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debug mixed_prec flatmm
This commit is contained in:
Feng Shijie
89
example/ck_tile/18_flatmm/mixed_input/a16w4_flatmm.hpp
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89
example/ck_tile/18_flatmm/mixed_input/a16w4_flatmm.hpp
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@@ -0,0 +1,89 @@
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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 "ck_tile/core.hpp"
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template <typename DataType>
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struct A16W4_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 = 64;
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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 = 16;
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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 A16W4_FlatmmConfig32_950 : public A16W4_FlatmmConfig32<DataType>
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{
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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 A16W4_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 = 64;
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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 = 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 int N_Repeat = N_Tile / N_Warp_Tile / N_Warp;
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static constexpr bool TiledMMAPermuteN = N_Repeat % 2 == 0;
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};
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template <typename DataType>
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struct A16W4_FlatmmConfig16_950 : public A16W4_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 = 128;
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static constexpr int kBlockPerCu = 1;
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static constexpr int N_Repeat = N_Tile / A16W4_FlatmmConfig16<DataType>::N_Warp_Tile /
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A16W4_FlatmmConfig16<DataType>::N_Warp;
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static constexpr bool TiledMMAPermuteN = N_Repeat % 2 == 0;
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};
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441
example/ck_tile/18_flatmm/mixed_input/mixed_prec_flatmm.cpp
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441
example/ck_tile/18_flatmm/mixed_input/mixed_prec_flatmm.cpp
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@@ -0,0 +1,441 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
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#include <hip/hip_runtime.h>
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#include <cstring>
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#include <iostream>
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#include <ostream>
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#include <string>
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#include <tuple>
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#include <type_traits>
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#include "ck_tile/host.hpp"
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#include "mixed_prec_flatmm.hpp"
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template <typename Layout>
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static constexpr inline auto is_row_major(Layout layout_)
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{
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return ck_tile::bool_constant<std::is_same_v<ck_tile::remove_cvref_t<decltype(layout_)>,
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ck_tile::tensor_layout::gemm::RowMajor>>{};
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}
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template <typename FlatmmConfig,
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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 ELayout,
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typename ScaleM,
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typename ScaleN,
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bool persistent,
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typename CDEElementWise>
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float mixed_prec_flatmm_calc(const ck_tile::ScaleFlatmmHostArgs<ScaleM, ScaleN>& args,
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const ck_tile::stream_config& s)
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{
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using CodegenFlatmmShape = ck_tile::TileGemmShape<
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ck_tile::sequence<FlatmmConfig::M_Tile, FlatmmConfig::N_Tile, FlatmmConfig::K_Tile>,
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ck_tile::sequence<FlatmmConfig::M_Warp, FlatmmConfig::N_Warp, FlatmmConfig::K_Warp>,
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ck_tile::sequence<FlatmmConfig::M_Warp_Tile,
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FlatmmConfig::N_Warp_Tile,
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FlatmmConfig::K_Warp_Tile>>;
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using TilePartitioner =
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ck_tile::GemmSpatiallyLocalTilePartitioner<CodegenFlatmmShape,
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FlatmmConfig::TileParitionerGroupNum,
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FlatmmConfig::TileParitionerM01>;
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using Traits = ck_tile::TileGemmTraits<FlatmmConfig::kPadM,
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FlatmmConfig::kPadN,
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FlatmmConfig::kPadK,
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ALayout,
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BLayout,
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ELayout,
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FlatmmConfig::NumWaveGroups>;
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using CodegenGemmTraits = ck_tile::TileGemmUniversalTraits<FlatmmConfig::kPadM,
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FlatmmConfig::kPadN,
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FlatmmConfig::kPadK,
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FlatmmConfig::DoubleSmemBuffer,
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ALayout,
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BLayout,
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ELayout,
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FlatmmConfig::TransposeC,
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FlatmmConfig::UseStructuredSparsity,
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persistent,
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FlatmmConfig::NumWaveGroups,
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true>;
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using ComputeDataType = ADataType;
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static_assert(sizeof(ComputeDataType) >= sizeof(BDataType),
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"mixed_prec_flatmm requires ADataType is a wider type than BDataType");
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using GemmPipelineProblem = ck_tile::GemmPipelineProblem<ComputeDataType,
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ComputeDataType,
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AccDataType,
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CodegenFlatmmShape,
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Traits>;
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using BaseGemmPipeline = ck_tile::BaseFlatmmPipelineAGmemBGmemCRegV1<GemmPipelineProblem>;
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const ck_tile::index_t k_grain = args.k_batch * FlatmmConfig::K_Tile;
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const ck_tile::index_t K_split = (args.K + k_grain - 1) / k_grain * FlatmmConfig::K_Tile;
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const ck_tile::index_t num_loop = TilePartitioner::GetLoopNum(K_split);
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const bool has_hot_loop = BaseGemmPipeline::BlockHasHotloop(num_loop);
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const ck_tile::TailNumber tail_num = BaseGemmPipeline::GetBlockLoopTailNum(num_loop);
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float ave_time{0};
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const auto Run = [&](const auto has_hot_loop_,
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const auto tail_number_,
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const auto memory_operation_) {
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constexpr bool has_hot_loop_v = has_hot_loop_.value;
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constexpr auto tail_number_v = tail_number_.value;
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constexpr auto scheduler = FlatmmConfig::Scheduler;
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constexpr auto memory_operation = memory_operation_.value;
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using CodegenPipelineProblem = ck_tile::MixedPrecFlatmmPipelineProblem<ADataType,
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BDataType,
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AccDataType,
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CodegenFlatmmShape,
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CodegenGemmTraits,
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scheduler,
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has_hot_loop_v,
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tail_number_v>;
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using CodegenFlatmmPipeline =
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ck_tile::FlatmmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem>;
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using GemmEpilogue = ck_tile::CShuffleEpilogue<
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ck_tile::CShuffleEpilogueProblem<ComputeDataType,
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ComputeDataType,
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DsDatatype,
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AccDataType,
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CDataType,
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DsLayout,
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ELayout,
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CDEElementWise,
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CodegenPipelineProblem::kBlockSize,
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TilePartitioner::MPerBlock,
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TilePartitioner::NPerBlock,
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FlatmmConfig::M_Warp,
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FlatmmConfig::N_Warp,
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FlatmmConfig::M_Warp_Tile,
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FlatmmConfig::N_Warp_Tile,
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FlatmmConfig::K_Warp_Tile,
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CodegenPipelineProblem::TransposeC,
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memory_operation,
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FlatmmConfig::NumWaveGroups,
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false,
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1,
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FlatmmConfig::TiledMMAPermuteN>>;
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using Kernel =
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ck_tile::MixedPrecFlatmmKernel<TilePartitioner, CodegenFlatmmPipeline, GemmEpilogue>;
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auto kargs = Kernel::MakeKernelArgs(args);
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const dim3 grids = Kernel::GridSize(kargs);
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constexpr 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:" << CodegenFlatmmShape::GetName() << "\n"
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<< "Shape: " << CodegenFlatmmShape::GetName() << "\n"
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<< "problem: " << CodegenPipelineProblem::GetName() << "\n"
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<< "pipeline: " << CodegenFlatmmPipeline::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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if(s.flush_cache_)
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{
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std::cout << "Flushing cache..." << std::endl;
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static constexpr ck_tile::index_t APackedSize =
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std::is_same_v<BDataType, ck_tile::pk_int4_t> ? 2 : 1;
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static constexpr ck_tile::index_t BPackedSize =
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std::is_same_v<BDataType, ck_tile::pk_int4_t> ? 2 : 1;
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ck_tile::HostTensor<ADataType> 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<BDataType> 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() / APackedSize;
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auto size_b_buffer = b_n.get_element_space_size_in_bytes() / BPackedSize;
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ck_tile::RotatingMemWrapper<ADataType, BDataType> rotating_mem(
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kargs.a_ptr, kargs.b_ptr, s.rotating_count_, size_a_buffer, size_b_buffer);
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rotating_mem.Print();
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auto run_flush_cache = [&]() {
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// flush icache
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ck_tile::flush_icache();
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// rotating mem
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rotating_mem.Next();
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// clear c mem
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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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ave_time = ck_tile::launch_kernel_preprocess(
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s,
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run_flush_cache,
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ck_tile::make_kernel<blocks.x, FlatmmConfig::kBlockPerCu>(
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Kernel{}, grids, blocks, 0, kargs));
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}
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else
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{
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ave_time =
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ck_tile::launch_kernel(s,
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ck_tile::make_kernel<blocks.x, FlatmmConfig::kBlockPerCu>(
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Kernel{}, grids, blocks, 0, kargs));
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}
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return ave_time;
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};
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const auto RunSplitk = [&](const auto has_hot_loop_, const auto tail_number_) {
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if(args.k_batch == 1)
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{
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Run(has_hot_loop_,
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tail_number_,
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ck_tile::integral_constant<ck_tile::memory_operation_enum,
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ck_tile::memory_operation_enum::set>{});
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}
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else
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{
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Run(has_hot_loop_,
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tail_number_,
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ck_tile::integral_constant<ck_tile::memory_operation_enum,
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ck_tile::memory_operation_enum::atomic_add>{});
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}
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};
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BaseGemmPipeline::TailHandler(RunSplitk, has_hot_loop, tail_num);
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return ave_time;
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}
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template <typename FlatmmConfig,
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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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typename ScaleN,
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bool UsePersistentKernel = false,
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typename CDEElementWise = ck_tile::element_wise::PassThrough>
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float invoke_mixed_prec_flatmm(ck_tile::DeviceMem& a_dev_buf,
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ck_tile::DeviceMem& b_shuffle_dev_buf,
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ck_tile::DeviceMem& c_dev_buf,
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ck_tile::index_t M,
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ck_tile::index_t N,
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ck_tile::index_t K,
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ck_tile::index_t stride_A,
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ck_tile::index_t stride_B,
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ck_tile::index_t stride_C,
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ck_tile::index_t kbatch,
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ScaleN dequant_scale_n,
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int n_warmup,
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int n_repeat)
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{
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// Activation has no scale
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using ActScaleType = ck_tile::FlatmmScalePointer<-1>;
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ck_tile::ScaleFlatmmHostArgs<ActScaleType, ScaleN> args = {a_dev_buf.GetDeviceBuffer(),
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b_shuffle_dev_buf.GetDeviceBuffer(),
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{},
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c_dev_buf.GetDeviceBuffer(),
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kbatch,
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M,
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N,
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K,
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stride_A,
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stride_B,
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{},
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stride_C,
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{},
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dequant_scale_n};
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float ave_time = mixed_prec_flatmm_calc<FlatmmConfig,
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ADataType,
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BDataType,
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DsDatatype,
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AccDataType,
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CDataType,
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ALayout,
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BLayout,
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DsLayout,
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CLayout,
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ActScaleType,
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ScaleN,
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UsePersistentKernel,
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CDEElementWise>(
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args, ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat, true, true, 50});
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// TODO (sizeof(BDataType) / 2)
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std::size_t flop = std::size_t(2) * M * N * K;
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std::size_t num_byte =
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sizeof(ADataType) * M * K + sizeof(BDataType) * N * K + sizeof(CDataType) * M * N;
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float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
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float gb_per_sec = num_byte / 1.E6 / ave_time;
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std::cout << "Run A16W4_Flatmm kernel "
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<< " M =" << M << " N =" << N << " K =" << K << " StrideA =" << stride_A
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<< " StrideB =" << stride_B << " StrideC =" << stride_C << " : " << ave_time
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<< " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, " << std::endl;
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return ave_time;
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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", "512", "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 GPU")
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.insert(
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"mixed_prec",
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"bf16xfp4",
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"data type for activation and weight, support: bf16xbf4, bf16xfp4, fp16xfp4, fp16xbf4")
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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:constant(1)")
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.insert("persistent", "0", "0: no persistent, 1: persistent kernel")
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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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|
||||
template <typename FlatmmConfig, typename T>
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||||
auto shuffle_subbyte_b(const ck_tile::HostTensor<T>& t)
|
||||
{
|
||||
constexpr int PackSize = 2;
|
||||
|
||||
assert(t.get_lengths().size() == 2);
|
||||
int n_ = t.get_lengths()[1];
|
||||
int k_ = t.get_lengths()[0] / 2;
|
||||
constexpr int divisor = FlatmmConfig::N_Warp_Tile == 32 ? 2 : 4;
|
||||
ck_tile::HostTensor<T> t_view({n_ / FlatmmConfig::N_Warp_Tile,
|
||||
FlatmmConfig::N_Warp_Tile,
|
||||
k_ / FlatmmConfig::K_Warp_Tile,
|
||||
divisor,
|
||||
FlatmmConfig::K_Warp_Tile / divisor / 2});
|
||||
std::copy(t.begin(), t.end(), t_view.begin());
|
||||
return ck_tile::reference_permute(t_view, {0, 2, 3, 1, 4});
|
||||
}
|
||||
|
||||
#include "run_mixed_prec_flatmm.inc"
|
||||
|
||||
template <template <typename PrecType> typename FlatmmConfig>
|
||||
int run_mixed_prec_flatmm_example(int argc, char* argv[])
|
||||
{
|
||||
auto [result, arg_parser] = create_args(argc, argv);
|
||||
if(!result)
|
||||
return -1;
|
||||
|
||||
using Row = ck_tile::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
std::string mixed_prec = arg_parser.get_str("mixed_prec");
|
||||
std::string a_layout = arg_parser.get_str("a_layout");
|
||||
std::string b_layout = arg_parser.get_str("b_layout");
|
||||
int persistent_opt = arg_parser.get_int("persistent");
|
||||
|
||||
if(a_layout == "R" && b_layout == "C")
|
||||
{
|
||||
if(mixed_prec == "bf16xfp4")
|
||||
{
|
||||
if(persistent_opt == 0)
|
||||
{
|
||||
run_mixed_prec_flatmm_with_layouts<ck_tile::bf16_t,
|
||||
ck_tile::pk_fp4_t,
|
||||
FlatmmConfig<ck_tile::bf16_t>,
|
||||
false>(argc, argv, Row{}, Col{}, Row{});
|
||||
}
|
||||
else
|
||||
{
|
||||
// run_mixed_prec_flatmm_with_layouts<ck_tile::bf16_t,
|
||||
// ck_tile::pk_fp4_t,
|
||||
// FlatmmConfig<ck_tile::bf16_t>,
|
||||
// true>(argc, argv, Row{}, Col{}, Row{});
|
||||
}
|
||||
}
|
||||
else if(mixed_prec == "fp16xfp4")
|
||||
{
|
||||
// if(persistent_opt == 0)
|
||||
// {
|
||||
// run_mixed_prec_flatmm_with_layouts<ck_tile::fp16_t,
|
||||
// ck_tile::pk_fp4_t,
|
||||
// FlatmmConfig<ck_tile::fp16_t>,
|
||||
// false>(argc, argv, Row{}, Col{}, Row{});
|
||||
// }
|
||||
// else
|
||||
// {
|
||||
// run_mixed_prec_flatmm_with_layouts<ck_tile::fp16_t,
|
||||
// ck_tile::pk_fp4_t,
|
||||
// FlatmmConfig<ck_tile::fp16_t>,
|
||||
// true>(argc, argv, Row{}, Col{}, Row{});
|
||||
// }
|
||||
}
|
||||
else
|
||||
{
|
||||
throw std::runtime_error("Unsupported data_type!");
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
throw std::runtime_error("Unsupported data layout configuration for A,B and C tensors!");
|
||||
}
|
||||
return -1;
|
||||
}
|
||||
|
||||
int main(int argc, char* argv[])
|
||||
{
|
||||
auto [result, arg_parser] = create_args(argc, argv);
|
||||
if(!result)
|
||||
return EXIT_FAILURE;
|
||||
try
|
||||
{
|
||||
int warp_tile = arg_parser.get_int("warp_tile");
|
||||
if(warp_tile == 0)
|
||||
{
|
||||
return !run_mixed_prec_flatmm_example<A16W4_FlatmmConfig16_950>(argc, argv);
|
||||
}
|
||||
// else if(warp_tile == 1)
|
||||
// {
|
||||
// return !run_mixed_prec_flatmm_example<A16W4_FlatmmConfig32_950>(argc, argv);
|
||||
// }
|
||||
else
|
||||
{
|
||||
throw std::runtime_error("Unsupported warp_tile!");
|
||||
}
|
||||
}
|
||||
catch(const std::runtime_error& e)
|
||||
{
|
||||
std::cerr << "Runtime error: " << e.what() << '\n';
|
||||
return EXIT_FAILURE;
|
||||
}
|
||||
}
|
||||
15
example/ck_tile/18_flatmm/mixed_input/mixed_prec_flatmm.hpp
Normal file
15
example/ck_tile/18_flatmm/mixed_input/mixed_prec_flatmm.hpp
Normal file
@@ -0,0 +1,15 @@
|
||||
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <string>
|
||||
|
||||
#include "ck_tile/core.hpp"
|
||||
#include "ck_tile/host/kernel_launch.hpp"
|
||||
#include "ck_tile/ops/epilogue.hpp"
|
||||
#include "ck_tile/ops/flatmm.hpp"
|
||||
#include "ck_tile/ops/gemm.hpp"
|
||||
|
||||
#include "a16w4_flatmm.hpp"
|
||||
172
example/ck_tile/18_flatmm/mixed_input/run_mixed_prec_flatmm.inc
Normal file
172
example/ck_tile/18_flatmm/mixed_input/run_mixed_prec_flatmm.inc
Normal file
@@ -0,0 +1,172 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
template <typename PrecActType,
|
||||
typename PrecWeightType,
|
||||
typename FlatmmConfig,
|
||||
bool UsePersistentKernel = false,
|
||||
typename ALayout,
|
||||
typename BLayout,
|
||||
typename CLayout>
|
||||
int run_mixed_prec_flatmm_with_layouts(int argc,
|
||||
char* argv[],
|
||||
const ALayout a_layout = ALayout{},
|
||||
const BLayout b_layout = BLayout{},
|
||||
[[maybe_unused]] const CLayout c_layout = CLayout{})
|
||||
{
|
||||
auto [result, arg_parser] = create_args(argc, argv);
|
||||
if(!result)
|
||||
return -1;
|
||||
|
||||
using ADataType = PrecActType;
|
||||
using BDataType = PrecWeightType;
|
||||
using CDataType = PrecActType;
|
||||
using AccDataType = float;
|
||||
|
||||
constexpr int DequantGranularityN = 1;
|
||||
constexpr int DequantGranularityK = 32;
|
||||
|
||||
ck_tile::index_t M = arg_parser.get_int("m");
|
||||
ck_tile::index_t N = arg_parser.get_int("n");
|
||||
ck_tile::index_t K = arg_parser.get_int("k");
|
||||
|
||||
ck_tile::index_t stride_A = arg_parser.get_int("stride_a");
|
||||
ck_tile::index_t stride_B = arg_parser.get_int("stride_b");
|
||||
ck_tile::index_t stride_C = arg_parser.get_int("stride_c");
|
||||
|
||||
ck_tile::index_t kbatch = arg_parser.get_int("split_k");
|
||||
ck_tile::index_t init_method = arg_parser.get_int("init");
|
||||
ck_tile::index_t n_warmup = arg_parser.get_int("warmup");
|
||||
ck_tile::index_t n_repeat = arg_parser.get_int("repeat");
|
||||
|
||||
stride_A = ck_tile::get_default_stride(M, K, stride_A, is_row_major(a_layout));
|
||||
stride_B = ck_tile::get_default_stride(K, N, stride_B, is_row_major(b_layout));
|
||||
stride_C = ck_tile::get_default_stride(M, N, stride_C, is_row_major(CLayout{}));
|
||||
|
||||
ck_tile::HostTensor<ADataType> a_host(
|
||||
ck_tile::host_tensor_descriptor(M, K, stride_A, is_row_major(a_layout)));
|
||||
ck_tile::HostTensor<BDataType> b_origin_host(
|
||||
ck_tile::host_tensor_descriptor(K, N, stride_B, is_row_major(b_layout)));
|
||||
ck_tile::HostTensor<CDataType> c_rslt_host(
|
||||
ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
|
||||
|
||||
ck_tile::HostTensor<AccDataType> weight_dequant_scale(ck_tile::HostTensorDescriptor(
|
||||
{N / DequantGranularityN, K / DequantGranularityK}, {1, N / DequantGranularityN}));
|
||||
|
||||
if(init_method == 0)
|
||||
{
|
||||
ck_tile::FillUniformDistribution<ADataType>{0.0f, 1.0f}(a_host);
|
||||
ck_tile::FillUniformDistribution<BDataType>{-4.f, 4.f}(b_origin_host);
|
||||
ck_tile::FillUniformDistribution<AccDataType>{-1.f, 1.f}(weight_dequant_scale);
|
||||
}
|
||||
else if(init_method == 1)
|
||||
{
|
||||
ck_tile::FillUniformDistribution<ADataType>{1.f, 1.f}(a_host);
|
||||
ck_tile::FillUniformDistribution<BDataType>{1.f, 1.f}(b_origin_host);
|
||||
ck_tile::FillUniformDistribution<AccDataType>{1.f, 1.f}(weight_dequant_scale);
|
||||
}
|
||||
|
||||
ck_tile::HostTensor<BDataType> b_shuffle_host = shuffle_subbyte_b<FlatmmConfig>(b_origin_host);
|
||||
|
||||
ck_tile::DeviceMem a_dev_buf(a_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem b_shuffle_dev_buf(b_shuffle_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem c_dev_buf(c_rslt_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem weight_dequant_scale_dev_buf(
|
||||
weight_dequant_scale.get_element_space_size_in_bytes());
|
||||
|
||||
a_dev_buf.ToDevice(a_host.data());
|
||||
b_shuffle_dev_buf.ToDevice(b_shuffle_host.data());
|
||||
c_rslt_host.SetZero();
|
||||
weight_dequant_scale_dev_buf.ToDevice(weight_dequant_scale.data());
|
||||
|
||||
auto weight_dequant_scale_dev_ptr =
|
||||
ck_tile::FlatmmScalePointer<DequantGranularityN, DequantGranularityK>{
|
||||
static_cast<float*>(weight_dequant_scale_dev_buf.GetDeviceBuffer()),
|
||||
N / DequantGranularityN};
|
||||
|
||||
invoke_mixed_prec_flatmm<FlatmmConfig,
|
||||
ADataType,
|
||||
BDataType,
|
||||
ck_tile::tuple<>,
|
||||
AccDataType,
|
||||
CDataType,
|
||||
ALayout,
|
||||
BLayout,
|
||||
ck_tile::tuple<>,
|
||||
CLayout,
|
||||
decltype(weight_dequant_scale_dev_ptr),
|
||||
UsePersistentKernel>(a_dev_buf,
|
||||
b_shuffle_dev_buf,
|
||||
c_dev_buf,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
stride_A,
|
||||
stride_B,
|
||||
stride_C,
|
||||
kbatch,
|
||||
weight_dequant_scale_dev_ptr,
|
||||
n_warmup,
|
||||
n_repeat);
|
||||
|
||||
c_dev_buf.FromDevice(c_rslt_host.data());
|
||||
|
||||
bool pass = true;
|
||||
if(arg_parser.get_int("v") == 1)
|
||||
{
|
||||
ck_tile::DeviceMem b_origin_dev_buf(b_origin_host.get_element_space_size_in_bytes());
|
||||
b_origin_dev_buf.ToDevice(b_origin_host.data());
|
||||
|
||||
ck_tile::HostTensor<CDataType> c_gpu_ref_host(
|
||||
ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
|
||||
ck_tile::DeviceMem c_gpu_ref_dev_buf(c_gpu_ref_host.get_element_space_size_in_bytes());
|
||||
|
||||
ck_tile::HostTensor<AccDataType> scale_A(
|
||||
ck_tile::HostTensorDescriptor({1, K / DequantGranularityK}, {1, 1}));
|
||||
// scaleA = 1 has no effect on the result
|
||||
ck_tile::FillUniformDistribution<AccDataType>{1.f, 1.f}(scale_A);
|
||||
|
||||
ck_tile::DeviceMem scale_A_dev_buf(scale_A.get_element_space_size_in_bytes());
|
||||
scale_A_dev_buf.ToDevice(scale_A.data());
|
||||
|
||||
c_gpu_ref_dev_buf.SetZero();
|
||||
ck_tile::reference_blockwise_gemm_gpu<ADataType,
|
||||
BDataType,
|
||||
AccDataType,
|
||||
CDataType,
|
||||
ALayout,
|
||||
BLayout,
|
||||
CLayout>(
|
||||
static_cast<ADataType*>(a_dev_buf.GetDeviceBuffer()),
|
||||
static_cast<BDataType*>(b_origin_dev_buf.GetDeviceBuffer()),
|
||||
static_cast<CDataType*>(c_gpu_ref_dev_buf.GetDeviceBuffer()),
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
stride_A,
|
||||
stride_B,
|
||||
stride_C,
|
||||
M,
|
||||
DequantGranularityN,
|
||||
DequantGranularityK,
|
||||
static_cast<float*>(scale_A_dev_buf.GetDeviceBuffer()),
|
||||
static_cast<float*>(weight_dequant_scale_dev_buf.GetDeviceBuffer()));
|
||||
|
||||
c_gpu_ref_dev_buf.FromDevice(c_gpu_ref_host.data());
|
||||
|
||||
const float max_accumulated_value =
|
||||
*std::max_element(c_gpu_ref_host.mData.begin(), c_gpu_ref_host.mData.end());
|
||||
|
||||
const float rtol = 1e-3;
|
||||
const float atol = 1e-3;
|
||||
|
||||
pass = ck_tile::check_err(
|
||||
c_rslt_host, c_gpu_ref_host, "Error: Incorrect results!", rtol, atol);
|
||||
|
||||
std::cout << "Relative error threshold: " << rtol << " Absolute error threshold: " << atol
|
||||
<< std::endl;
|
||||
std::cout << "The GPU veification result is: " << (pass ? "correct" : "fail") << std::endl;
|
||||
}
|
||||
|
||||
return pass;
|
||||
}
|
||||
Reference in New Issue
Block a user