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[CKTILE] Support A/B Quantization in Blockscale Grouped Gemm (#3452)

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* update grouped_gemm blockwise kernel

* update config

* update kernel

* update examples

* remove test code for now

* sync test files with origin/develop

* update example

* fix code lint

* fix code-lint

* update test code

* run clang format

* run pre-commit

* update api
This commit is contained in:
kyle-256
2026-01-07 04:36:04 +08:00
committed by GitHub
parent 2309c86054
commit 76696ace44
11 changed files with 1798 additions and 3 deletions
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3
example/ck_tile/17_grouped_gemm/CMakeLists.txt
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@@ -14,7 +14,7 @@ if(GPU_TARGETS MATCHES "gfx94|gfx95")
quant_grouped_gemm_bf8_rowcol.cpp
quant_grouped_gemm_bf8_tensor.cpp
)
add_executable(tile_example_abquant_grouped_gemm abquant_grouped_gemm.cpp)
add_executable(tile_example_grouped_gemm_preshuffle grouped_gemm_preshuffle.cpp)
add_executable(tile_example_grouped_gemm_multi_d grouped_gemm_multi_d.cpp)
set(EXAMPLE_GEMM_COMPILE_OPTIONS)
@@ -25,4 +25,5 @@ if(GPU_TARGETS MATCHES "gfx94|gfx95")
target_compile_options(tile_example_grouped_gemm_preshuffle PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
target_compile_options(tile_example_grouped_gemm_multi_d PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
target_compile_options(tile_example_quant_grouped_gemm PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
target_compile_options(tile_example_abquant_grouped_gemm PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
endif()

278
example/ck_tile/17_grouped_gemm/abquant_grouped_gemm.cpp Normal file
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@@ -0,0 +1,278 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#include <hip/hip_runtime.h>
#include <cstring>
#include <iostream>
#include <ostream>
#include <string>
#include <tuple>
#include <memory>
#include <type_traits>
#include "ck_tile/core.hpp"
#include "ck_tile/ops/epilogue.hpp"
#include "ck_tile/ops/gemm.hpp"
#include "ck_tile/ops/gemm/pipeline/tile_gemm_traits.hpp"
#include "ck_tile/ops/gemm_quant.hpp"
#include "ck_tile/host.hpp"
#include "abquant_grouped_gemm.hpp"
// Non-persistent grouped gemm for ABQuant
template <typename GemmConfig,
typename ALayout,
typename AQLayout,
typename BLayout,
typename BQLayout,
typename CLayout,
typename ADataType,
typename AQDataType,
typename BDataType,
typename BQDataType,
typename AccDataType,
typename CDataType,
typename AQuantGroupSize,
typename BQuantGroupSize,
ck_tile::QuantType QuantMode>
float grouped_gemm_abquant(const std::vector<grouped_gemm_kargs>& gemm_descs,
const ck_tile::stream_config& s,
void* kargs_ptr)
{
constexpr ck_tile::index_t TileParitionerGroupNum = 8;
constexpr ck_tile::index_t TileParitionerM01 = 4;
using GemmShape = ck_tile::TileGemmShape<
ck_tile::sequence<GemmConfig::M_Tile, GemmConfig::N_Tile, GemmConfig::K_Tile>,
ck_tile::sequence<GemmConfig::M_Warp, GemmConfig::N_Warp, GemmConfig::K_Warp>,
ck_tile::
sequence<GemmConfig::M_Warp_Tile, GemmConfig::N_Warp_Tile, GemmConfig::K_Warp_Tile>>;
using TilePartitioner = ck_tile::
GemmSpatiallyLocalTilePartitioner<GemmShape, TileParitionerGroupNum, TileParitionerM01>;
using Traits = ck_tile::TileGemmTraits<GemmConfig::kPadM,
GemmConfig::kPadN,
GemmConfig::kPadK,
ALayout,
BLayout,
CLayout>;
using GemmUniversalTraits = ck_tile::TileGemmQuantTraits<GemmConfig::kPadM,
GemmConfig::kPadN,
GemmConfig::kPadK,
false, // PreshuffleQuant
GemmConfig::PreshuffleB,
ALayout,
BLayout,
CLayout,
QuantMode,
AQLayout,
BQLayout,
GemmConfig::TransposeC,
GemmConfig::DoubleSmemBuffer,
GemmConfig::Persistent>;
using GemmPipelineProblem =
ck_tile::GemmPipelineProblem<ADataType, BDataType, AccDataType, GemmShape, Traits>;
using BaseGemmPipeline =
GemmQuantConfig<QuantMode>::template BaseGemmPipeline<GemmPipelineProblem,
GemmConfig::PreshuffleB>;
const ck_tile::index_t k_grain = gemm_descs[0].k_batch * GemmConfig::K_Tile;
const ck_tile::index_t K_split = (gemm_descs[0].K + k_grain - 1) / k_grain * GemmConfig::K_Tile;
const ck_tile::index_t num_loop = TilePartitioner::GetLoopNum(K_split);
const bool has_hot_loop = BaseGemmPipeline::BlockHasHotloop(num_loop);
const ck_tile::TailNumber tail_num = BaseGemmPipeline::GetBlockLoopTailNum(num_loop);
float ave_time{0};
const auto Run = [&](const auto has_hot_loop_, const auto tail_number_) {
constexpr bool has_hot_loop_v = has_hot_loop_.value;
constexpr auto tail_number_v = tail_number_.value;
constexpr auto scheduler = GemmConfig::Scheduler;
using QuantGemmProblem = ck_tile::GemmABQuantPipelineProblem<ADataType,
AQDataType,
BDataType,
BQDataType,
AccDataType,
GemmShape,
GemmUniversalTraits,
AQuantGroupSize,
BQuantGroupSize,
GemmConfig::TransposeC,
BDataType,
scheduler,
has_hot_loop_v,
tail_number_v>;
using GemmPipeline =
GemmQuantConfig<QuantMode>::template GemmPipeline<QuantGemmProblem,
GemmConfig::PreshuffleB>;
using GemmEpilogue = ck_tile::CShuffleEpilogue<
ck_tile::CShuffleEpilogueProblem<ADataType,
BDataType,
ck_tile::tuple<>,
AccDataType,
CDataType,
ck_tile::tuple<>,
CLayout,
ck_tile::element_wise::PassThrough,
TilePartitioner::MPerBlock,
TilePartitioner::NPerBlock,
GemmConfig::M_Warp,
GemmConfig::N_Warp,
GemmConfig::M_Warp_Tile,
GemmConfig::N_Warp_Tile,
GemmConfig::K_Warp_Tile,
QuantGemmProblem::TransposeC>>;
using Kernel = ck_tile::QuantGroupedGemmKernel<TilePartitioner,
GemmPipeline,
GemmEpilogue,
GemmUniversalTraits::kQuantType>;
auto kargs = Kernel::MakeKargs(gemm_descs);
if(!Kernel::IsSupportedArgument(kargs))
{
throw std::runtime_error("Kernel arguments not supported!");
}
const dim3 blocks = Kernel::BlockSize();
const dim3 grids = Kernel::GridSize(gemm_descs);
HIP_CHECK_ERROR(hipMemcpyWithStream(kargs_ptr,
kargs.data(),
get_workspace_size(gemm_descs),
hipMemcpyHostToDevice,
s.stream_id_));
if(s.log_level_ > 0)
{
std::cout << "Launching kernel: " << Kernel::GetName() << " with args:" << " grid: {"
<< grids.x << ", " << grids.y << ", " << grids.z << "}" << ", blocks: {"
<< blocks.x << ", " << blocks.y << ", " << blocks.z << "}" << std::endl;
}
return ave_time = ck_tile::launch_kernel(
s,
ck_tile::make_kernel<GemmConfig::kBlockPerCu>(
Kernel{},
grids,
blocks,
0,
ck_tile::cast_pointer_to_constant_address_space(kargs_ptr),
gemm_descs.size()));
};
return ave_time = BaseGemmPipeline::TailHandler(Run, has_hot_loop, tail_num);
}
// Persistent grouped gemm tileloop for ABQuant
template <typename GemmConfig,
typename ALayout,
typename AQLayout,
typename BLayout,
typename BQLayout,
typename CLayout,
typename ADataType,
typename AQDataType,
typename BDataType,
typename BQDataType,
typename AccDataType,
typename CDataType,
typename AQuantGroupSize,
typename BQuantGroupSize,
ck_tile::QuantType QuantMode>
float grouped_gemm_tileloop(const ck_tile::stream_config& s,
const ck_tile::index_t num_groups,
void* kargs_ptr)
{
constexpr ck_tile::index_t TileParitionerGroupNum = 8;
constexpr ck_tile::index_t TileParitionerM01 = 4;
using GemmShape = ck_tile::TileGemmShape<
ck_tile::sequence<GemmConfig::M_Tile, GemmConfig::N_Tile, GemmConfig::K_Tile>,
ck_tile::sequence<GemmConfig::M_Warp, GemmConfig::N_Warp, GemmConfig::K_Warp>,
ck_tile::
sequence<GemmConfig::M_Warp_Tile, GemmConfig::N_Warp_Tile, GemmConfig::K_Warp_Tile>>;
using TilePartitioner = ck_tile::
GemmSpatiallyLocalTilePartitioner<GemmShape, TileParitionerGroupNum, TileParitionerM01>;
using GemmUniversalTraits = ck_tile::TileGemmQuantTraits<GemmConfig::kPadM,
GemmConfig::kPadN,
GemmConfig::kPadK,
false, // PreshuffleQuant
GemmConfig::PreshuffleB,
ALayout,
BLayout,
CLayout,
QuantMode,
AQLayout,
BQLayout,
GemmConfig::TransposeC,
GemmConfig::DoubleSmemBuffer,
GemmConfig::Persistent>;
using QuantGemmProblem = ck_tile::GemmABQuantPipelineProblem<ADataType,
AQDataType,
BDataType,
BQDataType,
AccDataType,
GemmShape,
GemmUniversalTraits,
AQuantGroupSize,
BQuantGroupSize,
GemmConfig::TransposeC>;
using GemmPipeline = GemmQuantConfig<QuantMode>::template GemmPipeline<QuantGemmProblem,
GemmConfig::PreshuffleB>;
using GemmEpilogue = ck_tile::CShuffleEpilogue<
ck_tile::CShuffleEpilogueProblem<ADataType,
BDataType,
ck_tile::tuple<>,
AccDataType,
CDataType,
ck_tile::tuple<>,
CLayout,
ck_tile::element_wise::PassThrough,
TilePartitioner::MPerBlock,
TilePartitioner::NPerBlock,
GemmConfig::M_Warp,
GemmConfig::N_Warp,
GemmConfig::M_Warp_Tile,
GemmConfig::N_Warp_Tile,
GemmConfig::K_Warp_Tile,
QuantGemmProblem::TransposeC>>;
using Kernel = ck_tile::QuantGroupedGemmKernel<TilePartitioner,
GemmPipeline,
GemmEpilogue,
GemmUniversalTraits::kQuantType>;
const dim3 blocks = Kernel::BlockSize();
const dim3 grids = Kernel::MaxOccupancyGridSize(s);
if(s.log_level_ > 0)
{
std::cout << "Launching kernel: " << Kernel::GetName() << " with args:" << " grid: {"
<< grids.x << ", " << grids.y << ", " << grids.z << "}" << ", blocks: {"
<< blocks.x << ", " << blocks.y << ", " << blocks.z << "}" << std::endl;
}
return ck_tile::launch_kernel(s,
ck_tile::make_kernel<GemmConfig::kBlockPerCu>(
Kernel{},
grids,
blocks,
0,
ck_tile::cast_pointer_to_constant_address_space(kargs_ptr),
num_groups));
}
#include "run_grouped_gemm_abquant_example.inc"
int main(int argc, char* argv[])
{
int result1 = run_abquant_grouped_gemm_example(argc, argv);
return result1;
}

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example/ck_tile/17_grouped_gemm/abquant_grouped_gemm.hpp Normal file
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@@ -0,0 +1,171 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#pragma once
#include <string>
#include <tuple>
#include "ck_tile/core.hpp"
#include "ck_tile/host/kernel_launch.hpp"
#include "ck_tile/ops/gemm.hpp"
#include "ck_tile/utility/json_dump.hpp"
template <typename DataType>
struct GemmTypeConfig;
template <>
struct GemmTypeConfig<ck_tile::fp8_t>
{
using ADataType = ck_tile::fp8_t;
using BDataType = ck_tile::fp8_t;
using AccDataType = float;
using CDataType = ck_tile::half_t;
};
template <>
struct GemmTypeConfig<ck_tile::bf8_t>
{
using ADataType = ck_tile::bf8_t;
using BDataType = ck_tile::bf8_t;
using AccDataType = float;
using CDataType = ck_tile::half_t;
};
template <bool Persistent_>
struct GemmConfigBase
{
static constexpr bool kPadM = false;
static constexpr bool kPadN = false;
static constexpr bool kPadK = false;
static constexpr bool PermuteA = false;
static constexpr bool PermuteB = false;
static constexpr bool TransposeC = false;
static constexpr bool UseStructuredSparsity = false;
static constexpr int kBlockPerCu = 1;
static constexpr ck_tile::index_t TileParitionerGroupNum = 8;
static constexpr ck_tile::index_t TileParitionerM01 = 4;
static constexpr auto Scheduler = ck_tile::GemmPipelineScheduler::Intrawave;
static constexpr ck_tile::index_t NumWaveGroups = 1;
static constexpr bool DoubleSmemBuffer = false;
static constexpr bool PreshuffleB = false;
static constexpr bool Persistent = Persistent_;
};
template <typename PrecType, bool Persistent>
struct GemmConfigComputeV3_2 : public GemmConfigBase<Persistent>
{
static constexpr ck_tile::index_t M_Tile = 128;
static constexpr ck_tile::index_t N_Tile = 128;
static constexpr ck_tile::index_t K_Tile = 128 / sizeof(PrecType);
static constexpr ck_tile::index_t M_Warp = 1;
static constexpr ck_tile::index_t N_Warp = 4;
static constexpr ck_tile::index_t K_Warp = 1;
static constexpr ck_tile::index_t M_Warp_Tile = 16;
static constexpr ck_tile::index_t N_Warp_Tile = 16;
static constexpr ck_tile::index_t K_Warp_Tile =
ck_tile::get_k_warp_tile<PrecType, M_Warp_Tile>();
};
template <ck_tile::QuantType QuantMode>
struct GemmQuantConfig;
// ABQuant specialization for GemmQuantConfig
template <>
struct GemmQuantConfig<ck_tile::QuantType::ABQuantGrouped>
{
template <typename PrecType, bool Persistent>
using GemmConfig = GemmConfigComputeV3_2<PrecType, Persistent>;
template <typename GemmProblem, bool PreshuffleB = false>
using GemmPipeline = ck_tile::ABQuantGemmPipelineAgBgCrCompV3<GemmProblem>;
template <typename GemmProblem, bool PreshuffleB = false>
using BaseGemmPipeline = ck_tile::BaseGemmPipelineAgBgCrCompV3<GemmProblem>;
};
using grouped_gemm_kargs = ck_tile::QuantGroupedGemmHostArgs;
auto create_args(int argc, char* argv[])
{
ck_tile::ArgParser arg_parser;
arg_parser.insert("Ms", "", "M dimensions - empty by default.")
.insert("Ns", "", "N dimensions - empty by default.")
.insert("Ks", "", "K dimensions - empty by default.")
.insert(
"stride_As",
"",
"Tensor A strides - it is empty by default.") // stride_As/stride_Bs/stride_Cs/stride_AQs/stride_BQs
// can be set to zero if
// Ms/Ns/Ks is not empty
.insert("stride_Bs", "", "Tensor B strides - it is empty by default.")
.insert("stride_Cs", "", "Tensor C strides - it is empty by default.")
.insert("stride_AQs", "", "Tensor AQ strides - it is empty by default.")
.insert("stride_BQs", "", "Tensor BQ strides - it is empty by default.")
.insert("a_layout", "R", "A tensor data layout - Row by default.")
.insert("b_layout", "C", "B tensor data layout - Row by default.")
.insert("c_layout", "R", "C tensor data layout - Row by default.")
.insert("validate", "1", "0. No validation, 1. Validation on CPU.")
.insert("prec", "fp8", "data type. fp16/bf16/fp8/bf8")
.insert("warmup", "10", "number of iterations before benchmark the kernel.")
.insert("repeat", "100", "number of iterations to benchmark the kernel.")
.insert("group_count", "8", "group count.")
.insert("kbatch", "1", "kbatch for SplitK")
.insert("init", "0", "0. Random, 2. One(s) (Constant)")
.insert("persistent", "0", "Kernel persistency. 0: non-persistent. 1: persistent.")
.insert("bquant_group_size", "1x1x128", "BQuant group size. 1x1x128 (default) or 1x128x128")
.insert("json", "0", "0: No Json, 1: Dump Results in Json format")
.insert("jsonfile", "abquant_grouped_gemm.json", "json file name to dump results");
bool result = arg_parser.parse(argc, argv);
return std::make_tuple(result, arg_parser);
}
inline std::size_t get_workspace_size(const std::vector<grouped_gemm_kargs>& gemm_descs)
{
return gemm_descs.size() * sizeof(ck_tile::QuantGemmTransKernelArg);
}
// Forward declaration of the non-persistent version
template <typename GemmConfig,
typename ALayout,
typename AQLayout,
typename BLayout,
typename BQLayout,
typename CLayout,
typename ADataType,
typename AQDataType,
typename BDataType,
typename BQDataType,
typename AccDataType,
typename CDataType,
typename AQuantGroupSize,
typename BQuantGroupSize,
ck_tile::QuantType QuantMode = ck_tile::QuantType::ABQuantGrouped>
float grouped_gemm_abquant(const std::vector<grouped_gemm_kargs>& gemm_descs,
const ck_tile::stream_config& s,
void* kargs_ptr);
// Forward declaration of the tileloop version for persistent kernels
template <typename GemmConfig,
typename ALayout,
typename AQLayout,
typename BLayout,
typename BQLayout,
typename CLayout,
typename ADataType,
typename AQDataType,
typename BDataType,
typename BQDataType,
typename AccDataType,
typename CDataType,
typename AQuantGroupSize,
typename BQuantGroupSize,
ck_tile::QuantType QuantMode = ck_tile::QuantType::ABQuantGrouped>
float grouped_gemm_tileloop(const ck_tile::stream_config& s,
const ck_tile::index_t num_groups,
void* kargs_ptr);

604
example/ck_tile/17_grouped_gemm/run_grouped_gemm_abquant_example.inc Normal file
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@@ -0,0 +1,604 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#pragma once
template <typename Layout>
static constexpr inline auto is_row_major(Layout layout_)
{
return ck_tile::bool_constant<std::is_same_v<ck_tile::remove_cvref_t<decltype(layout_)>,
ck_tile::tensor_layout::gemm::RowMajor>>{};
}
template <typename ADataType, typename BDataType, typename AccDataType, typename CDataType>
auto calculate_rtol_atol(const ck_tile::index_t K,
const ck_tile::index_t kbatch,
const float max_accumulated_value)
{
using ComputeType =
std::conditional_t<sizeof(ADataType) < sizeof(BDataType), ADataType, BDataType>;
// Calculate thresholds
const auto rtol = ck_tile::get_relative_threshold<ComputeType, CDataType, AccDataType>(
ck_tile::integer_divide_ceil(K, kbatch));
const auto atol = ck_tile::get_absolute_threshold<ComputeType, CDataType, AccDataType>(
max_accumulated_value / kbatch, ck_tile::integer_divide_ceil(K, kbatch));
// Calculate error due to split_k accumulation
const auto rtol_split_k =
ck_tile::get_relative_threshold<CDataType, CDataType, CDataType>(kbatch);
const auto atol_split_k = ck_tile::get_absolute_threshold<CDataType, CDataType, CDataType>(
max_accumulated_value, kbatch);
// Use higher threshold
return ck_tile::make_tuple(std::max(rtol, rtol_split_k), std::max(atol, atol_split_k));
}
template <typename GemmConfig,
typename ADataType,
typename AQDataType,
typename BDataType,
typename BQDataType,
typename AccDataType,
typename CDataType,
typename ALayout,
typename AQLayout,
typename BLayout,
typename BQLayout,
typename CLayout,
typename AQuantGroupSize,
typename BQuantGroupSize,
ck_tile::QuantType QuantMode = ck_tile::QuantType::ABQuantGrouped,
typename CDEElementWise = ck_tile::element_wise::PassThrough>
float invoke_abquant_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;
if constexpr(!GemmConfig::Persistent)
{
ave_time = grouped_gemm_abquant<GemmConfig,
ALayout,
AQLayout,
BLayout,
BQLayout,
CLayout,
ADataType,
AQDataType,
BDataType,
BQDataType,
AccDataType,
CDataType,
AQuantGroupSize,
BQuantGroupSize,
QuantMode>(
args,
ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat},
gemm_workspace.GetDeviceBuffer());
}
else
{
// 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();
if(args[0].k_batch != 1)
{
throw std::runtime_error("Split-K not supported yet for persistent kernel");
}
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,
AQuantGroupSize,
BQuantGroupSize,
QuantMode>(stream, group_count, kargs_ptr);
}
return ave_time;
}
template <typename GemmConfig,
typename ADataType,
typename AQDataType,
typename BDataType,
typename BQDataType,
typename CDataType,
typename AccDataType,
typename AQuantGroupSize,
typename BQuantGroupSize,
ck_tile::QuantType QuantMode,
typename ALayout,
typename AQLayout,
typename BLayout,
typename BQLayout,
typename CLayout>
int run_abquant_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);
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::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> 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, stride_As, stride_Bs, stride_Cs, stride_AQs, stride_BQs))
{
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);
}
}
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];
// For ABQuantGrouped, both A and B need quantization
static_assert(QuantMode == ck_tile::QuantType::ABQuantGrouped,
"This file only supports ABQuantGrouped mode");
AQK = K / AQuantGroupSize::kK; // Group quantization: AQK = K / AQuantGroupSize
BQK = K / BQuantGroupSize::kK; // Group quantization: BQK = K / BQuantGroupSize
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(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(BQK, 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(BQK, 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;
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()));
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]});
}
float ave_time = invoke_abquant_gemm<GemmConfig,
ADataType,
AQDataType,
BDataType,
BQDataType,
AccDataType,
CDataType,
ALayout,
AQLayout,
BLayout,
BQLayout,
CLayout,
AQuantGroupSize,
BQuantGroupSize,
QuantMode>(warmup, repeat, group_count, gemm_descs);
std::string op_name = "ABQuant Grouped Gemm (" + ck_tile::quant_type_to_string(QuantMode) + ")";
std::size_t flop = 0, num_btype = 0;
for(int j = 0; j < group_count; ++j)
{
flop += std::size_t(2) * gemm_descs[j].M * gemm_descs[j].N * gemm_descs[j].K;
num_btype += sizeof(ADataType) * gemm_descs[j].M * gemm_descs[j].K +
sizeof(BDataType) * gemm_descs[j].K * gemm_descs[j].N +
sizeof(CDataType) * gemm_descs[j].M * gemm_descs[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;
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 implementation 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! in group [" + std::to_string(i) + "]",
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;
}
if(arg_parser.get_int("json") == 1)
{
dump_grouped_gemm_json_results<ALayout, BLayout, CLayout>(arg_parser.get_str("jsonfile"),
op_name,
group_count,
pass,
ave_time,
tflops,
gb_per_sec);
}
return pass;
}
template <typename PrecType, typename GemmConfig, typename BQuantGroupSize>
int run_abquant_grouped_gemm_example_prec_type_with_bquant(
std::string a_layout, std::string b_layout, std::string c_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;
using AQuantGroupSize = ck_tile::QuantGroupShape<ck_tile::sequence<1, 1, 128>>;
constexpr auto QuantMode = ck_tile::QuantType::ABQuantGrouped;
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,
QuantMode>(
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,
QuantMode>(
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,
QuantMode>(
argc, argv, Col{}, Row{}, Row{}, Col{}, Row{});
}
else
{
throw std::runtime_error("Unsupported data layout configuration for A,B and C tensors!");
}
}
template <typename PrecType, typename GemmConfig>
int run_abquant_grouped_gemm_example_prec_type(std::string a_layout,
std::string b_layout,
std::string c_layout,
std::string bquant_group_size,
int argc,
char* argv[])
{
if(bquant_group_size == "1x1x128")
{
using BQuantGroupSize = ck_tile::QuantGroupShape<ck_tile::sequence<1, 1, 128>>;
return run_abquant_grouped_gemm_example_prec_type_with_bquant<PrecType,
GemmConfig,
BQuantGroupSize>(
a_layout, b_layout, c_layout, argc, argv);
}
else if(bquant_group_size == "1x128x128")
{
using BQuantGroupSize = ck_tile::QuantGroupShape<ck_tile::sequence<1, 128, 128>>;
return run_abquant_grouped_gemm_example_prec_type_with_bquant<PrecType,
GemmConfig,
BQuantGroupSize>(
a_layout, b_layout, c_layout, argc, argv);
}
else
{
throw std::runtime_error("Unsupported BQuantGroupSize! Use 1x1x128 or 1x128x128.");
}
}
template <typename PrecType>
int run_abquant_gemm_example_persistency(std::string a_layout,
std::string b_layout,
std::string c_layout,
bool persistent,
std::string bquant_group_size,
int argc,
char* argv[])
{
if(persistent)
{
using GemmConfig = typename GemmQuantConfig<
ck_tile::QuantType::ABQuantGrouped>::template GemmConfig<PrecType, true>;
return run_abquant_grouped_gemm_example_prec_type<PrecType, GemmConfig>(
a_layout, b_layout, c_layout, bquant_group_size, argc, argv);
}
else
{
using GemmConfig = typename GemmQuantConfig<
ck_tile::QuantType::ABQuantGrouped>::template GemmConfig<PrecType, false>;
return run_abquant_grouped_gemm_example_prec_type<PrecType, GemmConfig>(
a_layout, b_layout, c_layout, bquant_group_size, argc, argv);
}
}
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");
bool persistent = arg_parser.get_bool("persistent");
const std::string bquant_group_size = arg_parser.get_str("bquant_group_size");
if(data_type == "fp8")
{
return run_abquant_gemm_example_persistency<ck_tile::fp8_t>(
a_layout, b_layout, c_layout, persistent, bquant_group_size, argc, argv);
}
else if(data_type == "bf8")
{
return run_abquant_gemm_example_persistency<ck_tile::bf8_t>(
a_layout, b_layout, c_layout, persistent, bquant_group_size, argc, argv);
}
else
{
throw std::runtime_error("Unsupported data type configuration.");
}
}

17
include/ck_tile/ops/gemm_quant/kernel/grouped_gemm_quant_kernel.hpp
View File

@@ -484,6 +484,17 @@ struct QuantGroupedGemmKernel
tail_num,
smem_ptr);
}
else if constexpr(kQuantType == QuantType::ABQuantGrouped)
{
return GemmPipeline{}.template operator()(a_block_window,
b_block_window,
aq_block_window,
bq_block_window,
num_loop,
has_hot_loop,
tail_num,
smem_ptr);
}
else if constexpr(kQuantType == QuantType::RowColQuant ||
kQuantType == QuantType::TensorQuant)
{
@@ -499,7 +510,8 @@ struct QuantGroupedGemmKernel
c_ptr, kargs, block_idx_m, block_idx_n);
if constexpr(kQuantType == QuantType::AQuantGrouped ||
kQuantType == QuantType::BQuantGrouped)
kQuantType == QuantType::BQuantGrouped ||
kQuantType == QuantType::ABQuantGrouped)
{
EpiloguePipeline{}(c_block_window, c_block_tile, c_block_window, smem_ptr);
}
@@ -527,7 +539,8 @@ struct QuantGroupedGemmKernel
c_ptr, kargs, block_idx_m, block_idx_n);
if constexpr(kQuantType == QuantType::AQuantGrouped ||
kQuantType == QuantType::BQuantGrouped)
kQuantType == QuantType::BQuantGrouped ||
kQuantType == QuantType::ABQuantGrouped)
{
EpiloguePipeline{}(c_block_window, c_block_tile, c_block_window, smem_ptr);
}

1
test/ck_tile/CMakeLists.txt
View File

@@ -9,6 +9,7 @@ add_subdirectory(grouped_gemm)
add_subdirectory(grouped_gemm_preshuffle)
add_subdirectory(grouped_gemm_multi_d)
add_subdirectory(grouped_gemm_quant)
add_subdirectory(grouped_gemm_abquant)
add_subdirectory(gemm_multi_d)
add_subdirectory(gemm_multi_abd)
add_subdirectory(gemm_streamk)

16
test/ck_tile/grouped_gemm_abquant/CMakeLists.txt Normal file
View File

@@ -0,0 +1,16 @@
# Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
# SPDX-License-Identifier: MIT
set(EXAMPLE_GEMM_COMPILE_OPTIONS)
if(CK_USE_OCP_FP8)
list(APPEND EXAMPLE_GEMM_COMPILE_OPTIONS -DCK_TILE_USE_OCP_FP8)
endif()
if(GPU_TARGETS MATCHES "gfx94|gfx95")
add_gtest_executable(test_ck_tile_grouped_gemm_abquant_1x1x128 test_grouped_gemm_abquant_1x1x128.cpp)
target_compile_options(test_ck_tile_grouped_gemm_abquant_1x1x128 PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
add_gtest_executable(test_ck_tile_grouped_gemm_abquant_1x128x128 test_grouped_gemm_abquant_1x128x128.cpp)
target_compile_options(test_ck_tile_grouped_gemm_abquant_1x128x128 PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
endif()

47
test/ck_tile/grouped_gemm_abquant/test_grouped_gemm_abquant_1x128x128.cpp Normal file
View File

@@ -0,0 +1,47 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#include <tuple>
#include "gtest/gtest.h"
#include "ck_tile/host.hpp"
#include "test_grouped_gemm_abquant_util.hpp"
using F16 = ck_tile::half_t;
using F32 = float;
using FP8 = ck_tile::fp8_t;
using BF8 = ck_tile::bf8_t;
using Row = ck_tile::tensor_layout::gemm::RowMajor;
using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
using True = ck_tile::bool_constant<true>;
using False = ck_tile::bool_constant<false>;
// AQuant group size is fixed at 1x1x128
using AQuantGroupSize = ck_tile::QuantGroupShape<ck_tile::sequence<1, 1, 128>>;
// BQuant group size: 1x128x128
using BQuantGroupSize_1x128x128 = ck_tile::QuantGroupShape<ck_tile::sequence<1, 128, 128>>;
// clang-format off
using KernelTypes_ABQuant_1x128x128 = ::testing::Types<
// ALayout, BLayout, CLayout, ADataType, AQDataType, BDataType, BQDataType, AccDataType, CDataType, AQuantGroupSize, BQuantGroupSize, Persistent
// FP8 variants
std::tuple< Row, Col, Row, FP8, F32, FP8, F32, F32, F16, AQuantGroupSize, BQuantGroupSize_1x128x128, False>,
std::tuple< Row, Col, Row, FP8, F32, FP8, F32, F32, F16, AQuantGroupSize, BQuantGroupSize_1x128x128, True>,
std::tuple< Row, Row, Row, FP8, F32, FP8, F32, F32, F16, AQuantGroupSize, BQuantGroupSize_1x128x128, False>,
std::tuple< Row, Row, Row, FP8, F32, FP8, F32, F32, F16, AQuantGroupSize, BQuantGroupSize_1x128x128, True>,
std::tuple< Col, Row, Row, FP8, F32, FP8, F32, F32, F16, AQuantGroupSize, BQuantGroupSize_1x128x128, False>,
std::tuple< Col, Row, Row, FP8, F32, FP8, F32, F32, F16, AQuantGroupSize, BQuantGroupSize_1x128x128, True>,
// BF8 variants
std::tuple< Row, Col, Row, BF8, F32, BF8, F32, F32, F16, AQuantGroupSize, BQuantGroupSize_1x128x128, False>,
std::tuple< Row, Col, Row, BF8, F32, BF8, F32, F32, F16, AQuantGroupSize, BQuantGroupSize_1x128x128, True>
>;
// clang-format on
TYPED_TEST_SUITE(TestCkTileGroupedGemmABQuant_1x128x128, KernelTypes_ABQuant_1x128x128);
#define TEST_CLASS_NAME TestCkTileGroupedGemmABQuant_1x128x128
#include "test_grouped_gemm_abquant_ut_cases.inc"
#undef TEST_CLASS_NAME

47
test/ck_tile/grouped_gemm_abquant/test_grouped_gemm_abquant_1x1x128.cpp Normal file
View File

@@ -0,0 +1,47 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#include <tuple>
#include "gtest/gtest.h"
#include "ck_tile/host.hpp"
#include "test_grouped_gemm_abquant_util.hpp"
using F16 = ck_tile::half_t;
using F32 = float;
using FP8 = ck_tile::fp8_t;
using BF8 = ck_tile::bf8_t;
using Row = ck_tile::tensor_layout::gemm::RowMajor;
using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
using True = ck_tile::bool_constant<true>;
using False = ck_tile::bool_constant<false>;
// AQuant group size is fixed at 1x1x128
using AQuantGroupSize = ck_tile::QuantGroupShape<ck_tile::sequence<1, 1, 128>>;
// BQuant group size: 1x1x128
using BQuantGroupSize_1x1x128 = ck_tile::QuantGroupShape<ck_tile::sequence<1, 1, 128>>;
// clang-format off
using KernelTypes_ABQuant_1x1x128 = ::testing::Types<
// ALayout, BLayout, CLayout, ADataType, AQDataType, BDataType, BQDataType, AccDataType, CDataType, AQuantGroupSize, BQuantGroupSize, Persistent
// FP8 variants
std::tuple< Row, Col, Row, FP8, F32, FP8, F32, F32, F16, AQuantGroupSize, BQuantGroupSize_1x1x128, False>,
std::tuple< Row, Col, Row, FP8, F32, FP8, F32, F32, F16, AQuantGroupSize, BQuantGroupSize_1x1x128, True>,
std::tuple< Row, Row, Row, FP8, F32, FP8, F32, F32, F16, AQuantGroupSize, BQuantGroupSize_1x1x128, False>,
std::tuple< Row, Row, Row, FP8, F32, FP8, F32, F32, F16, AQuantGroupSize, BQuantGroupSize_1x1x128, True>,
std::tuple< Col, Row, Row, FP8, F32, FP8, F32, F32, F16, AQuantGroupSize, BQuantGroupSize_1x1x128, False>,
std::tuple< Col, Row, Row, FP8, F32, FP8, F32, F32, F16, AQuantGroupSize, BQuantGroupSize_1x1x128, True>,
// BF8 variants
std::tuple< Row, Col, Row, BF8, F32, BF8, F32, F32, F16, AQuantGroupSize, BQuantGroupSize_1x1x128, False>,
std::tuple< Row, Col, Row, BF8, F32, BF8, F32, F32, F16, AQuantGroupSize, BQuantGroupSize_1x1x128, True>
>;
// clang-format on
TYPED_TEST_SUITE(TestCkTileGroupedGemmABQuant_1x1x128, KernelTypes_ABQuant_1x1x128);
#define TEST_CLASS_NAME TestCkTileGroupedGemmABQuant_1x1x128
#include "test_grouped_gemm_abquant_ut_cases.inc"
#undef TEST_CLASS_NAME

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// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#pragma once
TYPED_TEST(TEST_CLASS_NAME, Basic)
{
const int group_count = 6;
std::vector<int> Ms;
std::vector<int> Ns;
std::vector<int> Ks;
std::vector<int> stride_As;
std::vector<int> stride_Bs;
std::vector<int> stride_Cs;
std::vector<int> stride_AQs;
std::vector<int> stride_BQs;
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);
}
this->Run(Ms, Ns, Ks, stride_As, stride_Bs, stride_Cs, stride_AQs, stride_BQs, group_count);
}
// No Hot Loop Test Case, this is to test the correctness of the kernel when there is no hot loop
// Using 256x256x128 to match the test kernel's tile size (M_Tile=128, N_Tile=128, K_Tile=128)
TYPED_TEST(TEST_CLASS_NAME, SmallUniform)
{
const int group_count = 2;
std::vector<int> Ms;
std::vector<int> Ns;
std::vector<int> Ks;
std::vector<int> stride_As;
std::vector<int> stride_Bs;
std::vector<int> stride_Cs;
std::vector<int> stride_AQs;
std::vector<int> stride_BQs;
for(int i = 0; i < group_count; i++)
{
Ms.push_back(256);
Ns.push_back(256);
Ks.push_back(256);
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);
}
this->Run(Ms, Ns, Ks, stride_As, stride_Bs, stride_Cs, stride_AQs, stride_BQs, group_count);
}
TYPED_TEST(TEST_CLASS_NAME, OddTail)
{
const int group_count = 2;
std::vector<int> Ms;
std::vector<int> Ns;
std::vector<int> Ks;
std::vector<int> stride_As;
std::vector<int> stride_Bs;
std::vector<int> stride_Cs;
std::vector<int> stride_AQs;
std::vector<int> stride_BQs;
for(int i = 0; i < group_count; i++)
{
Ms.push_back(256);
Ns.push_back(256);
Ks.push_back(128);
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);
}
this->Run(Ms, Ns, Ks, stride_As, stride_Bs, stride_Cs, stride_AQs, stride_BQs, group_count);
}

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// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#pragma once
#include <sstream>
#include <gtest/gtest.h>
#include <type_traits>
#include "ck_tile/core.hpp"
#include "ck_tile/host.hpp"
#include "ck_tile/host/kernel_launch.hpp"
#include "ck_tile/ops/epilogue.hpp"
#include "ck_tile/ops/gemm.hpp"
#include "ck_tile/ops/gemm/pipeline/tile_gemm_traits.hpp"
#include "ck_tile/ops/gemm_quant.hpp"
template <typename Tuple>
class TestCkTileGroupedGemmABQuant : public ::testing::Test
{
protected:
using ALayout = std::tuple_element_t<0, Tuple>;
using BLayout = std::tuple_element_t<1, Tuple>;
using CLayout = std::tuple_element_t<2, Tuple>;
using ADataType = std::tuple_element_t<3, Tuple>;
using AQDataType = std::tuple_element_t<4, Tuple>;
using BDataType = std::tuple_element_t<5, Tuple>;
using BQDataType = std::tuple_element_t<6, Tuple>;
using AccDataType = std::tuple_element_t<7, Tuple>;
using CDataType = std::tuple_element_t<8, Tuple>;
using AQuantGroupSize = std::tuple_element_t<9, Tuple>;
using BQuantGroupSize = std::tuple_element_t<10, Tuple>;
static constexpr bool Persistent = std::tuple_element_t<11, Tuple>::value;
using Row = ck_tile::tensor_layout::gemm::RowMajor;
using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
using AQLayout = Row;
using BQLayout = Col;
static constexpr auto QuantMode = ck_tile::QuantType::ABQuantGrouped;
struct GemmConfig
{
static constexpr bool kPadM = false;
static constexpr bool kPadN = false;
static constexpr bool kPadK = false;
static constexpr int kBlockPerCu = 1;
static constexpr ck_tile::index_t M_Tile = 128;
static constexpr ck_tile::index_t N_Tile = 128;
static constexpr ck_tile::index_t K_Tile = 128 / sizeof(ADataType);
static constexpr ck_tile::index_t M_Warp = 1;
static constexpr ck_tile::index_t N_Warp = 4;
static constexpr ck_tile::index_t K_Warp = 1;
static constexpr ck_tile::index_t M_Warp_Tile = 16;
static constexpr ck_tile::index_t N_Warp_Tile = 16;
static constexpr ck_tile::index_t K_Warp_Tile =
ck_tile::get_k_warp_tile<ADataType, M_Warp_Tile>();
static constexpr bool PreshuffleB = false;
static constexpr bool TransposeC = false;
static constexpr bool DoubleSmemBuffer = false;
static constexpr auto Scheduler = ck_tile::GemmPipelineScheduler::Intrawave;
static constexpr bool IsPersistent = Persistent;
};
using grouped_gemm_kargs = ck_tile::QuantGroupedGemmHostArgs;
std::size_t get_workspace_size(const std::vector<grouped_gemm_kargs>& gemm_descs)
{
return gemm_descs.size() * sizeof(ck_tile::QuantGemmTransKernelArg);
}
template <typename Layout>
static constexpr inline auto is_row_major(Layout layout_)
{
return ck_tile::bool_constant<std::is_same_v<ck_tile::remove_cvref_t<decltype(layout_)>,
ck_tile::tensor_layout::gemm::RowMajor>>{};
}
auto calculate_rtol_atol(const ck_tile::index_t K,
const ck_tile::index_t kbatch,
const float max_accumulated_value)
{
using ComputeType =
std::conditional_t<sizeof(ADataType) < sizeof(BDataType), ADataType, BDataType>;
const auto rtol = ck_tile::get_relative_threshold<ComputeType, CDataType, AccDataType>(
ck_tile::integer_divide_ceil(K, kbatch));
const auto atol = ck_tile::get_absolute_threshold<ComputeType, CDataType, AccDataType>(
max_accumulated_value / kbatch, ck_tile::integer_divide_ceil(K, kbatch));
const auto rtol_split_k =
ck_tile::get_relative_threshold<CDataType, CDataType, CDataType>(kbatch);
const auto atol_split_k = ck_tile::get_absolute_threshold<CDataType, CDataType, CDataType>(
max_accumulated_value, kbatch);
return ck_tile::make_tuple(std::max(rtol, rtol_split_k), std::max(atol, atol_split_k));
}
template <typename Config>
float invoke_grouped_gemm_abquant(const std::vector<grouped_gemm_kargs>& gemm_descs,
const ck_tile::stream_config& s,
void* kargs_ptr)
{
constexpr ck_tile::index_t TileParitionerGroupNum = 8;
constexpr ck_tile::index_t TileParitionerM01 = 4;
using GemmShape = ck_tile::TileGemmShape<
ck_tile::sequence<Config::M_Tile, Config::N_Tile, Config::K_Tile>,
ck_tile::sequence<Config::M_Warp, Config::N_Warp, Config::K_Warp>,
ck_tile::sequence<Config::M_Warp_Tile, Config::N_Warp_Tile, Config::K_Warp_Tile>>;
using TilePartitioner = ck_tile::
GemmSpatiallyLocalTilePartitioner<GemmShape, TileParitionerGroupNum, TileParitionerM01>;
using Traits = ck_tile::
TileGemmTraits<Config::kPadM, Config::kPadN, Config::kPadK, ALayout, BLayout, CLayout>;
using GemmUniversalTraits = ck_tile::TileGemmQuantTraits<Config::kPadM,
Config::kPadN,
Config::kPadK,
false,
Config::PreshuffleB,
ALayout,
BLayout,
CLayout,
QuantMode,
AQLayout,
BQLayout,
Config::TransposeC,
Config::DoubleSmemBuffer,
Config::IsPersistent>;
using GemmPipelineProblem =
ck_tile::GemmPipelineProblem<ADataType, BDataType, AccDataType, GemmShape, Traits>;
using BaseGemmPipeline = ck_tile::BaseGemmPipelineAgBgCrCompV3<GemmPipelineProblem>;
const ck_tile::index_t k_grain = gemm_descs[0].k_batch * Config::K_Tile;
const ck_tile::index_t K_split = (gemm_descs[0].K + k_grain - 1) / k_grain * Config::K_Tile;
const ck_tile::index_t num_loop = TilePartitioner::GetLoopNum(K_split);
const bool has_hot_loop = BaseGemmPipeline::BlockHasHotloop(num_loop);
const ck_tile::TailNumber tail_num = BaseGemmPipeline::GetBlockLoopTailNum(num_loop);
float ave_time{0};
const auto Run = [&](const auto has_hot_loop_, const auto tail_number_) {
constexpr bool has_hot_loop_v = has_hot_loop_.value;
constexpr auto tail_number_v = tail_number_.value;
constexpr auto scheduler = Config::Scheduler;
using QuantGemmProblem = ck_tile::GemmABQuantPipelineProblem<ADataType,
AQDataType,
BDataType,
BQDataType,
AccDataType,
GemmShape,
GemmUniversalTraits,
AQuantGroupSize,
BQuantGroupSize,
Config::TransposeC,
BDataType,
scheduler,
has_hot_loop_v,
tail_number_v>;
using GemmPipeline = ck_tile::ABQuantGemmPipelineAgBgCrCompV3<QuantGemmProblem>;
using GemmEpilogue = ck_tile::CShuffleEpilogue<
ck_tile::CShuffleEpilogueProblem<ADataType,
BDataType,
ck_tile::tuple<>,
AccDataType,
CDataType,
ck_tile::tuple<>,
CLayout,
ck_tile::element_wise::PassThrough,
TilePartitioner::MPerBlock,
TilePartitioner::NPerBlock,
Config::M_Warp,
Config::N_Warp,
Config::M_Warp_Tile,
Config::N_Warp_Tile,
Config::K_Warp_Tile,
QuantGemmProblem::TransposeC>>;
using Kernel = ck_tile::QuantGroupedGemmKernel<TilePartitioner,
GemmPipeline,
GemmEpilogue,
GemmUniversalTraits::kQuantType>;
auto kargs = Kernel::MakeKargs(gemm_descs);
if(!Kernel::IsSupportedArgument(kargs))
{
throw std::runtime_error("Kernel arguments not supported!");
}
const dim3 blocks = Kernel::BlockSize();
const dim3 grids = Kernel::GridSize(gemm_descs);
HIP_CHECK_ERROR(hipMemcpyWithStream(kargs_ptr,
kargs.data(),
get_workspace_size(gemm_descs),
hipMemcpyHostToDevice,
s.stream_id_));
if(s.log_level_ > 0)
{
std::cout << "Launching kernel: " << Kernel::GetName()
<< " with args:" << " grid: {" << grids.x << ", " << grids.y << ", "
<< grids.z << "}" << ", blocks: {" << blocks.x << ", " << blocks.y << ", "
<< blocks.z << "}" << std::endl;
}
return ave_time = ck_tile::launch_kernel(
s,
ck_tile::make_kernel<Config::kBlockPerCu>(
Kernel{},
grids,
blocks,
0,
ck_tile::cast_pointer_to_constant_address_space(kargs_ptr),
gemm_descs.size()));
};
return ave_time = BaseGemmPipeline::TailHandler(Run, has_hot_loop, tail_num);
}
template <typename Config>
void invoke_grouped_gemm_persistent(const ck_tile::stream_config& s,
const ck_tile::index_t num_groups,
void* kargs_ptr)
{
constexpr ck_tile::index_t TileParitionerGroupNum = 8;
constexpr ck_tile::index_t TileParitionerM01 = 4;
using GemmShape = ck_tile::TileGemmShape<
ck_tile::sequence<Config::M_Tile, Config::N_Tile, Config::K_Tile>,
ck_tile::sequence<Config::M_Warp, Config::N_Warp, Config::K_Warp>,
ck_tile::sequence<Config::M_Warp_Tile, Config::N_Warp_Tile, Config::K_Warp_Tile>>;
using TilePartitioner = ck_tile::
GemmSpatiallyLocalTilePartitioner<GemmShape, TileParitionerGroupNum, TileParitionerM01>;
using GemmUniversalTraits = ck_tile::TileGemmQuantTraits<Config::kPadM,
Config::kPadN,
Config::kPadK,
false,
Config::PreshuffleB,
ALayout,
BLayout,
CLayout,
QuantMode,
AQLayout,
BQLayout,
Config::TransposeC,
Config::DoubleSmemBuffer,
Config::IsPersistent>;
using QuantGemmProblem = ck_tile::GemmABQuantPipelineProblem<ADataType,
AQDataType,
BDataType,
BQDataType,
AccDataType,
GemmShape,
GemmUniversalTraits,
AQuantGroupSize,
BQuantGroupSize,
Config::TransposeC>;
using GemmPipeline = ck_tile::ABQuantGemmPipelineAgBgCrCompV3<QuantGemmProblem>;
using GemmEpilogue = ck_tile::CShuffleEpilogue<
ck_tile::CShuffleEpilogueProblem<ADataType,
BDataType,
ck_tile::tuple<>,
AccDataType,
CDataType,
ck_tile::tuple<>,
CLayout,
ck_tile::element_wise::PassThrough,
TilePartitioner::MPerBlock,
TilePartitioner::NPerBlock,
Config::M_Warp,
Config::N_Warp,
Config::M_Warp_Tile,
Config::N_Warp_Tile,
Config::K_Warp_Tile,
QuantGemmProblem::TransposeC>>;
using Kernel = ck_tile::QuantGroupedGemmKernel<TilePartitioner,
GemmPipeline,
GemmEpilogue,
GemmUniversalTraits::kQuantType>;
const dim3 blocks = Kernel::BlockSize();
const dim3 grids = Kernel::MaxOccupancyGridSize(s);
if(s.log_level_ > 0)
{
std::cout << "Launching kernel: " << Kernel::GetName() << " with args:" << " grid: {"
<< grids.x << ", " << grids.y << ", " << grids.z << "}" << ", blocks: {"
<< blocks.x << ", " << blocks.y << ", " << blocks.z << "}" << std::endl;
}
ck_tile::launch_kernel(s,
ck_tile::make_kernel<Config::kBlockPerCu>(
Kernel{},
grids,
blocks,
0,
ck_tile::cast_pointer_to_constant_address_space(kargs_ptr),
num_groups));
}
public:
void Run(const std::vector<int>& Ms,
const std::vector<int>& Ns,
const std::vector<int>& Ks,
std::vector<int>& stride_As,
std::vector<int>& stride_Bs,
std::vector<int>& stride_Cs,
std::vector<int>& stride_AQs,
std::vector<int>& stride_BQs,
const int group_count = 8)
{
ck_tile::index_t AQK, BQK;
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 = 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;
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,
1, // k_batch
M,
N,
K,
AQK,
BQK,
stride_As[i],
stride_Bs[i],
stride_Cs[i],
stride_AQs[i],
stride_BQs[i]});
}
ck_tile::DeviceMem gemm_workspace;
gemm_workspace.Realloc(get_workspace_size(gemm_descs));
void* kargs_ptr = gemm_workspace.GetDeviceBuffer();
if constexpr(Persistent)
{
std::vector<ck_tile::QuantGemmTransKernelArg> kargs;
for(const auto& arg : gemm_descs)
{
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, false, 1};
ck_tile::hip_check_error(
hipMemcpyWithStream(kargs_ptr,
kargs.data(),
kargs.size() * sizeof(ck_tile::QuantGemmTransKernelArg),
hipMemcpyHostToDevice,
stream.stream_id_));
invoke_grouped_gemm_persistent<GemmConfig>(stream, group_count, kargs_ptr);
}
else
{
const auto stream = ck_tile::stream_config{nullptr, false, 1};
invoke_grouped_gemm_abquant<GemmConfig>(gemm_descs, stream, kargs_ptr);
}
// Copy results back to host for validation
for(int i = 0; i < group_count; i++)
{
c_m_n_dev_buf[i]->FromDevice(c_m_n_tensors[i].data());
}
bool pass{true};
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_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(Ks[i], 1, max_accumulated_value);
pass &=
ck_tile::check_err(c_m_n_tensors[i],
c_m_n_host_ref,
"Error: Incorrect results! in group [" + std::to_string(i) + "]",
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;
EXPECT_TRUE(pass);
}
};
// Aliases for split test files
template <typename Tuple>
using TestCkTileGroupedGemmABQuant_1x1x128 = TestCkTileGroupedGemmABQuant<Tuple>;
template <typename Tuple>
using TestCkTileGroupedGemmABQuant_1x128x128 = TestCkTileGroupedGemmABQuant<Tuple>;