2023-01-24 09:55:28 +08:00
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#################################################################################################
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#
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# Copyright (c) 2017 - 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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2022-09-04 06:48:46 +08:00
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import pycutlass
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from pycutlass import *
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from pycutlass.test import *
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import unittest
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from pycutlass.test.gemm_grouped_testbed import TestbedGrouped
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2022-12-08 10:53:33 +08:00
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from pycutlass.utils.device import device_cc
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2022-12-08 10:53:33 +08:00
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@unittest.skipIf(device_cc() < 80, "Device compute capability is insufficient for SM80 tests.")
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2022-09-04 06:48:46 +08:00
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class GemmGroupedSm80(unittest.TestCase):
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def test_SM80_Device_GemmGrouped_f16n_f16t_f32n_tensor_op_f32_128x128x32_64x64x32(self):
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math_inst = MathInstruction(
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instruction_shape=[16, 8, 16], element_a=cutlass.float16,
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element_b=cutlass.float16, element_accumulator=cutlass.float32,
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opcode_class=cutlass.OpClass.TensorOp,
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math_operation=MathOperation.multiply_add
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)
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tile_description = TileDescription(
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threadblock_shape=[128, 128, 32],
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stages=3, warp_count=[2, 2, 1],
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2022-09-13 09:26:30 +08:00
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math_instruction=math_inst
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)
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A = TensorDescription(
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element=cutlass.float16, layout=cutlass.ColumnMajor,
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alignment=8
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)
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B = TensorDescription(
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element=cutlass.float16, layout=cutlass.ColumnMajor,
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alignment=8
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)
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C = TensorDescription(
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element=cutlass.float32, layout=cutlass.ColumnMajor,
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alignment=4
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)
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element_epilogue = cutlass.float32
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epilogue_functor = LinearCombination(
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C.element, C.alignment,
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math_inst.element_accumulator, element_epilogue)
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swizzling_functor = cutlass.BatchedIdentitySwizzle
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for precompute_mode in [SchedulerMode.Device, SchedulerMode.Host]:
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operation = GemmOperationGrouped(
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80,
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tile_description, A, B, C,
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epilogue_functor, swizzling_functor,
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precompute_mode=precompute_mode
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)
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testbed = TestbedGrouped(operation=operation)
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self.assertTrue(testbed.run(24))
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def test_SM80_Device_GemmGrouped_f64t_f64t_f64n_tensor_op_f64_64x64x16_32x32x16(self):
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math_inst = MathInstruction(
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instruction_shape=[8, 8, 4], element_a=cutlass.float64,
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element_b=cutlass.float64, element_accumulator=cutlass.float64,
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opcode_class=cutlass.OpClass.TensorOp,
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math_operation=MathOperation.multiply_add
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)
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tile_description = TileDescription(
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threadblock_shape=[64, 64, 16],
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stages=4, warp_count=[2, 2, 1],
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math_instruction=math_inst
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)
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A = TensorDescription(
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element=cutlass.float64, layout=cutlass.RowMajor,
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alignment=1
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)
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B = TensorDescription(
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element=cutlass.float64, layout=cutlass.RowMajor,
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alignment=1
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)
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C = TensorDescription(
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element=cutlass.float64, layout=cutlass.ColumnMajor,
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alignment=1
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)
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element_epilogue = cutlass.float64
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epilogue_functor = LinearCombination(
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C.element, C.alignment,
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math_inst.element_accumulator, element_epilogue)
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swizzling_functor = cutlass.BatchedIdentitySwizzle
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for precompute_mode in [SchedulerMode.Device, SchedulerMode.Host]:
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operation = GemmOperationGrouped(
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80,
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tile_description, A, B, C,
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epilogue_functor, swizzling_functor,
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precompute_mode=precompute_mode
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)
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testbed = TestbedGrouped(operation=operation)
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self.assertTrue(testbed.run(24))
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def test_SM80_Device_GemmGrouped_f32t_f32t_f32t_simt_f32_128x64x8_64x32x1(self):
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math_inst = MathInstruction(
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instruction_shape=[1, 1, 1], element_a=cutlass.float32,
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element_b=cutlass.float32, element_accumulator=cutlass.float32,
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opcode_class=cutlass.OpClass.Simt,
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math_operation=MathOperation.multiply_add
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)
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tile_description = TileDescription(
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threadblock_shape=[128, 64, 8],
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stages=4, warp_count=[2, 2, 1],
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math_instruction=math_inst
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)
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A = TensorDescription(
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element=cutlass.float32, layout=cutlass.RowMajor,
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alignment=1
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)
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B = TensorDescription(
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element=cutlass.float32, layout=cutlass.RowMajor,
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alignment=1
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)
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C = TensorDescription(
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element=cutlass.float32, layout=cutlass.RowMajor,
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alignment=1
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)
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element_epilogue = cutlass.float32
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2022-09-13 09:26:30 +08:00
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epilogue_functor = LinearCombination(
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C.element, C.alignment,
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math_inst.element_accumulator, element_epilogue)
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swizzling_functor = cutlass.BatchedIdentitySwizzle
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for precompute_mode in [SchedulerMode.Device, SchedulerMode.Host]:
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operation = GemmOperationGrouped(
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80,
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tile_description, A, B, C,
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epilogue_functor, swizzling_functor,
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precompute_mode=precompute_mode
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)
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testbed = TestbedGrouped(operation=operation)
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self.assertTrue(testbed.run(27))
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def test_SM80_Device_GemmGrouped_f16n_f16t_f32n_tensor_op_f32_128x128x32_64x64x32_cache(self):
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math_inst = MathInstruction(
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instruction_shape=[16, 8, 16], element_a=cutlass.float16,
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element_b=cutlass.float16, element_accumulator=cutlass.float32,
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opcode_class=cutlass.OpClass.TensorOp,
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math_operation=MathOperation.multiply_add
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)
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tile_description = TileDescription(
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threadblock_shape=[128, 128, 32],
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stages=3, warp_count=[2, 2, 1],
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math_instruction=math_inst
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2022-09-04 06:48:46 +08:00
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)
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A = TensorDescription(
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element=cutlass.float16, layout=cutlass.ColumnMajor,
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alignment=8
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)
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B = TensorDescription(
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element=cutlass.float16, layout=cutlass.ColumnMajor,
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alignment=8
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)
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C = TensorDescription(
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element=cutlass.float32, layout=cutlass.ColumnMajor,
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alignment=4
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)
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element_epilogue = cutlass.float32
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2022-09-13 09:26:30 +08:00
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epilogue_functor = LinearCombination(
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C.element, C.alignment,
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math_inst.element_accumulator, element_epilogue)
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2022-09-04 06:48:46 +08:00
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swizzling_functor = cutlass.BatchedIdentitySwizzle
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for precompute_mode in [SchedulerMode.Device, SchedulerMode.Host]:
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operation = GemmOperationGrouped(
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80,
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tile_description, A, B, C,
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epilogue_functor, swizzling_functor,
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precompute_mode=precompute_mode
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)
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testbed = TestbedGrouped(operation=operation)
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self.assertTrue(testbed.run(5))
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if __name__ == '__main__':
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pycutlass.get_memory_pool(2**30, 2**30)
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unittest.main()
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