mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-02 04:31:25 +00:00
[CK_TILE] Update flatmm related kernels (#3022)
--------- Co-authored-by: Ding, Yi <yi.ding@amd.com> Co-authored-by: felix <felix.li@amd.com>
This commit is contained in:
lalala-sh
@@ -11,7 +11,102 @@
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#include "ck_tile/host.hpp"
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#include "flatmm_basic.hpp"
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#include "run_flatmm_example.inc"
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#include <type_traits>
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template <typename T>
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constexpr const char* DataTypeToString()
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{
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if constexpr(std::is_same_v<T, ck_tile::half_t>)
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{
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return "fp16";
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}
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else if constexpr(std::is_same_v<T, ck_tile::fp8_t>)
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{
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return "fp8";
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}
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else if constexpr(std::is_same_v<T, ck_tile::bf8_t>)
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{
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return "bf8";
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}
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else if constexpr(std::is_same_v<T, ck_tile::bf16_t>)
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{
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return "bf16";
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}
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else
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{
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return "unknown";
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}
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}
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template <typename Layout>
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static constexpr inline auto is_row_major(Layout layout_)
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{
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return ck_tile::bool_constant<std::is_same_v<ck_tile::remove_cvref_t<decltype(layout_)>,
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ck_tile::tensor_layout::gemm::RowMajor>>{};
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}
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// mfma_type, 0:32x32, 1:16x16
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template <typename FlatmmConfig, typename T>
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auto shuffle_b(const ck_tile::HostTensor<T>& t)
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{
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assert(t.get_lengths().size() == 2);
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int n_ = t.get_lengths()[1];
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int k_ = t.get_lengths()[0];
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constexpr int MaxVecSize = 16 / sizeof(T);
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constexpr int KLane = ck_tile::get_warp_size() / FlatmmConfig::N_Warp_Tile;
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constexpr int ItemsPerAccess = std::min(MaxVecSize, FlatmmConfig::K_Warp_Tile / KLane);
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ck_tile::HostTensor<T> t_view({n_ / FlatmmConfig::N_Warp_Tile,
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FlatmmConfig::N_Warp_Tile,
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k_ / ItemsPerAccess,
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ItemsPerAccess});
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std::copy(t.begin(), t.end(), t_view.begin());
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return ck_tile::reference_permute(t_view, {0, 2, 1, 3});
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}
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template <typename FlatmmConfig, typename T>
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auto shuffle_b_v1(const ck_tile::HostTensor<T>& t)
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{
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assert(t.get_lengths().size() == 2);
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int n_ = t.get_lengths()[1];
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int k_ = t.get_lengths()[0];
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constexpr int MaxVecSize = 16 / sizeof(T);
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constexpr int KLane = ck_tile::get_warp_size() / FlatmmConfig::N_Warp_Tile;
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constexpr int ItemsPerAccess = std::min(MaxVecSize, FlatmmConfig::K_Warp_Tile / KLane);
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constexpr int NRepeat = FlatmmConfig::N_Tile / FlatmmConfig::N_Warp_Tile / FlatmmConfig::N_Warp;
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ck_tile::HostTensor<T> t_view({n_ / FlatmmConfig::N_Tile,
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FlatmmConfig::N_Warp,
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FlatmmConfig::N_Warp_Tile,
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NRepeat,
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k_ / ItemsPerAccess,
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ItemsPerAccess});
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std::copy(t.begin(), t.end(), t_view.begin());
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return ck_tile::reference_permute(t_view, {0, 3, 1, 4, 2, 5});
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}
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template <typename ADataType, typename BDataType, typename AccDataType, typename CDataType>
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auto calculate_rtol_atol(const ck_tile::index_t K,
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const ck_tile::index_t kbatch,
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const float max_accumulated_value)
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{
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using ComputeType =
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std::conditional_t<sizeof(ADataType) < sizeof(BDataType), ADataType, BDataType>;
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// Calculate thresholds
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const auto rtol = ck_tile::get_relative_threshold<ComputeType, CDataType, AccDataType>(
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ck_tile::integer_divide_ceil(K, kbatch));
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const auto atol = ck_tile::get_absolute_threshold<ComputeType, CDataType, AccDataType>(
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max_accumulated_value / kbatch, ck_tile::integer_divide_ceil(K, kbatch));
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// Calculate error due to split_k accumulation
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const auto rtol_split_k =
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ck_tile::get_relative_threshold<CDataType, CDataType, CDataType>(kbatch);
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const auto atol_split_k = ck_tile::get_absolute_threshold<CDataType, CDataType, CDataType>(
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max_accumulated_value, kbatch);
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// Use higher threshold
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return ck_tile::make_tuple(std::max(rtol, rtol_split_k), std::max(atol, atol_split_k));
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}
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template <typename FlatmmConfig,
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typename ADataType,
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@@ -23,9 +118,12 @@ template <typename FlatmmConfig,
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typename BLayout,
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typename DsLayout,
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typename ELayout,
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typename ScaleM,
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typename ScaleN,
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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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float flatmm_calc(const ck_tile::ScaleFlatmmHostArgs<ScaleM, ScaleN>& args,
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const ck_tile::stream_config& s)
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{
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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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@@ -80,14 +178,14 @@ float flatmm_calc(const ck_tile::FlatmmHostArgs<>& args, const ck_tile::stream_c
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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 CodegenPipelineProblem = ck_tile::FlatmmPipelineProblem<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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@@ -110,7 +208,10 @@ float flatmm_calc(const ck_tile::FlatmmHostArgs<>& args, const ck_tile::stream_c
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FlatmmConfig::K_Warp_Tile,
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CodegenPipelineProblem::TransposeC,
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memory_operation,
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FlatmmConfig::NumWaveGroups>>;
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FlatmmConfig::NumWaveGroups,
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false,
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1,
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FlatmmConfig::TiledMMAPermuteN>>;
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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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@@ -118,8 +219,8 @@ float flatmm_calc(const ck_tile::FlatmmHostArgs<>& args, const ck_tile::stream_c
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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);
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const dim3 blocks = Kernel::BlockSize();
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const dim3 grids = Kernel::GridSize(kargs);
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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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@@ -167,40 +268,145 @@ float flatmm_calc(const ck_tile::FlatmmHostArgs<>& args, const ck_tile::stream_c
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hipGetErrorString(hipMemsetAsync(
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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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return ave_time = ck_tile::launch_kernel_time_mask(
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s,
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run_flush_cache,
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ck_tile::make_kernel<FlatmmConfig::kBlockPerCu>(
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Kernel{}, grids, blocks, 0, kargs));
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ave_time = ck_tile::launch_kernel_time_mask(
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s,
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run_flush_cache,
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ck_tile::make_kernel<FlatmmConfig::kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
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}
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else
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{
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return ave_time =
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ck_tile::launch_kernel(s,
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ck_tile::make_kernel<FlatmmConfig::kBlockPerCu>(
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Kernel{}, grids, blocks, 0, kargs));
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ave_time = ck_tile::launch_kernel(
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s,
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ck_tile::make_kernel<FlatmmConfig::kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
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}
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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(args.k_batch == 1)
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{
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return 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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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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return 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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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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return ave_time = BaseGemmPipeline::TailHandler(RunSplitk, has_hot_loop, tail_num);
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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 <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 ALayout,
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typename BLayout,
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typename DsLayout,
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typename CLayout,
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typename ScaleM,
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typename ScaleN,
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bool UsePersistentKernel = false,
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typename CDEElementWise = ck_tile::element_wise::PassThrough>
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float invoke_flatmm(ck_tile::DeviceMem& a_dev_buf,
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ck_tile::DeviceMem& b_shuffle_dev_buf,
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ck_tile::DeviceMem& c_dev_buf,
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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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ck_tile::index_t kbatch,
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ScaleM scale_m,
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ScaleN scale_n,
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int n_warmup,
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int n_repeat)
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{
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ck_tile::ScaleFlatmmHostArgs<ScaleM, ScaleN> args = {a_dev_buf.GetDeviceBuffer(),
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b_shuffle_dev_buf.GetDeviceBuffer(),
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{},
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c_dev_buf.GetDeviceBuffer(),
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kbatch,
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M,
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N,
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K,
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stride_A,
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stride_B,
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{},
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stride_C,
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scale_m,
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scale_n};
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float ave_time = flatmm_calc<FlatmmConfig,
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ADataType,
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BDataType,
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DsDatatype,
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AccDataType,
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CDataType,
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ALayout,
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BLayout,
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DsLayout,
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CLayout,
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ScaleM,
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ScaleN,
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UsePersistentKernel,
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CDEElementWise>(
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args, ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat, true, true, 50});
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std::size_t flop = std::size_t(2) * M * N * K;
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std::size_t num_byte =
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sizeof(ADataType) * M * K + sizeof(BDataType) * N * K + sizeof(CDataType) * M * N;
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float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
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float gb_per_sec = num_byte / 1.E6 / ave_time;
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std::cout << "Run Flatmm kernel with DataType = " << DataTypeToString<ADataType>()
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<< " M =" << M << " N =" << N << " K =" << K << " StrideA =" << stride_A
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<< " StrideB =" << stride_B << " StrideC =" << stride_C << " : " << ave_time
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<< " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, " << std::endl;
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return ave_time;
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}
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auto create_args(int argc, char* argv[])
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{
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ck_tile::ArgParser arg_parser;
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arg_parser.insert("m", "256", "m dimension")
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.insert("n", "256", "n dimension")
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.insert("k", "128", "k dimension")
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.insert("a_layout", "R", "A tensor data layout - Row by default")
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.insert("b_layout", "C", "B tensor data layout - Row by default")
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.insert("c_layout", "R", "C tensor data layout - Row by default")
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.insert("stride_a", "0", "Tensor A stride")
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.insert("stride_b", "0", "Tensor B stride")
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.insert("stride_c", "0", "Tensor C stride")
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.insert("v", "1", "0. No validation, 1. Validation on CPU, 2. Validation on GPU")
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.insert("prec", "fp8", "data type. fp16/bf16/fp8/bf8")
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.insert("wave_tile", "16", "only support 16(16x16) or 32(32x32)")
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.insert("warmup", "50", "number of iterations before benchmark the kernel")
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.insert("repeat", "100", "number of iterations to benchmark the kernel")
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.insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer")
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.insert("split_k", "1", "splitK value")
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.insert("init", "0", "0:random, 1:linear, 2:constant(1)")
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.insert("scale", "0", "0:without scale, 1:per-token/channel scale, only for fp8/bf8")
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.insert("persistent", "0", "0: no persistent, 1: persistent kernel")
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.insert("warp_tile",
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"0",
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"0: 16x16, 1: 32x32, 2: 16x16x128 (950 only), 3: 32x32x64 (950 only)");
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bool result = arg_parser.parse(argc, argv);
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return std::make_tuple(result, arg_parser);
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}
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#include "run_flatmm_example.inc"
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template <template <typename PreType> typename FlatmmConfig>
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int run_flatmm_example(int argc, char* argv[])
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{
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@@ -214,20 +420,10 @@ int run_flatmm_example(int argc, char* argv[])
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std::string data_type = arg_parser.get_str("prec");
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std::string a_layout = arg_parser.get_str("a_layout");
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std::string b_layout = arg_parser.get_str("b_layout");
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int k = arg_parser.get_int("k");
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int stride_b = arg_parser.get_int("stride_b");
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if(b_layout == "C" && stride_b > k)
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{
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throw std::runtime_error(
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"For ColumnMajor layout, StrideB must be smaller than or equal to K (" +
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std::to_string(k) + ")");
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}
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int scale_opt = arg_parser.get_int("scale");
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int persistent_opt = arg_parser.get_int("persistent");
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if(a_layout == "R" && b_layout == "C")
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{
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if(data_type == "fp16")
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{
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run_flatmm_example_with_layouts<ck_tile::half_t, FlatmmConfig<ck_tile::half_t>>(
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@@ -240,13 +436,53 @@ int run_flatmm_example(int argc, char* argv[])
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}
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else if(data_type == "fp8")
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{
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run_flatmm_example_with_layouts<ck_tile::fp8_t, FlatmmConfig<ck_tile::fp8_t>>(
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argc, argv, Row{}, Col{}, Row{});
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if(scale_opt == 0)
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{
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if(persistent_opt == 0)
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{
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run_flatmm_example_with_layouts<ck_tile::fp8_t, FlatmmConfig<ck_tile::fp8_t>>(
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argc, argv, Row{}, Col{}, Row{});
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}
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else
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{
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run_flatmm_example_with_layouts<ck_tile::fp8_t,
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FlatmmConfig<ck_tile::fp8_t>,
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-1,
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-1,
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true>(argc, argv, Row{}, Col{}, Row{});
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}
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}
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else
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{
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if(persistent_opt == 0)
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{
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run_flatmm_example_with_layouts<ck_tile::fp8_t,
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FlatmmConfig<ck_tile::fp8_t>,
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1,
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1>(argc, argv, Row{}, Col{}, Row{});
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}
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else
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{
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run_flatmm_example_with_layouts<ck_tile::fp8_t,
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FlatmmConfig<ck_tile::fp8_t>,
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1,
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1,
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true>(argc, argv, Row{}, Col{}, Row{});
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}
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}
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}
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else if(data_type == "bf8")
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{
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run_flatmm_example_with_layouts<ck_tile::bf8_t, FlatmmConfig<ck_tile::bf8_t>>(
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argc, argv, Row{}, Col{}, Row{});
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if(scale_opt == 0)
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{
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run_flatmm_example_with_layouts<ck_tile::bf8_t, FlatmmConfig<ck_tile::bf8_t>>(
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argc, argv, Row{}, Col{}, Row{});
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}
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else
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{
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run_flatmm_example_with_layouts<ck_tile::bf8_t, FlatmmConfig<ck_tile::bf8_t>, 1, 1>(
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argc, argv, Row{}, Col{}, Row{});
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}
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}
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else
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{
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@@ -268,9 +504,6 @@ int main(int argc, char* argv[])
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try
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{
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#if defined(CK_TILE_USE_WMMA)
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return !run_flatmm_example<FlatmmConfig16_Wmma>(argc, argv);
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#else
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int warp_tile = arg_parser.get_int("warp_tile");
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if(warp_tile == 0)
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{
|
||||
@@ -288,7 +521,6 @@ int main(int argc, char* argv[])
|
||||
{
|
||||
return !run_flatmm_example<FlatmmConfig32_950>(argc, argv);
|
||||
}
|
||||
#endif
|
||||
}
|
||||
catch(const std::runtime_error& e)
|
||||
{
|
||||
|
||||
Reference in New Issue
Block a user