ANIKET SHIVAM
GitHub
285
test/python/interface/conv2d_interface.py
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285
test/python/interface/conv2d_interface.py
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#################################################################################################
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#
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# Copyright (c) 2023 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: BSD-3-Clause
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# 1. Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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#
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# 2. Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# 3. Neither the name of the copyright holder nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#
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#################################################################################################
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"""
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Tests the high-level Conv2d interface
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"""
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from math import ceil
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import unittest
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import cutlass
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import cutlass_bindings
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import cutlass.utils.datatypes as datatypes
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from cutlass.backend.utils.device import device_cc
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from utils import ExpectException
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import os
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class Conv2dEquivalence:
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"""
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Helper class for testing the equivalence of different constructions of the Conv2d interface
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"""
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def __init__(self, conv_kind, element_A, element_B, element_C, element_D, element_accumulator,
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alignment_A, alignment_B, alignment_C):
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self.element_A = element_A
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self.element_B = element_B
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self.element_C = element_C
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self.element_D = element_D
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self.element_accumulator = element_accumulator
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self.alignment_A = alignment_A
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self.alignment_B = alignment_B
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self.alignment_C = alignment_C
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self.conv_kind = conv_kind
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self.plan = cutlass.op.Conv2d(
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kind=self.conv_kind, element_A=element_A, element_B=element_B, element_C=element_C,
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element_D=element_D, element_accumulator=element_accumulator)
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self.op = self.plan.construct(
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alignment_A=self.alignment_A, alignment_B=self.alignment_B,
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alignment_C=self.alignment_C)
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def _plans_equal(self, other_plan) -> bool:
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"""
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Compares whether two plans are equal
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:param other_plan: plan to compare against the default Conv2d
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:type other_plan: cutlass.op.Conv2d
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:return: whether `other_plan` is equivalent to `self.plan`
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:rtype: bool
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"""
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other_op = other_plan.construct(
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alignment_A=self.alignment_A, alignment_B=self.alignment_B,
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alignment_C=self.alignment_C)
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return self.op.rt_module.emit() == other_op.rt_module.emit()
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def generic_test(self):
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"""
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Tests the equivalence of various constructions of the Conv2d interface when using CUTLASS data types
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and layouts for constructing the Conv2d interface
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"""
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if not datatypes.numpy_available:
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return
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# Test when specifying all parameters
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plan_other = cutlass.op.Conv2d(
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kind=self.conv_kind,
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element_A=self.element_A, element_B=self.element_B, element_C=self.element_C,
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element_D=self.element_D, element_accumulator=self.element_accumulator)
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assert self._plans_equal(plan_other)
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# Test when specifying all parameters but A
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plan_other = cutlass.op.Conv2d(
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kind=self.conv_kind,
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element_B=self.element_B, element_C=self.element_C,
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element_D=self.element_D, element_accumulator=self.element_accumulator,
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element=self.element_A)
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assert self._plans_equal(plan_other)
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# Test when specifying all parameters but A and B as tensors using generic element and output
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plan_other = cutlass.op.Conv2d(
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kind=self.conv_kind,
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element_C=self.element_C,
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element_D=self.element_D, element_accumulator=self.element_accumulator,
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element=self.element_A)
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assert self._plans_equal(plan_other)
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# Test without explicit accumulator. Only run if the type of C and the accumulator are equal
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if self.element_C == self.element_accumulator:
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plan_other = cutlass.op.Conv2d(
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kind=self.conv_kind,
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element_C=self.element_C,
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element_D=self.element_D,
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element=self.element_A)
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assert self._plans_equal(plan_other)
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# Test with only the generic types. Only rune if the types of A, B, C, and D are the same
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if (self.element_A == self.element_B and self.element_A == self.element_C and self.element_A == self.element_D
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and self.element_A == self.element_accumulator):
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plan_other = cutlass.op.Conv2d(kind=self.conv_kind, element=self.element_A)
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assert self._plans_equal(plan_other)
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def numpy_test(self):
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"""
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Tests the equivalence of various constructions of the Conv2d interface when using numpy as a frontend
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"""
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if not datatypes.numpy_available:
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return
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import numpy as np
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type_A = datatypes.numpy_type(self.element_A)
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type_B = datatypes.numpy_type(self.element_B)
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type_C = datatypes.numpy_type(self.element_C)
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type_D = datatypes.numpy_type(self.element_D)
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type_accum = datatypes.numpy_type(self.element_accumulator)
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size = (2, 2)
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A = np.zeros(size, dtype=type_A)
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B = np.zeros(size, dtype=type_B)
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C = np.zeros(size, dtype=type_C)
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D = np.zeros(size, dtype=type_D)
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return self.tensor_test(type_A, type_B, type_C, type_D, type_accum, A, B, C, D)
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def torch_test(self):
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"""
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Tests the equivalence of various constructions of the Conv2d interface when using torch as a frontend
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"""
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if not datatypes.torch_available:
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return
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import torch
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type_A = datatypes.torch_type(self.element_A)
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type_B = datatypes.torch_type(self.element_B)
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type_C = datatypes.torch_type(self.element_C)
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type_D = datatypes.torch_type(self.element_D)
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type_accum = datatypes.torch_type(self.element_accumulator)
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size = (2, 2)
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A = torch.empty(size, dtype=type_A)
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B = torch.empty(size, dtype=type_B)
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C = torch.empty(size, dtype=type_C)
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D = torch.empty(size, dtype=type_D)
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return self.tensor_test(type_A, type_B, type_C, type_D, type_accum, A, B, C, D)
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def tensor_test(self, type_A, type_B, type_C, type_D, type_accum, A, B, C, D):
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# Test when specifying all parameters via tensors
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plan_np = cutlass.op.Conv2d(kind=self.conv_kind, A=A, B=B, C=C, D=D, element_accumulator=type_accum)
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assert self._plans_equal(plan_np)
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# Test when specifying all parameters but A as tensors
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plan_np = cutlass.op.Conv2d(kind=self.conv_kind, B=B, C=C, D=D, element_accumulator=type_accum, element_A=type_A)
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assert self._plans_equal(plan_np)
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# Test when specifying all parameters but A and B as tensors and using generic element and output
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if type_A == type_B:
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plan_np = cutlass.op.Conv2d(kind=self.conv_kind, C=C, D=D, element_accumulator=type_accum, element=type_A)
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assert self._plans_equal(plan_np)
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# Test without explicit accumulator. Only run if the type of C and the accumulator.
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if type_C == type_accum:
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plan_np = cutlass.op.Conv2d(kind=self.conv_kind, A=A, B=B, C=C, D=D)
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assert self._plans_equal(plan_np)
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# Test with only the generic types and layouts. Only run if types and layouts of A, B, C, and D are the same.
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if (type_A == type_B and type_A == type_C and type_A == type_D and type_A == type_accum):
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plan_np = cutlass.op.Conv2d(kind=self.conv_kind, element=type_A)
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assert self._plans_equal(plan_np)
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def test_all(self):
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"""
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Runs all tests on the Gemm interface
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"""
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self.generic_test()
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self.numpy_test()
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self.torch_test()
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@unittest.skipIf(device_cc() <= 80, 'Device compute capability is insufficient for SM80 tests.')
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class ConvEquivalenceTest(unittest.TestCase):
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"""
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Tests the equivalence of different constructions of the Conv2d interface
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"""
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pass
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type2alignment = {
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cutlass.DataType.f16: 8,
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cutlass.DataType.f32: 4
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}
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def add_test(conv_kind, element_A, element_B, element_C, element_D, element_accumulator):
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test_name = f"test_conv2d_{conv_kind}_{element_A}_{element_B}_{element_C}_{element_D}_{element_accumulator}"
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def run(self):
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conv2d_eq = Conv2dEquivalence(
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conv_kind=conv_kind,
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element_A=element_A, element_B=element_B,
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element_C=element_C, element_D=element_D,
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element_accumulator=element_accumulator,
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alignment_A=type2alignment[element_A], alignment_B=type2alignment[element_B],
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alignment_C=type2alignment[element_C]
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)
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conv2d_eq.test_all()
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setattr(ConvEquivalenceTest, test_name, run)
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for conv_kind in ["fprop", "wgrad", "dgrad"]:
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for types in [
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[cutlass.DataType.f16, cutlass.DataType.f16, cutlass.DataType.f16, cutlass.DataType.f16, cutlass.DataType.f16],
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[cutlass.DataType.f16, cutlass.DataType.f16, cutlass.DataType.f16, cutlass.DataType.f16, cutlass.DataType.f32],
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[cutlass.DataType.f16, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f32, cutlass.DataType.f16],
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[cutlass.DataType.f16, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f32, cutlass.DataType.f32],
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[cutlass.DataType.f32, cutlass.DataType.f32, cutlass.DataType.f32, cutlass.DataType.f32, cutlass.DataType.f32]
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]:
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add_test(conv_kind, types[0], types[1], types[2], types[3], types[4])
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@unittest.skipIf(device_cc() <= 80, 'Device compute capability is insufficient for SM80 tests.')
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class Conv2dErrorTests(unittest.TestCase):
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"""
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Tests various error scenarios that arise with the high-level Gemm interface
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"""
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def test_alignment(self):
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"""
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Tests case in which the alignment specified is unsupported
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"""
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plan = cutlass.op.Conv2d(kind="fprop", element=cutlass.DataType.f16)
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with ExpectException(True, 'Alignment 3 is not supported for F16. The construction should fail.'):
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op = plan.construct(alignment_A=3, alignment_B=3, alignment_C=3)
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def test_invalid_tile_description(self):
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"""
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Tests scenarios in which an invalid tile description is provided for a given CC
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"""
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plan = cutlass.op.Conv2d(kind="fprop", element=cutlass.DataType.f16)
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td = plan.tile_descriptions()[0]
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td.threadblock_shape=[17, 32, 5]
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plan.tile_description = td
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with ExpectException(True, 'The threadblock shape is invalid. The compilation should fail.'):
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plan.compile()
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# Clean up the error message
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os.remove("./cutlass_python_compilation_device_error.txt")
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if __name__ == '__main__':
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unittest.main()
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Block a user