mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-02 20:51:23 +00:00
[CK TILE] Refactor GemmKernel to be reused by other GEMM related operators (#1730)
* Gemm Kernel Refactor part1 * Gemm Kernel Refactor common gemm pipeline part2 * [CK TILE] Refactor batched gemm to reuse GemmKernel * [CK TILE] Refactor GemmKernel - review changes part1 * [CK TILE] Refactor GemmKernel - references fix * [CK TILE] Refactor GemmKernel - naming changes, add problem * [CK_TILE] Refactor GemmKernel - update tests * [CK_TILE] Refactor GemmKernel - review changes * [CK_TILE] Refactor GemmKernel - update test * [CK_TILE] Refactor GemmKernel - constness fixes * [CK_TILE] Refactor GemmKernel - update tests
This commit is contained in:
aledudek
@@ -3,90 +3,93 @@
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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/kernel/gemm_kernel.hpp"
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namespace ck_tile {
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struct BatchedGemmHostArgs
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struct BatchedGemmHostArgs : public ck_tile::GemmHostArgs
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{
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const void* a_ptr;
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const void* b_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 batch_stride_A;
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index_t batch_stride_B;
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index_t batch_stride_C;
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index_t batch_count;
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CK_TILE_HOST BatchedGemmHostArgs() = default;
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CK_TILE_HOST BatchedGemmHostArgs(const void* a_ptr_,
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const void* b_ptr_,
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void* c_ptr_,
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ck_tile::index_t k_batch_,
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ck_tile::index_t M_,
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ck_tile::index_t N_,
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ck_tile::index_t K_,
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ck_tile::index_t stride_A_,
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ck_tile::index_t stride_B_,
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ck_tile::index_t stride_C_,
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ck_tile::index_t batch_stride_A_,
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ck_tile::index_t batch_stride_B_,
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ck_tile::index_t batch_stride_C_,
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ck_tile::index_t batch_count_)
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: GemmHostArgs(
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a_ptr_, b_ptr_, c_ptr_, k_batch_, M_, N_, K_, stride_A_, stride_B_, stride_C_),
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batch_stride_A(batch_stride_A_),
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batch_stride_B(batch_stride_B_),
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batch_stride_C(batch_stride_C_),
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batch_count(batch_count_)
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{
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}
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ck_tile::index_t batch_stride_A;
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ck_tile::index_t batch_stride_B;
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ck_tile::index_t batch_stride_C;
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ck_tile::index_t batch_count;
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};
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template <typename TilePartitioner_, typename GemmPipeline_, typename EpiloguePipeline_>
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struct BatchedGemmKernel
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struct BatchedGemmKernel : public GemmKernel<TilePartitioner_, GemmPipeline_, EpiloguePipeline_>
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{
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using TilePartitioner = remove_cvref_t<TilePartitioner_>;
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using GemmPipeline = remove_cvref_t<GemmPipeline_>;
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using EpiloguePipeline = remove_cvref_t<EpiloguePipeline_>;
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using ALayout = remove_cvref_t<typename GemmPipeline::ALayout>;
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using BLayout = remove_cvref_t<typename GemmPipeline::BLayout>;
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using CLayout = remove_cvref_t<typename GemmPipeline::CLayout>;
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static constexpr index_t KernelBlockSize = GemmPipeline::BlockSize;
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using Base = GemmKernel<TilePartitioner_, GemmPipeline_, EpiloguePipeline_>;
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using ADataType = remove_cvref_t<typename GemmPipeline::ADataType>;
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using BDataType = remove_cvref_t<typename GemmPipeline::BDataType>;
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using CDataType = remove_cvref_t<typename EpiloguePipeline::ODataType>;
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using GemmKernelArgs = typename Base::GemmKernelArgs;
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struct BatchedGemmKargs
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using ADataType = typename Base::ADataType;
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using BDataType = typename Base::BDataType;
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using CDataType = typename Base::CDataType;
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using TilePartitioner = typename Base::TilePartitioner;
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using GemmPipeline = typename Base::GemmPipeline;
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using EpiloguePipeline = typename Base::EpiloguePipeline;
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using ALayout = typename Base::ALayout;
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using BLayout = typename Base::BLayout;
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using CLayout = typename Base::CLayout;
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struct BatchedGemmKernelArgs : GemmKernelArgs
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{
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const void* a_ptr;
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const void* b_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 batch_stride_A;
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index_t batch_stride_B;
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index_t batch_stride_C;
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index_t batch_count;
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};
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using Kargs = BatchedGemmKargs;
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using Hargs = BatchedGemmHostArgs;
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using KernelArgs = BatchedGemmKernelArgs;
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__host__ static constexpr auto GridSize(const Hargs& h)
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__host__ static constexpr auto GridSize(index_t M, index_t N, index_t batch_count)
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{
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return TilePartitioner::GridSize(h.M, h.N, h.batch_count);
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return TilePartitioner::GridSize(M, N, batch_count);
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}
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__host__ static constexpr auto BlockSize() { return dim3(KernelBlockSize); }
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__host__ static constexpr auto BlockSize() { return dim3(Base::KernelBlockSize); }
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CK_TILE_HOST static constexpr BatchedGemmKargs MakeKargs(const Hargs& h)
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CK_TILE_HOST static constexpr BatchedGemmKernelArgs
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MakeKernelArgs(const BatchedGemmHostArgs& hostArgs)
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{
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Kargs k;
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k.a_ptr = h.a_ptr;
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k.b_ptr = h.b_ptr;
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k.c_ptr = h.c_ptr;
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k.M = h.M;
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k.N = h.N;
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k.K = h.K;
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k.stride_A = h.stride_A;
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k.stride_B = h.stride_B;
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k.stride_C = h.stride_C;
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k.batch_stride_A = h.batch_stride_A;
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k.batch_stride_B = h.batch_stride_B;
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k.batch_stride_C = h.batch_stride_C;
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k.batch_count = h.batch_count;
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return k;
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return BatchedGemmKernelArgs{{hostArgs.a_ptr,
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hostArgs.b_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.batch_stride_A,
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hostArgs.batch_stride_B,
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hostArgs.batch_stride_C,
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hostArgs.batch_count};
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}
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CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
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@@ -94,7 +97,7 @@ struct BatchedGemmKernel
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return max(GemmPipeline::GetSmemSize(), EpiloguePipeline::GetSmemSize());
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}
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CK_TILE_DEVICE void operator()(Kargs kargs) const
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CK_TILE_DEVICE void operator()(BatchedGemmKernelArgs kargs) const
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{
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const auto [i_m, i_n] = TilePartitioner{}();
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const auto i_batch = __builtin_amdgcn_readfirstlane(blockIdx.z);
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@@ -102,156 +105,17 @@ struct BatchedGemmKernel
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// options
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const auto batch_stride_A = __builtin_amdgcn_readfirstlane(kargs.batch_stride_A);
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const auto batch_offset_A = __builtin_amdgcn_readfirstlane(i_batch * batch_stride_A);
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const ADataType* a_start = static_cast<const ADataType*>(kargs.a_ptr);
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const ADataType* a_ptr = static_cast<const ADataType*>(kargs.a_ptr) + batch_offset_A;
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const auto batch_stride_B = __builtin_amdgcn_readfirstlane(kargs.batch_stride_B);
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const auto batch_offset_B = __builtin_amdgcn_readfirstlane(i_batch * batch_stride_B);
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const BDataType* b_start = static_cast<const BDataType*>(kargs.b_ptr);
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// Convert pointers to tensor views
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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_start + batch_offset_A,
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make_tuple(kargs.M, kargs.K),
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make_tuple(kargs.stride_A, 1),
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number<GemmPipeline::VectorSizeA>{},
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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_start + batch_offset_A,
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make_tuple(kargs.M, kargs.K),
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make_tuple(1, kargs.stride_A),
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number<1>{},
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number<1>{});
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}
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}();
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auto b_tensor_view = [&]() {
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if constexpr(std::is_same_v<BLayout, 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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b_start + batch_offset_B,
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make_tuple(kargs.N, kargs.K),
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make_tuple(1, kargs.stride_B),
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number<1>{},
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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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b_start + batch_offset_B,
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make_tuple(kargs.N, kargs.K),
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make_tuple(kargs.stride_B, 1),
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number<GemmPipeline::VectorSizeB>{},
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number<1>{});
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}
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}();
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auto a_pad_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 pad_tensor_view(
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a_tensor_view,
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make_tuple(number<TilePartitioner::kM>{}, number<TilePartitioner::kK>{}),
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sequence<false, GemmPipeline::kPadK>{});
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}
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else
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{
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return pad_tensor_view(
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a_tensor_view,
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make_tuple(number<TilePartitioner::kM>{}, number<TilePartitioner::kK>{}),
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sequence<GemmPipeline::kPadM, false>{});
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}
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}();
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// clang-format on
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auto a_block_window = make_tile_window(
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a_pad_view,
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make_tuple(number<TilePartitioner::kM>{}, number<TilePartitioner::kK>{}),
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{i_m, 0});
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auto b_pad_view = [&]() {
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if constexpr(std::is_same_v<BLayout, tensor_layout::gemm::ColumnMajor>)
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{
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return pad_tensor_view(
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b_tensor_view,
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make_tuple(number<TilePartitioner::kN>{}, number<TilePartitioner::kK>{}),
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sequence<false, GemmPipeline::kPadK>{});
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}
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else
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{
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return pad_tensor_view(
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b_tensor_view,
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make_tuple(number<TilePartitioner::kN>{}, number<TilePartitioner::kK>{}),
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sequence<GemmPipeline::kPadN, false>{});
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}
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}();
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// clang-format on
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auto b_block_window = make_tile_window(
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b_pad_view,
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make_tuple(number<TilePartitioner::kN>{}, number<TilePartitioner::kK>{}),
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{i_n, 0});
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// allocate LDS
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__shared__ char smem_ptr[GetSmemSize()];
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const index_t num_loop = TilePartitioner::GetLoopNum(kargs.K);
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// Run GEMM cooperatively by whole wokrgroup.
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auto c_block_tile =
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GemmPipeline{}.template operator()(a_block_window, b_block_window, num_loop, smem_ptr);
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const BDataType* b_ptr = static_cast<const BDataType*>(kargs.b_ptr) + batch_offset_B;
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const auto batch_stride_C = __builtin_amdgcn_readfirstlane(kargs.batch_stride_C);
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const auto batch_offset_C = __builtin_amdgcn_readfirstlane(i_batch * batch_stride_C);
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CDataType* c_start = static_cast<CDataType*>(kargs.c_ptr);
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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_start + batch_offset_C,
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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<GemmPipeline::VectorSizeC>{},
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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_start + batch_offset_C,
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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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CDataType* c_ptr = static_cast<CDataType*>(kargs.c_ptr) + batch_offset_C;
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auto c_pad_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 pad_tensor_view(
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c_tensor_view,
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make_tuple(number<TilePartitioner::kM>{}, number<TilePartitioner::kN>{}),
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sequence<false, GemmPipeline::kPadN>{});
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}
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else
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{
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return pad_tensor_view(
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c_tensor_view,
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make_tuple(number<TilePartitioner::kM>{}, number<TilePartitioner::kN>{}),
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sequence<GemmPipeline::kPadM, false>{});
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}
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}();
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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::kM>{}, number<TilePartitioner::kN>{}),
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{i_m, i_n});
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EpiloguePipeline{}(c_block_window, c_block_tile);
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this->RunGemm(a_ptr, b_ptr, c_ptr, kargs, i_m, i_n);
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}
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};
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@@ -12,6 +12,50 @@
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namespace ck_tile {
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struct GemmProblem
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{
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CK_TILE_HOST GemmProblem() = default;
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CK_TILE_HOST GemmProblem(
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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 GemmHostArgs : public GemmProblem
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{
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CK_TILE_HOST GemmHostArgs() = default;
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CK_TILE_HOST GemmHostArgs(const void* a_ptr_,
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const void* b_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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: GemmProblem(M_, N_, K_, stride_A_, stride_B_, stride_C_),
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a_ptr(a_ptr_),
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b_ptr(b_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_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 GemmPipeline_, typename EpiloguePipeline_>
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struct GemmKernel
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{
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@@ -25,9 +69,12 @@ struct GemmKernel
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using ADataType = remove_cvref_t<typename GemmPipeline::ADataType>;
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using BDataType = remove_cvref_t<typename GemmPipeline::BDataType>;
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// using CAccDataType = remove_cvref_t<typename GemmPipeline::CDataType>;
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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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__host__ static constexpr auto GridSize(index_t M, index_t N, index_t KBatch)
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{
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return TilePartitioner::GridSize(M, N, KBatch);
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@@ -35,7 +82,7 @@ struct GemmKernel
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__host__ static constexpr auto BlockSize() { return dim3(KernelBlockSize); }
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struct GemmCommonKargs
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struct GemmKernelArgs
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{
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const void* a_ptr;
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const void* b_ptr;
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@@ -48,25 +95,37 @@ struct GemmKernel
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index_t stride_C;
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};
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CK_TILE_HOST static constexpr GemmCommonKargs MakeKargs(const void* a_ptr,
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const void* b_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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CK_TILE_HOST static constexpr GemmKernelArgs MakeKernelArgs(const GemmHostArgs& hostArgs)
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{
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return GemmCommonKargs{a_ptr, b_ptr, c_ptr, M, N, K, stride_A, stride_B, stride_C};
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return GemmKernelArgs{hostArgs.a_ptr,
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hostArgs.b_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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}
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// CK_TILE_HOST static constexpr GemmKernelArgs MakeKernelArgs(const void* a_ptr,
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// const void* b_ptr,
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// void* c_ptr,
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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)
|
||||
// {
|
||||
// return GemmKernelArgs{a_ptr, b_ptr, c_ptr, M, N, K, stride_A, stride_B, stride_C};
|
||||
// }
|
||||
|
||||
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
|
||||
{
|
||||
return max(GemmPipeline::GetSmemSize(), EpiloguePipeline::GetSmemSize());
|
||||
}
|
||||
|
||||
CK_TILE_HOST static bool IsSupportedArgument(const GemmCommonKargs& kargs)
|
||||
CK_TILE_HOST static bool IsSupportedArgument(const GemmKernelArgs& kargs)
|
||||
{
|
||||
if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
|
||||
{
|
||||
@@ -139,18 +198,16 @@ struct GemmKernel
|
||||
return true;
|
||||
}
|
||||
|
||||
CK_TILE_DEVICE void operator()(GemmCommonKargs kargs) const
|
||||
CK_TILE_DEVICE auto MakeGemmTensorViews(const ADataType* a_ptr,
|
||||
const BDataType* b_ptr,
|
||||
CDataType* c_ptr,
|
||||
const GemmKernelArgs& kargs) const
|
||||
{
|
||||
const auto [i_m, i_n] = TilePartitioner{}();
|
||||
// options
|
||||
const ADataType* a_start = static_cast<const ADataType*>(kargs.a_ptr);
|
||||
const BDataType* b_start = static_cast<const BDataType*>(kargs.b_ptr);
|
||||
// Convert pointers to tensor views
|
||||
auto a_tensor_view = [&]() {
|
||||
const auto& a_tensor_view = [&]() {
|
||||
if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
|
||||
{
|
||||
return make_naive_tensor_view<address_space_enum::global>(
|
||||
a_start,
|
||||
a_ptr,
|
||||
make_tuple(kargs.M, kargs.K),
|
||||
make_tuple(kargs.stride_A, 1),
|
||||
number<GemmPipeline::VectorSizeA>{},
|
||||
@@ -159,7 +216,7 @@ struct GemmKernel
|
||||
else
|
||||
{
|
||||
return make_naive_tensor_view<address_space_enum::global>(
|
||||
a_start,
|
||||
a_ptr,
|
||||
make_tuple(kargs.M, kargs.K),
|
||||
make_tuple(1, kargs.stride_A),
|
||||
number<1>{},
|
||||
@@ -167,11 +224,11 @@ struct GemmKernel
|
||||
}
|
||||
}();
|
||||
|
||||
auto b_tensor_view = [&]() {
|
||||
const auto& b_tensor_view = [&]() {
|
||||
if constexpr(std::is_same_v<BLayout, tensor_layout::gemm::RowMajor>)
|
||||
{
|
||||
return make_naive_tensor_view<address_space_enum::global>(
|
||||
b_start,
|
||||
b_ptr,
|
||||
make_tuple(kargs.N, kargs.K),
|
||||
make_tuple(1, kargs.stride_B),
|
||||
number<1>{},
|
||||
@@ -180,7 +237,7 @@ struct GemmKernel
|
||||
else
|
||||
{
|
||||
return make_naive_tensor_view<address_space_enum::global>(
|
||||
b_start,
|
||||
b_ptr,
|
||||
make_tuple(kargs.N, kargs.K),
|
||||
make_tuple(kargs.stride_B, 1),
|
||||
number<GemmPipeline::VectorSizeB>{},
|
||||
@@ -188,7 +245,35 @@ struct GemmKernel
|
||||
}
|
||||
}();
|
||||
|
||||
auto a_pad_view = [&]() {
|
||||
const auto& c_tensor_view = [&]() {
|
||||
if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
|
||||
{
|
||||
return make_naive_tensor_view<address_space_enum::global>(
|
||||
c_ptr,
|
||||
make_tuple(kargs.M, kargs.N),
|
||||
make_tuple(kargs.stride_C, 1),
|
||||
number<GemmPipeline::VectorSizeC>{},
|
||||
number<1>{});
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_naive_tensor_view<address_space_enum::global>(
|
||||
c_ptr,
|
||||
make_tuple(kargs.M, kargs.N),
|
||||
make_tuple(1, kargs.stride_C),
|
||||
number<1>{},
|
||||
number<1>{});
|
||||
}
|
||||
}();
|
||||
|
||||
return make_tuple(a_tensor_view, b_tensor_view, c_tensor_view);
|
||||
}
|
||||
|
||||
template <typename TensorView>
|
||||
CK_TILE_DEVICE auto MakeGemmPadViews(const TensorView& views) const
|
||||
{
|
||||
const auto& a_pad_view = [&]() {
|
||||
const auto& a_tensor_view = views.at(I0);
|
||||
if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
|
||||
{
|
||||
return pad_tensor_view(
|
||||
@@ -204,14 +289,9 @@ struct GemmKernel
|
||||
sequence<GemmPipeline::kPadM, false>{});
|
||||
}
|
||||
}();
|
||||
// clang-format on
|
||||
|
||||
auto a_block_window = make_tile_window(
|
||||
a_pad_view,
|
||||
make_tuple(number<TilePartitioner::kM>{}, number<TilePartitioner::kK>{}),
|
||||
{i_m, 0});
|
||||
|
||||
auto b_pad_view = [&]() {
|
||||
const auto& b_pad_view = [&]() {
|
||||
const auto& b_tensor_view = views.at(I1);
|
||||
if constexpr(std::is_same_v<BLayout, tensor_layout::gemm::ColumnMajor>)
|
||||
{
|
||||
return pad_tensor_view(
|
||||
@@ -228,43 +308,8 @@ struct GemmKernel
|
||||
}
|
||||
}();
|
||||
|
||||
auto b_block_window = make_tile_window(
|
||||
b_pad_view,
|
||||
make_tuple(number<TilePartitioner::kN>{}, number<TilePartitioner::kK>{}),
|
||||
{i_n, 0});
|
||||
|
||||
// allocate LDS
|
||||
__shared__ char smem_ptr[GetSmemSize()];
|
||||
|
||||
const index_t num_loop = TilePartitioner::GetLoopNum(kargs.K);
|
||||
|
||||
// Run GEMM cooperatively by whole wokrgroup.
|
||||
auto c_block_tile =
|
||||
GemmPipeline{}.template operator()(a_block_window, b_block_window, num_loop, smem_ptr);
|
||||
|
||||
CDataType* c_start = static_cast<CDataType*>(kargs.c_ptr);
|
||||
auto c_tensor_view = [&]() {
|
||||
if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
|
||||
{
|
||||
return make_naive_tensor_view<address_space_enum::global>(
|
||||
c_start,
|
||||
make_tuple(kargs.M, kargs.N),
|
||||
make_tuple(kargs.stride_C, 1),
|
||||
number<GemmPipeline::VectorSizeC>{},
|
||||
number<1>{});
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_naive_tensor_view<address_space_enum::global>(
|
||||
c_start,
|
||||
make_tuple(kargs.M, kargs.N),
|
||||
make_tuple(1, kargs.stride_C),
|
||||
number<1>{},
|
||||
number<1>{});
|
||||
}
|
||||
}();
|
||||
|
||||
auto c_pad_view = [&]() {
|
||||
const auto& c_pad_view = [&]() {
|
||||
const auto& c_tensor_view = views.at(I2);
|
||||
if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
|
||||
{
|
||||
return pad_tensor_view(
|
||||
@@ -280,12 +325,82 @@ struct GemmKernel
|
||||
sequence<GemmPipeline::kPadM, false>{});
|
||||
}
|
||||
}();
|
||||
auto CBlockWindow_pad = make_tile_window(
|
||||
|
||||
return make_tuple(a_pad_view, b_pad_view, c_pad_view);
|
||||
}
|
||||
|
||||
template <typename PadView>
|
||||
CK_TILE_DEVICE auto
|
||||
MakeGemmTileWindows(const PadView& views, const index_t i_m, const index_t i_n) const
|
||||
{
|
||||
const auto& a_pad_view = views.at(I0);
|
||||
const auto& a_block_window = make_tile_window(
|
||||
a_pad_view,
|
||||
make_tuple(number<TilePartitioner::kM>{}, number<TilePartitioner::kK>{}),
|
||||
{i_m, 0});
|
||||
|
||||
const auto& b_pad_view = views.at(I1);
|
||||
const auto& b_block_window = make_tile_window(
|
||||
b_pad_view,
|
||||
make_tuple(number<TilePartitioner::kN>{}, number<TilePartitioner::kK>{}),
|
||||
{i_n, 0});
|
||||
|
||||
const auto& c_pad_view = views.at(I2);
|
||||
auto c_block_window = make_tile_window(
|
||||
c_pad_view,
|
||||
make_tuple(number<TilePartitioner::kM>{}, number<TilePartitioner::kN>{}),
|
||||
{i_m, i_n});
|
||||
|
||||
EpiloguePipeline{}(CBlockWindow_pad, c_block_tile);
|
||||
return make_tuple(a_block_window, b_block_window, c_block_window);
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Runs single GEMM problem cooperatively by whole workgroup.
|
||||
*
|
||||
* @param a_ptr input A pointer
|
||||
* @param b_ptr input B pointer
|
||||
* @param c_ptr output C pointer
|
||||
* @param kargs GEMM kernel arguments
|
||||
* @param block_idx_m The GEMM's output M dimension tile index processed by this workgroup.
|
||||
* @param block_idx_n The GEMM's output N dimension tile index processed by this workgroup.
|
||||
*/
|
||||
CK_TILE_DEVICE void RunGemm(const ADataType* a_ptr,
|
||||
const BDataType* b_ptr,
|
||||
CDataType* c_ptr,
|
||||
const GemmKernelArgs& kargs,
|
||||
const index_t block_idx_m,
|
||||
const index_t block_idx_n) const
|
||||
{
|
||||
// Create Gemm tensor views, pad views and tile windows
|
||||
const auto& gemm_tensor_views_tuple = MakeGemmTensorViews(a_ptr, b_ptr, c_ptr, kargs);
|
||||
const auto& gemm_pad_views = MakeGemmPadViews(gemm_tensor_views_tuple);
|
||||
auto gemm_tile_windows = MakeGemmTileWindows(gemm_pad_views, block_idx_m, block_idx_n);
|
||||
|
||||
// allocate LDS
|
||||
__shared__ char smem_ptr[GetSmemSize()];
|
||||
|
||||
const index_t num_loop = TilePartitioner::GetLoopNum(kargs.K);
|
||||
|
||||
// Run GEMM cooperatively by whole workgroup.
|
||||
const auto& a_block_window = gemm_tile_windows.at(I0);
|
||||
const auto& b_block_window = gemm_tile_windows.at(I1);
|
||||
const auto& c_block_tile =
|
||||
GemmPipeline{}.template operator()(a_block_window, b_block_window, num_loop, smem_ptr);
|
||||
|
||||
// Run Epilogue Pipeline
|
||||
auto& c_block_window = gemm_tile_windows.at(I2);
|
||||
EpiloguePipeline{}(c_block_window, c_block_tile);
|
||||
}
|
||||
|
||||
CK_TILE_DEVICE void operator()(GemmKernelArgs kargs) const
|
||||
{
|
||||
const auto [i_m, i_n] = TilePartitioner{}();
|
||||
// options
|
||||
const ADataType* a_ptr = static_cast<const ADataType*>(kargs.a_ptr);
|
||||
const BDataType* b_ptr = static_cast<const BDataType*>(kargs.b_ptr);
|
||||
CDataType* c_ptr = static_cast<CDataType*>(kargs.c_ptr);
|
||||
|
||||
RunGemm(a_ptr, b_ptr, c_ptr, kargs, i_m, i_n);
|
||||
}
|
||||
};
|
||||
|
||||
|
||||
Reference in New Issue
Block a user