mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-03 05:01:25 +00:00
d239b91fd5
* Initial commit * Adding new tile partitioner to flatmm * intermediate changes * debugging kernels * Updating flatmm example to universal gemm example * updated flatmm kernel to run via gemmKernel * update universal gemm to incorporate flatmm * debug * Fix flatmm call * Fixing other kernels and tests for API changes * clang formatted * fixing gemm tests * added test for flatmm and simplify kernel arguments * adding flatmm test * fix test for flatmm * simplify gemm kernel with flatmm * remove flatmm related files * addressing review comments and code clean up * resolving empty file * resolving empty file * clang formatted * addressing review comments * enable persistent kernel for flatmm * reverted the removed files for flatmm * reverted the removed files for flatmm * changed flatmm to weightPReshuffle; removed the _1 added in teh faltmm example * some more renames * clang formatted
203 lines
6.4 KiB
C++
203 lines
6.4 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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// GEMM config with 32x132 warp tile
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template <typename DataType>
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struct FlatmmConfig32
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{
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static constexpr ck_tile::index_t M_Tile = 128;
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static constexpr ck_tile::index_t N_Tile = 128;
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static constexpr ck_tile::index_t K_Tile = 128 / sizeof(DataType);
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static constexpr ck_tile::index_t M_Warp = 1;
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static constexpr ck_tile::index_t N_Warp = 4;
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static constexpr ck_tile::index_t K_Warp = 1;
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static constexpr ck_tile::index_t M_Warp_Tile = 32;
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static constexpr ck_tile::index_t N_Warp_Tile = 32;
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static constexpr ck_tile::index_t K_Warp_Tile = sizeof(DataType) == 2 ? 16 : 32;
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static constexpr bool kPadM = false;
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static constexpr bool kPadN = false;
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static constexpr bool kPadK = false;
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static constexpr bool TransposeC = false;
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static constexpr bool UseStructuredSparsity = false;
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static constexpr int kBlockPerCu = 2;
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static constexpr int TileParitionerGroupNum = 8;
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static constexpr int TileParitionerM01 = 4;
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static constexpr auto Scheduler = ck_tile::GemmPipelineScheduler::Default;
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static constexpr ck_tile::index_t NumWaveGroups = 1;
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static constexpr bool DoubleSmemBuffer = false;
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};
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template <typename DataType>
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struct FlatmmConfig32_950 : public FlatmmConfig32<DataType>
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{
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static constexpr ck_tile::index_t K_Warp_Tile = sizeof(DataType) == 2 ? 16 : 64;
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};
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// GEMM config with 16x16 warp tile
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template <typename DataType>
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struct FlatmmConfig16
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{
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static constexpr ck_tile::index_t M_Tile = 128;
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static constexpr ck_tile::index_t N_Tile = 128;
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static constexpr ck_tile::index_t K_Tile = 128 / sizeof(DataType);
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static constexpr ck_tile::index_t M_Warp = 1;
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static constexpr ck_tile::index_t N_Warp = 4;
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static constexpr ck_tile::index_t K_Warp = 1;
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static constexpr ck_tile::index_t M_Warp_Tile = 16;
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static constexpr ck_tile::index_t N_Warp_Tile = 16;
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static constexpr ck_tile::index_t K_Warp_Tile = sizeof(DataType) == 2 ? 32 : 64;
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static constexpr bool kPadM = false;
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static constexpr bool kPadN = false;
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static constexpr bool kPadK = false;
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static constexpr bool TransposeC = false;
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static constexpr bool UseStructuredSparsity = false;
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static constexpr int kBlockPerCu = 2;
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static constexpr int TileParitionerGroupNum = 8;
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static constexpr int TileParitionerM01 = 4;
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static constexpr auto Scheduler = ck_tile::GemmPipelineScheduler::Default;
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static constexpr ck_tile::index_t NumWaveGroups = 1;
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static constexpr bool DoubleSmemBuffer = false;
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};
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template <typename DataType>
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struct FlatmmConfig16_950 : public FlatmmConfig16<DataType>
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{
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static constexpr ck_tile::index_t K_Warp_Tile = sizeof(DataType) == 2 ? 32 : 128;
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};
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template <typename ADataType>
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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 <>
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struct GemmBasicTypeConfig<ck_tile::bf16_t>
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{
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using ADataType = ck_tile::bf16_t;
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using BDataType = ck_tile::bf16_t;
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using AccDataType = float;
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using CDataType = ck_tile::bf16_t;
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};
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template <>
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struct GemmBasicTypeConfig<ck_tile::fp8_t>
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{
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using ADataType = ck_tile::fp8_t;
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using BDataType = ck_tile::fp8_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 <>
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struct GemmBasicTypeConfig<ck_tile::bf8_t>
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{
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using ADataType = ck_tile::bf8_t;
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using BDataType = ck_tile::bf8_t;
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using AccDataType = float;
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using CDataType = ck_tile::half_t;
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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<ck_tile::fp8_t>
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{
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static constexpr const char* name = "fp8";
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};
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template <>
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struct DataTypeTraits<ck_tile::bf8_t>
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{
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static constexpr const char* name = "bf8";
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};
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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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template <typename T>
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struct is_8bit_type
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: std::bool_constant<std::is_same_v<T, ck_tile::fp8_t> || std::is_same_v<T, ck_tile::bf8_t>>
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{
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};
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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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.insert("init", "0", "0:random, 1:linear, 2:constant(1)")
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.insert("warp_tile",
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"0",
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"0: 16x16, 1: 32x32, 2: 16x16x128 (950 only), 3: 32x32x64 (950 only)");
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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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template <typename ADataType,
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typename BDataType,
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typename AccDataType,
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typename CDataType,
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typename FlatmmConfig,
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typename ALayout,
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typename BLayout,
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typename CLayout>
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float flatmm_calc(const ck_tile::FlatmmHostArgs<>& args, const ck_tile::stream_config& s);
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