mirror of
https://github.com/ROCm/composable_kernel.git
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211d64e18a
--------- Co-authored-by: Ding, Yi <yi.ding@amd.com> Co-authored-by: felix <felix.li@amd.com>
203 lines
6.9 KiB
C++
203 lines
6.9 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <string>
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#include <tuple>
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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/moe_flatmm.hpp"
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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 = 64;
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static constexpr ck_tile::index_t N_Tile = 256;
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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 = 1;
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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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static constexpr bool TiledMMAPermuteN = false; // disable PermuteN when NWarpTile != 16
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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 = 64;
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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 = 1;
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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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static constexpr int N_Repeat = N_Tile / N_Warp_Tile / N_Warp;
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static constexpr bool TiledMMAPermuteN = 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 N_Tile = 256;
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static constexpr ck_tile::index_t K_Tile = 256 / sizeof(DataType);
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static constexpr ck_tile::index_t K_Warp_Tile = sizeof(DataType) == 2 ? 32 : 128;
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static constexpr int kBlockPerCu = 1;
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static constexpr int N_Repeat =
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N_Tile / FlatmmConfig16<DataType>::N_Warp_Tile / FlatmmConfig16<DataType>::N_Warp;
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static constexpr bool TiledMMAPermuteN = false; // N_Repeat % 2 == 0;
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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("experts", "8", "Num of experts - 8 by default")
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.insert("NumTokens", "128", "M dimensions - 128 by default.")
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.insert("TopK", "3", "Top K - 3 by default.")
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.insert("N", "4096", "N dimensions - 4096 by default.")
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.insert("K", "4096", "K dimensions - 4096 by default.")
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.insert("stride_A", "", "Tensor A strides - it is empty by default.")
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.insert("stride_B", "", "Tensor B strides - it is empty by default.")
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.insert("stride_C", "", "Tensor C strides - it is empty by default.")
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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 - Col by default.")
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.insert("c_layout", "R", "C tensor data layout - Row by default.")
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.insert("gemm_kind",
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"gemm1_gate_only",
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"Gemm kind in FFN network [gemm1_gate_only | gemm1_gate_up | gemm2] - "
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"gemm1_gate_only by default.")
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.insert("validate", "1", "0. No validation, 1. Validation on CPU.")
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.insert("warmup", "50", "number of iterations before benchmark the kernel")
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.insert("prec", "fp16", "data type. fp16/bf16/fp8/bf8")
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.insert(
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"warp_tile", "0", "0: 16x16, 1: 32x32, 2: 16x16x128 (950 only), 3: 32x32x64 (950 only)")
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.insert("repeat", "10", "number of iterations to benchmark the kernel.");
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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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