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Ck tile batched gemm example (#1615)
* [CK Tile] Batched GEMM Example * [CK Tile] Batched GEMM Example - minor refactor * [CK Tile] Batched GEMM Example - README update * [CK Tile] Batched Gemm Example - review changes - Added tensor data layours as input parameters - Changed structure of Host and Kernel args - Removed bug with invalid vector read on non-contiguous memory * [CK Tile] Batched Gemm Example - remove comment * [CK Tile] Batched Gemm Example - Add GTests part1 * [CK Tile] Batched Gemm Example - GTests part2 + review changes * [CK TILE] Batched GEMM post merge fixes * [CK Tile] Batched GEMM Example - fix pad views
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aledudek
258
include/ck_tile/ops/gemm/kernel/batched_gemm_kernel.hpp
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258
include/ck_tile/ops/gemm/kernel/batched_gemm_kernel.hpp
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2024, 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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namespace ck_tile {
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struct BatchedGemmHostArgs
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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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template <typename TilePartitioner_, typename GemmPipeline_, typename EpiloguePipeline_>
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struct BatchedGemmKernel
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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 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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struct BatchedGemmKargs
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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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__host__ static constexpr auto GridSize(const Hargs& h)
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{
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return TilePartitioner::GridSize(h.M, h.N, h.batch_count);
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}
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__host__ static constexpr auto BlockSize() { return dim3(KernelBlockSize); }
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CK_TILE_HOST static constexpr BatchedGemmKargs MakeKargs(const Hargs& h)
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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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}
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CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
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{
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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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{
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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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// 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 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 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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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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}
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};
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} // namespace ck_tile
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