mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-07-01 12:17:00 +00:00
update grouped_gemm blockwise kernel
This commit is contained in:
kyle-256
@@ -4,6 +4,7 @@
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if(GPU_TARGETS MATCHES "gfx94|gfx95")
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add_executable(tile_example_grouped_gemm grouped_gemm.cpp)
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add_executable(tile_example_quant_grouped_gemm quant_grouped_gemm.cpp)
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add_executable(tile_example_abquant_grouped_gemm abquant_grouped_gemm.cpp)
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add_executable(tile_example_grouped_gemm_preshuffle grouped_gemm_preshuffle.cpp)
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add_executable(tile_example_grouped_gemm_multi_d grouped_gemm_multi_d.cpp)
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set(EXAMPLE_GEMM_COMPILE_OPTIONS)
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@@ -14,4 +15,5 @@ if(GPU_TARGETS MATCHES "gfx94|gfx95")
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target_compile_options(tile_example_grouped_gemm_preshuffle PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
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target_compile_options(tile_example_grouped_gemm_multi_d PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
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target_compile_options(tile_example_quant_grouped_gemm PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
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target_compile_options(tile_example_abquant_grouped_gemm PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
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endif()
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140
example/ck_tile/17_grouped_gemm/abquant_grouped_gemm.cpp
Normal file
140
example/ck_tile/17_grouped_gemm/abquant_grouped_gemm.cpp
Normal file
@@ -0,0 +1,140 @@
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// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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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 <memory>
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#include <type_traits>
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#include "ck_tile/core.hpp"
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#include "ck_tile/ops/epilogue.hpp"
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#include "ck_tile/ops/gemm.hpp"
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#include "ck_tile/ops/gemm/pipeline/tile_gemm_traits.hpp"
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#include "ck_tile/ops/gemm_quant.hpp"
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#include "ck_tile/host.hpp"
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#include "abquant_grouped_gemm.hpp"
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template <typename GemmConfig,
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typename ALayout,
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typename AQLayout,
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typename BLayout,
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typename BQLayout,
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typename CLayout,
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typename ADataType,
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typename AQDataType,
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typename BDataType,
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typename BQDataType,
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typename AccDataType,
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typename CDataType,
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typename AQuantGroupSize,
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typename BQuantGroupSize,
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ck_tile::QuantType QuantMode = ck_tile::QuantType::ABQuantGrouped>
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float grouped_gemm_tileloop(const ck_tile::stream_config& s,
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const ck_tile::index_t num_groups,
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void* kargs_ptr)
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{
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constexpr ck_tile::index_t TileParitionerGroupNum = 8;
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constexpr ck_tile::index_t TileParitionerM01 = 4;
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using GemmShape = ck_tile::TileGemmShape<
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ck_tile::sequence<GemmConfig::M_Tile, GemmConfig::N_Tile, GemmConfig::K_Tile>,
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ck_tile::sequence<GemmConfig::M_Warp, GemmConfig::N_Warp, GemmConfig::K_Warp>,
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ck_tile::
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sequence<GemmConfig::M_Warp_Tile, GemmConfig::N_Warp_Tile, GemmConfig::K_Warp_Tile>>;
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using TilePartitioner = ck_tile::
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GemmSpatiallyLocalTilePartitioner<GemmShape, TileParitionerGroupNum, TileParitionerM01>;
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using GemmUniversalTraits = ck_tile::TileGemmQuantTraits<GemmConfig::kPadM,
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GemmConfig::kPadN,
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GemmConfig::kPadK,
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false, // PreshuffleQuant
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GemmConfig::PreshuffleB,
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ALayout,
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BLayout,
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CLayout,
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QuantMode,
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AQLayout,
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BQLayout,
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GemmConfig::TransposeC,
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GemmConfig::DoubleSmemBuffer,
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GemmConfig::Persistent>;
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float ave_time{0};
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const auto Run = [&](const auto memory_operation_) {
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constexpr auto memory_operation = memory_operation_.value;
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using QuantGemmProblem = ck_tile::GemmABQuantPipelineProblem<ADataType,
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AQDataType,
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BDataType,
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BQDataType,
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AccDataType,
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GemmShape,
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GemmUniversalTraits,
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AQuantGroupSize,
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BQuantGroupSize,
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GemmConfig::TransposeC>;
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using GemmPipeline =
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GemmQuantConfig<QuantMode>::template GemmPipeline<QuantGemmProblem,
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GemmConfig::PreshuffleB>;
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using GemmEpilogue = ck_tile::CShuffleEpilogue<
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ck_tile::CShuffleEpilogueProblem<ADataType,
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BDataType,
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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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GemmConfig::M_Warp,
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GemmConfig::N_Warp,
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GemmConfig::M_Warp_Tile,
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GemmConfig::N_Warp_Tile,
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GemmConfig::K_Warp_Tile,
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QuantGemmProblem::TransposeC,
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memory_operation>>;
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using Kernel = ck_tile::QuantGroupedGemmKernel<TilePartitioner,
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GemmPipeline,
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GemmEpilogue,
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GemmUniversalTraits::kQuantType>;
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const dim3 blocks = Kernel::BlockSize();
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const dim3 grids = Kernel::MaxOccupancyGridSize(s);
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if(s.log_level_ > 0)
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{
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std::cout << "Launching kernel: " << Kernel::GetName() << " with args:" << " grid: {"
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<< grids.x << ", " << grids.y << ", " << grids.z << "}" << ", blocks: {"
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<< blocks.x << ", " << blocks.y << ", " << blocks.z << "}" << std::endl;
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}
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return ave_time = ck_tile::launch_kernel(
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s,
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ck_tile::make_kernel<GemmConfig::kBlockPerCu>(
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Kernel{},
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grids,
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blocks,
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0,
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ck_tile::cast_pointer_to_constant_address_space(kargs_ptr),
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num_groups));
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};
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return ave_time = Run(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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#include "abquant_run_grouped_gemm_example.inc"
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int main(int argc, char* argv[])
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{
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int result1 = run_abquant_grouped_gemm_example(argc, argv);
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return result1;
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}
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164
example/ck_tile/17_grouped_gemm/abquant_grouped_gemm.hpp
Normal file
164
example/ck_tile/17_grouped_gemm/abquant_grouped_gemm.hpp
Normal file
@@ -0,0 +1,164 @@
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// 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 <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/gemm.hpp"
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#include "ck_tile/ops/elementwise/unary_element_wise_operation.hpp"
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template <typename PrecType, ck_tile::index_t M_Warp_Tile>
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constexpr ck_tile::index_t get_k_warp_tile()
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{
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#if defined(CK_GFX950_SUPPORT)
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constexpr bool is_8bit_float =
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std::is_same_v<PrecType, ck_tile::fp8_t> || std::is_same_v<PrecType, ck_tile::bf8_t>;
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if constexpr(M_Warp_Tile == 32)
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return is_8bit_float ? 64 : 16;
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else
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return is_8bit_float ? 128 : 32;
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#else
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if constexpr(M_Warp_Tile == 32)
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return 16;
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else
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return 32;
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#endif
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}
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template <typename DataType>
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struct GemmTypeConfig;
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template <>
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struct GemmTypeConfig<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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};
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template <>
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struct GemmTypeConfig<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 <bool Persistent_>
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struct GemmConfigBase
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{
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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 PermuteA = false;
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static constexpr bool PermuteB = 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 ck_tile::index_t TileParitionerGroupNum = 8;
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static constexpr ck_tile::index_t TileParitionerM01 = 4;
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static constexpr auto Scheduler = ck_tile::GemmPipelineScheduler::Intrawave;
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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 PreshuffleB = false;
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static constexpr bool Persistent = Persistent_;
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};
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template <typename PrecType, bool Persistent>
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struct GemmConfigComputeV3_2 : public GemmConfigBase<Persistent>
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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(PrecType);
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static constexpr ck_tile::index_t M_Warp = 2;
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static constexpr ck_tile::index_t N_Warp = 2;
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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 = get_k_warp_tile<PrecType, M_Warp_Tile>();
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};
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template <ck_tile::QuantType QuantMode>
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struct GemmQuantConfig;
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// ABQuant specialization for GemmQuantConfig
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template <>
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struct GemmQuantConfig<ck_tile::QuantType::ABQuantGrouped>
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{
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template <typename PrecType, bool Persistent>
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using GemmConfig = GemmConfigComputeV3_2<PrecType, Persistent>;
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template <typename GemmProblem, bool PreshuffleB = false>
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using GemmPipeline = ck_tile::ABQuantGemmPipelineAgBgCrCompV3<GemmProblem>;
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template <typename GemmProblem, bool PreshuffleB = false>
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using BaseGemmPipeline = ck_tile::BaseGemmPipelineAgBgCrCompV3<GemmProblem>;
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};
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using grouped_gemm_kargs = ck_tile::QuantGroupedGemmHostArgs;
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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("Ms", "", "M dimensions - empty by default.")
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.insert("Ns", "", "N dimensions - empty by default.")
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.insert("Ks", "", "K dimensions - empty by default.")
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.insert(
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"stride_As",
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"",
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"Tensor A strides - it is empty by default.") // stride_As/stride_Bs/stride_Cs/stride_AQs/stride_BQs
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// can be set to zero if
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// Ms/Ns/Ks is not empty
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.insert("stride_Bs", "", "Tensor B strides - it is empty by default.")
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.insert("stride_Cs", "", "Tensor C strides - it is empty by default.")
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.insert("stride_AQs", "", "Tensor AQ strides - it is empty by default.")
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.insert("stride_BQs", "", "Tensor BQ 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 - Row by default.")
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.insert("c_layout", "R", "C tensor data layout - Row by default.")
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.insert("validate", "1", "0. No validation, 1. Validation on CPU.")
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.insert("prec", "fp8", "data type. fp16/bf16/fp8/bf8")
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.insert("warmup", "10", "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("group_count", "8", "group count.")
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.insert("kbatch", "1", "kbatch for SplitK")
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.insert("quant_mode", "bquant", "Choose aquant, bquant (default), tensor, or rowcol")
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.insert("init", "0", "0. Random, 2. One(s) (Constant)")
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.insert("persistent", "0", "Kernel persistency. 0: non-persistent. 1: persistent.");
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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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inline std::size_t get_workspace_size(const std::vector<grouped_gemm_kargs>& gemm_descs)
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{
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return gemm_descs.size() * sizeof(ck_tile::QuantGemmTransKernelArg);
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}
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// Forward declaration of the tileloop version for persistent kernels
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template <typename GemmConfig,
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typename ALayout,
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typename AQLayout,
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typename BLayout,
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typename BQLayout,
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typename CLayout,
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typename ADataType,
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typename AQDataType,
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typename BDataType,
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typename BQDataType,
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typename AccDataType,
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typename CDataType,
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typename AQuantGroupSize,
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typename BQuantGroupSize>
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float grouped_gemm_abquant_tileloop(const ck_tile::stream_config& s,
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const ck_tile::index_t num_groups,
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void* kargs_ptr);
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@@ -0,0 +1,540 @@
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// 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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struct MultiplyMultiply
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{
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template <typename E, typename C, typename D0, typename D1>
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CK_TILE_HOST_DEVICE auto operator()(E& e, const C& c, const D0& d0, const D1& d1) const -> void
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{
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const float x0_f = ck_tile::type_convert<float>(c) * ck_tile::type_convert<float>(d0) *
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ck_tile::type_convert<float>(d1);
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e = ck_tile::type_convert<E>(x0_f);
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}
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};
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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 ADataType, typename BDataType, typename AccDataType, typename CDataType>
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auto calculate_rtol_atol(const ck_tile::index_t K,
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const ck_tile::index_t kbatch,
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const float max_accumulated_value)
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{
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using ComputeType =
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std::conditional_t<sizeof(ADataType) < sizeof(BDataType), ADataType, BDataType>;
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// Calculate thresholds
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const auto rtol = ck_tile::get_relative_threshold<ComputeType, CDataType, AccDataType>(
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ck_tile::integer_divide_ceil(K, kbatch));
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const auto atol = ck_tile::get_absolute_threshold<ComputeType, CDataType, AccDataType>(
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max_accumulated_value / kbatch, ck_tile::integer_divide_ceil(K, kbatch));
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// Calculate error due to split_k accumulation
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const auto rtol_split_k =
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ck_tile::get_relative_threshold<CDataType, CDataType, CDataType>(kbatch);
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const auto atol_split_k = ck_tile::get_absolute_threshold<CDataType, CDataType, CDataType>(
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max_accumulated_value, kbatch);
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// Use higher threshold
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return ck_tile::make_tuple(std::max(rtol, rtol_split_k), std::max(atol, atol_split_k));
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}
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// This file contains the example infrastructure for ABQuant grouped GEMM
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// It reuses most of the code from quant_run_grouped_gemm_example.inc but with ABQuantGrouped support
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template <typename GemmConfig,
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typename ADataType,
|
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typename AQDataType,
|
||||
typename BDataType,
|
||||
typename BQDataType,
|
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typename AccDataType,
|
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typename CDataType,
|
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typename ALayout,
|
||||
typename AQLayout,
|
||||
typename BLayout,
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typename BQLayout,
|
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typename CLayout,
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typename AQuantGroupSize,
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typename BQuantGroupSize,
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typename CDEElementWise = ck_tile::element_wise::PassThrough>
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float invoke_abquant_gemm(int n_warmup,
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int n_repeat,
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int group_count,
|
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const std::vector<grouped_gemm_kargs>& args)
|
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{
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constexpr ck_tile::QuantType QuantMode = ck_tile::QuantType::ABQuantGrouped;
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// Workspace memory allocated to hold the gemm descriptions.
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ck_tile::DeviceMem gemm_workspace;
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gemm_workspace.Realloc(get_workspace_size(args));
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|
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float ave_time = 0;
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||||
|
||||
// Persistent TileLoop kernel only
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std::vector<ck_tile::QuantGemmTransKernelArg> kargs;
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void* kargs_ptr = gemm_workspace.GetDeviceBuffer();
|
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if(args[0].k_batch != 1)
|
||||
{
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throw std::runtime_error("Split-K not supported yet for persistent kernel");
|
||||
}
|
||||
|
||||
for(const auto& arg : args)
|
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{
|
||||
kargs.emplace_back(ck_tile::QuantGroupedGemmKernelArgs{arg.a_ptr,
|
||||
arg.b_ptr,
|
||||
arg.aq_ptr,
|
||||
arg.bq_ptr,
|
||||
arg.e_ptr,
|
||||
arg.M,
|
||||
arg.N,
|
||||
arg.K,
|
||||
arg.QK_A,
|
||||
arg.QK_B,
|
||||
arg.stride_A,
|
||||
arg.stride_B,
|
||||
arg.stride_E,
|
||||
arg.stride_AQ,
|
||||
arg.stride_BQ,
|
||||
arg.k_batch});
|
||||
}
|
||||
const auto stream = ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat};
|
||||
HIP_CHECK_ERROR(hipMemcpyWithStream(kargs_ptr,
|
||||
kargs.data(),
|
||||
kargs.size() * sizeof(ck_tile::QuantGemmTransKernelArg),
|
||||
hipMemcpyHostToDevice,
|
||||
stream.stream_id_));
|
||||
ave_time = grouped_gemm_tileloop<GemmConfig,
|
||||
ALayout,
|
||||
AQLayout,
|
||||
BLayout,
|
||||
BQLayout,
|
||||
CLayout,
|
||||
ADataType,
|
||||
AQDataType,
|
||||
BDataType,
|
||||
BQDataType,
|
||||
AccDataType,
|
||||
CDataType,
|
||||
AQuantGroupSize,
|
||||
BQuantGroupSize,
|
||||
QuantMode>(stream, group_count, kargs_ptr);
|
||||
|
||||
std::string op_name = "ABQuant Grouped Gemm";
|
||||
|
||||
std::size_t flop = 0, num_btype = 0;
|
||||
for(int j = 0; j < group_count; ++j)
|
||||
{
|
||||
flop += std::size_t(2) * args[j].M * args[j].N * args[j].K;
|
||||
|
||||
num_btype += sizeof(ADataType) * args[j].M * args[j].K +
|
||||
sizeof(BDataType) * args[j].K * args[j].N +
|
||||
sizeof(CDataType) * args[j].M * args[j].N;
|
||||
}
|
||||
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
||||
float gb_per_sec = num_btype / 1.E6 / ave_time;
|
||||
|
||||
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
|
||||
<< gb_per_sec << " GB/s, " << op_name << std::endl;
|
||||
|
||||
return ave_time;
|
||||
}
|
||||
|
||||
template <typename GemmConfig,
|
||||
typename ADataType,
|
||||
typename AQDataType,
|
||||
typename BDataType,
|
||||
typename BQDataType,
|
||||
typename CDataType,
|
||||
typename AccDataType,
|
||||
typename AQuantGroupSize,
|
||||
typename BQuantGroupSize,
|
||||
typename ALayout,
|
||||
typename AQLayout,
|
||||
typename BLayout,
|
||||
typename BQLayout,
|
||||
typename CLayout>
|
||||
int run_abquant_grouped_gemm_example_with_layouts(int argc,
|
||||
char* argv[],
|
||||
[[maybe_unused]] const ALayout a_layout = ALayout{},
|
||||
[[maybe_unused]] const AQLayout aq_layout = AQLayout{},
|
||||
[[maybe_unused]] const BLayout b_layout = BLayout{},
|
||||
[[maybe_unused]] const BQLayout bq_layout = BQLayout{},
|
||||
[[maybe_unused]] const CLayout c_layout = CLayout{})
|
||||
{
|
||||
[[maybe_unused]] constexpr ck_tile::QuantType QuantMode = ck_tile::QuantType::ABQuantGrouped;
|
||||
|
||||
auto [result, arg_parser] = create_args(argc, argv);
|
||||
|
||||
if(!result)
|
||||
{
|
||||
return -1;
|
||||
};
|
||||
|
||||
auto valid_input_data = [&](int group_count, const auto&... args) {
|
||||
return group_count != 0 && ((args.size() == static_cast<size_t>(group_count)) && ...);
|
||||
};
|
||||
|
||||
const int group_count = arg_parser.get_int("group_count");
|
||||
const int repeat = arg_parser.get_int("repeat");
|
||||
const int warmup = arg_parser.get_int("warmup");
|
||||
const int kbatch = arg_parser.get_int("kbatch");
|
||||
const int init_method = arg_parser.get_int("init");
|
||||
bool validate = arg_parser.get_bool("validate");
|
||||
|
||||
if(kbatch > 1 && validate && warmup + repeat > 1)
|
||||
{
|
||||
std::cerr << "WARNING: Validation with split-K may be incorrect when warmup + repeat > 1"
|
||||
<< std::endl;
|
||||
}
|
||||
|
||||
std::vector<ck_tile::index_t> Ms = arg_parser.get_int_vec("Ms");
|
||||
std::vector<ck_tile::index_t> Ns = arg_parser.get_int_vec("Ns");
|
||||
std::vector<ck_tile::index_t> Ks = arg_parser.get_int_vec("Ks");
|
||||
std::vector<ck_tile::index_t> AQs; // dimension of AQ tensor is calculated from A tensor
|
||||
std::vector<ck_tile::index_t> BQs; // dimension of BQ tensor is calculated from B tensor
|
||||
std::vector<ck_tile::index_t> stride_As = arg_parser.get_int_vec("stride_As");
|
||||
std::vector<ck_tile::index_t> stride_Bs = arg_parser.get_int_vec("stride_Bs");
|
||||
std::vector<ck_tile::index_t> stride_Cs = arg_parser.get_int_vec("stride_Cs");
|
||||
std::vector<ck_tile::index_t> stride_AQs = arg_parser.get_int_vec("stride_AQs");
|
||||
std::vector<ck_tile::index_t> stride_BQs = arg_parser.get_int_vec("stride_BQs");
|
||||
|
||||
ck_tile::index_t AQK, BQK;
|
||||
|
||||
if(!valid_input_data(group_count, Ms, Ns, Ks))
|
||||
{
|
||||
std::cout << "Please check the input data. Default values will be used." << std::endl;
|
||||
|
||||
// Clear existing (invalid) data before adding defaults
|
||||
Ms.clear();
|
||||
Ns.clear();
|
||||
Ks.clear();
|
||||
stride_As.clear();
|
||||
stride_Bs.clear();
|
||||
stride_Cs.clear();
|
||||
stride_AQs.clear();
|
||||
stride_BQs.clear();
|
||||
|
||||
for(int i = 0; i < group_count; i++)
|
||||
{
|
||||
|
||||
Ms.push_back(256 + 256 * i);
|
||||
Ns.push_back(256 + 512 * i);
|
||||
Ks.push_back(512 + 128 * i);
|
||||
|
||||
// Let get_default_stride calculate based on layout
|
||||
stride_As.push_back(0);
|
||||
stride_Bs.push_back(0);
|
||||
stride_Cs.push_back(0);
|
||||
stride_AQs.push_back(0);
|
||||
stride_BQs.push_back(0);
|
||||
}
|
||||
}
|
||||
|
||||
// Create tensors and device buffers
|
||||
std::vector<ck_tile::HostTensor<ADataType>> a_m_k_tensors;
|
||||
std::vector<ck_tile::HostTensor<BDataType>> b_k_n_tensors;
|
||||
std::vector<ck_tile::HostTensor<CDataType>> c_m_n_tensors;
|
||||
std::vector<ck_tile::HostTensor<AQDataType>> aq_tensors;
|
||||
std::vector<ck_tile::HostTensor<BQDataType>> bq_tensors;
|
||||
|
||||
a_m_k_tensors.reserve(group_count);
|
||||
b_k_n_tensors.reserve(group_count);
|
||||
c_m_n_tensors.reserve(group_count);
|
||||
aq_tensors.reserve(group_count);
|
||||
bq_tensors.reserve(group_count);
|
||||
|
||||
std::vector<std::unique_ptr<ck_tile::DeviceMem>> a_m_k_dev_buf;
|
||||
std::vector<std::unique_ptr<ck_tile::DeviceMem>> b_k_n_dev_buf;
|
||||
std::vector<std::unique_ptr<ck_tile::DeviceMem>> c_m_n_dev_buf;
|
||||
std::vector<std::unique_ptr<ck_tile::DeviceMem>> aq_dev_buf;
|
||||
std::vector<std::unique_ptr<ck_tile::DeviceMem>> bq_dev_buf;
|
||||
|
||||
a_m_k_dev_buf.reserve(group_count);
|
||||
b_k_n_dev_buf.reserve(group_count);
|
||||
c_m_n_dev_buf.reserve(group_count);
|
||||
aq_dev_buf.reserve(group_count);
|
||||
bq_dev_buf.reserve(group_count);
|
||||
|
||||
std::vector<grouped_gemm_kargs> gemm_descs;
|
||||
gemm_descs.reserve(group_count);
|
||||
|
||||
for(int i = 0; i < group_count; ++i)
|
||||
{
|
||||
const ck_tile::index_t M = Ms[i];
|
||||
const ck_tile::index_t N = Ns[i];
|
||||
const ck_tile::index_t K = Ks[i];
|
||||
|
||||
// ABQuantGrouped mode: both A and B are quantized
|
||||
AQK = K / AQuantGroupSize::kK;
|
||||
BQK = K / BQuantGroupSize::kK;
|
||||
|
||||
if(K % AQuantGroupSize::kK != 0)
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"K must be divisible by AQuantGroupSize::kK for ABQuantGrouped mode");
|
||||
}
|
||||
if(K % BQuantGroupSize::kK != 0)
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"K must be divisible by BQuantGroupSize::kK for ABQuantGrouped mode");
|
||||
}
|
||||
|
||||
stride_As[i] = ck_tile::get_default_stride(M, K, stride_As[i], is_row_major(ALayout{}));
|
||||
stride_Bs[i] = ck_tile::get_default_stride(K, N, stride_Bs[i], is_row_major(BLayout{}));
|
||||
stride_Cs[i] = ck_tile::get_default_stride(M, N, stride_Cs[i], is_row_major(CLayout{}));
|
||||
stride_AQs[i] = ck_tile::get_default_stride(M, AQK, stride_AQs[i], is_row_major(AQLayout{}));
|
||||
stride_BQs[i] = ck_tile::get_default_stride(BQK, N, stride_BQs[i], is_row_major(BQLayout{}));
|
||||
|
||||
a_m_k_tensors.push_back(ck_tile::HostTensor<ADataType>(
|
||||
ck_tile::host_tensor_descriptor(M, K, stride_As[i], is_row_major(ALayout{}))));
|
||||
b_k_n_tensors.push_back(ck_tile::HostTensor<BDataType>(
|
||||
ck_tile::host_tensor_descriptor(K, N, stride_Bs[i], is_row_major(BLayout{}))));
|
||||
c_m_n_tensors.push_back(ck_tile::HostTensor<CDataType>(
|
||||
ck_tile::host_tensor_descriptor(M, N, stride_Cs[i], is_row_major(CLayout{}))));
|
||||
aq_tensors.push_back(ck_tile::HostTensor<AQDataType>(
|
||||
ck_tile::host_tensor_descriptor(M, AQK, stride_AQs[i], is_row_major(AQLayout{}))));
|
||||
bq_tensors.push_back(ck_tile::HostTensor<BQDataType>(
|
||||
ck_tile::host_tensor_descriptor(BQK, N, stride_BQs[i], is_row_major(BQLayout{}))));
|
||||
|
||||
std::cout << "gemm[" << i << "]" << " a_m_k: " << a_m_k_tensors[i].mDesc
|
||||
<< " b_k_n: " << b_k_n_tensors[i].mDesc << " c_m_n: " << c_m_n_tensors[i].mDesc
|
||||
<< " aq: " << aq_tensors[i].mDesc << " bq: " << bq_tensors[i].mDesc << std::endl;
|
||||
|
||||
if(init_method == 2)
|
||||
{
|
||||
ck_tile::FillUniformDistribution<ADataType>{1.f, 1.f}(a_m_k_tensors[i]);
|
||||
ck_tile::FillUniformDistribution<BDataType>{1.f, 1.f}(b_k_n_tensors[i]);
|
||||
ck_tile::FillUniformDistribution<AQDataType>{1.f, 1.f}(aq_tensors[i]);
|
||||
ck_tile::FillUniformDistribution<BQDataType>{1.f, 1.f}(bq_tensors[i]);
|
||||
}
|
||||
else
|
||||
{
|
||||
ck_tile::FillUniformDistribution<ADataType>{-1.f, 1.f}(a_m_k_tensors[i]);
|
||||
ck_tile::FillUniformDistribution<BDataType>{-1.f, 1.f}(b_k_n_tensors[i]);
|
||||
ck_tile::FillUniformDistribution<AQDataType>{-1.f, 1.f}(aq_tensors[i]);
|
||||
ck_tile::FillUniformDistribution<BQDataType>{-1.f, 1.f}(bq_tensors[i]);
|
||||
}
|
||||
|
||||
a_m_k_dev_buf.push_back(std::make_unique<ck_tile::DeviceMem>(
|
||||
a_m_k_tensors[i].get_element_space_size_in_bytes()));
|
||||
b_k_n_dev_buf.push_back(std::make_unique<ck_tile::DeviceMem>(
|
||||
b_k_n_tensors[i].get_element_space_size_in_bytes()));
|
||||
c_m_n_dev_buf.push_back(std::make_unique<ck_tile::DeviceMem>(
|
||||
c_m_n_tensors[i].get_element_space_size_in_bytes()));
|
||||
aq_dev_buf.push_back(
|
||||
std::make_unique<ck_tile::DeviceMem>(aq_tensors[i].get_element_space_size_in_bytes()));
|
||||
bq_dev_buf.push_back(
|
||||
std::make_unique<ck_tile::DeviceMem>(bq_tensors[i].get_element_space_size_in_bytes()));
|
||||
|
||||
b_k_n_dev_buf[i]->ToDevice(b_k_n_tensors[i].data());
|
||||
a_m_k_dev_buf[i]->ToDevice(a_m_k_tensors[i].data());
|
||||
aq_dev_buf[i]->ToDevice(aq_tensors[i].data());
|
||||
bq_dev_buf[i]->ToDevice(bq_tensors[i].data());
|
||||
c_m_n_dev_buf[i]->SetZero();
|
||||
c_m_n_tensors[i].SetZero();
|
||||
|
||||
const void* p_a = a_m_k_dev_buf[i]->GetDeviceBuffer();
|
||||
const void* p_b = b_k_n_dev_buf[i]->GetDeviceBuffer();
|
||||
void* p_c = c_m_n_dev_buf[i]->GetDeviceBuffer();
|
||||
const void* p_aq = aq_dev_buf[i]->GetDeviceBuffer();
|
||||
const void* p_bq = bq_dev_buf[i]->GetDeviceBuffer();
|
||||
|
||||
gemm_descs.push_back({p_a,
|
||||
p_b,
|
||||
p_c,
|
||||
p_aq,
|
||||
p_bq,
|
||||
kbatch,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
AQK,
|
||||
BQK,
|
||||
stride_As[i],
|
||||
stride_Bs[i],
|
||||
stride_Cs[i],
|
||||
stride_AQs[i],
|
||||
stride_BQs[i]});
|
||||
}
|
||||
|
||||
invoke_abquant_gemm<GemmConfig,
|
||||
ADataType,
|
||||
AQDataType,
|
||||
BDataType,
|
||||
BQDataType,
|
||||
AccDataType,
|
||||
CDataType,
|
||||
ALayout,
|
||||
AQLayout,
|
||||
BLayout,
|
||||
BQLayout,
|
||||
CLayout,
|
||||
AQuantGroupSize,
|
||||
BQuantGroupSize>(warmup, repeat, group_count, gemm_descs);
|
||||
|
||||
for(int i = 0; i < group_count; i++)
|
||||
{
|
||||
c_m_n_dev_buf[i]->FromDevice(c_m_n_tensors[i].data());
|
||||
}
|
||||
|
||||
bool pass{true};
|
||||
if(validate)
|
||||
{
|
||||
for(int i = 0; i < group_count; ++i)
|
||||
{
|
||||
ck_tile::HostTensor<CDataType> c_m_n_host_ref(ck_tile::host_tensor_descriptor(
|
||||
Ms[i], Ns[i], stride_Cs[i], is_row_major(CLayout{})));
|
||||
c_m_n_host_ref.SetZero();
|
||||
|
||||
// Reference computation for ABQuantGrouped
|
||||
ck_tile::reference_gemm_abquant<ADataType,
|
||||
AQDataType,
|
||||
BDataType,
|
||||
BQDataType,
|
||||
AccDataType,
|
||||
CDataType,
|
||||
AQuantGroupSize,
|
||||
BQuantGroupSize>(a_m_k_tensors[i],
|
||||
aq_tensors[i],
|
||||
b_k_n_tensors[i],
|
||||
bq_tensors[i],
|
||||
c_m_n_host_ref);
|
||||
|
||||
const float max_accumulated_value =
|
||||
*std::max_element(c_m_n_host_ref.mData.begin(), c_m_n_host_ref.mData.end());
|
||||
const auto rtol_atol =
|
||||
calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>(
|
||||
Ks[i], kbatch, max_accumulated_value);
|
||||
pass &= ck_tile::check_err(c_m_n_tensors[i],
|
||||
c_m_n_host_ref,
|
||||
"Error: Incorrect results!",
|
||||
rtol_atol.at(ck_tile::number<0>{}),
|
||||
rtol_atol.at(ck_tile::number<1>{}));
|
||||
std::cout << "gemm[" << i
|
||||
<< "] Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
|
||||
<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
|
||||
<< std::endl;
|
||||
}
|
||||
std::cout << "The CPU verification result is:" << (pass ? "correct" : "fail") << std::endl;
|
||||
}
|
||||
|
||||
return pass;
|
||||
}
|
||||
|
||||
template <typename PrecType>
|
||||
int run_abquant_grouped_gemm_example_prec_type(std::string a_layout,
|
||||
std::string b_layout,
|
||||
std::string c_layout,
|
||||
[[maybe_unused]] bool persistent,
|
||||
int argc,
|
||||
char* argv[])
|
||||
{
|
||||
using Row = ck_tile::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
|
||||
using Types = GemmTypeConfig<PrecType>;
|
||||
|
||||
using ADataType = typename Types::ADataType;
|
||||
using BDataType = typename Types::BDataType;
|
||||
using AccDataType = typename Types::AccDataType;
|
||||
using CDataType = typename Types::CDataType;
|
||||
using AQDataType = typename Types::AccDataType;
|
||||
using BQDataType = typename Types::AccDataType;
|
||||
using AQuantGroupSize = ck_tile::QuantGroupShape<ck_tile::sequence<1, 1, 128>>;
|
||||
using BQuantGroupSize = ck_tile::QuantGroupShape<ck_tile::sequence<1, 1, 128>>;
|
||||
|
||||
using GemmConfig = typename GemmQuantConfig<ck_tile::QuantType::ABQuantGrouped>::
|
||||
template GemmConfig<PrecType, true>;
|
||||
|
||||
// Support RCR, RRR, CRR layouts
|
||||
if(a_layout == "R" && b_layout == "C" && c_layout == "R")
|
||||
{
|
||||
return run_abquant_grouped_gemm_example_with_layouts<GemmConfig,
|
||||
ADataType,
|
||||
AQDataType,
|
||||
BDataType,
|
||||
BQDataType,
|
||||
CDataType,
|
||||
AccDataType,
|
||||
AQuantGroupSize,
|
||||
BQuantGroupSize>(
|
||||
argc, argv, Row{}, Row{}, Col{}, Col{}, Row{});
|
||||
}
|
||||
else if(a_layout == "R" && b_layout == "R" && c_layout == "R")
|
||||
{
|
||||
return run_abquant_grouped_gemm_example_with_layouts<GemmConfig,
|
||||
ADataType,
|
||||
AQDataType,
|
||||
BDataType,
|
||||
BQDataType,
|
||||
CDataType,
|
||||
AccDataType,
|
||||
AQuantGroupSize,
|
||||
BQuantGroupSize>(
|
||||
argc, argv, Row{}, Row{}, Row{}, Col{}, Row{});
|
||||
}
|
||||
else if(a_layout == "C" && b_layout == "R" && c_layout == "R")
|
||||
{
|
||||
return run_abquant_grouped_gemm_example_with_layouts<GemmConfig,
|
||||
ADataType,
|
||||
AQDataType,
|
||||
BDataType,
|
||||
BQDataType,
|
||||
CDataType,
|
||||
AccDataType,
|
||||
AQuantGroupSize,
|
||||
BQuantGroupSize>(
|
||||
argc, argv, Col{}, Row{}, Row{}, Col{}, Row{});
|
||||
}
|
||||
else
|
||||
{
|
||||
throw std::runtime_error("Unsupported data layout configuration! Supported: RCR, RRR, CRR");
|
||||
}
|
||||
}
|
||||
|
||||
int run_abquant_grouped_gemm_example(int argc, char* argv[])
|
||||
{
|
||||
auto [result, arg_parser] = create_args(argc, argv);
|
||||
if(!result)
|
||||
{
|
||||
return -1;
|
||||
}
|
||||
|
||||
const std::string a_layout = arg_parser.get_str("a_layout");
|
||||
const std::string b_layout = arg_parser.get_str("b_layout");
|
||||
const std::string c_layout = arg_parser.get_str("c_layout");
|
||||
const std::string data_type = arg_parser.get_str("prec");
|
||||
const bool persistent = arg_parser.get_bool("persistent");
|
||||
|
||||
// Validate layout combinations
|
||||
if(!((a_layout == "R" && b_layout == "C" && c_layout == "R") ||
|
||||
(a_layout == "R" && b_layout == "R" && c_layout == "R") ||
|
||||
(a_layout == "C" && b_layout == "R" && c_layout == "R")))
|
||||
{
|
||||
std::cerr << "Error: Unsupported layout combination: " << a_layout << b_layout << c_layout
|
||||
<< ". Supported layouts are: RCR, RRR, CRR" << std::endl;
|
||||
return -1;
|
||||
}
|
||||
|
||||
if(data_type == "fp8")
|
||||
{
|
||||
return run_abquant_grouped_gemm_example_prec_type<ck_tile::fp8_t>(
|
||||
a_layout, b_layout, c_layout, persistent, argc, argv);
|
||||
}
|
||||
else if(data_type == "bf8")
|
||||
{
|
||||
return run_abquant_grouped_gemm_example_prec_type<ck_tile::bf8_t>(
|
||||
a_layout, b_layout, c_layout, persistent, argc, argv);
|
||||
}
|
||||
else
|
||||
{
|
||||
std::cerr << "Error: Unsupported data type: " << data_type
|
||||
<< ". Supported types are: fp8, bf8" << std::endl;
|
||||
return -1;
|
||||
}
|
||||
}
|
||||
|
||||
@@ -500,6 +500,25 @@ struct QuantGroupedGemmKernel
|
||||
// Run Epilogue Pipeline
|
||||
EpiloguePipeline{}(c_block_window, c_block_tile, c_block_window, smem_ptr_0);
|
||||
}
|
||||
else if constexpr(kQuantType == QuantType::ABQuantGrouped)
|
||||
{
|
||||
const auto& aq_block_window = gemm_tile_windows.at(Base::I1);
|
||||
const auto& bq_block_window = gemm_tile_windows.at(Base::I3);
|
||||
// Run GEMM pipeline
|
||||
const auto& c_block_tile = GemmPipeline{}.template operator()(a_block_window,
|
||||
b_block_window,
|
||||
aq_block_window,
|
||||
bq_block_window,
|
||||
num_loop,
|
||||
has_hot_loop,
|
||||
tail_num,
|
||||
smem_ptr_0);
|
||||
|
||||
auto& c_block_window = gemm_tile_windows.at(Base::I4);
|
||||
|
||||
// Run Epilogue Pipeline
|
||||
EpiloguePipeline{}(c_block_window, c_block_tile, c_block_window, smem_ptr_0);
|
||||
}
|
||||
else
|
||||
{
|
||||
// Run GEMM pipeline
|
||||
|
||||
Reference in New Issue
Block a user