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https://github.com/ROCm/composable_kernel.git
synced 2026-05-02 20:51:23 +00:00
Added unit tests for CK Tile compute bound gemm pipeline (#1728)
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jakpiase
349
test/ck_tile/gemm/test_gemm_pipeline_util.hpp
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349
test/ck_tile/gemm/test_gemm_pipeline_util.hpp
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <sstream>
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#include <gtest/gtest.h>
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#include "ck_tile/core.hpp"
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#include "ck_tile/host.hpp"
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#include "ck_tile/host/kernel_launch.hpp"
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#include "ck_tile/ops/epilogue.hpp"
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#include "ck_tile/ops/gemm.hpp"
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enum struct GemmPipelineType
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{
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Mem,
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Comp
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};
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template <typename Tuple>
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class TestCkTileGemmPipeline : public ::testing::Test
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{
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protected:
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using ALayout = std::tuple_element_t<0, Tuple>;
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using BLayout = std::tuple_element_t<1, Tuple>;
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using CLayout = std::tuple_element_t<2, Tuple>;
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using ADataType = std::tuple_element_t<3, Tuple>;
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using BDataType = std::tuple_element_t<4, Tuple>;
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using AccDataType = std::tuple_element_t<5, Tuple>;
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using CDataType = std::tuple_element_t<6, Tuple>;
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static constexpr auto Scheduler = std::tuple_element_t<7, Tuple>::value;
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static constexpr auto PipelineType = std::tuple_element_t<8, Tuple>::value;
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// TODO: expose tile size through test t-param ?
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struct gemm_args
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{
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const void* p_a;
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const void* p_b;
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void* p_c;
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ck_tile::index_t kbatch;
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ck_tile::index_t M;
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ck_tile::index_t N;
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ck_tile::index_t K;
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ck_tile::index_t stride_A;
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ck_tile::index_t stride_B;
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ck_tile::index_t stride_C;
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};
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template <bool PadM, bool PadN, bool PadK>
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void invoke_gemm(const gemm_args& args, const ck_tile::stream_config& s)
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{
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// TODO: This should be parameterized in tests
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constexpr ck_tile::index_t M_Tile = 128;
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constexpr ck_tile::index_t N_Tile = 128;
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constexpr ck_tile::index_t K_Tile = 32;
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constexpr ck_tile::index_t M_Warp = 2;
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constexpr ck_tile::index_t N_Warp = 2;
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constexpr ck_tile::index_t K_Warp = 1;
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constexpr ck_tile::index_t M_Warp_Tile = 32;
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constexpr ck_tile::index_t N_Warp_Tile = 32;
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constexpr ck_tile::index_t K_Warp_Tile = 8;
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constexpr bool kPadM = PadM;
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constexpr bool kPadN = PadN;
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constexpr bool kPadK = PadK;
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constexpr int kBlockPerCu = 1;
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// ===============================================
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using GemmShape =
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ck_tile::TileGemmShape<ck_tile::sequence<M_Tile, N_Tile, K_Tile>,
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ck_tile::sequence<M_Warp, N_Warp, K_Warp>,
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ck_tile::sequence<M_Warp_Tile, N_Warp_Tile, K_Warp_Tile>>;
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using TilePartitioner = ck_tile::GemmTilePartitioner<GemmShape>;
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using GemmEpilogue = ck_tile::Default2DEpilogue<
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ck_tile::Default2DEpilogueProblem<AccDataType, CDataType, kPadM, kPadN>>;
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using Traits = ck_tile::TileGemmTraits<kPadM, kPadN, kPadK, ALayout, BLayout, CLayout>;
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using BaseGemmPipeline = std::conditional_t<
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PipelineType == GemmPipelineType::Mem,
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ck_tile::BaseGemmPipelineAgBgCrMem<
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ck_tile::GemmPipelineProblem<ADataType, BDataType, AccDataType, GemmShape, Traits>>,
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ck_tile::BaseGemmPipelineAgBgCrCompV3<
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ck_tile::
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GemmPipelineProblem<ADataType, BDataType, AccDataType, GemmShape, Traits>>>;
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const ck_tile::index_t num_loop = TilePartitioner::GetLoopNum(args.K);
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const bool has_hot_loop = BaseGemmPipeline::BlockHasHotloop(num_loop);
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const ck_tile::TailNumber tail_num = BaseGemmPipeline::GetBlockLoopTailNum(num_loop);
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const auto Run = [&](const auto has_hot_loop_, const auto tail_number_) {
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constexpr bool has_hot_loop_v = has_hot_loop_.value;
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constexpr auto tail_number_v = tail_number_.value;
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using GemmPipeline =
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std::conditional_t<PipelineType == GemmPipelineType::Mem,
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ck_tile::GemmPipelineAgBgCrMem<
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ck_tile::UniversalGemmPipelineProblem<ADataType,
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BDataType,
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AccDataType,
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GemmShape,
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Traits,
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Scheduler,
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has_hot_loop_v,
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tail_number_v>>,
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ck_tile::GemmPipelineAgBgCrCompV3<
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ck_tile::UniversalGemmPipelineProblem<ADataType,
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BDataType,
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AccDataType,
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GemmShape,
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Traits,
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Scheduler,
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has_hot_loop_v,
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tail_number_v>>>;
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using Kernel = ck_tile::GemmKernel<TilePartitioner, GemmPipeline, GemmEpilogue>;
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auto kargs = Kernel::MakeKargs(args.p_a,
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args.p_b,
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args.p_c,
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args.M,
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args.N,
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args.K,
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args.stride_A,
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args.stride_B,
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args.stride_C);
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const dim3 grids = Kernel::GridSize(args.M, args.N, args.kbatch);
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constexpr dim3 blocks = Kernel::BlockSize();
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if(!Kernel::IsSupportedArgument(kargs))
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{
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throw std::runtime_error("Wrong! Arguments not supported! Skipping gemm!\n");
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}
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if(s.log_level_ > 0)
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{
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std::cout << "Launching kernel with args:"
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<< " grid: {" << grids.x << ", " << grids.y << ", " << grids.z << "}"
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<< ", blocks: {" << blocks.x << ", " << blocks.y << ", " << blocks.z
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<< "}" << std::endl;
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}
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ck_tile::launch_kernel(
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s, ck_tile::make_kernel<blocks.x, kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
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};
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if(has_hot_loop)
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{
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// Tail pipeline One to Seven
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if(tail_num == ck_tile::TailNumber::One)
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{
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Run(ck_tile::bool_constant<true>{},
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ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::One>{});
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}
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else if(tail_num == ck_tile::TailNumber::Full)
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{
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Run(ck_tile::bool_constant<true>{},
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ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Full>{});
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}
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if constexpr(BaseGemmPipeline::PrefetchStages > 2)
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{
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if(tail_num == ck_tile::TailNumber::Two)
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{
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Run(ck_tile::bool_constant<true>{},
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ck_tile::integral_constant<ck_tile::TailNumber,
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ck_tile::TailNumber::Two>{});
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}
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}
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if constexpr(BaseGemmPipeline::PrefetchStages > 3)
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{
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if(tail_num == ck_tile::TailNumber::Three)
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{
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Run(ck_tile::bool_constant<true>{},
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ck_tile::integral_constant<ck_tile::TailNumber,
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ck_tile::TailNumber::Three>{});
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}
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}
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if constexpr(BaseGemmPipeline::PrefetchStages > 4)
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{
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if(tail_num == ck_tile::TailNumber::Four)
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{
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Run(ck_tile::bool_constant<true>{},
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ck_tile::integral_constant<ck_tile::TailNumber,
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ck_tile::TailNumber::Four>{});
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}
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}
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if constexpr(BaseGemmPipeline::PrefetchStages > 5)
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{
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if(tail_num == ck_tile::TailNumber::Five)
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{
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Run(ck_tile::bool_constant<true>{},
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ck_tile::integral_constant<ck_tile::TailNumber,
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ck_tile::TailNumber::Five>{});
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}
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}
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if constexpr(BaseGemmPipeline::PrefetchStages > 6)
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{
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if(tail_num == ck_tile::TailNumber::Six)
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{
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Run(ck_tile::bool_constant<true>{},
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ck_tile::integral_constant<ck_tile::TailNumber,
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ck_tile::TailNumber::Six>{});
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}
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}
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if constexpr(BaseGemmPipeline::PrefetchStages > 7)
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{
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if(tail_num == ck_tile::TailNumber::Seven)
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{
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Run(ck_tile::bool_constant<true>{},
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ck_tile::integral_constant<ck_tile::TailNumber,
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ck_tile::TailNumber::Seven>{});
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}
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}
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}
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else
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{
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// Tail number always Full - #PrefetchStages
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if(tail_num == ck_tile::TailNumber::Full)
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{
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Run(ck_tile::bool_constant<false>{},
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ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Full>{});
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}
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else
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{
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std::ostringstream err;
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err << "When there's no hot loop, this tail number \"" << tail_num
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<< "\" is not supported! " << __FILE__ << ":" << __LINE__
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<< ", in function: " << __func__;
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throw std::runtime_error(err.str());
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}
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}
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}
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public:
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std::vector<int> k_batches_;
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void SetUp() override { k_batches_ = {1}; }
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template <bool PadM = true, bool PadN = true, bool PadK = true>
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void Run(const int M,
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const int N,
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const int K,
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const int StrideA = 0,
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const int StrideB = 0,
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const int StrideC = 0)
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{
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for(auto kb : k_batches_)
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{
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RunSingle<PadM, PadN, PadK>(M, N, K, StrideA, StrideB, StrideC, kb);
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}
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}
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template <bool PadM, bool PadN, bool PadK>
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void RunSingle(const int M,
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const int N,
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const int K,
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const int StrideA,
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const int StrideB,
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const int StrideC,
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int kbatch = 1)
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{
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using namespace ck_tile::literals;
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auto f_host_tensor_descriptor = [](std::size_t row,
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std::size_t col,
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std::size_t stride,
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auto layout) {
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if constexpr(std::is_same_v<decltype(layout), ck_tile::tensor_layout::gemm::RowMajor>)
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{
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return ck_tile::HostTensorDescriptor({row, col}, {stride, 1_uz});
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}
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else
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{
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return ck_tile::HostTensorDescriptor({row, col}, {1_uz, stride});
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}
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};
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auto f_get_default_stride =
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[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
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if(stride == 0)
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{
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// give a chance if stride is zero, return a default packed stride
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if constexpr(std::is_same_v<decltype(layout),
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ck_tile::tensor_layout::gemm::RowMajor>)
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{
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return col;
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}
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else
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{
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return row;
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}
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}
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else
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return stride;
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};
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std::size_t stride_A = f_get_default_stride(M, K, StrideA, ALayout{});
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std::size_t stride_B = f_get_default_stride(K, N, StrideB, BLayout{});
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std::size_t stride_C = f_get_default_stride(M, N, StrideC, CLayout{});
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ck_tile::HostTensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, stride_A, ALayout{}));
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ck_tile::HostTensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, stride_B, BLayout{}));
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ck_tile::HostTensor<CDataType> c_m_n_dev_result(
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f_host_tensor_descriptor(M, N, stride_C, CLayout{}));
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ck_tile::FillUniformDistributionIntegerValue<ADataType>{-5, 5}(a_m_k);
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ck_tile::FillUniformDistributionIntegerValue<BDataType>{-5, 5}(b_k_n);
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ck_tile::DeviceMem a_m_k_dev_buf(a_m_k.get_element_space_size_in_bytes());
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ck_tile::DeviceMem b_k_n_dev_buf(b_k_n.get_element_space_size_in_bytes());
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ck_tile::DeviceMem c_m_n_dev_buf(c_m_n_dev_result.get_element_space_size_in_bytes());
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a_m_k_dev_buf.ToDevice(a_m_k.data());
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b_k_n_dev_buf.ToDevice(b_k_n.data());
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c_m_n_dev_buf.SetZero();
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c_m_n_dev_result.SetZero();
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gemm_args args;
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args.p_a = a_m_k_dev_buf.GetDeviceBuffer();
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args.p_b = b_k_n_dev_buf.GetDeviceBuffer();
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args.p_c = c_m_n_dev_buf.GetDeviceBuffer();
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args.kbatch = kbatch;
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args.M = M;
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args.N = N;
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args.K = K;
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args.stride_A = stride_A;
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args.stride_B = stride_B;
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args.stride_C = stride_C;
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invoke_gemm<PadM, PadN, PadK>(args, ck_tile::stream_config{nullptr, false});
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c_m_n_dev_buf.FromDevice(c_m_n_dev_result.data());
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bool pass = true;
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ck_tile::HostTensor<CDataType> c_m_n_host_ref(
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f_host_tensor_descriptor(M, N, stride_C, CLayout{}));
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c_m_n_host_ref.SetZero();
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ck_tile::reference_gemm<ADataType, BDataType, AccDataType, CDataType>(
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a_m_k, b_k_n, c_m_n_host_ref);
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pass = ck_tile::check_err(c_m_n_dev_result, c_m_n_host_ref);
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EXPECT_TRUE(pass);
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}
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};
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