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https://github.com/ROCm/composable_kernel.git
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eaf1f0bf3b
* [flatmm] implement basic fp16 flatmm * fix CI build fail --------- Co-authored-by: root <root@hjbog-srdc-50.amd.com> Co-authored-by: solin <bingzhou@amd.com>
101 lines
3.1 KiB
C++
101 lines
3.1 KiB
C++
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <string>
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#include "ck_tile/core.hpp"
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#include "ck_tile/host/kernel_launch.hpp"
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#include "ck_tile/ops/epilogue.hpp"
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#include "ck_tile/ops/flatmm.hpp"
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#include "ck_tile/ops/gemm.hpp"
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#define CK_TILE_PIPELINE_COMPUTE 1
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#define CK_TILE_PIPELINE_MEMORY 2
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#ifndef CK_TILE_PIPELINE_DEFAULT
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#define CK_TILE_PIPELINE_DEFAULT CK_TILE_PIPELINE_COMPUTE
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#endif
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#if(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_MEMORY)
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#define GEMM_PIPELINE ck_tile::GemmPipelineAgBgCrMem
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#define UNIVERSAL_GEMM_PIPELINE ck_tile::BaseGemmPipelineAgBgCrMem
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#define GEMM_PIPELINE_SCHEDULER ck_tile::GemmPipelineScheduler::Interwave
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#elif(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_COMPUTE)
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#define GEMM_PIPELINE ck_tile::GemmPipelineAgBgCrCompV3
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#define UNIVERSAL_GEMM_PIPELINE ck_tile::BaseGemmPipelineAgBgCrCompV3
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#define GEMM_PIPELINE_SCHEDULER ck_tile::GemmPipelineScheduler::Intrawave
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#else
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#error "unsupported CK_TILE_PIPELINE_DEFAULT value"
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#endif
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template <typename DataType>
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struct GemmBasicTypeConfig;
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template <>
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struct GemmBasicTypeConfig<ck_tile::half_t>
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{
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using ADataType = ck_tile::half_t;
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using BDataType = ck_tile::half_t;
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using AccDataType = float;
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using CDataType = ck_tile::half_t;
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// ToDo: Add more bias config to support different categories of GEMM.
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};
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template <typename T>
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struct DataTypeTraits;
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template <>
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struct DataTypeTraits<float>
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{
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static constexpr const char* name = "fp32";
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};
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template <>
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struct DataTypeTraits<double>
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{
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static constexpr const char* name = "fp64";
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};
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template <>
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struct DataTypeTraits<ck_tile::half_t>
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{
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static constexpr const char* name = "fp16";
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};
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using Types = GemmBasicTypeConfig<ck_tile::half_t>;
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// Specific type aliases for easy access
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using ADataType = Types::ADataType;
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using BDataType = Types::BDataType;
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using AccDataType = Types::AccDataType;
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using CDataType = Types::CDataType;
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auto create_args(int argc, char* argv[])
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{
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ck_tile::ArgParser arg_parser;
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arg_parser.insert("m", "256", "m dimension")
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.insert("n", "256", "n dimension")
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.insert("k", "128", "k dimension")
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.insert("a_layout", "R", "A tensor data layout - Row by default")
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.insert("b_layout", "C", "B tensor data layout - Row by default")
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.insert("c_layout", "R", "C tensor data layout - Row by default")
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.insert("stride_a", "0", "Tensor A stride")
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.insert("stride_b", "0", "Tensor B stride")
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.insert("stride_c", "0", "Tensor C stride")
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.insert("v", "1", "0. No validation, 1. Validation on CPU, 2. Validation on GPU")
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.insert("prec", "fp16", "data type. fp16/bf16/fp8/bf8")
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.insert("warmup", "50", "number of iterations before benchmark the kernel")
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.insert("repeat", "100", "number of iterations to benchmark the kernel")
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.insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer")
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.insert("split_k", "1", "splitK value");
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bool result = arg_parser.parse(argc, argv);
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return std::make_tuple(result, arg_parser);
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}
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// host API
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float flatmm_calc(const ck_tile::FlatmmHostArgs& args, const ck_tile::stream_config& s);
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