From 326413b55f5282bb5b263e21086055fe9c2bb003 Mon Sep 17 00:00:00 2001 From: Konstantin Milanovic Date: Thu, 14 Nov 2024 16:11:33 +0000 Subject: [PATCH] Tests for transpose op --- forge/test/operators/pytorch/tm/__init__.py | 3 + .../operators/pytorch/tm/test_transpose.py | 282 ++++++++++++++++++ 2 files changed, 285 insertions(+) create mode 100644 forge/test/operators/pytorch/tm/__init__.py create mode 100644 forge/test/operators/pytorch/tm/test_transpose.py diff --git a/forge/test/operators/pytorch/tm/__init__.py b/forge/test/operators/pytorch/tm/__init__.py new file mode 100644 index 00000000..2332467e --- /dev/null +++ b/forge/test/operators/pytorch/tm/__init__.py @@ -0,0 +1,3 @@ +# SPDX-FileCopyrightText: © 2024 Tenstorrent AI ULC + +# SPDX-License-Identifier: Apache-2.0 diff --git a/forge/test/operators/pytorch/tm/test_transpose.py b/forge/test/operators/pytorch/tm/test_transpose.py new file mode 100644 index 00000000..174de4b1 --- /dev/null +++ b/forge/test/operators/pytorch/tm/test_transpose.py @@ -0,0 +1,282 @@ +# SPDX-FileCopyrightText: © 2024 Tenstorrent AI ULC + +# SPDX-License-Identifier: Apache-2.0 +# +# Tests for testing of transpose operators +# +# In this test we test pytorch transpose 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 + + +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 ShapeUtils +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 + + +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 = "Transpose_pytorch_operator_" + opname + "_test_op_src_from_another_op" + self.operator = operator + self.opname = opname + self.shape = shape + self.kwargs = kwargs + + def forward(self, x: torch.Tensor): + # we use Add operator to create one operand which is input for the transpose operator + add = torch.add(x, x) + output = self.operator(add, **self.kwargs) + 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 = "Transpose_pytorch_operator_" + opname + "_test_op_src_from_host" + self.operator = operator + self.opname = opname + self.shape = shape + self.kwargs = kwargs + + def forward(self, x: torch.Tensor): + output = self.operator(x, **self.kwargs) + 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 = "Transpose_pytorch_operator_" + opname + "_test_op_src_const_eval_pass" + self.operator = operator + self.opname = opname + self.shape = shape + self.kwargs = kwargs + + self.c1 = torch.rand(*self.shape) - 0.5 + + def forward(self, x: torch.Tensor): + v1 = self.operator(self.c1, **self.kwargs) + v2 = self.operator(x, **self.kwargs) + # 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, + number_of_operands: int = 1, + input_params: List[Dict] = [], + 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, 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}") + + 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, + ) + + +class TestParamsData: + + __test__ = False # Avoid collecting TestParamsData as a pytest test + + test_plan: TestPlan = None + + @classmethod + def generate_kwargs(cls, test_vector: TestVector): + size = len(test_vector.input_shape) + kwarg_list = [] + for dim0 in list(range(0, size, 1)): + for dim1 in list(range(dim0 + 1, size, 1)): + kwargs = {} + kwargs["dim0"] = dim0 + kwargs["dim1"] = dim1 + kwarg_list.append(kwargs) + return kwarg_list + + +class TestCollectionData: + + __test__ = False # Avoid collecting TestCollectionData as a pytest test + + all = TestCollection( + operators=[ + "transpose", # 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=[ + item + for item in TestCollectionData.all.dev_data_formats + if item not in TestCollectionData.single.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=[ + # Skip all tests with input shapes with 2 dimensions + TestCollection( + criteria=lambda test_vector: len(test_vector.input_shape) == 2, + skip_reason=FailingReasons.NOT_IMPLEMENTED, + ), + ], +) + + +def get_test_plans() -> List[TestPlan]: + return [ + TestParamsData.test_plan, + ]