293 lines
9.1 KiB
C++
293 lines
9.1 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017-2021, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without modification, are permitted
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* provided that the following conditions are met:
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* * Redistributions of source code must retain the above copyright notice, this list of
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* conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright notice, this list of
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* conditions and the following disclaimer in the documentation and/or other materials
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* provided with the distribution.
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* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
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* to endorse or promote products derived from this software without specific prior written
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* permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
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* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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* STRICT LIABILITY, OR TOR (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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* Copyright (c) 2021 Iluvatar CoreX. All rights reserved.
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* Copyright Declaration: This software, including all of its code and documentation,
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* except for the third-party software it contains, is a copyrighted work of Shanghai Iluvatar CoreX
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* Semiconductor Co., Ltd. and its affiliates ("Iluvatar CoreX") in accordance with the PRC Copyright
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* Law and relevant international treaties, and all rights contained therein are enjoyed by Iluvatar
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* CoreX. No user of this software shall have any right, ownership or interest in this software and
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* any use of this software shall be in compliance with the terms and conditions of the End User
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* License Agreement.
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**************************************************************************************************/
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/*! \file
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\brief Definitions for GEMM structures
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*/
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#pragma once
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#include "cutlass/cutlass.h"
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#include "cutlass/numeric_types.h"
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#include "cutlass/arch/arch.h"
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#include "cutlass/arch/mma.h"
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#include "cutlass/gemm/gemm.h"
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#include "cutlass/epilogue/thread/linear_combination.h"
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#include "cutlass/epilogue/thread/linear_combination_clamp.h"
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////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace gemm {
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namespace device {
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////////////////////////////////////////////////////////////////////////////////
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template <
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typename OperatorClass,
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typename ArchTag,
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typename ElementA,
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typename ElementB,
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typename ElementC,
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typename ElementAccumulator
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>
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struct DefaultGemmConfiguration;
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////////////////////////////////////////////////////////////////////////////////
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/// FIXME(Peter Han): Need to update configuration according to perf results, so
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/// that could archieve good performance by default.
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template <
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typename ArchTag,
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typename ElementA,
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typename ElementB,
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typename ElementC,
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typename ElementAccumulator>
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struct DefaultGemmConfiguration<
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arch::OpClassSimt,
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ArchTag,
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ElementA,
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ElementB,
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ElementC,
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ElementAccumulator> {
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static int const kAlignmentA = 1;
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static int const kAlignmentB = 1;
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using ThreadblockShape = GemmShape<128, 128, 8>;
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using WarpShape = GemmShape<64, 64, 8>;
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using InstructionShape = GemmShape<1, 1, 1>;
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static int const kStages = 2;
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using EpilogueOutputOp = epilogue::thread::LinearCombination<
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ElementC,
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1,
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ElementAccumulator,
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ElementAccumulator
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>;
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using Operator = arch::OpMultiplyAdd;
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};
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////////////////////////////////////////////////////////////////////////////////
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template <
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typename ArchTag,
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typename ElementC>
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struct DefaultGemmConfiguration<arch::OpClassSimt, ArchTag, int8_t, int8_t, ElementC, int32_t> {
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static int const kAlignmentA = 4;
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static int const kAlignmentB = 4;
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using ThreadblockShape = GemmShape<128, 128, 32>;
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using WarpShape = GemmShape<64, 64, 32>;
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using InstructionShape = GemmShape<1, 1, 4>;
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static int const kStages = 2;
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using EpilogueOutputOp = epilogue::thread::LinearCombinationClamp<
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ElementC,
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1,
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int32_t,
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float
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>;
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using Operator = arch::OpMultiplyAdd;
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};
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////////////////////////////////////////////////////////////////////////////////
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template <
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typename ElementC>
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struct DefaultGemmConfiguration<
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arch::OpClassTensorOp,
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arch::Cu10,
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int8_t,
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int8_t,
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ElementC,
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int32_t> {
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using ElementA = int8_t;
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using ElementB = int8_t;
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using ElementAccumulator = int32_t;
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static int const kAlignmentA = MEMORY_ACCESS_SIZE / sizeof_bits<ElementA>::value;
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static int const kAlignmentB = MEMORY_ACCESS_SIZE / sizeof_bits<ElementB>::value;
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using ThreadblockShape = GemmShape<256, 256, 32>;
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using WarpShape = GemmShape<64, 64, 32>;
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using InstructionShape = GemmShape<16, 16, 16>;
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static int const kStages = 2;
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using EpilogueOutputOp = epilogue::thread::LinearCombination<
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ElementC,
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MEMORY_ACCESS_SIZE / sizeof_bits<ElementC>::value,
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ElementAccumulator,
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ElementAccumulator
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>;
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using Operator = arch::OpMultiplyAdd;
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};
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template <
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typename ElementC>
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struct DefaultGemmConfiguration<
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arch::OpClassTensorOp,
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arch::Cu10,
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uint8_t,
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uint8_t,
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ElementC,
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uint32_t> {
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using ElementA = uint8_t;
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using ElementB = uint8_t;
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using ElementAccumulator = uint32_t;
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static int const kAlignmentA = MEMORY_ACCESS_SIZE / sizeof_bits<ElementA>::value;
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static int const kAlignmentB = MEMORY_ACCESS_SIZE / sizeof_bits<ElementB>::value;
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using ThreadblockShape = GemmShape<256, 256, 32>;
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using WarpShape = GemmShape<64, 64, 32>;
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using InstructionShape = GemmShape<16, 16, 16>;
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static int const kStages = 2;
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using EpilogueOutputOp = epilogue::thread::LinearCombination<
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ElementC,
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MEMORY_ACCESS_SIZE / sizeof_bits<ElementC>::value,
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ElementAccumulator,
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ElementAccumulator
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>;
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using Operator = arch::OpMultiplyAdd;
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};
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template <
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typename ElementC>
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struct DefaultGemmConfiguration<
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arch::OpClassTensorOp,
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arch::Cu10,
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half_t,
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half_t,
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ElementC,
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float> {
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using ElementA = half_t;
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using ElementB = half_t;
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using ElementAccumulator = float;
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static int const kAlignmentA = MEMORY_ACCESS_SIZE / sizeof_bits<ElementA>::value;
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static int const kAlignmentB = MEMORY_ACCESS_SIZE / sizeof_bits<ElementB>::value;
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using ThreadblockShape = GemmShape<128, 128, 32>;
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using WarpShape = GemmShape<32, 32, 32>;
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using InstructionShape = GemmShape<16, 16, 16>;
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static int const kStages = 2;
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using EpilogueOutputOp = epilogue::thread::LinearCombination<
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ElementC,
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MEMORY_ACCESS_SIZE / sizeof_bits<ElementC>::value,
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ElementAccumulator,
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ElementAccumulator
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>;
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using Operator = arch::OpMultiplyAdd;
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};
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template <
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typename ElementC>
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struct DefaultGemmConfiguration<
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arch::OpClassTensorOp,
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arch::Cu10,
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bfloat16_t,
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bfloat16_t,
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ElementC,
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float> {
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using ElementA = bfloat16_t;
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using ElementB = bfloat16_t;
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using ElementAccumulator = float;
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static int const kAlignmentA = 32 / sizeof_bits<ElementA>::value;
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static int const kAlignmentB = 32 / sizeof_bits<ElementB>::value;
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using ThreadblockShape = GemmShape<128, 128, 32>;
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using WarpShape = GemmShape<32, 32, 32>;
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using InstructionShape = GemmShape<16, 16, 16>;
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static int const kStages = 2;
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using EpilogueOutputOp = epilogue::thread::LinearCombination<
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ElementC,
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MEMORY_ACCESS_SIZE / sizeof_bits<ElementC>::value,
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ElementAccumulator,
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ElementAccumulator
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>;
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using Operator = arch::OpMultiplyAdd;
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};
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template <
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typename ElementC>
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struct DefaultGemmConfiguration<
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arch::OpClassTensorOp,
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arch::Cu10,
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float,
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float,
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ElementC,
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float> {
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using ElementA = float;
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using ElementB = float;
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using ElementAccumulator = float;
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static int const kAlignmentA = 32 / sizeof_bits<ElementA>::value;
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static int const kAlignmentB = 32 / sizeof_bits<ElementB>::value;
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using ThreadblockShape = GemmShape<128, 128, 32>;
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using WarpShape = GemmShape<32, 32, 32>;
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using InstructionShape = GemmShape<16, 16, 16>;
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static int const kStages = 2;
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using EpilogueOutputOp = epilogue::thread::LinearCombination<
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ElementC,
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MEMORY_ACCESS_SIZE / sizeof_bits<ElementC>::value,
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ElementAccumulator,
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ElementAccumulator
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>;
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using Operator = arch::OpMultiplyAdd;
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};
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////////////////////////////////////////////////////////////////////////////////
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} // namespace device
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} // namespace gemm
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} // namespace cutlass
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////////////////////////////////////////////////////////////////////////////////
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