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d460ab35b6
[CK_TILE] add tf32 support MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Proposed changes TF32 is added in CK on gfx942 and gfx950. This PR is to initiate tf32 in CK_TILE on gfx942 and gfx950. ## Checklist Please put an into the boxes that apply. You can also fill these out after creating the PR. If you're not sure, please don't hesitate to ask. - [ ] I have added tests relevant to the introduced functionality, and the unit tests are passing locally - [ ] I have added the test to REGRESSION_TESTS list defined at the top of CMakeLists.txt in tests/CMakeLists.txt, **IF** the test takes more than 30 seconds to run. - [ ] I have added inline documentation which enables the maintainers with understanding the motivation - [ ] I have removed the stale documentation which is no longer relevant after this pull request - [ ] (If this change is user-facing) I have added release notes which provide the end users with a brief summary of the improvement from this pull request - [x] I have run on all changed files - [ ] Any dependent changes have been merged ## Discussion
184 lines
8.2 KiB
C++
184 lines
8.2 KiB
C++
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#pragma once
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#include "gemm_utils.hpp"
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struct BasicInvoker
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{
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template <typename GemmConfig,
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typename ADataType_,
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typename BDataType_,
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typename DsDataType,
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typename AccDataType,
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typename CDataType,
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typename ALayout,
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typename BLayout,
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typename DsLayout,
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typename CLayout,
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bool Persistent,
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typename CDEElementWise>
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static float gemm(const ck_tile::GemmHostArgs& args, const ck_tile::stream_config& s)
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{
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// ADataTypeCompute: compute type (tf32_t for TF32 mode, used for warp gemm selection)
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// ADataTypeBuf: buffer/storage type (fp32 when tf32)
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using ADataTypeCompute = ADataType_;
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using BDataTypeCompute = BDataType_;
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using ADataTypeBuf = ck_tile::if_select_t<ADataType_, ck_tile::tf32_t, float, ADataType_>;
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using BDataTypeBuf = ck_tile::if_select_t<BDataType_, ck_tile::tf32_t, float, BDataType_>;
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if constexpr(std::is_same_v<ADataTypeCompute, ck_tile::tf32_t>)
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{
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static_assert(std::is_same_v<ADataTypeCompute, BDataTypeCompute>,
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"ADataTypeCompute and BDataTypeCompute must be the same");
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}
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if constexpr(Persistent)
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{
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std::cout << "WARNING: Ignoring persistent kernel option for basic gemm." << std::endl;
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}
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constexpr bool is_fp32_input = std::is_same_v<ADataTypeBuf, float>;
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constexpr bool is_tf32_compute = std::is_same_v<ADataTypeCompute, ck_tile::tf32_t>;
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// This part comes from the Codegen
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constexpr ck_tile::index_t M_Tile = is_fp32_input ? 128 : 256;
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constexpr ck_tile::index_t N_Tile = is_fp32_input ? 128 : 256;
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constexpr ck_tile::index_t K_Tile = 64;
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#if CK_TILE_USE_WMMA
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constexpr ck_tile::index_t M_Warp = 4;
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constexpr ck_tile::index_t N_Warp = 2;
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constexpr ck_tile::index_t K_Warp = 1;
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constexpr ck_tile::index_t M_Warp_Tile = 16;
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constexpr ck_tile::index_t N_Warp_Tile = 16;
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constexpr ck_tile::index_t K_Warp_Tile = 16;
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#else
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// gfx950: fp32 uses 16x16x16 tile (native MFMA)
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// tf32 uses 32x32x16 tile (3x bf16 32x32x16 MFMA emulation)
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constexpr ck_tile::index_t M_Warp = (is_fp32_input && !is_tf32_compute) ? 4 : 2;
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constexpr ck_tile::index_t N_Warp = (is_fp32_input && !is_tf32_compute) ? 4 : 2;
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constexpr ck_tile::index_t K_Warp = 1;
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constexpr ck_tile::index_t M_Warp_Tile = (is_fp32_input && !is_tf32_compute) ? 16 : 32;
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constexpr ck_tile::index_t N_Warp_Tile = (is_fp32_input && !is_tf32_compute) ? 16 : 32;
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constexpr ck_tile::index_t K_Warp_Tile = 16;
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#endif
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using CodegenGemmShape =
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ck_tile::TileGemmShape<ck_tile::sequence<M_Tile, N_Tile, K_Tile>,
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ck_tile::sequence<M_Warp, N_Warp, K_Warp>,
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ck_tile::sequence<M_Warp_Tile, N_Warp_Tile, K_Warp_Tile>>;
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using TilePartitioner = ck_tile::GemmTile1DPartitioner<CodegenGemmShape>;
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using CodegenGemmTraits = ck_tile::TileGemmTraits<GemmConfig::kPadM,
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GemmConfig::kPadN,
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GemmConfig::kPadK,
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ALayout,
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BLayout,
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CLayout>;
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using CodegenPipelineProblem =
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ck_tile::GemmPipelineProblem<ADataTypeBuf,
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BDataTypeBuf,
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AccDataType,
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CodegenGemmShape,
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CodegenGemmTraits,
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ck_tile::element_wise::PassThrough,
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ck_tile::element_wise::PassThrough,
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ADataTypeCompute>;
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using CodegenGemmPipeline = ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem>;
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using GemmEpilogue = ck_tile::CShuffleEpilogue<
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ck_tile::CShuffleEpilogueProblem<ADataTypeCompute,
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BDataTypeCompute,
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ck_tile::tuple<>,
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AccDataType,
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CDataType,
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ck_tile::tuple<>,
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CLayout,
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ck_tile::element_wise::PassThrough,
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TilePartitioner::MPerBlock,
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TilePartitioner::NPerBlock,
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M_Warp,
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N_Warp,
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M_Warp_Tile,
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N_Warp_Tile,
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K_Warp_Tile,
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CodegenPipelineProblem::TransposeC>>;
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// ToDo: Will add the codegen part to test different pipeline policies in GEMM.
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// Now we only use the BlockGemmASmemBSmemCRegV1DefaultPolicy.
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using Kernel = ck_tile::GemmKernel<TilePartitioner, CodegenGemmPipeline, GemmEpilogue>;
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auto kargs = Kernel::MakeKernelArgs(args);
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const dim3 grids = Kernel::GridSize(args.M, args.N, args.k_batch);
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const dim3 blocks = Kernel::BlockSize();
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if(!Kernel::IsSupportedArgument(kargs))
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{
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throw std::runtime_error("Wrong! Arguments not supported! Skipping gemm!\n");
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}
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if(s.log_level_ > 0)
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{
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std::cout << "Launching kernel with args: " << Kernel::GetName() << '\n'
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<< "shape: " << CodegenGemmShape::GetName() << '\n'
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<< "problem: " << CodegenPipelineProblem::GetName() << '\n'
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<< "pipeline: " << CodegenGemmPipeline::GetName() << '\n'
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<< "grid: {" << grids.x << ", " << grids.y << ", " << grids.z << "}"
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<< ", blocks: {" << blocks.x << ", " << blocks.y << ", " << blocks.z << "}"
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<< std::endl;
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}
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// Declare rotating_mem_ptr here so it stays in scope until it is needed
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std::unique_ptr<ck_tile::RotatingMemWrapper<ADataTypeBuf, BDataTypeBuf>> rotating_mem_ptr;
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std::function<void()> preprocess;
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auto clear_gemm_output = [&]() {
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if(args.k_batch > 1)
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hipGetErrorString(hipMemsetAsync(
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args.e_ptr, 0, args.M * args.N * sizeof(CDataType), s.stream_id_));
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};
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if(s.flush_cache_)
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{
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std::cout << "Flushing cache..." << std::endl;
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ck_tile::HostTensor<ADataTypeBuf> a_m(ck_tile::host_tensor_descriptor(
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args.M, args.K, args.stride_A, is_row_major(ALayout{})));
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ck_tile::HostTensor<BDataTypeBuf> b_n(ck_tile::host_tensor_descriptor(
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args.K, args.N, args.stride_B, is_row_major(BLayout{})));
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auto size_a_buffer = a_m.get_element_space_size_in_bytes();
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auto size_b_buffer = b_n.get_element_space_size_in_bytes();
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rotating_mem_ptr =
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std::make_unique<ck_tile::RotatingMemWrapper<ADataTypeBuf, BDataTypeBuf>>(
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kargs.as_ptr[0],
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kargs.bs_ptr[0],
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s.rotating_count_,
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size_a_buffer,
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size_b_buffer);
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rotating_mem_ptr->Print();
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preprocess = [&]() {
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ck_tile::flush_icache();
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rotating_mem_ptr->Next();
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clear_gemm_output();
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};
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}
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else
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{
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preprocess = clear_gemm_output;
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}
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return ck_tile::launch_kernel_time_mask(
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s,
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preprocess,
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ck_tile::make_kernel<GemmConfig::kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
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}
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};
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