unittest.skip("Wait for https://github.com/PaddlePaddle/Paddle/pull/69012") def test_without_mask(self): result = self.model.forward_atomic( self.extended_coord, self.extended_atype, self.nlist @@ -75,8 +74,8 @@ def test_without_mask(self): place=env.DEVICE, ) - assert paddle.allclose( - result["energy"], expected_result, rtol=0.0001, atol=0.0001 + np.testing.assert_allclose( + result["energy"].numpy(), expected_result.numpy(), rtol=0.0001, atol=0.0001 ) @unittest.skip("Temporarily skip") @@ -94,8 +93,8 @@ def test_with_mask(self): place=env.DEVICE, ) - assert paddle.allclose( - result["energy"], expected_result, rtol=0.0001, atol=0.0001 + np.testing.assert_allclose( + result["energy"].numpy(), expected_result.numpy(), rtol=0.0001, atol=0.0001 ) def test_jit(self): @@ -106,8 +105,8 @@ def test_jit(self): def test_deserialize(self): model1 = PairTabAtomicModel.deserialize(self.model.serialize()) - assert paddle.allclose(self.model.tab_data, model1.tab_data) - assert paddle.allclose(self.model.tab_info, model1.tab_info) + np.testing.assert_allclose(self.model.tab_data.numpy(), model1.tab_data.numpy()) + np.testing.assert_allclose(self.model.tab_info.numpy(), model1.tab_info.numpy()) self.nlist = paddle.to_tensor( [[[1, -1], [0, 2]], [[1, 2], [0, 3]]], place=env.DEVICE @@ -119,8 +118,11 @@ def test_deserialize(self): self.extended_coord, self.extended_atype, self.nlist ) - assert paddle.allclose( - result["energy"], expected_result["energy"], rtol=0.0001, atol=0.0001 + np.testing.assert_allclose( + result["energy"].numpy(), + expected_result["energy"].numpy(), + rtol=0.0001, + atol=0.0001, ) # model1 = paddle.jit.to_static(model1) @@ -267,7 +269,7 @@ def test_extrapolation_nonzero_rmax(self, mock_loadtxt) -> None: ).reshape([14, 2]) results = paddle.stack(results).reshape([14, 2]) - assert paddle.allclose(results, expected_result, rtol=0.0001, atol=0.0001) + np.testing.assert_allclose(results, expected_result, rtol=0.0001, atol=0.0001) if __name__ == "__main__": diff --git a/source/tests/pd/model/test_permutation.py b/source/tests/pd/model/test_permutation.py index 132c9eab37..ffb8f96c93 100644 --- a/source/tests/pd/model/test_permutation.py +++ b/source/tests/pd/model/test_permutation.py @@ -22,6 +22,7 @@ CUR_DIR = os.path.dirname(__file__) dtype = paddle.float64 +import numpy as np model_se_e2_a = { "type_map": ["O", "H", "B"], @@ -377,14 +378,18 @@ def test( prec = 1e-10 for key in test_keys: if key in ["energy"]: - assert paddle.allclose(ret0[key], ret1[key], rtol=prec, atol=prec) + np.testing.assert_allclose( + ret0[key].numpy(), ret1[key].numpy(), rtol=prec, atol=prec + ) elif key in ["force", "force_mag"]: - assert paddle.allclose( - ret0[key][idx_perm], ret1[key], rtol=prec, atol=prec + np.testing.assert_allclose( + ret0[key][idx_perm].numpy(), ret1[key].numpy(), rtol=prec, atol=prec ) elif key == "virial": if not hasattr(self, "test_virial") or self.test_virial: - assert paddle.allclose(ret0[key], ret1[key], rtol=prec, atol=prec) + np.testing.assert_allclose( + ret0[key], ret1[key], rtol=prec, atol=prec + ) else: raise RuntimeError(f"Unexpected test key {key}") diff --git a/source/tests/pd/model/test_permutation_denoise.py b/source/tests/pd/model/test_permutation_denoise.py index 435f4c0d46..f147e360f7 100644 --- a/source/tests/pd/model/test_permutation_denoise.py +++ b/source/tests/pd/model/test_permutation_denoise.py @@ -2,6 +2,7 @@ import copy import unittest +import numpy as np import paddle from deepmd.pd.infer.deep_eval import ( @@ -61,11 +62,17 @@ def test( ) ret1 = {"updated_coord": updated_c1.squeeze(0), "logits": logits1.squeeze(0)} prec = 1e-10 - assert paddle.allclose( - ret0["updated_coord"][idx_perm], ret1["updated_coord"], rtol=prec, atol=prec + np.testing.assert_allclose( + ret0["updated_coord"][idx_perm].numpy(), + ret1["updated_coord"].numpy(), + rtol=prec, + atol=prec, ) - assert paddle.allclose( - ret0["logits"][idx_perm], ret1["logits"], rtol=prec, atol=prec + np.testing.assert_allclose( + ret0["logits"][idx_perm].numpy(), + ret1["logits"].numpy(), + rtol=prec, + atol=prec, ) diff --git a/source/tests/pd/model/test_region.py b/source/tests/pd/model/test_region.py index b3a89a39f8..7878e73cab 100644 --- a/source/tests/pd/model/test_region.py +++ b/source/tests/pd/model/test_region.py @@ -38,8 +38,11 @@ def test_inter_to_phys(self): for ii in range(4): for jj in range(5): expected_phys = paddle.matmul(inter[ii, jj], self.cell[ii, jj]) - assert paddle.allclose( - phys[ii, jj], expected_phys, rtol=self.prec, atol=self.prec + np.testing.assert_allclose( + phys[ii, jj].numpy(), + expected_phys.numpy(), + rtol=self.prec, + atol=self.prec, ) def test_to_face_dist(self): @@ -57,8 +60,11 @@ def test_to_face_dist(self): expected = paddle.to_tensor([dx, dy, dz], dtype=dists.dtype).to(device="cpu") for ii in range(4): for jj in range(5): - assert paddle.allclose( - dists[ii][jj], expected, rtol=self.prec, atol=self.prec + np.testing.assert_allclose( + dists[ii][jj].numpy(), + expected.numpy(), + rtol=self.prec, + atol=self.prec, ) @@ -75,14 +81,18 @@ def test_inter_to_phys(self): reg = Region3D(self.cell) phys = reg.inter2phys(inter) expected_phys = paddle.matmul(inter, self.cell) - assert paddle.allclose(phys, expected_phys, rtol=self.prec, atol=self.prec) + np.testing.assert_allclose( + phys.numpy(), expected_phys.numpy(), rtol=self.prec, atol=self.prec + ) def test_inter_to_inter(self): generator = paddle.seed(GLOBAL_SEED) inter = paddle.rand([3, 3], dtype=dtype).to(device=env.DEVICE) reg = Region3D(self.cell) new_inter = reg.phys2inter(reg.inter2phys(inter)) - assert paddle.allclose(inter, new_inter, rtol=self.prec, atol=self.prec) + np.testing.assert_allclose( + inter.numpy(), new_inter.numpy(), rtol=self.prec, atol=self.prec + ) def test_to_face_dist(self): pass diff --git a/source/tests/pd/model/test_rot.py b/source/tests/pd/model/test_rot.py index 2e2094b750..fe807c9b2d 100644 --- a/source/tests/pd/model/test_rot.py +++ b/source/tests/pd/model/test_rot.py @@ -2,6 +2,7 @@ import copy import unittest +import numpy as np import paddle from deepmd.pd.model.model import ( @@ -78,18 +79,23 @@ def test( ret1 = {key: result_1[key].squeeze(0) for key in test_keys} for key in test_keys: if key in ["energy"]: - assert paddle.allclose(ret0[key], ret1[key], rtol=prec, atol=prec) + np.testing.assert_allclose( + ret0[key].numpy(), ret1[key].numpy(), rtol=prec, atol=prec + ) elif key in ["force", "force_mag"]: - assert paddle.allclose( - paddle.matmul(ret0[key], rmat), ret1[key], rtol=prec, atol=prec + np.testing.assert_allclose( + paddle.matmul(ret0[key], rmat).numpy(), + ret1[key].numpy(), + rtol=prec, + atol=prec, ) elif key == "virial": if not hasattr(self, "test_virial") or self.test_virial: - assert paddle.allclose( + np.testing.assert_allclose( paddle.matmul( rmat.T, paddle.matmul(ret0[key].reshape([3, 3]), rmat) - ), - ret1[key].reshape([3, 3]), + ).numpy(), + ret1[key].reshape([3, 3]).numpy(), rtol=prec, atol=prec, ) @@ -126,18 +132,23 @@ def test( ret1 = {key: result_1[key].squeeze(0) for key in test_keys} for key in test_keys: if key in ["energy"]: - assert paddle.allclose(ret0[key], ret1[key], rtol=prec, atol=prec) + np.testing.assert_allclose( + ret0[key].numpy(), ret1[key].numpy(), rtol=prec, atol=prec + ) elif key in ["force", "force_mag"]: - assert paddle.allclose( - paddle.matmul(ret0[key], rmat), ret1[key], rtol=prec, atol=prec + np.testing.assert_allclose( + paddle.matmul(ret0[key], rmat).numpy(), + ret1[key].numpy(), + rtol=prec, + atol=prec, ) elif key == "virial": if not hasattr(self, "test_virial") or self.test_virial: - assert paddle.allclose( + np.testing.assert_allclose( paddle.matmul( rmat.T, paddle.matmul(ret0[key].reshape([3, 3]), rmat) - ), - ret1[key].reshape([3, 3]), + ).numpy(), + ret1[key].reshape([3, 3]).numpy(), rtol=prec, atol=prec, ) diff --git a/source/tests/pd/model/test_rot_denoise.py b/source/tests/pd/model/test_rot_denoise.py index 562943f395..bd1c858339 100644 --- a/source/tests/pd/model/test_rot_denoise.py +++ b/source/tests/pd/model/test_rot_denoise.py @@ -2,6 +2,7 @@ import copy import unittest +import numpy as np import paddle from deepmd.pd.infer.deep_eval import ( @@ -62,13 +63,15 @@ def test( ) update_c1 = update_c1 - (coord_rot + shift).unsqueeze(0) ret1 = {"updated_coord": update_c1.squeeze(0), "logits": logits1.squeeze(0)} - assert paddle.allclose( - paddle.matmul(ret0["updated_coord"], rmat), - ret1["updated_coord"], + np.testing.assert_allclose( + paddle.matmul(ret0["updated_coord"], rmat).numpy(), + ret1["updated_coord"].numpy(), rtol=prec, atol=prec, ) - assert paddle.allclose(ret0["logits"], ret1["logits"], rtol=prec, atol=prec) + np.testing.assert_allclose( + ret0["logits"].numpy(), ret1["logits"].numpy(), rtol=prec, atol=prec + ) # rotate coord and cell paddle.seed(0) @@ -91,10 +94,12 @@ def test( denoise=True, ) ret1 = {"updated_coord": update_c1.squeeze(0), "logits": logits1.squeeze(0)} - assert paddle.allclose(ret0["logits"], ret1["logits"], rtol=prec, atol=prec) - assert paddle.allclose( - paddle.matmul(ret0["updated_coord"], rmat), - ret1["updated_coord"], + np.testing.assert_allclose( + ret0["logits"].numpy(), ret1["logits"].numpy(), rtol=prec, atol=prec + ) + np.testing.assert_allclose( + paddle.matmul(ret0["updated_coord"], rmat).numpy(), + ret1["updated_coord"].numpy(), rtol=prec, atol=prec, ) diff --git a/source/tests/pd/model/test_rotation.py b/source/tests/pd/model/test_rotation.py index d5cf118c84..94e3442631 100644 --- a/source/tests/pd/model/test_rotation.py +++ b/source/tests/pd/model/test_rotation.py @@ -93,18 +93,19 @@ def test_rotation(self): result1 = self.model(**get_data(self.origin_batch)) result2 = self.model(**get_data(self.rotated_batch)) rotation = paddle.to_tensor(self.rotation).to(env.DEVICE) - assert paddle.allclose(result1["energy"], result2["energy"]) + np.testing.assert_allclose(result1["energy"].numpy(), result2["energy"].numpy()) if "force" in result1: - assert paddle.allclose( - result2["force"][0], paddle.matmul(rotation, result1["force"][0].T).T + np.testing.assert_allclose( + result2["force"][0].numpy(), + paddle.matmul(rotation, result1["force"][0].T).T.numpy(), ) if "virial" in result1: - assert paddle.allclose( - result2["virial"][0].view([3, 3]), + np.testing.assert_allclose( + result2["virial"][0].view([3, 3]).numpy(), paddle.matmul( paddle.matmul(rotation, result1["virial"][0].view([3, 3]).T), rotation.T, - ), + ).numpy(), ) diff --git a/source/tests/pd/model/test_saveload_dpa1.py b/source/tests/pd/model/test_saveload_dpa1.py index c9fac19d3f..04ddd6cb86 100644 --- a/source/tests/pd/model/test_saveload_dpa1.py +++ b/source/tests/pd/model/test_saveload_dpa1.py @@ -132,7 +132,6 @@ def get_data(self): label_dict[item] = batch_data[item].to(env.DEVICE) return input_dict, label_dict - @unittest.skip("Wait for https://github.com/PaddlePaddle/Paddle/pull/69012") def test_saveload(self): result1 = self.get_model_result() result2 = self.get_model_result(read=True) diff --git a/source/tests/pd/model/test_smooth.py b/source/tests/pd/model/test_smooth.py index 59ce2bdce6..7ad7152b60 100644 --- a/source/tests/pd/model/test_smooth.py +++ b/source/tests/pd/model/test_smooth.py @@ -2,6 +2,7 @@ import copy import unittest +import numpy as np import paddle from deepmd.pd.infer.deep_eval import ( @@ -123,16 +124,24 @@ def test( def compare(ret0, ret1): for key in test_keys: if key in ["energy"]: - assert paddle.allclose(ret0[key], ret1[key], rtol=rprec, atol=aprec) + np.testing.assert_allclose( + ret0[key].numpy(), ret1[key].numpy(), rtol=rprec, atol=aprec + ) elif key in ["force", "force_mag"]: # plus 1. to avoid the divided-by-zero issue - assert paddle.allclose( - 1.0 + ret0[key], 1.0 + ret1[key], rtol=rprec, atol=aprec + np.testing.assert_allclose( + (1.0 + ret0[key]).numpy(), + (1.0 + ret1[key]).numpy(), + rtol=rprec, + atol=aprec, ) elif key == "virial": if not hasattr(self, "test_virial") or self.test_virial: - assert paddle.allclose( - 1.0 + ret0[key], 1.0 + ret1[key], rtol=rprec, atol=aprec + np.testing.assert_allclose( + (1.0 + ret0[key]).numpy(), + (1.0 + ret1[key]).numpy(), + rtol=rprec, + atol=aprec, ) else: raise RuntimeError(f"Unexpected test key {key}") diff --git a/source/tests/pd/model/test_smooth_denoise.py b/source/tests/pd/model/test_smooth_denoise.py index db9592b05a..1563981e96 100644 --- a/source/tests/pd/model/test_smooth_denoise.py +++ b/source/tests/pd/model/test_smooth_denoise.py @@ -2,6 +2,7 @@ import copy import unittest +import numpy as np import paddle from deepmd.pd.infer.deep_eval import ( @@ -87,11 +88,14 @@ def test( ret3 = {"updated_coord": update_c3.squeeze(0), "logits": logits3.squeeze(0)} def compare(ret0, ret1): - assert paddle.allclose( - ret0["updated_coord"], ret1["updated_coord"], rtol=rprec, atol=aprec + np.testing.assert_allclose( + ret0["updated_coord"].numpy(), + ret1["updated_coord"].numpy(), + rtol=rprec, + atol=aprec, ) - assert paddle.allclose( - ret0["logits"], ret1["logits"], rtol=rprec, atol=aprec + np.testing.assert_allclose( + ret0["logits"].numpy(), ret1["logits"].numpy(), rtol=rprec, atol=aprec ) compare(ret0, ret1) diff --git a/source/tests/pd/model/test_trans.py b/source/tests/pd/model/test_trans.py index 1d0abfd5c7..ac5fce2dc3 100644 --- a/source/tests/pd/model/test_trans.py +++ b/source/tests/pd/model/test_trans.py @@ -2,6 +2,7 @@ import copy import unittest +import numpy as np import paddle from deepmd.pd.model.model import ( @@ -75,10 +76,14 @@ def test( prec = 1e-7 for key in test_keys: if key in ["energy", "force", "force_mag"]: - assert paddle.allclose(ret0[key], ret1[key], rtol=prec, atol=prec) + np.testing.assert_allclose( + ret0[key].numpy(), ret1[key].numpy(), rtol=prec, atol=prec + ) elif key == "virial": if not hasattr(self, "test_virial") or self.test_virial: - assert paddle.allclose(ret0[key], ret1[key], rtol=prec, atol=prec) + np.testing.assert_allclose( + ret0[key].numpy(), ret1[key].numpy(), rtol=prec, atol=prec + ) else: raise RuntimeError(f"Unexpected test key {key}") diff --git a/source/tests/pd/model/test_trans_denoise.py b/source/tests/pd/model/test_trans_denoise.py index 17e910e8a6..600a96ad8e 100644 --- a/source/tests/pd/model/test_trans_denoise.py +++ b/source/tests/pd/model/test_trans_denoise.py @@ -2,6 +2,7 @@ import copy import unittest +import numpy as np import paddle from deepmd.pd.infer.deep_eval import ( @@ -55,10 +56,15 @@ def test( updated_c1 = updated_c1 - coord_s.unsqueeze(0) ret1 = {"updated_coord": updated_c1.squeeze(0), "logits": logits1.squeeze(0)} prec = 1e-10 - assert paddle.allclose( - ret0["updated_coord"], ret1["updated_coord"], rtol=prec, atol=prec + np.testing.assert_allclose( + ret0["updated_coord"].numpy(), + ret1["updated_coord"].numpy(), + rtol=prec, + atol=prec, + ) + np.testing.assert_allclose( + ret0["logits"].numpy(), ret1["logits"].numpy(), rtol=prec, atol=prec ) - assert paddle.allclose(ret0["logits"], ret1["logits"], rtol=prec, atol=prec) @unittest.skip("support of the denoise is temporally disabled") diff --git a/source/tests/pd/test_change_bias.py b/source/tests/pd/test_change_bias.py index f0b31454ba..2d87b739ff 100644 --- a/source/tests/pd/test_change_bias.py +++ b/source/tests/pd/test_change_bias.py @@ -101,7 +101,7 @@ def test_change_bias_with_data(self): _bias_adjust_mode="change-by-statistic", ) expected_bias = expected_model.get_out_bias() - assert paddle.allclose(updated_bias, expected_bias) + np.testing.assert_allclose(updated_bias.numpy(), expected_bias.numpy()) def test_change_bias_with_data_sys_file(self): tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".txt") @@ -122,7 +122,7 @@ def test_change_bias_with_data_sys_file(self): _bias_adjust_mode="change-by-statistic", ) expected_bias = expected_model.get_out_bias() - assert paddle.allclose(updated_bias, expected_bias) + np.testing.assert_allclose(updated_bias.numpy(), expected_bias.numpy()) def test_change_bias_with_user_defined(self): user_bias = [0.1, 3.2, -0.5] @@ -138,7 +138,7 @@ def test_change_bias_with_user_defined(self): expected_bias = to_paddle_tensor(np.array(user_bias)).reshape( updated_bias.shape ) - assert paddle.allclose(updated_bias, expected_bias) + np.testing.assert_allclose(updated_bias.numpy(), expected_bias.numpy()) def tearDown(self): for f in os.listdir("."): diff --git a/source/tests/pd/test_multitask.py b/source/tests/pd/test_multitask.py index bb58311a0a..4dce6114da 100644 --- a/source/tests/pd/test_multitask.py +++ b/source/tests/pd/test_multitask.py @@ -10,7 +10,7 @@ Path, ) -import paddle +import numpy as np from deepmd.pd.entrypoints.main import ( get_trainer, @@ -62,9 +62,9 @@ def test_multitask_train(self): if "model_2" in state_key: self.assertIn(state_key.replace("model_2", "model_1"), multi_state_dict) if "model_1.descriptor" in state_key: - assert paddle.allclose( - multi_state_dict[state_key], - multi_state_dict[state_key.replace("model_1", "model_2")], + np.testing.assert_allclose( + multi_state_dict[state_key].numpy(), + multi_state_dict[state_key.replace("model_1", "model_2")].numpy(), ) # test multitask fine-tuning @@ -145,33 +145,29 @@ def test_multitask_train(self): multi_state_dict_finetuned = trainer_finetune.wrapper.model.state_dict() for state_key in multi_state_dict_finetuned: if "model_1" in state_key: - assert paddle.allclose( - multi_state_dict[state_key].astype("float32"), - multi_state_dict_finetuned[state_key].astype("float32"), - ).item() + np.testing.assert_allclose( + multi_state_dict[state_key].numpy(), + multi_state_dict_finetuned[state_key].numpy(), + ) elif "model_2" in state_key and "out_bias" not in state_key: - assert paddle.allclose( - multi_state_dict[state_key].astype("float32"), - multi_state_dict_finetuned[state_key].astype("float32"), - ).item() + np.testing.assert_allclose( + multi_state_dict[state_key].numpy(), + multi_state_dict_finetuned[state_key].numpy(), + ) elif "model_3" in state_key and "out_bias" not in state_key: - assert paddle.allclose( - multi_state_dict[state_key.replace("model_3", "model_2")].astype( - "float32" - ), - multi_state_dict_finetuned[state_key].astype("float32"), - ).item() + np.testing.assert_allclose( + multi_state_dict[state_key.replace("model_3", "model_2")].numpy(), + multi_state_dict_finetuned[state_key].numpy(), + ) elif ( "model_4" in state_key and "fitting_net" not in state_key and "out_bias" not in state_key ): - assert paddle.allclose( - multi_state_dict[state_key.replace("model_4", "model_2")].astype( - "float32" - ), - multi_state_dict_finetuned[state_key].astype("float32"), - ).item() + np.testing.assert_allclose( + multi_state_dict[state_key.replace("model_4", "model_2")].numpy(), + multi_state_dict_finetuned[state_key].numpy(), + ) # check running trainer_finetune.run() diff --git a/source/tests/pd/test_tabulate_fusion_se_a.py b/source/tests/pd/test_tabulate_fusion_se_a.py index eeddb48d30..11c7711105 100644 --- a/source/tests/pd/test_tabulate_fusion_se_a.py +++ b/source/tests/pd/test_tabulate_fusion_se_a.py @@ -1,6 +1,7 @@ # SPDX-License-Identifier: LGPL-3.0-or-later import unittest +import numpy as np import paddle from deepmd.pd.cxx_op import ( @@ -1452,9 +1453,9 @@ def test_forward(self): self.assertEqual(descriptor_tensor.shape, self.expected_descriptor_tensor.shape) # Check the values - assert paddle.allclose( - descriptor_tensor, - self.expected_descriptor_tensor, + np.testing.assert_allclose( + descriptor_tensor.numpy(), + self.expected_descriptor_tensor.numpy(), atol=self.prec, rtol=self.prec, ) @@ -1472,9 +1473,9 @@ def test_backward(self): descriptor_tensor = forward_result[0] # Check the forward - assert paddle.allclose( - descriptor_tensor, - self.expected_descriptor_tensor, + np.testing.assert_allclose( + descriptor_tensor.numpy(), + self.expected_descriptor_tensor.numpy(), atol=self.prec, rtol=self.prec, ) @@ -1492,16 +1493,16 @@ def test_backward(self): self.assertEqual(self.em_tensor.grad.shape, self.expected_dy_dem.shape) # Check the values of the gradients - assert paddle.allclose( - self.em_x_tensor.grad, - self.expected_dy_dem_x, + np.testing.assert_allclose( + self.em_x_tensor.grad.numpy(), + self.expected_dy_dem_x.numpy(), atol=self.prec, rtol=self.prec, ) - assert paddle.allclose( - self.em_tensor.grad, - self.expected_dy_dem, + np.testing.assert_allclose( + self.em_tensor.grad.numpy(), + self.expected_dy_dem.numpy(), atol=self.prec, rtol=self.prec, ) diff --git a/source/tests/pd/test_tabulate_fusion_se_atten.py b/source/tests/pd/test_tabulate_fusion_se_atten.py index 1608f8f8b9..a7322862c8 100644 --- a/source/tests/pd/test_tabulate_fusion_se_atten.py +++ b/source/tests/pd/test_tabulate_fusion_se_atten.py @@ -1,6 +1,7 @@ # SPDX-License-Identifier: LGPL-3.0-or-later import unittest +import numpy as np import paddle from deepmd.pd.cxx_op import ( @@ -1592,9 +1593,9 @@ def test_forward(self): self.assertEqual(descriptor_tensor.shape, self.expected_descriptor_tensor.shape) # Check the values - assert paddle.allclose( - descriptor_tensor, - self.expected_descriptor_tensor, + np.testing.assert_allclose( + descriptor_tensor.numpy(), + self.expected_descriptor_tensor.numpy(), atol=self.prec, rtol=self.prec, ) @@ -1614,9 +1615,9 @@ def test_backward(self): descriptor_tensor = forward_result[0] # Check the forward - assert paddle.allclose( - descriptor_tensor, - self.expected_descriptor_tensor, + np.testing.assert_allclose( + descriptor_tensor.numpy(), + self.expected_descriptor_tensor.numpy(), atol=self.prec, rtol=self.prec, ) @@ -1634,15 +1635,18 @@ def test_backward(self): self.assertEqual(self.em_tensor.grad.shape, self.expected_dy_dem.shape) # Check the values of the gradients - assert paddle.allclose( - self.em_x_tensor.grad, - self.expected_dy_dem_x, + np.testing.assert_allclose( + self.em_x_tensor.grad.numpy(), + self.expected_dy_dem_x.numpy(), atol=self.prec, rtol=self.prec, ) - assert paddle.allclose( - self.em_tensor.grad, self.expected_dy_dem, atol=self.prec, rtol=self.prec + np.testing.assert_allclose( + self.em_tensor.grad.numpy(), + self.expected_dy_dem.numpy(), + atol=self.prec, + rtol=self.prec, ) diff --git a/source/tests/pd/test_tabulate_fusion_se_r.py b/source/tests/pd/test_tabulate_fusion_se_r.py index e4c491ca9f..acb90e6e23 100644 --- a/source/tests/pd/test_tabulate_fusion_se_r.py +++ b/source/tests/pd/test_tabulate_fusion_se_r.py @@ -1,6 +1,7 @@ # SPDX-License-Identifier: LGPL-3.0-or-later import unittest +import numpy as np import paddle from deepmd.pd.cxx_op import ( @@ -1303,9 +1304,9 @@ def test_forward(self): self.assertEqual(descriptor_tensor.shape, self.expected_descriptor_tensor.shape) # Check the values - assert paddle.allclose( - descriptor_tensor, - self.expected_descriptor_tensor, + np.testing.assert_allclose( + descriptor_tensor.numpy(), + self.expected_descriptor_tensor.numpy(), atol=self.prec, rtol=self.prec, ) @@ -1322,9 +1323,9 @@ def test_backward(self): descriptor_tensor = forward_result[0] # Check the forward - assert paddle.allclose( - descriptor_tensor, - self.expected_descriptor_tensor, + np.testing.assert_allclose( + descriptor_tensor.numpy(), + self.expected_descriptor_tensor.numpy(), atol=self.prec, rtol=self.prec, ) @@ -1340,8 +1341,11 @@ def test_backward(self): self.assertEqual(self.em_tensor.grad.shape, self.expected_dy_dem.shape) # Check the values of the gradients - assert paddle.allclose( - self.em_tensor.grad, self.expected_dy_dem, atol=self.prec, rtol=self.prec + np.testing.assert_allclose( + self.em_tensor.grad.numpy(), + self.expected_dy_dem.numpy(), + atol=self.prec, + rtol=self.prec, ) diff --git a/source/tests/pd/test_tabulate_fusion_se_t.py b/source/tests/pd/test_tabulate_fusion_se_t.py index d46bcb492c..c9f182df45 100644 --- a/source/tests/pd/test_tabulate_fusion_se_t.py +++ b/source/tests/pd/test_tabulate_fusion_se_t.py @@ -1,6 +1,7 @@ # SPDX-License-Identifier: LGPL-3.0-or-later import unittest +import numpy as np import paddle from deepmd.pd.cxx_op import ( @@ -1709,9 +1710,9 @@ def test_forward(self): self.assertEqual(descriptor_tensor.shape, self.expected_descriptor_tensor.shape) # Check the values - assert paddle.allclose( - descriptor_tensor, - self.expected_descriptor_tensor, + np.testing.assert_allclose( + descriptor_tensor.numpy(), + self.expected_descriptor_tensor.numpy(), atol=self.prec, rtol=self.prec, ) @@ -1719,8 +1720,8 @@ def test_forward(self): def test_backward(self): # Call the forward function forward_result = paddle.ops.deepmd.tabulate_fusion_se_t( - self.table_tensor, - self.table_info_tensor, + self.table_tensor.numpy(), + self.table_info_tensor.numpy(), self.em_x_tensor, self.em_tensor, self.last_layer_size, @@ -1729,9 +1730,9 @@ def test_backward(self): descriptor_tensor = forward_result[0] # Check the forward - assert paddle.allclose( - descriptor_tensor, - self.expected_descriptor_tensor, + np.testing.assert_allclose( + descriptor_tensor.numpy(), + self.expected_descriptor_tensor.numpy(), atol=self.prec, rtol=self.prec, ) @@ -1749,16 +1750,16 @@ def test_backward(self): self.assertEqual(self.em_tensor.grad.shape, self.expected_dy_dem.shape) # Check the values of the gradients - assert paddle.allclose( - self.em_x_tensor.grad, - self.expected_dy_dem_x, + np.testing.assert_allclose( + self.em_x_tensor.grad.numpy(), + self.expected_dy_dem_x.numpy(), atol=self.prec, rtol=self.prec, ) - assert paddle.allclose( - self.em_tensor.grad, - self.expected_dy_dem, + np.testing.assert_allclose( + self.em_tensor.grad.numpy(), + self.expected_dy_dem.numpy(), atol=self.prec, rtol=self.prec, ) From dad72c81946b1ac55cb51e8b42a440b04f8ab0df Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 29 Oct 2024 20:41:48 +0800 Subject: [PATCH 86/93] use np.testing.assert_allclose instead of paddle.allclose --- source/tests/pd/model/test_saveload_dpa1.py | 7 +++---- source/tests/pd/model/test_saveload_se_e2_a.py | 7 +++---- 2 files changed, 6 insertions(+), 8 deletions(-) diff --git a/source/tests/pd/model/test_saveload_dpa1.py b/source/tests/pd/model/test_saveload_dpa1.py index 04ddd6cb86..54a82e479a 100644 --- a/source/tests/pd/model/test_saveload_dpa1.py +++ b/source/tests/pd/model/test_saveload_dpa1.py @@ -7,6 +7,7 @@ Path, ) +import numpy as np import paddle from paddle.io import ( DataLoader, @@ -135,10 +136,8 @@ def get_data(self): def test_saveload(self): result1 = self.get_model_result() result2 = self.get_model_result(read=True) - final_result = all( - paddle.allclose(result1[item], result2[item]) for item in result1 - ) - self.assertTrue(final_result) + for item in result1: + np.testing.assert_allclose(result1[item].numpy(), result2[item].numpy()) if __name__ == "__main__": diff --git a/source/tests/pd/model/test_saveload_se_e2_a.py b/source/tests/pd/model/test_saveload_se_e2_a.py index 35d8eb6d43..c1c2ba2cdd 100644 --- a/source/tests/pd/model/test_saveload_se_e2_a.py +++ b/source/tests/pd/model/test_saveload_se_e2_a.py @@ -7,6 +7,7 @@ Path, ) +import numpy as np import paddle from paddle.io import ( DataLoader, @@ -129,10 +130,8 @@ def get_data(self): def test_saveload(self): result1 = self.get_model_result() result2 = self.get_model_result(read=True) - final_result = all( - paddle.allclose(result1[item], result2[item]) for item in result1 - ) - self.assertTrue(final_result) + for item in result1: + np.testing.assert_allclose(result1[item].numpy(), result2[item].numpy()) if __name__ == "__main__": From cbc9c6515ce64565770353bc095301f741ab1a95 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 29 Oct 2024 20:42:19 +0800 Subject: [PATCH 87/93] reduce prec from 1e-10 to 1e-9 for test_rot --- source/tests/pd/model/test_rot.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/source/tests/pd/model/test_rot.py b/source/tests/pd/model/test_rot.py index fe807c9b2d..4e5f2b950f 100644 --- a/source/tests/pd/model/test_rot.py +++ b/source/tests/pd/model/test_rot.py @@ -36,7 +36,7 @@ def test( self, ): generator = paddle.seed(GLOBAL_SEED) - prec = 1e-10 + prec = 1e-9 natoms = 5 cell = 10.0 * paddle.eye(3, dtype=dtype).to(device=env.DEVICE) coord = 2 * paddle.rand([natoms, 3], dtype=dtype).to(device=env.DEVICE) From 7b2476fa84d80073c15ec2b6937f32bd7513a3cd Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Thu, 31 Oct 2024 19:08:59 +0800 Subject: [PATCH 88/93] support LKF optimizer --- README.md | 2 +- deepmd/pd/optimizer/KFWrapper.py | 8 +++++--- deepmd/pd/optimizer/LKF.py | 1 + deepmd/pd/train/training.py | 13 ++++++++----- source/tests/pd/test_LKF.py | 1 - 5 files changed, 15 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 626d93f1bb..100dcec4c2 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@ For more information, check the [documentation](https://deepmd.readthedocs.io/). ### Highlighted features -- **interfaced with multiple backends**, including TensorFlow, PyTorch, JAX and Paddle the most popular deep learning frameworks, making the training process highly automatic and efficient. +- **interfaced with multiple backends**, including TensorFlow, PyTorch, JAX and Paddle, the most popular deep learning frameworks, making the training process highly automatic and efficient. - **interfaced with high-performance classical MD and quantum (path-integral) MD packages**, including LAMMPS, i-PI, AMBER, CP2K, GROMACS, OpenMM, and ABUCUS. - **implements the Deep Potential series models**, which have been successfully applied to finite and extended systems, including organic molecules, metals, semiconductors, insulators, etc. - **implements MPI and GPU supports**, making it highly efficient for high-performance parallel and distributed computing. diff --git a/deepmd/pd/optimizer/KFWrapper.py b/deepmd/pd/optimizer/KFWrapper.py index 7fd8506300..32838fc389 100644 --- a/deepmd/pd/optimizer/KFWrapper.py +++ b/deepmd/pd/optimizer/KFWrapper.py @@ -58,7 +58,8 @@ def update_energy( mask = error < 0 error = error * update_prefactor - error[mask] = -1 * error[mask] + # error[mask] = -1 * error[mask] + error = _mask_update(error, mask, -error[mask]) error = error.mean() if self.is_distributed: @@ -66,7 +67,8 @@ def update_energy( error /= dist.get_world_size() Etot_predict = update_prefactor * Etot_predict - Etot_predict[mask] = -Etot_predict[mask] + # Etot_predict[mask] = -Etot_predict[mask] + Etot_predict = _mask_update(Etot_predict, mask, -Etot_predict[mask]) Etot_predict.sum().backward() error = error * math.sqrt(bs) @@ -91,7 +93,7 @@ def update_force( error_tmp = Force_label[:, index[i]] - force_predict[:, index[i]] error_tmp = update_prefactor * error_tmp mask = error_tmp < 0 - error_tmp = _mask_update(error_tmp, mask, -1 * error_tmp[mask]) + error_tmp = _mask_update(error_tmp, mask, -error_tmp[mask]) # error_tmp[mask] = -1 * error_tmp[mask] error = error_tmp.mean() / natoms_sum diff --git a/deepmd/pd/optimizer/LKF.py b/deepmd/pd/optimizer/LKF.py index 06e4e2e156..d77b4a9232 100644 --- a/deepmd/pd/optimizer/LKF.py +++ b/deepmd/pd/optimizer/LKF.py @@ -265,6 +265,7 @@ def __update(self, H, error, weights): def set_grad_prefactor(self, grad_prefactor): self.grad_prefactor = grad_prefactor + @paddle.no_grad() def step(self, error): params_packed_index = self._state.get("params_packed_index") diff --git a/deepmd/pd/train/training.py b/deepmd/pd/train/training.py index 17b9319f5b..f0d11a4a81 100644 --- a/deepmd/pd/train/training.py +++ b/deepmd/pd/train/training.py @@ -42,8 +42,9 @@ get_model, get_zbl_model, ) -from deepmd.pd.optimizer import ( # LKFOptimizer, +from deepmd.pd.optimizer import ( KFOptimizerWrapper, + LKFOptimizer, ) from deepmd.pd.train.wrapper import ( ModelWrapper, @@ -601,10 +602,12 @@ def warm_up_linear(step, warmup_steps): if optimizer_state_dict is not None and self.restart_training: self.optimizer.set_state_dict(optimizer_state_dict) elif self.opt_type == "LKF": - raise NotImplementedError("LKF is not supported yet in Paddle backend.") - # self.optimizer = LKFOptimizer( - # [{'params': self.wrapper.parameters()}], 0.98, 0.99870, self.opt_param["kf_blocksize"] - # ) + self.optimizer = LKFOptimizer( + [{"params": self.wrapper.parameters()}], + 0.98, + 0.99870, + self.opt_param["kf_blocksize"], + ) else: raise ValueError(f"Not supported optimizer type '{self.opt_type}'") diff --git a/source/tests/pd/test_LKF.py b/source/tests/pd/test_LKF.py index ae9508c149..81f69041da 100644 --- a/source/tests/pd/test_LKF.py +++ b/source/tests/pd/test_LKF.py @@ -11,7 +11,6 @@ ) -@unittest.skip("Paddle do not support LKF now") class TestLKF(unittest.TestCase): def test_lkf(self): with open(str(Path(__file__).parent / "water/lkf.json")) as fin: From 26047e959407eb4c1154022affdc8e5fea7bb8e5 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Thu, 31 Oct 2024 21:01:45 +0800 Subject: [PATCH 89/93] fix condition block dtype mismatch in jit.save and enable 2 unitest --- deepmd/pd/utils/nlist.py | 6 +++++- source/tests/pd/model/test_jit.py | 1 - source/tests/pd/test_multitask.py | 1 - 3 files changed, 5 insertions(+), 3 deletions(-) diff --git a/deepmd/pd/utils/nlist.py b/deepmd/pd/utils/nlist.py index 923d43cbfd..58d02b8aec 100644 --- a/deepmd/pd/utils/nlist.py +++ b/deepmd/pd/utils/nlist.py @@ -385,7 +385,11 @@ def build_multiple_neighbor_list( ).to(device=nlist.place) # nb x nloc x nsel nlist = paddle.concat([nlist, pad], axis=-1) - nsel = nsels[-1] + if paddle.is_tensor(nsel): + nsel = paddle.to_tensor(nsels[-1], dtype=nsel.dtype) + else: + nsel = nsels[-1] + # nb x nall x 3 coord1 = coord.reshape([nb, -1, 3]) nall = coord1.shape[1] diff --git a/source/tests/pd/model/test_jit.py b/source/tests/pd/model/test_jit.py index 20a3f67a71..f4f755d2eb 100644 --- a/source/tests/pd/model/test_jit.py +++ b/source/tests/pd/model/test_jit.py @@ -115,7 +115,6 @@ def tearDown(self): JITTest.tearDown(self) -@unittest.skip("var dtype int32/int64 confused in if block") class TestEnergyModelDPA2(unittest.TestCase, JITTest): def setUp(self): input_json = str(Path(__file__).parent / "water/se_atten.json") diff --git a/source/tests/pd/test_multitask.py b/source/tests/pd/test_multitask.py index 4dce6114da..8c7ceb5e90 100644 --- a/source/tests/pd/test_multitask.py +++ b/source/tests/pd/test_multitask.py @@ -183,7 +183,6 @@ def tearDown(self): shutil.rmtree(f) -@unittest.skip("Paddle do not support MultiTaskSeA.") class TestMultiTaskSeA(unittest.TestCase, MultiTaskTrainTest): def setUp(self): multitask_se_e2_a = deepcopy(multitask_template) From d03702a690743cbfc73638c0c144c0d0e96663ad Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Fri, 1 Nov 2024 11:21:00 +0800 Subject: [PATCH 90/93] fix bugs and enable more pd unitests --- deepmd/pd/infer/deep_eval.py | 365 +++++++----------- deepmd/pd/utils/env.py | 3 +- source/tests/pd/model/test_forward_lower.py | 6 +- .../tests/pd/model/test_make_hessian_model.py | 2 +- .../pd/model/test_permutation_denoise.py | 6 +- source/tests/pd/model/test_rot_denoise.py | 6 +- source/tests/pd/model/test_smooth.py | 6 +- source/tests/pd/model/test_smooth_denoise.py | 6 +- source/tests/pd/model/test_trans_denoise.py | 6 +- source/tests/pd/model/test_unused_params.py | 6 +- 10 files changed, 158 insertions(+), 254 deletions(-) diff --git a/deepmd/pd/infer/deep_eval.py b/deepmd/pd/infer/deep_eval.py index a8347ac7c0..d939c6ef7f 100644 --- a/deepmd/pd/infer/deep_eval.py +++ b/deepmd/pd/infer/deep_eval.py @@ -10,6 +10,7 @@ import numpy as np import paddle +from deepmd.dpmodel.common import PRECISION_DICT as NP_PRECISION_DICT from deepmd.dpmodel.output_def import ( ModelOutputDef, OutputVariableCategory, @@ -32,12 +33,18 @@ from deepmd.infer.deep_pot import ( DeepPot, ) +from deepmd.infer.deep_property import ( + DeepProperty, +) from deepmd.infer.deep_wfc import ( DeepWFC, ) from deepmd.pd.model.model import ( get_model, ) +from deepmd.pd.model.network.network import ( + TypeEmbedNetConsistent, +) from deepmd.pd.train.wrapper import ( ModelWrapper, ) @@ -47,9 +54,11 @@ from deepmd.pd.utils.env import ( DEVICE, GLOBAL_PD_FLOAT_PRECISION, + RESERVED_PRECISON_DICT, enable_prim, ) from deepmd.pd.utils.utils import ( + to_numpy_array, to_paddle_tensor, ) @@ -58,7 +67,7 @@ class DeepEval(DeepEvalBackend): - """Paddle backend implementaion of DeepEval. + """Paddle backend implementation of DeepEval. Parameters ---------- @@ -85,7 +94,7 @@ def __init__( *args: Any, auto_batch_size: Union[bool, int, AutoBatchSize] = True, neighbor_list: Optional["ase.neighborlist.NewPrimitiveNeighborList"] = None, - head: Optional[str] = None, + head: Optional[Union[str, int]] = None, **kwargs: Any, ): enable_prim(True) @@ -96,9 +105,12 @@ def __init__( if "model" in state_dict: state_dict = state_dict["model"] self.input_param = state_dict["_extra_state"]["model_params"] + self.model_def_script = self.input_param self.multi_task = "model_dict" in self.input_param if self.multi_task: model_keys = list(self.input_param["model_dict"].keys()) + if isinstance(head, int): + head = model_keys[0] assert ( head is not None ), f"Head must be set for multitask model! Available heads are: {model_keys}" @@ -120,7 +132,6 @@ def __init__( else: # self.dp = paddle.jit.load(self.model_path.split(".json")[0]) raise ValueError(f"Unknown model file format: {self.model_path}!") - self.rcut = self.dp.model["Default"].get_rcut() self.type_map = self.dp.model["Default"].get_type_map() if isinstance(auto_batch_size, bool): @@ -158,6 +169,9 @@ def get_dim_aparam(self) -> int: """Get the number (dimension) of atomic parameters of this DP.""" return self.dp.model["Default"].get_dim_aparam() + def get_intensive(self) -> bool: + return self.dp.model["Default"].get_intensive() + @property def model_type(self) -> type["DeepEvalWrapper"]: """The the evaluator of the model type.""" @@ -174,6 +188,8 @@ def model_type(self) -> type["DeepEvalWrapper"]: return DeepGlobalPolar elif "wfc" in model_output_type: return DeepWFC + elif "property" in model_output_type: + return DeepProperty else: raise RuntimeError("Unknown model type") @@ -190,6 +206,10 @@ def get_numb_dos(self) -> int: """Get the number of DOS.""" return self.dp.model["Default"].get_numb_dos() + def get_task_dim(self) -> int: + """Get the output dimension.""" + return self.dp.model["Default"].get_task_dim() + def get_has_efield(self): """Check if the model has efield.""" return False @@ -365,6 +385,7 @@ def _eval_model( request_defs: list[OutputVariableDef], ): model = self.dp.to(DEVICE) + prec = NP_PRECISION_DICT[RESERVED_PRECISON_DICT[GLOBAL_PD_FLOAT_PRECISION]] nframes = coords.shape[0] if len(atom_types.shape) == 1: @@ -374,15 +395,21 @@ def _eval_model( natoms = len(atom_types[0]) coord_input = paddle.to_tensor( - coords.reshape([nframes, natoms, 3]), + coords.reshape([nframes, natoms, 3]).astype(prec), dtype=GLOBAL_PD_FLOAT_PRECISION, - ).to(DEVICE) - type_input = paddle.to_tensor(atom_types, dtype=paddle.int64).to(DEVICE) + place=DEVICE, + ) + type_input = paddle.to_tensor( + atom_types.astype(NP_PRECISION_DICT[RESERVED_PRECISON_DICT[paddle.int64]]), + dtype=paddle.int64, + place=DEVICE, + ) if cells is not None: box_input = paddle.to_tensor( cells.reshape([nframes, 3, 3]), dtype=GLOBAL_PD_FLOAT_PRECISION, - ).to(DEVICE) + place=DEVICE, + ) else: box_input = None if fparam is not None: @@ -421,7 +448,7 @@ def _eval_model( else: shape = self._get_output_shape(odef, nframes, natoms) results.append( - np.full(np.abs(shape), np.nan) # pylint: disable=no-explicit-dtype + np.full(np.abs(shape), np.nan, dtype=prec) ) # this is kinda hacky return tuple(results) @@ -447,17 +474,20 @@ def _eval_model_spin( coord_input = paddle.to_tensor( coords.reshape([nframes, natoms, 3]), dtype=GLOBAL_PD_FLOAT_PRECISION, - ).to(DEVICE) - type_input = paddle.to_tensor(atom_types, dtype=paddle.int64).to(DEVICE) + place=DEVICE, + ) + type_input = paddle.to_tensor(atom_types, dtype=paddle.int64, place=DEVICE) spin_input = paddle.to_tensor( spins.reshape([nframes, natoms, 3]), dtype=GLOBAL_PD_FLOAT_PRECISION, - ).to(DEVICE) + place=DEVICE, + ) if cells is not None: box_input = paddle.to_tensor( cells.reshape([nframes, 3, 3]), dtype=GLOBAL_PD_FLOAT_PRECISION, - ).to(DEVICE) + place=DEVICE, + ) else: box_input = None if fparam is not None: @@ -498,7 +528,13 @@ def _eval_model_spin( else: shape = self._get_output_shape(odef, nframes, natoms) results.append( - np.full(np.abs(shape), np.nan) # pylint: disable=no-explicit-dtype + np.full( + np.abs(shape), + np.nan, + dtype=NP_PRECISION_DICT[ + RESERVED_PRECISON_DICT[GLOBAL_PD_FLOAT_PRECISION] + ], + ) ) # this is kinda hacky return tuple(results) @@ -523,222 +559,91 @@ def _get_output_shape(self, odef, nframes, natoms): else: raise RuntimeError("unknown category") + def eval_typeebd(self) -> np.ndarray: + """Evaluate output of type embedding network by using this model. -# For tests only -def eval_model( - model, - coords: Union[np.ndarray, paddle.Tensor], - cells: Optional[Union[np.ndarray, paddle.Tensor]], - atom_types: Union[np.ndarray, paddle.to_tensor, list[int]], - spins: Optional[Union[np.ndarray, paddle.Tensor]] = None, - atomic: bool = False, - infer_batch_size: int = 2, - denoise: bool = False, -): - model = model.to(DEVICE) - energy_out = [] - atomic_energy_out = [] - force_out = [] - force_mag_out = [] - virial_out = [] - atomic_virial_out = [] - updated_coord_out = [] - logits_out = [] - err_msg = ( - f"All inputs should be the same format, " - f"but found {type(coords)}, {type(cells)}, {type(atom_types)} instead! " - ) - return_tensor = True - if isinstance(coords, paddle.Tensor): - if cells is not None: - assert isinstance(cells, paddle.Tensor), err_msg - if spins is not None: - assert isinstance(spins, paddle.Tensor), err_msg - assert isinstance(atom_types, paddle.Tensor) or isinstance(atom_types, list) - atom_types = paddle.to_tensor(atom_types, dtype=paddle.int64).to(DEVICE) - elif isinstance(coords, np.ndarray): - if cells is not None: - assert isinstance(cells, np.ndarray), err_msg - if spins is not None: - assert isinstance(spins, np.ndarray), err_msg - assert isinstance(atom_types, np.ndarray) or isinstance(atom_types, list) - atom_types = np.array(atom_types, dtype=np.int32) - return_tensor = False - - nframes = coords.shape[0] - if len(atom_types.shape) == 1: - natoms = len(atom_types) - if isinstance(atom_types, paddle.Tensor): - atom_types = paddle.tile(atom_types.unsqueeze(0), [nframes, 1]).reshape( - [nframes, -1] - ) - else: - atom_types = np.tile(atom_types, nframes).reshape([nframes, -1]) - else: - natoms = len(atom_types[0]) - - coord_input = paddle.to_tensor( - coords.reshape([-1, natoms, 3]), dtype=GLOBAL_PD_FLOAT_PRECISION - ).to(DEVICE) - spin_input = None - if spins is not None: - spin_input = paddle.to_tensor( - spins.reshape([-1, natoms, 3]), - dtype=GLOBAL_PD_FLOAT_PRECISION, - ).to(DEVICE) - has_spin = getattr(model, "has_spin", False) - if callable(has_spin): - has_spin = has_spin() - type_input = paddle.to_tensor(atom_types, dtype=paddle.int64).to(DEVICE) - box_input = None - if cells is None: - pbc = False - else: - pbc = True - box_input = paddle.to_tensor( - cells.reshape([-1, 3, 3]), dtype=GLOBAL_PD_FLOAT_PRECISION - ).to(DEVICE) - num_iter = int((nframes + infer_batch_size - 1) / infer_batch_size) - - for ii in range(num_iter): - batch_coord = coord_input[ii * infer_batch_size : (ii + 1) * infer_batch_size] - batch_atype = type_input[ii * infer_batch_size : (ii + 1) * infer_batch_size] - batch_box = None - batch_spin = None - if spin_input is not None: - batch_spin = spin_input[ii * infer_batch_size : (ii + 1) * infer_batch_size] - if pbc: - batch_box = box_input[ii * infer_batch_size : (ii + 1) * infer_batch_size] - input_dict = { - "coord": batch_coord, - "atype": batch_atype, - "box": batch_box, - "do_atomic_virial": atomic, - } - if has_spin: - input_dict["spin"] = batch_spin - batch_output = model(**input_dict) - if isinstance(batch_output, tuple): - batch_output = batch_output[0] - if not return_tensor: - if "energy" in batch_output: - energy_out.append(batch_output["energy"].numpy()) - if "atom_energy" in batch_output: - atomic_energy_out.append(batch_output["atom_energy"].numpy()) - if "force" in batch_output: - force_out.append(batch_output["force"].numpy()) - if "force_mag" in batch_output: - force_mag_out.append(batch_output["force_mag"].numpy()) - if "virial" in batch_output: - virial_out.append(batch_output["virial"].numpy()) - if "atom_virial" in batch_output: - atomic_virial_out.append(batch_output["atom_virial"].numpy()) - if "updated_coord" in batch_output: - updated_coord_out.append(batch_output["updated_coord"].numpy()) - if "logits" in batch_output: - logits_out.append(batch_output["logits"].numpy()) - else: - if "energy" in batch_output: - energy_out.append(batch_output["energy"]) - if "atom_energy" in batch_output: - atomic_energy_out.append(batch_output["atom_energy"]) - if "force" in batch_output: - force_out.append(batch_output["force"]) - if "force_mag" in batch_output: - force_mag_out.append(batch_output["force_mag"]) - if "virial" in batch_output: - virial_out.append(batch_output["virial"]) - if "atom_virial" in batch_output: - atomic_virial_out.append(batch_output["atom_virial"]) - if "updated_coord" in batch_output: - updated_coord_out.append(batch_output["updated_coord"]) - if "logits" in batch_output: - logits_out.append(batch_output["logits"]) - if not return_tensor: - energy_out = ( - np.concatenate(energy_out) if energy_out else np.zeros([nframes, 1]) # pylint: disable=no-explicit-dtype - ) - atomic_energy_out = ( - np.concatenate(atomic_energy_out) - if atomic_energy_out - else np.zeros([nframes, natoms, 1]) # pylint: disable=no-explicit-dtype - ) - force_out = ( - np.concatenate(force_out) if force_out else np.zeros([nframes, natoms, 3]) # pylint: disable=no-explicit-dtype - ) - force_mag_out = ( - np.concatenate(force_mag_out) - if force_mag_out - else np.zeros([nframes, natoms, 3]) # pylint: disable=no-explicit-dtype - ) - virial_out = ( - np.concatenate(virial_out) if virial_out else np.zeros([nframes, 3, 3]) # pylint: disable=no-explicit-dtype - ) - atomic_virial_out = ( - np.concatenate(atomic_virial_out) - if atomic_virial_out - else np.zeros([nframes, natoms, 3, 3]) # pylint: disable=no-explicit-dtype - ) - updated_coord_out = ( - np.concatenate(updated_coord_out) if updated_coord_out else None - ) - logits_out = np.concatenate(logits_out) if logits_out else None - else: - energy_out = ( - paddle.concat(energy_out) - if energy_out - else paddle.zeros([nframes, 1], dtype=GLOBAL_PD_FLOAT_PRECISION).to(DEVICE) - ) - atomic_energy_out = ( - paddle.concat(atomic_energy_out) - if atomic_energy_out - else paddle.zeros([nframes, natoms, 1], dtype=GLOBAL_PD_FLOAT_PRECISION).to( - DEVICE - ) - ) - force_out = ( - paddle.concat(force_out) - if force_out - else paddle.zeros([nframes, natoms, 3], dtype=GLOBAL_PD_FLOAT_PRECISION).to( - DEVICE - ) - ) - force_mag_out = ( - paddle.concat(force_mag_out) - if force_mag_out - else paddle.zeros([nframes, natoms, 3], dtype=GLOBAL_PD_FLOAT_PRECISION).to( - DEVICE - ) - ) - virial_out = ( - paddle.concat(virial_out) - if virial_out - else paddle.zeros([nframes, 3, 3], dtype=GLOBAL_PD_FLOAT_PRECISION).to( - DEVICE - ) - ) - atomic_virial_out = ( - paddle.concat(atomic_virial_out) - if atomic_virial_out - else paddle.zeros( - [nframes, natoms, 3, 3], dtype=GLOBAL_PD_FLOAT_PRECISION - ).to(DEVICE) - ) - updated_coord_out = ( - paddle.concat(updated_coord_out) if updated_coord_out else None + Returns + ------- + np.ndarray + The output of type embedding network. The shape is [ntypes, o_size] or [ntypes + 1, o_size], + where ntypes is the number of types, and o_size is the number of nodes + in the output layer. If there are multiple type embedding networks, + these outputs will be concatenated along the second axis. + + Raises + ------ + KeyError + If the model does not enable type embedding. + + See Also + -------- + deepmd.pd.model.network.network.TypeEmbedNetConsistent : + The type embedding network. + """ + out = [] + for mm in self.dp.model["Default"].modules(): + if mm.original_name == TypeEmbedNetConsistent.__name__: + out.append(mm(DEVICE)) + if not out: + raise KeyError("The model has no type embedding networks.") + typeebd = paddle.concat(out, axis=1) + return to_numpy_array(typeebd) + + def get_model_def_script(self) -> str: + """Get model definition script.""" + return self.model_def_script + + def eval_descriptor( + self, + coords: np.ndarray, + cells: Optional[np.ndarray], + atom_types: np.ndarray, + fparam: Optional[np.ndarray] = None, + aparam: Optional[np.ndarray] = None, + **kwargs: Any, + ) -> np.ndarray: + """Evaluate descriptors by using this DP. + + Parameters + ---------- + coords + The coordinates of atoms. + The array should be of size nframes x natoms x 3 + cells + The cell of the region. + If None then non-PBC is assumed, otherwise using PBC. + The array should be of size nframes x 9 + atom_types + The atom types + The list should contain natoms ints + fparam + The frame parameter. + The array can be of size : + - nframes x dim_fparam. + - dim_fparam. Then all frames are assumed to be provided with the same fparam. + aparam + The atomic parameter + The array can be of size : + - nframes x natoms x dim_aparam. + - natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. + - dim_aparam. Then all frames and atoms are provided with the same aparam. + + Returns + ------- + descriptor + Descriptors. + """ + model = self.dp.model["Default"] + model.set_eval_descriptor_hook(True) + self.eval( + coords, + cells, + atom_types, + atomic=False, + fparam=fparam, + aparam=aparam, + **kwargs, ) - logits_out = paddle.concat(logits_out) if logits_out else None - if denoise: - return updated_coord_out, logits_out - else: - results_dict = { - "energy": energy_out, - "force": force_out, - "virial": virial_out, - } - if has_spin: - results_dict["force_mag"] = force_mag_out - if atomic: - results_dict["atom_energy"] = atomic_energy_out - results_dict["atom_virial"] = atomic_virial_out - return results_dict + descriptor = model.eval_descriptor() + model.set_eval_descriptor_hook(False) + return to_numpy_array(descriptor) diff --git a/deepmd/pd/utils/env.py b/deepmd/pd/utils/env.py index 37b6259b61..e9593d4c50 100644 --- a/deepmd/pd/utils/env.py +++ b/deepmd/pd/utils/env.py @@ -15,8 +15,6 @@ set_default_nthreads, ) -log = logging.getLogger(__name__) - SAMPLER_RECORD = os.environ.get("SAMPLER_RECORD", False) try: # only linux @@ -87,6 +85,7 @@ def enable_prim(enable: bool = True): core.set_prim_eager_enabled(True) core._set_prim_all_enabled(True) + log = logging.getLogger(__name__) log.info("Enable prim in eager and static mode.") diff --git a/source/tests/pd/model/test_forward_lower.py b/source/tests/pd/model/test_forward_lower.py index efd0b638d8..dc348b5a37 100644 --- a/source/tests/pd/model/test_forward_lower.py +++ b/source/tests/pd/model/test_forward_lower.py @@ -5,9 +5,6 @@ import numpy as np import paddle -from deepmd.pd.infer.deep_eval import ( - eval_model, -) from deepmd.pd.model.model import ( get_model, ) @@ -22,6 +19,9 @@ from ...seed import ( GLOBAL_SEED, ) +from ..common import ( + eval_model, +) from .test_permutation import ( # model_dpau, model_dpa1, model_dpa2, diff --git a/source/tests/pd/model/test_make_hessian_model.py b/source/tests/pd/model/test_make_hessian_model.py index 30171342aa..79a7c4f163 100644 --- a/source/tests/pd/model/test_make_hessian_model.py +++ b/source/tests/pd/model/test_make_hessian_model.py @@ -137,7 +137,7 @@ def ff(xx): ) -@unittest.skip("TODO") +@unittest.skip("Skip temporarily") class TestDPModel(unittest.TestCase, HessianTest): def setUp(self): paddle.seed(2) diff --git a/source/tests/pd/model/test_permutation_denoise.py b/source/tests/pd/model/test_permutation_denoise.py index f147e360f7..0f3dc9e871 100644 --- a/source/tests/pd/model/test_permutation_denoise.py +++ b/source/tests/pd/model/test_permutation_denoise.py @@ -5,9 +5,6 @@ import numpy as np import paddle -from deepmd.pd.infer.deep_eval import ( - eval_model, -) from deepmd.pd.model.model import ( get_model, ) @@ -18,6 +15,9 @@ from ...seed import ( GLOBAL_SEED, ) +from ..common import ( + eval_model, +) from .test_permutation import ( # model_dpau, model_dpa1, model_dpa2, diff --git a/source/tests/pd/model/test_rot_denoise.py b/source/tests/pd/model/test_rot_denoise.py index bd1c858339..9526084efe 100644 --- a/source/tests/pd/model/test_rot_denoise.py +++ b/source/tests/pd/model/test_rot_denoise.py @@ -5,9 +5,6 @@ import numpy as np import paddle -from deepmd.pd.infer.deep_eval import ( - eval_model, -) from deepmd.pd.model.model import ( get_model, ) @@ -18,6 +15,9 @@ from ...seed import ( GLOBAL_SEED, ) +from ..common import ( + eval_model, +) from .test_permutation_denoise import ( model_dpa1, model_dpa2, diff --git a/source/tests/pd/model/test_smooth.py b/source/tests/pd/model/test_smooth.py index 7ad7152b60..796b15faf4 100644 --- a/source/tests/pd/model/test_smooth.py +++ b/source/tests/pd/model/test_smooth.py @@ -5,9 +5,6 @@ import numpy as np import paddle -from deepmd.pd.infer.deep_eval import ( - eval_model, -) from deepmd.pd.model.model import ( get_model, ) @@ -18,6 +15,9 @@ from ...seed import ( GLOBAL_SEED, ) +from ..common import ( + eval_model, +) from .test_permutation import ( # model_dpau, model_dos, model_dpa1, diff --git a/source/tests/pd/model/test_smooth_denoise.py b/source/tests/pd/model/test_smooth_denoise.py index 1563981e96..d94f15863d 100644 --- a/source/tests/pd/model/test_smooth_denoise.py +++ b/source/tests/pd/model/test_smooth_denoise.py @@ -5,9 +5,6 @@ import numpy as np import paddle -from deepmd.pd.infer.deep_eval import ( - eval_model, -) from deepmd.pd.model.model import ( get_model, ) @@ -18,6 +15,9 @@ from ...seed import ( GLOBAL_SEED, ) +from ..common import ( + eval_model, +) from .test_permutation_denoise import ( model_dpa2, ) diff --git a/source/tests/pd/model/test_trans_denoise.py b/source/tests/pd/model/test_trans_denoise.py index 600a96ad8e..8317d4d2ae 100644 --- a/source/tests/pd/model/test_trans_denoise.py +++ b/source/tests/pd/model/test_trans_denoise.py @@ -5,9 +5,6 @@ import numpy as np import paddle -from deepmd.pd.infer.deep_eval import ( - eval_model, -) from deepmd.pd.model.model import ( get_model, ) @@ -18,6 +15,9 @@ from ...seed import ( GLOBAL_SEED, ) +from ..common import ( + eval_model, +) from .test_permutation_denoise import ( model_dpa1, model_dpa2, diff --git a/source/tests/pd/model/test_unused_params.py b/source/tests/pd/model/test_unused_params.py index e634ecb022..3424e9dafa 100644 --- a/source/tests/pd/model/test_unused_params.py +++ b/source/tests/pd/model/test_unused_params.py @@ -4,9 +4,6 @@ import paddle -from deepmd.pd.infer.deep_eval import ( - eval_model, -) from deepmd.pd.model.model import ( get_model, ) @@ -17,6 +14,9 @@ from ...seed import ( GLOBAL_SEED, ) +from ..common import ( + eval_model, +) from .test_permutation import ( model_dpa2, ) From c6119552439011b777e36fb2f2486cc6a64eef3f Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Fri, 1 Nov 2024 11:26:21 +0800 Subject: [PATCH 91/93] fix the last 2 files --- deepmd/pd/model/network/init.py | 42 +++++++++----------------------- deepmd/pd/model/task/property.py | 7 +++--- 2 files changed, 15 insertions(+), 34 deletions(-) diff --git a/deepmd/pd/model/network/init.py b/deepmd/pd/model/network/init.py index 7a83877f8f..dbdad56794 100644 --- a/deepmd/pd/model/network/init.py +++ b/deepmd/pd/model/network/init.py @@ -1,4 +1,16 @@ # SPDX-License-Identifier: LGPL-3.0-or-later + +# Copyright (c) 2024 The PyTorch Authors. All rights reserved. +# +# This file includes source code from PyTorch of version v2.3.0, which is released under the BSD-3-Clause license. +# For more information about PyTorch, visit https://pytorch.org/. + + +# These no_grad_* functions are necessary as wrappers around the parts of these +# functions that use `with paddle.no_grad()`. The JIT doesn't support context +# managers, so these need to be implemented as builtins. Using these wrappers +# lets us keep those builtins small and re-usable. + from __future__ import ( annotations, ) @@ -13,16 +25,7 @@ PaddleGenerator = paddle.base.libpaddle.Generator -# Copyright (c) 2024 The PyTorch Authors. All rights reserved. -# -# This file includes source code from PyTorch of version v2.3.0, which is released under the BSD-3-Clause license. -# For more information about PyTorch, visit https://pytorch.org/. - -# These no_grad_* functions are necessary as wrappers around the parts of these -# functions that use `with paddle.no_grad()`. The JIT doesn't support context -# managers, so these need to be implemented as builtins. Using these wrappers -# lets us keep those builtins small and re-usable. def _no_grad_uniform_(tensor: paddle.Tensor, a, b, generator=None): with paddle.no_grad(): return tensor.uniform_(a, b) @@ -167,8 +170,6 @@ def _calculate_fan_in_and_fan_out(tensor, reverse=False): receptive_field_size = 1 if tensor.ndim > 2: - # math.prod is not always available, accumulate the product manually - # we could use functools.reduce but that is not supported by TorchScript for s in tensor.shape[2:]: receptive_field_size *= s fan_in = num_input_fmaps * receptive_field_size @@ -227,10 +228,6 @@ def constant_(tensor: Tensor, val: float) -> Tensor: >>> w = paddle.empty(3, 5) >>> nn.init.constant_(w, 0.3) """ - # if paddle.overrides.has_torch_function_variadic(tensor): - # return paddle.overrides.handle_torch_function( - # constant_, (tensor,), tensor=tensor, val=val - # ) return _no_grad_fill_(tensor, val) @@ -255,10 +252,6 @@ def normal_( >>> w = paddle.empty(3, 5) >>> nn.init.normal_(w) """ - # if paddle.overrides.has_torch_function_variadic(tensor): - # return paddle.overrides.handle_torch_function( - # normal_, (tensor,), tensor=tensor, mean=mean, std=std - # ) return _no_grad_normal_(tensor, mean, std, generator) @@ -333,17 +326,6 @@ def kaiming_uniform_( >>> w = paddle.empty(3, 5) >>> nn.init.kaiming_uniform_(w, mode="fan_in", nonlinearity="relu") """ - # if paddle.overrides.has_torch_function_variadic(tensor): - # return paddle.overrides.handle_torch_function( - # kaiming_uniform_, - # (tensor,), - # tensor=tensor, - # a=a, - # mode=mode, - # nonlinearity=nonlinearity, - # generator=generator, - # ) - if 0 in tensor.shape: warnings.warn("Initializing zero-element tensors is a no-op") return tensor diff --git a/deepmd/pd/model/task/property.py b/deepmd/pd/model/task/property.py index 600b5c265f..0d95d24a47 100644 --- a/deepmd/pd/model/task/property.py +++ b/deepmd/pd/model/task/property.py @@ -5,7 +5,7 @@ Optional, ) -import torch +import paddle from deepmd.dpmodel import ( FittingOutputDef, @@ -47,7 +47,7 @@ class PropertyFittingNet(InvarFitting): The dimension of outputs of fitting net. neuron : list[int] Number of neurons in each hidden layers of the fitting net. - bias_atom_p : torch.Tensor, optional + bias_atom_p : paddle.Tensor, optional Average property per atom for each element. intensive : bool, optional Whether the fitting property is intensive. @@ -78,7 +78,7 @@ def __init__( dim_descrpt: int, task_dim: int = 1, neuron: list[int] = [128, 128, 128], - bias_atom_p: Optional[torch.Tensor] = None, + bias_atom_p: Optional[paddle.Tensor] = None, intensive: bool = False, bias_method: str = "normal", resnet_dt: bool = True, @@ -147,5 +147,4 @@ def serialize(self) -> dict: return dd - # make jit happy with torch 2.0.0 exclude_types: list[int] From 3b27c4958d4297dea27754ef3a1bba8580c67f0f Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Fri, 1 Nov 2024 17:00:22 +0800 Subject: [PATCH 92/93] rename aux to decomp --- deepmd/pd/loss/ener.py | 6 ++++-- .../model/atomic_model/pairtab_atomic_model.py | 8 ++++---- deepmd/pd/model/descriptor/descriptor.py | 6 +++--- deepmd/pd/model/descriptor/dpa2.py | 4 ++-- deepmd/pd/model/descriptor/env_mat.py | 6 +++--- deepmd/pd/model/descriptor/gaussian_lcc.py | 8 ++++---- deepmd/pd/model/descriptor/repformer_layer.py | 8 ++++---- deepmd/pd/model/descriptor/repformers.py | 6 +++--- deepmd/pd/model/descriptor/se_atten.py | 12 ++++++------ deepmd/pd/model/descriptor/se_t_tebd.py | 4 ++-- deepmd/pd/model/model/make_model.py | 8 ++++---- deepmd/pd/model/model/spin_model.py | 8 +++++--- deepmd/pd/model/model/transform_output.py | 6 +++--- deepmd/pd/utils/{aux.py => decomp.py} | 14 ++++++++++++-- deepmd/pd/utils/exclude_mask.py | 4 ++-- deepmd/pd/utils/nlist.py | 16 ++++++++-------- deepmd/pd/utils/preprocess.py | 10 +++++----- deepmd/pd/utils/region.py | 14 +++++++------- source/tests/pd/model/test_descriptor.py | 4 ++-- source/tests/pd/model/test_ener_spin_model.py | 14 +++++++------- source/tests/pd/model/test_forward_lower.py | 6 +++--- .../pd/model/test_polarizability_fitting.py | 5 ++++- source/tests/pd/test_init_frz_model.py | 5 ++++- 23 files changed, 101 insertions(+), 81 deletions(-) rename deepmd/pd/utils/{aux.py => decomp.py} (91%) diff --git a/deepmd/pd/loss/ener.py b/deepmd/pd/loss/ener.py index 036325205d..7c5d848b45 100644 --- a/deepmd/pd/loss/ener.py +++ b/deepmd/pd/loss/ener.py @@ -10,7 +10,7 @@ TaskLoss, ) from deepmd.pd.utils import ( - aux, + decomp, env, ) from deepmd.pd.utils.env import ( @@ -225,7 +225,9 @@ def forward(self, input_dict, model, label, natoms, learning_rate, mae=False): if self.relative_f is not None: force_label_3 = force_label.reshape([-1, 3]) # norm_f = force_label_3.norm(axis=1, keepdim=True) + self.relative_f - norm_f = aux.norm(force_label_3, axis=1, keepdim=True) + self.relative_f + norm_f = ( + decomp.norm(force_label_3, axis=1, keepdim=True) + self.relative_f + ) diff_f_3 = diff_f.reshape([-1, 3]) diff_f_3 = diff_f_3 / norm_f diff_f = diff_f_3.reshape([-1]) diff --git a/deepmd/pd/model/atomic_model/pairtab_atomic_model.py b/deepmd/pd/model/atomic_model/pairtab_atomic_model.py index ae6990cee4..6c6c498050 100644 --- a/deepmd/pd/model/atomic_model/pairtab_atomic_model.py +++ b/deepmd/pd/model/atomic_model/pairtab_atomic_model.py @@ -13,7 +13,7 @@ OutputVariableDef, ) from deepmd.pd.utils import ( - aux, + decomp, env, ) from deepmd.utils.pair_tab import ( @@ -382,11 +382,11 @@ def _get_pairwise_dist( coord_l = coords[:, :nloc].reshape([nframes, -1, 1, 3]) index = nlist.reshape([nframes, -1]).unsqueeze(-1).expand([-1, -1, 3]) # coord_r = paddle.take_along_axis(coords, axis=1, indices=index) - coord_r = aux.take_along_axis(coords, axis=1, indices=index) + coord_r = decomp.take_along_axis(coords, axis=1, indices=index) coord_r = coord_r.reshape([nframes, nloc, nnei, 3]) diff = coord_r - coord_l # pairwise_rr = paddle.linalg.norm(diff, axis=-1, keepdim=True).squeeze(-1) - pairwise_rr = aux.norm(diff, axis=-1, keepdim=True).squeeze(-1) + pairwise_rr = decomp.norm(diff, axis=-1, keepdim=True).squeeze(-1) return pairwise_rr @staticmethod @@ -440,7 +440,7 @@ def _extract_spline_coefficient( # final_coef = paddle.take_along_axis( # tab_data, axis=0, indices=tab_data_idx # ).reshape([nframes, nloc, nnei, 4]) - final_coef = aux.take_along_axis( + final_coef = decomp.take_along_axis( tab_data, axis=0, indices=tab_data_idx ).reshape([nframes, nloc, nnei, 4]) diff --git a/deepmd/pd/model/descriptor/descriptor.py b/deepmd/pd/model/descriptor/descriptor.py index 5d29a1cf35..b27facd0ae 100644 --- a/deepmd/pd/model/descriptor/descriptor.py +++ b/deepmd/pd/model/descriptor/descriptor.py @@ -187,9 +187,9 @@ def need_sorted_nlist_for_lower(self) -> bool: def make_default_type_embedding( ntypes, ): - aux = {} - aux["tebd_dim"] = 8 - return TypeEmbedNet(ntypes, aux["tebd_dim"]), aux + decomp = {} + decomp["tebd_dim"] = 8 + return TypeEmbedNet(ntypes, decomp["tebd_dim"]), decomp def extend_descrpt_stat(des, type_map, des_with_stat=None): diff --git a/deepmd/pd/model/descriptor/dpa2.py b/deepmd/pd/model/descriptor/dpa2.py index 8bbfc4b5c6..8fbffe2d90 100644 --- a/deepmd/pd/model/descriptor/dpa2.py +++ b/deepmd/pd/model/descriptor/dpa2.py @@ -25,7 +25,7 @@ TypeEmbedNetConsistent, ) from deepmd.pd.utils import ( - aux, + decomp, env, ) from deepmd.pd.utils.nlist import ( @@ -793,7 +793,7 @@ def forward( .unsqueeze(-1) .expand([-1, -1, g1.shape[-1]]) ) - g1_ext = aux.take_along_axis(g1, mapping_ext, 1) + g1_ext = decomp.take_along_axis(g1, mapping_ext, 1) g1 = g1_ext # repformer g1, g2, h2, rot_mat, sw = self.repformers( diff --git a/deepmd/pd/model/descriptor/env_mat.py b/deepmd/pd/model/descriptor/env_mat.py index abeb8a1c1f..3a9daec1e8 100644 --- a/deepmd/pd/model/descriptor/env_mat.py +++ b/deepmd/pd/model/descriptor/env_mat.py @@ -3,7 +3,7 @@ import paddle from deepmd.pd.utils import ( - aux, + decomp, ) from deepmd.pd.utils.preprocess import ( compute_smooth_weight, @@ -28,11 +28,11 @@ def _make_env_mat( coord_l = coord[:, :natoms].reshape([bsz, -1, 1, 3]) index = nlist.reshape([bsz, -1]).unsqueeze(-1).expand([-1, -1, 3]) # coord_r = paddle.take_along_axis(coord, axis=1, indices=index) - coord_r = aux.take_along_axis(coord, axis=1, indices=index) + coord_r = decomp.take_along_axis(coord, axis=1, indices=index) coord_r = coord_r.reshape([bsz, natoms, nnei, 3]) diff = coord_r - coord_l # length = paddle.linalg.norm(diff, axis=-1, keepdim=True) - length = aux.norm(diff, axis=-1, keepdim=True) + length = decomp.norm(diff, axis=-1, keepdim=True) # for index 0 nloc atom length = length + (~mask.unsqueeze(-1)).astype(length.dtype) t0 = 1 / (length + protection) diff --git a/deepmd/pd/model/descriptor/gaussian_lcc.py b/deepmd/pd/model/descriptor/gaussian_lcc.py index 8f58faa57f..038198dfac 100644 --- a/deepmd/pd/model/descriptor/gaussian_lcc.py +++ b/deepmd/pd/model/descriptor/gaussian_lcc.py @@ -15,7 +15,7 @@ TypeEmbedNet, ) from deepmd.pd.utils import ( - aux, + decomp, env, ) from deepmd.utils.path import ( @@ -244,7 +244,7 @@ def forward( # Atomic feature # [(nframes x nloc) x (1 + nnei2) x tebd_dim] - atom_feature = aux.take_along_axis( + atom_feature = decomp.take_along_axis( atype_tebd, axis=1, indices=nlist_loc2.reshape([nframes, -1]) @@ -256,7 +256,7 @@ def forward( if first_dim == nframes * nloc: atom_feature += seq_input elif first_dim == nframes: - atom_feature_seq = aux.take_along_axis( + atom_feature_seq = decomp.take_along_axis( seq_input, axis=1, indices=nlist_loc2.reshape([nframes, -1]) @@ -294,7 +294,7 @@ def forward( axis=-1, ) # [(nframes x nloc) x (1 + nnei2) x 3] - coord_selected = aux.take_along_axis( + coord_selected = decomp.take_along_axis( extended_coord.unsqueeze(1) .expand([-1, nloc, -1, -1]) .reshape([nframes * nloc, nall, 3]), diff --git a/deepmd/pd/model/descriptor/repformer_layer.py b/deepmd/pd/model/descriptor/repformer_layer.py index 45fc0e4d23..91413badbe 100644 --- a/deepmd/pd/model/descriptor/repformer_layer.py +++ b/deepmd/pd/model/descriptor/repformer_layer.py @@ -21,7 +21,7 @@ MLPLayer, ) from deepmd.pd.utils import ( - aux, + decomp, env, ) from deepmd.pd.utils.env import ( @@ -110,7 +110,7 @@ def _make_nei_g1( # index: nb x (nloc x nnei) x ng1 index = nlist.reshape([nb, nloc * nnei]).unsqueeze(-1).expand([-1, -1, ng1]) # gg1 : nb x (nloc x nnei) x ng1 - gg1 = aux.take_along_axis(g1_ext, axis=1, indices=index) + gg1 = decomp.take_along_axis(g1_ext, axis=1, indices=index) # gg1 : nb x nloc x nnei x ng1 gg1 = gg1.reshape([nb, nloc, nnei, ng1]) return gg1 @@ -200,7 +200,7 @@ def forward( # nb x nloc x (nh x 2) x nnei x nd g2qk = paddle.transpose(g2qk, (0, 1, 4, 2, 3)) # nb x nloc x nh x nnei x nd - g2q, g2k = paddle.split(g2qk, aux.sec(g2qk.shape[2], nh), axis=2) + g2q, g2k = paddle.split(g2qk, decomp.sec(g2qk.shape[2], nh), axis=2) # g2q = paddle.nn.functional.normalize(g2q, axis=-1) # g2k = paddle.nn.functional.normalize(g2k, axis=-1) # nb x nloc x nh x nnei x nnei @@ -1013,7 +1013,7 @@ def _cal_grrg(h2g2: paddle.Tensor, axis_neuron: int) -> paddle.Tensor: # nb x nloc x 3 x ng2 nb, nloc, _, ng2 = h2g2.shape # nb x nloc x 3 x axis - h2g2m = paddle.split(h2g2, aux.sec(h2g2.shape[-1], axis_neuron), axis=-1)[0] + h2g2m = paddle.split(h2g2, decomp.sec(h2g2.shape[-1], axis_neuron), axis=-1)[0] # nb x nloc x axis x ng2 g1_13 = paddle.matmul(paddle.transpose(h2g2m, [0, 1, 3, 2]), h2g2) / (3.0**1) # nb x nloc x (axisxng2) diff --git a/deepmd/pd/model/descriptor/repformers.py b/deepmd/pd/model/descriptor/repformers.py index 37979f3c54..fbbfcc1216 100644 --- a/deepmd/pd/model/descriptor/repformers.py +++ b/deepmd/pd/model/descriptor/repformers.py @@ -20,7 +20,7 @@ MLPLayer, ) from deepmd.pd.utils import ( - aux, + decomp, env, ) from deepmd.pd.utils.env_mat_stat import ( @@ -430,7 +430,7 @@ def forward( g2, h2 = paddle.split(dmatrix, [1, 3], axis=-1) else: # g2, h2 = paddle.linalg.norm(diff, axis=-1, keepdim=True), diff - g2, h2 = aux.norm(diff, axis=-1, keepdim=True), diff + g2, h2 = decomp.norm(diff, axis=-1, keepdim=True), diff g2 = g2 / self.rcut h2 = h2 / self.rcut # nb x nloc x nnei x ng2 @@ -452,7 +452,7 @@ def forward( # g1_ext: nb x nall x ng1 if comm_dict is None: assert mapping is not None - g1_ext = aux.take_along_axis(g1, axis=1, indices=mapping) + g1_ext = decomp.take_along_axis(g1, axis=1, indices=mapping) else: n_padding = nall - nloc g1 = paddle.nn.functional.pad( diff --git a/deepmd/pd/model/descriptor/se_atten.py b/deepmd/pd/model/descriptor/se_atten.py index 53627e0082..2eaeb7f7a7 100644 --- a/deepmd/pd/model/descriptor/se_atten.py +++ b/deepmd/pd/model/descriptor/se_atten.py @@ -27,7 +27,7 @@ NetworkCollection, ) from deepmd.pd.utils import ( - aux, + decomp, env, ) from deepmd.pd.utils.env import ( @@ -455,7 +455,7 @@ def forward( index = nlist.reshape([nb, nloc * nnei]).unsqueeze(-1).expand([-1, -1, nt]) # nb x (nloc x nnei) x nt # atype_tebd_nlist = paddle.take_along_axis(atype_tebd_ext, axis=1, index=index) - atype_tebd_nlist = aux.take_along_axis(atype_tebd_ext, axis=1, indices=index) + atype_tebd_nlist = decomp.take_along_axis(atype_tebd_ext, axis=1, indices=index) # nb x nloc x nnei x nt atype_tebd_nlist = atype_tebd_nlist.reshape([nb, nloc, nnei, nt]) # beyond the cutoff sw should be 0.0 @@ -502,7 +502,7 @@ def forward( # input_r = paddle.nn.functional.normalize( # rr.reshape([-1, self.nnei, 4])[:, :, 1:4], axis=-1 # ) - input_r = aux.normalize(rr.reshape([-1, self.nnei, 4])[:, :, 1:4], axis=-1) + input_r = decomp.normalize(rr.reshape([-1, self.nnei, 4])[:, :, 1:4], axis=-1) gg = self.dpa1_attention( gg, nlist_mask, input_r=input_r, sw=sw ) # shape is [nframes*nloc, self.neei, out_size] @@ -882,9 +882,9 @@ def forward( # q = paddle_func.normalize(q, axis=-1) # k = paddle_func.normalize(k, axis=-1) # v = paddle_func.normalize(v, axis=-1) - q = aux.normalize(q, axis=-1) - k = aux.normalize(k, axis=-1) - v = aux.normalize(v, axis=-1) + q = decomp.normalize(q, axis=-1) + k = decomp.normalize(k, axis=-1) + v = decomp.normalize(v, axis=-1) q = q * self.scaling # (nf x nloc) x num_heads x head_dim x nnei diff --git a/deepmd/pd/model/descriptor/se_t_tebd.py b/deepmd/pd/model/descriptor/se_t_tebd.py index 5ce6da5d09..606056437c 100644 --- a/deepmd/pd/model/descriptor/se_t_tebd.py +++ b/deepmd/pd/model/descriptor/se_t_tebd.py @@ -26,7 +26,7 @@ TypeEmbedNetConsistent, ) from deepmd.pd.utils import ( - aux, + decomp, env, ) from deepmd.pd.utils.env import ( @@ -791,7 +791,7 @@ def forward( index = nlist.reshape([nb, nloc * nnei]).unsqueeze(-1).expand([-1, -1, nt]) # nb x (nloc x nnei) x nt # atype_tebd_nlist = paddle.take_along_axis(atype_tebd_ext, axis=1, index=index) - atype_tebd_nlist = aux.take_along_axis(atype_tebd_ext, axis=1, indices=index) + atype_tebd_nlist = decomp.take_along_axis(atype_tebd_ext, axis=1, indices=index) # nb x nloc x nnei x nt atype_tebd_nlist = atype_tebd_nlist.reshape([nb, nloc, nnei, nt]) # beyond the cutoff sw should be 0.0 diff --git a/deepmd/pd/model/model/make_model.py b/deepmd/pd/model/model/make_model.py index 26bc6f91c8..258ba5d2fc 100644 --- a/deepmd/pd/model/model/make_model.py +++ b/deepmd/pd/model/model/make_model.py @@ -25,7 +25,7 @@ fit_output_to_model_output, ) from deepmd.pd.utils import ( - aux, + decomp, ) from deepmd.pd.utils.env import ( GLOBAL_PD_ENER_FLOAT_PRECISION, @@ -432,18 +432,18 @@ def _format_nlist( coord0 = extended_coord[:, :n_nloc, :] # nf x (nloc x nnei) x 3 index = nlist.reshape([n_nf, n_nloc * n_nnei, 1]).expand([-1, -1, 3]) - coord1 = aux.take_along_axis(extended_coord, axis=1, indices=index) + coord1 = decomp.take_along_axis(extended_coord, axis=1, indices=index) # nf x nloc x nnei x 3 coord1 = coord1.reshape([n_nf, n_nloc, n_nnei, 3]) # nf x nloc x nnei # rr = paddle.linalg.norm(coord0[:, :, None, :] - coord1, axis=-1) - rr = aux.norm(coord0[:, :, None, :] - coord1, axis=-1) + rr = decomp.norm(coord0[:, :, None, :] - coord1, axis=-1) rr = paddle.where(m_real_nei, rr, float("inf")) rr, nlist_mapping = ( paddle.sort(rr, axis=-1), paddle.argsort(rr, axis=-1), ) - nlist = aux.take_along_axis(nlist, axis=2, indices=nlist_mapping) + nlist = decomp.take_along_axis(nlist, axis=2, indices=nlist_mapping) nlist = paddle.where(rr > rcut, paddle.full_like(nlist, -1), nlist) nlist = nlist[..., :nnei] else: # not extra_nlist_sort and n_nnei <= nnei: diff --git a/deepmd/pd/model/model/spin_model.py b/deepmd/pd/model/model/spin_model.py index de1f2504b4..0fcf90a7af 100644 --- a/deepmd/pd/model/model/spin_model.py +++ b/deepmd/pd/model/model/spin_model.py @@ -16,7 +16,7 @@ DPAtomicModel, ) from deepmd.pd.utils import ( - aux, + decomp, ) from deepmd.pd.utils.utils import ( to_paddle_tensor, @@ -204,9 +204,11 @@ def extend_nlist(extended_atype, nlist): first_part_index = (nloc <= extended_nlist) & (extended_nlist < nall) second_part_index = (nall <= extended_nlist) & (extended_nlist < (nall + nloc)) # extended_nlist[first_part_index] += nloc - extended_nlist = aux.masked_add_(extended_nlist, first_part_index, nloc) + extended_nlist = decomp.masked_add_(extended_nlist, first_part_index, nloc) # extended_nlist[second_part_index] -= nall - nloc - entended_nlist = aux.masked_add_(extended_nlist, second_part_index, nloc - nall) + entended_nlist = decomp.masked_add_( + extended_nlist, second_part_index, nloc - nall + ) return extended_nlist @staticmethod diff --git a/deepmd/pd/model/model/transform_output.py b/deepmd/pd/model/model/transform_output.py index 52939980ec..148258d8f2 100644 --- a/deepmd/pd/model/model/transform_output.py +++ b/deepmd/pd/model/model/transform_output.py @@ -10,7 +10,7 @@ get_reduce_name, ) from deepmd.pd.utils import ( - aux, + decomp, env, ) @@ -233,7 +233,7 @@ def communicate_extended_output( device=vv.place ) # nf x nloc x nvar x 3 - new_ret[kk_derv_r] = aux.scatter_reduce( + new_ret[kk_derv_r] = decomp.scatter_reduce( force, 1, index=mapping, @@ -252,7 +252,7 @@ def communicate_extended_output( device=vv.place ) # nf x nloc x nvar x 9 - new_ret[kk_derv_c] = aux.scatter_reduce( + new_ret[kk_derv_c] = decomp.scatter_reduce( virial, 1, index=mapping, diff --git a/deepmd/pd/utils/aux.py b/deepmd/pd/utils/decomp.py similarity index 91% rename from deepmd/pd/utils/aux.py rename to deepmd/pd/utils/decomp.py index d07ac0caf4..79e4630e51 100644 --- a/deepmd/pd/utils/aux.py +++ b/deepmd/pd/utils/decomp.py @@ -1,8 +1,8 @@ # SPDX-License-Identifier: LGPL-3.0-or-later -# This file is used to implement some paddle functions with composite APi, +# This file is used to implement some paddle functions with composite API, # so as to support high-order differentation when double-backward is needed. - +# For example: [norm] --decomposition--> [multiply, power, sum] # This file will be removed when implmented functions are decomposed into primitive # function in Paddle framework in the future. @@ -21,6 +21,16 @@ # decomposition for forward function def softmax_decomp(x: paddle.Tensor, axis: int = -1) -> paddle.Tensor: + """Forward decompsition function of softmax. + + Args: + x (paddle.Tensor): Input. + axis (int, optional): A dimension along which softmax will be computed. Defaults to -1. + + Returns + ------- + paddle.Tensor: Computed output. + """ x_max = paddle.max(x, axis=axis, keepdim=True) x = x - x_max return paddle.exp(x) / paddle.sum(paddle.exp(x), axis=axis, keepdim=True) diff --git a/deepmd/pd/utils/exclude_mask.py b/deepmd/pd/utils/exclude_mask.py index 98057eaf1b..088ac186a8 100644 --- a/deepmd/pd/utils/exclude_mask.py +++ b/deepmd/pd/utils/exclude_mask.py @@ -4,7 +4,7 @@ import paddle from deepmd.pd.utils import ( - aux, + decomp, ) from deepmd.pd.utils.utils import ( to_paddle_tensor, @@ -149,7 +149,7 @@ def forward( # type_j = paddle.take_along_axis(ae, axis=1, indices=index).reshape( # [nf, nloc, nnei] # ) - type_j = aux.take_along_axis(ae, axis=1, indices=index).reshape( + type_j = decomp.take_along_axis(ae, axis=1, indices=index).reshape( [nf, nloc, nnei] ) type_ij = type_i[:, :, None] + type_j diff --git a/deepmd/pd/utils/nlist.py b/deepmd/pd/utils/nlist.py index 58d02b8aec..851ff5293d 100644 --- a/deepmd/pd/utils/nlist.py +++ b/deepmd/pd/utils/nlist.py @@ -7,7 +7,7 @@ import paddle from deepmd.pd.utils import ( - aux, + decomp, env, ) from deepmd.pd.utils.region import ( @@ -119,7 +119,7 @@ def build_neighbor_list( assert list(diff.shape) == [batch_size, nloc, nall, 3] # nloc x nall # rr = paddle.linalg.norm(diff, axis=-1) - rr = aux.norm(diff, axis=-1) + rr = decomp.norm(diff, axis=-1) # if central atom has two zero distances, sorting sometimes can not exclude itself rr = rr - paddle.eye(nloc, nall, dtype=rr.dtype).to(device=rr.place).unsqueeze(0) rr, nlist = paddle.sort(rr, axis=-1), paddle.argsort(rr, axis=-1) @@ -268,7 +268,7 @@ def build_directional_neighbor_list( assert list(diff.shape) == [batch_size, nloc_cntl, nall_neig, 3] # nloc x nall # rr = paddle.linalg.norm(diff, axis=-1) - rr = aux.norm(diff, axis=-1) + rr = decomp.norm(diff, axis=-1) rr, nlist = paddle.sort(rr, axis=-1), paddle.argsort(rr, axis=-1) # We assume that the central and neighbor atoms are diffferent, @@ -305,7 +305,7 @@ def nlist_distinguish_types( # axis=2, # indices=nlist.masked_fill(mask, 0), # ) - tnlist = aux.take_along_axis( + tnlist = decomp.take_along_axis( tmp_atype, axis=2, indices=nlist.masked_fill(mask, 0), @@ -323,7 +323,7 @@ def nlist_distinguish_types( ) # nloc x s(nsel) # inlist = paddle.take_along_axis(nlist, axis=2, indices=imap) - inlist = aux.take_along_axis(nlist, axis=2, indices=imap) + inlist = decomp.take_along_axis(nlist, axis=2, indices=imap) inlist = inlist.masked_fill(~(pick_mask.to(paddle.bool)), -1) # nloc x nsel[ii] ret_nlist.append(paddle.split(inlist, [ss, snsel - ss], axis=-1)[0]) @@ -407,14 +407,14 @@ def build_multiple_neighbor_list( # coord2 = paddle.take_along_axis(coord1, axis=1, index=index).reshape( # [nb, nloc, nsel, 3] # ) - coord2 = aux.take_along_axis(coord1, axis=1, indices=index).reshape( + coord2 = decomp.take_along_axis(coord1, axis=1, indices=index).reshape( [nb, nloc, nsel, 3] ) # nb x nloc x nsel x 3 diff = coord2 - coord0[:, :, None, :] # nb x nloc x nsel # rr = paddle.linalg.norm(diff, axis=-1) - rr = aux.norm(diff, axis=-1) + rr = decomp.norm(diff, axis=-1) rr.masked_fill(nlist_mask, float("inf")) nlist0 = nlist ret = {} @@ -516,7 +516,7 @@ def extend_coord_with_ghosts( # xyz = xyz.to(device=device) # ns x 3 # shift_idx = xyz[paddle.argsort(paddle.norm(xyz, axis=1))] - shift_idx = xyz[paddle.argsort(aux.norm(xyz, axis=1))] + shift_idx = xyz[paddle.argsort(decomp.norm(xyz, axis=1))] ns, _ = shift_idx.shape nall = ns * nloc # nf x ns x 3 diff --git a/deepmd/pd/utils/preprocess.py b/deepmd/pd/utils/preprocess.py index 5a3c10f441..052d9941f8 100644 --- a/deepmd/pd/utils/preprocess.py +++ b/deepmd/pd/utils/preprocess.py @@ -7,7 +7,7 @@ import paddle from deepmd.pd.utils import ( - aux, + decomp, env, ) @@ -28,13 +28,13 @@ def __init__(self, boxt): # boxt = boxt.permute(1, 0) c_yz = paddle.cross(boxt[1], boxt[2]) # self._h2yz = self.volume / paddle.linalg.norm(c_yz) - self._h2yz = self.volume / aux.norm(c_yz) + self._h2yz = self.volume / decomp.norm(c_yz) c_zx = paddle.cross(boxt[2], boxt[0]) # self._h2zx = self.volume / paddle.linalg.norm(c_zx) - self._h2zx = self.volume / aux.norm(c_zx) + self._h2zx = self.volume / decomp.norm(c_zx) c_xy = paddle.cross(boxt[0], boxt[1]) # self._h2xy = self.volume / paddle.linalg.norm(c_xy) - self._h2xy = self.volume / aux.norm(c_xy) + self._h2xy = self.volume / decomp.norm(c_xy) def phys2inter(self, coord): """Convert physical coordinates to internal ones.""" @@ -189,7 +189,7 @@ def build_neighbor_list( coord_r = coord.reshape([1, -1, 3]) distance = coord_l - coord_r # distance = paddle.linalg.norm(distance, axis=-1) - distance = aux.norm(distance, axis=-1) + distance = decomp.norm(distance, axis=-1) DISTANCE_INF = distance.max().detach() + rcut distance[:nloc, :nloc] += paddle.eye(nloc, dtype=paddle.bool) * DISTANCE_INF # pylint: disable=no-explicit-device if min_check: diff --git a/deepmd/pd/utils/region.py b/deepmd/pd/utils/region.py index 21ce2b5e75..160a4d124e 100644 --- a/deepmd/pd/utils/region.py +++ b/deepmd/pd/utils/region.py @@ -2,7 +2,7 @@ import paddle from deepmd.pd.utils import ( - aux, + decomp, ) @@ -83,13 +83,13 @@ def _to_face_distance(cell): volume = paddle.linalg.det(cell) c_yz = paddle.cross(cell[1], cell[2]) # _h2yz = volume / paddle.linalg.norm(c_yz) - _h2yz = volume / aux.norm(c_yz) + _h2yz = volume / decomp.norm(c_yz) c_zx = paddle.cross(cell[2], cell[0]) # _h2zx = volume / paddle.linalg.norm(c_zx) - _h2zx = volume / aux.norm(c_zx) + _h2zx = volume / decomp.norm(c_zx) c_xy = paddle.cross(cell[0], cell[1]) # _h2xy = volume / paddle.linalg.norm(c_xy) - _h2xy = volume / aux.norm(c_xy) + _h2xy = volume / decomp.norm(c_xy) return paddle.stack([_h2yz, _h2zx, _h2xy]) @@ -97,13 +97,13 @@ def b_to_face_distance(cell): volume = paddle.linalg.det(cell) c_yz = paddle.cross(cell[:, 1], cell[:, 2], axis=-1) # _h2yz = volume / paddle.linalg.norm(c_yz, axis=-1) - _h2yz = volume / aux.norm(c_yz, axis=-1) + _h2yz = volume / decomp.norm(c_yz, axis=-1) c_zx = paddle.cross(cell[:, 2], cell[:, 0], axis=-1) # _h2zx = volume / paddle.linalg.norm(c_zx, axis=-1) - _h2zx = volume / aux.norm(c_zx, axis=-1) + _h2zx = volume / decomp.norm(c_zx, axis=-1) c_xy = paddle.cross(cell[:, 0], cell[:, 1], axis=-1) # _h2xy = volume / paddle.linalg.norm(c_xy, axis=-1) - _h2xy = volume / aux.norm(c_xy, axis=-1) + _h2xy = volume / decomp.norm(c_xy, axis=-1) return paddle.stack([_h2yz, _h2zx, _h2xy], axis=1) diff --git a/source/tests/pd/model/test_descriptor.py b/source/tests/pd/model/test_descriptor.py index dd622fb40b..386c68595b 100644 --- a/source/tests/pd/model/test_descriptor.py +++ b/source/tests/pd/model/test_descriptor.py @@ -17,7 +17,7 @@ prod_env_mat, ) from deepmd.pd.utils import ( - aux, + decomp, dp_random, env, ) @@ -179,7 +179,7 @@ def test_consistency(self): my_nlist = nlist.reshape([bsz, -1]).cpu() mask = my_nlist == -1 my_nlist = my_nlist * (~mask).astype(my_nlist.dtype) - my_nlist = aux.take_along_axis(mapping, axis=-1, indices=my_nlist) + my_nlist = decomp.take_along_axis(mapping, axis=-1, indices=my_nlist) my_nlist = my_nlist * (~mask).astype(my_nlist.dtype) - mask.astype( my_nlist.dtype ) diff --git a/source/tests/pd/model/test_ener_spin_model.py b/source/tests/pd/model/test_ener_spin_model.py index 701528c72a..b4573b6a45 100644 --- a/source/tests/pd/model/test_ener_spin_model.py +++ b/source/tests/pd/model/test_ener_spin_model.py @@ -11,7 +11,7 @@ get_model, ) from deepmd.pd.utils import ( - aux, + decomp, env, ) from deepmd.pd.utils.nlist import ( @@ -46,7 +46,7 @@ def reduce_tensor(extended_tensor, mapping, nloc: int): [-1] * len(mldims) + list(ext_dims) ) # nf x nloc x (*ext_dims) - reduced_tensor = aux.scatter_reduce( + reduced_tensor = decomp.scatter_reduce( reduced_tensor, 1, index=mapping, @@ -168,7 +168,7 @@ def test_input_output_process(self): ) nall = extended_coord.shape[1] nnei = nlist.shape[-1] - extended_spin = aux.take_along_axis( + extended_spin = decomp.take_along_axis( self.spin, indices=mapping.unsqueeze(-1).tile((1, 1, 3)), axis=1 ) ( @@ -247,16 +247,16 @@ def test_input_output_process(self): loc_atoms_mask = (nlist < nloc) & (nlist != -1) ghost_atoms_mask = nlist >= nloc real_neighbors = nlist.clone() - aux.masked_add_(real_neighbors, ghost_atoms_mask, nloc) + decomp.masked_add_(real_neighbors, ghost_atoms_mask, nloc) # real_neighbors[ghost_atoms_mask] += nloc assert np.allclose( nlist_updated[:, :nloc, 1 : 1 + nnei].numpy(), real_neighbors.numpy() ) virtual_neighbors = nlist.clone() # virtual_neighbors[loc_atoms_mask] += nloc - aux.masked_add_(virtual_neighbors, loc_atoms_mask, nloc) + decomp.masked_add_(virtual_neighbors, loc_atoms_mask, nloc) # virtual_neighbors[ghost_atoms_mask] += nall - aux.masked_add_(virtual_neighbors, ghost_atoms_mask, nall) + decomp.masked_add_(virtual_neighbors, ghost_atoms_mask, nall) assert np.allclose( nlist_updated[:, :nloc, 1 + nnei :].numpy(), virtual_neighbors.numpy() ) @@ -365,7 +365,7 @@ def test_dp_consistency(self): mixed_types=self.model.mixed_types(), box=self.cell, ) - extended_spin = aux.take_along_axis( + extended_spin = decomp.take_along_axis( self.spin, indices=mapping.unsqueeze(-1).tile((1, 1, 3)), axis=1 ) dp_ret_lower = dp_model.call_lower( diff --git a/source/tests/pd/model/test_forward_lower.py b/source/tests/pd/model/test_forward_lower.py index dc348b5a37..213369ea12 100644 --- a/source/tests/pd/model/test_forward_lower.py +++ b/source/tests/pd/model/test_forward_lower.py @@ -9,7 +9,7 @@ get_model, ) from deepmd.pd.utils import ( - aux, + decomp, env, ) from deepmd.pd.utils.nlist import ( @@ -45,7 +45,7 @@ def reduce_tensor(extended_tensor, mapping, nloc: int): [-1] * len(mldims) + list(ext_dims) ) # nf x nloc x (*ext_dims) - reduced_tensor = aux.scatter_reduce( + reduced_tensor = decomp.scatter_reduce( reduced_tensor, 1, index=mapping, @@ -96,7 +96,7 @@ def test( mixed_types=self.model.mixed_types(), box=cell.unsqueeze(0), ) - extended_spin = aux.take_along_axis( + extended_spin = decomp.take_along_axis( spin.unsqueeze(0), indices=mapping.unsqueeze(-1).tile((1, 1, 3)), axis=1 ) input_dict = { diff --git a/source/tests/pd/model/test_polarizability_fitting.py b/source/tests/pd/model/test_polarizability_fitting.py index 87b62ba679..7280fbd1b0 100644 --- a/source/tests/pd/model/test_polarizability_fitting.py +++ b/source/tests/pd/model/test_polarizability_fitting.py @@ -361,7 +361,10 @@ def setUp(self): self.model = PolarModel(self.dd0, self.ft0, self.type_mapping) self.file_path = "model_output.pd" - @unittest.skip("Paddle do not eval on frozen model yet.") + @unittest.skip( + "Paddle do not support finetune in frozen models(.json and .pdiparams file), " + "will be supported in the future." + ) def test_deepdipole_infer(self): atype = self.atype.reshape([self.nf, self.natoms]) coord = self.coord.reshape([1, 5, 3]) diff --git a/source/tests/pd/test_init_frz_model.py b/source/tests/pd/test_init_frz_model.py index 2938131a60..9b824b3886 100644 --- a/source/tests/pd/test_init_frz_model.py +++ b/source/tests/pd/test_init_frz_model.py @@ -29,7 +29,10 @@ ) -@unittest.skip("froze model only used to inference in paddle backend") +@unittest.skip( + "Paddle do not support finetune in frozen models(.json and .pdiparams file), " + "will be supported in the future." +) class TestInitFrzModel(unittest.TestCase): def setUp(self): input_json = str(Path(__file__).parent / "water/se_atten.json") From 541dae620bf14f7cbe8e079103116f9d02caacbf Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Fri, 1 Nov 2024 17:19:14 +0800 Subject: [PATCH 93/93] polish decomp.py --- deepmd/pd/utils/decomp.py | 139 ++++++++++++++++++++++++++++++++++---- 1 file changed, 127 insertions(+), 12 deletions(-) diff --git a/deepmd/pd/utils/decomp.py b/deepmd/pd/utils/decomp.py index 79e4630e51..25eac1b6d5 100644 --- a/deepmd/pd/utils/decomp.py +++ b/deepmd/pd/utils/decomp.py @@ -6,6 +6,9 @@ # This file will be removed when implmented functions are decomposed into primitive # function in Paddle framework in the future. +from __future__ import ( + annotations, +) import paddle @@ -23,13 +26,17 @@ def softmax_decomp(x: paddle.Tensor, axis: int = -1) -> paddle.Tensor: """Forward decompsition function of softmax. - Args: - x (paddle.Tensor): Input. - axis (int, optional): A dimension along which softmax will be computed. Defaults to -1. + Parameters + ---------- + x : paddle.Tensor + Input. + axis : int, defaults: -1. + A dimension along which softmax will be computed. Returns ------- - paddle.Tensor: Computed output. + paddle.Tensor + Computed output. """ x_max = paddle.max(x, axis=axis, keepdim=True) x = x - x_max @@ -39,6 +46,25 @@ def softmax_decomp(x: paddle.Tensor, axis: int = -1) -> paddle.Tensor: def norm_decomp( x: paddle.Tensor, p: float = 2, axis: bool = -1, keepdim: bool = False ) -> paddle.Tensor: + """Forward decompsition function of norm. + + Parameters + ---------- + x : paddle.Tensor + Input + p : float, default: 2 + Order of norm + axis : bool, default: -1 + Dimensions over which to compute the vector or matrix norm + keepdim : bool, default: False + If set to True, the reduced dimensions are retained in the result as dimensions + with size one + + Returns + ------- + paddle.Tensor + A real-valued tensor, even when A is complex. + """ if p == 2 or p == 2.0: # clip for negative indexing, or 1/(0^(k-1)) will cause inf in backward return (x * x).sum(axis=axis, keepdim=keepdim).clip(1e-12) ** 0.5 @@ -47,9 +73,27 @@ def norm_decomp( def take_along_axis_decomp( x: paddle.Tensor, indices: paddle.Tensor, axis: int, broadcast: bool = True -): - """Broadcast no used now.""" - # manually contruct indices for gather_nd(ind_gather_nd.ndim == indices.ndim + 1, the lsat 1 represents the number of dimension(s) of indices) +) -> paddle.Tensor: + """Forward decompsition function of take_along_axis. + + Parameters + ---------- + x : paddle.Tensor + The input tensor. + indices : paddle.Tensor + Indices to take along each 1d slice of array. + axis : int + The axis to take 1d slices along. + broadcast : bool, default: True + Whether the indices broadcast. + + Returns + ------- + paddle.Tensor + Computed output. + """ + # manually contruct indices for gather_nd(ind_gather_nd.ndim == indices.ndim + 1, + # the lsat 1 represents the number of dimension(s) of indices) ind_gather_nd = paddle.stack( paddle.meshgrid(*[paddle.arange(v) for v in indices.shape], indexing="ij"), axis=-1, @@ -67,6 +111,27 @@ def scatter_reduce_decomp( src: paddle.Tensor, reduce: str, ) -> paddle.Tensor: + """Forward decompsition function of scatter_reduce. + + Parameters + ---------- + input : paddle.Tensor + Input tensor. + axis : int + The axis along which to index. + index : paddle.Tensor + The indices of elements to scatter and reduce. + src : paddle.Tensor + The source elements to scatter and reduce. + reduce : str + The reduction operation to apply for non-unique indices. + Supported modes: ("sum", "prod", "mean", "amax", "amin"). + + Returns + ------- + paddle.Tensor + Computed output. + """ # reduce: "sum", "prod", "mean", "amax", "amin" if reduce == "sum": input.put_along_axis_(indices=index, values=src, axis=axis, reduce="add") @@ -86,17 +151,49 @@ def scatter_reduce_decomp( return input -def sec(l: int, size: int) -> list[int]: - assert l > 0 +def sec(length: int, size: int) -> list[int]: + """Auxiliary function for decomposed functions. + + If length is not divisible by size, the last chunk will be smaller. + + Parameters + ---------- + length : int + Length to be chunked. + size : int + Chunk size. + + Returns + ------- + list[int] + Chunked output list. + """ + assert length > 0 assert size > 0 - if l % size == 0: - return [size] * (l // size) - return [size] * (l // size) + [l % size] + if length % size == 0: + return [size] * (length // size) + return [size] * (length // size) + [length % size] def masked_add__decomp( x: paddle.Tensor, mask: paddle.Tensor, v: paddle.Tensor ) -> paddle.Tensor: + """Forward decompsition function of masked_add_(inplace operator). + + Parameters + ---------- + x : paddle.Tensor + Input tensor. + mask : paddle.Tensor + Mask tensor. + v : paddle.Tensor + Value to add. + + Returns + ------- + paddle.Tensor + Computed output. + """ assert mask.dtype == paddle.bool, f"mask must be bool type, but got {mask.dtype}" # indices is bool mask mask_coord = paddle.concat( @@ -120,6 +217,24 @@ def normalize_decomp( axis: int = 1, epsilon: float = 1e-12, ) -> paddle.Tensor: + """Forward decompsition function of normalize. + + Parameters + ---------- + x : paddle.Tensor + Input tensor. + p : float, optional + Order of the norm, default: 2 + axis : int, optional + Axis on which to perform normalization, default: 1 + epsilon : float, optional + Epislon value, default: 1e-12 + + Returns + ------- + paddle.Tensor + Computed output. + """ return x / (norm(x, p=p, axis=axis, keepdim=True).clip(min=epsilon))

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25.541034 5.1893967 1.1060305 25.541034 5.1893967 1.1060305 +0.975 25.4504678 5.1726057 1.1028081 25.4504678 5.1726057 1.1028081 +0.976 25.3602728 5.1558791 1.0995971 25.3602728 5.1558791 1.0995971 +0.977 25.2704474 5.1392165 1.0963975 25.2704474 5.1392165 1.0963975 +0.978 25.1809897 5.1226177 1.0932092 25.1809897 5.1226177 1.0932092 +0.979 25.091898 5.1060825 1.0900323 25.091898 5.1060825 1.0900323 +0.98 25.0031705 5.0896104 1.0868665 25.0031705 5.0896104 1.0868665 +0.981 24.9148055 5.0732013 1.083712 24.9148055 5.0732013 1.083712 +0.982 24.8268013 5.0568548 1.0805686 24.8268013 5.0568548 1.0805686 +0.983 24.7391563 5.0405707 1.0774363 24.7391563 5.0405707 1.0774363 +0.984 24.6518686 5.0243487 1.074315 24.6518686 5.0243487 1.074315 +0.985 24.5649365 5.0081885 1.0712048 24.5649365 5.0081885 1.0712048 +0.986 24.4783585 4.9920898 1.0681055 24.4783585 4.9920898 1.0681055 +0.987 24.3921328 4.9760523 1.0650171 24.3921328 4.9760523 1.0650171 +0.988 24.3062578 4.9600758 1.0619396 24.3062578 4.9600758 1.0619396 +0.989 24.2207318 4.94416 1.0588729 24.2207318 4.94416 1.0588729 +0.99 24.1355532 4.9283046 1.0558169 24.1355532 4.9283046 1.0558169 +0.991 24.0507203 4.9125093 1.0527717 24.0507203 4.9125093 1.0527717 +0.992 23.9662314 4.896774 1.0497372 23.9662314 4.896774 1.0497372 +0.993 23.8820851 4.8810982 1.0467134 23.8820851 4.8810982 1.0467134 +0.994 23.7982796 4.8654818 1.0437001 23.7982796 4.8654818 1.0437001 +0.995 23.7148134 4.8499244 1.0406973 23.7148134 4.8499244 1.0406973 +0.996 23.6316848 4.8344259 1.0377051 23.6316848 4.8344259 1.0377051 +0.997 23.5488924 4.8189859 1.0347233 23.5488924 4.8189859 1.0347233 +0.998 23.4664344 4.8036042 1.0317519 23.4664344 4.8036042 1.0317519 +0.999 23.3843094 4.7882805 1.0287909 23.3843094 4.7882805 1.0287909 +1.0 23.3025158 4.7730145 1.0258403 23.3025158 4.7730145 1.0258403 diff --git a/source/tests/pd/model/water/lkf.json b/source/tests/pd/model/water/lkf.json new file mode 100644 index 0000000000..377679c7ee --- /dev/null +++ b/source/tests/pd/model/water/lkf.json @@ -0,0 +1,79 @@ +{ + "model": { + "type_map": [ + "O", + "H" + ], + "descriptor": { + "type": "se_e2_a", + "sel": [ + 46, + 92 + ], + "rcut_smth": 0.50, + "rcut": 6.00, + "neuron": [ + 25, + 25, + 25 + ], + "resnet_dt": false, + "axis_neuron": 16, + "seed": 1, + "_comment": " that's all" + }, + "fitting_net": { + "neuron": [ + 100, + 100, + 100 + ], + "resnet_dt": true, + "seed": 1, + "_comment": " that's all" + }, + "data_stat_nbatch": 20, + "_comment": " that's all" + }, + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.001, + "stop_lr": 3.51e-8, + "_comment": "that's all" + }, + "loss": { + "type": "ener", + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "_comment": " that's all" + }, + "training": { + "training_data": { + "systems": [ + "pd/water/data/data_0" + ], + "batch_size": 3, + "_comment": "that's all" + }, + "validation_data": { + "systems": [ + "pd/water/data/data_0" + ], + "batch_size": 1, + "numb_btch": 3, + "_comment": "that's all" + }, + "numb_steps": 1, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 1, + "save_freq": 1, + "opt_type": "LKF", + "kf_blocksize": 1024, + "_comment": "that's all" + }, + "_comment": "that's all" +} diff --git a/source/tests/pd/model/water/multitask.json b/source/tests/pd/model/water/multitask.json new file mode 100644 index 0000000000..83524a8b77 --- /dev/null +++ b/source/tests/pd/model/water/multitask.json @@ -0,0 +1,140 @@ +{ + "model": { + "shared_dict": { + "my_type_map": [ + "O", + "H", + "B" + ], + "my_descriptor": { + "type": "se_e2_a", + "sel": [ + 46, + 92 + ], + "rcut_smth": 0.50, + "rcut": 6.00, + "neuron": [ + 25, + 50, + 100 + ], + "resnet_dt": false, + "axis_neuron": 16, + "seed": 1, + "_comment": " that's all" + }, + "_comment": "that's all" + }, + "model_dict": { + "model_1": { + "type_map": "my_type_map", + "descriptor": "my_descriptor", + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1, + "_comment": " that's all" + }, + "data_stat_nbatch": 1 + }, + "model_2": { + "type_map": "my_type_map", + "descriptor": "my_descriptor", + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1, + "_comment": " that's all" + }, + "data_stat_nbatch": 1 + } + } + }, + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.0002, + "decay_rate": 0.98, + "stop_lr": 3.51e-08, + "_comment": "that's all" + }, + "loss_dict": { + "model_1": { + "type": "ener", + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "start_pref_v": 0, + "limit_pref_v": 0 + }, + "model_2": { + "type": "ener", + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "start_pref_v": 0, + "limit_pref_v": 0 + } + }, + "training": { + "model_prob": { + "model_1": 0.5, + "model_2": 0.5 + }, + "data_dict": { + "model_1": { + "stat_file": "./stat_files/model_1.hdf5", + "training_data": { + "systems": [ + "pd/water/data/data_0" + ], + "batch_size": 1, + "_comment": "that's all" + }, + "validation_data": { + "systems": [ + "pd/water/data/data_0" + ], + "batch_size": 1, + "_comment": "that's all" + } + }, + "model_2": { + "stat_file": "./stat_files/model_2.hdf5", + "training_data": { + "systems": [ + "pd/water/data/data_0" + ], + "batch_size": 1, + "_comment": "that's all" + }, + "validation_data": { + "systems": [ + "pd/water/data/data_0" + ], + "batch_size": 1, + "_comment": "that's all" + } + } + }, + "numb_steps": 100000, + "warmup_steps": 0, + "gradient_max_norm": 5.0, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 100, + "_comment": "that's all" + } +} diff --git a/source/tests/pd/model/water/se_atten.json b/source/tests/pd/model/water/se_atten.json new file mode 100644 index 0000000000..70abf6759c --- /dev/null +++ b/source/tests/pd/model/water/se_atten.json @@ -0,0 +1,83 @@ +{ + "_comment": "that's all", + "model": { + "type_map": [ + "O", + "H" + ], + "descriptor": { + "type": "se_atten", + "sel": 40, + "rcut_smth": 0.5, + "rcut": 4.0, + "neuron": [ + 25, + 50, + 100 + ], + "axis_neuron": 16, + "type_one_side": true, + "attn": 64, + "attn_layer": 2, + "attn_dotr": true, + "attn_mask": false, + "activation_function": "tanh", + "scaling_factor": 1.0, + "normalize": false, + "temperature": 1.0, + "seed": 1 + }, + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1, + "_comment": " that's all" + }, + "_comment": " that's all" + }, + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.001, + "stop_lr": 3.51e-08, + "_comment": "that's all" + }, + "loss": { + "type": "ener", + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "start_pref_v": 0, + "limit_pref_v": 0, + "_comment": " that's all" + }, + "training": { + "training_data": { + "systems": [ + "pd/water/data/data_0" + ], + "batch_size": 1, + "_comment": "that's all" + }, + "validation_data": { + "systems": [ + "pd/water/data/data_0" + ], + "batch_size": 1, + "numb_btch": 1, + "_comment": "that's all" + }, + "numb_steps": 1000000, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 1000, + "save_ckpt": "model", + "_comment": "that's all" + } +} diff --git a/source/tests/pd/model/water/se_e2_a.json b/source/tests/pd/model/water/se_e2_a.json new file mode 100644 index 0000000000..96f51ba5aa --- /dev/null +++ b/source/tests/pd/model/water/se_e2_a.json @@ -0,0 +1,77 @@ +{ + "model": { + "type_map": [ + "O", + "H" + ], + "descriptor": { + "type": "se_e2_a", + "sel": [ + 46, + 92 + ], + "rcut_smth": 0.50, + "rcut": 6.00, + "neuron": [ + 25, + 50, + 100 + ], + "resnet_dt": false, + "axis_neuron": 16, + "seed": 1, + "_comment": " that's all" + }, + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1, + "_comment": " that's all" + }, + "data_stat_nbatch": 20, + "_comment": " that's all" + }, + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.001, + "stop_lr": 3.51e-8, + "_comment": "that's all" + }, + "loss": { + "type": "ener", + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "_comment": " that's all" + }, + "training": { + "training_data": { + "systems": [ + "pd/water/data/data_0" + ], + "batch_size": 1, + "_comment": "that's all" + }, + "validation_data": { + "systems": [ + "pd/water/data/data_0" + ], + "batch_size": 1, + "numb_btch": 3, + "_comment": "that's all" + }, + "numb_steps": 100000, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 10000, + "_comment": "that's all" + }, + "_comment": "that's all" +} diff --git a/source/tests/pd/model/water/zbl.json b/source/tests/pd/model/water/zbl.json new file mode 100644 index 0000000000..cb5602d92d --- /dev/null +++ b/source/tests/pd/model/water/zbl.json @@ -0,0 +1,92 @@ +{ + "_comment1": " model parameters", + "model": { + "use_srtab": "H2O_tab_potential.txt", + "smin_alpha": 0.1, + "sw_rmin": 0.8, + "sw_rmax": 1.0, + "type_map": [ + "O", + "H" + ], + "descriptor": { + "type": "se_e2_a", + "sel": [ + 46, + 92 + ], + "rcut_smth": 0.50, + "rcut": 6.00, + "neuron": [ + 25, + 50, + 100 + ], + "resnet_dt": false, + "axis_neuron": 16, + "type_one_side": true, + "precision": "float64", + "seed": 1, + "_comment2": " that's all" + }, + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "precision": "float64", + "seed": 1, + "_comment3": " that's all" + }, + "_comment4": " that's all" + }, + + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.001, + "stop_lr": 3.51e-8, + "_comment5": "that's all" + }, + + "loss": { + "type": "ener", + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "start_pref_v": 0, + "limit_pref_v": 0, + "_comment6": " that's all" + }, + + "training": { + "training_data": { + "systems": [ + "../data/data_0/", + "../data/data_1/", + "../data/data_2/" + ], + "batch_size": "auto", + "_comment7": "that's all" + }, + "validation_data": { + "systems": [ + "../data/data_3" + ], + "batch_size": 1, + "numb_btch": 3, + "_comment8": "that's all" + }, + "numb_steps": 1000000, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 1000, + "_comment9": "that's all" + }, + + "_comment10": "that's all" +} diff --git a/source/tests/pd/property/input.json b/source/tests/pd/property/input.json new file mode 100644 index 0000000000..4e005f8277 --- /dev/null +++ b/source/tests/pd/property/input.json @@ -0,0 +1,77 @@ +{ + "_comment": "that's all", + "model": { + "type_map": [ + "H", + "C", + "N", + "O" + ], + "descriptor": { + "type": "se_e2_a", + "sel": [ + 90 + ], + "rcut_smth": 1.8, + "rcut": 6.0, + "neuron": [ + 25, + 50, + 100 + ], + "resnet_dt": false, + "axis_neuron": 8, + "precision": "float64", + "seed": 1 + }, + "fitting_net": { + "type": "property", + "intensive": true, + "task_dim": 3, + "neuron": [ + 100, + 100, + 100 + ], + "resnet_dt": true, + "seed": 1, + "_comment": " that's all" + }, + "_comment": " that's all" + }, + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.0002, + "stop_lr": 3.51e-08, + "_comment": "that's all" + }, + "loss": { + "type": "property", + "_comment": " that's all" + }, + "training": { + "training_data": { + "systems": [ + "pt/property/single" + ], + "batch_size": 1, + "_comment": "that's all" + }, + "validation_data": { + "systems": [ + "pt/property/single" + ], + "batch_size": 1, + "_comment": "that's all" + }, + "numb_steps": 1000000, + "warmup_steps": 0, + "gradient_max_norm": 5.0, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 2000, + "_comment": "that's all" + } +} diff --git a/source/tests/pd/property/single/nopbc 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b/source/tests/pd/property/single/set.000000/real_atom_types.npy new file mode 100644 index 0000000000000000000000000000000000000000..256dbe7122021ee7ba58bd0baa222ad3cb20945f GIT binary patch literal 288 zcmbR27wQ`j$;eQ~P_3SlTAW;@Zl$1ZlWC!@qoAIaUsO_*m=~X4l#&V(cT3DEP6dh= mXCxM+0{I$-ItoSxnmP)#3giMV1{wiIsJX<_Oi=aAXfyziog0q; literal 0 HcmV?d00001 diff --git a/source/tests/pd/property/single/type.raw b/source/tests/pd/property/single/type.raw new file mode 100644 index 0000000000..d677b495ec --- /dev/null +++ b/source/tests/pd/property/single/type.raw @@ -0,0 +1,20 @@ +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 diff --git a/source/tests/pd/property/single/type_map.raw b/source/tests/pd/property/single/type_map.raw new file mode 100644 index 0000000000..c8a39f3a9e --- /dev/null +++ b/source/tests/pd/property/single/type_map.raw @@ -0,0 +1,4 @@ +H +C +N +O diff --git a/source/tests/pd/requirements.txt b/source/tests/pd/requirements.txt new file mode 100644 index 0000000000..74abad719e --- /dev/null +++ b/source/tests/pd/requirements.txt @@ -0,0 +1,6 @@ +tensorflow>=2.14.0 +deepmd-kit>=2.2.7 +dpdata +ase +coverage +pytest diff --git a/source/tests/pd/test_dp_test.py b/source/tests/pd/test_dp_test.py index 6d525b1251..e188eb3d6b 100644 --- a/source/tests/pd/test_dp_test.py +++ b/source/tests/pd/test_dp_test.py @@ -26,6 +26,7 @@ ) from .model.test_permutation import ( + model_property, model_se_e2_a, model_spin, ) @@ -78,7 +79,7 @@ def test_dp_test_1_frame(self): system=self.config["training"]["validation_data"]["systems"][0], datafile=None, set_prefix="set", - numb_test=2, + numb_test=0, rand_seed=None, shuffle_test=False, detail_file=self.detail_file, @@ -169,5 +170,63 @@ def setUp(self): json.dump(self.config, fp, indent=4) +class TestDPTestPropertySeA(unittest.TestCase): + def setUp(self): + self.detail_file = "test_dp_test_property_detail" + input_json = str(Path(__file__).parent / "property/input.json") + with open(input_json) as f: + self.config = json.load(f) + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + data_file = [str(Path(__file__).parent / "property/single")] + self.config["training"]["training_data"]["systems"] = data_file + self.config["training"]["validation_data"]["systems"] = data_file + self.config["model"] = deepcopy(model_property) + self.input_json = "test_dp_test_property.json" + with open(self.input_json, "w") as fp: + json.dump(self.config, fp, indent=4) + + @unittest.skip( + "Paddle do not support testing in frozen models(.json and .pdiparams file), " + "will be supported in the future." + ) + def test_dp_test_1_frame(self): + trainer = get_trainer(deepcopy(self.config)) + input_dict, label_dict, _ = trainer.get_data(is_train=False) + input_dict.pop("spin", None) + result = trainer.model(**input_dict) + model = paddle.jit.to_static(trainer.model) + tmp_model = tempfile.NamedTemporaryFile(delete=False, suffix=".json") + paddle.jit.save(model, tmp_model.name) + dp_test( + model=tmp_model.name, + system=self.config["training"]["validation_data"]["systems"][0], + datafile=None, + set_prefix="set", + numb_test=0, + rand_seed=None, + shuffle_test=False, + detail_file=self.detail_file, + atomic=True, + ) + os.unlink(tmp_model.name) + pred_property = np.loadtxt(self.detail_file + ".property.out.0")[:, 1] + np.testing.assert_almost_equal( + pred_property, + to_numpy_array(result["property"])[0], + ) + + def tearDown(self): + for f in os.listdir("."): + if f.startswith("model") and f.endswith(".pt"): + os.remove(f) + if f.startswith(self.detail_file): + os.remove(f) + if f in ["lcurve.out", self.input_json]: + os.remove(f) + if f in ["stat_files"]: + shutil.rmtree(f) + + if __name__ == "__main__": unittest.main() diff --git a/source/tests/pd/water_tensor/dipole/atomic_system/nopbc b/source/tests/pd/water_tensor/dipole/atomic_system/nopbc new file mode 100644 index 0000000000..e69de29bb2 diff --git 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