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https://github.com/ROCm/composable_kernel.git
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add a pipeline which copies from v4
This commit is contained in:
joye
@@ -0,0 +1,475 @@
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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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#pragma once
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#include "ck_tile/core.hpp"
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#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_scheduler.hpp"
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#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_base.hpp"
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#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_comp_v4_default_policy.hpp"
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namespace ck_tile {
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// A Tile Window: global memory
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// B Tile Window: global memory
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// C Distributed tensor: register
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template <typename Problem>
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struct BaseGemmPipelineAgBgCrCompV4
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{
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static constexpr index_t PrefetchStages = 2;
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static constexpr index_t PrefillStages = 1;
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static constexpr index_t GlobalBufferNum = 1;
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CK_TILE_HOST static constexpr bool BlockHasHotloop(index_t num_loop)
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{
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return num_loop > PrefetchStages;
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}
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CK_TILE_HOST static constexpr TailNumber GetBlockLoopTailNum(index_t num_loop)
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{
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if(num_loop % PrefetchStages == 1)
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{
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return TailNumber::Three;
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}
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else
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{
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return TailNumber::Two;
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}
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}
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};
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/**
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* @brief Compute optimized pipeline version async; which is changed from V4.
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*
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* This version introduces a dual LDS window mechanism using a ping-pong buffer approach
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* for more efficient data handling from global memory. Unlike compute version 3, this method
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* allows one LDS to fetch data from global memory while the other LDS executes warps for MFMA
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* matrix multiplication. This dual operation helps in keeping the Warp unit continuously busy,
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* thereby significantly reducing memory load times and enhancing overall performance.
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*
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* @note This version shows improved performance over Compute Version 3 with the same block tile.
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* It is particularly more efficient for large matrices where M, N, and K are greater than 8K,
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* even when Compute Version 3's block size is twice that of Compute Version 4.
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*/
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template <typename Problem, typename Policy = GemmPipelineAgBgCrCompV4DefaultPolicy>
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struct GemmPipelineAgBgCrCompAsync : public BaseGemmPipelineAgBgCrCompV4<Problem>
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{
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using Base = BaseGemmPipelineAgBgCrCompV4<Problem>;
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using PipelineImplBase = GemmPipelineAgBgCrImplBase<Problem, Policy>;
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using ADataType = remove_cvref_t<typename Problem::ADataType>;
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using BDataType = remove_cvref_t<typename Problem::BDataType>;
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using CDataType = remove_cvref_t<typename Problem::CDataType>;
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using BlockGemmShape = remove_cvref_t<typename Problem::BlockGemmShape>;
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static_assert(!std::is_same_v<BDataType, pk_int4_t>, "Not implemented");
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static constexpr index_t APackedSize =
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ck_tile::numeric_traits<remove_cvref_t<ADataType>>::PackedSize;
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static constexpr index_t BPackedSize =
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ck_tile::numeric_traits<remove_cvref_t<BDataType>>::PackedSize;
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using ALayout = remove_cvref_t<typename Problem::ALayout>;
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using BLayout = remove_cvref_t<typename Problem::BLayout>;
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using CLayout = remove_cvref_t<typename Problem::CLayout>;
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using BlockGemm = remove_cvref_t<decltype(Policy::template GetBlockGemm<Problem>())>;
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using I0 = number<0>;
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using I1 = number<1>;
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using I2 = number<2>;
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static constexpr index_t BlockSize = Problem::kBlockSize;
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static constexpr index_t MPerBlock = BlockGemmShape::kM;
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static constexpr index_t NPerBlock = BlockGemmShape::kN;
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static constexpr index_t KPerBlock = BlockGemmShape::kK;
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static constexpr index_t GetVectorSizeA() { return Policy::template GetVectorSizeA<Problem>(); }
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static constexpr index_t GetVectorSizeB() { return Policy::template GetVectorSizeB<Problem>(); }
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static constexpr index_t GetVectorSizeC() { return Policy::template GetVectorSizeC<Problem>(); }
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static constexpr index_t GetSmemPackA() { return Policy::template GetSmemPackA<Problem>(); }
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static constexpr index_t GetSmemPackB() { return Policy::template GetSmemPackB<Problem>(); }
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static constexpr bool kPadM = Problem::kPadM;
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static constexpr bool kPadN = Problem::kPadN;
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static constexpr bool kPadK = Problem::kPadK;
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static constexpr bool DoubleSmemBuffer = Problem::DoubleSmemBuffer;
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static constexpr bool HasHotLoop = Problem::HasHotLoop;
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static constexpr auto TailNum = Problem::TailNum;
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static constexpr auto Scheduler = Problem::Scheduler;
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CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
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{
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return Policy::template GetSmemSize<Problem>();
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}
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CK_TILE_HOST_DEVICE static constexpr auto IsTransposeC()
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{
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return Policy::template IsTransposeC<Problem>();
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}
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template <GemmPipelineScheduler Scheduler>
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struct PipelineImpl : public PipelineImplBase
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{
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};
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template <>
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struct PipelineImpl<GemmPipelineScheduler::Intrawave> : public PipelineImplBase
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{
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using Base = PipelineImplBase;
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CK_TILE_DEVICE static constexpr auto HotLoopScheduler()
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{
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constexpr index_t MPerXDL = BlockGemmShape::WarpTile::at(I0{});
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constexpr index_t NPerXDL = BlockGemmShape::WarpTile::at(I1{});
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constexpr index_t KPerXDL = BlockGemmShape::WarpTile::at(I2{});
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constexpr index_t WaveSize = 64;
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constexpr index_t WaveNumM = BlockGemmShape::BlockWarps::at(I0{});
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constexpr index_t WaveNumN = BlockGemmShape::BlockWarps::at(I1{});
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constexpr index_t A_LDS_Read_Width = KPerXDL;
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constexpr index_t B_LDS_Read_Width = KPerXDL;
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constexpr index_t A_Buffer_Load_Inst_Num =
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MPerBlock * KPerBlock / (BlockSize * GetVectorSizeA());
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constexpr index_t B_Buffer_Load_Inst_Num =
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NPerBlock * KPerBlock / (BlockSize * GetVectorSizeB());
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constexpr index_t A_LDS_Write_Inst_Num = MPerBlock * KPerBlock / (BlockSize * KPerXDL);
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constexpr index_t B_LDS_Write_Inst_Num = NPerBlock * KPerBlock / (BlockSize * KPerXDL);
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constexpr index_t A_LDS_Read_Inst_Num =
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WaveNumN * MPerBlock * KPerBlock / (BlockSize * KPerXDL);
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constexpr index_t B_LDS_Read_Inst_Num =
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WaveNumM * NPerBlock * KPerBlock / (BlockSize * KPerXDL);
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constexpr index_t C_MFMA_Inst_Num = MPerBlock * NPerBlock * KPerBlock /
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(BlockSize / WaveSize) /
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(MPerXDL * NPerXDL * KPerXDL);
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constexpr auto num_ds_read_inst_a =
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A_LDS_Read_Width * sizeof(ADataType) / APackedSize == 16 ? A_LDS_Read_Inst_Num
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: A_LDS_Read_Inst_Num / 2;
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constexpr auto num_ds_read_inst_b =
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B_LDS_Read_Width * sizeof(BDataType) / BPackedSize == 16 ? B_LDS_Read_Inst_Num
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: B_LDS_Read_Inst_Num / 2;
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constexpr auto num_ds_read_inst = num_ds_read_inst_a + num_ds_read_inst_b;
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constexpr auto num_ds_write_inst = A_LDS_Write_Inst_Num + B_LDS_Write_Inst_Num;
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constexpr auto num_buffer_load_inst = A_Buffer_Load_Inst_Num + B_Buffer_Load_Inst_Num;
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constexpr auto num_issue = num_buffer_load_inst;
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static_for<0, num_buffer_load_inst, 1>{}([&](auto i) {
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ignore = i;
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__builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA : 1
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__builtin_amdgcn_sched_group_barrier(
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0x100, num_ds_read_inst / num_issue, 0); // DS read : 2
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__builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA: 1
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__builtin_amdgcn_sched_group_barrier(
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0x200, num_ds_write_inst / num_issue, 0); // DS write : 1
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__builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA : 1
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__builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read :1
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__builtin_amdgcn_sched_group_barrier(
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0x008, C_MFMA_Inst_Num / num_issue - 3, 0); // MFMA : 5
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});
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__builtin_amdgcn_sched_barrier(0);
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}
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template <bool HasHotLoop,
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TailNumber TailNum,
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typename ADramBlockWindowTmp,
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typename BDramBlockWindowTmp,
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typename AElementFunction,
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typename BElementFunction>
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CK_TILE_DEVICE auto operator()(const ADramBlockWindowTmp& a_dram_block_window_tmp,
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const AElementFunction& a_element_func,
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const BDramBlockWindowTmp& b_dram_block_window_tmp,
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const BElementFunction& b_element_func,
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index_t num_loop,
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void* __restrict__ p_smem_0,
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void* __restrict__ p_smem_1) const
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{
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static_assert(
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std::is_same_v<ADataType, remove_cvref_t<typename ADramBlockWindowTmp::DataType>> &&
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std::is_same_v<BDataType,
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remove_cvref_t<typename BDramBlockWindowTmp::DataType>>,
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"Data Type conflict on A and B matrix input data type.");
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constexpr bool is_a_col_major =
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std::is_same_v<ALayout, tensor_layout::gemm::ColumnMajor>;
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constexpr bool is_b_row_major = std::is_same_v<BLayout, tensor_layout::gemm::RowMajor>;
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// TODO currently only support A matrix row major, B matrix col major; if A matrix is
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// col major or B is row major, need to combine with transpose load api
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static_assert(!(is_a_col_major || is_b_row_major),
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"only support A matrix is row major, B matrix is col major!");
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static_assert(is_a_col_major
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? (KPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
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MPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I1{}])
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: (MPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
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KPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I1{}]),
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"A block window has incorrect lengths for defined ALayout!");
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static_assert(is_b_row_major
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? (KPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
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NPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I1{}])
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: (NPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
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KPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I1{}]),
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"B block window has incorrect lengths for defined BLayout!");
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////////////// global window & register /////////////////
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// A DRAM tile window for load
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auto a_copy_dram_window =
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make_tile_window(a_dram_block_window_tmp.get_bottom_tensor_view(),
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make_tuple(number<MPerBlock>{}, number<KPerBlock>{}),
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a_dram_block_window_tmp.get_window_origin(),
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Policy::template MakeADramTileDistribution<Problem>());
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// B DRAM tile window for load
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auto b_copy_dram_window =
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make_tile_window(b_dram_block_window_tmp.get_bottom_tensor_view(),
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make_tuple(number<NPerBlock>{}, number<KPerBlock>{}),
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b_dram_block_window_tmp.get_window_origin(),
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Policy::template MakeBDramTileDistribution<Problem>());
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// A register tile for global load
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constexpr auto ABlockTileDistr = a_copy_dram_window.get_tile_distribution();
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constexpr auto BBlockTileDistr = b_copy_dram_window.get_tile_distribution();
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// using ABlockTile =
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// decltype(make_static_distributed_tensor<ADataType>(ABlockTileDistr)); using
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// BBlockTile = decltype(make_static_distributed_tensor<BDataType>(BBlockTileDistr));
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// ABlockTile a_global_load_tile;
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// BBlockTile b_global_load_tile;
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auto&& [a_lds_block0, b_lds_block0] = Base::GetABLdsTensorViews(p_smem_0);
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auto&& [a_lds_block1, b_lds_block1] = Base::GetABLdsTensorViews(p_smem_1);
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auto a_copy_lds_window0 = make_tile_window(
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a_lds_block0, make_tuple(number<MPerBlock>{}, number<KPerBlock>{}), {0, 0});
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auto a_copy_lds_window1 = make_tile_window(
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a_lds_block1, make_tuple(number<MPerBlock>{}, number<KPerBlock>{}), {0, 0});
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auto b_copy_lds_window0 = make_tile_window(
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b_lds_block0, make_tuple(number<NPerBlock>{}, number<KPerBlock>{}), {0, 0});
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auto b_copy_lds_window1 = make_tile_window(
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b_lds_block1, make_tuple(number<NPerBlock>{}, number<KPerBlock>{}), {0, 0});
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using ADramTileWindowStep = typename ADramBlockWindowTmp::BottomTensorIndex;
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using BDramTileWindowStep = typename BDramBlockWindowTmp::BottomTensorIndex;
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constexpr ADramTileWindowStep a_dram_tile_window_step =
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is_a_col_major ? make_array(KPerBlock, 0) : make_array(0, KPerBlock);
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constexpr BDramTileWindowStep b_dram_tile_window_step =
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is_b_row_major ? make_array(KPerBlock, 0) : make_array(0, KPerBlock);
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// the below is used for async cnt calculation
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using ACopyDramWindow = remove_cvref_t<decltype(a_copy_dram_window)>;
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using BCopyDramWindow = remove_cvref_t<decltype(b_copy_dram_window)>;
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constexpr auto a_number_of_access = ACopyDramWindow{}.get_num_of_access();
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constexpr auto b_number_of_access = BCopyDramWindow{}.get_num_of_access();
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// global prefetch 0
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// global read 0
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Base::GlobalPrefetchAsync(
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a_copy_lds_window0, a_copy_dram_window, a_dram_tile_window_step);
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Base::GlobalPrefetchAsync(
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b_copy_lds_window0, b_copy_dram_window, b_dram_tile_window_step);
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////////////// LDS desc, window & register /////////////////
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// Block GEMM
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auto block_gemm = BlockGemm();
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auto c_block_tile = block_gemm.MakeCBlockTile();
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// initialize C
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tile_elementwise_inout([](auto& c) { c = 0; }, c_block_tile);
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// global read 1
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Base::GlobalPrefetchAsync(
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a_copy_lds_window1, a_copy_dram_window, a_dram_tile_window_step);
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Base::GlobalPrefetchAsync(
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b_copy_lds_window1, b_copy_dram_window, b_dram_tile_window_step);
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buffer_load_fence(a_number_of_access + b_number_of_access);
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constexpr auto ALdsTileDistr = decltype(make_static_tile_distribution(
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BlockGemm::MakeABlockDistributionEncode())){};
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constexpr auto BLdsTileDistr = decltype(make_static_tile_distribution(
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BlockGemm::MakeBBlockDistributionEncode())){};
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using ALdsTile = decltype(make_static_distributed_tensor<ADataType>(ALdsTileDistr));
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using BLdsTile = decltype(make_static_distributed_tensor<BDataType>(BLdsTileDistr));
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ALdsTile a_block_tile0;
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ALdsTile a_block_tile1;
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BLdsTile b_block_tile0;
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BLdsTile b_block_tile1;
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auto a_lds_ld_window0 =
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make_tile_window(a_lds_block0,
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make_tuple(number<MPerBlock>{}, number<KPerBlock>{}),
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{0, 0},
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ALdsTileDistr);
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auto a_lds_ld_window1 =
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make_tile_window(a_lds_block1,
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make_tuple(number<MPerBlock>{}, number<KPerBlock>{}),
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{0, 0},
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ALdsTileDistr);
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auto b_lds_ld_window0 =
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make_tile_window(b_lds_block0,
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make_tuple(number<NPerBlock>{}, number<KPerBlock>{}),
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{0, 0},
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BLdsTileDistr);
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auto b_lds_ld_window1 =
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make_tile_window(b_lds_block1,
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make_tuple(number<NPerBlock>{}, number<KPerBlock>{}),
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{0, 0},
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BLdsTileDistr);
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static_assert(
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!(is_tile_window_linear_v<decltype(a_lds_ld_window0)>)&&!(is_tile_window_linear_v<decltype(a_lds_ld_window1)>)&&!(
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is_tile_window_linear_v<
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decltype(b_lds_ld_window0)>)&&!(is_tile_window_linear_v<decltype(b_lds_ld_window1)>),
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"LDS windows must not be linear");
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Base::LocalPrefetch(a_block_tile0, a_lds_ld_window0);
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Base::LocalPrefetch(b_block_tile0, b_lds_ld_window0);
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Base::GlobalPrefetchAsync(
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a_copy_lds_window0, a_copy_dram_window, a_dram_tile_window_step);
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Base::GlobalPrefetchAsync(
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b_copy_lds_window0, b_copy_dram_window, b_dram_tile_window_step);
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if(HasHotLoop)
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{
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// minus 2 because we have ping-pong double buffer.
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index_t iCounter = __builtin_amdgcn_readfirstlane(num_loop - 2);
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do
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{
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// ping
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{
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buffer_load_fence(a_number_of_access + b_number_of_access);
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Base::LocalPrefetch(a_block_tile1, a_lds_ld_window1);
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Base::LocalPrefetch(b_block_tile1, b_lds_ld_window1);
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Base::GlobalPrefetchAsync(
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a_copy_lds_window1, a_copy_dram_window, a_dram_tile_window_step);
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Base::GlobalPrefetchAsync(
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b_copy_lds_window1, b_copy_dram_window, b_dram_tile_window_step);
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// gemm
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block_gemm(c_block_tile, a_block_tile0, b_block_tile0);
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HotLoopScheduler();
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__builtin_amdgcn_sched_barrier(0);
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}
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// pong
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{
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buffer_load_fence(a_number_of_access + b_number_of_access);
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Base::LocalPrefetch(a_block_tile0, a_lds_ld_window0);
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Base::LocalPrefetch(b_block_tile0, b_lds_ld_window0);
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block_sync_lds();
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Base::GlobalPrefetchAsync(
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a_copy_lds_window0, a_copy_dram_window, a_dram_tile_window_step);
|
||||
Base::GlobalPrefetchAsync(
|
||||
b_copy_lds_window0, b_copy_dram_window, b_dram_tile_window_step);
|
||||
// gemm
|
||||
block_gemm(c_block_tile, a_block_tile1, b_block_tile1);
|
||||
HotLoopScheduler();
|
||||
__builtin_amdgcn_sched_barrier(0);
|
||||
}
|
||||
iCounter -= 2;
|
||||
} while(iCounter > 1);
|
||||
}
|
||||
|
||||
// tail 3
|
||||
if(TailNum == TailNumber::Three)
|
||||
{
|
||||
// 3
|
||||
{
|
||||
block_sync_lds();
|
||||
Base::LocalPrefetch(a_block_tile1, a_lds_ld_window1);
|
||||
Base::LocalPrefetch(b_block_tile1, b_lds_ld_window1);
|
||||
block_gemm(c_block_tile, a_block_tile0, b_block_tile0);
|
||||
}
|
||||
// 2
|
||||
{
|
||||
block_sync_lds();
|
||||
Base::LocalPrefetch(a_block_tile0, a_lds_ld_window0);
|
||||
Base::LocalPrefetch(a_block_tile0, a_lds_ld_window0);
|
||||
block_gemm(c_block_tile, a_block_tile1, b_block_tile1);
|
||||
}
|
||||
// 1
|
||||
{
|
||||
block_gemm(c_block_tile, a_block_tile0, b_block_tile0);
|
||||
__builtin_amdgcn_sched_barrier(0);
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
// 2
|
||||
{
|
||||
block_sync_lds();
|
||||
Base::LocalPrefetch(a_block_tile1, a_lds_ld_window1);
|
||||
Base::LocalPrefetch(b_block_tile1, b_lds_ld_window1);
|
||||
block_gemm(c_block_tile, a_block_tile0, b_block_tile0);
|
||||
static_for<0, 8, 1>{}([&](auto i) {
|
||||
ignore = i;
|
||||
__builtin_amdgcn_sched_group_barrier(0x100, 1, 0); // DS read
|
||||
__builtin_amdgcn_sched_group_barrier(0x008, 8, 0); // MFMA
|
||||
});
|
||||
__builtin_amdgcn_sched_barrier(0);
|
||||
}
|
||||
// 1
|
||||
{
|
||||
block_gemm(c_block_tile, a_block_tile1, b_block_tile1);
|
||||
__builtin_amdgcn_sched_barrier(0);
|
||||
}
|
||||
}
|
||||
return c_block_tile;
|
||||
}
|
||||
};
|
||||
|
||||
template <typename ADramBlockWindowTmp,
|
||||
typename BDramBlockWindowTmp,
|
||||
typename AElementFunction,
|
||||
typename BElementFunction>
|
||||
CK_TILE_DEVICE auto operator()(const ADramBlockWindowTmp& a_dram_block_window_tmp,
|
||||
const AElementFunction& a_element_func,
|
||||
const BDramBlockWindowTmp& b_dram_block_window_tmp,
|
||||
const BElementFunction& b_element_func,
|
||||
index_t num_loop,
|
||||
void* p_smem_0,
|
||||
void* p_smem_1) const
|
||||
{
|
||||
return PipelineImpl<Scheduler>{}.template operator()<HasHotLoop, TailNum>(
|
||||
a_dram_block_window_tmp,
|
||||
a_element_func,
|
||||
b_dram_block_window_tmp,
|
||||
b_element_func,
|
||||
num_loop,
|
||||
p_smem_0,
|
||||
p_smem_1);
|
||||
}
|
||||
|
||||
public:
|
||||
template <typename ADramBlockWindowTmp, typename BDramBlockWindowTmp>
|
||||
CK_TILE_DEVICE auto operator()(const ADramBlockWindowTmp& a_dram_block_window_tmp,
|
||||
const BDramBlockWindowTmp& b_dram_block_window_tmp,
|
||||
const index_t num_loop,
|
||||
void* __restrict__ p_smem_0,
|
||||
void* __restrict__ p_smem_1) const
|
||||
{
|
||||
return PipelineImpl<Scheduler>{}.template operator()<HasHotLoop, TailNum>(
|
||||
a_dram_block_window_tmp,
|
||||
[](const ADataType& a) { return a; },
|
||||
b_dram_block_window_tmp,
|
||||
[](const BDataType& b) { return b; },
|
||||
num_loop,
|
||||
p_smem_0,
|
||||
p_smem_1);
|
||||
}
|
||||
};
|
||||
} // namespace ck_tile
|
||||
Reference in New Issue
Block a user