diff --git a/forge/test/operators/pytorch/nn/test_embedding.py b/forge/test/operators/pytorch/nn/test_embedding.py new file mode 100644 index 000000000..72d0306df --- /dev/null +++ b/forge/test/operators/pytorch/nn/test_embedding.py @@ -0,0 +1,422 @@ +# SPDX-FileCopyrightText: © 2024 Tenstorrent AI ULC + +# SPDX-License-Identifier: Apache-2.0 +# +# Tests for testing of embedding operators +# +# In this test we test pytorch embedding operator + +# GENERAL OP SUPPORT TEST PLAN: +# 1. Operand type - any supported type +# 2. Operand source(s): +# (+) 2.1 From another op +# - Operator -> input +# (+) 2.2 From DRAM queue +# - Operator is first node in network +# - Input_queue flag = false +# (+) 2.3 Const Inputs (const eval pass) +# - Operator where all inputs are constants. +# (+) 2.4 From host +# - Input tensor as input of network +# - Operator is first node in network +# - Input_queue flag = true +# 3 Operand shapes type(s): +# (+) 3.1 Full tensor (i.e. full expected shape) +# - 3-4 by default P1 (high prioriy) +# - 2, 5, ++ include P2 (lower prioriy) +# (+) 3.2 Tensor reduce on one or more dims to 1 +# - Vector +# - Only one dim is not equal to 1 +# (+) 3.3 Scalar P2 +# - Create tensor of dimension equal to 0 (tensor from scalar) or just to use scalar as simple value +# 4. Operand / output size of dimensions (few examples of each, 10 values total) +# (+) 4.1 Divisible by 32 +# (+) 4.2 Prime numbers +# (+) 4.3 Very large (thousands, 10s of thousands) +# - 100x100, 100x1000 +# - maybe nightly only +# (+) 4.4 Extreme ratios between height/width +# 4.5 ...probably many more interesting combinations here +# 5. Data format - all supported formats +# (/) 5.1 Output DF +# (/) 5.2 Intermediate DF +# (/) 5.3 Accumulation DF +# (+) 5.4 Operand DFs +# - Fix HiFi4 for math fidelity value +# (+) 6. Math fidelity - LoFi, HiFi2a, Hifi2b, Hifi3, Hifi4 +# - Fix fp16b (default) for data format value +# (/) 7. Special attributes - if applicable.. like approx_mode for Exp, for example +# (/) 8. Special cases - if applicable +# 9. Variable number of operands - if applicable +# (/) Few representative values +# (/) Reuse inputs for selected operators + + +from functools import reduce +import random +import pytest + +from typing import List, Dict, Type, Optional, Any +from loguru import logger + +import torch +import forge +import forge.op + + +from test.operators.utils import InputSourceFlags, VerifyUtils +from test.operators.utils import InputSource +from test.operators.utils import TestVector +from test.operators.utils import TestPlan +from test.operators.utils import FailingReasons +from test.operators.utils.compat import TestDevice +from test.operators.utils import TestCollection +from test.operators.utils import TestCollectionCommon +from test.operators.utils.datatypes import ValueRange + + +class ModelFromAnotherOp(torch.nn.Module): + + model_name = "model_op_src_from_another_op" + + def __init__(self, operator, opname, shape, kwargs): + super(ModelFromAnotherOp, self).__init__() + self.testname = "Embedding_pytorch_operator_" + opname + "_test_op_src_from_another_op" + self.operator = operator + self.opname = opname + self.shape = shape + self.kwargs = { + "num_embeddings": kwargs["num_embeddings"], + "embedding_dim": kwargs["embedding_dim"], + } + + self.weight = torch.rand( + (self.kwargs["num_embeddings"], self.kwargs["embedding_dim"]), dtype=kwargs["weight_dtype"] + ) + self.embedding = self.operator(**self.kwargs, _weight=self.weight) + + def forward(self, x: torch.Tensor): + # we use Add operator to create one operands which is input for the embedding operator + add = torch.add(x, x) + output = self.embedding(add) + return output + + +class ModelDirect(torch.nn.Module): + + model_name = "model_op_src_from_host" + + def __init__(self, operator, opname, shape, kwargs): + super(ModelDirect, self).__init__() + self.testname = "Embedding_pytorch_operator_" + opname + "_test_op_src_from_host" + self.operator = operator + self.opname = opname + self.shape = shape + self.kwargs = { + "num_embeddings": kwargs["num_embeddings"], + "embedding_dim": kwargs["embedding_dim"], + } + + self.weight = torch.rand( + (self.kwargs["num_embeddings"], self.kwargs["embedding_dim"]), dtype=kwargs["weight_dtype"] + ) + self.embedding = self.operator(**self.kwargs, _weight=self.weight) + + def forward(self, x: torch.Tensor): + output = self.embedding(x) + return output + + +class ModelConstEvalPass(torch.nn.Module): + + model_name = "model_op_src_const_eval_pass" + + def __init__(self, operator, opname, shape, kwargs): + super(ModelConstEvalPass, self).__init__() + self.testname = "Embedding_pytorch_operator_" + opname + "_test_op_src_const_eval_pass" + self.operator = operator + self.opname = opname + self.shape = shape + self.kwargs = { + "num_embeddings": kwargs["num_embeddings"], + "embedding_dim": kwargs["embedding_dim"], + } + + self.constant = torch.randint(0, self.kwargs["num_embeddings"] - 1, self.shape, dtype=torch.int32) + + self.weight = torch.rand( + (self.kwargs["num_embeddings"], self.kwargs["embedding_dim"]), dtype=kwargs["weight_dtype"] + ) + self.embedding = self.operator(**self.kwargs, _weight=self.weight) + + def forward(self, x: torch.Tensor): + v1 = self.embedding(self.constant) + # v2 = torch.add(x, x) + v2 = self.embedding(x) + # add consume inputs + add = torch.add(v1, v2) + return add + + +class TestVerification: + + MODEL_TYPES = { + InputSource.FROM_ANOTHER_OP: ModelFromAnotherOp, + InputSource.FROM_HOST: ModelDirect, + InputSource.FROM_DRAM_QUEUE: ModelDirect, + InputSource.CONST_EVAL_PASS: ModelConstEvalPass, + } + + @classmethod + def verify( + cls, + test_device: TestDevice, + test_vector: TestVector, + input_params: List[Dict] = [], + number_of_operands: int = 1, + warm_reset: bool = False, + ): + """Common verification function for all tests""" + + input_source_flag: InputSourceFlags = None + if test_vector.input_source in (InputSource.FROM_DRAM_QUEUE,): + input_source_flag = InputSourceFlags.FROM_DRAM + + operator = getattr(torch.nn, test_vector.operator) + + kwargs = test_vector.kwargs if test_vector.kwargs else {} + + model_type = cls.MODEL_TYPES[test_vector.input_source] + pytorch_model = model_type( + operator=operator, + opname=test_vector.operator, + shape=test_vector.input_shape, + kwargs=kwargs, + ) + + input_shapes = tuple([test_vector.input_shape for _ in range(number_of_operands)]) + logger.trace(f"***input_shapes: {input_shapes}") + + min = 0 + max = test_vector.kwargs["num_embeddings"] - 1 + match test_vector.input_source: + case InputSource.FROM_ANOTHER_OP: + max = int(max / 2) + value_range = ValueRange(min, max) + + VerifyUtils.verify( + model=pytorch_model, + test_device=test_device, + input_shapes=input_shapes, + input_params=input_params, + input_source_flag=input_source_flag, + dev_data_format=test_vector.dev_data_format, + math_fidelity=test_vector.math_fidelity, + pcc=test_vector.pcc, + warm_reset=warm_reset, + value_range=value_range, + ) + + +class TestParamsData: + + __test__ = False # Avoid collecting TestParamsData as a pytest test + + test_plan: TestPlan = None + + rng = random.Random(31) + INPUT_SHAPE_THRESHOLD = 100000000 + MAX_EMBEDDING_DIM = 10000 + + embedding_dims = [1000, 3200, MAX_EMBEDDING_DIM] + # weight_dtypes = [torch.bfloat16, torch.float32] + + @classmethod + def generate_kwargs(cls, test_vector: TestVector, weight_dtype: Type[torch.dtype]): + num_embedding_limit = 0 + num_embeddings = [] + match test_vector.dev_data_format: + case forge.DataFormat.RawUInt8: + num_embedding_limit = 2**8 - 1 # 255 + num_embeddings = [cls.rng.randint(2, num_embedding_limit)] + case forge.DataFormat.RawUInt16: + num_embedding_limit = 2**16 - 1 # 65535 + num_embeddings = [cls.rng.randint(2, num_embedding_limit)] + case forge.DataFormat.RawUInt32: + num_embedding_limit = 2**32 - 1 # 4294967295 + num_embeddings = [cls.rng.randint(2, 32000)] + case forge.DataFormat.Int8: + num_embedding_limit = 2**7 - 1 # 127 + num_embeddings = [cls.rng.randint(2, num_embedding_limit)] + case forge.DataFormat.UInt16: + num_embedding_limit = 2**16 - 1 # 65535 + num_embeddings = [cls.rng.randint(2, num_embedding_limit)] + case forge.DataFormat.Int32: + num_embedding_limit = 2**31 - 1 # 2147483647 + num_embeddings = [cls.rng.randint(2, 32000)] + + kwarg_list = [] + for num_embeddings in num_embeddings: + for embedding_dim in cls.embedding_dims: + kwarg_list.append( + { + "num_embeddings": num_embeddings, + "embedding_dim": embedding_dim, + "weight_dtype": weight_dtype, + } + ) + return kwarg_list + + +class TestCollectionData: + + __test__ = False # Avoid collecting TestCollectionData as a pytest test + + all = TestCollection( + operators=[ + "Embedding", # 00 + ], + input_sources=TestCollectionCommon.all.input_sources, + input_shapes=[ + input_shape + for input_shape in TestCollectionCommon.all.input_shapes + if reduce(lambda x, y: x * y, input_shape) * TestParamsData.MAX_EMBEDDING_DIM + < TestParamsData.INPUT_SHAPE_THRESHOLD + ], + dev_data_formats=TestCollectionCommon.int.dev_data_formats, + math_fidelities=TestCollectionCommon.all.math_fidelities, + ) + + single = TestCollection( + input_sources=TestCollectionCommon.single.input_sources, + input_shapes=TestCollectionCommon.single.input_shapes, + dev_data_formats=[ + pytest.param(forge.DataFormat.Int32, id="Int32"), + ], + math_fidelities=TestCollectionCommon.single.math_fidelities, + ) + + +TestParamsData.test_plan = TestPlan( + verify=lambda test_device, test_vector: TestVerification.verify( + test_device, + test_vector, + ), + collections=[ + # Test plan: + # 2. Operand source(s): + # 3. Operand shapes type(s): + # 4. Operand / output size of dimensions + TestCollection( + operators=TestCollectionData.all.operators, + input_sources=[ + InputSource.FROM_ANOTHER_OP, + ], + input_shapes=TestCollectionData.single.input_shapes, + dev_data_formats=TestCollectionData.single.dev_data_formats, + kwargs=lambda test_vector: TestParamsData.generate_kwargs(test_vector, torch.bfloat16), + ), + TestCollection( + operators=TestCollectionData.all.operators, + input_sources=[ + InputSource.FROM_HOST, + InputSource.FROM_DRAM_QUEUE, + InputSource.CONST_EVAL_PASS, + ], + input_shapes=TestCollectionData.single.input_shapes, + dev_data_formats=TestCollectionData.single.dev_data_formats, + kwargs=lambda test_vector: TestParamsData.generate_kwargs(test_vector, torch.float32), + ), + TestCollection( + operators=TestCollectionData.all.operators, + input_sources=[ + InputSource.FROM_HOST, + InputSource.FROM_DRAM_QUEUE, + InputSource.CONST_EVAL_PASS, + ], + input_shapes=TestCollectionData.all.input_shapes, + dev_data_formats=TestCollectionData.all.dev_data_formats, + kwargs=lambda test_vector: TestParamsData.generate_kwargs(test_vector, torch.bfloat16), + ), + # Test plan: + # 6. Math fidelity + TestCollection( + operators=TestCollectionData.all.operators, + input_sources=TestCollectionData.single.input_sources, + input_shapes=TestCollectionData.single.input_shapes, + kwargs=lambda test_vector: TestParamsData.generate_kwargs(test_vector, torch.bfloat16), + dev_data_formats=TestCollectionData.single.dev_data_formats, + math_fidelities=TestCollectionData.all.math_fidelities, + ), + ], + failing_rules=[ + TestCollection( + input_sources=[ + InputSource.FROM_ANOTHER_OP, + ], + failing_reason=FailingReasons.INFERENCE_FAILED, + ), + TestCollection( + input_sources=[ + InputSource.FROM_HOST, + InputSource.FROM_DRAM_QUEUE, + ], + criteria=lambda test_vector: test_vector.kwargs["weight_dtype"] == torch.bfloat16, + failing_reason=FailingReasons.DATA_MISMATCH, + ), + TestCollection( + input_sources=[ + InputSource.CONST_EVAL_PASS, + ], + criteria=lambda test_vector: test_vector.kwargs["weight_dtype"] == torch.bfloat16, + failing_reason=FailingReasons.INFERENCE_FAILED, + ), + TestCollection( + input_sources=[ + InputSource.FROM_HOST, + InputSource.FROM_DRAM_QUEUE, + InputSource.CONST_EVAL_PASS, + ], + criteria=lambda test_vector: test_vector.kwargs["weight_dtype"] == torch.float32, + failing_reason=FailingReasons.INFERENCE_FAILED, + ), + TestCollection( + input_sources=[ + InputSource.FROM_HOST, + InputSource.FROM_DRAM_QUEUE, + ], + input_shapes=[ + (9920, 1), + ], + kwargs=[ + { + "embedding_dim": 10000, + }, + ], + failing_reason=FailingReasons.ALLOCATION_FAILED, + ), + TestCollection( + input_sources=[ + InputSource.FROM_HOST, + InputSource.FROM_DRAM_QUEUE, + InputSource.CONST_EVAL_PASS, + ], + input_shapes=[ + (1, 9920, 1), + (1, 1, 9920, 1), + ], + kwargs=[ + { + "embedding_dim": 10000, + }, + ], + skip_reason=FailingReasons.SEG_FAULT, + ), + ], +) + + +def get_test_plans() -> List[TestPlan]: + return [ + TestParamsData.test_plan, + ] diff --git a/forge/test/operators/utils/failing_reasons.py b/forge/test/operators/utils/failing_reasons.py index 3ba36eed2..af8f7f7bf 100644 --- a/forge/test/operators/utils/failing_reasons.py +++ b/forge/test/operators/utils/failing_reasons.py @@ -127,6 +127,9 @@ def validate_exception_message( lambda ex: isinstance(ex, RuntimeError) and "tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/allocator.cpp:143" in f"{ex}", + lambda ex: isinstance(ex, RuntimeError) + and "tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/allocator.cpp:145" + in f"{ex}", ], FailingReasons.ATTRIBUTE_ERROR: [ lambda ex: isinstance(ex, AttributeError), @@ -152,6 +155,11 @@ def validate_exception_message( lambda ex: isinstance(ex, RuntimeError) and "tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/reduction/generic/generic_reductions.cpp" in f"{ex}", + lambda ex: isinstance(ex, RuntimeError) + and "input_tensor_arg.get_layout() == ttnn::ROW_MAJOR_LAYOUT" in f"{ex}", + lambda ex: isinstance(ex, RuntimeError) and "weights.get_dtype() == DataType::BFLOAT16" in f"{ex}", + lambda ex: isinstance(ex, RuntimeError) + and "Tensor 1 - data type mismatch: expected BFloat16, got Float32" in f"{ex}", ], }