diff --git a/.vscode/cspell.json b/.vscode/cspell.json index d12ead35..6f3eae5e 100644 --- a/.vscode/cspell.json +++ b/.vscode/cspell.json @@ -51,6 +51,7 @@ "jaxtyping", "kaiming", "keepdim", + "logit", "lognormal", "loguniform", "loguniformvalues", @@ -73,6 +74,7 @@ "neox", "nonlinerity", "numel", + "onebit", "openwebtext", "optim", "penality", @@ -116,6 +118,7 @@ "venv", "virtualenv", "virtualenvs", - "wandb" + "wandb", + "zoadam" ] } diff --git a/poetry.lock b/poetry.lock index ce2b723f..842aca64 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2,13 +2,13 @@ [[package]] name = "accelerate" -version = "0.25.0" +version = "0.26.1" description = "Accelerate" optional = false python-versions = ">=3.8.0" files = [ - {file = "accelerate-0.25.0-py3-none-any.whl", hash = "sha256:c7bb817eb974bba0ff3ea1ba0f24d55afb86d50e3d4fe98d6922dc69cf2ccff1"}, - {file = "accelerate-0.25.0.tar.gz", hash = "sha256:ecf55b0ab278a1dac8539dde0d276977aff04683f07ede73eaf02478538576a1"}, + {file = "accelerate-0.26.1-py3-none-any.whl", hash = "sha256:04df826b84ac7bad8a0a8ab90e6aeacdecb1ea5a2d744d7e94f6735c29183227"}, + {file = "accelerate-0.26.1.tar.gz", hash = "sha256:bf63716b6bd9460d87da970cf4d833abb824ca0aa633be36b741e63a1b504f89"}, ] [package.dependencies] @@ -367,22 +367,36 @@ test-tox-coverage = ["coverage (>=5.5)"] [[package]] name = "beautifulsoup4" -version = "4.12.2" +version = "4.12.3" description = "Screen-scraping library" optional = false python-versions = ">=3.6.0" files = [ - {file = "beautifulsoup4-4.12.2-py3-none-any.whl", hash = "sha256:bd2520ca0d9d7d12694a53d44ac482d181b4ec1888909b035a3dbf40d0f57d4a"}, - {file = "beautifulsoup4-4.12.2.tar.gz", hash = "sha256:492bbc69dca35d12daac71c4db1bfff0c876c00ef4a2ffacce226d4638eb72da"}, + {file = "beautifulsoup4-4.12.3-py3-none-any.whl", hash = "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed"}, + {file = "beautifulsoup4-4.12.3.tar.gz", hash = "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051"}, ] [package.dependencies] soupsieve = ">1.2" [package.extras] +cchardet = ["cchardet"] +chardet = ["chardet"] +charset-normalizer = ["charset-normalizer"] html5lib = ["html5lib"] lxml = ["lxml"] +[[package]] +name = "better-abc" +version = "0.0.3" +description = "Python ABC plus abstract attributes" +optional = false +python-versions = "*" +files = [ + {file = "better-abc-0.0.3.tar.gz", hash = "sha256:a880fd6bc9675da2ec991e8712a555bffa0f12722efed78c739f78343cf989f6"}, + {file = "better_abc-0.0.3-py3-none-any.whl", hash = "sha256:3ae73b473fbeb536a548f542984976e80b821676ae6e18f14e24d8e180647187"}, +] + [[package]] name = "bleach" version = "6.1.0" @@ -1079,30 +1093,30 @@ smmap = ">=3.0.1,<6" [[package]] name = "gitpython" -version = "3.1.40" +version = "3.1.41" description = "GitPython is a Python library used to interact with Git repositories" optional = false python-versions = ">=3.7" files = [ - {file = "GitPython-3.1.40-py3-none-any.whl", hash = "sha256:cf14627d5a8049ffbf49915732e5eddbe8134c3bdb9d476e6182b676fc573f8a"}, - {file = "GitPython-3.1.40.tar.gz", hash = "sha256:22b126e9ffb671fdd0c129796343a02bf67bf2994b35449ffc9321aa755e18a4"}, + {file = "GitPython-3.1.41-py3-none-any.whl", hash = "sha256:c36b6634d069b3f719610175020a9aed919421c87552185b085e04fbbdb10b7c"}, + {file = "GitPython-3.1.41.tar.gz", hash = "sha256:ed66e624884f76df22c8e16066d567aaa5a37d5b5fa19db2c6df6f7156db9048"}, ] [package.dependencies] gitdb = ">=4.0.1,<5" [package.extras] -test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest", "pytest-cov", "pytest-instafail", "pytest-subtests", "pytest-sugar"] +test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "sumtypes"] [[package]] name = "griffe" -version = "0.38.1" +version = "0.39.1" description = "Signatures for entire Python programs. 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"evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic (<2)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.19)", "torch (>=1.11,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"] +docs = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.19)", "torch (>=1.11,!=1.12.0)", "torchaudio", "torchvision"] docs-specific = ["hf-doc-builder"] flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"] flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] @@ -4561,7 +4605,7 @@ ftfy = ["ftfy"] integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"] ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"] modelcreation = ["cookiecutter (==1.7.3)"] -natten = ["natten (>=0.14.6)"] +natten = ["natten (>=0.14.6,<0.15.0)"] onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"] onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"] optuna = ["optuna"] @@ -4580,10 +4624,10 @@ tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6, tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] timm = ["timm"] tokenizers = ["tokenizers (>=0.14,<0.19)"] -torch = ["accelerate (>=0.21.0)", "torch (>=1.10,!=1.12.0)"] +torch = ["accelerate (>=0.21.0)", "torch (>=1.11,!=1.12.0)"] torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"] -torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.14,<0.19)", "torch (>=1.10,!=1.12.0)", "tqdm (>=4.27)"] +torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.14,<0.19)", "torch (>=1.11,!=1.12.0)", "tqdm (>=4.27)"] video = ["av (==9.2.0)", "decord (==0.6.0)"] vision = ["Pillow (>=10.0.1,<=15.0)"] @@ -4629,13 +4673,13 @@ test = ["mypy", "pytest", "typing-extensions"] [[package]] name = "types-python-dateutil" -version = "2.8.19.14" +version = "2.8.19.20240106" description = "Typing stubs for python-dateutil" optional = false -python-versions = "*" +python-versions = ">=3.8" files = [ - {file = "types-python-dateutil-2.8.19.14.tar.gz", hash = "sha256:1f4f10ac98bb8b16ade9dbee3518d9ace017821d94b057a425b069f834737f4b"}, - {file = "types_python_dateutil-2.8.19.14-py3-none-any.whl", hash = "sha256:f977b8de27787639986b4e28963263fd0e5158942b3ecef91b9335c130cb1ce9"}, + {file = "types-python-dateutil-2.8.19.20240106.tar.gz", hash = "sha256:1f8db221c3b98e6ca02ea83a58371b22c374f42ae5bbdf186db9c9a76581459f"}, + {file = "types_python_dateutil-2.8.19.20240106-py3-none-any.whl", hash = "sha256:efbbdc54590d0f16152fa103c9879c7d4a00e82078f6e2cf01769042165acaa2"}, ] [[package]] @@ -4692,13 +4736,13 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "wandb" -version = "0.16.1" +version = "0.16.2" description = "A CLI and library for interacting with the Weights & Biases API." optional = false python-versions = ">=3.7" files = [ - {file = "wandb-0.16.1-py3-none-any.whl", hash = "sha256:1d7423f92520984585bae9693bb637ae08d3e0c1d75ad4b34215bc44431f114c"}, - {file = "wandb-0.16.1.tar.gz", hash = "sha256:ffe6e8dd8cc8fcd72010c1246fb3d6d226b37c4f111f3f94308a1c0ae28a2fec"}, + {file = "wandb-0.16.2-py3-none-any.whl", hash = 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"azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "typing-extensions"] +media = ["bokeh", "moviepy", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit-pypi", "soundfile"] models = ["cloudpickle"] perf = ["orjson"] sweeps = ["sweeps (>=0.2.0)"] @@ -4768,13 +4811,13 @@ watchmedo = ["PyYAML (>=3.10)"] [[package]] name = "wcwidth" -version = "0.2.12" +version = "0.2.13" description = "Measures the displayed width of unicode strings in a terminal" optional = false python-versions = "*" files = [ - {file = "wcwidth-0.2.12-py2.py3-none-any.whl", hash = "sha256:f26ec43d96c8cbfed76a5075dac87680124fa84e0855195a6184da9c187f133c"}, - {file = "wcwidth-0.2.12.tar.gz", hash = "sha256:f01c104efdf57971bcb756f054dd58ddec5204dd15fa31d6503ea57947d97c02"}, + {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"}, + {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"}, ] [[package]] @@ -5114,4 +5157,4 @@ cffi = ["cffi (>=1.11)"] [metadata] lock-version = "2.0" python-versions = ">=3.10, <3.12" -content-hash = "ecbd7cbeb4c7821efd7a3279e20893011882715222f6230b5fb552db82e83ffb" +content-hash = "762049bdd3e2518e103ae7c66bdfae8d2d5d43bcbf9795efd7e949a9ed78d93f" diff --git a/pyproject.toml b/pyproject.toml index 347bb10f..157a969b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -31,7 +31,7 @@ pytest-integration=">=0.2.3" pytest-timeout=">=2.2.0" pytest-xdist="^3.5.0" - ruff=">=0.1.4" + ruff=">=0.1.14" syrupy=">=4.6.0" [tool.poetry.group.demos.dependencies] diff --git a/sparse_autoencoder/activation_resampler/activation_resampler.py b/sparse_autoencoder/activation_resampler/activation_resampler.py index e2562bcd..d46ffcaf 100644 --- a/sparse_autoencoder/activation_resampler/activation_resampler.py +++ b/sparse_autoencoder/activation_resampler/activation_resampler.py @@ -7,6 +7,7 @@ from pydantic import Field, NonNegativeInt, PositiveInt, validate_call import torch from torch import Tensor +from torch.nn.parallel import DataParallel from torch.utils.data import DataLoader from sparse_autoencoder.activation_resampler.utils.component_slice_tensor import ( @@ -17,7 +18,6 @@ from sparse_autoencoder.loss.abstract_loss import AbstractLoss from sparse_autoencoder.tensor_types import Axis from sparse_autoencoder.train.utils.get_model_device import get_model_device -from sparse_autoencoder.utils.data_parallel import DataParallelWithModelAttributes @dataclass @@ -207,7 +207,7 @@ def _get_dead_neuron_indices( def compute_loss_and_get_activations( self, store: ActivationStore, - autoencoder: SparseAutoencoder | DataParallelWithModelAttributes[SparseAutoencoder], + autoencoder: SparseAutoencoder | DataParallel[SparseAutoencoder], loss_fn: AbstractLoss, train_batch_size: int, ) -> LossInputActivationsTuple: @@ -440,7 +440,7 @@ def renormalize_and_scale( def resample_dead_neurons( self, activation_store: ActivationStore, - autoencoder: SparseAutoencoder | DataParallelWithModelAttributes[SparseAutoencoder], + autoencoder: SparseAutoencoder | DataParallel[SparseAutoencoder], loss_fn: AbstractLoss, train_batch_size: int, ) -> list[ParameterUpdateResults]: @@ -530,7 +530,7 @@ def step_resampler( self, batch_neuron_activity: Int64[Tensor, Axis.names(Axis.COMPONENT, Axis.LEARNT_FEATURE)], activation_store: ActivationStore, - autoencoder: SparseAutoencoder | DataParallelWithModelAttributes[SparseAutoencoder], + autoencoder: SparseAutoencoder | DataParallel[SparseAutoencoder], loss_fn: AbstractLoss, train_batch_size: int, ) -> list[ParameterUpdateResults] | None: diff --git a/sparse_autoencoder/autoencoder/model.py b/sparse_autoencoder/autoencoder/model.py index 0ae8a9a9..34e031e6 100644 --- a/sparse_autoencoder/autoencoder/model.py +++ b/sparse_autoencoder/autoencoder/model.py @@ -276,7 +276,7 @@ def load( The loaded model. """ # Load the file - serialized_state = torch.load(file_path) + serialized_state = torch.load(file_path, map_location=torch.device("cpu")) state = SparseAutoencoderState.model_validate(serialized_state) # Initialise the model diff --git a/sparse_autoencoder/metrics/validate/tests/test_model_reconstruction_score.py b/sparse_autoencoder/metrics/validate/tests/test_model_reconstruction_score.py index 1cf8739c..bb1424ff 100644 --- a/sparse_autoencoder/metrics/validate/tests/test_model_reconstruction_score.py +++ b/sparse_autoencoder/metrics/validate/tests/test_model_reconstruction_score.py @@ -1,15 +1,13 @@ """Test the model reconstruction score metric.""" -from jaxtyping import Float import pytest from syrupy.session import SnapshotSession import torch -from torch import Tensor +from torch import Tensor, tensor from sparse_autoencoder.metrics.utils.find_metric_result import find_metric_result from sparse_autoencoder.metrics.validate.abstract_validate_metric import ValidationMetricData from sparse_autoencoder.metrics.validate.model_reconstruction_score import ModelReconstructionScore -from sparse_autoencoder.tensor_types import Axis def test_model_reconstruction_score_empty_data() -> None: @@ -19,9 +17,9 @@ def test_model_reconstruction_score_empty_data() -> None: is provided (i.e., at the end of training or in similar scenarios). """ data = ValidationMetricData( - source_model_loss=Float[Tensor, Axis.ITEMS]([]), - source_model_loss_with_reconstruction=Float[Tensor, Axis.ITEMS]([]), - source_model_loss_with_zero_ablation=Float[Tensor, Axis.ITEMS]([]), + source_model_loss=tensor([]), + source_model_loss_with_reconstruction=tensor([]), + source_model_loss_with_zero_ablation=tensor([]), ) metric = ModelReconstructionScore() result = metric.calculate(data) @@ -41,13 +39,9 @@ def test_model_reconstruction_score_empty_data() -> None: ), ( ValidationMetricData( - source_model_loss=Float[Tensor, Axis.ITEMS]([[0.5], [1.5], [2.5]]), - source_model_loss_with_reconstruction=Float[Tensor, Axis.ITEMS]( - [[1.5], [2.5], [3.5]] - ), - source_model_loss_with_zero_ablation=Float[Tensor, Axis.ITEMS]( - [[8.0], [7.0], [4.0]] - ), + source_model_loss=tensor([[0.5], [1.5], [2.5]]), + source_model_loss_with_reconstruction=tensor([[1.5], [2.5], [3.5]]), + source_model_loss_with_zero_ablation=tensor([[8.0], [7.0], [4.0]]), ), 0.79, ), diff --git a/sparse_autoencoder/optimizer/adam_with_reset.py b/sparse_autoencoder/optimizer/adam_with_reset.py index 4401c29f..0f83bd34 100644 --- a/sparse_autoencoder/optimizer/adam_with_reset.py +++ b/sparse_autoencoder/optimizer/adam_with_reset.py @@ -42,7 +42,7 @@ def __init__( # (extending existing implementation) lr: float | Float[Tensor, Axis.names(Axis.SINGLE_ITEM)] = 1e-3, betas: tuple[float, float] = (0.9, 0.999), eps: float = 1e-8, - weight_decay: float = 0, + weight_decay: float = 0.0, *, amsgrad: bool = False, foreach: bool | None = None, diff --git a/sparse_autoencoder/source_model/replace_activations_hook.py b/sparse_autoencoder/source_model/replace_activations_hook.py index e0cf6874..377d6124 100644 --- a/sparse_autoencoder/source_model/replace_activations_hook.py +++ b/sparse_autoencoder/source_model/replace_activations_hook.py @@ -1,33 +1,36 @@ """Replace activations hook.""" from typing import TYPE_CHECKING +from jaxtyping import Float from torch import Tensor +from torch.nn.parallel import DataParallel from transformer_lens.hook_points import HookPoint from sparse_autoencoder.autoencoder.model import SparseAutoencoder -from sparse_autoencoder.utils.data_parallel import DataParallelWithModelAttributes if TYPE_CHECKING: from sparse_autoencoder.tensor_types import Axis -from jaxtyping import Float def replace_activations_hook( value: Tensor, hook: HookPoint, # noqa: ARG001 - sparse_autoencoder: SparseAutoencoder | DataParallelWithModelAttributes[SparseAutoencoder], + sparse_autoencoder: SparseAutoencoder | DataParallel[SparseAutoencoder], component_idx: int | None = None, + n_components: int | None = None, ) -> Tensor: """Replace activations hook. + This should be pre-initialised with `functools.partial`. + Args: value: The activations to replace. hook: The hook point. - sparse_autoencoder: The sparse autoencoder. This should be pre-initialised with - `functools.partial`. + sparse_autoencoder: The sparse autoencoder. component_idx: The component index to replace the activations with, if just replacing activations for a single component. Requires the model to have a component axis. + n_components: The number of components that the SAE is trained on. Returns: Replaced activations. @@ -43,11 +46,8 @@ def replace_activations_hook( ) if component_idx is not None: - if sparse_autoencoder.config.n_components is None: - error_message = ( - "Cannot replace for a specific component, if the model does not have a " - "component axis." - ) + if n_components is None: + error_message = "The number of model components must be set if component_idx is set." raise RuntimeError(error_message) # The approach here is to run a forward pass with dummy values for all components other than @@ -56,7 +56,7 @@ def replace_activations_hook( # components. expanded_shape = [ squashed_value.shape[0], - sparse_autoencoder.config.n_components, + n_components, squashed_value.shape[-1], ] expanded = squashed_value.unsqueeze(1).expand(*expanded_shape) diff --git a/sparse_autoencoder/source_model/tests/test_replace_activations_hook.py b/sparse_autoencoder/source_model/tests/test_replace_activations_hook.py index efee221f..165c74df 100644 --- a/sparse_autoencoder/source_model/tests/test_replace_activations_hook.py +++ b/sparse_autoencoder/source_model/tests/test_replace_activations_hook.py @@ -66,7 +66,12 @@ def test_hook_replaces_activations_2_components() -> None: fwd_hooks=[ ( "blocks.0.hook_mlp_out", - partial(replace_activations_hook, sparse_autoencoder=autoencoder, component_idx=1), + partial( + replace_activations_hook, + sparse_autoencoder=autoencoder, + component_idx=1, + n_components=2, + ), ) ], ) diff --git a/sparse_autoencoder/train/pipeline.py b/sparse_autoencoder/train/pipeline.py index bccad468..4c2bcc09 100644 --- a/sparse_autoencoder/train/pipeline.py +++ b/sparse_autoencoder/train/pipeline.py @@ -9,6 +9,7 @@ from pydantic import NonNegativeInt, PositiveInt, validate_call import torch from torch import Tensor +from torch.nn.parallel import DataParallel from torch.optim.lr_scheduler import LRScheduler from torch.utils.data import DataLoader from tqdm.auto import tqdm @@ -32,7 +33,6 @@ from sparse_autoencoder.source_model.zero_ablate_hook import zero_ablate_hook from sparse_autoencoder.tensor_types import Axis from sparse_autoencoder.train.utils.get_model_device import get_model_device -from sparse_autoencoder.utils.data_parallel import DataParallelWithModelAttributes if TYPE_CHECKING: @@ -51,9 +51,15 @@ class Pipeline: activation_resampler: ActivationResampler | None """Activation resampler to use.""" - autoencoder: SparseAutoencoder | DataParallelWithModelAttributes[SparseAutoencoder] + autoencoder: SparseAutoencoder | DataParallel[SparseAutoencoder] """Sparse autoencoder to train.""" + n_input_features: int + """Number of input features in the sparse autoencoder.""" + + n_learned_features: int + """Number of learned features in the sparse autoencoder.""" + cache_names: list[str] """Names of the cache hook points to use in the source model.""" @@ -81,7 +87,7 @@ class Pipeline: source_dataset: SourceDataset """Source dataset to generate activation data from (tokenized prompts).""" - source_model: HookedTransformer | DataParallelWithModelAttributes[HookedTransformer] + source_model: HookedTransformer | DataParallel[HookedTransformer] """Source model to get activations from.""" total_activations_trained_on: int = 0 @@ -97,13 +103,15 @@ def n_components(self) -> int: def __init__( self, activation_resampler: ActivationResampler | None, - autoencoder: SparseAutoencoder | DataParallelWithModelAttributes[SparseAutoencoder], + autoencoder: SparseAutoencoder | DataParallel[SparseAutoencoder], cache_names: list[str], layer: NonNegativeInt, loss: AbstractLoss, optimizer: AbstractOptimizerWithReset, source_dataset: SourceDataset, - source_model: HookedTransformer | DataParallelWithModelAttributes[HookedTransformer], + source_model: HookedTransformer | DataParallel[HookedTransformer], + n_input_features: int, + n_learned_features: int, run_name: str = "sparse_autoencoder", checkpoint_directory: Path = DEFAULT_CHECKPOINT_DIRECTORY, lr_scheduler: LRScheduler | None = None, @@ -124,6 +132,8 @@ def __init__( optimizer: Optimizer to use. source_dataset: Source dataset to get data from. source_model: Source model to get activations from. + n_input_features: Number of input features in the sparse autoencoder. + n_learned_features: Number of learned features in the sparse autoencoder. run_name: Name of the run for saving checkpoints. checkpoint_directory: Directory to save checkpoints to. lr_scheduler: Learning rate scheduler to use. @@ -146,6 +156,8 @@ def __init__( self.source_data_batch_size = source_data_batch_size self.source_dataset = source_dataset self.source_model = source_model + self.n_input_features = n_input_features + self.n_learned_features = n_learned_features # Create a stateful iterator source_dataloader = source_dataset.get_dataloader( @@ -175,9 +187,10 @@ def generate_activations(self, store_size: PositiveInt) -> TensorActivationStore raise ValueError(error_message) # Setup the store - n_neurons: int = self.autoencoder.config.n_input_features source_model_device: torch.device = get_model_device(self.source_model) - store = TensorActivationStore(store_size, n_neurons, n_components=self.n_components) + store = TensorActivationStore( + store_size, self.n_input_features, n_components=self.n_components + ) # Add the hook to the model (will automatically store the activations every time the model # runs) @@ -225,9 +238,9 @@ def train_autoencoder( learned_activations_fired_count: Int64[ Tensor, Axis.names(Axis.COMPONENT, Axis.LEARNT_FEATURE) ] = torch.zeros( - (self.n_components, self.autoencoder.config.n_learned_features), + (self.n_components, self.n_learned_features), dtype=torch.int64, - device=autoencoder_device, + device=torch.device("cpu"), ) for store_batch in activations_dataloader: @@ -260,7 +273,7 @@ def train_autoencoder( # Store count of how many neurons have fired with torch.no_grad(): fired = learned_activations > 0 - learned_activations_fired_count.add_(fired.sum(dim=0)) + learned_activations_fired_count.add_(fired.sum(dim=0).cpu()) # Backwards pass total_loss.backward() @@ -358,6 +371,7 @@ def validate_sae(self, validation_n_activations: PositiveInt) -> None: replace_activations_hook, sparse_autoencoder=self.autoencoder, component_idx=component_idx, + n_components=self.n_components, ) with torch.no_grad(): diff --git a/sparse_autoencoder/train/sweep.py b/sparse_autoencoder/train/sweep.py index 2adbdc43..3faeac37 100644 --- a/sparse_autoencoder/train/sweep.py +++ b/sparse_autoencoder/train/sweep.py @@ -5,9 +5,9 @@ import traceback import torch +from torch.nn.parallel import DataParallel from torch.optim.lr_scheduler import CosineAnnealingLR, LRScheduler, ReduceLROnPlateau from transformer_lens import HookedTransformer -from transformer_lens.utils import get_device from transformers import AutoTokenizer import wandb @@ -28,7 +28,6 @@ RuntimeHyperparameters, SweepConfig, ) -from sparse_autoencoder.utils.data_parallel import DataParallelWithModelAttributes def setup_activation_resampler(hyperparameters: RuntimeHyperparameters) -> ActivationResampler: @@ -71,14 +70,13 @@ def setup_source_model(hyperparameters: RuntimeHyperparameters) -> HookedTransfo ) -def setup_autoencoder( - hyperparameters: RuntimeHyperparameters, device: torch.device -) -> SparseAutoencoder: +def setup_autoencoder_optimizer_scheduler( + hyperparameters: RuntimeHyperparameters, +) -> tuple[SparseAutoencoder, AdamWithReset, LRScheduler | None]: """Setup the sparse autoencoder. Args: hyperparameters: The hyperparameters dictionary. - device: The computation device. Returns: The initialized sparse autoencoder. @@ -91,41 +89,12 @@ def setup_autoencoder( n_learned_features=autoencoder_input_dim * expansion_factor, n_components=len(hyperparameters["source_model"]["cache_names"]), ) - return SparseAutoencoder(config).to(device) - - -def setup_loss_function(hyperparameters: RuntimeHyperparameters) -> LossReducer: - """Setup the loss function for the autoencoder. - Args: - hyperparameters: The hyperparameters dictionary. + model = SparseAutoencoder(config) - Returns: - The combined loss function. - """ - return LossReducer( - LearnedActivationsL1Loss( - l1_coefficient=hyperparameters["loss"]["l1_coefficient"], - ), - L2ReconstructionLoss(), - ) - - -def setup_optimizer( - autoencoder: SparseAutoencoder, hyperparameters: RuntimeHyperparameters -) -> tuple[AdamWithReset, LRScheduler | None]: - """Setup the optimizer for the autoencoder. - - Args: - autoencoder: The sparse autoencoder model. - hyperparameters: The hyperparameters dictionary. - - Returns: - The initialized optimizer & learning rate scheduler. - """ optim = AdamWithReset( - params=autoencoder.parameters(), - named_parameters=autoencoder.named_parameters(), + params=model.parameters(), + named_parameters=model.named_parameters(), lr=hyperparameters["optimizer"]["lr"], betas=( hyperparameters["optimizer"]["adam_beta_1"], @@ -148,7 +117,24 @@ def setup_optimizer( T_max=10, ) - return optim, lr_scheduler + return (model, optim, lr_scheduler) + + +def setup_loss_function(hyperparameters: RuntimeHyperparameters) -> LossReducer: + """Setup the loss function for the autoencoder. + + Args: + hyperparameters: The hyperparameters dictionary. + + Returns: + The combined loss function. + """ + return LossReducer( + LearnedActivationsL1Loss( + l1_coefficient=hyperparameters["loss"]["l1_coefficient"], + ), + L2ReconstructionLoss(), + ) def setup_source_data(hyperparameters: RuntimeHyperparameters) -> SourceDataset: @@ -257,8 +243,8 @@ def stop_layer_from_cache_names(cache_names: list[str]) -> int: def run_training_pipeline( hyperparameters: RuntimeHyperparameters, - source_model: HookedTransformer | DataParallelWithModelAttributes[HookedTransformer], - autoencoder: SparseAutoencoder | DataParallelWithModelAttributes[SparseAutoencoder], + source_model: HookedTransformer | DataParallel[HookedTransformer], + autoencoder: SparseAutoencoder | DataParallel[SparseAutoencoder], loss: LossReducer, optimizer: AdamWithReset, lr_scheduler: LRScheduler | None, @@ -303,6 +289,11 @@ def run_training_pipeline( run_name=run_name, num_workers_data_loading=hyperparameters["pipeline"]["num_workers_data_loading"], lr_scheduler=lr_scheduler, + n_input_features=hyperparameters["source_model"]["hook_dimension"], + n_learned_features=int( + hyperparameters["autoencoder"]["expansion_factor"] + * hyperparameters["source_model"]["hook_dimension"] + ), ) pipeline.run_pipeline( @@ -322,21 +313,17 @@ def train() -> None: hyperparameters = setup_wandb() run_name: str = wandb.run.name # type: ignore - # Setup the device for training - device = get_device() - # Set up the source model source_model = setup_source_model(hyperparameters) - # Set up the autoencoder - autoencoder = setup_autoencoder(hyperparameters, device) + # Set up the autoencoder, optimizer and learning rate scheduler + autoencoder, optimizer, lr_scheduler = setup_autoencoder_optimizer_scheduler( + hyperparameters + ) # Set up the loss function loss_function = setup_loss_function(hyperparameters) - # Set up the optimizer - optimizer, lr_scheduler = setup_optimizer(autoencoder, hyperparameters) - # Set up the activation resampler activation_resampler = setup_activation_resampler(hyperparameters) @@ -347,7 +334,7 @@ def train() -> None: run_training_pipeline( hyperparameters=hyperparameters, source_model=source_model, - autoencoder=DataParallelWithModelAttributes(autoencoder), + autoencoder=autoencoder, loss=loss_function, optimizer=optimizer, lr_scheduler=lr_scheduler, @@ -361,7 +348,10 @@ def train() -> None: sys.exit(1) -def sweep(sweep_config: SweepConfig | None = None, sweep_id: str | None = None) -> None: +def sweep( + sweep_config: SweepConfig | None = None, + sweep_id: str | None = None, +) -> None: """Run the training pipeline with wandb hyperparameter sweep. Warning: diff --git a/sparse_autoencoder/train/tests/test_pipeline.py b/sparse_autoencoder/train/tests/test_pipeline.py index fbb46588..b6416244 100644 --- a/sparse_autoencoder/train/tests/test_pipeline.py +++ b/sparse_autoencoder/train/tests/test_pipeline.py @@ -70,6 +70,8 @@ def pipeline_fixture() -> Pipeline: source_dataset=source_data, source_model=src_model, source_data_batch_size=10, + n_input_features=src_model.cfg.d_model, + n_learned_features=int(src_model.cfg.d_model * 2), ) diff --git a/sparse_autoencoder/train/tests/test_sweep.py b/sparse_autoencoder/train/tests/test_sweep.py index 4647ff75..4f1da6df 100644 --- a/sparse_autoencoder/train/tests/test_sweep.py +++ b/sparse_autoencoder/train/tests/test_sweep.py @@ -2,11 +2,10 @@ import pytest from syrupy.session import SnapshotSession -import torch from sparse_autoencoder.train.sweep import ( setup_activation_resampler, - setup_autoencoder, + setup_autoencoder_optimizer_scheduler, setup_loss_function, ) from sparse_autoencoder.train.sweep_config import ( @@ -30,7 +29,7 @@ def dummy_hyperparameters() -> RuntimeHyperparameters: "optimizer": { "adam_beta_1": 0.9, "adam_beta_2": 0.99, - "adam_weight_decay": 0, + "adam_weight_decay": 0.0, "amsgrad": False, "fused": False, "lr": 1e-05, @@ -81,7 +80,7 @@ def test_setup_autoencoder( dummy_hyperparameters: RuntimeHyperparameters, snapshot: SnapshotSession ) -> None: """Test the setup_autoencoder function.""" - autoencoder = setup_autoencoder(dummy_hyperparameters, device=torch.device("cpu")) + autoencoder, _, _ = setup_autoencoder_optimizer_scheduler(dummy_hyperparameters) assert snapshot == str(autoencoder), "Autoencoder string representation has changed." diff --git a/sparse_autoencoder/train/utils/get_model_device.py b/sparse_autoencoder/train/utils/get_model_device.py index 0d49bbd5..2ebfb52a 100644 --- a/sparse_autoencoder/train/utils/get_model_device.py +++ b/sparse_autoencoder/train/utils/get_model_device.py @@ -1,9 +1,10 @@ """Get the device that the model is on.""" import torch from torch.nn import Module +from torch.nn.parallel import DataParallel -def get_model_device(model: Module) -> torch.device: +def get_model_device(model: Module | DataParallel) -> torch.device: """Get the device on which a PyTorch model is on. Args: @@ -15,6 +16,10 @@ def get_model_device(model: Module) -> torch.device: Raises: ValueError: If the model has no parameters. """ + # Deepspeed models already have a device property, so just return that + if hasattr(model, "device"): + return model.device + # Check if the model has parameters if len(list(model.parameters())) == 0: exception_message = "The model has no parameters." diff --git a/sparse_autoencoder/train/utils/wandb_sweep_types.py b/sparse_autoencoder/train/utils/wandb_sweep_types.py index 5df4aa5b..a8530041 100644 --- a/sparse_autoencoder/train/utils/wandb_sweep_types.py +++ b/sparse_autoencoder/train/utils/wandb_sweep_types.py @@ -165,7 +165,7 @@ class Distribution(LowercaseStrEnum): class Controller: """Controller.""" - type: ControllerType # noqa: A003 + type: ControllerType @dataclass @@ -180,7 +180,7 @@ class HyperbandStopping: HyperbandStopping(type=hyperband) """ - type: HyperbandStoppingType | None = HyperbandStoppingType.HYPERBAND # noqa: A003 + type: HyperbandStoppingType | None = HyperbandStoppingType.HYPERBAND eta: float | None = None """ETA. @@ -286,10 +286,10 @@ class Parameter(Generic[ParamType]): Specifies the single valid value for this hyperparameter. Compatible with grid. """ - max: ParamType | None = None # noqa: A003 + max: ParamType | None = None """Maximum value.""" - min: ParamType | None = None # noqa: A003 + min: ParamType | None = None """Minimum value.""" distribution: Distribution | None = None diff --git a/sparse_autoencoder/utils/data_parallel.py b/sparse_autoencoder/utils/data_parallel.py deleted file mode 100644 index 4895b412..00000000 --- a/sparse_autoencoder/utils/data_parallel.py +++ /dev/null @@ -1,40 +0,0 @@ -"""Data parallel utils.""" -from typing import Any, Generic, TypeVar - -from torch.nn import DataParallel, Module - - -T = TypeVar("T", bound=Module) - - -class DataParallelWithModelAttributes(DataParallel[T], Generic[T]): - """Data parallel with access to underlying model attributes/methods. - - Allows access to underlying model attributes/methods, which is not possible with the default - `DataParallel` class. Based on: - https://pytorch.org/tutorials/beginner/former_torchies/parallelism_tutorial.html - - Example: - >>> from sparse_autoencoder import SparseAutoencoder, SparseAutoencoderConfig - >>> model = SparseAutoencoder(SparseAutoencoderConfig( - ... n_input_features=2, - ... n_learned_features=4, - ... )) - >>> distributed_model = DataParallelWithModelAttributes(model) - >>> distributed_model.config.n_learned_features - 4 - """ - - def __getattr__(self, name: str) -> Any: # noqa: ANN401 - """Allow access to underlying model attributes/methods. - - Args: - name: Attribute/method name. - - Returns: - Attribute value/method. - """ - try: - return super().__getattr__(name) - except AttributeError: - return getattr(self.module, name)