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https://github.com/ROCm/composable_kernel.git
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[CK_TILE] Multiple-D GEMM example (#2219)
* Multiple d, initial commit * Check Ds Layout * Readme and clang format * Update branch & conflicts * Multiple D - fix clang-formatter * Rename elemetwise_op * Fix CI * Code review part1 * Remove printf * Remove unnecessary comment * Add new tests with Col layout * Review part 2 * Added support for Multiple D GEMM * Update comment * Remove maybe_unused * Clang-format * Review part 3 * Add comment to function * Add comment to function: another * Take number of params for a refrence function * Remove additional d param for 0 tensor * Change name of function * Fix CI fails
This commit is contained in:
Mateusz Ozga
1
example/ck_tile/19_gemm_multi_d/CMakeLists.txt
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1
example/ck_tile/19_gemm_multi_d/CMakeLists.txt
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add_executable(tile_example_gemm_multi_d_fp16 EXCLUDE_FROM_ALL gemm_multi_d_fp16.cpp)
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35
example/ck_tile/19_gemm_multi_d/README.md
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35
example/ck_tile/19_gemm_multi_d/README.md
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#Multiple D GEMM
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This folder contains example for Multiple D GEMM using ck_tile tile-programming implementation.
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## build
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```
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#in the root of ck_tile
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mkdir build && cd build
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#you can replace < arch> with the appropriate architecture(for example gfx90a or gfx942) or \
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leave it blank
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sh ../script/cmake-ck-dev.sh ../ <arch>
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#The basic pipeline method on the gemm calculation
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make tile_example_gemm_multi_d_fp16 -j
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```
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This will result in an executable `build/bin/tile_example_gemm_multi_d_fp16`
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## example
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```
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args:
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-m M dimensions - (Default: 3840)
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-n N dimensions - (Default: 4096)
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-k K dimensions - (Default: 4096)
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-a_layout Tensor A layout (default:R)
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-b_layout Tensor B layout (default:C)
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-ds_layout Tensor D layout (default:R)
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-e_layout Tensor E layout (default:R)
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-stride_a Tensor A strides - (Default: 0)
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-stride_b Tensor B strides - (Default: 0)
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-stride_e Tensor C strides - (Default: 0)
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-stride_ds Tensor D strides - (Default: 0)
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-validate 0. No validation, 1. Validation on GPU. (Default: 1)
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-warmup Number of iterations before benchmark the kernel. (Default: 10)
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-repeat Number of iterations to benchmark the kernel. (Default: 100)
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-kbatch kbatch for SplitK. (Default 1)
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```
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296
example/ck_tile/19_gemm_multi_d/gemm_multi_d_fp16.cpp
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296
example/ck_tile/19_gemm_multi_d/gemm_multi_d_fp16.cpp
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// SPDX-License-Identifier: MIT
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// Copyright (c) 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 <memory>
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#include "ck_tile/core.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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#include "ck_tile/host.hpp"
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#include "gemm_multi_d_fp16.hpp"
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#include "utils.hpp"
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template <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 EDataType,
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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 CDEElementWise = ck_tile::element_wise::PassThrough>
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auto gemm_multi_d(const gemm_multi_d_kargs& args, const ck_tile::stream_config& s) -> float
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{
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#if(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_MEMORY)
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// Memory friendly for Interwave scheduler
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constexpr ck_tile::index_t M_Tile = 128;
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constexpr ck_tile::index_t N_Tile = 32;
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constexpr ck_tile::index_t K_Tile = 64;
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constexpr ck_tile::index_t M_Warp = 4;
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constexpr ck_tile::index_t N_Warp = 1;
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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 = 8;
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constexpr bool DoubleSmemBuffer = false;
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#endif
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#if(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_COMPUTE_V3)
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// Compute friendly for Intrawave scheduler
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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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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 DoubleSmemBuffer = false;
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#elif(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_COMPUTE_V4)
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// Compute friendly for Intrawave scheduler
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// Using the ping pong reader in the lds level
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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 = 32;
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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 DoubleSmemBuffer = true;
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#endif
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constexpr bool kPadM = false;
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constexpr bool kPadN = false;
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constexpr bool kPadK = false;
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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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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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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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using GemmPipelineProblem =
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ck_tile::GemmPipelineProblem<ADataType, BDataType, AccDataType, GemmShape, Traits>;
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using BaseGemmPipeline = UNIVERSAL_GEMM_PIPELINE<GemmPipelineProblem>;
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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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float ave_time{0};
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const auto Run =
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[&](const auto has_hot_loop_, const auto tail_number_, 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 = GEMM_PIPELINE_SCHEDULER;
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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 = GEMM_PIPELINE<UniversalGemmProblem>;
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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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EDataType,
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DsLayout,
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CLayout,
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CDEElementWise,
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GemmPipelineProblem::kBlockSize,
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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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const dim3 grids = Kernel::GridSize(args.M, args.N, args.k_batch);
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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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ave_time = 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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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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if(has_hot_loop)
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{
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#if(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_COMPUTE_V3)
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if(tail_num == ck_tile::TailNumber::Full)
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{
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RunSplitk(ck_tile::bool_constant<true>{},
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ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Full>{});
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}
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else if(tail_num == ck_tile::TailNumber::Odd)
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{
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RunSplitk(ck_tile::bool_constant<true>{},
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ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Odd>{});
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}
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else if(tail_num == ck_tile::TailNumber::Even)
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{
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RunSplitk(ck_tile::bool_constant<true>{},
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ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Even>{});
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}
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else
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{
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std::ostringstream err;
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err << "For compute pipeline tail number should always be Full, but have \"" << tail_num
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<< "\" which is not supported! PrefetchStages: " << BaseGemmPipeline::PrefetchStages
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<< "\n File: " << __FILE__ << ":" << __LINE__ << ", in function: " << __func__;
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throw std::runtime_error(err.str());
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}
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#elif(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_MEMORY)
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if(tail_num == ck_tile::TailNumber::One)
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{
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RunSplitk(ck_tile::bool_constant<true>{},
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ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::One>{});
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}
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else if(tail_num == ck_tile::TailNumber::Full)
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{
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RunSplitk(ck_tile::bool_constant<true>{},
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ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Full>{});
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}
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auto check_tail = [&](auto... TNs) {
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(try_run<BaseGemmPipeline, decltype(TNs)::value>(tail_num), ...);
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};
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check_tail(ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Two>{},
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ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Three>{},
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ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Four>{},
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ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Five>{},
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ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Six>{},
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ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Seven>{});
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#elif(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_COMPUTE_V4)
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if(tail_num == ck_tile::TailNumber::Three)
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{
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RunSplitk(
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ck_tile::bool_constant<true>{},
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ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Three>{});
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}
|
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else
|
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{
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RunSplitk(ck_tile::bool_constant<true>{},
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ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Two>{});
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}
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#endif
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}
|
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else
|
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{
|
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if(tail_num == ck_tile::TailNumber::Full)
|
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{
|
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RunSplitk(ck_tile::bool_constant<false>{},
|
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ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Full>{});
|
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}
|
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else if(tail_num == ck_tile::TailNumber::Odd)
|
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{
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RunSplitk(ck_tile::bool_constant<false>{},
|
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ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Odd>{});
|
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}
|
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else if(tail_num == ck_tile::TailNumber::Even)
|
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{
|
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RunSplitk(ck_tile::bool_constant<false>{},
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ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Even>{});
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}
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else
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{
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std::ostringstream err;
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err << "Num K loop must be larger than number of prefetech stages."
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<< "\n PrefetchStages: " << BaseGemmPipeline::PrefetchStages
|
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<< "\n File: " << __FILE__ << ":" << __LINE__ << ", in function: " << __func__;
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throw std::runtime_error(err.str());
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}
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}
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return ave_time;
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}
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#include "run_gemm_multi_d_fp16_example.inc"
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|
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int main(int argc, char* argv[]) { return !run_multiple_d_gemm_example(argc, argv); }
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79
example/ck_tile/19_gemm_multi_d/gemm_multi_d_fp16.hpp
Normal file
79
example/ck_tile/19_gemm_multi_d/gemm_multi_d_fp16.hpp
Normal file
@@ -0,0 +1,79 @@
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// SPDX-License-Identifier: MIT
|
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// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
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|
||||
#include <string>
|
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|
||||
#include "ck_tile/core.hpp"
|
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#include "ck_tile/host/kernel_launch.hpp"
|
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#include "ck_tile/ops/elementwise/unary_element_wise_operation.hpp"
|
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|
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#define CK_TILE_PIPELINE_COMPUTE_V3 1
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#define CK_TILE_PIPELINE_MEMORY 2
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#define CK_TILE_PIPELINE_COMPUTE_V4 3
|
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|
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#ifndef CK_TILE_PIPELINE_DEFAULT
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#define CK_TILE_PIPELINE_DEFAULT CK_TILE_PIPELINE_COMPUTE_V3
|
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#endif
|
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|
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#if(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_MEMORY)
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#define GEMM_PIPELINE ck_tile::GemmPipelineAgBgCrMem
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#define UNIVERSAL_GEMM_PIPELINE ck_tile::BaseGemmPipelineAgBgCrMem
|
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#define GEMM_PIPELINE_SCHEDULER ck_tile::GemmPipelineScheduler::Interwave
|
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#elif(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_COMPUTE_V3)
|
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#define GEMM_PIPELINE ck_tile::GemmPipelineAgBgCrCompV3
|
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#define UNIVERSAL_GEMM_PIPELINE ck_tile::BaseGemmPipelineAgBgCrCompV3
|
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#define GEMM_PIPELINE_SCHEDULER ck_tile::GemmPipelineScheduler::Intrawave
|
||||
#elif(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_COMPUTE_V4)
|
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#define GEMM_PIPELINE ck_tile::GemmPipelineAgBgCrCompV4
|
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#define UNIVERSAL_GEMM_PIPELINE ck_tile::BaseGemmPipelineAgBgCrCompV4
|
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#define GEMM_PIPELINE_SCHEDULER ck_tile::GemmPipelineScheduler::Intrawave
|
||||
#else
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||||
#error "unsupported CK_TILE_PIPELINE_DEFAULT value"
|
||||
#endif
|
||||
|
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using ADataType = ck_tile::half_t;
|
||||
using BDataType = ck_tile::half_t;
|
||||
using D0DataType = ck_tile::half_t;
|
||||
using D1DataType = ck_tile::half_t;
|
||||
using EDataType = ck_tile::half_t;
|
||||
using DsDataType = ck_tile::tuple<D0DataType, D1DataType>;
|
||||
using AccDataType = float;
|
||||
|
||||
auto create_args(int argc, char* argv[])
|
||||
{
|
||||
ck_tile::ArgParser arg_parser;
|
||||
arg_parser.insert("m", "3840", "m dimension")
|
||||
.insert("n", "4096", "n dimension")
|
||||
.insert("k", "4096", "k dimension")
|
||||
.insert("a_layout", "R", "A tensor data layout - Row by default")
|
||||
.insert("b_layout", "C", "B tensor data layout - Col by default")
|
||||
.insert("ds_layout", "R", "Ds tensor data layout - Row by default")
|
||||
.insert("e_layout", "R", "E tensor data layout - Row by default")
|
||||
.insert("stride_a", "0", "Tensor A stride")
|
||||
.insert("stride_b", "0", "Tensor B stride")
|
||||
.insert("stride_ds", "0", "Tensor Ds stride")
|
||||
.insert("stride_e", "0", "Tensor E stride")
|
||||
.insert("v", "1", "0. No validation, 1. Validation on GPU")
|
||||
.insert("warmup", "50", "number of iterations before benchmark the kernel")
|
||||
.insert("repeat", "100", "number of iterations to benchmark the kernel")
|
||||
.insert("kbatch", "1", "kbatch for SplitK");
|
||||
|
||||
bool result = arg_parser.parse(argc, argv);
|
||||
return std::make_tuple(result, arg_parser);
|
||||
}
|
||||
|
||||
using gemm_multi_d_kargs = ck_tile::GemmHostArgs<DsDataType::size()>;
|
||||
|
||||
template <typename ADataType,
|
||||
typename BDataType,
|
||||
typename DsDataType,
|
||||
typename AccDataType,
|
||||
typename EDataType,
|
||||
typename ALayout,
|
||||
typename BLayout,
|
||||
typename DsLayout,
|
||||
typename CLayout,
|
||||
typename CDEElementWise>
|
||||
float gemm_multi_d(const gemm_multi_d_kargs& kargs, const ck_tile::stream_config& s);
|
||||
@@ -0,0 +1,247 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
#include <cstddef>
|
||||
|
||||
template <typename ADataType,
|
||||
typename BDataType,
|
||||
typename DsDataType,
|
||||
typename AccDataType,
|
||||
typename EDataType,
|
||||
typename ALayout,
|
||||
typename BLayout,
|
||||
typename DsLayout,
|
||||
typename ELayout,
|
||||
typename CDEElementWise = ck_tile::element_wise::PassThrough>
|
||||
float invoke_gemm_multi_d(const void* a_m_k_dev_buf,
|
||||
const void* b_k_n_dev_buf,
|
||||
const std::array<const void*, DsDataType::size()>& ds_m_n_dev_buf,
|
||||
void* e_m_n_dev_buf,
|
||||
ck_tile::index_t M,
|
||||
ck_tile::index_t N,
|
||||
ck_tile::index_t K,
|
||||
ck_tile::index_t StrideA,
|
||||
ck_tile::index_t StrideB,
|
||||
const std::array<ck_tile::index_t, DsDataType::size()>& StrideDs,
|
||||
ck_tile::index_t StrideE,
|
||||
int n_warmup,
|
||||
int n_repeat,
|
||||
int k_batch)
|
||||
{
|
||||
gemm_multi_d_kargs gemm_descs({a_m_k_dev_buf,
|
||||
b_k_n_dev_buf,
|
||||
ds_m_n_dev_buf,
|
||||
e_m_n_dev_buf,
|
||||
k_batch,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideDs,
|
||||
StrideE});
|
||||
|
||||
float ave_time = gemm_multi_d<ADataType,
|
||||
BDataType,
|
||||
DsDataType,
|
||||
AccDataType,
|
||||
EDataType,
|
||||
ALayout,
|
||||
BLayout,
|
||||
DsLayout,
|
||||
ELayout,
|
||||
CDEElementWise>(
|
||||
gemm_descs, ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat});
|
||||
|
||||
std::string op_name{"Gemm Multiple-D"};
|
||||
static constexpr ck_tile::index_t NumDTensor = DsDataType::size();
|
||||
|
||||
std::size_t flop = 0, num_btype = 0;
|
||||
|
||||
flop += std::size_t(2) * M * N * K;
|
||||
|
||||
ck_tile::static_for<0, NumDTensor, 1>{}([&](auto i) {
|
||||
num_btype += sizeof(ck_tile::remove_cvref_t<std::tuple_element_t<i, DsDataType>>) * M * N;
|
||||
flop += sizeof(ck_tile::remove_cvref_t<std::tuple_element_t<i, DsDataType>>) * M * N;
|
||||
});
|
||||
|
||||
num_btype += sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(EDataType) * M * N;
|
||||
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
||||
float gb_per_sec = num_btype / 1.E6 / ave_time;
|
||||
|
||||
std::cout << "Run Gemm Multiple-D kernel with:\n";
|
||||
std::cout << "M =" << M << " N =" << N << " K =" << K << "\n";
|
||||
std::cout << "StrideA = " << StrideA << " StrideB = " << StrideB << " StrideE = " << StrideE
|
||||
<< "\n";
|
||||
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
|
||||
<< "\n";
|
||||
|
||||
return ave_time;
|
||||
}
|
||||
|
||||
template <typename ALayout,
|
||||
typename BLayout,
|
||||
typename D0Layout,
|
||||
typename D1Layout,
|
||||
typename ELayout>
|
||||
int run_multiple_d_gemm_example_with_layouts(int argc,
|
||||
char* argv[],
|
||||
const ALayout a_layout = ALayout{},
|
||||
const BLayout b_layout = BLayout{},
|
||||
const D0Layout d0_layout = D0Layout{},
|
||||
const D1Layout d1_layout = D1Layout{},
|
||||
const ELayout e_layout = ELayout{})
|
||||
{
|
||||
auto [result, arg_parser] = create_args(argc, argv);
|
||||
if(!result)
|
||||
{
|
||||
return -1;
|
||||
}
|
||||
using CDElementWiseFn = MultiplyMultiply;
|
||||
using DsLayout = ck_tile::tuple<D0Layout, D1Layout>;
|
||||
|
||||
ck_tile::index_t M = arg_parser.get_int("m");
|
||||
ck_tile::index_t N = arg_parser.get_int("n");
|
||||
ck_tile::index_t K = arg_parser.get_int("k");
|
||||
|
||||
ck_tile::index_t StrideA = arg_parser.get_int("stride_a");
|
||||
ck_tile::index_t StrideB = arg_parser.get_int("stride_b");
|
||||
ck_tile::index_t StrideD = arg_parser.get_int("stride_ds");
|
||||
ck_tile::index_t StrideE = arg_parser.get_int("stride_e");
|
||||
|
||||
ck_tile::index_t StrideD0 = StrideD;
|
||||
ck_tile::index_t StrideD1 = StrideD;
|
||||
|
||||
const int n_warmup = arg_parser.get_int("warmup");
|
||||
const int n_repeat = arg_parser.get_int("repeat");
|
||||
const int k_batch = arg_parser.get_int("kbatch");
|
||||
|
||||
StrideA = get_default_stride(M, K, StrideA, is_row_major(a_layout));
|
||||
StrideB = get_default_stride(K, N, StrideB, is_row_major(b_layout));
|
||||
StrideD0 = get_default_stride(M, N, StrideD0, is_row_major(d0_layout));
|
||||
StrideD1 = get_default_stride(M, N, StrideD1, is_row_major(d1_layout));
|
||||
StrideE = get_default_stride(M, N, StrideE, is_row_major(e_layout));
|
||||
|
||||
ck_tile::HostTensor<ADataType> a_m_k_tesnor(
|
||||
host_tensor_descriptor(M, K, StrideA, is_row_major(a_layout)));
|
||||
ck_tile::HostTensor<BDataType> b_k_n_tensors(
|
||||
host_tensor_descriptor(K, N, StrideB, is_row_major(b_layout)));
|
||||
ck_tile::HostTensor<D0DataType> d0_m_n_tensors(
|
||||
host_tensor_descriptor(M, N, StrideD0, is_row_major(d0_layout)));
|
||||
ck_tile::HostTensor<D1DataType> d1_m_n_tensors(
|
||||
host_tensor_descriptor(M, N, StrideD1, is_row_major(d1_layout)));
|
||||
ck_tile::HostTensor<EDataType> e_m_n_device_result(
|
||||
host_tensor_descriptor(M, N, StrideE, is_row_major(e_layout)));
|
||||
|
||||
ck_tile::FillUniformDistribution<ADataType>{-5.f, 5.f}(a_m_k_tesnor);
|
||||
ck_tile::FillUniformDistribution<BDataType>{-5.f, 5.f}(b_k_n_tensors);
|
||||
ck_tile::FillUniformDistribution<D0DataType>{-1.f, 1.f}(d0_m_n_tensors);
|
||||
ck_tile::FillUniformDistribution<D1DataType>{-1.f, 1.f}(d1_m_n_tensors);
|
||||
|
||||
ck_tile::DeviceMem a_m_k_dev_buf(a_m_k_tesnor.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem b_k_n_dev_buf(b_k_n_tensors.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem d0_m_n_dev_buf(d0_m_n_tensors.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem d1_m_n_dev_buf(d1_m_n_tensors.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem e_m_n_dev_buf(e_m_n_device_result.get_element_space_size_in_bytes());
|
||||
|
||||
a_m_k_dev_buf.ToDevice(a_m_k_tesnor.mData.data());
|
||||
b_k_n_dev_buf.ToDevice(b_k_n_tensors.mData.data());
|
||||
d0_m_n_dev_buf.ToDevice(d0_m_n_tensors.mData.data());
|
||||
d1_m_n_dev_buf.ToDevice(d1_m_n_tensors.mData.data());
|
||||
|
||||
e_m_n_dev_buf.SetZero();
|
||||
e_m_n_device_result.SetZero();
|
||||
|
||||
std::array<const void*, DsDataType::size()> ds_ptr_buf = {d0_m_n_dev_buf.GetDeviceBuffer(),
|
||||
d1_m_n_dev_buf.GetDeviceBuffer()};
|
||||
|
||||
std::array<ck_tile::index_t, DsDataType::size()> stridesDs = {StrideD0, StrideD1};
|
||||
|
||||
invoke_gemm_multi_d<ADataType,
|
||||
BDataType,
|
||||
DsDataType,
|
||||
AccDataType,
|
||||
EDataType,
|
||||
ALayout,
|
||||
BLayout,
|
||||
DsLayout,
|
||||
ELayout,
|
||||
CDElementWiseFn>(a_m_k_dev_buf.GetDeviceBuffer(),
|
||||
b_k_n_dev_buf.GetDeviceBuffer(),
|
||||
ds_ptr_buf,
|
||||
e_m_n_dev_buf.GetDeviceBuffer(),
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
stridesDs,
|
||||
StrideE,
|
||||
n_warmup,
|
||||
n_repeat,
|
||||
k_batch);
|
||||
|
||||
e_m_n_dev_buf.FromDevice(e_m_n_device_result.data());
|
||||
|
||||
ck_tile::HostTensor<EDataType> e_m_n_host_ref(
|
||||
host_tensor_descriptor(M, N, StrideE, is_row_major(e_layout)));
|
||||
e_m_n_host_ref.SetZero();
|
||||
|
||||
ck_tile::reference_gemm_multiple_d<ADataType,
|
||||
BDataType,
|
||||
DsDataType,
|
||||
AccDataType,
|
||||
EDataType,
|
||||
CDElementWiseFn>(
|
||||
a_m_k_tesnor, b_k_n_tensors, {d0_m_n_tensors, d1_m_n_tensors}, e_m_n_host_ref);
|
||||
|
||||
bool pass{true};
|
||||
if(arg_parser.get_int("v"))
|
||||
{
|
||||
const float max_accumulated_value =
|
||||
*std::max_element(e_m_n_host_ref.mData.begin(), e_m_n_host_ref.mData.end());
|
||||
|
||||
const auto rtol_atol = calculate_rtol_atol(K, 1, max_accumulated_value);
|
||||
|
||||
pass &= ck_tile::check_err(e_m_n_device_result,
|
||||
e_m_n_host_ref,
|
||||
"Error: Incorrect results!",
|
||||
rtol_atol.at(ck_tile::number<0>{}),
|
||||
rtol_atol.at(ck_tile::number<1>{}));
|
||||
|
||||
std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
|
||||
<< std::endl;
|
||||
std::cout << "Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
|
||||
<< std::endl;
|
||||
std::cout << "The CPU veification result is: " << (pass ? "correct" : "fail") << std::endl;
|
||||
}
|
||||
return pass;
|
||||
}
|
||||
|
||||
int run_multiple_d_gemm_example(int argc, char* argv[])
|
||||
{
|
||||
auto [result, arg_parser] = create_args(argc, argv);
|
||||
if(!result)
|
||||
{
|
||||
return -1;
|
||||
}
|
||||
|
||||
const std::string a_layout = arg_parser.get_str("a_layout");
|
||||
const std::string b_layout = arg_parser.get_str("b_layout");
|
||||
const std::string ds_layout = arg_parser.get_str("ds_layout");
|
||||
|
||||
using Row = ck_tile::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
if(a_layout == "R" && b_layout == "C" && ds_layout == "R")
|
||||
{
|
||||
return run_multiple_d_gemm_example_with_layouts(
|
||||
argc, argv, Row{}, Col{}, Row{}, Row{}, Row{});
|
||||
}
|
||||
else
|
||||
{
|
||||
throw std::runtime_error("Unsupported data layout configuration for provided tensors!");
|
||||
}
|
||||
}
|
||||
50
example/ck_tile/19_gemm_multi_d/utils.hpp
Normal file
50
example/ck_tile/19_gemm_multi_d/utils.hpp
Normal file
@@ -0,0 +1,50 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
struct MultiplyMultiply
|
||||
{
|
||||
template <typename E, typename C, typename D0, typename D1>
|
||||
CK_TILE_HOST_DEVICE auto operator()(E& e, const C& c, const D0& d0, const D1& d1) const -> void
|
||||
{
|
||||
const float x0_f = ck_tile::type_convert<float>(c) * ck_tile::type_convert<float>(d0) *
|
||||
ck_tile::type_convert<float>(d1);
|
||||
|
||||
e = ck_tile::type_convert<E>(x0_f);
|
||||
}
|
||||
};
|
||||
|
||||
template <typename Layout>
|
||||
static constexpr inline auto is_row_major(Layout layout_)
|
||||
{
|
||||
return ck_tile::bool_constant<std::is_same_v<ck_tile::remove_cvref_t<decltype(layout_)>,
|
||||
ck_tile::tensor_layout::gemm::RowMajor>>{};
|
||||
}
|
||||
|
||||
auto calculate_rtol_atol(const ck_tile::index_t K,
|
||||
const ck_tile::index_t kbatch,
|
||||
const float max_accumulated_value)
|
||||
{
|
||||
using ComputeTypeAB =
|
||||
std::conditional_t<sizeof(ADataType) < sizeof(BDataType), ADataType, BDataType>;
|
||||
|
||||
using ComputeType =
|
||||
std::conditional_t<sizeof(ComputeTypeAB) < sizeof(D0DataType), ComputeTypeAB, D0DataType>;
|
||||
// Calculate thresholds
|
||||
const auto rtol = ck_tile::get_relative_threshold<ComputeType, EDataType, AccDataType>(
|
||||
ck_tile::integer_divide_ceil(K, kbatch));
|
||||
|
||||
const auto atol = ck_tile::get_absolute_threshold<ComputeType, EDataType, AccDataType>(
|
||||
max_accumulated_value / kbatch, ck_tile::integer_divide_ceil(K, kbatch));
|
||||
|
||||
// Calculate error due to split_k accumulation
|
||||
const auto rtol_split_k =
|
||||
ck_tile::get_relative_threshold<EDataType, EDataType, EDataType>(kbatch);
|
||||
|
||||
const auto atol_split_k = ck_tile::get_absolute_threshold<EDataType, EDataType, EDataType>(
|
||||
max_accumulated_value, kbatch);
|
||||
|
||||
// Use higher threshold
|
||||
return ck_tile::make_tuple(std::max(rtol, rtol_split_k), std::max(atol, atol_split_k));
|
||||
}
|
||||
Reference in New Issue
Block a user