mirror of
https://github.com/ROCm/composable_kernel.git
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[CK_TILE] Implement Row/Col quant grouped gemm (#2786)
* Add cshuffle epilogue test
* add the poc implementation to the epilogue and tests
* refactor cshuffle epilogue
* WIP: adding tensor/tile usage to scale_tile
* fix usage of tile_elementwise_inout
* add gemm_quant_kernel for generalizing gemm quant kernel
* Add problem specific to different quants, add QuantType to Traits
* Add quant_type to quant_kernel template parameters
* Create aq/bq_block_windows and views depending on QuantType
* Use tile windows as inputs in cshuffle epilogue
* Fix some issues in epilogue
* initial new example code for new general gemm quant kernel test
* Fix issues in kernel
* Add verification check for rowcol Quantmode
* use AccDataType instead of AQ in pipeline
* fix aquant preshuffle
* fix formatting
* some cleanup
* remove gemm_aquant_basic.cpp
* remove gemm_aquant_kernel.hpp
* fix tests for the renamed quant kernel
* fix formatting
* clean example files
* fix some merge conflicts
* fix preshufflequant rename issue
* updating
* fix some templates after merging with develop
* fix test preshuffle parameter
* fix formatting
* updating kernels
* change update user
* test username
* update quant_grouped_gemm example
* update example
* Unify bquant kernel to the common quant kernel
* remove bquant kernel also from common header
* fix formatting
* clean up commented code
* update grouped_gemm_quant example
* fix formatting config hpp
* fix merge mistake
* Non-const for movable windows
* fix formatting
* update tileloop pipleline
* Fix grammar in README
Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com>
* Remove #include<bit> and clean up example
* fix strides
* Add some descriptions for move_windows
* fix tensor print bug
* update quant_grouped_gemm example
* remove useless code
* cleanup code
* clean up code & format code
* fix compile & running bug in grouped_gemm example
---------
Co-authored-by: Sami Remes <samremes@amd.com>
Co-authored-by: Mohsen Saffari <mohsen.saffari@amd.com>
Co-authored-by: liyingli <liyingli@amd.com>
Co-authored-by: kyle-256 <Kyle.Zhao@amd.com>
Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com>
[ROCm/composable_kernel commit: 4eb415829e]
This commit is contained in:
kyle-256
@@ -1,2 +1,3 @@
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add_executable(tile_example_grouped_gemm EXCLUDE_FROM_ALL grouped_gemm.cpp)
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add_executable(tile_example_quant_grouped_gemm EXCLUDE_FROM_ALL quant_grouped_gemm.cpp)
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add_executable(tile_example_grouped_gemm_preshuffle EXCLUDE_FROM_ALL grouped_gemm_preshuffle.cpp)
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@@ -175,6 +175,8 @@ mkdir build && cd build
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make tile_example_grouped_gemm -j
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# The preshuffle example
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make tile_example_grouped_gemm_preshuffle -j
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# The quant grouped gemm fp8 example
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make tile_example_quant_grouped_gemm -j
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```
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This will result in an executable `build/bin/tile_example_grouped_gemm`
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@@ -321,6 +321,36 @@ int run_gemm_example_prec_type(std::string a_layout, std::string b_layout, int a
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throw std::runtime_error("Unsupported data layout configuration for A and B tensors!");
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}
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}
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template <template <typename PrecType> typename GemmConfig>
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int run_grouped_gemm_example(int argc, char* argv[])
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{
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auto [result, arg_parser] = create_args(argc, argv);
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if(!result)
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{
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return -1;
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}
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const std::string a_layout = arg_parser.get_str("a_layout");
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const std::string b_layout = arg_parser.get_str("b_layout");
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const std::string data_type = arg_parser.get_str("prec");
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if(data_type == "fp16")
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{
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return run_gemm_example_prec_type<GemmConfig<ck_tile::half_t>, ck_tile::half_t>(
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a_layout, b_layout, argc, argv);
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}
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else if(data_type == "fp8")
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{
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return run_gemm_example_prec_type<GemmConfig<ck_tile::fp8_t>, ck_tile::fp8_t>(
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a_layout, b_layout, argc, argv);
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}
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else
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{
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throw std::runtime_error("Unsupported data type configuration.");
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}
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}
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int main(int argc, char* argv[])
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{
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return !run_grouped_gemm_example<GemmConfigComputeV4>(argc, argv);
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136
example/ck_tile/17_grouped_gemm/quant_grouped_gemm.cpp
Normal file
136
example/ck_tile/17_grouped_gemm/quant_grouped_gemm.cpp
Normal file
@@ -0,0 +1,136 @@
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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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#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 "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_group_quant.hpp"
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#include "ck_tile/host.hpp"
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#include "quant_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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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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constexpr ck_tile::QuantType QuantMode = ck_tile::QuantType::RowColQuant;
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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,
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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::DoubleSmemBuffer,
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true>;
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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 scheduler = GemmConfig::Scheduler;
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constexpr auto memory_operation = memory_operation_.value;
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constexpr bool transpose_c = false;
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using QuantGemmProblem = ck_tile::GemmRowColQuantPipelineProblem<ADataType,
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BDataType,
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AccDataType,
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AccDataType,
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GemmShape,
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GemmUniversalTraits,
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transpose_c,
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BDataType,
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scheduler>;
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using GemmPipeline = typename PipelineTypeTraits<
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GemmConfig::Pipeline>::template GemmPipeline<QuantGemmProblem>;
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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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ave_time =
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ck_tile::launch_kernel(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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return ave_time;
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};
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Run(ck_tile::integral_constant<ck_tile::memory_operation_enum,
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ck_tile::memory_operation_enum::set>{});
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return ave_time;
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}
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#include "quant_run_grouped_gemm_example.inc"
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int main(int argc, char* argv[])
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{
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return !run_grouped_gemm_example<GemmConfigComputeV3_2>(argc, argv);
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}
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157
example/ck_tile/17_grouped_gemm/quant_grouped_gemm.hpp
Normal file
157
example/ck_tile/17_grouped_gemm/quant_grouped_gemm.hpp
Normal file
@@ -0,0 +1,157 @@
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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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#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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#define CK_TILE_PIPELINE_COMPUTE_V3 1
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#define CK_TILE_PIPELINE_MEMORY 2
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#define CK_TILE_PIPELINE_COMPUTE_V4 3
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#ifndef CK_TILE_PIPELINE_DEFAULT
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#define CK_TILE_PIPELINE_DEFAULT CK_TILE_PIPELINE_COMPUTE_V3
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#endif
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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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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 Pipeline = CK_TILE_PIPELINE_COMPUTE_V3;
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static constexpr ck_tile::index_t NumWaveGroups = 1;
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static constexpr bool Preshuffle = false;
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};
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template <typename PrecType>
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struct GemmConfigComputeV3_2 : public GemmConfigBase
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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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static constexpr bool DoubleSmemBuffer = false;
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static constexpr ck_tile::index_t Pipeline = CK_TILE_PIPELINE_COMPUTE_V3;
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static constexpr int kBlockPerCu = 1;
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};
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template <ck_tile::index_t PipelineId>
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struct PipelineTypeTraits;
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template <>
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struct PipelineTypeTraits<CK_TILE_PIPELINE_COMPUTE_V3>
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{
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template <typename PipelineProblem>
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using GemmPipeline = ck_tile::GemmPipelineAgBgCrCompV3<PipelineProblem>;
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template <typename PipelineProblem>
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using UniversalGemmPipeline = ck_tile::BaseGemmPipelineAgBgCrCompV3<PipelineProblem>;
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};
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template <>
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struct PipelineTypeTraits<CK_TILE_PIPELINE_COMPUTE_V4>
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{
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template <typename PipelineProblem>
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using GemmPipeline = ck_tile::GemmPipelineAgBgCrCompV4<PipelineProblem>;
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template <typename PipelineProblem>
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using UniversalGemmPipeline = ck_tile::BaseGemmPipelineAgBgCrCompV4<PipelineProblem>;
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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("stride_As", "", "Tensor A strides - it is empty by default.")
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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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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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template <typename GemmConfig,
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typename ALayout,
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typename BLayout,
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typename CLayout,
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typename ADataType,
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typename BDataType,
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typename AccDataType,
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typename CDataType>
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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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bool splitk = false);
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@@ -0,0 +1,443 @@
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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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#pragma once
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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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template <typename GemmConfig,
|
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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,
|
||||
typename ALayout,
|
||||
typename AQLayout,
|
||||
typename BLayout,
|
||||
typename BQLayout,
|
||||
typename CLayout,
|
||||
typename CDEElementWise = ck_tile::element_wise::PassThrough>
|
||||
float invoke_gemm(int n_warmup,
|
||||
int n_repeat,
|
||||
int group_count,
|
||||
const std::vector<grouped_gemm_kargs>& args)
|
||||
{
|
||||
// Workspace memory allocated to hold the gemm descriptions.
|
||||
ck_tile::DeviceMem gemm_workspace;
|
||||
gemm_workspace.Realloc(get_workspace_size(args));
|
||||
|
||||
float ave_time = 0;
|
||||
|
||||
// NOTE: With the persistent TileLoop kernel, we do not necessarily need to have
|
||||
// the gemm problems known on the host. Instead, we can just pass the pointer
|
||||
// to the kernel and let the workgroups figure out which tiles to work on.
|
||||
// This is useful when the gemm problems are generated dynamically.
|
||||
// In this example however, we generate the `kargs` using the known gemm_descs,
|
||||
// and copy the gemm descriptions to the device memory.
|
||||
// The contents of the memory pointed to by `kargs_ptr` pointer could be
|
||||
// written by e.g. another kernel from earlier stage.
|
||||
std::vector<ck_tile::QuantGemmTransKernelArg> kargs;
|
||||
void* kargs_ptr = gemm_workspace.GetDeviceBuffer();
|
||||
assert(args[0].k_batch == 1);
|
||||
for(const auto& arg : args)
|
||||
{
|
||||
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>(stream, group_count, kargs_ptr);
|
||||
|
||||
std::string op_name{"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 ALayout,
|
||||
typename AQLayout,
|
||||
typename BLayout,
|
||||
typename BQLayout,
|
||||
typename CLayout>
|
||||
int run_grouped_gemm_example_with_layouts(int argc,
|
||||
char* argv[],
|
||||
const ALayout a_layout = ALayout{},
|
||||
const AQLayout aq_layout = AQLayout{},
|
||||
const BLayout b_layout = BLayout{},
|
||||
const BQLayout bq_layout = BQLayout{},
|
||||
[[maybe_unused]] const CLayout c_layout = CLayout{})
|
||||
{
|
||||
auto [result, arg_parser] = create_args(argc, argv);
|
||||
|
||||
if(!result)
|
||||
{
|
||||
return -1;
|
||||
};
|
||||
|
||||
auto valid_input_data = [&](int group_count, const auto&... args) {
|
||||
return !(args.empty() || ...) && group_count == (args.size() == ...);
|
||||
};
|
||||
|
||||
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");
|
||||
bool validate = arg_parser.get_bool("validate");
|
||||
|
||||
if(kbatch > 1 && validate && warmup + repeat > 1)
|
||||
{
|
||||
std::cout << "WARNING: Data validation enabled with SplitK and more than"
|
||||
<< "1 warmup/repeat. Disabling validation." << std::endl;
|
||||
validate = false;
|
||||
}
|
||||
|
||||
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> 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, stride_As, stride_Bs, stride_Cs))
|
||||
{
|
||||
std::cout << "Please check the input data. Default values will be used." << std::endl;
|
||||
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);
|
||||
|
||||
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);
|
||||
}
|
||||
}
|
||||
|
||||
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];
|
||||
|
||||
AQK = 1; // Row quantization: tensor shape [M, 1]. Only for NT
|
||||
BQK = N; // Column quantization: tensor shape [1, N]. Only for NT
|
||||
|
||||
stride_As[i] = ck_tile::get_default_stride(M, K, stride_As[i], is_row_major(a_layout));
|
||||
stride_Bs[i] = ck_tile::get_default_stride(K, N, stride_Bs[i], is_row_major(b_layout));
|
||||
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(aq_layout));
|
||||
stride_BQs[i] = ck_tile::get_default_stride(1, N, stride_BQs[i], is_row_major(bq_layout));
|
||||
a_m_k_tensors.push_back(ck_tile::HostTensor<ADataType>(
|
||||
ck_tile::host_tensor_descriptor(M, K, stride_As[i], is_row_major(a_layout))));
|
||||
b_k_n_tensors.push_back(ck_tile::HostTensor<BDataType>(
|
||||
ck_tile::host_tensor_descriptor(K, N, stride_Bs[i], is_row_major(b_layout))));
|
||||
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(aq_layout))));
|
||||
bq_tensors.push_back(ck_tile::HostTensor<BQDataType>(
|
||||
ck_tile::host_tensor_descriptor(1, N, stride_BQs[i], is_row_major(bq_layout))));
|
||||
|
||||
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;
|
||||
|
||||
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()));
|
||||
|
||||
a_m_k_dev_buf[i]->ToDevice(a_m_k_tensors[i].data());
|
||||
b_k_n_dev_buf[i]->ToDevice(b_k_n_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_gemm<GemmConfig,
|
||||
ADataType,
|
||||
AQDataType,
|
||||
BDataType,
|
||||
BQDataType,
|
||||
AccDataType,
|
||||
CDataType,
|
||||
ALayout,
|
||||
AQLayout,
|
||||
BLayout,
|
||||
BQLayout,
|
||||
CLayout>(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();
|
||||
ck_tile::reference_gemm_rowcol_quant<ADataType,
|
||||
AQDataType,
|
||||
BDataType,
|
||||
BQDataType,
|
||||
AccDataType,
|
||||
CDataType>(
|
||||
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 GemmConfig, typename PrecType>
|
||||
int run_gemm_example_prec_type(std::string a_layout, std::string b_layout, int argc, char* argv[])
|
||||
{
|
||||
using Row = ck_tile::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
|
||||
using Types = GemmTypeConfig<PrecType>;
|
||||
// Specific type aliases for easy access
|
||||
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;
|
||||
if(a_layout == "R" && b_layout == "C")
|
||||
{
|
||||
return run_grouped_gemm_example_with_layouts<GemmConfig,
|
||||
ADataType,
|
||||
AQDataType,
|
||||
BDataType,
|
||||
BQDataType,
|
||||
CDataType,
|
||||
AccDataType>(
|
||||
argc, argv, Row{}, Row{}, Col{}, Col{}, Row{});
|
||||
}
|
||||
else if(a_layout == "R" && b_layout == "R")
|
||||
{
|
||||
return run_grouped_gemm_example_with_layouts<GemmConfig,
|
||||
ADataType,
|
||||
AQDataType,
|
||||
BDataType,
|
||||
BQDataType,
|
||||
CDataType,
|
||||
AccDataType>(
|
||||
argc, argv, Row{}, Row{}, Row{}, Row{}, Row{});
|
||||
}
|
||||
else if(a_layout == "C" && b_layout == "R")
|
||||
{
|
||||
return run_grouped_gemm_example_with_layouts<GemmConfig,
|
||||
ADataType,
|
||||
AQDataType,
|
||||
BDataType,
|
||||
BQDataType,
|
||||
CDataType,
|
||||
AccDataType>(
|
||||
argc, argv, Row{}, Row{}, Col{}, Col{}, Row{});
|
||||
}
|
||||
else if(a_layout == "C" && b_layout == "C")
|
||||
{
|
||||
return run_grouped_gemm_example_with_layouts<GemmConfig,
|
||||
ADataType,
|
||||
AQDataType,
|
||||
BDataType,
|
||||
BQDataType,
|
||||
CDataType,
|
||||
AccDataType>(
|
||||
argc, argv, Col{}, Col{}, Col{}, Col{}, Row{});
|
||||
}
|
||||
else
|
||||
{
|
||||
throw std::runtime_error("Unsupported data layout configuration for A,B and C tensors!");
|
||||
}
|
||||
}
|
||||
|
||||
template <template <typename PrecType> typename GemmConfig>
|
||||
int run_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 data_type = arg_parser.get_str("prec");
|
||||
|
||||
if(data_type == "fp8")
|
||||
{
|
||||
return run_gemm_example_prec_type<GemmConfig<ck_tile::fp8_t>, ck_tile::fp8_t>(
|
||||
a_layout, b_layout, argc, argv);
|
||||
}
|
||||
else
|
||||
{
|
||||
throw std::runtime_error("Unsupported data type configuration.");
|
||||
}
|
||||
}
|
||||
@@ -210,7 +210,7 @@ int run_grouped_gemm_example_with_layouts(int argc,
|
||||
const ck_tile::index_t N = Ns[i];
|
||||
const ck_tile::index_t K = Ks[i];
|
||||
|
||||
stride_As[i] = ck_tile::get_default_stride(M, N, stride_As[i], is_row_major(a_layout));
|
||||
stride_As[i] = ck_tile::get_default_stride(M, K, stride_As[i], is_row_major(a_layout));
|
||||
stride_Bs[i] = ck_tile::get_default_stride(K, N, stride_Bs[i], is_row_major(b_layout));
|
||||
stride_Cs[i] = ck_tile::get_default_stride(M, N, stride_Cs[i], is_row_major(CLayout{}));
|
||||
|
||||
|
||||
Reference in New Issue
Block a user