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453ca37347
* Gemm Kernel Refactor part1 * Gemm Kernel Refactor common gemm pipeline part2 * [CK TILE] Refactor batched gemm to reuse GemmKernel * [CK TILE] Refactor GemmKernel - review changes part1 * [CK TILE] Refactor GemmKernel - references fix * [CK TILE] Refactor GemmKernel - naming changes, add problem * [CK_TILE] Refactor GemmKernel - update tests * [CK_TILE] Refactor GemmKernel - review changes * [CK_TILE] Refactor GemmKernel - update test * [CK_TILE] Refactor GemmKernel - constness fixes * [CK_TILE] Refactor GemmKernel - update tests
104 lines
4.2 KiB
C++
104 lines
4.2 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
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#include <hip/hip_runtime.h>
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#include <cstring>
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#include <iostream>
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#include <ostream>
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#include <string>
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#include <tuple>
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#include "ck_tile/core.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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#include "ck_tile/host.hpp"
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#include "batched_gemm.hpp"
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template <typename ALayout, typename BLayout, typename CLayout>
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float batched_gemm(const ck_tile::BatchedGemmHostArgs& args, const ck_tile::stream_config& s)
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{
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// The kPadM, kPadN, kPadK & kBlockPerCu should also come from the Codegen part.
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constexpr bool kPadM = false;
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constexpr bool kPadN = false;
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constexpr bool kPadK = false;
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constexpr bool kTilePermute = false;
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// The rank and permutation will also be generate out by the CodeGen part.
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constexpr ck_tile::index_t kOutputRank = 2;
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constexpr int kBlockPerCu = 1;
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// This part comes from the Codegen
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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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// Whether doing the CShuffle (transpose before the global memory), depending on the output
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// layout.
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constexpr bool CShuffleEpilogue =
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std::is_same_v<CLayout, ck_tile::tensor_layout::gemm::ColumnMajor>;
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using CodegenGemmShape =
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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<CodegenGemmShape>;
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using GemmEpilogue = std::conditional_t<
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CShuffleEpilogue,
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ck_tile::CShuffleEpilogue<ck_tile::CShuffleEpilogueProblem<AccDataType,
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CDataType,
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kPadM,
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kPadN,
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kTilePermute,
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kOutputRank,
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1,
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0,
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TilePartitioner::kM,
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TilePartitioner::kN>>,
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ck_tile::Default2DEpilogue<
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ck_tile::Default2DEpilogueProblem<AccDataType, CDataType, kPadM, kPadN>>>;
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using CodegenGemmTraits =
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ck_tile::TileGemmTraits<kPadM, kPadN, kPadK, ALayout, BLayout, CLayout>;
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using CodegenPipelineProblem = ck_tile::
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GemmPipelineProblem<ADataType, BDataType, AccDataType, CodegenGemmShape, CodegenGemmTraits>;
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using CodegenGemmPipeline = ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem>;
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// ToDo: Will add the codegen part to test different pipeline policies in GEMM.
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// Now we only use the BlockGemmASmemBSmemCRegV1DefaultPolicy.
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using Kernel = ck_tile::BatchedGemmKernel<TilePartitioner, CodegenGemmPipeline, GemmEpilogue>;
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auto kargs = Kernel::MakeKernelArgs(args);
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const dim3 grids = Kernel::GridSize(args.M, args.N, args.batch_count);
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constexpr dim3 blocks = Kernel::BlockSize();
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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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float ave_time = 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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return ave_time;
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}
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#include "run_batched_gemm_example.inc"
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int main(int argc, char* argv[]) { return !run_batched_gemm_example(argc, argv); }
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