mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-02 20:51:23 +00:00
7ea1508b59
* Add TailHandler for V3, V4 and Mem pipelines * Adapt examples and tests to use TailHandler * move tail-handling logic to pipeline in persistent grouped gemm * Fix Mem pipeline dispatching, add CompV4 dispatching * Use a macro for handling the many tails of Mem pipeline * Fix formatting again * Use const-ref RunFunction, remove unnecessary try_run
365 lines
16 KiB
C++
365 lines
16 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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#pragma once
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#include <sstream>
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#include <gtest/gtest.h>
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#include "ck_tile/core.hpp"
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#include "ck_tile/host.hpp"
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#include "ck_tile/host/kernel_launch.hpp"
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#include "ck_tile/ops/epilogue.hpp"
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#include "ck_tile/ops/gemm.hpp"
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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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enum struct GemmPipelineType
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{
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Mem,
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CompV3,
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CompV4
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};
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template <GemmPipelineType PT, typename Problem>
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struct GemmPipelineTypeSelector;
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template <typename Problem>
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struct GemmPipelineTypeSelector<GemmPipelineType::Mem, Problem>
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{
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using base_pipeline = ck_tile::BaseGemmPipelineAgBgCrMem<Problem>;
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using pipeline = ck_tile::GemmPipelineAgBgCrMem<Problem>;
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};
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template <typename Problem>
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struct GemmPipelineTypeSelector<GemmPipelineType::CompV3, Problem>
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{
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using base_pipeline = ck_tile::BaseGemmPipelineAgBgCrCompV3<Problem>;
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using pipeline = ck_tile::GemmPipelineAgBgCrCompV3<Problem>;
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};
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template <typename Problem>
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struct GemmPipelineTypeSelector<GemmPipelineType::CompV4, Problem>
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{
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using base_pipeline = ck_tile::BaseGemmPipelineAgBgCrCompV4<Problem>;
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using pipeline = ck_tile::GemmPipelineAgBgCrCompV4<Problem>;
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};
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template <typename Tuple>
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class TestCkTileGemmPipeline : public ::testing::Test
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{
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protected:
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using ALayout = std::tuple_element_t<0, Tuple>;
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using BLayout = std::tuple_element_t<1, Tuple>;
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using CLayout = std::tuple_element_t<2, Tuple>;
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using ADataType = std::tuple_element_t<3, Tuple>;
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using BDataType = std::tuple_element_t<4, Tuple>;
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using AccDataType = std::tuple_element_t<5, Tuple>;
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using CDataType = std::tuple_element_t<6, Tuple>;
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static constexpr auto Scheduler = std::tuple_element_t<7, Tuple>::value;
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static constexpr auto PipelineType = std::tuple_element_t<8, Tuple>::value;
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static constexpr bool Persistent =
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ck_tile::tuple_element_or_default_t<Tuple, 9, std::false_type>::value;
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// TODO: expose tile size through test t-param ?
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template <bool PadM, bool PadN, bool PadK>
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void invoke_gemm(const ck_tile::GemmHostArgs& args, const ck_tile::stream_config& s)
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{
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// TODO: This should be parameterized in tests
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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 = (PipelineType == GemmPipelineType::CompV4) ? 32 : 64;
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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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constexpr bool kPadM = PadM;
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constexpr bool kPadN = PadN;
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constexpr bool kPadK = PadK;
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constexpr bool DoubleSmemBuffer = (PipelineType == GemmPipelineType::CompV4) ? true : false;
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// TODO: For now - but this should also be a test parameter
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constexpr bool TransposeC = false;
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constexpr int kBlockPerCu = 1;
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constexpr ck_tile::index_t TileParitionerGroupNum = 8;
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constexpr ck_tile::index_t TileParitionerM01 = 4;
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// ===============================================
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using GemmShape =
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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::
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GemmSpatiallyLocalTilePartitioner<GemmShape, TileParitionerGroupNum, TileParitionerM01>;
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using Traits = ck_tile::TileGemmTraits<kPadM, kPadN, kPadK, ALayout, BLayout, CLayout>;
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static constexpr bool StructuredSparsity = false;
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using GemmUniversalTraits = ck_tile::TileGemmUniversalTraits<kPadM,
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kPadN,
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kPadK,
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DoubleSmemBuffer,
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ALayout,
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BLayout,
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CLayout,
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TransposeC,
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StructuredSparsity,
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Persistent>;
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using GemmPipelineProblem =
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ck_tile::GemmPipelineProblem<ADataType, BDataType, AccDataType, GemmShape, Traits>;
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using BaseGemmPipeline =
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typename GemmPipelineTypeSelector<PipelineType, GemmPipelineProblem>::base_pipeline;
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const ck_tile::index_t k_grain = args.k_batch * K_Tile;
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const ck_tile::index_t K_split = (args.K + k_grain - 1) / k_grain * 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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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 memory_operation = memory_operation_.value;
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using UniversalGemmProblem = ck_tile::UniversalGemmPipelineProblem<ADataType,
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BDataType,
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AccDataType,
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GemmShape,
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GemmUniversalTraits,
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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 GemmPipeline =
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typename GemmPipelineTypeSelector<PipelineType, UniversalGemmProblem>::pipeline;
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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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AccDataType,
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CDataType,
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CLayout,
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GemmPipeline::BlockSize,
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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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UniversalGemmProblem::TransposeC,
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memory_operation>>;
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using Kernel = ck_tile::GemmKernel<TilePartitioner, GemmPipeline, GemmEpilogue>;
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auto kargs = Kernel::MakeKernelArgs(args);
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dim3 grids;
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if constexpr(Persistent)
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{
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grids = Kernel::MaxOccupancyGridSize(s);
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}
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else
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{
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grids = Kernel::GridSize(args.M, args.N, args.k_batch);
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}
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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:"
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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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ck_tile::launch_kernel(
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s, ck_tile::make_kernel<blocks.x, kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
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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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}
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public:
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std::vector<int> k_batches_;
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void SetUp() override
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{
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if constexpr(PipelineType == GemmPipelineType::CompV4)
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{
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// Only do k_batch = 1 when pipeline is CompV4
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k_batches_ = {1};
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}
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else
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{
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// Otherwise, use k_batch = 1 and 2
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k_batches_ = {1, 2};
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}
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}
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template <bool PadM = true, bool PadN = true, bool PadK = true>
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void Run(const int M,
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const int N,
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const int K,
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const int StrideA = 0,
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const int StrideB = 0,
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const int StrideC = 0)
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{
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for(auto kb : k_batches_)
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{
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RunSingle<PadM, PadN, PadK>(M, N, K, StrideA, StrideB, StrideC, kb);
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}
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}
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template <bool PadM, bool PadN, bool PadK>
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void RunSingle(const int M,
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const int N,
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const int K,
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const int StrideA,
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const int StrideB,
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const int StrideC,
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int kbatch = 1)
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{
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using namespace ck_tile::literals;
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auto f_host_tensor_descriptor = [](std::size_t row,
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std::size_t col,
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std::size_t stride,
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auto layout) {
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if constexpr(std::is_same_v<decltype(layout), ck_tile::tensor_layout::gemm::RowMajor>)
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{
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return ck_tile::HostTensorDescriptor({row, col}, {stride, 1_uz});
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}
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else
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{
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return ck_tile::HostTensorDescriptor({row, col}, {1_uz, stride});
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}
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};
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auto f_get_default_stride =
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[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
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if(stride == 0)
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{
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// give a chance if stride is zero, return a default packed stride
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if constexpr(std::is_same_v<decltype(layout),
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ck_tile::tensor_layout::gemm::RowMajor>)
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{
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return col;
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}
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else
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{
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return row;
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}
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}
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else
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return stride;
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};
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std::size_t stride_A = f_get_default_stride(M, K, StrideA, ALayout{});
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std::size_t stride_B = f_get_default_stride(K, N, StrideB, BLayout{});
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std::size_t stride_C = f_get_default_stride(M, N, StrideC, CLayout{});
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ck_tile::HostTensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, stride_A, ALayout{}));
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ck_tile::HostTensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, stride_B, BLayout{}));
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ck_tile::HostTensor<CDataType> c_m_n_dev_result(
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f_host_tensor_descriptor(M, N, stride_C, CLayout{}));
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ck_tile::FillUniformDistributionIntegerValue<ADataType>{-5, 5}(a_m_k);
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ck_tile::FillUniformDistributionIntegerValue<BDataType>{-5, 5}(b_k_n);
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ck_tile::DeviceMem a_m_k_dev_buf(a_m_k.get_element_space_size_in_bytes());
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ck_tile::DeviceMem b_k_n_dev_buf(b_k_n.get_element_space_size_in_bytes());
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ck_tile::DeviceMem c_m_n_dev_buf(c_m_n_dev_result.get_element_space_size_in_bytes());
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a_m_k_dev_buf.ToDevice(a_m_k.data());
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b_k_n_dev_buf.ToDevice(b_k_n.data());
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c_m_n_dev_buf.SetZero();
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c_m_n_dev_result.SetZero();
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ck_tile::GemmHostArgs args;
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args.a_ptr = a_m_k_dev_buf.GetDeviceBuffer();
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args.b_ptr = b_k_n_dev_buf.GetDeviceBuffer();
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args.c_ptr = c_m_n_dev_buf.GetDeviceBuffer();
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args.k_batch = kbatch;
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args.M = M;
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args.N = N;
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args.K = K;
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args.stride_A = stride_A;
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args.stride_B = stride_B;
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args.stride_C = stride_C;
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invoke_gemm<PadM, PadN, PadK>(args, ck_tile::stream_config{nullptr, false});
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c_m_n_dev_buf.FromDevice(c_m_n_dev_result.data());
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bool pass = true;
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ck_tile::HostTensor<CDataType> c_m_n_host_ref(
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f_host_tensor_descriptor(M, N, stride_C, CLayout{}));
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c_m_n_host_ref.SetZero();
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ck_tile::reference_gemm<ADataType, BDataType, AccDataType, CDataType>(
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a_m_k, b_k_n, c_m_n_host_ref);
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const float max_accumulated_value =
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*std::max_element(c_m_n_host_ref.mData.begin(), c_m_n_host_ref.mData.end());
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const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>(
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K, kbatch, max_accumulated_value);
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pass = ck_tile::check_err(c_m_n_dev_result,
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c_m_n_host_ref,
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"Error: Incorrect results!",
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rtol_atol.at(ck_tile::number<0>{}),
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rtol_atol.at(ck_tile::number<1>{}));
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EXPECT_TRUE(pass);
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}
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};
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