mirror of
https://github.com/ROCm/composable_kernel.git
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1acd8e041c
* Fix a typo * Use std::variant to call run_gemm_example_with_layouts with the available layout variant combinations * Use a unified run_gemm_example_prec_type for basic gemm and universal gemm * Factor out run_gemm_example_prec_type * Refactor argument parsing in gemm_splitk_two_stage_reduce.cpp * Parse arguments outside of create_args * Move the gemm operators to separate structs to facilitate their reuse * Move the invokers to separate files to facilitate their reuse * Rename the invoker files for consistency with the examples that use them * Add fp32 support to the elementwise examples, and produce an error message for unsupported types * Get rid of four unused variables * Make two variables const * Add support for different input-output type combinations in elementwise examples * Test support for different input and output types in elementwise examples * Add support for different operations in the elementwise unary tests * Add support for UnaryConvert in the elementwise unary tests * Add support for bf16 in elementwise examples, excluding unsupported type combinations * Make some operator parameters const in ElementWiseKernel * Remove some unnecessary include statements * Implement a two-stage GEMM that does a type conversion in the second stage using the elementwise kernel * Clear workspace instead of output when flushing the cache in SplitKTwoStageInvoker::gemm * Fix formatting issues reported by clang * Add back CK_TILE_USE_WMMA related changes * Use the right prec type for bf16 in the universal GEMM and two stage split K examples * Add some brackets * Add some brackets * Separate the clearing of the GEMM output memory from the cache flushing in the universal GEMM example * Separate the clearing of the GEMM output memory from the cache flushing in the split K two stage example * Fix formatting * No need to call SetZero on ws_m_n_dev_buf here, as clear_gemm_output now does this as part of the kernel preprocessing * Add fp16 data type to splitk two stage example * Add preprocessing with optional cache flushing and clearing of output for k_batch > 1 to the basic GEMM example
177 lines
7.5 KiB
C++
177 lines
7.5 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include "gemm_utils.hpp"
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struct BasicInvoker
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{
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template <typename GemmConfig,
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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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bool Persistent,
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typename CDEElementWise>
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static float gemm(const ck_tile::GemmHostArgs& args, const ck_tile::stream_config& s)
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{
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if constexpr(Persistent)
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{
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std::cout << "WARNING: Ignoring persistent kernel option for basic gemm." << std::endl;
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}
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// This part comes from the Codegen
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constexpr ck_tile::index_t M_Tile = 256;
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constexpr ck_tile::index_t N_Tile = 256;
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constexpr ck_tile::index_t K_Tile = 64;
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#if CK_TILE_USE_WMMA
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constexpr ck_tile::index_t M_Warp = 4;
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constexpr ck_tile::index_t N_Warp = 2;
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constexpr ck_tile::index_t K_Warp = 1;
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constexpr ck_tile::index_t M_Warp_Tile = 16;
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constexpr ck_tile::index_t N_Warp_Tile = 16;
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constexpr ck_tile::index_t K_Warp_Tile = 16;
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#else
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constexpr ck_tile::index_t M_Warp = 2;
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constexpr ck_tile::index_t N_Warp = 2;
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constexpr ck_tile::index_t K_Warp = 1;
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constexpr ck_tile::index_t M_Warp_Tile = 32;
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constexpr ck_tile::index_t N_Warp_Tile = 32;
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constexpr ck_tile::index_t K_Warp_Tile = 16;
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#endif
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using CodegenGemmShape =
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ck_tile::TileGemmShape<ck_tile::sequence<M_Tile, N_Tile, K_Tile>,
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ck_tile::sequence<M_Warp, N_Warp, K_Warp>,
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ck_tile::sequence<M_Warp_Tile, N_Warp_Tile, K_Warp_Tile>>;
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using TilePartitioner = ck_tile::GemmTile1DPartitioner<CodegenGemmShape>;
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using CodegenGemmTraits = ck_tile::TileGemmTraits<GemmConfig::kPadM,
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GemmConfig::kPadN,
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GemmConfig::kPadK,
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ALayout,
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BLayout,
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CLayout>;
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using CodegenPipelineProblem = ck_tile::GemmPipelineProblem<ADataType,
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BDataType,
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AccDataType,
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CodegenGemmShape,
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CodegenGemmTraits>;
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using CodegenGemmPipeline = ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem>;
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const auto Run = [&](const auto memory_operation_) {
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constexpr auto memory_operation = memory_operation_.value;
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using GemmEpilogue = ck_tile::CShuffleEpilogue<
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ck_tile::CShuffleEpilogueProblem<ADataType,
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BDataType,
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ck_tile::tuple<>,
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AccDataType,
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CDataType,
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ck_tile::tuple<>,
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CLayout,
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ck_tile::element_wise::PassThrough,
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TilePartitioner::MPerBlock,
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TilePartitioner::NPerBlock,
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M_Warp,
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N_Warp,
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M_Warp_Tile,
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N_Warp_Tile,
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K_Warp_Tile,
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CodegenPipelineProblem::TransposeC,
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memory_operation>>;
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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 = ck_tile::GemmKernel<TilePartitioner, CodegenGemmPipeline, GemmEpilogue>;
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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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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: " << Kernel::GetName() << '\n'
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<< "shape: " << CodegenGemmShape::GetName() << '\n'
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<< "problem: " << CodegenPipelineProblem::GetName() << '\n'
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<< "pipeline: " << CodegenGemmPipeline::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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// Declare rotating_mem_ptr here so it stays in scope until it is needed
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std::unique_ptr<ck_tile::RotatingMemWrapper<ADataType, BDataType>> rotating_mem_ptr;
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std::function<void()> preprocess;
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auto clear_gemm_output = [&]() {
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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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if(s.flush_cache_)
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{
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std::cout << "Flushing cache..." << std::endl;
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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();
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auto size_b_buffer = b_n.get_element_space_size_in_bytes();
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rotating_mem_ptr =
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std::make_unique<ck_tile::RotatingMemWrapper<ADataType, BDataType>>(
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kargs.as_ptr[0],
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kargs.bs_ptr[0],
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s.rotating_count_,
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size_a_buffer,
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size_b_buffer);
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rotating_mem_ptr->Print();
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preprocess = [&]() {
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ck_tile::flush_icache();
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rotating_mem_ptr->Next();
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clear_gemm_output();
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};
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}
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else
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{
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preprocess = clear_gemm_output;
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}
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return ck_tile::launch_kernel_time_mask(
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s,
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preprocess,
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ck_tile::make_kernel<GemmConfig::kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
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};
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if(args.k_batch == 1)
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{
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return Run(MemoryOpSet{});
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}
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else
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{
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return Run(MemoryOpAtomicAdd{});
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}
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}
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};
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