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ANIKET SHIVAM
138
test/python/conv2d/conv2d_sm80.py
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138
test/python/conv2d/conv2d_sm80.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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Low-level functionality tests for Conv2d operands on SM80
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"""
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from conv2d_test_utils import *
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import cutlass
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import logging
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cutlass.set_log_level(logging.WARNING)
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cc = 80
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@unittest.skipIf(device_cc() != cc, 'Device compute capability is invalid for SM80 tests.')
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class Conv2dSm80(unittest.TestCase):
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"""
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Wrapper class to which tests will be added dynamically in __main__
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"""
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pass
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conv_problems = get_conv_problems()
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# Tests for optimized & analytic
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for conv_kind in ["fprop", "wgrad", "dgrad"]:
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# F16, simt
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add_test(
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Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16,
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opclass="simt", threadblock_shape=[128, 128, 8],
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warp_count=[4, 2, 1], stages=2, instruction_shape=[1, 1, 1])
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# F16, tensor op
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add_test(
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Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16,
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opclass="tensor_op", threadblock_shape=[128, 128, 64],
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warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 16])
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# F16, tensor op, analytic iterator
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add_test(
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Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f16, cutlass.DataType.f16,
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opclass="tensor_op", threadblock_shape=[128, 128, 64],
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warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 16], iterator_algorithm="analytic")
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# F16, tensor op, f32 output
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add_test(
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Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f32,
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opclass="tensor_op", threadblock_shape=[128, 128, 64],
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warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 16])
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# F16, tensor op, different tile description
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add_test(
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Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16,
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opclass="tensor_op", threadblock_shape=[128, 64, 32],
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warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 8])
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# F32, simt
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add_test(
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Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f32, cutlass.DataType.f32, cutlass.DataType.f32,
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opclass="simt", threadblock_shape=[128, 128, 8],
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warp_count=[4, 2, 1], stages=4, instruction_shape=[1, 1, 1])
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# Tf32, tensorop
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add_test(
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Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f32, cutlass.DataType.f32, cutlass.DataType.f32,
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opclass="tensor_op", threadblock_shape=[128, 128, 16],
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warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 8]
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)
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# Split-K
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add_test(
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Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16,
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opclass="tensor_op", threadblock_shape=[128, 128, 64],
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warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 16], split_k_mode="serial",
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split_k_slices=2)
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add_test(
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Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16,
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opclass="tensor_op", threadblock_shape=[128, 128, 64],
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warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 16], split_k_mode="parallel",
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split_k_slices=5)
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# Swizzling functor
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add_test(
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Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16,
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opclass="tensor_op", threadblock_shape=[128, 64, 32],
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warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 8], swizzle=4)
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# Tests for few channels and fixed channels
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# F16, tensor op, few channels
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for c, tb, stage, inst in zip([2, 1],
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[[128, 128, 64], [128, 128, 32]],
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[3, 2],
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[[16, 8, 16], [16, 8, 8]]):
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add_test(
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Conv2dSm80, cc, "fprop", conv2d_few_channel_problemsizes(c), cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16,
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opclass="tensor_op", threadblock_shape=tb,
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warp_count=[2, 2, 1], stages=stage, instruction_shape=inst, iterator_algorithm="few_channels"
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)
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# F16, tensor op, fixed channels
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for c in [8, 4, 2]:
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add_test(
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Conv2dSm80, cc, "fprop", conv2d_few_channel_problemsizes(c), cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16,
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opclass="tensor_op", threadblock_shape=[128, 128, 64],
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warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 16], iterator_algorithm="fixed_channels"
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)
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# Test activations
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for activation in ["relu", "leaky_relu"]:
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for split_k_mode, split_k_slices in zip(["parallel", "serial", "parallel"], [1, 7, 5]):
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add_test(
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Conv2dSm80, cc, "fprop", conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16,
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opclass="tensor_op", threadblock_shape=[128, 128, 64],
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warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 16], split_k_mode=split_k_mode,
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split_k_slices=split_k_slices, activation=activation)
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if __name__ == '__main__':
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unittest.main()
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