mirror of
https://github.com/ROCm/composable_kernel.git
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Merge commit 'e135dd518d19a36466ce7c61bb9d3203ec18c8af' into develop
This commit is contained in:
assistant-librarian[bot]
@@ -902,8 +902,8 @@ struct FlatmmKernel
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{
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const auto [iM, iN] =
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TilePartitioner{kargs.M, kargs.N}.GetOutputTileIndex(partition_idx);
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const index_t i_m = __builtin_amdgcn_readfirstlane(iM * TilePartitioner::MPerBlock);
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const index_t i_n = __builtin_amdgcn_readfirstlane(iN * TilePartitioner::NPerBlock);
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const index_t i_m = amd_wave_read_first_lane(iM * TilePartitioner::MPerBlock);
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const index_t i_n = amd_wave_read_first_lane(iN * TilePartitioner::NPerBlock);
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const SplitKBatchOffset splitk_batch_offset(kargs);
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// options
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518
include/ck_tile/ops/flatmm/kernel/mx_flatmm_kernel.hpp
Normal file
518
include/ck_tile/ops/flatmm/kernel/mx_flatmm_kernel.hpp
Normal file
@@ -0,0 +1,518 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <iostream>
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#include <string>
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#include "ck_tile/core.hpp"
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#include "ck_tile/ops/common.hpp"
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#include "ck_tile/ops/flatmm/kernel/flatmm_kernel.hpp"
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namespace ck_tile {
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template <typename TilePartitioner_, typename MXFlatmmPipeline_, typename EpiloguePipeline_>
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struct MXFlatmmKernel : FlatmmKernel<TilePartitioner_, MXFlatmmPipeline_, EpiloguePipeline_>
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{
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using Underlying = FlatmmKernel<TilePartitioner_, MXFlatmmPipeline_, EpiloguePipeline_>;
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using TilePartitioner = remove_cvref_t<TilePartitioner_>;
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using FlatmmPipeline = remove_cvref_t<MXFlatmmPipeline_>;
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using BlockGemmShape =
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remove_cvref_t<typename MXFlatmmPipeline_::BlockGemmShape>; // TileFlatmmShape
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using EpiloguePipeline = remove_cvref_t<EpiloguePipeline_>;
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using ALayout = remove_cvref_t<typename FlatmmPipeline::ALayout>;
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using BLayout = remove_cvref_t<typename FlatmmPipeline::BLayout>;
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using ELayout = remove_cvref_t<typename FlatmmPipeline::CLayout>;
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using DsLayout = remove_cvref_t<typename EpiloguePipeline::DsLayout>;
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using DsDataType = remove_cvref_t<typename EpiloguePipeline::DsDataType>;
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static constexpr index_t KernelBlockSize = FlatmmPipeline::BlockSize;
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static constexpr bool UsePersistentKernel = FlatmmPipeline::UsePersistentKernel;
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using ADataType = remove_cvref_t<typename FlatmmPipeline::ADataType>;
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using BDataType = remove_cvref_t<typename FlatmmPipeline::BDataType>;
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// Below type is actually accumulation data type - the output of block GEMM.
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using EDataType = remove_cvref_t<typename EpiloguePipeline::ODataType>;
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static constexpr int MThreadPerXdl = BlockGemmShape::WarpTile::at(number<0>{});
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static constexpr int NThreadPerXdl = BlockGemmShape::WarpTile::at(number<1>{});
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static constexpr int KThreadPerXdl = 64 / MThreadPerXdl;
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static constexpr int APackedSize = numeric_traits<ADataType>::PackedSize;
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static constexpr int BPackedSize = numeric_traits<BDataType>::PackedSize;
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static constexpr int MXdlPack = FlatmmPipeline::MXdlPack;
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static constexpr int NXdlPack = FlatmmPipeline::NXdlPack;
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static constexpr int KXdlPack = FlatmmPipeline::KXdlPack;
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static constexpr index_t NumDTensor = DsDataType::size();
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static constexpr auto I0 = number<0>();
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static constexpr auto I1 = number<1>();
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static constexpr auto I2 = number<2>();
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static constexpr auto I3 = number<3>();
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static constexpr auto I4 = number<4>();
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static constexpr auto I5 = number<5>();
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static_assert(DsLayout::size() == DsDataType::size(),
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"The size of DsLayout and DsDataType should be the same");
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// using KernelArgs = FlatmmKernelArgs<DsLayout::size()>;
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[[nodiscard]] CK_TILE_HOST static const std::string GetName()
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{
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// clang-format off
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return concat('_', "mx_flatmm_gemm", gemm_prec_str<ADataType, BDataType>, FlatmmPipeline::GetName());
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// clang-format on
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}
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template <class ScaleM, class ScaleN>
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CK_TILE_HOST static constexpr auto
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GridSize(const FlatmmKernelArgs<ScaleM, ScaleN, DsDataType::size()>& kargs)
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{
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if constexpr(UsePersistentKernel)
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{
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hipDeviceProp_t prop;
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int deviceId = 0; // default device
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constexpr int block_size = MXFlatmmKernel::BlockSize().x;
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int dync_smem_size = 0;
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int maxActiveBlocksPerCU = 0;
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if(hipGetDeviceProperties(&prop, deviceId) != hipSuccess)
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throw std::runtime_error(std::string("hipGetDeviceProperties failed: ") +
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hipGetErrorName(hipGetLastError()));
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if(hipOccupancyMaxActiveBlocksPerMultiprocessor(
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&maxActiveBlocksPerCU,
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reinterpret_cast<void*>(
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kentry<1, MXFlatmmKernel, remove_cvref_t<decltype(kargs)>>),
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block_size,
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dync_smem_size) != hipSuccess)
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throw std::runtime_error(
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std::string("hipOccupancyMaxActiveBlocksPerMultiprocessor failed: ") +
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hipGetErrorName(hipGetLastError()));
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const int persistent_block_size = prop.multiProcessorCount * maxActiveBlocksPerCU;
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const int total_work_tile_cnt = TilePartitioner::GridSize(kargs.M, kargs.N);
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// std::cout << "maxActiveBlocksPerCU: " << maxActiveBlocksPerCU
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// << ", persistent_block_size: " << persistent_block_size
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// << ", total_work_tile_cnt: " << total_work_tile_cnt << std::endl;
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if(kargs.k_batch != 1)
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throw std::runtime_error("Wrong! k_batch != 1 not supported in persistent kernel");
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return dim3(min(persistent_block_size, total_work_tile_cnt), 1, kargs.k_batch);
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}
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else
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{
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return dim3(TilePartitioner::GridSize(kargs.M, kargs.N), 1, kargs.k_batch);
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}
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}
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using SplitKBatchOffset = typename Underlying::SplitKBatchOffset;
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template <memory_operation_enum DstInMemOp = memory_operation_enum::set, class KernelArgs>
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CK_TILE_DEVICE static auto
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MakeGemmTensorViews(const ADataType* a_ptr,
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const BDataType* b_flat_ptr,
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const std::array<const void*, NumDTensor>& ds_ptr,
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EDataType* e_ptr,
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const KernelArgs& kargs,
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const SplitKBatchOffset& splitk_batch_offset)
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{
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const auto& a_tensor_view = [&]() {
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if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
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{
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return make_naive_tensor_view<address_space_enum::global>(
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a_ptr,
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make_tuple(kargs.M, splitk_batch_offset.splitted_k),
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make_tuple(kargs.stride_A, 1),
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number<FlatmmPipeline::GetVectorSizeA()>{},
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number<1>{});
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}
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else
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{
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return make_naive_tensor_view<address_space_enum::global>(
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a_ptr,
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make_tuple(splitk_batch_offset.splitted_k, kargs.M),
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make_tuple(kargs.stride_A, 1),
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number<FlatmmPipeline::GetVectorSizeA()>{},
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number<1>{});
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}
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}();
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index_t kFlatK = kargs.K * BlockGemmShape::WarpTile::at(I1);
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index_t kFlatN = kargs.N * kargs.K / kFlatK;
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const auto& b_flat_tensor_view = [&]() {
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return make_naive_tensor_view<address_space_enum::global>(
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b_flat_ptr,
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make_tuple(kFlatN, kFlatK),
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make_tuple(kFlatK, 1),
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number<FlatmmPipeline::GetVectorSizeB()>{},
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number<1>{});
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}();
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const auto& ds_tensor_view = generate_tuple(
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[&](auto i) {
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using DiLayout = remove_cvref_t<std::tuple_element_t<i.value, DsLayout>>;
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using DDataType_ = remove_cvref_t<std::tuple_element_t<i.value, DsDataType>>;
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if constexpr(std::is_same_v<DiLayout, 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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static_cast<const DDataType_*>(ds_ptr[i]),
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make_tuple(kargs.M, kargs.N),
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make_tuple(kargs.stride_Ds[i], 1),
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number<EpiloguePipeline::GetVectorSizeD(i)>{},
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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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static_cast<const DDataType_*>(ds_ptr[i]),
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make_tuple(kargs.N, kargs.M),
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make_tuple(kargs.stride_Ds[i], 1),
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number<EpiloguePipeline::GetVectorSizeD(i)>{},
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number<1>{});
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}
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},
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number<NumDTensor>{});
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// TODO: enable vector write for C in ColMajor
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const auto& e_tensor_view = [&]() {
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if constexpr(std::is_same_v<ELayout, tensor_layout::gemm::RowMajor>)
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{
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return make_naive_tensor_view<address_space_enum::global, DstInMemOp>(
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e_ptr,
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make_tuple(kargs.M, kargs.N),
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make_tuple(kargs.stride_E, 1),
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number<EpiloguePipeline::GetVectorSizeC()>{},
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number<1>{});
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}
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else
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{
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return make_naive_tensor_view<address_space_enum::global, DstInMemOp>(
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e_ptr,
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make_tuple(kargs.N, kargs.M),
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make_tuple(kargs.stride_E, 1),
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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 scale_a = kargs.scale_m_ptr;
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auto scale_b = kargs.scale_n_ptr;
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static constexpr int BlockScaleSize = 32; // decltype(scale_n)::GranularityK;
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const auto&& scale_packs_m = integer_divide_ceil(kargs.M, (MXdlPack * MThreadPerXdl));
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const auto&& scale_packs_n = integer_divide_ceil(kargs.N, (NXdlPack * NThreadPerXdl));
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const auto&& scale_packs_k = kargs.K / BlockScaleSize / (KXdlPack * KThreadPerXdl);
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// A scale tensor view
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const auto& scale_a_tensor_view = [&]() {
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// Pack 2x2 e8m0 over M/K dimension into 1 int32_t to trigger dword width load
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const auto scale_a_naive_desc = make_naive_tensor_descriptor_packed(
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make_tuple(scale_packs_m, scale_packs_k, KThreadPerXdl, MThreadPerXdl));
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const auto scale_a_desc = transform_tensor_descriptor(
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scale_a_naive_desc,
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make_tuple(make_merge_transform(make_tuple(scale_packs_m, MThreadPerXdl)),
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make_merge_transform(make_tuple(scale_packs_k, KThreadPerXdl))),
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make_tuple(sequence<0, 3>{}, sequence<1, 2>{}),
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make_tuple(sequence<0>{}, sequence<1>{}));
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return make_tensor_view<address_space_enum::global>(
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reinterpret_cast<const int32_t*>(scale_a.ptr), scale_a_desc);
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}();
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// B scale tensor view
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const auto& scale_b_tensor_view = [&]() {
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const auto scale_b_navie_desc = make_naive_tensor_descriptor_packed(
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make_tuple(scale_packs_n, scale_packs_k, KThreadPerXdl, NThreadPerXdl));
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const auto scale_b_desc = transform_tensor_descriptor(
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scale_b_navie_desc,
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make_tuple(make_merge_transform(make_tuple(scale_packs_n, NThreadPerXdl)),
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make_merge_transform(make_tuple(scale_packs_k, KThreadPerXdl))),
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make_tuple(sequence<0, 3>{}, sequence<1, 2>{}),
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make_tuple(sequence<0>{}, sequence<1>{}));
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return make_tensor_view<address_space_enum::global>(
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reinterpret_cast<const int32_t*>(scale_b.ptr), scale_b_desc);
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}();
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return make_tuple(a_tensor_view,
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b_flat_tensor_view,
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ds_tensor_view,
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e_tensor_view,
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scale_a_tensor_view,
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scale_b_tensor_view);
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}
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template <typename TensorView>
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CK_TILE_DEVICE static auto MakeGemmPadViews(const TensorView& views)
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{
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const auto& a_pad_view = [&]() {
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const auto& a_tensor_view = views.at(I0);
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if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
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{
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return pad_tensor_view(a_tensor_view,
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make_tuple(number<TilePartitioner::MPerBlock>{},
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number<TilePartitioner::KPerBlock>{}),
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sequence<false, FlatmmPipeline::kPadK>{});
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}
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else
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{
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return pad_tensor_view(a_tensor_view,
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make_tuple(number<TilePartitioner::KPerBlock>{},
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number<TilePartitioner::MPerBlock>{}),
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sequence<false, FlatmmPipeline::kPadM>{});
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}
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}();
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const auto& b_flat_tensor_view = views.at(I1);
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const auto& ds_pad_view = generate_tuple(
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[&](auto i) {
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const auto& d_tensor_view = views.at(I2);
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using DiLayout = remove_cvref_t<std::tuple_element_t<i.value, DsLayout>>;
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if constexpr(std::is_same_v<DiLayout, tensor_layout::gemm::RowMajor>)
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{
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return pad_tensor_view(d_tensor_view[i],
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make_tuple(number<TilePartitioner::MPerBlock>{},
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number<TilePartitioner::NPerBlock>{}),
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sequence<false, FlatmmPipeline::kPadN>{});
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}
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else
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{
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return pad_tensor_view(d_tensor_view[i],
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make_tuple(number<TilePartitioner::NPerBlock>{},
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number<TilePartitioner::MPerBlock>{}),
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sequence<false, FlatmmPipeline::kPadM>{});
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}
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},
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number<NumDTensor>{});
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// TODO vector write in for C in ColMajor
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const auto& e_pad_view = [&]() {
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const auto& e_tensor_view = views.at(I3);
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if constexpr(std::is_same_v<ELayout, tensor_layout::gemm::RowMajor>)
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{
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return pad_tensor_view(e_tensor_view,
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make_tuple(number<TilePartitioner::MPerBlock>{},
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number<TilePartitioner::NPerBlock>{}),
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sequence<false, FlatmmPipeline::kPadN>{});
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}
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else
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{
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return pad_tensor_view(e_tensor_view,
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make_tuple(number<TilePartitioner::MPerBlock>{},
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number<TilePartitioner::NPerBlock>{}),
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sequence<FlatmmPipeline::kPadM, false>{});
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}
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}();
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return make_tuple(
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a_pad_view, b_flat_tensor_view, ds_pad_view, e_pad_view, views.at(I4), views.at(I5));
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}
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template <typename PadView>
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CK_TILE_DEVICE static auto
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MakeGemmTileWindows(const PadView& views, const index_t i_m, const index_t i_n)
|
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{
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const auto& a_pad_view = views.at(I0);
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const auto& b_flat_pad_view = views.at(I1);
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const auto& ds_pad_view = views.at(I2);
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const auto& e_pad_view = views.at(I3);
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||||
|
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const auto& a_block_window = [&]() {
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if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
|
||||
{
|
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return make_tile_window(a_pad_view,
|
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make_tuple(number<TilePartitioner::MPerBlock>{},
|
||||
number<TilePartitioner::KPerBlock>{}),
|
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{i_m, 0});
|
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}
|
||||
else
|
||||
{
|
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return make_tile_window(a_pad_view,
|
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make_tuple(number<TilePartitioner::KPerBlock>{},
|
||||
number<TilePartitioner::MPerBlock>{}),
|
||||
{0, i_m});
|
||||
}
|
||||
}();
|
||||
|
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const auto& b_flat_block_window =
|
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make_tile_window(b_flat_pad_view,
|
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make_tuple(number<FlatmmPipeline::flatNPerWarp>{},
|
||||
number<FlatmmPipeline::flatKPerWarp>{}),
|
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{static_cast<int>(i_n / BlockGemmShape::WarpTile::at(I1)), 0});
|
||||
|
||||
const auto ds_block_window = generate_tuple(
|
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[&](auto i) {
|
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using DiLayout = remove_cvref_t<std::tuple_element_t<i.value, DsLayout>>;
|
||||
if constexpr(std::is_same_v<DiLayout, tensor_layout::gemm::RowMajor>)
|
||||
{
|
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return make_tile_window(ds_pad_view[i],
|
||||
make_tuple(number<TilePartitioner::MPerBlock>{},
|
||||
number<TilePartitioner::NPerBlock>{}),
|
||||
{i_m, i_n});
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_tile_window(ds_pad_view[i],
|
||||
make_tuple(number<TilePartitioner::NPerBlock>{},
|
||||
number<TilePartitioner::MPerBlock>{}),
|
||||
{i_n, i_m});
|
||||
}
|
||||
},
|
||||
number<NumDTensor>{});
|
||||
|
||||
auto e_block_window = make_tile_window(
|
||||
e_pad_view,
|
||||
make_tuple(number<TilePartitioner::MPerBlock>{}, number<TilePartitioner::NPerBlock>{}),
|
||||
{i_m, i_n});
|
||||
|
||||
static constexpr int BlockScaleSize = 32;
|
||||
|
||||
auto scale_a_block_window = make_tile_window(
|
||||
views.at(I4),
|
||||
make_tuple(number<TilePartitioner::MPerBlock / MXdlPack>{},
|
||||
number<TilePartitioner::KPerBlock / (BlockScaleSize * KXdlPack)>{}),
|
||||
{i_m / MXdlPack, 0});
|
||||
|
||||
auto scale_b_block_window = make_tile_window(
|
||||
views.at(I5),
|
||||
make_tuple(number<TilePartitioner::NPerBlock / NXdlPack>{},
|
||||
number<TilePartitioner::KPerBlock / (BlockScaleSize * KXdlPack)>{}),
|
||||
{i_n / NXdlPack, 0});
|
||||
|
||||
return make_tuple(a_block_window,
|
||||
b_flat_block_window,
|
||||
ds_block_window,
|
||||
e_block_window,
|
||||
scale_a_block_window,
|
||||
scale_b_block_window);
|
||||
}
|
||||
|
||||
template <class ScaleM, class ScaleN, bool UseDefaultScheduler = true>
|
||||
CK_TILE_DEVICE static void
|
||||
RunFlatmm(const ADataType* a_ptr,
|
||||
const BDataType* b_flat_ptr,
|
||||
const std::array<const void*, NumDTensor>& ds_ptr,
|
||||
EDataType* e_ptr,
|
||||
void* smem_ptr_ping,
|
||||
void* smem_ptr_pong,
|
||||
const FlatmmKernelArgs<ScaleM, ScaleN, DsDataType::size()>& kargs,
|
||||
const SplitKBatchOffset& splitk_batch_offset,
|
||||
const index_t block_idx_m,
|
||||
const index_t block_idx_n)
|
||||
{
|
||||
// Create Gemm tensor views, pad views and tile windows
|
||||
const auto& gemm_tensor_views_tuple =
|
||||
MakeGemmTensorViews<EpiloguePipeline::MemoryOperation>(
|
||||
a_ptr, b_flat_ptr, ds_ptr, e_ptr, kargs, splitk_batch_offset);
|
||||
const auto& gemm_pad_views = MakeGemmPadViews(gemm_tensor_views_tuple);
|
||||
auto gemm_tile_windows = MakeGemmTileWindows(gemm_pad_views, block_idx_m, block_idx_n);
|
||||
|
||||
const index_t num_loop = TilePartitioner::GetLoopNum(splitk_batch_offset.splitted_k);
|
||||
|
||||
// Run GEMM cooperatively by whole workgroup.
|
||||
const auto& a_block_window = gemm_tile_windows.at(I0);
|
||||
const auto& b_flat_block_window = gemm_tile_windows.at(I1);
|
||||
const auto& d_block_window = gemm_tile_windows.at(I2);
|
||||
const auto& scale_a_block_window = gemm_tile_windows.at(I4);
|
||||
const auto& scale_b_block_window = gemm_tile_windows.at(I5);
|
||||
|
||||
static_assert(ScaleM::GranularityK == ScaleN::GranularityK // have the same granK
|
||||
|| ScaleM::GranularityMN == -1 // or ScaleA is disable
|
||||
|| ScaleN::GranularityMN == -1, // or ScaleB is disable
|
||||
"ScaleM and ScaleN should have the same GranularityK");
|
||||
constexpr bool DoEpiScale =
|
||||
(ScaleM::GranularityMN != -1 && ScaleM::GranularityK == 0) || // per token
|
||||
(ScaleN::GranularityMN != -1 && ScaleN::GranularityK == 0); // per channel
|
||||
|
||||
auto a_block_window_with_distr =
|
||||
ck_tile::make_tile_window(a_block_window.get_bottom_tensor_view(),
|
||||
a_block_window.get_window_lengths(),
|
||||
a_block_window.get_window_origin(),
|
||||
FlatmmPipeline::GetADramTileDistribution());
|
||||
const auto& c_block_tile = FlatmmPipeline{}(a_block_window_with_distr,
|
||||
b_flat_block_window,
|
||||
scale_a_block_window,
|
||||
scale_b_block_window,
|
||||
num_loop,
|
||||
smem_ptr_ping,
|
||||
smem_ptr_pong);
|
||||
|
||||
// Run Epilogue Pipeline
|
||||
if constexpr(DoEpiScale)
|
||||
{
|
||||
auto& c_block_window = gemm_tile_windows.at(I3);
|
||||
EpiloguePipeline{}(c_block_window,
|
||||
c_block_tile,
|
||||
d_block_window,
|
||||
smem_ptr_ping,
|
||||
kargs.scale_m_ptr + block_idx_m,
|
||||
kargs.scale_n_ptr + block_idx_n);
|
||||
}
|
||||
else if(UseDefaultScheduler || (get_warp_id() == 0))
|
||||
{
|
||||
// Run Epilogue Pipeline
|
||||
auto& c_block_window = gemm_tile_windows.at(I3);
|
||||
EpiloguePipeline{}(c_block_window, c_block_tile, d_block_window, smem_ptr_ping);
|
||||
}
|
||||
}
|
||||
|
||||
template <class ScaleM, class ScaleN>
|
||||
CK_TILE_DEVICE void operator()(FlatmmKernelArgs<ScaleM, ScaleN, DsDataType::size()> kargs,
|
||||
int partition_idx = blockIdx.x) const
|
||||
{
|
||||
int total_work_tile_cnt = TilePartitioner::GridSize(kargs.M, kargs.N);
|
||||
|
||||
do
|
||||
{
|
||||
const auto [iM, iN] =
|
||||
TilePartitioner{kargs.M, kargs.N}.GetOutputTileIndex(partition_idx);
|
||||
const index_t i_m = amd_wave_read_first_lane(iM * TilePartitioner::MPerBlock);
|
||||
const index_t i_n = amd_wave_read_first_lane(iN * TilePartitioner::NPerBlock);
|
||||
|
||||
const SplitKBatchOffset splitk_batch_offset(kargs);
|
||||
// options
|
||||
const ADataType* a_ptr = static_cast<const ADataType*>(kargs.a_ptr) +
|
||||
splitk_batch_offset.a_k_split_offset / APackedSize;
|
||||
const BDataType* b_flat_ptr = static_cast<const BDataType*>(kargs.b_ptr) +
|
||||
splitk_batch_offset.b_k_split_offset / BPackedSize;
|
||||
EDataType* e_ptr = static_cast<EDataType*>(kargs.e_ptr);
|
||||
|
||||
// allocate LDS
|
||||
__shared__ char smem_ptr_ping[Underlying::GetSmemPingSize()];
|
||||
__shared__ char smem_ptr_pong[Underlying::GetSmemPongSize()];
|
||||
|
||||
if constexpr(!(EpiloguePipeline::MemoryOperation == memory_operation_enum::atomic_add &&
|
||||
EpiloguePipeline::GetVectorSizeC() % 2 != 0 &&
|
||||
is_any_of<EDataType, fp16_t, bf16_t>::value))
|
||||
{
|
||||
constexpr auto scheduler_type = (FlatmmPipeline::NumWaveGroups == 1);
|
||||
RunFlatmm<ScaleM, ScaleN, scheduler_type>(a_ptr,
|
||||
b_flat_ptr,
|
||||
kargs.ds_ptr,
|
||||
e_ptr,
|
||||
smem_ptr_ping,
|
||||
smem_ptr_pong,
|
||||
kargs,
|
||||
splitk_batch_offset,
|
||||
i_m,
|
||||
i_n);
|
||||
}
|
||||
else
|
||||
{
|
||||
static_assert(false,
|
||||
"Unimplemented: atomic_add with odd vector size for fp16/bf16");
|
||||
}
|
||||
partition_idx += gridDim.x;
|
||||
} while(UsePersistentKernel && partition_idx < total_work_tile_cnt);
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace ck_tile
|
||||
Reference in New Issue
Block a user