From 36cfac9734d4cd0fb9ec1276403af8032a3576aa Mon Sep 17 00:00:00 2001 From: Konstantin Milanovic Date: Mon, 23 Dec 2024 09:32:21 +0000 Subject: [PATCH 1/2] Tests for linear op --- .../test/operators/pytorch/nn/test_linear.py | 345 ++++++++++++++++++ forge/test/operators/utils/failing_reasons.py | 5 + 2 files changed, 350 insertions(+) create mode 100644 forge/test/operators/pytorch/nn/test_linear.py diff --git a/forge/test/operators/pytorch/nn/test_linear.py b/forge/test/operators/pytorch/nn/test_linear.py new file mode 100644 index 000000000..e51bb57bc --- /dev/null +++ b/forge/test/operators/pytorch/nn/test_linear.py @@ -0,0 +1,345 @@ +# 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 forge.verify.config import VerifyConfig +from forge.verify.value_checkers import AllCloseValueChecker + +from test.operators.utils import InputSourceFlags, VerifyUtils, ValueRanges +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.compat import TestTensorsUtils +from test.operators.utils import TestCollection +from test.operators.utils import TestCollectionCommon + + +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 = { + "in_features": kwargs["in_features"], + "out_features": kwargs["out_features"], + } + + self.l1 = self.operator(**self.kwargs) + + 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.l1(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 = { + "in_features": kwargs["in_features"], + "out_features": kwargs["out_features"], + } + + self.l1 = self.operator(**self.kwargs) + + def forward(self, x: torch.Tensor): + output = self.l1(x) + return output + + +class ModelConstEvalPass(torch.nn.Module): + + model_name = "model_op_src_const_eval_pass" + + def __init__(self, operator, opname, shape, kwargs, dtype): + 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 = { + "in_features": kwargs["in_features"], + "out_features": kwargs["out_features"], + } + + self.constant = torch.rand(self.shape, dtype=dtype) + self.l1 = self.operator(**self.kwargs) + + def forward(self, x: torch.Tensor): + v1 = self.l1(self.constant) + # v2 = torch.add(x, x) + v2 = self.l1(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] + if test_vector.input_source == InputSource.CONST_EVAL_PASS: + pytorch_model = model_type( + operator=operator, + opname=test_vector.operator, + shape=test_vector.input_shape, + kwargs=kwargs, + dtype=TestTensorsUtils.get_dtype_for_df(test_vector.dev_data_format), + ) + else: + 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}") + + 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, + deprecated_verification=False, + verify_config=VerifyConfig(value_checker=AllCloseValueChecker(rtol=1e-2, atol=1e-2)), + value_range=ValueRanges.SMALL, + ) + + +class TestParamsData: + + __test__ = False # Avoid collecting TestParamsData as a pytest test + + test_plan: TestPlan = None + + @classmethod + def get_out_features(cls, input_shape: List[int]): + treshold = 10000 + out_features = [] + rng = random.Random(sum(input_shape)) + for _ in range(2): + out_features.append(rng.randint(1, 1000)) + out_features.append(sum(input_shape) % treshold) + return out_features + + @classmethod + def generate_kwargs(cls, test_vector: TestVector): + kwarg_list = [] + in_features = test_vector.input_shape[-1] + out_features_list = TestParamsData.get_out_features(test_vector.input_shape) + bias_list = [True, False] + for out_features in out_features_list: + for bias in bias_list: + kwarg_list.append( + { + "in_features": in_features, + "out_features": out_features, + "bias": bias, + } + ) + return kwarg_list + + +class TestCollectionData: + + __test__ = False # Avoid collecting TestCollectionData as a pytest test + + all = TestCollection( + operators=[ + "Linear", # 00 + ], + input_sources=TestCollectionCommon.all.input_sources, + input_shapes=TestCollectionCommon.all.input_shapes, + dev_data_formats=TestCollectionCommon.all.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=TestCollectionCommon.single.dev_data_formats, + 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=TestCollectionData.all.input_sources, + input_shapes=TestCollectionData.all.input_shapes, + kwargs=lambda test_vector: TestParamsData.generate_kwargs(test_vector), + ), + # Test plan: + # 5. Data format + 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), + dev_data_formats=TestCollectionCommon.float.dev_data_formats, + math_fidelities=TestCollectionData.single.math_fidelities, + ), + # 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), + dev_data_formats=TestCollectionData.single.dev_data_formats, + math_fidelities=TestCollectionData.all.math_fidelities, + ), + ], + failing_rules=[ + # E RuntimeError: The expanded size of the tensor (x) must match the existing size (y) at non-singleton dimension 0. Target sizes: [x]. Tensor sizes: [y] + TestCollection( + input_sources=TestCollectionData.all.input_sources, + criteria=lambda test_vector: len(test_vector.input_shape) == 4 and test_vector.input_shape[0] > 1, + failing_reason=FailingReasons.MICROBATCHING_UNSUPPORTED, + ), + # E ValueError: Data mismatch -> AllCloseValueChecker (all_close): + TestCollection( + input_shapes=[ + (1, 10000), + ], + failing_reason=FailingReasons.DATA_MISMATCH, + ), + # # THIS ERROR OCCURES WHEN USING DEPRICATED VERIFICATION METHOD (NOT ALLCLOSE VALUE CHECKER) + # # E AssertionError: PCC check failed + # # this also happens for other 2 dim ipnut shapes where microbatch size is 1 and out_features is 1 - not all cases are failing + # TestCollection( + # input_shapes=[ + # (1, 10000), + # ], + # kwargs=[ + # { + # "out_features": 1, + # }, + # ], + # failing_reason=FailingReasons.DATA_MISMATCH, + # ), + ], +) + + +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 4c456233e..815ed7d47 100644 --- a/forge/test/operators/utils/failing_reasons.py +++ b/forge/test/operators/utils/failing_reasons.py @@ -164,6 +164,11 @@ def validate_exception_message( in f"{ex}", lambda ex: isinstance(ex, RuntimeError) and "Index is out of bounds for the rank, should be between 0 and 0 however is 1" in f"{ex}", + lambda ex: isinstance(ex, RuntimeError) + and "mat1 and mat2 must have the same dtype, but got Int and Float" in f"{ex}", + ], + FailingReasons.MICROBATCHING_UNSUPPORTED: [ + lambda ex: isinstance(ex, RuntimeError) and "The expanded size of the tensor" in f"{ex}", ], } From e22add7d1b53757317dc6bcd27f2a1e8f0f9e9a4 Mon Sep 17 00:00:00 2001 From: Konstantin Milanovic Date: Mon, 3 Feb 2025 09:47:18 +0000 Subject: [PATCH 2/2] Tests for convTranspose2d (cherry picked from commit bde6e4d3f0c0b1dca8581c483659a24aa7dcbe2c) --- .../pytorch/nn/test_convtranspose2d.py | 271 ++++++++++++++++++ 1 file changed, 271 insertions(+) create mode 100644 forge/test/operators/pytorch/nn/test_convtranspose2d.py diff --git a/forge/test/operators/pytorch/nn/test_convtranspose2d.py b/forge/test/operators/pytorch/nn/test_convtranspose2d.py new file mode 100644 index 000000000..d4b1d5ba5 --- /dev/null +++ b/forge/test/operators/pytorch/nn/test_convtranspose2d.py @@ -0,0 +1,271 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +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 forge.verify.config import VerifyConfig +from forge.verify.value_checkers import AllCloseValueChecker + +from test.operators.utils import InputSourceFlags, VerifyUtils, ValueRanges +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.compat import TestTensorsUtils +from test.operators.utils import TestCollection +from test.operators.utils import TestCollectionCommon + + +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 = "ConvTranspose2d_pytorch_operator_" + opname + "_test_op_src_from_another_op" + self.operator = operator + self.opname = opname + self.shape = shape + self.kwargs = {} + + self.ct1 = self.operator(**self.kwargs) + + def forward(self, x: torch.Tensor): + # we use Add operator to create one operands which is input for the ConvTranspose2d operator + add = torch.add(x, x) + output = self.ct1(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 = "ConvTranspose2d_pytorch_operator_" + opname + "_test_op_src_from_host" + self.operator = operator + self.opname = opname + self.shape = shape + self.kwargs = {} + + self.ct1 = self.operator(**self.kwargs) + + def forward(self, x: torch.Tensor): + output = self.ct1(x) + return output + + +class ModelConstEvalPass(torch.nn.Module): + + model_name = "model_op_src_const_eval_pass" + + def __init__(self, operator, opname, shape, kwargs, dtype): + super(ModelConstEvalPass, self).__init__() + self.testname = "ConvTranspose2d_pytorch_operator_" + opname + "_test_op_src_const_eval_pass" + self.operator = operator + self.opname = opname + self.shape = shape + self.kwargs = {} + + self.constant = torch.rand(self.shape, dtype=dtype) + self.ct1 = self.operator(**self.kwargs) + + def forward(self, x: torch.Tensor): + v1 = self.ct1(self.constant) + # v2 = torch.add(x, x) + v2 = self.ct1(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] + if test_vector.input_source == InputSource.CONST_EVAL_PASS: + pytorch_model = model_type( + operator=operator, + opname=test_vector.operator, + shape=test_vector.input_shape, + kwargs=kwargs, + dtype=TestTensorsUtils.get_dtype_for_df(test_vector.dev_data_format), + ) + else: + 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}") + + 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, + deprecated_verification=False, + verify_config=VerifyConfig(value_checker=AllCloseValueChecker(rtol=1e-2, atol=1e-2)), + value_range=ValueRanges.SMALL, + ) + + +class TestParamsData: + + __test__ = False # Avoid collecting TestParamsData as a pytest test + + test_plan: TestPlan = None + + @classmethod + def generate_kwargs(cls, test_vector: TestVector): + kwarg_list = [] + rng = random.Random(sum(test_vector.input_shape)) + # if len(test_vector.input_shape) == 4: + # N = test_vector.input_shape[-4] + N = test_vector.input_shape[0] + C_in = test_vector.input_shape[-3] + H_in = test_vector.input_shape[-2] + W_in = test_vector.input_shape[-1] + + in_channels = C_in + out_channels = rng.randint(1, C_in + 100) # it can be less, equal or greater than in_channels + + dilation = rng.randint(1, 3) + + # Two cases for kernel size + k_maxh = (H_in if H_in > 3 else 3) / dilation + 1 + k_maxw = (W_in if W_in > 3 else 3) / dilation + 1 + kernel_size_option1 = rng.randint(3, k_maxh) + kernel_size_option2 = rng.randint(3, k_maxw) + # 1. kernel is equal to integer + kernel_size = random.choice(kernel_size_option1, kernel_size_option2) + # make it odd number + kernel_size = kernel_size if kernel_size % 2 != 0 else kernel_size + 1 + # 2. kernel is equal to tuple + kernel_size = (kernel_size_option1, kernel_size_option2) + # assert that kernel value will fit in the input shape + # if isinstance(kernel_size, tuple): + # assert dilation * (kernel_size[0] - 1) < H_in, "Invalid kernel height!" + # assert dilation * (kernel_size[1] - 1) < W_in, "Invalid kernel width!" + # else: + # assert dilation * (kernel_size - 1) < H_in, "Invalid height" + # assert dilation * (kernel_size - 1) < W_in, "Invalid width" + + kwarg_list.append( + {"in_channels": in_channels, "out_channels": out_channels, "kernel_size": kernel_size, "dilation": dilation} + ) + return kwarg_list + + +class TestCollectionData: + + __test__ = False # Avoid collecting TestCollectionData as a pytest test + + all = TestCollection( + operators=[ + "ConvTranspose2d", # 00 + ], + input_sources=TestCollectionCommon.all.input_sources, + # only 4D input tensors are supported + input_shapes=[input_shape for input_shape in TestCollectionCommon.all.input_shapes if len(input_shape) == 4], + dev_data_formats=TestCollectionCommon.all.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=TestCollectionCommon.single.dev_data_formats, + 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=TestCollectionData.single.input_sources, + input_shapes=TestCollectionData.all.input_shapes, + kwargs=lambda test_vector: TestParamsData.generate_kwargs(test_vector), + ), + # # Test plan: + # # 5. Data format + # 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), + # dev_data_formats=TestCollectionCommon.float.dev_data_formats, + # math_fidelities=TestCollectionData.single.math_fidelities, + # ), + # # 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), + # dev_data_formats=TestCollectionData.single.dev_data_formats, + # math_fidelities=TestCollectionData.all.math_fidelities, + # ), + ], + failing_rules=[], +) + + +def get_test_plans() -> List[TestPlan]: + return [ + TestParamsData.test_plan, + ]