141 lines
8.7 KiB
Python
141 lines
8.7 KiB
Python
#################################################################################################
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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 GEMM with F16 operands on SM90
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"""
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from functools import partial
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import cutlass
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import logging
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import unittest
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from cutlass.backend.test.utils import LayoutCombination, add_test_gemm
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from cutlass.backend.utils.device import device_cc
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cutlass.set_log_level(logging.WARNING)
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cc = 90
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@unittest.skipIf(device_cc() < cc, 'Device compute capability is insufficient for SM90 tests.')
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class GemmF16Sm90(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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add_test_specialized = partial(add_test_gemm, cls=GemmF16Sm90, element=cutlass.DataType.f16,
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warp_count=None, compilation_modes=['nvcc'])
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add_test_tensorop = partial(add_test_specialized, opclass=cutlass.OpcodeClass.TensorOp)
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# Tests with 1x1x1 clusters
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add_test_unit_cluster = partial(add_test_tensorop, cluster_shape=[1, 1, 1])
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add_test_unit_cluster(layouts=LayoutCombination.NNN, alignments=[8, 8, 8], element_output=cutlass.DataType.f16,
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element_accumulator=cutlass.DataType.f32, threadblock_shape=[128, 128, 32], stages=3)
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add_test_unit_cluster(layouts=LayoutCombination.NNT, alignments=[8, 8, 8], element_output=cutlass.DataType.f16,
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element_accumulator=cutlass.DataType.f32, threadblock_shape=[128, 128, 32], stages=None)
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add_test_unit_cluster(layouts=LayoutCombination.NTN, alignments=[8, 8, 8], element_output=cutlass.DataType.f16,
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element_accumulator=cutlass.DataType.f32, threadblock_shape=[128, 128, 32], stages=None)
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add_test_unit_cluster(layouts=LayoutCombination.NTT, alignments=[8, 8, 8], element_output=cutlass.DataType.f16,
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element_accumulator=cutlass.DataType.f32, threadblock_shape=[128, 128, 32], stages=None)
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add_test_unit_cluster(layouts=LayoutCombination.TNN, alignments=[8, 8, 8], element_output=cutlass.DataType.f16,
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element_accumulator=cutlass.DataType.f32, threadblock_shape=[128, 128, 32], stages=None)
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add_test_unit_cluster(layouts=LayoutCombination.TNT, alignments=[4, 4, 8], element_output=cutlass.DataType.f16,
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element_accumulator=cutlass.DataType.f32, threadblock_shape=[128, 128, 32], stages=None)
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add_test_unit_cluster(layouts=LayoutCombination.TNT, alignments=[4, 4, 8], element_output=cutlass.DataType.f16,
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element_accumulator=cutlass.DataType.f16, threadblock_shape=[128, 128, 32], stages=None)
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add_test_unit_cluster(layouts=LayoutCombination.TNT, alignments=[8, 8, 8], element_output=cutlass.DataType.f16,
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element_accumulator=cutlass.DataType.f16, threadblock_shape=[128, 128, 32], stages=None)
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add_test_unit_cluster(layouts=LayoutCombination.TNT, alignments=[8, 8, 8], element_output=cutlass.DataType.f16,
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element_accumulator=cutlass.DataType.f32, threadblock_shape=[ 64, 64, 64], stages=5)
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add_test_unit_cluster(layouts=LayoutCombination.TNT, alignments=[2, 2, 2], element_output=cutlass.DataType.f16,
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element_accumulator=cutlass.DataType.f16, threadblock_shape=[128, 128, 32], stages=None)
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# Tests with different cluster shapes
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add_test_cluster_shape = partial(add_test_tensorop, threadblock_shape=[64, 128, 64], stages=None)
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add_test_cluster_shape(layouts=LayoutCombination.TTN, alignments=[8, 8, 8], element_output=cutlass.DataType.f16,
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element_accumulator=cutlass.DataType.f16, cluster_shape=[2, 2, 1])
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add_test_cluster_shape(layouts=LayoutCombination.TNN, alignments=[8, 8, 4], element_output=cutlass.DataType.f32,
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element_accumulator=cutlass.DataType.f32, cluster_shape=[2, 2, 1])
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add_test_cluster_shape(layouts=LayoutCombination.NTN, alignments=[8, 8, 4], element_output=cutlass.DataType.f32,
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element_accumulator=cutlass.DataType.f32, cluster_shape=[2, 2, 1])
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add_test_cluster_shape(layouts=LayoutCombination.NNN, alignments=[8, 8, 4], element_output=cutlass.DataType.f32,
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element_accumulator=cutlass.DataType.f32, cluster_shape=[2, 2, 1])
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add_test_cluster_shape(layouts=LayoutCombination.TTN, alignments=[8, 8, 4], element_output=cutlass.DataType.f32,
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element_accumulator=cutlass.DataType.f32, cluster_shape=[1, 4, 1])
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add_test_cluster_shape(layouts=LayoutCombination.TTN, alignments=[8, 8, 4], element_output=cutlass.DataType.f32,
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element_accumulator=cutlass.DataType.f32, cluster_shape=[2, 4, 1])
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add_test_cluster_shape(layouts=LayoutCombination.TTN, alignments=[8, 8, 4], element_output=cutlass.DataType.f32,
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element_accumulator=cutlass.DataType.f32, cluster_shape=[4, 1, 1])
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add_test_cluster_shape(layouts=LayoutCombination.TTN, alignments=[8, 8, 4], element_output=cutlass.DataType.f32,
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element_accumulator=cutlass.DataType.f32, cluster_shape=[4, 2, 1])
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# Tests for different schedule modes
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add_test_schedule = partial(add_test_specialized, layouts=LayoutCombination.TTN, alignments=[8, 8, 4],
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element_output=cutlass.DataType.f32, element_accumulator=cutlass.DataType.f32,
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opclass=cutlass.OpcodeClass.TensorOp, threadblock_shape=[128, 128, 64], stages=None)
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add_test_schedule(
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cluster_shape=[1, 1, 1],
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kernel_schedule=cutlass.KernelScheduleType.TmaWarpSpecializedPingpong,
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epilogue_schedule=cutlass.EpilogueScheduleType.TmaWarpSpecialized
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)
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add_test_schedule(
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cluster_shape=[1, 1, 1],
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kernel_schedule=cutlass.KernelScheduleType.TmaWarpSpecializedCooperative,
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epilogue_schedule=cutlass.EpilogueScheduleType.TmaWarpSpecializedCooperative
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)
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add_test_schedule(
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cluster_shape=[2, 1, 1],
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kernel_schedule=cutlass.KernelScheduleType.TmaWarpSpecializedPingpong,
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epilogue_schedule=cutlass.EpilogueScheduleType.TmaWarpSpecialized
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)
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add_test_schedule(
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cluster_shape=[2, 1, 1],
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kernel_schedule=cutlass.KernelScheduleType.TmaWarpSpecializedCooperative,
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epilogue_schedule=cutlass.EpilogueScheduleType.TmaWarpSpecializedCooperative
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)
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# Tests using SIMT
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add_test_simt = partial(add_test_specialized, opclass=cutlass.OpcodeClass.Simt, alignments=[1, 1, 1], cluster_shape=[1, 1, 1], stages=2)
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add_test_simt(layouts=LayoutCombination.NNN, element_output=cutlass.DataType.f16, element_accumulator=cutlass.DataType.f32, threadblock_shape=[128, 128, 8])
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add_test_simt(layouts=LayoutCombination.TNN, element_output=cutlass.DataType.f16, element_accumulator=cutlass.DataType.f32, threadblock_shape=[ 64, 128, 8])
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add_test_simt(layouts=LayoutCombination.NTN, element_output=cutlass.DataType.f16, element_accumulator=cutlass.DataType.f32, threadblock_shape=[128, 64, 8])
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add_test_simt(layouts=LayoutCombination.TTN, element_output=cutlass.DataType.f16, element_accumulator=cutlass.DataType.f32, threadblock_shape=[ 64, 64, 8])
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add_test_simt(layouts=LayoutCombination.NNT, element_output=cutlass.DataType.f16, element_accumulator=cutlass.DataType.f16, threadblock_shape=[128, 128, 8])
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if __name__ == '__main__':
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unittest.main()
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