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https://github.com/ROCm/composable_kernel.git
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[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
364
example/ck_tile/18_flatmm/grouped_flatmm.cpp
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364
example/ck_tile/18_flatmm/grouped_flatmm.cpp
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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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#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 "flatmm_basic.hpp"
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#include "ck_tile/host.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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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("Ms", "1,1,1", "m dimension")
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.insert("Ns", "5120,5120,5120", "n dimension")
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.insert("Ks", "6144,6144,6144", "k dimension")
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.insert("group_count", "3", "group count")
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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("mode",
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"masked",
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"grouped gemm mode: [general | contiguous | masked], general by default")
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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("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 <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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bool persistent,
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typename CDEElementWise,
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typename KernelArguments>
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float grouped_flatmm(const KernelArguments& args, 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 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::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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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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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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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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// ToDo: Will add the codegen part to test different pipeline policies in GEMM.
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// Now we only use the BlockGemmASmemBSmemCRegV1DefaultPolicy.
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using Kernel =
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ck_tile::GroupedFlatmmKernel<TilePartitioner, CodegenFlatmmPipeline, 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.flush_cache_)
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{
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std::cout << "Flushing cache..." << std::endl;
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static constexpr ck_tile::index_t APackedSize =
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std::is_same_v<BDataType, ck_tile::pk_int4_t> ? 2 : 1;
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static constexpr ck_tile::index_t BPackedSize =
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std::is_same_v<BDataType, ck_tile::pk_int4_t> ? 2 : 1;
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ck_tile::HostTensor<ADataType> a_m(ck_tile::host_tensor_descriptor(
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args.group_count * 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.group_count * 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_shuffle_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(
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hipMemsetAsync(args.e_ptr,
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0,
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args.group_count * args.M * args.N * sizeof(CDataType),
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s.stream_id_));
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};
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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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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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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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#include "run_grouped_flatmm_example.inc"
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template <template <typename PreType> typename FlatmmConfig>
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int run_grouped_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 data_type = arg_parser.get_str("prec");
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std::string mode = arg_parser.get_str("mode");
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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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if(a_layout == "R" && b_layout == "C")
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{
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if(mode == "contiguous")
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{
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if(data_type == "fp16")
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{
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run_contiguous_grouped_flatmm_example_with_layouts<ck_tile::half_t,
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FlatmmConfig<ck_tile::half_t>>(
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argc, argv, Row{}, Col{}, Row{});
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}
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else if(data_type == "bf16")
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{
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run_contiguous_grouped_flatmm_example_with_layouts<ck_tile::bf16_t,
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FlatmmConfig<ck_tile::bf16_t>>(
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argc, argv, Row{}, Col{}, Row{});
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}
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else if(data_type == "fp8")
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{
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run_contiguous_grouped_flatmm_example_with_layouts<ck_tile::fp8_t,
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FlatmmConfig<ck_tile::fp8_t>>(
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argc, argv, Row{}, Col{}, Row{});
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}
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else if(data_type == "bf8")
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{
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run_contiguous_grouped_flatmm_example_with_layouts<ck_tile::bf8_t,
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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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throw std::runtime_error("Unsupported data_type!");
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}
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}
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else if(mode == "masked")
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{
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if(data_type == "fp16")
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{
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run_masked_grouped_flatmm_example_with_layouts<ck_tile::half_t,
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FlatmmConfig<ck_tile::half_t>>(
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argc, argv, Row{}, Col{}, Row{});
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}
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else if(data_type == "bf16")
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{
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run_masked_grouped_flatmm_example_with_layouts<ck_tile::bf16_t,
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FlatmmConfig<ck_tile::bf16_t>>(
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argc, argv, Row{}, Col{}, Row{});
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}
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else if(data_type == "fp8")
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{
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run_masked_grouped_flatmm_example_with_layouts<ck_tile::fp8_t,
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FlatmmConfig<ck_tile::fp8_t>>(
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argc, argv, Row{}, Col{}, Row{});
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}
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else if(data_type == "bf8")
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{
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run_masked_grouped_flatmm_example_with_layouts<ck_tile::bf8_t,
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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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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 mode!");
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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_grouped_flatmm_example<FlatmmConfig16>(argc, argv);
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}
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// else if(warp_tile == 1)
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// {
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// return !run_grouped_flatmm_example<FlatmmConfig32>(argc, argv);
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// }
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// else if(warp_tile == 2)
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// {
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// return !run_grouped_flatmm_example<FlatmmConfig16_950>(argc, argv);
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// }
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// else
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// {
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// return !run_grouped_flatmm_example<FlatmmConfig32_950>(argc, argv);
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// }
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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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