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https://github.com/ROCm/composable_kernel.git
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Merge flatmm Operator with universal gemm (#2434)
* Initial commit * Adding new tile partitioner to flatmm * intermediate changes * debugging kernels * Updating flatmm example to universal gemm example * updated flatmm kernel to run via gemmKernel * update universal gemm to incorporate flatmm * debug * Fix flatmm call * Fixing other kernels and tests for API changes * clang formatted * fixing gemm tests * added test for flatmm and simplify kernel arguments * adding flatmm test * fix test for flatmm * simplify gemm kernel with flatmm * remove flatmm related files * addressing review comments and code clean up * resolving empty file * resolving empty file * clang formatted * addressing review comments * enable persistent kernel for flatmm * reverted the removed files for flatmm * reverted the removed files for flatmm * changed flatmm to weightPReshuffle; removed the _1 added in teh faltmm example * some more renames * clang formatted
This commit is contained in:
Khushbu Agarwal
@@ -13,50 +13,94 @@
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#include "flatmm_basic.hpp"
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#include "run_flatmm_example.inc"
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template <typename ADataType,
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template <typename FlatmmConfig,
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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 FlatmmConfig,
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typename ALayout,
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typename BLayout,
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typename CLayout>
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float flatmm_calc(const ck_tile::FlatmmHostArgs& args, const ck_tile::stream_config& s)
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typename DsLayout,
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typename ELayout,
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bool persistent,
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typename CDEElementWise>
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float flatmm_calc(const ck_tile::FlatmmHostArgs<>& args, const ck_tile::stream_config& s)
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{
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using CodegenFlatmmShape = ck_tile::TileFlatmmShape<
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using CodegenFlatmmShape = ck_tile::TileGemmShape<
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ck_tile::sequence<FlatmmConfig::M_Tile, FlatmmConfig::N_Tile, FlatmmConfig::K_Tile>,
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ck_tile::sequence<FlatmmConfig::M_Warp, FlatmmConfig::N_Warp, FlatmmConfig::K_Warp>,
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ck_tile::sequence<FlatmmConfig::M_Warp_Tile,
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FlatmmConfig::N_Warp_Tile,
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FlatmmConfig::K_Warp_Tile>>;
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using TilePartitioner = ck_tile::GemmTile1DPartitioner<CodegenFlatmmShape>;
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using TilePartitioner =
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ck_tile::GemmSpatiallyLocalTilePartitioner<CodegenFlatmmShape,
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FlatmmConfig::TileParitionerGroupNum,
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FlatmmConfig::TileParitionerM01>;
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using CodegenGemmTraits = ck_tile::TileGemmTraits<FlatmmConfig::kPadM,
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FlatmmConfig::kPadN,
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FlatmmConfig::kPadK,
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ALayout,
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BLayout,
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CLayout>;
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using Traits = ck_tile::TileGemmTraits<FlatmmConfig::kPadM,
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FlatmmConfig::kPadN,
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FlatmmConfig::kPadK,
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ALayout,
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BLayout,
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ELayout,
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FlatmmConfig::NumWaveGroups>;
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using CodegenPipelineProblem = ck_tile::GemmPipelineProblem<ADataType,
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BDataType,
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AccDataType,
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CodegenFlatmmShape,
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CodegenGemmTraits>;
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using CodegenGemmTraits = ck_tile::TileGemmUniversalTraits<FlatmmConfig::kPadM,
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FlatmmConfig::kPadN,
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FlatmmConfig::kPadK,
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FlatmmConfig::DoubleSmemBuffer,
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ALayout,
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BLayout,
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ELayout,
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FlatmmConfig::TransposeC,
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FlatmmConfig::UseStructuredSparsity,
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persistent,
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FlatmmConfig::NumWaveGroups,
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true>;
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const auto Run = [&](const auto memory_operation_) {
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using GemmPipelineProblem =
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ck_tile::GemmPipelineProblem<ADataType, BDataType, AccDataType, CodegenFlatmmShape, Traits>;
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using BaseGemmPipeline = ck_tile::BaseFlatmmPipelineAGmemBGmemCRegV1<GemmPipelineProblem>;
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const ck_tile::index_t k_grain = args.k_batch * FlatmmConfig::K_Tile;
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const ck_tile::index_t K_split = (args.K + k_grain - 1) / k_grain * FlatmmConfig::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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constexpr auto scheduler = FlatmmConfig::Scheduler;
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constexpr auto memory_operation = memory_operation_.value;
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using CodegenPipelineProblem = ck_tile::UniversalGemmPipelineProblem<ADataType,
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BDataType,
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AccDataType,
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CodegenFlatmmShape,
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CodegenGemmTraits,
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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 CodegenFlatmmPipeline =
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ck_tile::FlatmmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem>;
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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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ck_tile::tuple<>,
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DsDatatype,
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AccDataType,
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CDataType,
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ck_tile::tuple<>,
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CLayout,
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ck_tile::element_wise::PassThrough,
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DsLayout,
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ELayout,
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CDEElementWise,
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CodegenPipelineProblem::kBlockSize,
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TilePartitioner::MPerBlock,
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TilePartitioner::NPerBlock,
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@@ -66,11 +110,8 @@ float flatmm_calc(const ck_tile::FlatmmHostArgs& args, const ck_tile::stream_con
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FlatmmConfig::N_Warp_Tile,
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FlatmmConfig::K_Warp_Tile,
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CodegenPipelineProblem::TransposeC,
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memory_operation>>;
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using CodegenFlatmmPolicy = ck_tile::UniversalFlatmmPipelineAgBgCrPolicy;
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using CodegenFlatmmPipeline =
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ck_tile::FlatmmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem, CodegenFlatmmPolicy>;
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memory_operation,
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FlatmmConfig::NumWaveGroups>>;
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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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@@ -88,14 +129,15 @@ float flatmm_calc(const ck_tile::FlatmmHostArgs& args, const ck_tile::stream_con
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if(s.log_level_ > 0)
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{
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std::cout << "Launching kernel with args:" << CodegenFlatmmShape::GetName()
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<< CodegenPipelineProblem::GetName() << " grid: {" << grids.x << ", "
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<< grids.y << ", " << grids.z << "}"
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std::cout << "Launching kernel with args:" << CodegenFlatmmShape::GetName() << "\n"
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<< "Shape: " << CodegenFlatmmShape::GetName() << "\n"
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<< "problem: " << CodegenPipelineProblem::GetName() << "\n"
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<< "pipeline: " << CodegenFlatmmPipeline::GetName() << "\n"
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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{0};
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if(s.flush_cache_)
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{
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std::cout << "Flushing cache..." << std::endl;
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@@ -113,7 +155,7 @@ float flatmm_calc(const ck_tile::FlatmmHostArgs& args, const ck_tile::stream_con
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auto size_b_buffer = b_n.get_element_space_size_in_bytes() / BPackedSize;
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ck_tile::RotatingMemWrapper<ADataType, BDataType> rotating_mem(
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kargs.a_ptr, kargs.b_shuffle_ptr, s.rotating_count_, size_a_buffer, size_b_buffer);
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kargs.a_ptr, kargs.b_ptr, s.rotating_count_, size_a_buffer, size_b_buffer);
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rotating_mem.Print();
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auto run_flush_cache = [&]() {
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@@ -124,7 +166,7 @@ float flatmm_calc(const ck_tile::FlatmmHostArgs& args, const ck_tile::stream_con
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// clear c mem
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if(args.k_batch > 1)
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hipGetErrorString(hipMemsetAsync(
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args.c_ptr, 0, args.M * args.N * sizeof(CDataType), s.stream_id_));
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args.e_ptr, 0, args.M * args.N * sizeof(CDataType), s.stream_id_));
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};
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ave_time = ck_tile::launch_kernel_preprocess(
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s,
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@@ -141,16 +183,25 @@ float flatmm_calc(const ck_tile::FlatmmHostArgs& args, const ck_tile::stream_con
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}
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return ave_time;
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};
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if(args.k_batch == 1)
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{
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return 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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return 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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const auto RunSplitk = [&](const auto has_hot_loop_, const auto tail_number_) {
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if(args.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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return ave_time;
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}
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template <template <typename PreType> typename FlatmmConfig>
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