mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-02 04:31:25 +00:00
6a3960c1e1
* sync with function interface of cshuffleepiloge,fix flatmm build fail * move code from solin/flatmm which add mfma16*16*32fp8 and optimize flatmm --------- Co-authored-by: solin <bingzhou@amd.com>
487 lines
19 KiB
C++
487 lines
19 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <iostream>
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#include <string>
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#include "ck_tile/core.hpp"
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#include "ck_tile/ops/common.hpp"
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#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_scheduler.hpp"
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namespace ck_tile {
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struct FlatmmProblem
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{
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CK_TILE_HOST FlatmmProblem() = default;
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CK_TILE_HOST FlatmmProblem(
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index_t M_, index_t N_, index_t K_, index_t stride_A_, index_t stride_B_, index_t stride_C_)
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: M(M_), N(N_), K(K_), stride_A(stride_A_), stride_B(stride_B_), stride_C(stride_C_)
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{
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}
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index_t M;
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index_t N;
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index_t K;
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index_t stride_A;
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index_t stride_B;
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index_t stride_C;
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};
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struct FlatmmHostArgs : public FlatmmProblem
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{
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CK_TILE_HOST FlatmmHostArgs() = default;
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CK_TILE_HOST FlatmmHostArgs(const void* a_ptr_,
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const void* b_shuffle_ptr_,
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void* c_ptr_,
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index_t k_batch_,
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index_t M_,
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index_t N_,
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index_t K_,
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index_t stride_A_,
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index_t stride_B_,
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index_t stride_C_)
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: FlatmmProblem(M_, N_, K_, stride_A_, stride_B_, stride_C_),
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a_ptr(a_ptr_),
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b_shuffle_ptr(b_shuffle_ptr_),
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c_ptr(c_ptr_),
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k_batch(k_batch_)
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{
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}
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const void* a_ptr;
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const void* b_shuffle_ptr;
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void* c_ptr;
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index_t k_batch;
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};
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template <typename TilePartitioner_, typename FlatmmPipeline_, typename EpiloguePipeline_>
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struct FlatmmKernel
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{
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using TilePartitioner = remove_cvref_t<TilePartitioner_>;
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using FlatmmPipeline = remove_cvref_t<FlatmmPipeline_>;
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using BlockGemmShape =
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remove_cvref_t<typename FlatmmPipeline::BlockGemmShape>; // TileFlatmmShape
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using EpiloguePipeline = remove_cvref_t<EpiloguePipeline_>;
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using ALayout = remove_cvref_t<typename FlatmmPipeline::ALayout>;
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using BLayout = remove_cvref_t<typename FlatmmPipeline::BLayout>;
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using CLayout = remove_cvref_t<typename FlatmmPipeline::CLayout>;
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static constexpr index_t KernelBlockSize = FlatmmPipeline::BlockSize;
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using ADataType = remove_cvref_t<typename FlatmmPipeline::ADataType>;
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using BDataType = remove_cvref_t<typename FlatmmPipeline::BDataType>;
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// Below type is actually accumulation data type - the output of block GEMM.
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using CDataType = remove_cvref_t<typename EpiloguePipeline::ODataType>;
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static constexpr auto I0 = number<0>();
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static constexpr auto I1 = number<1>();
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static constexpr auto I2 = number<2>();
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static constexpr auto idxM = I0;
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static constexpr auto idxN = I1;
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static constexpr auto idxK = I2;
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[[nodiscard]] CK_TILE_HOST static const std::string GetName()
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{
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// clang-format off
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return concat('_', "gemm", gemm_prec_str<ADataType, BDataType>, FlatmmPipeline::GetName());
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// clang-format on
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}
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CK_TILE_HOST static constexpr auto GridSize(index_t M, index_t N, index_t KBatch)
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{
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return dim3(TilePartitioner::GridSize(M, N), 1, KBatch);
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}
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CK_TILE_HOST static constexpr auto BlockSize() { return dim3(KernelBlockSize); }
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struct FlatmmKernelArgs
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{
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const void* a_ptr;
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const void* b_shuffle_ptr;
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void* c_ptr;
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index_t M;
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index_t N;
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index_t K;
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index_t stride_A;
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index_t stride_B;
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index_t stride_C;
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index_t k_batch;
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};
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CK_TILE_HOST static constexpr FlatmmKernelArgs MakeKernelArgs(const FlatmmHostArgs& hostArgs)
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{
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return FlatmmKernelArgs{hostArgs.a_ptr,
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hostArgs.b_shuffle_ptr,
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hostArgs.c_ptr,
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hostArgs.M,
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hostArgs.N,
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hostArgs.K,
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hostArgs.stride_A,
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hostArgs.stride_B,
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hostArgs.stride_C,
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hostArgs.k_batch};
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}
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CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
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{
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return max(FlatmmPipeline::GetSmemSize(), EpiloguePipeline::GetSmemSize());
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}
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struct SplitKBatchOffset
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{
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__device__ SplitKBatchOffset(const FlatmmKernelArgs& kargs,
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const std::size_t k_id = blockIdx.z)
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{
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constexpr auto K1 = TilePartitioner::BlockGemmShape::WarpTile::at(number<2>{});
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const index_t K_t = kargs.k_batch * K1;
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const index_t KRead = (kargs.K + K_t - 1) / K_t * K1;
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if constexpr(std::is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
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{
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a_k_split_offset = k_id * KRead;
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}
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else if constexpr(std::is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
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{
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a_k_split_offset = k_id * KRead * kargs.stride_A;
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}
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if constexpr(std::is_same_v<tensor_layout::gemm::RowMajor, BLayout>)
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{
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b_k_split_offset = k_id * KRead * kargs.stride_B;
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}
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else if constexpr(std::is_same_v<tensor_layout::gemm::ColumnMajor, BLayout>)
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{
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b_k_split_offset = k_id * KRead;
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}
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if(k_id < static_cast<uint32_t>(kargs.k_batch - 1))
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{
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splitted_k = KRead;
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}
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else
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{
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splitted_k = kargs.K - KRead * (kargs.k_batch - 1);
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}
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}
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index_t a_k_split_offset;
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index_t b_k_split_offset;
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index_t splitted_k;
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};
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CK_TILE_HOST static bool IsSupportedArgument(const FlatmmKernelArgs& kargs)
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{
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if constexpr(EpiloguePipeline::GetVectorSizeC() % 2 != 0 &&
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is_any_of<CDataType, fp16_t, bf16_t>::value)
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{
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if(kargs.k_batch != 1)
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{
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std::cerr << "Conditions not met for Kbatch >1 !" << std::endl;
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return false;
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}
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}
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if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
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{
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if(kargs.K % TilePartitioner::KPerBlock != 0 && FlatmmPipeline::kPadK == false)
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{
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std::cerr << "Can't support K that is not a multiple of KPerBlock"
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" without padding!"
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<< std::endl;
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return false;
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}
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if(kargs.K % FlatmmPipeline::GetVectorSizeA() != 0)
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{
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std::cerr << "K is not a multiple of vector load size for A tensor!" << std::endl;
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return false;
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}
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}
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else
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{
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if(kargs.M % TilePartitioner::MPerBlock != 0 && FlatmmPipeline::kPadM == false)
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{
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std::cerr << "Can't support M that is not a multiple of MPerBlock"
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" without padding!"
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<< std::endl;
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return false;
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}
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if(kargs.M % FlatmmPipeline::GetVectorSizeA() != 0)
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{
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std::cerr << "M is not a multiple of vector load size for A tensor!" << std::endl;
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return false;
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}
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}
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if constexpr(std::is_same_v<BLayout, tensor_layout::gemm::RowMajor>)
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{
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if(kargs.N % TilePartitioner::NPerBlock != 0 && FlatmmPipeline::kPadN == false)
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{
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std::cerr << "Can't support N that is not a multiple of NPerBlock"
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" without padding!"
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<< std::endl;
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return false;
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}
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if(kargs.N % FlatmmPipeline::GetVectorSizeB() != 0)
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{
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std::cerr << "N is not a multiple of vector load size for B tensor!" << std::endl;
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return false;
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}
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}
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else
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{
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if(kargs.K % TilePartitioner::KPerBlock != 0 && FlatmmPipeline::kPadK == false)
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{
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std::cerr << "Can't support K that is not a multiple of KPerBlock"
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" without padding!"
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<< std::endl;
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return false;
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}
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if(kargs.K % FlatmmPipeline::GetVectorSizeB() != 0)
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{
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std::cerr << "K is not a multiple of vector load size for B tensor!" << std::endl;
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return false;
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}
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}
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if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
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{
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if(kargs.N % TilePartitioner::NPerBlock != 0 && FlatmmPipeline::kPadN == false)
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{
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std::cerr << "Can't support N that is not a multiple of NPerBlock"
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" without padding!"
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<< std::endl;
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return false;
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}
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if(kargs.N % EpiloguePipeline::GetVectorSizeC() != 0)
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{
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std::cerr << "N is not a multiple of vector load size for C tensor!" << std::endl;
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return false;
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}
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}
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else
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{
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if(kargs.M % TilePartitioner::MPerBlock != 0 && FlatmmPipeline::kPadM == false)
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{
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std::cerr << "Can't support M that is not a multiple of MPerBlock"
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" without padding!"
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<< std::endl;
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return false;
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}
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if(kargs.M % EpiloguePipeline::GetVectorSizeC() != 0)
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{
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std::cerr << "M is not a multiple of vector load size for C tensor!" << std::endl;
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return false;
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}
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}
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return true;
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}
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template <memory_operation_enum DstInMemOp = memory_operation_enum::set>
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CK_TILE_DEVICE static auto MakeGemmTensorViews(const ADataType* a_ptr,
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const BDataType* b_flat_ptr,
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CDataType* c_ptr,
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const FlatmmKernelArgs& kargs,
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const SplitKBatchOffset& splitk_batch_offset)
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{
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const auto& a_tensor_view = [&]() {
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if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
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{
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return make_naive_tensor_view<address_space_enum::global>(
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a_ptr,
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make_tuple(kargs.M, splitk_batch_offset.splitted_k),
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make_tuple(kargs.stride_A, 1),
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number<FlatmmPipeline::GetVectorSizeA()>{},
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number<1>{});
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}
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else
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{
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return make_naive_tensor_view<address_space_enum::global>(
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a_ptr,
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make_tuple(splitk_batch_offset.splitted_k, kargs.M),
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make_tuple(kargs.stride_A, 1),
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number<FlatmmPipeline::GetVectorSizeA()>{},
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number<1>{});
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}
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}();
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index_t kFlatK = FlatmmPipeline::flatKPerWarp * (splitk_batch_offset.splitted_k /
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BlockGemmShape::WarpTile::at(number<2>{}));
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index_t kFlatN = kargs.N * kargs.K / kFlatK;
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const auto& b_flat_tensor_view = [&]() {
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return make_naive_tensor_view<address_space_enum::global>(
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b_flat_ptr,
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make_tuple(kFlatN, kFlatK),
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make_tuple(kFlatK, 1),
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number<FlatmmPipeline::GetVectorSizeB()>{},
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number<1>{});
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}();
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// TODO: enable vector write for C in ColMajor
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const auto& c_tensor_view = [&]() {
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if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
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{
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return make_naive_tensor_view<address_space_enum::global>(
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c_ptr,
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make_tuple(kargs.M, kargs.N),
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make_tuple(kargs.stride_C, 1),
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number<EpiloguePipeline::GetVectorSizeC()>{},
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number<1>{});
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}
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else
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{
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return make_naive_tensor_view<address_space_enum::global>(
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c_ptr,
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make_tuple(kargs.M, kargs.N),
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make_tuple(1, kargs.stride_C),
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number<1>{},
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number<1>{});
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}
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}();
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return make_tuple(a_tensor_view, b_flat_tensor_view, c_tensor_view);
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}
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template <typename TensorView>
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CK_TILE_DEVICE static auto MakeGemmPadViews(const TensorView& views)
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{
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const auto& a_pad_view = [&]() {
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const auto& a_tensor_view = views.at(I0);
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if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
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{
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return pad_tensor_view(a_tensor_view,
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make_tuple(number<TilePartitioner::MPerBlock>{},
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number<TilePartitioner::KPerBlock>{}),
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sequence<false, FlatmmPipeline::kPadK>{});
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}
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else
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{
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return pad_tensor_view(a_tensor_view,
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make_tuple(number<TilePartitioner::KPerBlock>{},
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number<TilePartitioner::MPerBlock>{}),
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sequence<false, FlatmmPipeline::kPadM>{});
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}
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}();
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const auto& b_flat_tensor_view = views.at(I1);
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// TODO vector write in for C in ColMajor
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const auto& c_pad_view = [&]() {
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const auto& c_tensor_view = views.at(I2);
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if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
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{
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return pad_tensor_view(c_tensor_view,
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make_tuple(number<TilePartitioner::MPerBlock>{},
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number<TilePartitioner::NPerBlock>{}),
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sequence<false, FlatmmPipeline::kPadN>{});
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}
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else
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{
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return pad_tensor_view(c_tensor_view,
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make_tuple(number<TilePartitioner::MPerBlock>{},
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number<TilePartitioner::NPerBlock>{}),
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sequence<FlatmmPipeline::kPadM, false>{});
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}
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}();
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return make_tuple(a_pad_view, b_flat_tensor_view, c_pad_view);
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}
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template <typename PadView>
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CK_TILE_DEVICE static auto
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MakeGemmTileWindows(const PadView& views, const index_t i_m, const index_t i_n)
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{
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const auto& a_pad_view = views.at(I0);
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const auto& b_flat_pad_view = views.at(I1);
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const auto& c_pad_view = views.at(I2);
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const auto& a_block_window = [&]() {
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if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
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{
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return make_tile_window(a_pad_view,
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make_tuple(number<TilePartitioner::MPerBlock>{},
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number<TilePartitioner::KPerBlock>{}),
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{i_m, 0});
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}
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else
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{
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return make_tile_window(a_pad_view,
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make_tuple(number<TilePartitioner::KPerBlock>{},
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number<TilePartitioner::MPerBlock>{}),
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{0, i_m});
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}
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}();
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const auto& b_flat_block_window =
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make_tile_window(b_flat_pad_view,
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make_tuple(number<FlatmmPipeline::flatNPerWarp>{},
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number<FlatmmPipeline::flatKPerWarp>{}),
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{static_cast<int>(i_n / BlockGemmShape::WarpTile::at(idxN)), 0});
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auto c_block_window = make_tile_window(
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c_pad_view,
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make_tuple(number<TilePartitioner::MPerBlock>{}, number<TilePartitioner::NPerBlock>{}),
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{i_m, i_n});
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return make_tuple(a_block_window, b_flat_block_window, c_block_window);
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}
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CK_TILE_DEVICE static void RunFlatmm(const ADataType* a_ptr,
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const BDataType* b_flat_ptr,
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CDataType* c_ptr,
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void* smem_ptr,
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const FlatmmKernelArgs& kargs,
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const SplitKBatchOffset& splitk_batch_offset,
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const index_t block_idx_m,
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const index_t block_idx_n)
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{
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// Create Gemm tensor views, pad views and tile windows
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const auto& gemm_tensor_views_tuple =
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MakeGemmTensorViews<EpiloguePipeline::MemoryOperation>(
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a_ptr, b_flat_ptr, c_ptr, kargs, splitk_batch_offset);
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const auto& gemm_pad_views = MakeGemmPadViews(gemm_tensor_views_tuple);
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auto gemm_tile_windows = MakeGemmTileWindows(gemm_pad_views, block_idx_m, block_idx_n);
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const index_t num_loop = TilePartitioner::GetLoopNum(splitk_batch_offset.splitted_k);
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// Run GEMM cooperatively by whole workgroup.
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const auto& a_block_window = gemm_tile_windows.at(I0);
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const auto& b_flat_block_window = gemm_tile_windows.at(I1);
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const auto& c_block_tile = FlatmmPipeline{}.template operator()(
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a_block_window, b_flat_block_window, num_loop, smem_ptr);
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// Run Epilogue Pipeline
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auto& c_block_window = gemm_tile_windows.at(I2);
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EpiloguePipeline{}.template operator()<decltype(c_block_window), decltype(c_block_tile)>(
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c_block_window, c_block_tile, smem_ptr);
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}
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CK_TILE_DEVICE void operator()(FlatmmKernelArgs kargs) const
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{
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const auto [iM, iN] = TilePartitioner{kargs.M, kargs.N}.GetOutputTileIndex(blockIdx.x);
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const index_t i_m = __builtin_amdgcn_readfirstlane(iM * TilePartitioner::MPerBlock);
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const index_t i_n = __builtin_amdgcn_readfirstlane(iN * TilePartitioner::NPerBlock);
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const SplitKBatchOffset splitk_batch_offset(kargs);
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// options
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const ADataType* a_ptr =
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static_cast<const ADataType*>(kargs.a_ptr) + splitk_batch_offset.a_k_split_offset;
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const BDataType* b_flat_ptr = static_cast<const BDataType*>(kargs.b_shuffle_ptr) +
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splitk_batch_offset.b_k_split_offset;
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CDataType* c_ptr = static_cast<CDataType*>(kargs.c_ptr);
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// allocate LDS
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__shared__ char smem_ptr[GetSmemSize()];
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if constexpr(!(EpiloguePipeline::MemoryOperation == memory_operation_enum::atomic_add &&
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EpiloguePipeline::GetVectorSizeC() % 2 != 0 &&
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is_any_of<CDataType, fp16_t, bf16_t>::value))
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{
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RunFlatmm(a_ptr, b_flat_ptr, c_ptr, smem_ptr, kargs, splitk_batch_offset, i_m, i_n);
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}
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}
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};
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} // namespace ck_tile
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