ANIKET SHIVAM
GitHub
167
test/python/gemm/gemm_f16_sm80.py
Normal file
167
test/python/gemm/gemm_f16_sm80.py
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@ -0,0 +1,167 @@
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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:
|
||||
#
|
||||
# 1. Redistributions of source code must retain the above copyright notice, this
|
||||
# list of conditions and the following disclaimer.
|
||||
#
|
||||
# 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
# this list of conditions and the following disclaimer in the documentation
|
||||
# and/or other materials provided with the distribution.
|
||||
#
|
||||
# 3. Neither the name of the copyright holder nor the names of its
|
||||
# contributors may be used to endorse or promote products derived from
|
||||
# this software without specific prior written permission.
|
||||
#
|
||||
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
# 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 SM80
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"""
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from functools import partial
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import cutlass
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from cutlass.utils.datatypes import binding_opclass, binding_type
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from cutlass.backend.test.gemm_testbed import test_all_gemm
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import unittest
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from cutlass.backend.test.utils import LayoutCombination, get_name
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from cutlass.backend.utils.device import device_cc
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cc = 80
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# Partial specialziation for naming tests
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bound_type = binding_type(cutlass.DataType.f16)
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name_fn = partial(get_name, element_a=bound_type, element_b=bound_type, arch=cc)
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def add_test(cls, layouts, alignments, element_output, element_accumulator,
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threadblock_shape, warp_count, stages, opclass, swizzle=None):
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"""
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Create a test-running function with the given specification and set it as a method of `cls`.
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:param cls: class to which the generated method will be added
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:type cls: type
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:param layouts: layouts of A, B, and C operands
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:type layouts: list or tuple
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:param alignments: alingments of A, B, and C operands
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:type alignments: list or tuple
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:param element_output: data type of the output element
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:type element_output: cutlass.DataType
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:param element_accumulator: data type used in accumulation
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:type element_accumulator: cutlass.DataType
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:param threadblock_shape: dimensions of threadblock tiles
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:type threadblock_shape: list or tuple
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:param warp_count: warps to be launched per threadblock dimension
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:type warp_count: list or tuple
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:param stages: number of pipeline stages to use in the kernel
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:type stages: int
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:param opclass: class of operation being performed (e.g., SIMT, Tensor Core)
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:type opclass: cutlass.OpClass
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:param swizzle: threadblock swizzling functor
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"""
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cluster_shape = [1, 1, 1]
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def run(self):
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"""
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Dynamically-generated function that constructs a GEMM operation and verifies it against
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multiple test cases.
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"""
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element_A = cutlass.DataType.f16
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element_B = cutlass.DataType.f16
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layout_A, layout_B, layout_C = layouts
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alignment_A, alignment_B, alignment_C = alignments
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plan = cutlass.op.Gemm(element_A=element_A, element_B=element_B,
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element_C=element_output, element_D=element_output,
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layout_A=layout_A, layout_B=layout_B, layout_C=layout_C,
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element_accumulator=element_accumulator,
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kernel_cc=cc)
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plan.opclass = opclass
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if swizzle is not None:
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plan.swizzling_functor = swizzle
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td = plan.tile_descriptions()[0]
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td.threadblock_shape = threadblock_shape
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td.stages = stages
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td.warp_count = warp_count
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td.cluster_shape = cluster_shape
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op = plan.construct(tile_description=td, alignment_A=alignment_A, alignment_B=alignment_B, alignment_C=alignment_C)
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self.assertTrue(test_all_gemm(op, 'universal'))
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element_epilogue = element_accumulator
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name = name_fn(layouts, alignments, binding_type(element_output), binding_type(element_accumulator),
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binding_type(element_epilogue), cluster_shape, threadblock_shape, stages, opclass=binding_opclass(opclass))
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setattr(cls, name, run)
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return run
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@unittest.skipIf(device_cc() < cc, 'Device compute capability is insufficient for SM80 tests.')
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class GemmF16Sm80(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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@unittest.skipIf(device_cc() < cc, 'Device compute capability is insufficient for SM80 tests.')
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class GemmF16Sm80StreamK(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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# Tests using TensorOp
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add_test_tensorop = partial(add_test, opclass=cutlass.OpcodeClass.TensorOp)
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add_test_tensorop(GemmF16Sm80, LayoutCombination.NNN, [8, 8, 8], cutlass.DataType.f16, cutlass.DataType.f32, [128, 128, 32], [2, 2, 1], 3)
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add_test_tensorop(GemmF16Sm80, LayoutCombination.NNT, [8, 8, 8], cutlass.DataType.f16, cutlass.DataType.f32, [128, 128, 32], [2, 2, 1], 3)
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add_test_tensorop(GemmF16Sm80, LayoutCombination.NTN, [8, 8, 8], cutlass.DataType.f16, cutlass.DataType.f32, [128, 128, 32], [2, 2, 1], 3)
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add_test_tensorop(GemmF16Sm80, LayoutCombination.NTT, [8, 8, 8], cutlass.DataType.f16, cutlass.DataType.f32, [128, 128, 32], [2, 2, 1], 3)
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add_test_tensorop(GemmF16Sm80, LayoutCombination.TNN, [8, 8, 8], cutlass.DataType.f16, cutlass.DataType.f32, [128, 128, 32], [2, 2, 1], 3)
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add_test_tensorop(GemmF16Sm80, LayoutCombination.TNT, [8, 8, 8], cutlass.DataType.f16, cutlass.DataType.f32, [128, 128, 32], [2, 2, 1], 3)
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add_test_tensorop(GemmF16Sm80, LayoutCombination.TTN, [8, 8, 8], cutlass.DataType.f16, cutlass.DataType.f32, [128, 128, 32], [2, 2, 1], 3)
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add_test_tensorop(GemmF16Sm80, LayoutCombination.TTT, [8, 8, 8], cutlass.DataType.f16, cutlass.DataType.f32, [128, 128, 32], [2, 2, 1], 3)
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add_test_tensorop(GemmF16Sm80, LayoutCombination.TNT, [8, 8, 8], cutlass.DataType.f16, cutlass.DataType.f32, [64, 128, 32], [1, 2, 1], 3)
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add_test_tensorop(GemmF16Sm80, LayoutCombination.TNT, [8, 8, 8], cutlass.DataType.f16, cutlass.DataType.f32, [128, 64, 32], [2, 1, 1], 3)
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add_test_tensorop(GemmF16Sm80, LayoutCombination.TNT, [8, 8, 8], cutlass.DataType.f16, cutlass.DataType.f32, [64, 64, 64], [1, 1, 1], 3)
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add_test_tensorop(GemmF16Sm80, LayoutCombination.TNT, [4, 4, 8], cutlass.DataType.f16, cutlass.DataType.f32, [128, 128, 32], [2, 2, 1], 3)
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add_test_tensorop(GemmF16Sm80, LayoutCombination.TNT, [4, 4, 8], cutlass.DataType.f16, cutlass.DataType.f16, [128, 128, 32], [2, 2, 1], 3)
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add_test_tensorop(GemmF16Sm80, LayoutCombination.TNT, [8, 8, 8], cutlass.DataType.f16, cutlass.DataType.f16, [128, 128, 32], [2, 2, 1], 3)
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add_test_tensorop(GemmF16Sm80, LayoutCombination.TNT, [8, 8, 8], cutlass.DataType.f16, cutlass.DataType.f32, [64, 64, 64], [1, 1, 1], 5)
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add_test_tensorop(GemmF16Sm80, LayoutCombination.TNT, [2, 2, 2], cutlass.DataType.f16, cutlass.DataType.f16, [128, 128, 32], [2, 2, 1], 3)
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# Tests using SIMT
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add_test_simt = partial(add_test, opclass=cutlass.OpcodeClass.Simt)
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add_test_simt(GemmF16Sm80, LayoutCombination.NNN, [1, 1, 1], cutlass.DataType.f16, cutlass.DataType.f32, [128, 128, 8], [2, 2, 1], 2)
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add_test_simt(GemmF16Sm80, LayoutCombination.TNN, [1, 1, 1], cutlass.DataType.f16, cutlass.DataType.f32, [64, 128, 8], [1, 2, 1], 2)
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add_test_simt(GemmF16Sm80, LayoutCombination.NTN, [1, 1, 1], cutlass.DataType.f16, cutlass.DataType.f32, [128, 64, 8], [2, 1, 1], 2)
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add_test_simt(GemmF16Sm80, LayoutCombination.TTN, [1, 1, 1], cutlass.DataType.f16, cutlass.DataType.f32, [64, 64, 8], [1, 1, 1], 2)
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add_test_simt(GemmF16Sm80, LayoutCombination.NNT, [1, 1, 1], cutlass.DataType.f16, cutlass.DataType.f16, [128, 128, 8], [2, 2, 1], 2)
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# Stream K tests
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add_test_streamk = partial(add_test, opclass=cutlass.OpcodeClass.TensorOp, swizzle=cutlass.swizzle.ThreadblockSwizzleStreamK)
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add_test_streamk(GemmF16Sm80StreamK, LayoutCombination.NNN, [8, 8, 8], cutlass.DataType.f16, cutlass.DataType.f32, [128, 128, 32], [2, 2, 1], 3)
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add_test_streamk(GemmF16Sm80StreamK, LayoutCombination.TNT, [8, 8, 8], cutlass.DataType.f16, cutlass.DataType.f32, [64, 64, 64], [1, 1, 1], 5)
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if __name__ == '__main__':
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unittest.main()
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173
test/python/gemm/gemm_f16_sm90.py
Normal file
173
test/python/gemm/gemm_f16_sm90.py
Normal file
@ -0,0 +1,173 @@
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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
|
||||
# modification, are permitted provided that the following conditions are met:
|
||||
#
|
||||
# 1. Redistributions of source code must retain the above copyright notice, this
|
||||
# list of conditions and the following disclaimer.
|
||||
#
|
||||
# 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
# this list of conditions and the following disclaimer in the documentation
|
||||
# and/or other materials provided with the distribution.
|
||||
#
|
||||
# 3. Neither the name of the copyright holder nor the names of its
|
||||
# contributors may be used to endorse or promote products derived from
|
||||
# this software without specific prior written permission.
|
||||
#
|
||||
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
# 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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from cutlass.utils.datatypes import binding_opclass, binding_type
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from cutlass.backend.test.gemm_testbed import test_all_gemm
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import unittest
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from cutlass.backend.test.utils import LayoutCombination, get_name
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from cutlass.backend.utils.device import device_cc
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cc = 90
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# Partial specialziation for naming tests
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bound_type = binding_type(cutlass.DataType.f16)
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name_fn = partial(get_name, element_a=bound_type, element_b=bound_type, arch=cc)
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def add_test(cls, layouts, alignments, element_output, element_accumulator,
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cluster_shape, threadblock_shape, stages, opclass,
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kernel_schedule=cutlass.KernelScheduleType.ScheduleAuto,
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swizzle=None):
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"""
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Create a test-running function with the given specification and set it as a method of `cls`.
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:param cls: class to which the generated method will be added
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:type cls: type
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:param layouts: layouts of A, B, and C operands
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:type layouts: list or tuple
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:param alignments: alingments of A, B, and C operands
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:type alignments: list or tuple
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:param element_output: data type of the output element
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:type element_output: cutlass.DataType
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:param element_accumulator: data type used in accumulation
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:type element_accumulator: cutlass.DataType
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:param cluster_shape: dimensions of threadblock cluster
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:type cluster_shape: list or tuple
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:param threadblock_shape: dimensions of threadblock tiles
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:type threadblock_shape: list or tuple
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:param warp_count: warps to be launched per threadblock dimension
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:type warp_count: list or tuple
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:param stages: number of pipeline stages to use in the kernel
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:type stages: int
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:param opclass: class of operation being performed (e.g., SIMT, Tensor Core)
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:type opclass: cutlass.OpClass
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:param kernel_schedule: kernel schedule type
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:type kernel_schedule: cutlass.KernelScheduleType
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:param swizzle: threadblock swizzling functor
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"""
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def run(self):
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"""
|
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Dynamically-generated function that constructs a GEMM operation and verifies it against
|
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multiple test cases.
|
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"""
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element_A = cutlass.DataType.f16
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element_B = cutlass.DataType.f16
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layout_A, layout_B, layout_C = layouts
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alignment_A, alignment_B, alignment_C = alignments
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plan = cutlass.op.Gemm(element_A=element_A, element_B=element_B,
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element_C=element_output, element_D=element_output,
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layout_A=layout_A, layout_B=layout_B, layout_C=layout_C,
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element_accumulator=element_accumulator)
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plan.opclass = opclass
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if swizzle is not None:
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plan.swizzling_functor = swizzle
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td = plan.tile_descriptions()[0]
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td.threadblock_shape = threadblock_shape
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td.stages = stages
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td.cluster_shape = cluster_shape
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td.kernel_schedule = kernel_schedule
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op = plan.construct(tile_description=td, alignment_A=alignment_A, alignment_B=alignment_B, alignment_C=alignment_C)
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self.assertTrue(test_all_gemm(op, 'universal'))
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element_epilogue = element_accumulator
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name = name_fn(layouts, alignments, binding_type(element_output), binding_type(element_accumulator),
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binding_type(element_epilogue), cluster_shape, threadblock_shape, stages,
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opclass=binding_opclass(opclass), kernel_schedule=kernel_schedule)
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setattr(cls, name, run)
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return run
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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_tensorop = partial(add_test, opclass=cutlass.OpcodeClass.TensorOp)
|
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# Tests with 1x1x1 clusters
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add_test_tensorop(GemmF16Sm90, LayoutCombination.NNN, [8, 8, 8], cutlass.DataType.f16, cutlass.DataType.f32, [1, 1, 1], [128, 128, 32], 3)
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add_test_tensorop(GemmF16Sm90, LayoutCombination.NNT, [8, 8, 8], cutlass.DataType.f16, cutlass.DataType.f32, [1, 1, 1], [128, 128, 32], None)
|
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add_test_tensorop(GemmF16Sm90, LayoutCombination.NTN, [8, 8, 8], cutlass.DataType.f16, cutlass.DataType.f32, [1, 1, 1], [128, 128, 32], None)
|
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add_test_tensorop(GemmF16Sm90, LayoutCombination.NTT, [8, 8, 8], cutlass.DataType.f16, cutlass.DataType.f32, [1, 1, 1], [128, 128, 32], None)
|
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add_test_tensorop(GemmF16Sm90, LayoutCombination.TNN, [8, 8, 8], cutlass.DataType.f16, cutlass.DataType.f32, [1, 1, 1], [128, 128, 32], None)
|
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add_test_tensorop(GemmF16Sm90, LayoutCombination.TNT, [4, 4, 8], cutlass.DataType.f16, cutlass.DataType.f32, [1, 1, 1], [128, 128, 32], None)
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add_test_tensorop(GemmF16Sm90, LayoutCombination.TNT, [4, 4, 8], cutlass.DataType.f16, cutlass.DataType.f16, [1, 1, 1], [128, 128, 32], None)
|
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add_test_tensorop(GemmF16Sm90, LayoutCombination.TNT, [8, 8, 8], cutlass.DataType.f16, cutlass.DataType.f16, [1, 1, 1], [128, 128, 32], None)
|
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add_test_tensorop(GemmF16Sm90, LayoutCombination.TNT, [8, 8, 8], cutlass.DataType.f16, cutlass.DataType.f32, [1, 1, 1], [64, 64, 64], 5)
|
||||
add_test_tensorop(GemmF16Sm90, LayoutCombination.TNT, [2, 2, 2], cutlass.DataType.f16, cutlass.DataType.f16, [1, 1, 1], [128, 128, 32], None)
|
||||
|
||||
# Tests with different cluster shapes
|
||||
add_test_tensorop(GemmF16Sm90, LayoutCombination.TTN, [8, 8, 8], cutlass.DataType.f16, cutlass.DataType.f16, [2, 2, 1], [64, 128, 64], None)
|
||||
add_test_tensorop(GemmF16Sm90, LayoutCombination.TNN, [8, 8, 4], cutlass.DataType.f32, cutlass.DataType.f32, [2, 2, 1], [64, 128, 64], None)
|
||||
add_test_tensorop(GemmF16Sm90, LayoutCombination.NTN, [8, 8, 4], cutlass.DataType.f32, cutlass.DataType.f32, [2, 2, 1], [64, 128, 64], None)
|
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add_test_tensorop(GemmF16Sm90, LayoutCombination.NNN, [8, 8, 4], cutlass.DataType.f32, cutlass.DataType.f32, [2, 2, 1], [64, 128, 64], None)
|
||||
add_test_tensorop(GemmF16Sm90, LayoutCombination.TTN, [8, 8, 4], cutlass.DataType.f32, cutlass.DataType.f32, [1, 4, 1], [64, 128, 64], None)
|
||||
add_test_tensorop(GemmF16Sm90, LayoutCombination.TTN, [8, 8, 4], cutlass.DataType.f32, cutlass.DataType.f32, [2, 4, 1], [64, 128, 64], None)
|
||||
add_test_tensorop(GemmF16Sm90, LayoutCombination.TTN, [8, 8, 4], cutlass.DataType.f32, cutlass.DataType.f32, [4, 1, 1], [64, 128, 64], None)
|
||||
add_test_tensorop(GemmF16Sm90, LayoutCombination.TTN, [8, 8, 4], cutlass.DataType.f32, cutlass.DataType.f32, [4, 2, 1], [64, 128, 64], None)
|
||||
|
||||
# Tests for different schedule modes
|
||||
add_test_schedule = partial(add_test, GemmF16Sm90, LayoutCombination.TTN, [8, 8, 4], cutlass.DataType.f32, cutlass.DataType.f32, opclass=cutlass.OpcodeClass.TensorOp)
|
||||
add_test_schedule([1, 1, 1], [128, 128, 64], None, kernel_schedule=cutlass.KernelScheduleType.TmaWarpSpecializedPingpong)
|
||||
add_test_schedule([1, 1, 1], [128, 128, 64], None, kernel_schedule=cutlass.KernelScheduleType.TmaWarpSpecializedCooperative)
|
||||
add_test_schedule([2, 1, 1], [128, 128, 64], None, kernel_schedule=cutlass.KernelScheduleType.TmaWarpSpecializedPingpong)
|
||||
add_test_schedule([2, 1, 1], [128, 128, 64], None, kernel_schedule=cutlass.KernelScheduleType.TmaWarpSpecializedCooperative)
|
||||
add_test_schedule([2, 1, 1], [256, 128, 64], None, kernel_schedule=cutlass.KernelScheduleType.TmaWarpSpecializedCooperative)
|
||||
add_test_schedule([2, 1, 1], [128, 128, 64], 5, kernel_schedule=cutlass.KernelScheduleType.TmaWarpSpecializedPingpong)
|
||||
add_test_schedule([2, 1, 1], [128, 128, 64], 5, kernel_schedule=cutlass.KernelScheduleType.TmaWarpSpecializedCooperative)
|
||||
|
||||
# Tests using SIMT
|
||||
add_test_simt = partial(add_test, opclass=cutlass.OpcodeClass.Simt)
|
||||
add_test_simt(GemmF16Sm90, LayoutCombination.NNN, [1, 1, 1], cutlass.DataType.f16, cutlass.DataType.f32, [1, 1, 1], [128, 128, 8], 2)
|
||||
add_test_simt(GemmF16Sm90, LayoutCombination.TNN, [1, 1, 1], cutlass.DataType.f16, cutlass.DataType.f32, [1, 1, 1], [64, 128, 8], 2)
|
||||
add_test_simt(GemmF16Sm90, LayoutCombination.NTN, [1, 1, 1], cutlass.DataType.f16, cutlass.DataType.f32, [1, 1, 1], [128, 64, 8], 2)
|
||||
add_test_simt(GemmF16Sm90, LayoutCombination.TTN, [1, 1, 1], cutlass.DataType.f16, cutlass.DataType.f32, [1, 1, 1], [64, 64, 8], 2)
|
||||
add_test_simt(GemmF16Sm90, LayoutCombination.NNT, [1, 1, 1], cutlass.DataType.f16, cutlass.DataType.f16, [1, 1, 1], [128, 128, 8], 2)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
155
test/python/gemm/gemm_f32_sm80.py
Normal file
155
test/python/gemm/gemm_f32_sm80.py
Normal file
@ -0,0 +1,155 @@
|
||||
#################################################################################################
|
||||
#
|
||||
# Copyright (c) 2023 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
# SPDX-License-Identifier: BSD-3-Clause
|
||||
#
|
||||
# Redistribution and use in source and binary forms, with or without
|
||||
# modification, are permitted provided that the following conditions are met:
|
||||
#
|
||||
# 1. Redistributions of source code must retain the above copyright notice, this
|
||||
# list of conditions and the following disclaimer.
|
||||
#
|
||||
# 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
# this list of conditions and the following disclaimer in the documentation
|
||||
# and/or other materials provided with the distribution.
|
||||
#
|
||||
# 3. Neither the name of the copyright holder nor the names of its
|
||||
# contributors may be used to endorse or promote products derived from
|
||||
# this software without specific prior written permission.
|
||||
#
|
||||
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
#
|
||||
#################################################################################################
|
||||
|
||||
"""
|
||||
Low-level functionality tests for GEMM with F32 operands on SM80
|
||||
"""
|
||||
|
||||
from functools import partial
|
||||
|
||||
import cutlass
|
||||
from cutlass.utils.datatypes import binding_opclass, binding_type
|
||||
from cutlass.backend.test.gemm_testbed import test_all_gemm
|
||||
import unittest
|
||||
|
||||
from cutlass.backend.test.utils import LayoutCombination, get_name
|
||||
from cutlass.backend.utils.device import device_cc
|
||||
|
||||
cc = 80
|
||||
|
||||
# Partial specialziation for naming tests
|
||||
bound_type = binding_type(cutlass.DataType.f32)
|
||||
name_fn = partial(get_name, element_a=bound_type, element_b=bound_type, arch=cc)
|
||||
|
||||
|
||||
def add_test(cls, layouts, alignments, element_output, element_accumulator,
|
||||
threadblock_shape, warp_count, stages, opclass, swizzle=None):
|
||||
"""
|
||||
Create a test-running function with the given specification and set it as a method of `cls`.
|
||||
|
||||
:param cls: class to which the generated method will be added
|
||||
:type cls: type
|
||||
:param layouts: layouts of A, B, and C operands
|
||||
:type layouts: list or tuple
|
||||
:param alignments: alingments of A, B, and C operands
|
||||
:type alignments: list or tuple
|
||||
:param element_output: data type of the output element
|
||||
:type element_output: cutlass.DataType
|
||||
:param element_accumulator: data type used in accumulation
|
||||
:type element_accumulator: cutlass.DataType
|
||||
:param threadblock_shape: dimensions of threadblock tiles
|
||||
:type threadblock_shape: list or tuple
|
||||
:param warp_count: warps to be launched per threadblock dimension
|
||||
:type warp_count: list or tuple
|
||||
:param stages: number of pipeline stages to use in the kernel
|
||||
:type stages: int
|
||||
:param opclass: class of operation being performed (e.g., SIMT, Tensor Core)
|
||||
:type opclass: cutlass.OpClass
|
||||
:param swizzle: threadblock swizzling functor
|
||||
"""
|
||||
|
||||
cluster_shape = [1, 1, 1]
|
||||
|
||||
def run(self):
|
||||
"""
|
||||
Dynamically-generated function that constructs a GEMM operation and verifies it against
|
||||
multiple test cases.
|
||||
"""
|
||||
element_A = cutlass.DataType.f32
|
||||
element_B = cutlass.DataType.f32
|
||||
layout_A, layout_B, layout_C = layouts
|
||||
alignment_A, alignment_B, alignment_C = alignments
|
||||
|
||||
plan = cutlass.op.Gemm(element_A=element_A, element_B=element_B,
|
||||
element_C=element_output, element_D=element_output,
|
||||
layout_A=layout_A, layout_B=layout_B, layout_C=layout_C,
|
||||
element_accumulator=element_accumulator,
|
||||
kernel_cc=cc)
|
||||
|
||||
plan.opclass = opclass
|
||||
if swizzle is not None:
|
||||
plan.swizzling_functor = swizzle
|
||||
td = plan.tile_descriptions()[0]
|
||||
td.threadblock_shape = threadblock_shape
|
||||
td.stages = stages
|
||||
td.warp_count = warp_count
|
||||
td.cluster_shape = cluster_shape
|
||||
op = plan.construct(tile_description=td, alignment_A=alignment_A, alignment_B=alignment_B, alignment_C=alignment_C)
|
||||
|
||||
self.assertTrue(test_all_gemm(op, 'universal'))
|
||||
|
||||
element_epilogue = element_accumulator
|
||||
name = name_fn(layouts, alignments, binding_type(element_output), binding_type(element_accumulator),
|
||||
binding_type(element_epilogue), cluster_shape, threadblock_shape, stages, opclass=binding_opclass(opclass))
|
||||
setattr(cls, name, run)
|
||||
|
||||
return run
|
||||
|
||||
@unittest.skipIf(device_cc() < cc, 'Device compute capability is insufficient for SM80 tests.')
|
||||
class GemmF32Sm80(unittest.TestCase):
|
||||
"""
|
||||
Wrapper class to which tests will be added dynamically in __main__
|
||||
"""
|
||||
pass
|
||||
|
||||
|
||||
@unittest.skipIf(device_cc() < cc, 'Device compute capability is insufficient for SM80 tests.')
|
||||
class GemmF32Sm80StreamK(unittest.TestCase):
|
||||
"""
|
||||
Wrapper class to which tests will be added dynamically in __main__
|
||||
"""
|
||||
pass
|
||||
|
||||
|
||||
# Tests using TensorOp
|
||||
add_test_tensorop = partial(add_test, opclass=cutlass.OpcodeClass.TensorOp)
|
||||
|
||||
add_test_tensorop(GemmF32Sm80, LayoutCombination.NNN, [4, 4, 4], cutlass.DataType.f32, cutlass.DataType.f32, [128, 128, 32], [2, 2, 1], 3)
|
||||
add_test_tensorop(GemmF32Sm80, LayoutCombination.NNT, [4, 4, 4], cutlass.DataType.f32, cutlass.DataType.f32, [128, 128, 32], [2, 2, 1], 3)
|
||||
add_test_tensorop(GemmF32Sm80, LayoutCombination.NTN, [4, 4, 4], cutlass.DataType.f32, cutlass.DataType.f32, [64, 128, 32], [1, 2, 1], 3)
|
||||
add_test_tensorop(GemmF32Sm80, LayoutCombination.NTN, [4, 4, 4], cutlass.DataType.f32, cutlass.DataType.f32, [64, 64, 32], [1, 1, 1], 4)
|
||||
# Tests using SIMT
|
||||
add_test_simt = partial(add_test, opclass=cutlass.OpcodeClass.Simt)
|
||||
|
||||
add_test_simt(GemmF32Sm80, LayoutCombination.NNN, [1, 1, 1], cutlass.DataType.f32, cutlass.DataType.f32, [128, 128, 8], [2, 2, 1], 2)
|
||||
add_test_simt(GemmF32Sm80, LayoutCombination.TNN, [1, 1, 1], cutlass.DataType.f32, cutlass.DataType.f32, [64, 128, 8], [1, 2, 1], 2)
|
||||
add_test_simt(GemmF32Sm80, LayoutCombination.NTN, [1, 1, 1], cutlass.DataType.f32, cutlass.DataType.f32, [128, 64, 8], [2, 1, 1], 2)
|
||||
add_test_simt(GemmF32Sm80, LayoutCombination.TTN, [1, 1, 1], cutlass.DataType.f32, cutlass.DataType.f32, [64, 64, 8], [1, 1, 1], 2)
|
||||
add_test_simt(GemmF32Sm80, LayoutCombination.NNT, [1, 1, 1], cutlass.DataType.f32, cutlass.DataType.f32, [128, 128, 8], [2, 2, 1], 2)
|
||||
|
||||
# Stream K tests
|
||||
add_test_streamk = partial(add_test, opclass=cutlass.OpcodeClass.TensorOp, swizzle=cutlass.swizzle.ThreadblockSwizzleStreamK)
|
||||
add_test_streamk(GemmF32Sm80StreamK, LayoutCombination.TTN, [4, 4, 4], cutlass.DataType.f32, cutlass.DataType.f32, [128, 128, 32], [2, 2, 1], 3)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
156
test/python/gemm/gemm_f64_sm80.py
Normal file
156
test/python/gemm/gemm_f64_sm80.py
Normal file
@ -0,0 +1,156 @@
|
||||
#################################################################################################
|
||||
#
|
||||
# Copyright (c) 2023 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
# SPDX-License-Identifier: BSD-3-Clause
|
||||
#
|
||||
# Redistribution and use in source and binary forms, with or without
|
||||
# modification, are permitted provided that the following conditions are met:
|
||||
#
|
||||
# 1. Redistributions of source code must retain the above copyright notice, this
|
||||
# list of conditions and the following disclaimer.
|
||||
#
|
||||
# 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
# this list of conditions and the following disclaimer in the documentation
|
||||
# and/or other materials provided with the distribution.
|
||||
#
|
||||
# 3. Neither the name of the copyright holder nor the names of its
|
||||
# contributors may be used to endorse or promote products derived from
|
||||
# this software without specific prior written permission.
|
||||
#
|
||||
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
#
|
||||
#################################################################################################
|
||||
|
||||
"""
|
||||
Low-level functionality tests for GEMM with F64 operands on SM80
|
||||
"""
|
||||
|
||||
from functools import partial
|
||||
|
||||
import cutlass
|
||||
from cutlass.utils.datatypes import binding_opclass, binding_type
|
||||
from cutlass.backend.test.gemm_testbed import test_all_gemm
|
||||
import unittest
|
||||
|
||||
from cutlass.backend.test.utils import LayoutCombination, get_name
|
||||
from cutlass.backend.utils.device import device_cc
|
||||
|
||||
cc = 80
|
||||
|
||||
# Partial specialziation for naming tests
|
||||
bound_type = binding_type(cutlass.DataType.f64)
|
||||
name_fn = partial(get_name, element_a=bound_type, element_b=bound_type, arch=cc)
|
||||
|
||||
|
||||
def add_test(cls, layouts, alignments, element_output, element_accumulator,
|
||||
threadblock_shape, warp_count, stages, opclass, swizzle=None):
|
||||
"""
|
||||
Create a test-running function with the given specification and set it as a method of `cls`.
|
||||
|
||||
:param cls: class to which the generated method will be added
|
||||
:type cls: type
|
||||
:param layouts: layouts of A, B, and C operands
|
||||
:type layouts: list or tuple
|
||||
:param alignments: alingments of A, B, and C operands
|
||||
:type alignments: list or tuple
|
||||
:param element_output: data type of the output element
|
||||
:type element_output: cutlass.DataType
|
||||
:param element_accumulator: data type used in accumulation
|
||||
:type element_accumulator: cutlass.DataType
|
||||
:param threadblock_shape: dimensions of threadblock tiles
|
||||
:type threadblock_shape: list or tuple
|
||||
:param warp_count: warps to be launched per threadblock dimension
|
||||
:type warp_count: list or tuple
|
||||
:param stages: number of pipeline stages to use in the kernel
|
||||
:type stages: int
|
||||
:param opclass: class of operation being performed (e.g., SIMT, Tensor Core)
|
||||
:type opclass: cutlass.OpClass
|
||||
:param swizzle: threadblock swizzling functor
|
||||
"""
|
||||
|
||||
cluster_shape = [1, 1, 1]
|
||||
|
||||
def run(self):
|
||||
"""
|
||||
Dynamically-generated function that constructs a GEMM operation and verifies it against
|
||||
multiple test cases.
|
||||
"""
|
||||
element_A = cutlass.DataType.f64
|
||||
element_B = cutlass.DataType.f64
|
||||
layout_A, layout_B, layout_C = layouts
|
||||
alignment_A, alignment_B, alignment_C = alignments
|
||||
|
||||
plan = cutlass.op.Gemm(element_A=element_A, element_B=element_B,
|
||||
element_C=element_output, element_D=element_output,
|
||||
layout_A=layout_A, layout_B=layout_B, layout_C=layout_C,
|
||||
element_accumulator=element_accumulator,
|
||||
kernel_cc=cc)
|
||||
|
||||
plan.opclass = opclass
|
||||
if swizzle is not None:
|
||||
plan.swizzling_functor = swizzle
|
||||
td = plan.tile_descriptions()[0]
|
||||
td.threadblock_shape = threadblock_shape
|
||||
td.stages = stages
|
||||
td.warp_count = warp_count
|
||||
td.cluster_shape = cluster_shape
|
||||
op = plan.construct(tile_description=td, alignment_A=alignment_A, alignment_B=alignment_B, alignment_C=alignment_C)
|
||||
|
||||
self.assertTrue(test_all_gemm(op, 'universal'))
|
||||
|
||||
element_epilogue = element_accumulator
|
||||
name = name_fn(layouts, alignments, binding_type(element_output), binding_type(element_accumulator),
|
||||
binding_type(element_epilogue), cluster_shape, threadblock_shape, stages, opclass=binding_opclass(opclass))
|
||||
setattr(cls, name, run)
|
||||
|
||||
return run
|
||||
|
||||
|
||||
@unittest.skipIf(device_cc() < cc, 'Device compute capability is insufficient for SM80 tests.')
|
||||
class GemmF64Sm80(unittest.TestCase):
|
||||
"""
|
||||
Wrapper class to which tests will be added dynamically in __main__
|
||||
"""
|
||||
pass
|
||||
|
||||
|
||||
@unittest.skipIf(device_cc() < cc, 'Device compute capability is insufficient for SM80 tests.')
|
||||
class GemmF64Sm80StreamK(unittest.TestCase):
|
||||
"""
|
||||
Wrapper class to which tests will be added dynamically in __main__
|
||||
"""
|
||||
pass
|
||||
|
||||
|
||||
# Tests using TensorOp
|
||||
add_test_tensorop = partial(add_test, opclass=cutlass.OpcodeClass.TensorOp)
|
||||
|
||||
add_test_tensorop(GemmF64Sm80, LayoutCombination.NNN, [1, 1, 1], cutlass.DataType.f64, cutlass.DataType.f64, [128, 128, 16], [4, 2, 1], 3)
|
||||
add_test_tensorop(GemmF64Sm80, LayoutCombination.NTN, [1, 1, 1], cutlass.DataType.f64, cutlass.DataType.f64, [64, 64, 16], [2, 2, 1], 4)
|
||||
add_test_tensorop(GemmF64Sm80, LayoutCombination.TTN, [1, 1, 1], cutlass.DataType.f64, cutlass.DataType.f64, [32, 32, 16], [2, 1, 1], 5)
|
||||
|
||||
# Tests using SIMT
|
||||
add_test_simt = partial(add_test, opclass=cutlass.OpcodeClass.Simt)
|
||||
|
||||
add_test_simt(GemmF64Sm80, LayoutCombination.NNN, [1, 1, 1], cutlass.DataType.f64, cutlass.DataType.f64, [128, 128, 8], [2, 2, 1], 2)
|
||||
add_test_simt(GemmF64Sm80, LayoutCombination.TNN, [1, 1, 1], cutlass.DataType.f64, cutlass.DataType.f64, [64, 128, 8], [1, 2, 1], 2)
|
||||
add_test_simt(GemmF64Sm80, LayoutCombination.NTN, [1, 1, 1], cutlass.DataType.f64, cutlass.DataType.f64, [128, 64, 8], [2, 1, 1], 2)
|
||||
add_test_simt(GemmF64Sm80, LayoutCombination.TTN, [1, 1, 1], cutlass.DataType.f64, cutlass.DataType.f64, [64, 64, 8], [1, 1, 1], 2)
|
||||
add_test_simt(GemmF64Sm80, LayoutCombination.NNT, [1, 1, 1], cutlass.DataType.f64, cutlass.DataType.f64, [128, 128, 8], [2, 2, 1], 2)
|
||||
|
||||
# Stream K tests
|
||||
add_test_streamk = partial(add_test, opclass=cutlass.OpcodeClass.TensorOp, swizzle=cutlass.swizzle.ThreadblockSwizzleStreamK)
|
||||
add_test_streamk(GemmF64Sm80StreamK, LayoutCombination.NTT, [1, 1, 1], cutlass.DataType.f64, cutlass.DataType.f64, [128, 128, 16], [4, 2, 1], 3)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
142
test/python/gemm/gemm_f64_sm90.py
Normal file
142
test/python/gemm/gemm_f64_sm90.py
Normal file
@ -0,0 +1,142 @@
|
||||
#################################################################################################
|
||||
#
|
||||
# Copyright (c) 2023 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
# SPDX-License-Identifier: BSD-3-Clause
|
||||
#
|
||||
# Redistribution and use in source and binary forms, with or without
|
||||
# modification, are permitted provided that the following conditions are met:
|
||||
#
|
||||
# 1. Redistributions of source code must retain the above copyright notice, this
|
||||
# list of conditions and the following disclaimer.
|
||||
#
|
||||
# 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
# this list of conditions and the following disclaimer in the documentation
|
||||
# and/or other materials provided with the distribution.
|
||||
#
|
||||
# 3. Neither the name of the copyright holder nor the names of its
|
||||
# contributors may be used to endorse or promote products derived from
|
||||
# this software without specific prior written permission.
|
||||
#
|
||||
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
#
|
||||
#################################################################################################
|
||||
|
||||
"""
|
||||
Low-level functionality tests for GEMM with F64 operands on SM90
|
||||
"""
|
||||
|
||||
from functools import partial
|
||||
|
||||
import cutlass
|
||||
from cutlass.utils.datatypes import binding_opclass, binding_type
|
||||
from cutlass.backend.test.gemm_testbed import test_all_gemm
|
||||
import unittest
|
||||
|
||||
from cutlass.backend.test.utils import LayoutCombination, get_name
|
||||
from cutlass.backend.utils.device import device_cc
|
||||
|
||||
cc = 90
|
||||
|
||||
# Partial specialziation for naming tests
|
||||
bound_type = binding_type(cutlass.DataType.f64)
|
||||
name_fn = partial(get_name, element_a=bound_type, element_b=bound_type, arch=cc)
|
||||
|
||||
|
||||
def add_test(cls, layouts, alignments, element_output, element_accumulator,
|
||||
cluster_shape, threadblock_shape, stages, opclass, persistent=False, swizzle=None):
|
||||
"""
|
||||
Create a test-running function with the given specification and set it as a method of `cls`.
|
||||
|
||||
:param cls: class to which the generated method will be added
|
||||
:type cls: type
|
||||
:param layouts: layouts of A, B, and C operands
|
||||
:type layouts: list or tuple
|
||||
:param alignments: alingments of A, B, and C operands
|
||||
:type alignments: list or tuple
|
||||
:param element_output: data type of the output element
|
||||
:type element_output: cutlass.DataType
|
||||
:param element_accumulator: data type used in accumulation
|
||||
:type element_accumulator: cutlass.DataType
|
||||
:param cluster_shape: dimensions of threadblock cluster
|
||||
:type cluster_shape: list or tuple
|
||||
:param threadblock_shape: dimensions of threadblock tiles
|
||||
:type threadblock_shape: list or tuple
|
||||
:param warp_count: warps to be launched per threadblock dimension
|
||||
:type warp_count: list or tuple
|
||||
:param stages: number of pipeline stages to use in the kernel
|
||||
:type stages: int
|
||||
:param opclass: class of operation being performed (e.g., SIMT, Tensor Core)
|
||||
:type opclass: cutlass.OpClass
|
||||
:param persistent: whether this is a persistent warp-specialized kernel
|
||||
:type persistent: bool
|
||||
:param swizzle: threadblock swizzling functor
|
||||
"""
|
||||
|
||||
def run(self):
|
||||
"""
|
||||
Dynamically-generated function that constructs a GEMM operation and verifies it against
|
||||
multiple test cases.
|
||||
"""
|
||||
element_A = cutlass.DataType.f64
|
||||
element_B = cutlass.DataType.f64
|
||||
layout_A, layout_B, layout_C = layouts
|
||||
alignment_A, alignment_B, alignment_C = alignments
|
||||
|
||||
plan = cutlass.op.Gemm(element_A=element_A, element_B=element_B,
|
||||
element_C=element_output, element_D=element_output,
|
||||
layout_A=layout_A, layout_B=layout_B, layout_C=layout_C,
|
||||
element_accumulator=element_accumulator)
|
||||
|
||||
plan.opclass = opclass
|
||||
if swizzle is not None:
|
||||
plan.swizzling_functor = swizzle
|
||||
td = plan.tile_descriptions()[0]
|
||||
td.threadblock_shape = threadblock_shape
|
||||
td.stages = stages
|
||||
td.cluster_shape = cluster_shape
|
||||
td.persistent = persistent
|
||||
op = plan.construct(tile_description=td, alignment_A=alignment_A, alignment_B=alignment_B, alignment_C=alignment_C)
|
||||
self.assertTrue(test_all_gemm(op, 'universal'))
|
||||
|
||||
if persistent:
|
||||
suffix = "_persistent"
|
||||
else:
|
||||
suffix = ""
|
||||
|
||||
element_epilogue = element_accumulator
|
||||
name = name_fn(layouts, alignments, binding_type(element_output), binding_type(element_accumulator),
|
||||
binding_type(element_epilogue), cluster_shape, threadblock_shape, stages,
|
||||
opclass=binding_opclass(opclass), suffix=suffix)
|
||||
setattr(cls, name, run)
|
||||
|
||||
return run
|
||||
|
||||
|
||||
@unittest.skipIf(device_cc() < cc, 'Device compute capability is insufficient for SM90 tests.')
|
||||
class GemmF64Sm90(unittest.TestCase):
|
||||
"""
|
||||
Wrapper class to which tests will be added dynamically in __main__
|
||||
"""
|
||||
pass
|
||||
|
||||
|
||||
add_test_tensorop = partial(add_test, opclass=cutlass.OpcodeClass.TensorOp)
|
||||
add_test_simt = partial(add_test, opclass=cutlass.OpcodeClass.Simt)
|
||||
|
||||
add_test_tensorop(GemmF64Sm90, LayoutCombination.NNT, [1, 1, 1], cutlass.DataType.f64, cutlass.DataType.f64, [1, 1, 1], [128, 128, 32], 3)
|
||||
add_test_tensorop(GemmF64Sm90, LayoutCombination.TNN, [1, 1, 1], cutlass.DataType.f64, cutlass.DataType.f64, [1, 1, 1], [128, 128, 32], 3)
|
||||
add_test_simt(GemmF64Sm90, LayoutCombination.NNN, [1, 1, 1], cutlass.DataType.f64, cutlass.DataType.f64, [1, 1, 1], [128, 128, 8], 2)
|
||||
add_test_simt(GemmF64Sm90, LayoutCombination.TTT, [1, 1, 1], cutlass.DataType.f64, cutlass.DataType.f64, [1, 1, 1], [64, 128, 8], 2)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
156
test/python/gemm/gemm_s8_sm80.py
Normal file
156
test/python/gemm/gemm_s8_sm80.py
Normal file
@ -0,0 +1,156 @@
|
||||
#################################################################################################
|
||||
#
|
||||
# Copyright (c) 2023 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
# SPDX-License-Identifier: BSD-3-Clause
|
||||
#
|
||||
# Redistribution and use in source and binary forms, with or without
|
||||
# modification, are permitted provided that the following conditions are met:
|
||||
#
|
||||
# 1. Redistributions of source code must retain the above copyright notice, this
|
||||
# list of conditions and the following disclaimer.
|
||||
#
|
||||
# 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
# this list of conditions and the following disclaimer in the documentation
|
||||
# and/or other materials provided with the distribution.
|
||||
#
|
||||
# 3. Neither the name of the copyright holder nor the names of its
|
||||
# contributors may be used to endorse or promote products derived from
|
||||
# this software without specific prior written permission.
|
||||
#
|
||||
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
#
|
||||
#################################################################################################
|
||||
|
||||
"""
|
||||
Low-level functionality tests for GEMM with S8 operands on SM80
|
||||
"""
|
||||
|
||||
from functools import partial
|
||||
|
||||
import cutlass
|
||||
from cutlass.utils.datatypes import binding_opclass, binding_type
|
||||
from cutlass.backend.test.gemm_testbed import test_all_gemm
|
||||
import unittest
|
||||
|
||||
from cutlass.backend.test.utils import LayoutCombination, get_name
|
||||
from cutlass.backend.utils.device import device_cc
|
||||
|
||||
cc = 80
|
||||
|
||||
# Partial specialziation for naming tests
|
||||
bound_type = binding_type(cutlass.DataType.s8)
|
||||
name_fn = partial(get_name, element_a=bound_type, element_b=bound_type, arch=cc)
|
||||
|
||||
|
||||
def add_test(cls, layouts, alignments, element_output, element_accumulator,
|
||||
threadblock_shape, warp_count, stages, opclass, swizzle=None):
|
||||
"""
|
||||
Create a test-running function with the given specification and set it as a method of `cls`.
|
||||
|
||||
:param cls: class to which the generated method will be added
|
||||
:type cls: type
|
||||
:param layouts: layouts of A, B, and C operands
|
||||
:type layouts: list or tuple
|
||||
:param alignments: alingments of A, B, and C operands
|
||||
:type alignments: list or tuple
|
||||
:param element_output: data type of the output element
|
||||
:type element_output: cutlass.DataType
|
||||
:param element_accumulator: data type used in accumulation
|
||||
:type element_accumulator: cutlass.DataType
|
||||
:param threadblock_shape: dimensions of threadblock tiles
|
||||
:type threadblock_shape: list or tuple
|
||||
:param warp_count: warps to be launched per threadblock dimension
|
||||
:type warp_count: list or tuple
|
||||
:param stages: number of pipeline stages to use in the kernel
|
||||
:type stages: int
|
||||
:param opclass: class of operation being performed (e.g., SIMT, Tensor Core)
|
||||
:type opclass: cutlass.OpClass
|
||||
:param swizzle: threadblock swizzling functor
|
||||
"""
|
||||
|
||||
cluster_shape = [1, 1, 1]
|
||||
|
||||
def run(self):
|
||||
"""
|
||||
Dynamically-generated function that constructs a GEMM operation and verifies it against
|
||||
multiple test cases.
|
||||
"""
|
||||
element_A = cutlass.DataType.s8
|
||||
element_B = cutlass.DataType.s8
|
||||
layout_A, layout_B, layout_C = layouts
|
||||
alignment_A, alignment_B, alignment_C = alignments
|
||||
|
||||
plan = cutlass.op.Gemm(element_A=element_A, element_B=element_B,
|
||||
element_C=element_output, element_D=element_output,
|
||||
layout_A=layout_A, layout_B=layout_B, layout_C=layout_C,
|
||||
element_accumulator=element_accumulator,
|
||||
kernel_cc=cc)
|
||||
|
||||
plan.opclass = opclass
|
||||
if swizzle is not None:
|
||||
plan.swizzling_functor = swizzle
|
||||
td = plan.tile_descriptions()[0]
|
||||
td.threadblock_shape = threadblock_shape
|
||||
td.stages = stages
|
||||
td.warp_count = warp_count
|
||||
td.cluster_shape = cluster_shape
|
||||
op = plan.construct(tile_description=td, alignment_A=alignment_A, alignment_B=alignment_B, alignment_C=alignment_C)
|
||||
|
||||
self.assertTrue(test_all_gemm(op, 'universal'))
|
||||
|
||||
element_epilogue = element_accumulator
|
||||
name = name_fn(layouts, alignments, binding_type(element_output), binding_type(element_accumulator),
|
||||
binding_type(element_epilogue), cluster_shape, threadblock_shape, stages, opclass=binding_opclass(opclass))
|
||||
setattr(cls, name, run)
|
||||
|
||||
return run
|
||||
|
||||
|
||||
@unittest.skipIf(device_cc() < cc, 'Device compute capability is insufficient for SM80 tests.')
|
||||
class GemmS8Sm80(unittest.TestCase):
|
||||
"""
|
||||
Wrapper class to which tests will be added dynamically in __main__
|
||||
"""
|
||||
pass
|
||||
|
||||
|
||||
@unittest.skipIf(device_cc() < cc, 'Device compute capability is insufficient for SM80 tests.')
|
||||
class GemmS8Sm80StreamK(unittest.TestCase):
|
||||
"""
|
||||
Wrapper class to which tests will be added dynamically in __main__
|
||||
"""
|
||||
pass
|
||||
|
||||
|
||||
# Tests using TensorOp
|
||||
add_test_tensorop = partial(add_test, opclass=cutlass.OpcodeClass.TensorOp)
|
||||
|
||||
add_test_tensorop(GemmS8Sm80, LayoutCombination.TNN, [16, 16, 16], cutlass.DataType.s8, cutlass.DataType.s32, [256, 128, 64], [4, 2, 1], 3)
|
||||
add_test_tensorop(GemmS8Sm80, LayoutCombination.TNT, [16, 16, 16], cutlass.DataType.s8, cutlass.DataType.s32, [128, 256, 64], [2, 4, 1], 3)
|
||||
add_test_tensorop(GemmS8Sm80, LayoutCombination.TNN, [16, 16, 4], cutlass.DataType.s32, cutlass.DataType.s32, [64, 64, 64], [1, 1, 1], 4)
|
||||
|
||||
# Tests using SIMT
|
||||
add_test_simt = partial(add_test, opclass=cutlass.OpcodeClass.Simt)
|
||||
|
||||
add_test_simt(GemmS8Sm80, LayoutCombination.NNN, [1, 1, 1], cutlass.DataType.s8, cutlass.DataType.s32, [128, 128, 8], [2, 2, 1], 2)
|
||||
add_test_simt(GemmS8Sm80, LayoutCombination.TNN, [1, 1, 1], cutlass.DataType.s8, cutlass.DataType.s32, [64, 128, 8], [1, 2, 1], 2)
|
||||
add_test_simt(GemmS8Sm80, LayoutCombination.NTN, [1, 1, 1], cutlass.DataType.s8, cutlass.DataType.s32, [128, 64, 8], [2, 1, 1], 2)
|
||||
add_test_simt(GemmS8Sm80, LayoutCombination.TTN, [1, 1, 1], cutlass.DataType.s32, cutlass.DataType.s32, [64, 64, 8], [1, 1, 1], 2)
|
||||
add_test_simt(GemmS8Sm80, LayoutCombination.NNT, [1, 1, 1], cutlass.DataType.s32, cutlass.DataType.s32, [128, 128, 8], [2, 2, 1], 2)
|
||||
|
||||
# Stream K tests
|
||||
add_test_streamk = partial(add_test, opclass=cutlass.OpcodeClass.TensorOp, swizzle=cutlass.swizzle.ThreadblockSwizzleStreamK)
|
||||
add_test_streamk(GemmS8Sm80StreamK, LayoutCombination.TNT, [16, 16, 16], cutlass.DataType.s8, cutlass.DataType.s32, [128, 256, 64], [2, 4, 1], 3)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
155
test/python/gemm/gemm_s8_sm90.py
Normal file
155
test/python/gemm/gemm_s8_sm90.py
Normal file
@ -0,0 +1,155 @@
|
||||
#################################################################################################
|
||||
#
|
||||
# Copyright (c) 2023 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
# SPDX-License-Identifier: BSD-3-Clause
|
||||
#
|
||||
# Redistribution and use in source and binary forms, with or without
|
||||
# modification, are permitted provided that the following conditions are met:
|
||||
#
|
||||
# 1. Redistributions of source code must retain the above copyright notice, this
|
||||
# list of conditions and the following disclaimer.
|
||||
#
|
||||
# 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
# this list of conditions and the following disclaimer in the documentation
|
||||
# and/or other materials provided with the distribution.
|
||||
#
|
||||
# 3. Neither the name of the copyright holder nor the names of its
|
||||
# contributors may be used to endorse or promote products derived from
|
||||
# this software without specific prior written permission.
|
||||
#
|
||||
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
#
|
||||
#################################################################################################
|
||||
|
||||
"""
|
||||
Low-level functionality tests for GEMM with S8 operands on SM90
|
||||
"""
|
||||
|
||||
from functools import partial
|
||||
|
||||
import cutlass
|
||||
from cutlass.utils.datatypes import binding_opclass, binding_type
|
||||
from cutlass.backend.test.gemm_testbed import test_all_gemm
|
||||
import unittest
|
||||
|
||||
from cutlass.backend.test.utils import LayoutCombination, get_name
|
||||
from cutlass.backend.utils.device import device_cc
|
||||
|
||||
cc = 90
|
||||
|
||||
# Partial specialziation for naming tests
|
||||
bound_type = binding_type(cutlass.DataType.s8)
|
||||
name_fn = partial(get_name, element_a=bound_type, element_b=bound_type, arch=cc)
|
||||
|
||||
|
||||
def add_test(cls, layouts, alignments, element_output, element_accumulator,
|
||||
cluster_shape, threadblock_shape, stages, opclass, persistent=False, swizzle=None):
|
||||
"""
|
||||
Create a test-running function with the given specification and set it as a method of `cls`.
|
||||
|
||||
:param cls: class to which the generated method will be added
|
||||
:type cls: type
|
||||
:param layouts: layouts of A, B, and C operands
|
||||
:type layouts: list or tuple
|
||||
:param alignments: alingments of A, B, and C operands
|
||||
:type alignments: list or tuple
|
||||
:param element_output: data type of the output element
|
||||
:type element_output: cutlass.DataType
|
||||
:param element_accumulator: data type used in accumulation
|
||||
:type element_accumulator: cutlass.DataType
|
||||
:param cluster_shape: dimensions of threadblock cluster
|
||||
:type cluster_shape: list or tuple
|
||||
:param threadblock_shape: dimensions of threadblock tiles
|
||||
:type threadblock_shape: list or tuple
|
||||
:param warp_count: warps to be launched per threadblock dimension
|
||||
:type warp_count: list or tuple
|
||||
:param stages: number of pipeline stages to use in the kernel
|
||||
:type stages: int
|
||||
:param opclass: class of operation being performed (e.g., SIMT, Tensor Core)
|
||||
:type opclass: cutlass.OpClass
|
||||
:param persistent: whether this is a persistent warp-specialized kernel
|
||||
:type persistent: bool
|
||||
:param swizzle: threadblock swizzling functor
|
||||
"""
|
||||
|
||||
def run(self):
|
||||
"""
|
||||
Dynamically-generated function that constructs a GEMM operation and verifies it against
|
||||
multiple test cases.
|
||||
"""
|
||||
element_A = cutlass.DataType.s8
|
||||
element_B = cutlass.DataType.s8
|
||||
layout_A, layout_B, layout_C = layouts
|
||||
alignment_A, alignment_B, alignment_C = alignments
|
||||
|
||||
plan = cutlass.op.Gemm(element_A=element_A, element_B=element_B,
|
||||
element_C=element_output, element_D=element_output,
|
||||
layout_A=layout_A, layout_B=layout_B, layout_C=layout_C,
|
||||
element_accumulator=element_accumulator)
|
||||
|
||||
plan.opclass = opclass
|
||||
if swizzle is not None:
|
||||
plan.swizzling_functor = swizzle
|
||||
td = plan.tile_descriptions()[0]
|
||||
td.threadblock_shape = threadblock_shape
|
||||
td.stages = stages
|
||||
td.cluster_shape = cluster_shape
|
||||
td.persistent = persistent
|
||||
op = plan.construct(tile_description=td, alignment_A=alignment_A, alignment_B=alignment_B, alignment_C=alignment_C)
|
||||
self.assertTrue(test_all_gemm(op, 'universal'))
|
||||
|
||||
if persistent:
|
||||
suffix = "_persistent"
|
||||
else:
|
||||
suffix = ""
|
||||
|
||||
element_epilogue = element_accumulator
|
||||
name = name_fn(layouts, alignments, binding_type(element_output), binding_type(element_accumulator),
|
||||
binding_type(element_epilogue), cluster_shape, threadblock_shape, stages,
|
||||
opclass=binding_opclass(opclass), suffix=suffix)
|
||||
setattr(cls, name, run)
|
||||
|
||||
return run
|
||||
|
||||
|
||||
@unittest.skipIf(device_cc() < cc, 'Device compute capability is insufficient for SM90 tests.')
|
||||
class GemmS8Sm90(unittest.TestCase):
|
||||
"""
|
||||
Wrapper class to which tests will be added dynamically in __main__
|
||||
"""
|
||||
pass
|
||||
|
||||
|
||||
add_test_tensorop = partial(add_test, opclass=cutlass.OpcodeClass.TensorOp)
|
||||
|
||||
# Tests with 1x1x1 clusters
|
||||
add_test_tensorop(GemmS8Sm90, LayoutCombination.TNN, [16, 16, 16], cutlass.DataType.s8, cutlass.DataType.s32, [1, 1, 1], [128, 128, 128], 3)
|
||||
add_test_tensorop(GemmS8Sm90, LayoutCombination.TNT, [16, 16, 16], cutlass.DataType.s8, cutlass.DataType.s32, [1, 1, 1], [128, 128, 128], None)
|
||||
add_test_tensorop(GemmS8Sm90, LayoutCombination.TNT, [16, 16, 8], cutlass.DataType.s8, cutlass.DataType.s32, [1, 1, 1], [128, 128, 128], None)
|
||||
add_test_tensorop(GemmS8Sm90, LayoutCombination.TNT, [16, 16, 16], cutlass.DataType.s8, cutlass.DataType.s32, [1, 1, 1], [64, 128, 128], None)
|
||||
add_test_tensorop(GemmS8Sm90, LayoutCombination.TNT, [16, 16, 16], cutlass.DataType.s8, cutlass.DataType.s32, [1, 1, 1], [128, 64, 32], None)
|
||||
add_test_tensorop(GemmS8Sm90, LayoutCombination.TNT, [4, 4, 16], cutlass.DataType.s8, cutlass.DataType.s32, [1, 1, 1], [128, 128, 128], None)
|
||||
|
||||
# Tests with different cluster shapes
|
||||
add_test_tensorop(GemmS8Sm90, LayoutCombination.TNT, [16, 16, 16], cutlass.DataType.s8, cutlass.DataType.s32, [2, 2, 1], [128, 128, 128], None)
|
||||
add_test_tensorop(GemmS8Sm90, LayoutCombination.TNT, [16, 16, 16], cutlass.DataType.s8, cutlass.DataType.s32, [1, 4, 1], [128, 128, 128], None)
|
||||
|
||||
# Tests with persistent warp-specialized threadblocks
|
||||
add_test_tensorop(GemmS8Sm90, LayoutCombination.TNT, [16, 16, 16], cutlass.DataType.s8, cutlass.DataType.s32, [2, 1, 1], [128, 128, 128], None, persistent=True)
|
||||
|
||||
# Tests for SIMT
|
||||
add_test_simt = partial(add_test, opclass=cutlass.OpcodeClass.Simt)
|
||||
add_test_simt(GemmS8Sm90, LayoutCombination.TNN, [1, 1, 1], cutlass.DataType.s8, cutlass.DataType.s32, [1, 1, 1], [64, 32, 8], 2)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
42
test/python/gemm/run_all_tests.py
Normal file
42
test/python/gemm/run_all_tests.py
Normal file
@ -0,0 +1,42 @@
|
||||
#################################################################################################
|
||||
#
|
||||
# Copyright (c) 2023 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
# SPDX-License-Identifier: BSD-3-Clause
|
||||
#
|
||||
# Redistribution and use in source and binary forms, with or without
|
||||
# modification, are permitted provided that the following conditions are met:
|
||||
#
|
||||
# 1. Redistributions of source code must retain the above copyright notice, this
|
||||
# list of conditions and the following disclaimer.
|
||||
#
|
||||
# 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
# this list of conditions and the following disclaimer in the documentation
|
||||
# and/or other materials provided with the distribution.
|
||||
#
|
||||
# 3. Neither the name of the copyright holder nor the names of its
|
||||
# contributors may be used to endorse or promote products derived from
|
||||
# this software without specific prior written permission.
|
||||
#
|
||||
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
#
|
||||
#################################################################################################
|
||||
|
||||
import unittest
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
loader = unittest.TestLoader()
|
||||
tests = loader.discover('./', 'gemm_*.py')
|
||||
testRunner = unittest.runner.TextTestRunner()
|
||||
results = testRunner.run(tests)
|
||||
if not results.wasSuccessful():
|
||||
raise Exception('Test cases failed')
|
||||
Reference in New Issue
Block a user