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Jing's contribution: prototype of mixed precision gemm FP16/BF16xint4…
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… GEMM (#1762)

* add a prototype of int4

* clean

* debug

* clean

* clean

* move packed into dynamic_buffer

* fixed coord reset

* add fast pki4 to half conversion

* fix

* fixed reference and host_tensor

* fixed tensor init

* format

* debug i4_to_f16_convert

* format

* fixed splitk

* weight permute

* add b tile permute

* clean

* weight permute with splitki

* format

* improve weight layout

* add and_or_b32

* fixed splitk crush

* add permute switch as a template

* recover v3r1

* clean

* failure with intrawave v2

* fixed

* fixed

* add ckProfiler

* add bfp16 support

* add bf16 example

* fixed int4 to bhalf_t conversion

* format

* fixed int4 to bf16 conversion

* clean

* add instances for mem

* clean

* fixed host tensor size

* fixed

* debug

* fixed

* add pk_i4_t as a struct

* fix

* Update example/01_gemm/gemm_xdl_bf16_pk_i4_v3.cpp

Co-authored-by: Adam Osewski <[email protected]>

* Update example/01_gemm/gemm_xdl_bf16_pk_i4_v3.cpp

Co-authored-by: Adam Osewski <[email protected]>

* Update example/01_gemm/gemm_xdl_bf16_pk_i4_v3.cpp

Co-authored-by: Adam Osewski <[email protected]>

* revert

* Update example/01_gemm/gemm_xdl_bf16_pk_i4_v3.cpp

Co-authored-by: Adam Osewski <[email protected]>

* Update example/01_gemm/gemm_xdl_fp16_pk_i4_v3.cpp

Co-authored-by: Adam Osewski <[email protected]>

* Update example/01_gemm/gemm_xdl_fp16_pk_i4_v3.cpp

Co-authored-by: Adam Osewski <[email protected]>

* Update example/01_gemm/gemm_xdl_fp16_pk_i4_v3.cpp

Co-authored-by: Adam Osewski <[email protected]>

* Update example/01_gemm/gemm_xdl_fp16_pk_i4_v3.cpp

Co-authored-by: Adam Osewski <[email protected]>

* fixed comments

* revert

* clean

* revert

* revert

* fixed

* Update CMakeLists.txt

* Update script/cmake-ck-dev.sh

Co-authored-by: Adam Osewski <[email protected]>

* Update include/ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp

Co-authored-by: Adam Osewski <[email protected]>

* Update CMakeLists.txt

Co-authored-by: Adam Osewski <[email protected]>

* fixed

* fixed

* fixed

* revert

* revert

* add comments

* format

* fixed assert

* fixed

* Fix I4 define in ckProfiler

* Fixed example_gemm_xdl_bf16_pk_i4_v3 test failed issue

---------

Co-authored-by: Jing Zhang <[email protected]>
Co-authored-by: zjing14 <[email protected]>
Co-authored-by: mtgu0705 <[email protected]>
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4 people authored Jan 2, 2025
1 parent 159fa31 commit 1d8e4ec
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Showing 37 changed files with 1,583 additions and 350 deletions.
2 changes: 1 addition & 1 deletion CMakeLists.txt
Original file line number Diff line number Diff line change
Expand Up @@ -585,7 +585,7 @@ if(NOT GPU_ARCHS AND USER_GPU_TARGETS)
)
add_subdirectory(example)
if(BUILD_TESTING)
add_subdirectory(test)
add_subdirectory(test)
endif()
endif()

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2 changes: 1 addition & 1 deletion cmake/EnableCompilerWarnings.cmake
Original file line number Diff line number Diff line change
Expand Up @@ -66,7 +66,7 @@ else()
-Wunreachable-code
-Wunused
-Wno-reserved-identifier
-Werror
-Werror
-Wno-option-ignored
-Wsign-compare
-Wno-extra-semi-stmt
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2 changes: 2 additions & 0 deletions example/01_gemm/CMakeLists.txt
Original file line number Diff line number Diff line change
Expand Up @@ -29,6 +29,8 @@ add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp16_v3)
add_example_executable(example_gemm_xdl_fp8_v3 gemm_xdl_fp8_v3.cpp)
add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp8_v3)
add_example_executable(example_gemm_xdl_fp16_fp8_v3 gemm_xdl_fp16_fp8_v3.cpp)
add_example_executable(example_gemm_xdl_fp16_pk_i4_v3 gemm_xdl_fp16_pk_i4_v3.cpp)
add_example_executable(example_gemm_xdl_bf16_pk_i4_v3 gemm_xdl_bf16_pk_i4_v3.cpp)
add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp16_fp8_v3)
add_example_executable(example_gemm_xdl_bf16_v3 gemm_xdl_bf16_v3.cpp)
add_example_dependencies(example_gemm_xdl example_gemm_xdl_bf16_v3)
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82 changes: 82 additions & 0 deletions example/01_gemm/common.hpp
Original file line number Diff line number Diff line change
Expand Up @@ -287,3 +287,85 @@ bool parse_cmd_args<ProblemSizeSplitK>(int argc,

return true;
}

template <typename DataType>
inline __host__ __device__ constexpr double get_rtol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 1e-1; // 240 and 224 are acceptable
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 1.5e-1; // 57344 and 49152 are acceptable
}
else
{
return 1e-3;
}
}

template <typename DataType>
inline __host__ __device__ constexpr double get_atol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 16.1; // 240 and 224 are acceptable
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 8192.1; // 57344 and 49152 are acceptable
}
else
{
return 1e-3;
}
}
253 changes: 253 additions & 0 deletions example/01_gemm/gemm_xdl_bf16_pk_i4_v3.cpp
Original file line number Diff line number Diff line change
@@ -0,0 +1,253 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.

#include "common.hpp"

#include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle_v3.hpp"

using ADataType = ck::bhalf_t;
using BDataType = ck::pk_i4_t;
using AccDataType = float;
using CShuffleDataType = ck::bhalf_t;
using CDataType = ck::bhalf_t;

using ALayout = Row;
using BLayout = Col;
using CLayout = Row;

using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CElementOp = PassThrough;

static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
static constexpr bool PermuteA = false;
static constexpr bool PermuteB = true;
static constexpr ck::index_t KPerBlock = 128;

// clang-format off
using DeviceGemmV2Instance =
ck::tensor_operation::device::DeviceGemm_Xdl_CShuffleV3<
ALayout, BLayout, CLayout,
ADataType, BDataType, CDataType, AccDataType, CShuffleDataType,
AElementOp, BElementOp, CElementOp, GemmDefault,
128,
16, 64,
KPerBlock, 8, 32,
16, 16,
1, 2,
S<16, 8, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 8, 8, 0,
S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 32, 32, 0,
1, 1, S<1, 16, 1, 8>, 4,
ck::BlockGemmPipelineScheduler::Interwave, ck::BlockGemmPipelineVersion::v2, ADataType, ADataType, PermuteA, PermuteB>;

// clang-format on

using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
CDataType,
AccDataType,
PassThrough,
PassThrough,
PassThrough>;
template <typename ProblemType>
bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
{
using namespace ck::literals;

auto M = problem_size.M;
auto N = problem_size.N;
auto K = problem_size.K;
auto StrideA = problem_size.StrideA;
auto StrideB = problem_size.StrideB;
auto StrideC = problem_size.StrideC;
auto KBatch = problem_size.KBatch;

auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};

auto f_get_default_stride =
[](std::size_t row, std::size_t col, ck::index_t stride, auto layout) {
if(stride == -1)
{
// give a chance if stride is -1, return a default packed stride
if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
{
return static_cast<std::size_t>(col);
}
else
{
return static_cast<std::size_t>(row);
}
}
else
return static_cast<std::size_t>(stride);
};

StrideA = f_get_default_stride(M, K, StrideA, ALayout{});
StrideB = f_get_default_stride(K, N, StrideB, BLayout{});
StrideC = f_get_default_stride(M, N, StrideC, CLayout{});

Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
Tensor<BDataType> b_k_n_permute(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));

switch(config.init_method)
{
case 0:
a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
break;
case 1:
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
break;
case 2:
a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
break;
case 3:
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
break;
default:
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0, 1.0});
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
}

Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));

std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;

DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n_permute.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());

// weight permute
if constexpr(PermuteB)
{
int K1 = KPerBlock;
int K0 = K / KPerBlock;

// int K0, N, K1
for(int j = 0; j < K0; j++)
{
for(int i = 0; i < N; i++)
{
for(int jj = 0; jj < K1; jj++)
{
b_k_n_permute(j * N * K1 + i * K1 + jj) = b_k_n(i * K + (j * K1 + jj));
}
}
}
}
else
{
for(int i = 0; i < N; i++)
{
for(int j = 0; j < K; j++)
{
b_k_n_permute(i * K + j) = b_k_n(i * K + j);
}
}
}

a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n_permute.mData.data());
DeviceMem workspace;

auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto c_element_op = CElementOp{};

// do GEMM
auto gemm = DeviceGemmV2Instance{};
auto invoker = gemm.MakeInvoker();
float ave_time = 0;

auto argument = gemm.MakeArgument(static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
M,
N,
K,
StrideA,
StrideB,
StrideC,
KBatch,
a_element_op,
b_element_op,
c_element_op);

if(!gemm.IsSupportedArgument(argument))
{
std::cerr << gemm.GetTypeString() << " does not support this problem" << std::endl;

return true;
}

bool pass = true;
if(config.do_verification)
{
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();

auto ref_argument = ref_gemm.MakeArgument(
a_m_k, b_k_n, c_m_n_host_result, PassThrough{}, PassThrough{}, PassThrough{});

ref_invoker.Run(ref_argument);

ave_time = invoker.Run(argument, StreamConfig{nullptr, false, 0});
c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());

pass &= ck::utils::check_err(c_m_n_device_result,
c_m_n_host_result,
"Error: Incorrect results!",
get_rtol<CDataType>(),
get_atol<CDataType>());
}

if(config.time_kernel)
{
ave_time =
invoker.Run(argument, StreamConfig{nullptr, config.time_kernel, 0, 20, 50, true, 50});

std::size_t flop = 2_uz * M * N * K;
std::size_t num_btype =
sizeof(ADataType) * M * K +
sizeof(BDataType) * K * N /
(ck::is_same_v<ck::remove_cvref_t<BDataType>, ck::pk_i4_t> ? 2 : 1) +
sizeof(CDataType) * M * N;

float tflops = static_cast<float>(flop) / 1.E9 / ave_time;

float gb_per_sec = num_btype / 1.E6 / ave_time;

std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
<< " GB/s, " << gemm.GetTypeString() << std::endl;
}
return pass;
}

bool run_gemm_splitk_example(int argc, char* argv[])
{
ProblemSizeSplitK problem_size;
ExecutionConfig config;

return parse_cmd_args(argc, argv, problem_size, config) && run_gemm(problem_size, config);
}

int main(int argc, char* argv[]) { return !run_gemm_splitk_example(argc, argv); }
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