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https://github.com/ROCm/composable_kernel.git
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ce448002ee
* Add spatially local tile partitioner
* Use 1D Grid size & create partitioner object.
* Docs & use 1D partitioner in example.
* Clang format.
* Change kernel grid size
Now: X is the # of output C-tiles,
Y is the batch count
Z is the splitK
* Formatting & more doc.
* Clang format.
* Fix batched gemm test. Use 1d partitioner.
* Move condition.
* FIx ctor.
* clang-format.
97 lines
3.8 KiB
C++
97 lines
3.8 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2024-2025, 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 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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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::GemmTile1DPartitioner<CodegenGemmShape>;
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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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using GemmEpilogue = ck_tile::CShuffleEpilogue<
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ck_tile::CShuffleEpilogueProblem<AccDataType,
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CDataType,
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CLayout,
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CodegenPipelineProblem::kBlockSize,
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TilePartitioner::MPerBlock,
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TilePartitioner::NPerBlock,
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M_Warp,
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N_Warp,
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M_Warp_Tile,
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N_Warp_Tile,
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K_Warp_Tile,
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CodegenPipelineProblem::TransposeC>>;
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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.k_batch, args.batch_count);
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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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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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