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Merge commit '4208e2898818362735e1ae9980a4cc2fea607ab4' into develop

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assistant-librarian[bot]
2025-08-29 07:12:11 +00:00
parent c2d62a5d74
commit fbab1a7466
20 changed files with 2471 additions and 26 deletions
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1
CHANGELOG.md
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@@ -27,6 +27,7 @@ Documentation for Composable Kernel available at [https://rocm.docs.amd.com/proj
* Added int8 support for CK_TILE GEMM.
* Added support for elementwise kernel.
* Added benchmarking support for tile engine GEMM Multi D.
* Added block scaling support in CK_TILE GEMM, allowing flexible use of quantization matrices from either A or B operands.
### Optimized

5
example/ck_tile/38_block_scale_gemm/CMakeLists.txt
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@@ -8,9 +8,8 @@ list(APPEND EXAMPLE_GEMM_COMPILE_OPTIONS -mllvm -enable-noalias-to-md-conversion
if(GPU_TARGETS MATCHES "gfx94" OR GPU_TARGETS MATCHES "gfx95")
add_executable(tile_example_gemm_aquant_basic EXCLUDE_FROM_ALL gemm_aquant_basic.cpp)
target_compile_options(tile_example_gemm_aquant_basic PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
add_executable(tile_example_gemm_aquant_preshuffle EXCLUDE_FROM_ALL gemm_aquant_preshuffle.cpp)
target_compile_options(tile_example_gemm_aquant_preshuffle PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
add_executable(tile_example_gemm_bquant_basic EXCLUDE_FROM_ALL gemm_bquant_basic.cpp)
target_compile_options(tile_example_gemm_bquant_basic PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
else()
message(DEBUG "Skipping ck_tile quant gemm tests for current target")
endif()

1
example/ck_tile/38_block_scale_gemm/README.md
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@@ -10,6 +10,7 @@ mkdir build && cd build
../script/cmake-ck-dev.sh ../ <arch>
# The aquant pipeline method on the gemm calculation
make tile_example_gemm_aquant_basic -j
make tile_example_gemm_bquant_basic -j
```
This will result in an executable `build/bin/tile_example_gemm_aquant_basic`

8
example/ck_tile/38_block_scale_gemm/gemm_aquant_basic.cpp
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@@ -27,8 +27,6 @@ float gemm_calc_aquant(const ck_tile::AQuantGemmHostArgs& args, const ck_tile::s
constexpr bool kPadN = false;
constexpr bool kPadK = false;
constexpr int kBlockPerCu = 1;
static_assert(std::is_same_v<CLayout, ck_tile::tensor_layout::gemm::RowMajor>);
constexpr ck_tile::index_t M_Tile = GemmConfig::M_Tile;
@@ -139,7 +137,7 @@ float gemm_calc_aquant(const ck_tile::AQuantGemmHostArgs& args, const ck_tile::s
}
float ave_time = ck_tile::launch_kernel(
s, ck_tile::make_kernel<kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
s, ck_tile::make_kernel<GemmConfig::kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
return ave_time;
};
@@ -207,7 +205,7 @@ int run_gemm_example(int argc, char* argv[])
ck_tile::fp8_t,
ck_tile::half_t,
ck_tile::fp8_t>{});
return run_gemm_example_prec_type<GemmConfig<ck_tile::pk_int4_t>, TypeConfig, 128>(
return run_gemm_example_prec_type<GemmConfig<ck_tile::fp8_t>, TypeConfig, 128>(
a_layout, b_layout, argc, argv);
}
else if(data_type == "i4bf8")
@@ -216,7 +214,7 @@ int run_gemm_example(int argc, char* argv[])
ck_tile::bf8_t,
ck_tile::half_t,
ck_tile::bf8_t>{});
return run_gemm_example_prec_type<GemmConfig<ck_tile::pk_int4_t>, TypeConfig, 128>(
return run_gemm_example_prec_type<GemmConfig<ck_tile::bf8_t>, TypeConfig, 128>(
a_layout, b_layout, argc, argv);
}
else

229
example/ck_tile/38_block_scale_gemm/gemm_bquant_basic.cpp Normal file
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@@ -0,0 +1,229 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include <cstring>
#include <iostream>
#include <ostream>
#include <stdexcept>
#include <string>
#include <tuple>
#include "ck_tile/core/config.hpp"
#include "ck_tile/host.hpp"
#include "gemm_utils.hpp"
template <typename GemmConfig,
typename ADataType,
typename BDataType,
typename BQDataType,
typename AccDataType,
typename CDataType,
typename ComputeDataType,
typename ALayout,
typename BLayout,
typename CLayout,
uint32_t QuantGroupSize>
float gemm_calc_bquant(const ck_tile::BQuantGemmHostArgs& args, const ck_tile::stream_config& s)
{
constexpr bool kPadM = false;
constexpr bool kPadN = false;
constexpr bool kPadK = false;
static_assert(std::is_same_v<CLayout, ck_tile::tensor_layout::gemm::RowMajor>);
constexpr ck_tile::index_t M_Tile = GemmConfig::M_Tile;
constexpr ck_tile::index_t N_Tile = GemmConfig::N_Tile;
constexpr ck_tile::index_t K_Tile = GemmConfig::K_Tile;
constexpr ck_tile::index_t M_Warp = GemmConfig::M_Warp;
constexpr ck_tile::index_t N_Warp = GemmConfig::N_Warp;
constexpr ck_tile::index_t K_Warp = GemmConfig::K_Warp;
constexpr ck_tile::index_t M_Warp_Tile = GemmConfig::M_Warp_Tile;
constexpr ck_tile::index_t N_Warp_Tile = GemmConfig::N_Warp_Tile;
constexpr ck_tile::index_t K_Warp_Tile = GemmConfig::K_Warp_Tile;
using CodegenGemmShape =
ck_tile::TileGemmShape<ck_tile::sequence<M_Tile, N_Tile, K_Tile>,
ck_tile::sequence<M_Warp, N_Warp, K_Warp>,
ck_tile::sequence<M_Warp_Tile, N_Warp_Tile, K_Warp_Tile>>;
using TilePartitioner = ck_tile::GemmTile1DPartitioner<CodegenGemmShape>;
using CodegenGemmTraits = ck_tile::TileGemmBQuantTraits<kPadM,
kPadN,
kPadK,
GemmConfig::PreshuffleQuant,
ALayout,
BLayout,
CLayout>;
using GemmPipelineProblem = ck_tile::GemmPipelineProblemBase<ADataType,
BDataType,
AccDataType,
CodegenGemmShape,
CodegenGemmTraits,
ComputeDataType>;
using BaseGemmPipeline = ck_tile::BaseBQuantGemmPipelineAgBgCrCompV3<GemmPipelineProblem>;
const ck_tile::index_t K_split = (args.K + K_Tile - 1) / K_Tile * 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);
constexpr bool transposed_warp_gemm = false;
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;
using CodegenPipelineProblem =
ck_tile::GemmBQuantPipelineProblem<ADataType,
BDataType,
BQDataType,
AccDataType,
CodegenGemmShape,
CodegenGemmTraits,
QuantGroupSize,
ComputeDataType,
ck_tile::GemmPipelineScheduler::Intrawave,
has_hot_loop_v,
tail_number_v>;
using CodegenGemmPipeline = ck_tile::BQuantGemmPipelineAgBgCrCompV3<CodegenPipelineProblem>;
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,
M_Warp,
N_Warp,
M_Warp_Tile,
N_Warp_Tile,
K_Warp_Tile,
transposed_warp_gemm,
ck_tile::memory_operation_enum::set>>;
using Kernel =
ck_tile::BQuantGemmKernel<TilePartitioner, CodegenGemmPipeline, GemmEpilogue>;
auto kargs = Kernel::MakeKernelArgs(args);
const dim3 grids = Kernel::GridSize(args.M, args.N, args.k_batch);
const dim3 blocks = Kernel::BlockSize();
if(args.k_batch != 1)
{
throw std::runtime_error("split-k is not supported yet!");
}
if(!Kernel::IsSupportedArgument(kargs))
{
throw std::runtime_error("Wrong! Arguments not supported! Skipping gemm!\n");
}
if(s.log_level_ > 0)
{
std::cout << "Launching kernel with args: " << Kernel::GetName() << '\n'
<< "shape: " << CodegenGemmShape::GetName() << '\n'
<< "problem: " << CodegenPipelineProblem::GetName() << '\n'
<< "pipeline: " << CodegenGemmPipeline::GetName() << '\n'
<< "grid: {" << grids.x << ", " << grids.y << ", " << grids.z << "}"
<< ", blocks: {" << blocks.x << ", " << blocks.y << ", " << blocks.z << "}"
<< std::endl;
}
float ave_time = ck_tile::launch_kernel(
s, ck_tile::make_kernel<GemmConfig::kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
return ave_time;
};
return BaseGemmPipeline::TailHandler(Run, has_hot_loop, tail_num);;
}
#include "run_gemm_bquant_example.inc"
template <typename GemmConfig, typename TypeConfig, uint32_t QuantGroupSize>
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;
if constexpr(std::is_same_v<typename TypeConfig::BDataType, ck_tile::pk_int4_t> ||
std::is_same_v<typename TypeConfig::BDataType, ck_tile::fp8_t> ||
std::is_same_v<typename TypeConfig::BDataType, ck_tile::bf8_t>)
{
if(a_layout == "R" && b_layout == "C")
{
return run_gemm_example_with_layouts<GemmConfig, TypeConfig, QuantGroupSize>(
argc, argv, Row{}, Col{}, Col{}, Row{});
}
else
{
throw std::runtime_error("Unsupported memory layout for the input matrices!");
}
}
else
{
throw std::runtime_error("Unsupported data type for B.");
}
return 0;
}
template <template <typename PreType> typename GemmConfig>
int run_gemm_example(int argc, char* argv[])
{
auto [result, arg_parser] = create_args(argc, argv);
if(!result)
return -1;
std::string data_type = arg_parser.get_str("prec");
std::string a_layout = arg_parser.get_str("a_layout");
std::string b_layout = arg_parser.get_str("b_layout");
if(data_type == "fp8")
{
using TypeConfig =
decltype(GemmQuantTypeConfig<ck_tile::fp8_t, ck_tile::fp8_t, ck_tile::half_t, float>{});
return run_gemm_example_prec_type<GemmConfig<ck_tile::fp8_t>, TypeConfig, 128>(
a_layout, b_layout, argc, argv);
}
else if(data_type == "bf8")
{
using TypeConfig =
decltype(GemmQuantTypeConfig<ck_tile::bf8_t, ck_tile::bf8_t, ck_tile::half_t, float>{});
return run_gemm_example_prec_type<GemmConfig<ck_tile::bf8_t>, TypeConfig, 128>(
a_layout, b_layout, argc, argv);
}
else if(data_type == "fp8i4")
{
using TypeConfig = decltype(GemmQuantTypeConfig<ck_tile::fp8_t,
ck_tile::pk_int4_t,
ck_tile::half_t,
ck_tile::fp8_t>{});
return run_gemm_example_prec_type<GemmConfig<ck_tile::fp8_t>, TypeConfig, 128>(
a_layout, b_layout, argc, argv);
}
else if(data_type == "bf8i4")
{
using TypeConfig = decltype(GemmQuantTypeConfig<ck_tile::bf8_t,
ck_tile::pk_int4_t,
ck_tile::half_t,
ck_tile::bf8_t>{});
return run_gemm_example_prec_type<GemmConfig<ck_tile::bf8_t>, TypeConfig, 128>(
a_layout, b_layout, argc, argv);
}
else
{
throw std::runtime_error("Unsupported data type for this operation !!!");
}
}
int main(int argc, char* argv[]) { return !run_gemm_example<GemmConfigDecode>(argc, argv); }

8
example/ck_tile/38_block_scale_gemm/gemm_utils.hpp
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@@ -81,11 +81,10 @@ struct GemmConfigBase
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 PreshuffleQuant = false;
static constexpr bool DoubleSmemBuffer = false;
};
@@ -105,6 +104,8 @@ struct GemmConfigDecode : public GemmConfigBase
static constexpr ck_tile::index_t N_Warp_Tile = 16;
static constexpr ck_tile::index_t K_Warp_Tile = get_k_warp_tile<PrecType, M_Warp_Tile>();
static constexpr int kBlockPerCu = 1;
static constexpr auto Scheduler = ck_tile::GemmPipelineScheduler::Default;
static constexpr ck_tile::index_t Pipeline = CK_TILE_PIPELINE_DECODE;
};
@@ -145,6 +146,9 @@ struct GemmConfigPreshuffleQuant : public GemmConfigBase
static constexpr ck_tile::index_t K_Warp_Tile =
get_k_from_preshuffled_warp_tile<PrecType, M_Warp_Tile>();
static constexpr int kBlockPerCu = 1;
static constexpr auto Scheduler = ck_tile::GemmPipelineScheduler::Default;
static constexpr ck_tile::index_t Pipeline = CK_TILE_PIPELINE_PRESHUFFLEQUANT;
static constexpr bool PreshuffleQuant = true;

1
example/ck_tile/38_block_scale_gemm/run_gemm_aquant_example.inc
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@@ -1,3 +1,4 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.

286
example/ck_tile/38_block_scale_gemm/run_gemm_bquant_example.inc Normal file
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@@ -0,0 +1,286 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <bit>
#include <random>
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 T>
auto shuffle_bq(const ck_tile::HostTensor<T>& t, int block_bq_k)
{
if(t.get_lengths().size() != 2)
{
throw std::runtime_error("Host tensor is not rank 2 tensor.");
}
int n_ = t.get_lengths()[0];
int bqk_ = t.get_lengths()[1];
if(bqk_ % block_bq_k != 0)
{
throw std::runtime_error("shuffle_aq needs a bqk of multiple times of block_bq_k.");
}
ck_tile::HostTensor<T> t_view({n_, bqk_ / block_bq_k, block_bq_k});
std::copy(t.begin(), t.end(), t_view.begin());
return ck_tile::reference_permute(t_view, {1, 0, 2});
}
template <typename GemmConfig,
typename ADataType,
typename BDataType,
typename BQDataType,
typename DsDataType,
typename AccDataType,
typename CDataType,
typename ALayout,
typename BLayout,
typename BQLayout,
typename DsLayout,
typename CLayout,
uint32_t QuantGroupSize,
typename CDEElementWise = ck_tile::element_wise::PassThrough>
float invoke_gemm(ck_tile::DeviceMem& a_m_k_dev_buf,
ck_tile::DeviceMem& b_k_n_dev_buf,
ck_tile::DeviceMem& bq_bqk_n_dev_buf,
ck_tile::DeviceMem& c_m_n_dev_buf,
ck_tile::index_t M,
ck_tile::index_t N,
ck_tile::index_t K,
ck_tile::index_t BQK,
ck_tile::index_t stride_A,
ck_tile::index_t stride_B,
ck_tile::index_t stride_BQ,
ck_tile::index_t stride_C,
ck_tile::index_t kbatch,
int n_warmup,
int n_repeat)
{
ck_tile::BQuantGemmHostArgs args;
args.a_ptr = a_m_k_dev_buf.GetDeviceBuffer();
args.b_ptr = b_k_n_dev_buf.GetDeviceBuffer();
args.bq_ptr = bq_bqk_n_dev_buf.GetDeviceBuffer();
args.c_ptr = c_m_n_dev_buf.GetDeviceBuffer();
args.k_batch = kbatch;
args.M = M;
args.N = N;
args.K = K;
args.QK = BQK;
args.stride_A = stride_A;
args.stride_B = stride_B;
args.stride_C = stride_C;
args.stride_BQ = stride_BQ;
float ave_time = gemm_calc_bquant<GemmConfig,
ADataType,
BDataType,
BQDataType,
AccDataType,
CDataType,
ADataType, // computeDatatype
ALayout,
BLayout,
CLayout,
QuantGroupSize>(
args, ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat});
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_byte = sizeof(ADataType) * M * K + sizeof(BDataType) * N * K +
sizeof(BQDataType) * BQK * N + sizeof(CDataType) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_byte / 1.E6 / ave_time;
std::cout << "Run Gemm kernel with M =" << M << " N =" << N << " K =" << K
<< " StrideA =" << stride_A << " StrideB =" << stride_B << " StrideBQ =" << stride_BQ
<< " StrideC =" << stride_C << " A_Layout =" << ALayout::name
<< " B_Layout =" << BLayout::name << " C_Layout =" << CLayout::name
<< " A_Type = " << DataTypeTraits<ADataType>::name
<< " B_Type = " << DataTypeTraits<BDataType>::name
<< " BQ_Type = " << DataTypeTraits<BQDataType>::name
<< " Acc_Type = " << DataTypeTraits<AccDataType>::name
<< " C_Type = " << DataTypeTraits<CDataType>::name << " : " << ave_time << " ms, "
<< tflops << " TFlops, " << gb_per_sec << " GB/s, " << std::endl;
return ave_time;
}
template <typename GemmConfig,
typename TypeConfig,
uint32_t QuantGroupSize,
typename ALayout,
typename BLayout,
typename BQLayout,
typename CLayout>
int run_gemm_example_with_layouts(int argc,
char* argv[],
const ALayout a_layout = ALayout{},
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;
using ADataType = typename TypeConfig::ADataType;
using BDataType = typename TypeConfig::BDataType;
using BQDataType = typename TypeConfig::QDataType;
using AccDataType = typename TypeConfig::AccDataType;
using CDataType = typename TypeConfig::CDataType;
ck_tile::index_t M = arg_parser.get_int("m");
ck_tile::index_t N = arg_parser.get_int("n");
ck_tile::index_t K = arg_parser.get_int("k");
if(K % QuantGroupSize != 0)
{
throw std::runtime_error("K must be aligned with QuantGroupSize");
}
ck_tile::index_t BQK = K / QuantGroupSize;
ck_tile::index_t stride_A = arg_parser.get_int("stride_a");
ck_tile::index_t stride_B = arg_parser.get_int("stride_b");
ck_tile::index_t stride_BQ = arg_parser.get_int("stride_q");
ck_tile::index_t stride_C = arg_parser.get_int("stride_c");
ck_tile::index_t kbatch = arg_parser.get_int("split_k");
int n_warmup = arg_parser.get_int("warmup");
int n_repeat = arg_parser.get_int("repeat");
ck_tile::index_t init_method = arg_parser.get_int("init");
stride_A = ck_tile::get_default_stride(M, K, stride_A, is_row_major(a_layout));
stride_B = ck_tile::get_default_stride(K, N, stride_B, is_row_major(b_layout));
stride_BQ = ck_tile::get_default_stride(BQK, N, stride_BQ, is_row_major(bq_layout));
stride_C = ck_tile::get_default_stride(M, N, stride_C, is_row_major(CLayout{}));
ck_tile::HostTensor<ADataType> a_m_k(
ck_tile::host_tensor_descriptor(M, K, stride_A, is_row_major(a_layout)));
ck_tile::HostTensor<BDataType> b_k_n(
ck_tile::host_tensor_descriptor(K, N, stride_B, is_row_major(b_layout)));
ck_tile::HostTensor<BQDataType> bq_bqk_n(
ck_tile::host_tensor_descriptor(BQK, N, stride_BQ, is_row_major(bq_layout)));
ck_tile::HostTensor<CDataType> c_m_n_dev_result(
ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_int_distribution<std::uint32_t> fill_seed(0, 500);
if(init_method == 0)
{
if constexpr(std::is_same_v<BDataType, ck_tile::pk_int4_t>)
{
ck_tile::FillUniformDistribution<ck_tile::pk_int4_t>{-5.0f, 5.0f, fill_seed(gen)}(
b_k_n);
}
else
{
ck_tile::FillUniformDistribution<BDataType>{-2.0f, 3.0f, fill_seed(gen)}(b_k_n);
}
ck_tile::FillUniformDistribution<BQDataType>{-2.0f, 2.0f, fill_seed(gen)}(bq_bqk_n);
ck_tile::FillUniformDistribution<ADataType>{-5.0f, 5.0f, fill_seed(gen)}(a_m_k);
}
else if(init_method == 1)
{
std::cout << "Monotonic initialization is not supported." << std::endl;
return 0;
}
else if(init_method == 2)
{
ck_tile::FillConstant<ADataType>{static_cast<ADataType>(0x38)}(a_m_k);
ck_tile::FillConstant<BDataType>{static_cast<BDataType>(0x22)}(b_k_n);
ck_tile::FillConstant<BQDataType>{static_cast<BQDataType>(0.5f)}(bq_bqk_n);
}
else
{
a_m_k.SetZero();
b_k_n.SetZero();
bq_bqk_n.SetZero();
}
ck_tile::DeviceMem a_m_k_dev_buf(a_m_k.get_element_space_size_in_bytes());
ck_tile::DeviceMem b_k_n_dev_buf(b_k_n.get_element_space_size_in_bytes());
ck_tile::DeviceMem bq_bqk_n_dev_buf(bq_bqk_n.get_element_space_size_in_bytes());
ck_tile::DeviceMem c_m_n_dev_buf(c_m_n_dev_result.get_element_space_size_in_bytes());
a_m_k_dev_buf.ToDevice(a_m_k.data());
b_k_n_dev_buf.ToDevice(b_k_n.data());
bq_bqk_n_dev_buf.ToDevice(bq_bqk_n.data());
c_m_n_dev_buf.SetZero();
c_m_n_dev_result.SetZero();
invoke_gemm<GemmConfig,
ADataType,
BDataType,
BQDataType,
ck_tile::tuple<>,
AccDataType,
CDataType,
ALayout,
BLayout,
BQLayout,
ck_tile::tuple<>,
CLayout,
QuantGroupSize>(a_m_k_dev_buf,
b_k_n_dev_buf,
bq_bqk_n_dev_buf,
c_m_n_dev_buf,
M,
N,
K,
BQK,
stride_A,
stride_B,
stride_BQ,
stride_C,
kbatch,
n_warmup,
n_repeat);
c_m_n_dev_buf.FromDevice(c_m_n_dev_result.data());
bool pass = true;
if(arg_parser.get_int("v") == 1)
{
ck_tile::HostTensor<CDataType> c_m_n_host_ref(
ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
c_m_n_host_ref.SetZero();
ck_tile::reference_gemm_quant<ADataType,
BQDataType,
BDataType,
AccDataType,
CDataType,
QuantGroupSize,
false>(a_m_k, bq_bqk_n, b_k_n, 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>(
K, kbatch, max_accumulated_value);
pass = ck_tile::check_err(c_m_n_dev_result,
c_m_n_host_ref,
"Error: Incorrect results!",
rtol_atol.at(ck_tile::number<0>{}),
rtol_atol.at(ck_tile::number<1>{}));
if(!pass)
{
std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
<< std::endl;
}
std::cout << "CPU verification " << (pass ? "Passed!" : "Failed ...") << std::endl;
}
else if(arg_parser.get_int("v") == 2)
{
std::cout << "GPU verification is not implemented yet. Re-run with -v=1" << std::endl;
return false;
}
return pass;
}

6
include/ck_tile/core/numeric/pk_fp4.hpp
View File

@@ -250,8 +250,7 @@ CK_TILE_HOST_DEVICE constexpr pk_fp4_t fp16x2_to_pk_fp4(const fp16x2_t& x, float
#if CK_TILE_FP4_CVT_DEVICE
return impl::_to_f4(x, scale);
#else
return pk_fp4_t::pack(float_to_e2m1(type_convert<float>(x[0]), scale),
float_to_e2m1(type_convert<float>(x[1]), scale));
return pk_fp4_t::pack(float_to_e2m1(x[0], scale), float_to_e2m1(x[1], scale));
#endif
}
CK_TILE_HOST_DEVICE constexpr pk_fp4_t bf16x2_to_pk_fp4(const bf16x2_t& x, float scale)
@@ -259,8 +258,7 @@ CK_TILE_HOST_DEVICE constexpr pk_fp4_t bf16x2_to_pk_fp4(const bf16x2_t& x, float
#if CK_TILE_FP4_CVT_DEVICE
return impl::_to_f4(x, scale);
#else
return pk_fp4_t::pack(float_to_e2m1(type_convert<float>(x[0]), scale),
float_to_e2m1(type_convert<float>(x[1]), scale));
return pk_fp4_t::pack(float_to_e2m1(x[0], scale), float_to_e2m1(x[1], scale));
#endif
}
CK_TILE_HOST_DEVICE constexpr pk_fp4_t fp32x2_to_pk_fp4(const fp32x2_t& x, float scale)

9
include/ck_tile/ops/gemm_group_quant.hpp
View File

@@ -4,13 +4,18 @@
#pragma once
#include "ck_tile/ops/gemm_group_quant/block/block_universal_gemm_as_aquant_bs_cr.hpp"
#include "ck_tile/ops/gemm_group_quant/block/block_universal_gemm_as_bs_bquant_cr.hpp"
#include "ck_tile/ops/gemm_group_quant/kernel/gemm_aquant_kernel.hpp"
#include "ck_tile/ops/gemm_group_quant/kernel/gemm_bquant_kernel.hpp"
#include "ck_tile/ops/gemm_group_quant/pipeline/gemm_aquant_pipeline_ag_bg_cr_base.hpp"
#include "ck_tile/ops/gemm_group_quant/pipeline/gemm_aquant_pipeline_ag_bg_cr_policy.hpp"
#include "ck_tile/ops/gemm_group_quant/pipeline/gemm_aquant_pipeline_ag_bg_cr_v3.hpp"
#include "ck_tile/ops/gemm_group_quant/pipeline/gemm_aquant_pipeline_problem.hpp"
#include "ck_tile/ops/gemm_group_quant/pipeline/gemm_bquant_pipeline_ag_bg_cr_base.hpp"
#include "ck_tile/ops/gemm_group_quant/pipeline/gemm_bquant_pipeline_ag_bg_cr_policy.hpp"
#include "ck_tile/ops/gemm_group_quant/pipeline/gemm_bquant_pipeline_ag_bg_cr_v3.hpp"
#include "ck_tile/ops/gemm_group_quant/pipeline/gemm_quant_pipeline_problem.hpp"
#include "ck_tile/ops/gemm_group_quant/pipeline/gemm_group_quant_utils.hpp"
#include "ck_tile/ops/gemm_group_quant/pipeline/tile_gemm_aquant_traits.hpp"
#include "ck_tile/ops/gemm_group_quant/pipeline/tile_gemm_quant_traits.hpp"
#include "ck_tile/ops/common/generic_2d_block_shape.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
#include "ck_tile/ops/common/utils.hpp"

16
include/ck_tile/ops/gemm_group_quant/block/block_universal_gemm_as_aquant_bs_cr.hpp
View File

@@ -12,7 +12,7 @@
namespace ck_tile {
template <typename Problem, index_t UnaryOpSize_ = 8>
struct BlockGemmQuantBase
struct BlockGemmAQuantBase
{
using AQDataType = remove_cvref_t<typename Problem::AQDataType>;
using ComputeDataType = remove_cvref_t<typename Problem::ComputeDataType>;
@@ -67,7 +67,7 @@ struct BlockGemmQuantBase
// B is block window on shared memory
// C is block distributed tensor
template <typename Problem_, typename Policy_ = BlockGemmASmemBSmemCRegV1DefaultPolicy>
struct AQuantBlockUniversalGemmAsBsCr : public BlockGemmQuantBase<Problem_>
struct AQuantBlockUniversalGemmAsBsCr : public BlockGemmAQuantBase<Problem_>
{
private:
template <typename PipelineProblem_, typename GemmPolicy_>
@@ -103,13 +103,13 @@ struct AQuantBlockUniversalGemmAsBsCr : public BlockGemmQuantBase<Problem_>
using I1 = number<1>;
static_assert(MWarp == BlockGemmShape::BlockWarps::at(I0{}),
"Error! WarpGemm's MWarp is not consisten with BlockGemmShape!");
"Error! WarpGemm's MWarp is not consistent with BlockGemmShape!");
static_assert(NWarp == BlockGemmShape::BlockWarps::at(I1{}),
"Error! WarpGemm's NWarp is not consisten with BlockGemmShape!");
"Error! WarpGemm's NWarp is not consistent with BlockGemmShape!");
static_assert(WarpGemm::kM == BlockGemmShape::WarpTile::at(I0{}),
"Error! WarpGemm's M is not consisten with BlockGemmShape!");
"Error! WarpGemm's M is not consistent with BlockGemmShape!");
static_assert(WarpGemm::kN == BlockGemmShape::WarpTile::at(I1{}),
"Error! WarpGemm's N is not consisten with BlockGemmShape!");
"Error! WarpGemm's N is not consistent with BlockGemmShape!");
static constexpr index_t MIterPerWarp = MPerBlock / (MWarp * WarpGemm::kM);
static constexpr index_t NIterPerWarp = NPerBlock / (NWarp * WarpGemm::kN);
@@ -170,7 +170,7 @@ struct AQuantBlockUniversalGemmAsBsCr : public BlockGemmQuantBase<Problem_>
using ComputeDataType = remove_cvref_t<typename Traits::ComputeDataType>;
using CDataType = remove_cvref_t<typename Traits::CDataType>;
using Base = BlockGemmQuantBase<Problem_>;
using Base = BlockGemmAQuantBase<Problem_>;
using WarpGemm = remove_cvref_t<typename Traits::WarpGemm>;
@@ -323,7 +323,7 @@ struct AQuantBlockUniversalGemmAsBsCr : public BlockGemmQuantBase<Problem_>
[[maybe_unused]] BSmemBlockWindow& b_block_window)
{
static_assert(std::is_same_v<CDataType, typename CBlockTensor::DataType>,
"The CDataType as defined in traits should be the same as correspoinding "
"The CDataType as defined in traits should be the same as corresponding "
"C block tensor data type!");
constexpr auto warp_size = get_warp_size();

439
include/ck_tile/ops/gemm_group_quant/block/block_universal_gemm_as_bs_bquant_cr.hpp Normal file
View File

@@ -0,0 +1,439 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/core/arch/arch.hpp"
#include "ck_tile/ops/gemm/block/block_gemm_asmem_bsmem_creg_v1_default_policy.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_scheduler.hpp"
#include "ck_tile/ops/elementwise.hpp"
namespace ck_tile {
template <typename Problem, index_t UnaryOpSize_ = 8>
struct BlockGemmBQuantBase
{
using BQDataType = remove_cvref_t<typename Problem::BQDataType>;
using ComputeDataType = remove_cvref_t<typename Problem::ComputeDataType>;
static constexpr index_t UnaryOpSize = UnaryOpSize_;
template <typename T>
CK_TILE_DEVICE static float cvt_scale_to_fp32(T scale)
{
float scale_reg_f = 0.f;
if constexpr(std::is_same_v<BQDataType, ck_tile::fp8_t>)
{
scale_reg_f =
ck_tile::element_wise::amd_assembly_fp8_to_fp32(static_cast<uint32_t>(scale));
}
else if constexpr(std::is_same_v<BQDataType, ck_tile::bf8_t>)
{
scale_reg_f =
ck_tile::element_wise::amd_assembly_bf8_to_fp32(static_cast<uint32_t>(scale));
}
else if constexpr(std::is_same_v<BQDataType, float>)
{
scale_reg_f = ck_tile::bit_cast<float>(scale);
}
else
{
static_assert(false, "BQDataType must be float, fp8_t or bf8_t.");
}
return scale_reg_f;
}
// can be inherited from A
template <typename WarpWindow, typename WarpTile>
CK_TILE_DEVICE static void load_interleaved_pk_type(WarpTile& warp_tile,
const WarpWindow& warp_window)
{
const element_wise::PassThroughPack8 elementwise_op{};
static_assert(WarpTile::get_thread_buffer_size() % UnaryOpSize == 0);
constexpr index_t thread_buffer_size = WarpTile::get_thread_buffer_size() / UnaryOpSize;
const auto in_dstr_tensors = load_tile(warp_window);
using ComputeVectorType = ComputeDataType __attribute__((ext_vector_type(UnaryOpSize)));
static_for<0, thread_buffer_size, 1>{}([&](auto i) {
elementwise_op(warp_tile.get_thread_buffer().template get_as<ComputeVectorType>()(i),
in_dstr_tensors.get_thread_buffer().template get_as<pk_int4x4_t>()[i]);
});
}
};
// A is block window on shared memory
// BQ (scale tensor) is block distributed tensor.
// Consecutive kQuantGroupSize elements of B are quantized with a separate scale.
// B is block window on shared memory
// C is block distributed tensor
template <typename Problem_, typename Policy_ = BlockGemmASmemBSmemCRegV1DefaultPolicy>
struct BQuantBlockUniversalGemmAsBsCr : public BlockGemmBQuantBase<Problem_>
{
private:
template <typename PipelineProblem_, typename GemmPolicy_>
struct GemmTraits_
{
using Problem = remove_cvref_t<PipelineProblem_>;
using Policy = remove_cvref_t<GemmPolicy_>;
using ADataType = remove_cvref_t<typename Problem::ADataType>;
using BDataType = remove_cvref_t<typename Problem::BDataType>;
using BQDataType = remove_cvref_t<typename Problem::BQDataType>;
using ComputeDataType = remove_cvref_t<typename Problem::ComputeDataType>;
using CDataType = remove_cvref_t<typename Problem::CDataType>;
using BlockGemmShape = remove_cvref_t<typename Problem::BlockGemmShape>;
static constexpr index_t kQuantGroupSize = Problem::kQuantGroupSize;
static constexpr index_t kBlockSize = Problem::kBlockSize;
static constexpr auto Scheduler = Problem::Scheduler;
// Threadblock GEMM tile size
static constexpr index_t MPerBlock = BlockGemmShape::kM;
static constexpr index_t NPerBlock = BlockGemmShape::kN;
static constexpr index_t KPerBlock = BlockGemmShape::kK;
static constexpr index_t BQPerBlock = KPerBlock / kQuantGroupSize;
static constexpr auto config = Policy::template GetWarpGemmMWarpNWarp<Problem>();
using WarpGemm = remove_cvref_t<decltype(config.template at<0>())>;
// number of warps along M and N for threadblock's GEMM problem size
static constexpr index_t MWarp = config.template at<1>();
static constexpr index_t NWarp = config.template at<2>();
using I0 = number<0>;
using I1 = number<1>;
static_assert(MWarp == BlockGemmShape::BlockWarps::at(I0{}),
"Error! WarpGemm's MWarp is not consistent with BlockGemmShape!");
static_assert(NWarp == BlockGemmShape::BlockWarps::at(I1{}),
"Error! WarpGemm's NWarp is not consistent with BlockGemmShape!");
static_assert(WarpGemm::kM == BlockGemmShape::WarpTile::at(I0{}),
"Error! WarpGemm's M is not consistent with BlockGemmShape!");
static_assert(WarpGemm::kN == BlockGemmShape::WarpTile::at(I1{}),
"Error! WarpGemm's N is not consistent with BlockGemmShape!");
static constexpr index_t MIterPerWarp = MPerBlock / (MWarp * WarpGemm::kM);
static constexpr index_t NIterPerWarp = NPerBlock / (NWarp * WarpGemm::kN);
static constexpr index_t KIterPerWarp = KPerBlock / WarpGemm::kK;
static constexpr index_t QScalesPerBlockRow =
(KPerBlock + kQuantGroupSize - 1) / kQuantGroupSize;
static constexpr index_t QScalesPerWarpGemmRow =
(WarpGemm::kK + kQuantGroupSize - 1) / kQuantGroupSize;
static constexpr index_t KIterPerQScale = KIterPerWarp / QScalesPerBlockRow;
static_assert(kQuantGroupSize % WarpGemm::kK == 0,
"Error! WarpGemm::kK should be a multiple of kQuantGroupSize");
static_assert(QScalesPerWarpGemmRow == 1,
"Error! kQuantGroupSize shouldn't be smaller than WarpGemm::kK");
static_assert(KIterPerWarp % QScalesPerBlockRow == 0,
"Error! KItersPerWarp should be a multiple of QscalesPerBlockRow");
static_assert(KPerBlock / kQuantGroupSize > 0,
"Error! Each row of blockgemm should have a separate scale");
static_assert(MIterPerWarp * MWarp * WarpGemm::kM == MPerBlock,
"Error! Warps should cover all Block tile!");
static_assert(NIterPerWarp * NWarp * WarpGemm::kN == NPerBlock,
"Error! Warps should cover all Block tile!");
// Currently tested combinations (A, B, BQ)
// 1. fp8, fp8, fp32 -> f32
// 2. bf8, bf8, fp32 -> f32
// 3. i4, fp8, (fp8/fp32) -> f32
// 4. i4, bf8, (fp8/fp32) -> f32
static_assert((std::is_same_v<ADataType, fp8_t> || std::is_same_v<ADataType, bf8_t>) &&
(std::is_same_v<BDataType, fp8_t> || std::is_same_v<BDataType, bf8_t> ||
std::is_same_v<BDataType, ck_tile::pk_int4_t>) &&
(std::is_same_v<BQDataType, float> ||
std::is_same_v<BQDataType, ck_tile::fp8_t> ||
std::is_same_v<BQDataType, ck_tile::bf8_t>) &&
(std::is_same_v<ComputeDataType, fp8_t> ||
std::is_same_v<ComputeDataType, bf8_t>) &&
std::is_same_v<CDataType, fp32_t>);
static constexpr index_t InterWaveSchedulingMacClusters = 1;
static constexpr index_t KPack = WarpGemm::kKPerThread;
static constexpr index_t KPerThread = KIterPerWarp * WarpGemm::kKPerThread;
};
public:
using Traits = GemmTraits_<Problem_, Policy_>;
using ADataType = remove_cvref_t<typename Traits::ADataType>;
using BDataType = remove_cvref_t<typename Traits::BDataType>;
using BQDataType = remove_cvref_t<typename Traits::BQDataType>;
using ComputeDataType = remove_cvref_t<typename Traits::ComputeDataType>;
using CDataType = remove_cvref_t<typename Traits::CDataType>;
using Base = BlockGemmBQuantBase<Problem_>;
using WarpGemm = remove_cvref_t<typename Traits::WarpGemm>;
static constexpr index_t KIterPerWarp = Traits::KIterPerWarp;
static constexpr index_t MIterPerWarp = Traits::MIterPerWarp;
static constexpr index_t NIterPerWarp = Traits::NIterPerWarp;
static constexpr index_t MWarp = Traits::MWarp;
static constexpr index_t NWarp = Traits::NWarp;
static constexpr auto Scheduler = Traits::Scheduler;
static constexpr uint8_t kA_cvt_scale = std::is_same_v<ADataType, pk_int4_t> ? 16 : 1;
static constexpr uint8_t kB_cvt_scale = std::is_same_v<BDataType, pk_int4_t> ? 16 : 1;
using AWarpDstr = typename WarpGemm::AWarpDstr;
using BWarpDstr = typename WarpGemm::BWarpDstr;
using CWarpDstr = typename WarpGemm::CWarpDstr;
using AWarpTensor = typename WarpGemm::AWarpTensor;
using BWarpTensor = typename WarpGemm::BWarpTensor;
using CWarpTensor = typename WarpGemm::CWarpTensor;
static_assert(std::is_same_v<typename WarpGemm::CDataType, float>);
static constexpr auto a_warp_y_lengths =
to_sequence(AWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
static constexpr auto b_warp_y_lengths =
to_sequence(BWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
static constexpr auto c_warp_y_lengths =
to_sequence(CWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
static constexpr auto a_warp_y_index_zeros = uniform_sequence_gen_t<AWarpDstr::NDimY, 0>{};
static constexpr auto b_warp_y_index_zeros = uniform_sequence_gen_t<BWarpDstr::NDimY, 0>{};
static constexpr auto c_warp_y_index_zeros = uniform_sequence_gen_t<CWarpDstr::NDimY, 0>{};
static constexpr index_t APackedSize =
ck_tile::numeric_traits<remove_cvref_t<ADataType>>::PackedSize;
static constexpr index_t BPackedSize =
ck_tile::numeric_traits<remove_cvref_t<BDataType>>::PackedSize;
using I0 = number<0>;
using I1 = number<1>;
CK_TILE_DEVICE static constexpr auto MakeABlockDistributionEncode()
{
constexpr index_t KPerThread = Traits::KPerThread;
constexpr index_t NumMacClusters = Traits::InterWaveSchedulingMacClusters;
constexpr index_t KPerInnerLoop =
ck_tile::max(KPerThread / NumMacClusters, WarpGemm::kKPerThread);
constexpr index_t KIterInterwave = KPerInnerLoop / WarpGemm::kKPerThread;
using KIterSeq = std::conditional_t<Scheduler == GemmPipelineScheduler::Interwave,
sequence<KIterInterwave>,
sequence<KIterPerWarp>>;
constexpr auto a_block_outer_dstr_encoding =
tile_distribution_encoding<sequence<NWarp>,
tuple<sequence<MIterPerWarp, MWarp>, KIterSeq>,
tuple<sequence<1, 0>>,
tuple<sequence<1, 0>>,
sequence<1, 2>,
sequence<0, 0>>{};
constexpr auto a_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
a_block_outer_dstr_encoding, typename WarpGemm::AWarpDstrEncoding{});
return a_block_dstr_encode;
}
CK_TILE_DEVICE static constexpr auto MakeBBlockDistributionEncode()
{
constexpr index_t KPerThread = Traits::KPerThread;
constexpr index_t NumMacClusters = Traits::InterWaveSchedulingMacClusters;
constexpr index_t KPerInnerLoop =
ck_tile::max(KPerThread / NumMacClusters, WarpGemm::kKPerThread);
constexpr index_t KIterInterwave = KPerInnerLoop / WarpGemm::kKPerThread;
using KIterSeq = std::conditional_t<Scheduler == GemmPipelineScheduler::Interwave,
sequence<KIterInterwave>,
sequence<KIterPerWarp>>;
constexpr auto b_block_outer_dstr_encoding =
tile_distribution_encoding<sequence<MWarp>,
tuple<sequence<NIterPerWarp, NWarp>, KIterSeq>,
tuple<sequence<0, 1>>,
tuple<sequence<0, 1>>,
sequence<1, 2>,
sequence<0, 0>>{};
constexpr auto b_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
b_block_outer_dstr_encoding, typename WarpGemm::BWarpDstrEncoding{});
return b_block_dstr_encode;
}
private:
template <GemmPipelineScheduler Scheduler, typename GemmTraits>
struct BlockGemmImpl
{
};
template <typename GemmTraits>
struct BlockGemmImpl<GemmPipelineScheduler::Intrawave, GemmTraits>
{
static constexpr auto ALdsTileDistr =
decltype(make_static_tile_distribution(MakeABlockDistributionEncode())){};
static constexpr auto BLdsTileDistr =
decltype(make_static_tile_distribution(MakeBBlockDistributionEncode())){};
using ALdsTile = decltype(make_static_distributed_tensor<ComputeDataType>(ALdsTileDistr));
using BLdsTile = decltype(make_static_distributed_tensor<ComputeDataType>(BLdsTileDistr));
ALdsTile a_warp_tile_;
BLdsTile b_warp_tile_;
template <typename ASmemBlockWindow, typename BSmemBlockWindow>
CK_TILE_DEVICE void LocalPrefetch(const ASmemBlockWindow& a_block_window,
const BSmemBlockWindow& b_block_window)
{
if constexpr(std::is_same_v<ADataType, pk_int4_t>)
{
static_assert(std::is_same_v<ComputeDataType, fp8_t> ||
std::is_same_v<ComputeDataType, bf8_t>);
Base::load_interleaved_pk_type(a_warp_tile_, a_block_window);
}
else
{
load_tile(a_warp_tile_, a_block_window);
}
if constexpr(std::is_same_v<BDataType, pk_int4_t>)
{
static_assert(std::is_same_v<ComputeDataType, fp8_t> ||
std::is_same_v<ComputeDataType, bf8_t>);
Base::load_interleaved_pk_type(b_warp_tile_, b_block_window);
}
else
{
load_tile(b_warp_tile_, b_block_window);
}
}
// C += A * B
template <typename CBlockTensor,
typename BQBlockTensor,
typename ASmemBlockWindow,
typename BSmemBlockWindow>
CK_TILE_DEVICE void operator()(CBlockTensor& c_block_tensor,
BQBlockTensor& bq_block_tensor,
[[maybe_unused]] ASmemBlockWindow& a_block_window,
[[maybe_unused]] BSmemBlockWindow& b_block_window)
{
static_assert(std::is_same_v<CDataType, typename CBlockTensor::DataType>,
"The CDataType as defined in traits should be the same as corresponding "
"C block tensor data type!");
// hot loop:
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
CWarpTensor c_warp_tensor;
static_for<0, Traits::QScalesPerBlockRow, 1>{}([&](auto kQScale) {
static_for<0, Traits::KIterPerQScale, 1>{}([&](auto kIterInQScale) {
constexpr auto kIter = kQScale * Traits::KIterPerQScale + kIterInQScale;
AWarpTensor a_warp_tensor;
a_warp_tensor.get_thread_buffer() =
a_warp_tile_.get_y_sliced_thread_data(
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
BWarpTensor b_warp_tensor;
b_warp_tensor.get_thread_buffer() =
b_warp_tile_.get_y_sliced_thread_data(
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
if constexpr(kIterInQScale == 0)
{
c_warp_tensor = WarpGemm{}(a_warp_tensor, b_warp_tensor);
}
else
{
WarpGemm{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
}
});
// Need to multiply bquant with accumulated C
//
// The accumulated C tile has the standard distribution. For example
// lane 0 holds elements [0,0], [1,0], [2,0], [3,0], [8,0], [9,0],
// [10,0], [11,0], [16,0], [17,0], [18,0], [19,0], [24,0], [25,0],
// [26,0], [27,0].
//
// These elements are in different rows, need to get the scale value
// for the corresponding row.
// Based on bquant's tile distribution, it can be inferred which
// lane holds the relevant scale. For example, the scales corresponding
// to the 16 elements held by lane 0 are held by lanes 0, 1, 2, 3, 8, 9,
// 10, 11, 16, 17, 18, 19, 24, 25, 26, 27 respectively.
//
// These scales can be obtained using __builtin_amdgcn_ds_bpermute.
constexpr index_t reg_offset = nIter * Traits::BQPerBlock + kQScale;
constexpr auto tbuf_offset =
number<typename CBlockTensor::ThreadTensorDesc{}.calculate_offset(
merge_sequences(sequence<mIter, nIter>{},
c_warp_y_index_zeros)) /
CBlockTensor::PackedSize>{};
auto& scale_reg = bq_block_tensor.get_thread_buffer()[reg_offset];
float scale_reg_f = Base::cvt_scale_to_fp32(scale_reg);
static_for<0, WarpGemm::kM / 2, 1>{}([&](auto c_row) {
c_block_tensor.get_thread_buffer()[tbuf_offset + c_row] +=
(c_warp_tensor.get_thread_buffer()[c_row] * scale_reg_f *
kA_cvt_scale * kB_cvt_scale);
});
});
});
});
}
};
public:
CK_TILE_DEVICE static constexpr auto MakeCBlockTile()
{
constexpr auto c_block_outer_dstr_encoding = tile_distribution_encoding<
sequence<>,
tuple<sequence<MIterPerWarp, MWarp>, sequence<NIterPerWarp, NWarp>>,
tuple<sequence<1, 2>>,
tuple<sequence<1, 1>>,
sequence<1, 2>,
sequence<0, 0>>{};
constexpr auto c_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
c_block_outer_dstr_encoding, typename WarpGemm::CWarpDstrEncoding{});
constexpr auto c_block_dstr = make_static_tile_distribution(c_block_dstr_encode);
auto c_block_tensor = make_static_distributed_tensor<CDataType>(c_block_dstr);
return c_block_tensor;
}
template <typename ASmemBlockWindow, typename BSmemBlockWindow>
CK_TILE_DEVICE void LocalPrefetch(const ASmemBlockWindow& a_block_window,
const BSmemBlockWindow& b_block_window)
{
block_gemm_impl_.LocalPrefetch(a_block_window, b_block_window);
}
// C += A * B
template <typename CBlockTensor,
typename BQBlockTensor,
typename ASmemBlockWindow,
typename BSmemBlockWindow>
CK_TILE_DEVICE void operator()(CBlockTensor& c_block_tensor,
BQBlockTensor& bq_block_tensor,
const ASmemBlockWindow& a_block_window,
const BSmemBlockWindow& b_block_window)
{
block_gemm_impl_(c_block_tensor, bq_block_tensor, a_block_window, b_block_window);
}
private:
BlockGemmImpl<Scheduler, Traits> block_gemm_impl_{};
};
} // namespace ck_tile

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include/ck_tile/ops/gemm_group_quant/kernel/gemm_bquant_kernel.hpp Normal file
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// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iostream>
#include <string>
#include "ck_tile/core.hpp"
#include "ck_tile/ops/common.hpp"
#include "ck_tile/host/concat.hpp"
namespace ck_tile {
struct BQuantGemmProblem
{
CK_TILE_HOST BQuantGemmProblem() = default;
CK_TILE_HOST BQuantGemmProblem(index_t M_,
index_t N_,
index_t K_,
index_t QK_,
index_t stride_A_,
index_t stride_B_,
index_t stride_C_,
index_t stride_BQ_)
: M(M_),
N(N_),
K(K_),
QK(QK_),
stride_A(stride_A_),
stride_B(stride_B_),
stride_C(stride_C_),
stride_BQ(stride_BQ_)
{
}
index_t M;
index_t N;
index_t K;
index_t QK;
index_t stride_A;
index_t stride_B;
index_t stride_C;
index_t stride_BQ;
};
struct BQuantGemmHostArgs : public BQuantGemmProblem
{
CK_TILE_HOST BQuantGemmHostArgs() = default;
CK_TILE_HOST BQuantGemmHostArgs(const void* a_ptr_,
const void* b_ptr_,
void* c_ptr_,
const void* bq_ptr_,
index_t k_batch_,
index_t M_,
index_t N_,
index_t K_,
index_t QK_,
index_t stride_A_,
index_t stride_B_,
index_t stride_C_,
index_t stride_BQ_)
: BQuantGemmProblem(M_, N_, K_, QK_, stride_A_, stride_B_, stride_C_, stride_BQ_),
a_ptr(a_ptr_),
b_ptr(b_ptr_),
bq_ptr(bq_ptr_),
c_ptr(c_ptr_),
k_batch(k_batch_)
{
}
const void* a_ptr;
const void* b_ptr;
const void* bq_ptr;
void* c_ptr;
index_t k_batch;
};
struct BQuantGemmKernelArgs
{
const void* a_ptr;
const void* b_ptr;
const void* bq_ptr;
void* c_ptr;
index_t M;
index_t N;
index_t K;
index_t QK;
index_t stride_A;
index_t stride_B;
index_t stride_C;
index_t stride_BQ;
index_t k_batch;
};
template <typename TilePartitioner_, typename GemmPipeline_, typename EpiloguePipeline_>
struct BQuantGemmKernel
{
using TilePartitioner = remove_cvref_t<TilePartitioner_>;
using GemmPipeline = remove_cvref_t<GemmPipeline_>;
using EpiloguePipeline = remove_cvref_t<EpiloguePipeline_>;
using ALayout = remove_cvref_t<typename GemmPipeline::ALayout>;
using BLayout = remove_cvref_t<typename GemmPipeline::BLayout>;
using BQLayout = remove_cvref_t<typename GemmPipeline::BQLayout>;
using CLayout = remove_cvref_t<typename GemmPipeline::CLayout>;
static constexpr index_t kBlockSize = GemmPipeline::BlockSize;
using ADataType = remove_cvref_t<typename GemmPipeline::ADataType>;
using BDataType = remove_cvref_t<typename GemmPipeline::BDataType>;
using BQDataType = remove_cvref_t<typename GemmPipeline::BQDataType>;
using CDataType = remove_cvref_t<typename EpiloguePipeline::ODataType>;
static constexpr auto I0 = number<0>();
static constexpr auto I1 = number<1>();
static constexpr auto I2 = number<2>();
static constexpr auto I3 = number<3>();
[[nodiscard]] CK_TILE_HOST static const std::string GetName()
{
// clang-format off
return concat('_', "gemm", gemm_prec_str<ADataType, BDataType>, GemmPipeline::GetName());
// clang-format on
}
CK_TILE_HOST static constexpr auto GridSize(index_t M, index_t N, index_t KBatch)
{
return dim3(TilePartitioner::GridSize(M, N), 1, KBatch);
}
CK_TILE_HOST static constexpr auto BlockSize() { return dim3(kBlockSize); }
CK_TILE_HOST static constexpr BQuantGemmKernelArgs
MakeKernelArgs(const BQuantGemmHostArgs& hostArgs)
{
return BQuantGemmKernelArgs{hostArgs.a_ptr,
hostArgs.b_ptr,
hostArgs.bq_ptr,
hostArgs.c_ptr,
hostArgs.M,
hostArgs.N,
hostArgs.K,
hostArgs.QK,
hostArgs.stride_A,
hostArgs.stride_B,
hostArgs.stride_C,
hostArgs.stride_BQ,
hostArgs.k_batch};
}
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
{
return max(GemmPipeline::GetSmemSize(), EpiloguePipeline::GetSmemSize());
}
struct SplitKBatchOffset
{
__device__ SplitKBatchOffset(const BQuantGemmKernelArgs& kargs,
const std::size_t k_id = blockIdx.z)
{
constexpr auto K1 = TilePartitioner::BlockGemmShape::WarpTile::at(number<2>{});
const index_t K_t = __builtin_amdgcn_readfirstlane(kargs.k_batch * K1);
const index_t KRead = __builtin_amdgcn_readfirstlane((kargs.K + K_t - 1) / K_t * K1);
if constexpr(std::is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
{
a_k_split_offset = __builtin_amdgcn_readfirstlane(k_id * KRead);
}
else if constexpr(std::is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
{
a_k_split_offset = __builtin_amdgcn_readfirstlane(k_id * KRead * kargs.stride_A);
}
if constexpr(std::is_same_v<tensor_layout::gemm::RowMajor, BLayout>)
{
b_k_split_offset = __builtin_amdgcn_readfirstlane(k_id * KRead * kargs.stride_B);
}
else if constexpr(std::is_same_v<tensor_layout::gemm::ColumnMajor, BLayout>)
{
b_k_split_offset = __builtin_amdgcn_readfirstlane(k_id * KRead);
}
if(k_id < static_cast<uint32_t>(kargs.k_batch - 1))
{
splitted_k = __builtin_amdgcn_readfirstlane(KRead);
}
else
{
splitted_k = __builtin_amdgcn_readfirstlane(kargs.K - KRead * (kargs.k_batch - 1));
}
}
index_t a_k_split_offset;
index_t b_k_split_offset;
index_t splitted_k;
};
CK_TILE_HOST static bool IsSupportedArgument(const BQuantGemmKernelArgs& kargs)
{
if(kargs.k_batch != 1)
{
if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
{
CK_TILE_ERROR("Conditions not met for Kbatch >1 !");
}
return false;
}
static_assert(std::is_same_v<BQLayout, tensor_layout::gemm::ColumnMajor>);
if(kargs.QK % GemmPipeline::GetVectorSizeBQ() != 0)
{
if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
{
CK_TILE_ERROR("K is not a multiple of vector load size for A tensor!");
}
return false;
}
if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
{
if(kargs.K % (TilePartitioner::KPerBlock * kargs.k_batch) != 0 &&
GemmPipeline::kPadK == false)
{
if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
{
CK_TILE_ERROR("Can't support K that is not a multiple of k_batch * KPerBlock "
"without padding!");
}
return false;
}
if(kargs.K % GemmPipeline::GetVectorSizeA() != 0)
{
if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
{
CK_TILE_ERROR("K is not a multiple of vector load size for A tensor!");
}
return false;
}
}
else
{
if(kargs.M % TilePartitioner::MPerBlock != 0 && GemmPipeline::kPadM == false)
{
if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
{
CK_TILE_ERROR(
"Can't support M that is not a multiple of MPerBlock without padding!");
}
return false;
}
if(kargs.M % GemmPipeline::GetVectorSizeA() != 0)
{
if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
{
CK_TILE_ERROR("M is not a multiple of vector load size for A tensor!");
}
return false;
}
}
if constexpr(std::is_same_v<BLayout, tensor_layout::gemm::RowMajor>)
{
if(kargs.N % TilePartitioner::NPerBlock != 0 && GemmPipeline::kPadN == false)
{
if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
{
CK_TILE_ERROR(
"Can't support N that is not a multiple of NPerBlock without padding!");
}
return false;
}
if(kargs.N % GemmPipeline::GetVectorSizeB() != 0)
{
if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
{
CK_TILE_ERROR("N is not a multiple of vector load size for B tensor!");
}
return false;
}
}
else
{
if(kargs.K % (TilePartitioner::KPerBlock * kargs.k_batch) != 0 &&
GemmPipeline::kPadK == false)
{
if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
{
CK_TILE_ERROR("Can't support K that is not a multiple of k_batch * KPerBlock "
"without padding!");
}
return false;
}
if(kargs.K % GemmPipeline::GetVectorSizeB() != 0)
{
if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
{
CK_TILE_ERROR("K is not a multiple of vector load size for B tensor!");
}
return false;
}
}
if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
{
if(kargs.N % TilePartitioner::NPerBlock != 0 && GemmPipeline::kPadN == false)
{
if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
{
CK_TILE_ERROR(
"Can't support N that is not a multiple of NPerBlock without padding!");
}
return false;
}
if(kargs.N % EpiloguePipeline::GetVectorSizeC() != 0)
{
if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
{
CK_TILE_ERROR("N is not a multiple of vector load size for C tensor!");
}
return false;
}
}
else
{
if(kargs.M % TilePartitioner::MPerBlock != 0 && GemmPipeline::kPadM == false)
{
if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
{
CK_TILE_ERROR(
"Can't support M that is not a multiple of MPerBlock without padding!");
}
return false;
}
if(kargs.M % EpiloguePipeline::GetVectorSizeC() != 0)
{
if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
{
CK_TILE_ERROR("M is not a multiple of vector load size for C tensor!");
}
return false;
}
}
return true;
}
template <memory_operation_enum DstInMemOp = memory_operation_enum::set>
CK_TILE_DEVICE static auto MakeGemmTensorViews(const ADataType* a_ptr,
const BDataType* b_ptr,
const BQDataType* bq_ptr,
CDataType* c_ptr,
const BQuantGemmKernelArgs& kargs,
const SplitKBatchOffset& splitk_batch_offset)
{
static_assert(!TilePartitioner::BlockGemmShape::PermuteA, "Not implemented!");
const auto& a_tensor_view = [&]() {
if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
{
return make_naive_tensor_view<address_space_enum::global>(
a_ptr,
make_tuple(kargs.M, splitk_batch_offset.splitted_k),
make_tuple(kargs.stride_A, 1),
number<GemmPipeline::GetVectorSizeA()>{},
number<1>{});
}
else
{
return make_naive_tensor_view<address_space_enum::global>(
a_ptr,
make_tuple(splitk_batch_offset.splitted_k, kargs.M),
make_tuple(kargs.stride_A, 1),
number<GemmPipeline::GetVectorSizeA()>{},
number<1>{});
}
}();
const auto& bq_tensor_view = [&]() {
static_assert(std::is_same_v<BQLayout, tensor_layout::gemm::ColumnMajor>);
return make_naive_tensor_view<address_space_enum::global>(
bq_ptr,
make_tuple(kargs.N, kargs.QK),
make_tuple(kargs.stride_BQ, 1),
number<GemmPipeline::GetVectorSizeBQ()>{},
number<1>{});
}();
const auto& b_tensor_view = [&]() {
if constexpr(std::is_same_v<BLayout, tensor_layout::gemm::RowMajor>)
{
if constexpr(TilePartitioner::BlockGemmShape::PermuteB)
{
constexpr index_t K1 = GemmPipeline::GetSmemPackB();
const index_t K0 = splitk_batch_offset.splitted_k / K1;
constexpr index_t VectorSizeB = std::min(K1, GemmPipeline::GetVectorSizeB());
const auto b_k0_n_k1_desc =
make_naive_tensor_descriptor(make_tuple(K0, kargs.N, K1),
make_tuple(kargs.N * K1, K1, I1),
number<VectorSizeB>{},
number<1>{});
const auto b_n_k_desc = transform_tensor_descriptor(
b_k0_n_k1_desc,
make_tuple(make_merge_transform(make_tuple(K0, K1)),
make_pass_through_transform(kargs.N)),
make_tuple(sequence<0, 2>{}, sequence<1>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
return make_tensor_view<address_space_enum::global>(b_ptr, b_n_k_desc);
}
else
{
return make_naive_tensor_view<address_space_enum::global>(
b_ptr,
make_tuple(splitk_batch_offset.splitted_k, kargs.N),
make_tuple(kargs.stride_B, 1),
number<GemmPipeline::GetVectorSizeB()>{},
number<1>{});
}
}
else
{
if constexpr(TilePartitioner::BlockGemmShape::PermuteB)
{
constexpr index_t K1 = GemmPipeline::GetSmemPackB();
const index_t K0 = splitk_batch_offset.splitted_k / K1;
constexpr index_t VectorSizeB = std::min(K1, GemmPipeline::GetVectorSizeB());
const auto b_k0_n_k1_desc =
make_naive_tensor_descriptor(make_tuple(K0, kargs.N, K1),
make_tuple(kargs.N * K1, K1, I1),
number<VectorSizeB>{},
number<1>{});
const auto b_n_k_desc = transform_tensor_descriptor(
b_k0_n_k1_desc,
make_tuple(make_merge_transform(make_tuple(K0, K1)),
make_pass_through_transform(kargs.N)),
make_tuple(sequence<0, 2>{}, sequence<1>{}),
make_tuple(sequence<1>{}, sequence<0>{}));
return make_tensor_view<address_space_enum::global>(b_ptr, b_n_k_desc);
}
else
{
return make_naive_tensor_view<address_space_enum::global>(
b_ptr,
make_tuple(kargs.N, splitk_batch_offset.splitted_k),
make_tuple(kargs.stride_B, 1),
number<GemmPipeline::GetVectorSizeB()>{},
number<1>{});
}
}
}();
// TODO: enable vector write for C in ColMajor
const auto& c_tensor_view = [&]() {
if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
{
return make_naive_tensor_view<address_space_enum::global, DstInMemOp>(
c_ptr,
make_tuple(kargs.M, kargs.N),
make_tuple(kargs.stride_C, 1),
number<EpiloguePipeline::GetVectorSizeC()>{},
number<1>{});
}
else
{
return make_naive_tensor_view<address_space_enum::global, DstInMemOp>(
c_ptr,
make_tuple(kargs.M, kargs.N),
make_tuple(1, kargs.stride_C),
number<1>{},
number<1>{});
}
}();
return make_tuple(a_tensor_view, bq_tensor_view, b_tensor_view, c_tensor_view);
}
template <typename TensorView>
CK_TILE_DEVICE static auto MakeGemmPadViews(const TensorView& views)
{
const auto& a_pad_view = [&]() {
const auto& a_tensor_view = views.at(I0);
if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
{
return pad_tensor_view(a_tensor_view,
make_tuple(number<TilePartitioner::MPerBlock>{},
number<TilePartitioner::KPerBlock>{}),
sequence<false, GemmPipeline::kPadK>{});
}
else
{
return pad_tensor_view(a_tensor_view,
make_tuple(number<TilePartitioner::KPerBlock>{},
number<TilePartitioner::MPerBlock>{}),
sequence<false, GemmPipeline::kPadM>{});
}
}();
const auto& bq_pad_view = [&]() {
const auto& bq_tensor_view = views.at(I1);
static_assert(std::is_same_v<BQLayout, tensor_layout::gemm::ColumnMajor>);
return pad_tensor_view(
bq_tensor_view,
make_tuple(number<TilePartitioner::NPerBlock>{},
number<TilePartitioner::KPerBlock / GemmPipeline::QuantGroupSize>{}),
// TODO: Add support for padding.
sequence<false, false>{});
}();
const auto& b_pad_view = [&]() {
const auto& b_tensor_view = views.at(I2);
if constexpr(std::is_same_v<BLayout, tensor_layout::gemm::ColumnMajor>)
{
return pad_tensor_view(b_tensor_view,
make_tuple(number<TilePartitioner::NPerBlock>{},
number<TilePartitioner::KPerBlock>{}),
sequence<false, GemmPipeline::kPadK>{});
}
else
{
return pad_tensor_view(b_tensor_view,
make_tuple(number<TilePartitioner::KPerBlock>{},
number<TilePartitioner::NPerBlock>{}),
sequence<false, GemmPipeline::kPadN>{});
}
}();
// TODO vector write in for C in ColMajor
const auto& c_pad_view = [&]() {
const auto& c_tensor_view = views.at(I3);
if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
{
return pad_tensor_view(c_tensor_view,
make_tuple(number<TilePartitioner::MPerBlock>{},
number<TilePartitioner::NPerBlock>{}),
sequence<false, GemmPipeline::kPadN>{});
}
else
{
return pad_tensor_view(c_tensor_view,
make_tuple(number<TilePartitioner::MPerBlock>{},
number<TilePartitioner::NPerBlock>{}),
sequence<GemmPipeline::kPadM, false>{});
}
}();
return make_tuple(a_pad_view, bq_pad_view, b_pad_view, c_pad_view);
}
template <typename PadView>
CK_TILE_DEVICE static auto
MakeGemmTileWindows(const PadView& views, const index_t i_m, const index_t i_n)
{
const auto& a_pad_view = views.at(I0);
const auto& bq_pad_view = views.at(I1);
const auto& b_pad_view = views.at(I2);
const auto& c_pad_view = views.at(I3);
const auto& a_block_window = [&]() {
if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
{
return make_tile_window(a_pad_view,
make_tuple(number<TilePartitioner::MPerBlock>{},
number<TilePartitioner::KPerBlock>{}),
{i_m, 0});
}
else
{
return make_tile_window(a_pad_view,
make_tuple(number<TilePartitioner::KPerBlock>{},
number<TilePartitioner::MPerBlock>{}),
{0, i_m});
}
}();
const auto& bq_block_window = [&]() {
static_assert(std::is_same_v<BQLayout, tensor_layout::gemm::ColumnMajor>);
return make_tile_window(
bq_pad_view,
make_tuple(number<TilePartitioner::NPerBlock>{},
number<TilePartitioner::KPerBlock / GemmPipeline::QuantGroupSize>{}),
{i_n, 0});
}();
const auto& b_block_window = [&]() {
if constexpr(std::is_same_v<BLayout, tensor_layout::gemm::ColumnMajor>)
{
return make_tile_window(b_pad_view,
make_tuple(number<TilePartitioner::NPerBlock>{},
number<TilePartitioner::KPerBlock>{}),
{i_n, 0});
}
else
{
return make_tile_window(b_pad_view,
make_tuple(number<TilePartitioner::KPerBlock>{},
number<TilePartitioner::NPerBlock>{}),
{0, i_n});
}
}();
auto c_block_window = make_tile_window(
c_pad_view,
make_tuple(number<TilePartitioner::MPerBlock>{}, number<TilePartitioner::NPerBlock>{}),
{i_m, i_n});
return make_tuple(a_block_window, bq_block_window, b_block_window, c_block_window);
}
/**
* @brief Runs single GEMM problem cooperatively by whole workgroup.
*
* @param a_ptr input A pointer
* @param b_ptr input B pointer
* @param bq_ptr input BQ pointer
* @param c_ptr output C pointer
* @param smem_ptr_0 The start memory pointer of the shared memory block.
* @param kargs GEMM kernel arguments
* @param splitk_batch_offset splitk_batch_offset Utility structure used to calculate k batch.
* @param block_idx_m The GEMM's output M dimension tile index processed by this workgroup.
* @param block_idx_n The GEMM's output N dimension tile index processed by this workgroup.
*
* @tparam DstInMemOp Destination memory operation (default: set).
*/
template <memory_operation_enum DstInMemOp = memory_operation_enum::set>
CK_TILE_DEVICE static void RunGemm(const ADataType* a_ptr,
const BDataType* b_ptr,
const BQDataType* bq_ptr,
CDataType* c_ptr,
void* smem_ptr_0,
const BQuantGemmKernelArgs& kargs,
const SplitKBatchOffset& splitk_batch_offset,
const index_t block_idx_m,
const index_t block_idx_n)
{
// Create Gemm tensor views, pad views and tile windows
const auto& gemm_tensor_views_tuple = MakeGemmTensorViews<DstInMemOp>(
a_ptr, b_ptr, bq_ptr, c_ptr, kargs, splitk_batch_offset);
const auto& gemm_pad_views = MakeGemmPadViews(gemm_tensor_views_tuple);
auto gemm_tile_windows = MakeGemmTileWindows(gemm_pad_views, block_idx_m, block_idx_n);
const index_t num_loop = __builtin_amdgcn_readfirstlane(
TilePartitioner::GetLoopNum(splitk_batch_offset.splitted_k));
// Run GEMM cooperatively by whole workgroup.
const auto& a_block_window = gemm_tile_windows.at(I0);
const auto& bq_block_window = gemm_tile_windows.at(I1);
const auto& b_block_window = gemm_tile_windows.at(I2);
const auto& c_block_tile = GemmPipeline{}.template operator()(
a_block_window, b_block_window, bq_block_window, num_loop, smem_ptr_0);
// Run Epilogue Pipeline
auto& c_block_window = gemm_tile_windows.at(I3);
EpiloguePipeline{}.template
operator()<decltype(c_block_window), decltype(c_block_tile), decltype(c_block_window)>(
c_block_window, c_block_tile, c_block_window, smem_ptr_0);
}
CK_TILE_DEVICE void operator()(BQuantGemmKernelArgs kargs) const
{
const auto blockId = __builtin_amdgcn_readfirstlane(blockIdx.x);
const auto [iM, iN] = TilePartitioner{kargs.M, kargs.N}.GetOutputTileIndex(blockId);
const index_t i_m = __builtin_amdgcn_readfirstlane(iM * TilePartitioner::MPerBlock);
const index_t i_n = __builtin_amdgcn_readfirstlane(iN * TilePartitioner::NPerBlock);
const SplitKBatchOffset splitk_batch_offset(kargs);
// options
const ADataType* a_ptr = static_cast<const ADataType*>(kargs.a_ptr);
const BDataType* b_ptr = static_cast<const BDataType*>(kargs.b_ptr);
const BQDataType* bq_ptr = static_cast<const BQDataType*>(kargs.bq_ptr);
CDataType* c_ptr = static_cast<CDataType*>(kargs.c_ptr);
// allocate LDS
__shared__ char smem_ptr_0[GetSmemSize()];
assert(kargs.k_batch == 1);
RunGemm(a_ptr, b_ptr, bq_ptr, c_ptr, smem_ptr_0, kargs, splitk_batch_offset, i_m, i_n);
}
};
} // namespace ck_tile

2
include/ck_tile/ops/gemm_group_quant/pipeline/gemm_aquant_pipeline_ag_bg_cr_policy.hpp
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@@ -28,7 +28,7 @@ struct GemmAQuantPipelineAgBgCrDefaultPolicy : public UniversalGemmPipelineAgBgC
constexpr index_t KPerBlockAQ = KPerBlock / Problem::kQuantGroupSize;
static_assert(std::is_same_v<AQLayout, ck_tile::tensor_layout::gemm::RowMajor>);
return GetAQGlobalVectorLoadSize<Problem, AQDataType, MPerBlock, KPerBlockAQ>();
return GetABQGlobalVectorLoadSize<Problem, AQDataType, MPerBlock, KPerBlockAQ>();
}
template <typename Problem>

53
include/ck_tile/ops/gemm_group_quant/pipeline/gemm_bquant_pipeline_ag_bg_cr_base.hpp Normal file
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@@ -0,0 +1,53 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ops/common.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_base.hpp"
namespace ck_tile {
template <typename Problem, typename Policy>
struct GemmBQuantPipelineAgBgCrImplBase : public GemmPipelineAgBgCrImplBase<Problem, Policy>
{
using Base = GemmPipelineAgBgCrImplBase<Problem, Policy>;
using ADataType = typename Base::ADataType;
using ALayout = typename Base::ALayout;
using BDataType = typename Base::BDataType;
using BLayout = typename Base::BLayout;
using BlockGemmShape = typename Base::BlockGemmShape;
using BQLayout = remove_cvref_t<typename Problem::BQLayout>;
static constexpr index_t MPerBlock = BlockGemmShape::kM;
static constexpr index_t NPerBlock = BlockGemmShape::kN;
static constexpr index_t KPerBlock = BlockGemmShape::kK;
static constexpr index_t QuantGroupSize = Problem::kQuantGroupSize;
static constexpr index_t KPerBlockBQ = KPerBlock / QuantGroupSize;
static_assert(KPerBlock % QuantGroupSize == 0,
"KPerBlock must be a multiple of QuantGroupSize");
// Create DRAM tile window for BQ
template <typename BQDramBlockWindowTmp>
CK_TILE_DEVICE constexpr auto
GetBQDramLoadWindow(const BQDramBlockWindowTmp& bq_dram_block_window_tmp) const
{
static_assert(std::is_same_v<BQLayout, tensor_layout::gemm::ColumnMajor>);
using YPerTile = number<NPerBlock>;
using XPerTile = number<KPerBlockBQ>;
auto bq_copy_dram_window =
make_tile_window(bq_dram_block_window_tmp.get_bottom_tensor_view(),
make_tuple(YPerTile(), XPerTile()),
bq_dram_block_window_tmp.get_window_origin(),
Policy::template MakeBQDramTileDistribution<Problem>());
return bq_copy_dram_window;
}
};
} // namespace ck_tile

93
include/ck_tile/ops/gemm_group_quant/pipeline/gemm_bquant_pipeline_ag_bg_cr_policy.hpp Normal file
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@@ -0,0 +1,93 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/ops/gemm/pipeline/gemm_universal_pipeline_ag_bg_cr_policy.hpp"
#include "gemm_group_quant_utils.hpp"
namespace ck_tile {
struct GemmBQuantPipelineAgBgCrDefaultPolicy : public UniversalGemmPipelineAgBgCrPolicy
{
using Base = UniversalGemmPipelineAgBgCrPolicy;
using Base::I0;
using Base::I1;
using Base::I2;
using Base::ATileAccessPattern;
using Base::BTileAccessPattern;
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetVectorSizeBQ()
{
using BQLayout = remove_cvref_t<typename Problem::BQLayout>;
using BQDataType = remove_cvref_t<typename Problem::BQDataType>;
constexpr index_t NPerBlock = Problem::BlockGemmShape::kN;
constexpr index_t KPerBlock = Problem::BlockGemmShape::kK;
constexpr index_t KPerBlockBQ = KPerBlock / Problem::kQuantGroupSize;
static_assert(std::is_same_v<BQLayout, ck_tile::tensor_layout::gemm::ColumnMajor>);
return GetABQGlobalVectorLoadSize<Problem, BQDataType, NPerBlock, KPerBlockBQ>();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeBQDramTileDistribution()
{
using BQLayout = remove_cvref_t<typename Problem::BQLayout>;
using BlockGemmShape = typename Problem::BlockGemmShape;
constexpr index_t BlockSize = Problem::kBlockSize;
constexpr index_t NPerBlock = Problem::BlockGemmShape::kN;
constexpr index_t KPerBlock = Problem::BlockGemmShape::kK;
constexpr index_t KPerBlockBQ = KPerBlock / Problem::kQuantGroupSize;
constexpr index_t VecLoadSize = GetVectorSizeBQ<Problem>();
using WarpTile = typename Problem::BlockGemmShape::WarpTile;
using WarpGemm = WarpGemmDispatcher<typename Problem::ComputeDataType,
typename Problem::ComputeDataType,
typename Problem::CDataType,
WarpTile::at(I0),
WarpTile::at(I1),
WarpTile::at(I2),
Problem::TransposeC>;
static_assert(std::is_same_v<BQLayout, tensor_layout::gemm::ColumnMajor>);
using TileEncodingPattern = TileDistributionEncodingPatternBQ<BlockGemmShape,
WarpGemm,
BlockSize,
NPerBlock,
KPerBlockBQ,
VecLoadSize>;
return TileEncodingPattern::Make2DStaticTileDistribution();
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetBlockGemm()
{
using BlockWarps = typename Problem::BlockGemmShape::BlockWarps;
using WarpTile = typename Problem::BlockGemmShape::WarpTile;
static_assert(Problem::kQuantGroupSize % WarpTile::at(I2) == 0,
"KPerWarpGemm must be a multiple of kQuantGroupSize!");
using WarpGemm = WarpGemmDispatcher<typename Problem::ComputeDataType,
typename Problem::ComputeDataType,
typename Problem::CDataType,
WarpTile::at(I0),
WarpTile::at(I1),
WarpTile::at(I2),
Problem::TransposeC>;
static_assert(std::is_same_v<typename Problem::ComputeDataType, fp8_t> ||
std::is_same_v<typename Problem::ComputeDataType, bf8_t>);
static_assert(std::is_same_v<typename Problem::CDataType, float>);
using BlockGemmPolicy = BlockGemmASmemBSmemCRegV1CustomPolicy<typename Problem::ADataType,
typename Problem::BDataType,
typename Problem::CDataType,
BlockWarps,
WarpGemm>;
return BQuantBlockUniversalGemmAsBsCr<Problem, BlockGemmPolicy>{};
}
};
} // namespace ck_tile

475
include/ck_tile/ops/gemm_group_quant/pipeline/gemm_bquant_pipeline_ag_bg_cr_v3.hpp Normal file
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@@ -0,0 +1,475 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <string>
#include <sstream>
#include "ck_tile/core.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_universal_pipeline_ag_bg_cr_policy.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_scheduler.hpp"
#include "ck_tile/ops/gemm_group_quant/pipeline/gemm_bquant_pipeline_ag_bg_cr_base.hpp"
#include "ck_tile/host/concat.hpp"
namespace ck_tile {
// Compute optimized pipeline
// GlobalPrefetchStages: 2
// LocalPreFillStages: 1
// LocalPreFetchStages: 1
// LocalSharedMemoryBuffer: 1
template <typename Problem>
struct BaseBQuantGemmPipelineAgBgCrCompV3 : public BaseGemmPipelineAgBgCrCompV3<Problem>
{
template <typename RunFunction>
CK_TILE_HOST_DEVICE static auto
TailHandler(const RunFunction& run_func, bool has_hot_loop, TailNumber tail_number)
{
if(has_hot_loop)
{
if(tail_number == ck_tile::TailNumber::Full)
{
return run_func(
ck_tile::bool_constant<true>{},
ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Full>{});
}
else if(tail_number == ck_tile::TailNumber::Odd)
{
return run_func(
ck_tile::bool_constant<true>{},
ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Odd>{});
}
else if(tail_number == ck_tile::TailNumber::Even)
{
return run_func(
ck_tile::bool_constant<true>{},
ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Even>{});
}
else
{
throw std::runtime_error("Unsupported tail number for this operation !!!");
}
}
else
{
if(tail_number == ck_tile::TailNumber::Full)
{
return run_func(
ck_tile::bool_constant<false>{},
ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Full>{});
}
else if(tail_number == ck_tile::TailNumber::Odd)
{
return run_func(
ck_tile::bool_constant<false>{},
ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Odd>{});
}
else if(tail_number == ck_tile::TailNumber::Even)
{
return run_func(
ck_tile::bool_constant<false>{},
ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Even>{});
}
else
{
throw std::runtime_error("Unsupported tail number for this operation !!!");
}
}
}
};
template <typename Problem, typename Policy = GemmBQuantPipelineAgBgCrDefaultPolicy>
struct BQuantGemmPipelineAgBgCrCompV3 : public BaseBQuantGemmPipelineAgBgCrCompV3<Problem>
{
using Base = BaseGemmPipelineAgBgCrCompV3<Problem>;
using PipelineImplBase = GemmBQuantPipelineAgBgCrImplBase<Problem, Policy>;
using ADataType = remove_cvref_t<typename Problem::ADataType>;
using BDataType = remove_cvref_t<typename Problem::BDataType>;
using BQDataType = remove_cvref_t<typename Problem::BQDataType>;
using CDataType = remove_cvref_t<typename Problem::CDataType>;
using BlockGemmShape = remove_cvref_t<typename Problem::BlockGemmShape>;
using I0 = number<0>;
using I1 = number<1>;
using I2 = number<2>;
static constexpr index_t APackedSize =
ck_tile::numeric_traits<remove_cvref_t<ADataType>>::PackedSize;
static constexpr index_t BPackedSize =
ck_tile::numeric_traits<remove_cvref_t<BDataType>>::PackedSize;
static constexpr index_t BQPackedSize =
ck_tile::numeric_traits<remove_cvref_t<BQDataType>>::PackedSize;
using ALayout = remove_cvref_t<typename Problem::ALayout>;
using BQLayout = remove_cvref_t<typename Problem::BQLayout>;
using BLayout = remove_cvref_t<typename Problem::BLayout>;
using CLayout = remove_cvref_t<typename Problem::CLayout>;
using BlockGemm = remove_cvref_t<decltype(Policy::template GetBlockGemm<Problem>())>;
static constexpr index_t BlockSize = Problem::kBlockSize;
static constexpr index_t MPerBlock = BlockGemmShape::kM;
static constexpr index_t NPerBlock = BlockGemmShape::kN;
static constexpr index_t KPerBlock = BlockGemmShape::kK;
static constexpr index_t QuantGroupSize = Problem::kQuantGroupSize;
static constexpr index_t KPerBlockBQ = BlockGemmShape::kK / QuantGroupSize;
static constexpr index_t GetVectorSizeA() { return Policy::template GetVectorSizeA<Problem>(); }
static constexpr index_t GetVectorSizeB() { return Policy::template GetVectorSizeB<Problem>(); }
static constexpr index_t GetVectorSizeC() { return Policy::template GetVectorSizeC<Problem>(); }
static constexpr index_t GetVectorSizeBQ()
{
return Policy::template GetVectorSizeBQ<Problem>();
}
static constexpr index_t GetSmemPackA() { return Policy::template GetSmemPackA<Problem>(); }
static constexpr index_t GetSmemPackB() { return Policy::template GetSmemPackB<Problem>(); }
static constexpr bool kPadM = Problem::kPadM;
static constexpr bool kPadN = Problem::kPadN;
static constexpr bool kPadK = Problem::kPadK;
static constexpr bool DoubleSmemBuffer = Problem::DoubleSmemBuffer;
static constexpr bool HasHotLoop = Problem::HasHotLoop;
static constexpr auto TailNum = Problem::TailNum;
static constexpr auto Scheduler = Problem::Scheduler;
using Base::PrefetchStages;
[[nodiscard]] CK_TILE_HOST static const std::string GetName()
{
// clang-format off
constexpr index_t WaveNumM = BlockGemmShape::BlockWarps::at(I0{});
constexpr index_t WaveNumN = BlockGemmShape::BlockWarps::at(I1{});
return concat('_', "bquant_pipeline_AgBgCrCompV3",
concat('x', MPerBlock, NPerBlock, KPerBlock),
BlockSize,
concat('x', WaveNumM, WaveNumN),
concat('x', BlockGemm::WarpGemm::kM, BlockGemm::WarpGemm::kN, BlockGemm::WarpGemm::kK),
concat('x', kPadM, kPadN, kPadK), "QuantGroupSize", QuantGroupSize);
// clang-format on
}
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
{
return Policy::template GetSmemSize<Problem>();
}
CK_TILE_HOST static std::string Print()
{
constexpr index_t MPerXDL = BlockGemm::WarpGemm::kM;
constexpr index_t NPerXDL = BlockGemm::WarpGemm::kN;
constexpr index_t KPerXDL = BlockGemm::WarpGemm::WarpGemmAttribute::Impl::kK;
constexpr index_t WaveSize = 64;
constexpr index_t WaveNumM = BlockGemmShape::BlockWarps::at(I0{});
constexpr index_t WaveNumN = BlockGemmShape::BlockWarps::at(I1{});
constexpr index_t A_LDS_Read_Width = GetSmemPackA();
constexpr index_t B_LDS_Read_Width = GetSmemPackB();
constexpr index_t A_LDS_Write_Width = GetSmemPackA();
constexpr index_t B_LDS_Write_Width = GetSmemPackB();
constexpr index_t A_Buffer_Load_Inst_Num =
MPerBlock * KPerBlock / (BlockSize * GetVectorSizeA());
constexpr index_t B_Buffer_Load_Inst_Num =
NPerBlock * KPerBlock / (BlockSize * GetVectorSizeB());
constexpr index_t BQ_Buffer_Load_Inst_Num =
NPerBlock * KPerBlockBQ / (BlockSize * GetVectorSizeBQ());
constexpr index_t A_LDS_Write_Inst_Num =
MPerBlock * KPerBlock / (BlockSize * A_LDS_Write_Width);
constexpr index_t B_LDS_Write_Inst_Num =
NPerBlock * KPerBlock / (BlockSize * B_LDS_Write_Width);
constexpr index_t A_LDS_Read_Inst_Num =
WaveNumN * MPerBlock * KPerBlock / (BlockSize * A_LDS_Read_Width);
constexpr index_t B_LDS_Read_Inst_Num =
WaveNumM * NPerBlock * KPerBlock / (BlockSize * B_LDS_Read_Width);
constexpr index_t C_MFMA_Inst_Num = MPerBlock * NPerBlock * KPerBlock /
(BlockSize / WaveSize) / (MPerXDL * NPerXDL * KPerXDL);
auto str = std::stringstream{};
str << "A/B vector size: " << GetVectorSizeA() << ", " << GetVectorSizeB() << ", "
<< "BQ vector size: " << GetVectorSizeBQ() << "\n"
<< "A/B LDS read/write width: " << A_LDS_Read_Width << ", " << B_LDS_Read_Width << "\n"
<< "A/B buffer load inst: " << A_Buffer_Load_Inst_Num << ", " << B_Buffer_Load_Inst_Num
<< ", " << "BQ buffer load inst: " << BQ_Buffer_Load_Inst_Num << "\n"
<< "A/B LDS write inst: " << A_LDS_Write_Inst_Num << ", " << B_LDS_Write_Inst_Num
<< "\n"
<< "A/B LDS read inst: " << A_LDS_Read_Inst_Num << ", " << B_LDS_Read_Inst_Num << "\n"
<< "C MFMA inst: " << C_MFMA_Inst_Num << "\n"
<< "QuantGroupSize: " << QuantGroupSize << "\n"
<< "KPack: " << BlockGemm::Traits::KPack << "\n"
<< "PrefetchStages: " << PrefetchStages << "\n";
return str.str();
}
template <GemmPipelineScheduler Scheduler>
struct PipelineImpl : public PipelineImplBase
{
};
template <>
struct PipelineImpl<GemmPipelineScheduler::Intrawave> : public PipelineImplBase
{
using Base = PipelineImplBase;
template <bool HasHotLoop,
TailNumber TailNum,
typename ADramBlockWindowTmp,
typename BDramBlockWindowTmp,
typename BQDramBlockWindowTmp,
typename AElementFunction,
typename BElementFunction>
CK_TILE_DEVICE auto operator()(const ADramBlockWindowTmp& a_dram_block_window_tmp,
const AElementFunction& a_element_func,
const BDramBlockWindowTmp& b_dram_block_window_tmp,
const BElementFunction& b_element_func,
const BQDramBlockWindowTmp& bq_dram_block_window_tmp,
index_t num_loop,
void* p_smem) const
{
static_assert(
std::is_same_v<ADataType, remove_cvref_t<typename ADramBlockWindowTmp::DataType>> &&
std::is_same_v<BDataType,
remove_cvref_t<typename BDramBlockWindowTmp::DataType>> &&
std::is_same_v<BQDataType,
remove_cvref_t<typename BQDramBlockWindowTmp::DataType>>,
"A/B/BQ Dram block window should have the same data type as appropriate "
"([A|B|BQ]DataType) defined in Problem definition!");
constexpr bool is_a_col_major =
std::is_same_v<ALayout, tensor_layout::gemm::ColumnMajor>;
constexpr bool is_bq_col_major =
std::is_same_v<BQLayout, tensor_layout::gemm::ColumnMajor>;
constexpr bool is_b_row_major = std::is_same_v<BLayout, tensor_layout::gemm::RowMajor>;
static_assert(is_bq_col_major, "Bq must be col major (row major not supported yet)");
static_assert(NPerBlock == BQDramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
KPerBlockBQ == BQDramBlockWindowTmp{}.get_window_lengths()[I1{}],
"Bq block window has incorrect lengths for defined BqLayout!");
static_assert(is_a_col_major
? (KPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
MPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I1{}])
: (MPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
KPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I1{}]),
"A block window has incorrect lengths for defined ALayout!");
static_assert(is_b_row_major
? (KPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
NPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I1{}])
: (NPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
KPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I1{}]),
"B block window has incorrect lengths for defined BLayout!");
using ADramTileWindowStep = typename ADramBlockWindowTmp::BottomTensorIndex;
using BDramTileWindowStep = typename BDramBlockWindowTmp::BottomTensorIndex;
using BQDramTileWindowStep = typename BQDramBlockWindowTmp::BottomTensorIndex;
auto&& [a_lds_block, b_lds_block] = Base::GetABLdsTensorViews(p_smem);
constexpr auto a_lds_load_tile_distr =
make_static_tile_distribution(BlockGemm::MakeABlockDistributionEncode());
constexpr auto b_lds_load_tile_distr =
make_static_tile_distribution(BlockGemm::MakeBBlockDistributionEncode());
auto&& [a_copy_dram_window, a_copy_lds_window, a_lds_gemm_window] =
Base::GetAWindows(a_dram_block_window_tmp, a_lds_block, a_lds_load_tile_distr);
auto&& [b_copy_dram_window, b_copy_lds_window, b_lds_gemm_window] =
Base::GetBWindows(b_dram_block_window_tmp, b_lds_block, b_lds_load_tile_distr);
auto bq_copy_dram_window = Base::GetBQDramLoadWindow(bq_dram_block_window_tmp);
using ABlockTileDistr = decltype(a_copy_dram_window.get_tile_distribution());
using BBlockTileDistr = decltype(b_copy_dram_window.get_tile_distribution());
using BQBlockTileDistr = decltype(bq_copy_dram_window.get_tile_distribution());
using ABlockTile =
decltype(make_static_distributed_tensor<ADataType>(ABlockTileDistr{}));
using BBlockTile =
decltype(make_static_distributed_tensor<BDataType>(BBlockTileDistr{}));
using BQBlockTile =
decltype(make_static_distributed_tensor<BQDataType>(BQBlockTileDistr{}));
auto block_gemm = BlockGemm();
ABlockTile a_block_tile;
BBlockTile b_block_tile;
BQBlockTile bq_block_tile[2];
int currIdx = 0;
auto c_block_tile = block_gemm.MakeCBlockTile();
constexpr ADramTileWindowStep a_dram_tile_window_step =
is_a_col_major ? make_array(KPerBlock, 0) : make_array(0, KPerBlock);
constexpr BDramTileWindowStep b_dram_tile_window_step =
is_b_row_major ? make_array(KPerBlock, 0) : make_array(0, KPerBlock);
constexpr BQDramTileWindowStep bq_dram_tile_window_step =
is_bq_col_major ? make_array(0, KPerBlockBQ) : make_array(KPerBlockBQ, 0);
// DRAM prefetch (global read 0)
Base::GlobalPrefetch(a_block_tile, a_copy_dram_window, a_dram_tile_window_step);
Base::GlobalPrefetch(b_block_tile, b_copy_dram_window, b_dram_tile_window_step);
Base::GlobalPrefetch(
bq_block_tile[currIdx], bq_copy_dram_window, bq_dram_tile_window_step);
tile_elementwise_inout([](auto& c) { c = 0; }, c_block_tile);
if constexpr(is_a_col_major)
{
auto a_shuffle_tmp = make_static_distributed_tensor<ADataType>(
Policy::template MakeShuffled2DStaticTileDistribution<Problem>());
transpose_tile2d(a_shuffle_tmp, a_block_tile);
Base::LocalPrefill(a_copy_lds_window, a_shuffle_tmp, a_element_func);
}
else
{
Base::LocalPrefill(a_copy_lds_window, a_block_tile, a_element_func);
}
if constexpr(is_b_row_major)
{
auto b_shuffle_tmp = make_static_distributed_tensor<BDataType>(
Policy::template MakeShuffled2DStaticTileDistribution<Problem>());
transpose_tile2d(b_shuffle_tmp, b_block_tile);
Base::LocalPrefill(b_copy_lds_window, b_shuffle_tmp, b_element_func);
}
else
{
Base::LocalPrefill(b_copy_lds_window, b_block_tile, b_element_func);
}
Base::GlobalPrefetch(a_block_tile, a_copy_dram_window, a_dram_tile_window_step);
Base::GlobalPrefetch(b_block_tile, b_copy_dram_window, b_dram_tile_window_step);
block_sync_lds();
block_gemm.LocalPrefetch(a_lds_gemm_window, b_lds_gemm_window);
__builtin_amdgcn_sched_barrier(0);
if constexpr(HasHotLoop)
{
index_t i = 0;
do
{
block_sync_lds();
if constexpr(is_a_col_major)
{
auto a_shuffle_tmp = make_static_distributed_tensor<ADataType>(
Policy::template MakeShuffledARegTileDistribution<Problem>());
transpose_tile2d(a_shuffle_tmp, a_block_tile);
Base::LocalPrefill(a_copy_lds_window, a_shuffle_tmp, a_element_func);
}
else
{
Base::LocalPrefill(a_copy_lds_window, a_block_tile, a_element_func);
}
if constexpr(is_b_row_major)
{
auto b_shuffle_tmp = make_static_distributed_tensor<BDataType>(
Policy::template MakeShuffledBRegTileDistribution<Problem>());
transpose_tile2d(b_shuffle_tmp, b_block_tile);
Base::LocalPrefill(b_copy_lds_window, b_shuffle_tmp, b_element_func);
}
else
{
Base::LocalPrefill(b_copy_lds_window, b_block_tile, b_element_func);
}
Base::GlobalPrefetch(a_block_tile, a_copy_dram_window, a_dram_tile_window_step);
Base::GlobalPrefetch(b_block_tile, b_copy_dram_window, b_dram_tile_window_step);
Base::GlobalPrefetch(bq_block_tile[(currIdx + 1) % 2],
bq_copy_dram_window,
bq_dram_tile_window_step);
block_gemm(
c_block_tile, bq_block_tile[currIdx], a_lds_gemm_window, b_lds_gemm_window);
currIdx = (currIdx + 1) % 2;
block_sync_lds();
block_gemm.LocalPrefetch(a_lds_gemm_window, b_lds_gemm_window);
__builtin_amdgcn_sched_barrier(0);
i += 1;
} while(i < (num_loop - 1));
}
// tail
if constexpr((TailNum == TailNumber::Full) || (TailNum == TailNumber::Odd))
{
block_gemm(
c_block_tile, bq_block_tile[currIdx], a_lds_gemm_window, b_lds_gemm_window);
}
else
{
Base::GlobalPrefetch(bq_block_tile[(currIdx + 1) % 2],
bq_copy_dram_window,
bq_dram_tile_window_step);
block_gemm(
c_block_tile, bq_block_tile[currIdx], a_lds_gemm_window, b_lds_gemm_window);
block_sync_lds();
currIdx = (currIdx + 1) % 2;
if constexpr(is_a_col_major)
{
auto a_shuffle_tmp = make_static_distributed_tensor<ADataType>(
Policy::template MakeShuffledARegTileDistribution<Problem>());
transpose_tile2d(a_shuffle_tmp, a_block_tile);
Base::LocalPrefill(a_copy_lds_window, a_shuffle_tmp, a_element_func);
}
else
{
Base::LocalPrefill(a_copy_lds_window, a_block_tile, a_element_func);
}
if constexpr(is_b_row_major)
{
auto b_shuffle_tmp = make_static_distributed_tensor<BDataType>(
Policy::template MakeShuffledBRegTileDistribution<Problem>());
transpose_tile2d(b_shuffle_tmp, b_block_tile);
Base::LocalPrefill(b_copy_lds_window, b_shuffle_tmp, b_element_func);
}
else
{
Base::LocalPrefill(b_copy_lds_window, b_block_tile, b_element_func);
}
block_sync_lds();
block_gemm.LocalPrefetch(a_lds_gemm_window, b_lds_gemm_window);
block_gemm(
c_block_tile, bq_block_tile[currIdx], a_lds_gemm_window, b_lds_gemm_window);
}
return c_block_tile;
}
};
template <typename ADramBlockWindowTmp,
typename BDramBlockWindowTmp,
typename BQDramBlockWindowTmp>
CK_TILE_DEVICE auto operator()(const ADramBlockWindowTmp& a_dram_block_window_tmp,
const BDramBlockWindowTmp& b_dram_block_window_tmp,
const BQDramBlockWindowTmp& bq_dram_block_window_tmp,
index_t num_loop,
void* p_smem) const
{
return PipelineImpl<Scheduler>{}.template operator()<HasHotLoop, TailNum>(
a_dram_block_window_tmp,
[](const ADataType& a) { return a; },
b_dram_block_window_tmp,
[](const BDataType& b) { return b; },
bq_dram_block_window_tmp,
num_loop,
p_smem);
}
};
} // namespace ck_tile

54
include/ck_tile/ops/gemm_group_quant/pipeline/gemm_group_quant_utils.hpp
View File

@@ -8,7 +8,7 @@
namespace ck_tile {
template <typename Problem, typename DataType, index_t YPerTile, index_t XPerTile>
CK_TILE_HOST_DEVICE static constexpr auto GetAQGlobalVectorLoadSize()
CK_TILE_HOST_DEVICE static constexpr auto GetABQGlobalVectorLoadSize()
{
using I1 = number<1>;
constexpr index_t NWarps = Problem::BlockGemmShape::BlockWarps::at(I1{});
@@ -164,4 +164,56 @@ struct TileDistributionEncodingPatternAQTransposedC : public TileDistributionEnc
}
};
// TODO:: might need to update
template <typename BlockGemmShape,
typename WarpGemm,
index_t BlockSize,
index_t YPerTile,
index_t XPerTile,
index_t VecSize>
struct TileDistributionEncodingPatternBQ : public TileDistributionEncodingPattern
{
// TODO: make pattern where below condition does not need to hold - GGemmMultiDSplitk!
static_assert(XPerTile % VecSize == 0, "XPerTile must be a multiple of VecSize!");
static constexpr index_t warp_size = get_warp_size();
static constexpr index_t num_warps = BlockSize / get_warp_size();
static constexpr index_t MWarps = BlockGemmShape::BlockWarps::at(number<0>{});
static constexpr index_t NWarps = BlockGemmShape::BlockWarps::at(number<1>{});
static constexpr index_t KWarps = BlockGemmShape::BlockWarps::at(number<2>{});
static constexpr index_t NIterPerWarp = BlockGemmShape::kN / (NWarps * WarpGemm::kN);
static_assert(num_warps == MWarps * NWarps * KWarps);
// KWarps > 1 isn't supported
static_assert(KWarps == 1);
// # of elements per thread
static constexpr index_t X = XPerTile;
static constexpr index_t XR = 2;
// Number of iters per warp
// MIters are indexed using (Y0, Y1)
static constexpr index_t Y0 = NIterPerWarp;
// # of warps in Y dim
static constexpr index_t Y1 = NWarps;
static constexpr index_t Y2 = WarpGemm::kN;
static_assert(Y0 * Y1 * Y2 == YPerTile, "Y0, Y1, Y2 must cover the blocktile along Y.");
CK_TILE_HOST_DEVICE static constexpr auto Make2DStaticTileDistribution()
{
return make_static_tile_distribution(
tile_distribution_encoding<sequence<MWarps, XR>,
tuple<sequence<Y0, Y1, Y2>, sequence<X>>,
tuple<sequence<0, 1>, sequence<0, 1>>,
tuple<sequence<0, 1>, sequence<1, 2>>,
sequence<1, 2>,
sequence<0, 0>>{});
}
};
} // namespace ck_tile

103
include/ck_tile/ops/gemm_group_quant/pipeline/gemm_aquant_pipeline_problem.hpp → include/ck_tile/ops/gemm_group_quant/pipeline/gemm_quant_pipeline_problem.hpp
View File

@@ -121,4 +121,107 @@ using GemmAQuantPipelineProblem = GemmAQuantPipelineProblemBase<ADataType_,
HasHotLoop_,
TailNum_>;
template <typename ADataType_,
typename BDataType_,
typename BQDataType_,
typename CDataType_,
typename BlockGemmShape_,
typename Traits_,
uint32_t QuantGroupSize_,
typename ComputeDataType_ = ADataType_,
GemmPipelineScheduler Scheduler_ = GemmPipelineScheduler::Intrawave,
bool HasHotLoop_ = true,
TailNumber TailNum_ = TailNumber::Full>
struct GemmBQuantPipelineProblemBase : public GemmPipelineProblemBase<ADataType_,
BDataType_,
CDataType_,
BlockGemmShape_,
Traits_,
ComputeDataType_>
{
using Base = GemmPipelineProblemBase<ADataType_,
BDataType_,
CDataType_,
BlockGemmShape_,
Traits_,
ComputeDataType_>;
using Traits = typename Base::Traits;
using typename Base::ADataType;
using typename Base::BDataType;
using typename Base::CDataType;
using typename Base::ComputeDataType;
using BQDataType = remove_cvref_t<BQDataType_>;
using BlockGemmShape = typename Base::BlockGemmShape;
using typename Base::ALayout;
using typename Base::BLayout;
using typename Base::CLayout;
static constexpr bool TransposeC = Traits::TransposeC;
using Base::kBlockSize;
using Base::kPadK;
using Base::kPadM;
using Base::kPadN;
using Base::DoubleSmemBuffer;
using Base::VectorLoadSize;
using BQLayout = remove_cvref_t<typename Traits::BQLayout>;
static constexpr uint32_t kQuantGroupSize = QuantGroupSize_;
static constexpr auto Scheduler = Scheduler_;
static constexpr auto HasHotLoop = HasHotLoop_;
static constexpr auto TailNum = TailNum_;
static_assert(BlockGemmShape::kK % kQuantGroupSize == 0);
static_assert(Scheduler == GemmPipelineScheduler::Intrawave);
[[nodiscard]] CK_TILE_HOST static const std::string GetName()
{
// clang-format off
return concat('_', "gemm_bquant_problem",
concat('x', VectorLoadSize, kBlockSize),
concat('x', kPadM, kPadN, kPadK),
Scheduler,
"QuantGroupSize",
kQuantGroupSize);
// clang-format on
}
CK_TILE_HOST_DEVICE static constexpr auto GetAlignmentBQ()
{
return VectorLoadSize / sizeof(BQDataType);
}
static constexpr index_t VectorSizeBQ = []() { return kPadK ? 1 : GetAlignmentBQ(); }();
};
template <typename ADataType_,
typename BDataType_,
typename BQDataType_,
typename CDataType_,
typename BlockGemmShape_,
typename Traits_,
uint32_t QuantGroupSize_,
typename ComputeDataType_ = ADataType_,
GemmPipelineScheduler Scheduler_ = GemmPipelineScheduler::Intrawave,
bool HasHotLoop_ = true,
TailNumber TailNum_ = TailNumber::Full>
using GemmBQuantPipelineProblem = GemmBQuantPipelineProblemBase<ADataType_,
BDataType_,
BQDataType_,
CDataType_,
BlockGemmShape_,
Traits_,
QuantGroupSize_,
ComputeDataType_,
Scheduler_,
HasHotLoop_,
TailNum_>;
} // namespace ck_tile

29
include/ck_tile/ops/gemm_group_quant/pipeline/tile_gemm_aquant_traits.hpp → include/ck_tile/ops/gemm_group_quant/pipeline/tile_gemm_quant_traits.hpp
View File

@@ -28,6 +28,35 @@ struct TileGemmAQuantTraits
using CLayout = CLayout_;
using AQLayout = AQLayout_;
static constexpr bool TransposeC = false;
static constexpr bool UseStructuredSparsity = false;
static constexpr index_t NumWaveGroups = 1;
static constexpr bool PreshuffleQuant = PreshuffleQuant_;
};
template <bool kPadM_,
bool kPadN_,
bool kPadK_,
bool PreshuffleQuant_,
typename ALayout_,
typename BLayout_,
typename CLayout_,
typename BQLayout_ = BLayout_>
struct TileGemmBQuantTraits
{
static constexpr bool kPadM = kPadM_;
static constexpr bool kPadN = kPadN_;
static constexpr bool kPadK = kPadK_;
static constexpr int _VectorSize = 16;
using ALayout = ALayout_;
using BLayout = BLayout_;
using CLayout = CLayout_;
using BQLayout = BQLayout_;
static constexpr bool TransposeC = false;
static constexpr bool UseStructuredSparsity = false;
static constexpr index_t NumWaveGroups = 1;
static constexpr bool PreshuffleQuant = PreshuffleQuant_;