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https://github.com/ROCm/composable_kernel.git
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Persistent grouped gemm CompV4 Enablement & Polish (#2605)
* enable the persistent kernel for CompV4 * polish the example and clang format * fix the non-persistent kernel error --------- Co-authored-by: ThomasNing <thomasning@amd.com>
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Thomas Ning
@@ -1,5 +1,5 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
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// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
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#include <hip/hip_runtime.h>
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@@ -16,19 +16,11 @@
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#include "ck_tile/host.hpp"
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#include "grouped_gemm.hpp"
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template <typename ADataType,
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typename BDataType,
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typename DsDataType,
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typename AccDataType,
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typename CDataType,
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typename ALayout,
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typename BLayout,
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typename DsLayout,
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typename CLayout,
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typename CDEElementWise = ck_tile::element_wise::PassThrough>
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float grouped_gemm(const std::vector<grouped_gemm_kargs>& gemm_descs,
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const ck_tile::stream_config& s,
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void* kargs_ptr)
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template <typename ALayout, typename BLayout, typename CLayout>
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float grouped_gemm_tileloop(const ck_tile::stream_config& s,
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const ck_tile::index_t num_groups,
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void* kargs_ptr,
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bool splitk)
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{
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#if(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_MEMORY)
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// Memory friendly for Interwave scheduler
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@@ -83,8 +75,6 @@ float grouped_gemm(const std::vector<grouped_gemm_kargs>& gemm_descs,
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constexpr bool kPadN = false;
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constexpr bool kPadK = false;
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constexpr bool TransposeC = false;
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constexpr int kBlockPerCu = 1;
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constexpr ck_tile::index_t TileParitionerGroupNum = 8;
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constexpr ck_tile::index_t TileParitionerM01 = 4;
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@@ -97,54 +87,41 @@ float grouped_gemm(const std::vector<grouped_gemm_kargs>& gemm_descs,
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GemmSpatiallyLocalTilePartitioner<GemmShape, TileParitionerGroupNum, TileParitionerM01>;
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using Traits = ck_tile::TileGemmTraits<kPadM, kPadN, kPadK, ALayout, BLayout, CLayout>;
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using GemmUniversalTraits = ck_tile::TileGemmUniversalTraits<kPadM,
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kPadN,
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kPadK,
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DoubleSmemBuffer,
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ALayout,
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BLayout,
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CLayout,
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TransposeC>;
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using GemmUniversalTraits = ck_tile::PersistentTileGemmUniversalTraits<kPadM,
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kPadN,
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kPadK,
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DoubleSmemBuffer,
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ALayout,
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BLayout,
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CLayout>;
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using GemmPipelineProblem =
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ck_tile::GemmPipelineProblem<ADataType, BDataType, AccDataType, GemmShape, Traits>;
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using BaseGemmPipeline = UNIVERSAL_GEMM_PIPELINE<GemmPipelineProblem>;
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const ck_tile::index_t k_grain = gemm_descs[0].k_batch * K_Tile;
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const ck_tile::index_t K_split = (gemm_descs[0].K + k_grain - 1) / k_grain * K_Tile;
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const ck_tile::index_t num_loop = TilePartitioner::GetLoopNum(K_split);
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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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float ave_time{0};
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const auto Run = [&](const auto has_hot_loop_,
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const auto tail_number_,
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const auto memory_operation_) {
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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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const auto Run = [&](const auto memory_operation_) {
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constexpr auto scheduler = GEMM_PIPELINE_SCHEDULER;
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constexpr auto memory_operation = memory_operation_.value;
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// We create the GEMM pipeline without specifying hotloop or tailnumber.
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// These are automatically run inside the kernel based on the given input data.
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using UniversalGemmProblem = ck_tile::UniversalGemmPipelineProblem<ADataType,
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BDataType,
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AccDataType,
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GemmShape,
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GemmUniversalTraits,
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scheduler,
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has_hot_loop_v,
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tail_number_v>;
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scheduler>;
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using GemmPipeline = GEMM_PIPELINE<UniversalGemmProblem>;
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using GemmEpilogue = ck_tile::CShuffleEpilogue<
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ck_tile::CShuffleEpilogueProblem<ADataType,
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BDataType,
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DsDataType,
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ck_tile::tuple<>,
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AccDataType,
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CDataType,
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DsLayout,
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ck_tile::tuple<>,
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CLayout,
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CDEElementWise,
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ck_tile::element_wise::PassThrough,
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GemmPipelineProblem::kBlockSize,
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TilePartitioner::MPerBlock,
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TilePartitioner::NPerBlock,
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@@ -156,20 +133,8 @@ float grouped_gemm(const std::vector<grouped_gemm_kargs>& gemm_descs,
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UniversalGemmProblem::TransposeC,
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memory_operation>>;
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using Kernel = ck_tile::GroupedGemmKernel<TilePartitioner, GemmPipeline, GemmEpilogue>;
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auto kargs = Kernel::MakeKargs(gemm_descs);
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if(!Kernel::IsSupportedArgument(kargs))
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{
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throw std::runtime_error("Kernel arguments not supported!");
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}
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constexpr dim3 blocks = Kernel::BlockSize();
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const dim3 grids = Kernel::GridSize(gemm_descs);
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HIP_CHECK_ERROR(hipMemcpyWithStream(kargs_ptr,
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kargs.data(),
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get_workspace_size(gemm_descs),
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hipMemcpyHostToDevice,
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s.stream_id_));
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const dim3 grids = Kernel::MaxOccupancyGridSize(s);
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if(s.log_level_ > 0)
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{
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@@ -186,45 +151,26 @@ float grouped_gemm(const std::vector<grouped_gemm_kargs>& gemm_descs,
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blocks,
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0,
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ck_tile::cast_pointer_to_constant_address_space(kargs_ptr),
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gemm_descs.size()));
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num_groups));
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return ave_time;
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};
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const auto RunSplitk = [&](const auto has_hot_loop_, const auto tail_number_) {
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if(gemm_descs[0].k_batch == 1)
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{
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Run(has_hot_loop_,
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tail_number_,
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ck_tile::integral_constant<ck_tile::memory_operation_enum,
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ck_tile::memory_operation_enum::set>{});
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}
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else
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{
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Run(has_hot_loop_,
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tail_number_,
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ck_tile::integral_constant<ck_tile::memory_operation_enum,
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ck_tile::memory_operation_enum::atomic_add>{});
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}
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};
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BaseGemmPipeline::TailHandler(RunSplitk, has_hot_loop, tail_num);
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if(!splitk)
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{
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Run(ck_tile::integral_constant<ck_tile::memory_operation_enum,
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ck_tile::memory_operation_enum::set>{});
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}
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else
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{
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Run(ck_tile::integral_constant<ck_tile::memory_operation_enum,
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ck_tile::memory_operation_enum::atomic_add>{});
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}
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return ave_time;
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}
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#include "run_grouped_gemm_example.inc"
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constexpr bool Persistent = false;
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int main(int argc, char* argv[])
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{
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try
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{
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return !run_grouped_gemm_example<Persistent>(argc, argv);
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}
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catch(const std::runtime_error& e)
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{
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std::cerr << "Runtime error: " << e.what() << '\n';
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return EXIT_FAILURE;
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}
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}
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constexpr bool Persistent = true;
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int main(int argc, char* argv[]) { return !run_grouped_gemm_example<Persistent>(argc, argv); }
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