mirror of
https://github.com/ROCm/composable_kernel.git
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479 lines
20 KiB
C++
479 lines
20 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 <type_traits>
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#include "ck_tile/host.hpp"
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#include "mx_flatmm.hpp"
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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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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 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 mx_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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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 =
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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 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 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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using ComputeDataType = ADataType;
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static_assert(sizeof(ComputeDataType) >= sizeof(BDataType),
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"mixed_prec_flatmm requires ADataType is a wider type than BDataType");
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using GemmPipelineProblem = ck_tile::GemmPipelineProblem<ComputeDataType,
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ComputeDataType,
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AccDataType,
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CodegenFlatmmShape,
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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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constexpr int BlockedXDLN_PerWarp = 2; // determined by scale shuffle pattern
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using CodegenPipelineProblem = ck_tile::MXFlatmmPipelineProblem<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 CodegenMXFlatmmPipeline =
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ck_tile::MXF4FlatmmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem>;
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using GemmEpilogue = ck_tile::CShuffleEpilogue<
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ck_tile::CShuffleEpilogueProblem<ComputeDataType,
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ComputeDataType,
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DsDatatype,
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AccDataType,
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CDataType,
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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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FlatmmConfig::M_Warp,
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FlatmmConfig::N_Warp,
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FlatmmConfig::M_Warp_Tile,
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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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FlatmmConfig::NumWaveGroups,
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false, // FixedVectorSize
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1, // VectorSizeC
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FlatmmConfig::TiledMMAPermuteN,
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BlockedXDLN_PerWarp>>;
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using Kernel =
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ck_tile::MXFlatmmKernel<TilePartitioner, CodegenMXFlatmmPipeline, GemmEpilogue>;
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auto kargs = Kernel::MakeKernelArgs(args);
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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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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:" << 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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if(s.flush_cache_)
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{
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std::cout << "Flushing cache..." << std::endl;
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constexpr ck_tile::index_t APackedSize = ck_tile::numeric_traits<ADataType>::PackedSize;
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constexpr ck_tile::index_t BPackedSize = ck_tile::numeric_traits<BDataType>::PackedSize;
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ck_tile::HostTensor<ADataType> a_m(ck_tile::host_tensor_descriptor(
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args.M, args.K, args.stride_A, is_row_major(ALayout{})));
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ck_tile::HostTensor<BDataType> b_n(ck_tile::host_tensor_descriptor(
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args.K, args.N, args.stride_B, is_row_major(BLayout{})));
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auto size_a_buffer = a_m.get_element_space_size_in_bytes() / APackedSize;
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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_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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// flush icache
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ck_tile::flush_icache();
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// rotating mem
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rotating_mem.Next();
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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.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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run_flush_cache,
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ck_tile::make_kernel<blocks.x, FlatmmConfig::kBlockPerCu>(
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Kernel{}, grids, blocks, 0, kargs));
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}
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else
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{
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// ave_time =
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// ck_tile::launch_kernel(s,
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// ck_tile::make_kernel<blocks.x, FlatmmConfig::kBlockPerCu>(
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// 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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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 <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 ScaleA,
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typename ScaleB,
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bool UsePersistentKernel = false,
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typename CDEElementWise = ck_tile::element_wise::PassThrough>
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float invoke_mx_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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ScaleA scale_a,
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ScaleB scale_b,
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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<ScaleA, ScaleB> 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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{},
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scale_a,
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scale_b};
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float ave_time = mx_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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ScaleA,
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ScaleB,
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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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constexpr int APackedSize = ck_tile::numeric_traits<ADataType>::PackedSize;
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constexpr int BPackedSize = ck_tile::numeric_traits<BDataType>::PackedSize;
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std::size_t flop = std::size_t(2) * M * N * K + std::size_t(2) * M * N * K / 32;
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std::size_t num_byte = sizeof(ADataType) * M * K / APackedSize +
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sizeof(BDataType) * N * K / BPackedSize + 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 A16W4_Flatmm kernel "
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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", "32", "m dimension")
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.insert("n", "128", "n dimension")
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.insert("k", "512", "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(
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"mx_prec", "fp4xfp4", "data type for activation and weight, support: fp6xfp6, fp8xfp8")
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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:constant(1)")
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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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template <class FlatmmConfig, class IterSrc, class IterDst>
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void preShuffleWeight(const IterSrc src, IterDst dst, int N, int K)
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{
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int KPack = 16;
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int NLane = FlatmmConfig::N_Warp_Tile;
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int KLane = 64 / NLane;
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int K_pk = K / 2;
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int K0 = K_pk / (KLane * KPack);
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// K -> K0 KLane KPack
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// N -> N0 NLane
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// N, K -> N0 K0 KLane NLane KPack
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int tempk;
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for(int n = 0; n < N; ++n)
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{
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for(int k = 0; k < K_pk; ++k)
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{
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int n0 = n / NLane;
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int n1 = n % NLane;
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int k0 = k / (KLane * KPack);
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tempk = k % (KLane * KPack);
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int k1 = tempk / KPack;
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int k2 = tempk % KPack;
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int outputIndex = n0 * KPack * NLane * KLane * K0 + k0 * KPack * NLane * KLane +
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k1 * KPack * NLane + n1 * KPack + k2;
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dst[outputIndex] = src[n * K_pk + k];
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}
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}
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}
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template <class FlatmmConfig, class T>
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auto preShuffleScale(const ck_tile::HostTensor<T>& scale)
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{
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assert(scale.get_lengths().size() == 2);
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int n_ = scale.get_lengths()[1];
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int k_ = scale.get_lengths()[0];
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constexpr int K_Pack = 2; // fixed for mxfp4
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constexpr int N_Pack = 2; // fixed for mxfp4
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constexpr int GranularityK = 32; // fixed for mxfp4
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constexpr int K_Lane = 64 / FlatmmConfig::N_Warp_Tile; // 4
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static_assert(FlatmmConfig::N_Warp_Tile == 16, "only support XDL_N == 16");
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static_assert(FlatmmConfig::N_Repeat % N_Pack == 0);
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static_assert(FlatmmConfig::K_Tile % (K_Pack * K_Lane * GranularityK) == 0);
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ck_tile::HostTensor<T> shfl_scale({
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k_ / K_Pack / K_Lane,
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K_Pack,
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K_Lane,
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n_ / FlatmmConfig::N_Warp_Tile / N_Pack,
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N_Pack,
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FlatmmConfig::N_Warp_Tile,
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});
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std::copy(scale.begin(), scale.end(), shfl_scale.begin());
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return ck_tile::reference_permute(shfl_scale, {3, 0, 2, 5, 1, 4});
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}
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#include "run_mx_prec_flatmm.inc"
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template <typename FlatmmConfig>
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int run_mx_flatmm_example(int argc, char* argv[])
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{
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auto [result, arg_parser] = create_args(argc, argv);
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if(!result)
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return -1;
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using Row = ck_tile::tensor_layout::gemm::RowMajor;
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using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
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std::string mx_prec = arg_parser.get_str("mx_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 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(mx_prec == "fp4xfp4")
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{
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if(persistent_opt == 0)
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{
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run_mx_flatmm_with_layouts<ck_tile::pk_fp4_t,
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ck_tile::pk_fp4_t,
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ck_tile::fp16_t,
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FlatmmConfig,
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false>(argc, argv, Row{}, Col{}, Row{});
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}
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else
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{
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run_mx_flatmm_with_layouts<ck_tile::pk_fp4_t,
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ck_tile::pk_fp4_t,
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ck_tile::fp16_t,
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FlatmmConfig,
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true>(argc, argv, Row{}, Col{}, Row{});
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}
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}
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else if(mx_prec == "fp6xfp6")
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{
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throw std::runtime_error("Only support fp4xfp4 now!");
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}
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else if(mx_prec == "fp8xfp8")
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{
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throw std::runtime_error("Only support fp4xfp4 now!");
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}
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else
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{
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throw std::runtime_error("Unsupported data_type!");
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}
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}
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else
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{
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throw std::runtime_error("Unsupported data layout configuration for A,B and C tensors!");
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}
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return -1;
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}
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int main(int argc, char* argv[])
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{
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auto [result, arg_parser] = create_args(argc, argv);
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if(!result)
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return EXIT_FAILURE;
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try
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{
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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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{
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return !run_mx_flatmm_example<MXfp4_FlatmmConfig16>(argc, argv);
|
|
}
|
|
else if(warp_tile == 1)
|
|
{
|
|
thow std::runtime_error("Only support MFMA_16x16x128 now!");
|
|
}
|
|
else
|
|
{
|
|
throw std::runtime_error("Unsupported warp_tile!");
|
|
}
|
|
}
|
|
catch(const std::runtime_error& e)
|
|
{
|
|
std::cerr << "Runtime error: " << e.what() << '\n';
|
|
return EXIT_FAILURE;
|
|
}
|
|
}
|