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CGEMM examples bf16, fp32, int8 (#332)

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* Add int8 specialization for elementwise Add and Subtract.

* CGEMM examples bf16, fp32, int8

* Add convert reference output to CDataType.

* Skip BF16 data type during testing.

* Lower K value to get rid of accumulation error.

* Fix merge artifact.

* Fix changed function name: GetElementSpaceSize()

* Fix merge artifact.

Co-authored-by: Adam Osewski <aosewski@amd.com>
This commit is contained in:
Adam Osewski
2022-08-02 21:52:27 +02:00
committed by GitHub
parent 984b3722bf
commit fb0dc35861
9 changed files with 649 additions and 182 deletions
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10
example/22_cgemm/CMakeLists.txt
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@@ -1 +1,11 @@
add_custom_target(example_cgemm_xdl)
add_example_executable(example_cgemm_xdl_bf16 cgemm_xdl_bf16.cpp)
add_example_executable(example_cgemm_xdl_fp16 cgemm_xdl_fp16.cpp)
add_example_executable(example_cgemm_xdl_fp32 cgemm_xdl_fp32.cpp)
add_example_executable(example_cgemm_xdl_int8 cgemm_xdl_int8.cpp)
add_dependencies(example_cgemm_xdl example_cgemm_xdl_bf16)
add_dependencies(example_cgemm_xdl example_cgemm_xdl_fp16)
add_dependencies(example_cgemm_xdl example_cgemm_xdl_fp32)
add_dependencies(example_cgemm_xdl example_cgemm_xdl_int8)

132
example/22_cgemm/cgemm_xdl_bf16.cpp Normal file
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@@ -0,0 +1,132 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include "cgemm_xdl_common.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_cgemm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/device_cgemm_4gemm_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
using ADataType = BF16;
using BDataType = BF16;
using CDataType = BF16;
using AccDataType = F32;
using ALayout = ck::tensor_layout::gemm::RowMajor;
using BLayout = ck::tensor_layout::gemm::ColumnMajor;
using CLayout = ck::tensor_layout::gemm::RowMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
using ReferenceCGemmInstance = ck::tensor_operation::host::
ReferenceCGemm<ADataType, BDataType, CDataType, PassThrough, PassThrough, PassThrough>;
// clang-format off
using DeviceCGemmInstance = ck::tensor_operation::device::DeviceCGemm_4Gemm_Xdl_CShuffle
<ALayout, // typename ALayout
BLayout, // typename BLayout
CLayout, // typename CLayout
ADataType, // typename ADataType
BDataType, // typename BDataType
CDataType, // typename CDataType
AccDataType, // typename GemmAccDataType
CDataType, // typename CShuffleDataType
PassThrough, // typename AElementwiseOperation
PassThrough, // typename BElementwiseOperation
PassThrough, // typename CElementwiseOperation
GemmDefault, // GemmSpecialization GemmSpec
1, // index_t NumGemmKPrefetchStage
256, // index_t BlockSize
256, // index_t MPerBlock
128, // index_t NPerBlock
32, // index_t KPerBlock
8, // index_t AK1
8, // index_t BK1
32, // index_t MPerXDL
32, // index_t NPerXDL
4, // index_t MXdlPerWave
2, // index_t NXdlPerWave
S<4, 64, 1>, // typename ABlockTransferThreadClusterLengths_AK0_M_AK1
S<1, 0, 2>, // typename ABlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // typename ABlockTransferSrcAccessOrder
2, // index_t ABlockTransferSrcVectorDim
8, // index_t ABlockTransferSrcScalarPerVector
8, // index_t ABlockTransferDstScalarPerVector_AK1
1, // index_t ABlockLdsExtraM
S<4, 64, 1>, // typename BBlockTransferThreadClusterLengths_BK0_N_BK1
S<1, 0, 2>, // typename BBlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // typename BBlockTransferSrcAccessOrder
2, // index_t BBlockTransferSrcVectorDim
8, // index_t BBlockTransferSrcScalarPerVector
8, // index_t BBlockTransferDstScalarPerVector_BK1
1, // index_t BBlockLdsExtraN
1, // index_t CShuffleMXdlPerWavePerShuffle
1, // index_t CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8>; // index_t CShuffleBlockTransferScalarPerVector_NPerBlock
// clang-format on
int main(int argc, char* argv[])
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
// CGEMM shape
ck::index_t M = 3840;
ck::index_t N = 4096;
ck::index_t K = 416;
ck::index_t StrideA = 4096;
ck::index_t StrideB = 4096;
ck::index_t StrideC = 4096;
if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 10)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
StrideA = std::stoi(argv[7]);
StrideB = std::stoi(argv[8]);
StrideC = std::stoi(argv[9]);
}
else
{
std::cout << "arg1: verification (0=no, 1=yes)\n"
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
<< "arg3: run kernel # of times (>1)\n"
<< "arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n"
<< std::endl;
exit(0);
}
return run_cgemm_xdl<ADataType,
BDataType,
CDataType,
ALayout,
BLayout,
CLayout,
PassThrough,
PassThrough,
PassThrough,
DeviceCGemmInstance,
ReferenceCGemmInstance>(
M, N, K, StrideA, StrideB, StrideC, do_verification, init_method, time_kernel);
}

192
example/22_cgemm/cgemm_xdl_common.hpp Normal file
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@@ -0,0 +1,192 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/stream_config.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using F16 = ck::half_t;
using F32 = float;
using BF16 = ck::bhalf_t;
using INT8 = std::int8_t;
using INT32 = std::int32_t;
template <typename ADataType,
typename BDataType,
typename CDataType,
typename ALayout,
typename BLayout,
typename CLayout,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CElementwiseOperation,
typename DeviceCGemmInstance,
typename ReferenceCGemmInstance>
int run_cgemm_xdl(ck::index_t M,
ck::index_t N,
ck::index_t K,
ck::index_t StrideA,
ck::index_t StrideB,
ck::index_t StrideC,
bool do_verification,
int init_method,
bool time_kernel)
{
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({stride, 1}));
}
else
{
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({1, stride}));
}
};
Tensor<ADataType> a_m_k_real(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
Tensor<ADataType> a_m_k_imag(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
Tensor<BDataType> b_k_n_real(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
Tensor<BDataType> b_k_n_imag(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
Tensor<CDataType> c_m_n_real_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<CDataType> c_m_n_imag_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
std::cout << "a_m_k_real: " << a_m_k_real.mDesc << std::endl;
std::cout << "a_m_k_imag: " << a_m_k_imag.mDesc << std::endl;
std::cout << "b_k_n_real: " << b_k_n_real.mDesc << std::endl;
std::cout << "b_k_n_imag: " << b_k_n_imag.mDesc << std::endl;
std::cout << "c_m_n_real: " << c_m_n_real_device_result.mDesc << std::endl;
std::cout << "c_m_n_imag: " << c_m_n_imag_device_result.mDesc << std::endl;
switch(init_method)
{
case 0: break;
case 1:
a_m_k_real.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
a_m_k_imag.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
b_k_n_real.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
b_k_n_imag.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
break;
default:
a_m_k_real.GenerateTensorValue(GeneratorTensor_3<ADataType>{-0.5, 0.5});
a_m_k_imag.GenerateTensorValue(GeneratorTensor_3<ADataType>{-0.5, 0.5});
b_k_n_real.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
b_k_n_imag.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
}
auto cgemm = DeviceCGemmInstance{};
DeviceMem a_m_k_real_device_buf(sizeof(ADataType) * a_m_k_real.mDesc.GetElementSpaceSize());
DeviceMem a_m_k_imag_device_buf(sizeof(ADataType) * a_m_k_imag.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_real_device_buf(sizeof(BDataType) * b_k_n_real.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_imag_device_buf(sizeof(BDataType) * b_k_n_imag.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_real_device_buf(sizeof(CDataType) *
c_m_n_real_device_result.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_imag_device_buf(sizeof(CDataType) *
c_m_n_imag_device_result.mDesc.GetElementSpaceSize());
DeviceMem workspace_device_buf(cgemm.GetWorkspaceSize(M, N, K, StrideA, StrideB, StrideC));
a_m_k_real_device_buf.ToDevice(a_m_k_real.mData.data());
a_m_k_imag_device_buf.ToDevice(a_m_k_imag.mData.data());
b_k_n_real_device_buf.ToDevice(b_k_n_real.mData.data());
b_k_n_imag_device_buf.ToDevice(b_k_n_imag.mData.data());
auto a_element_op = AElementwiseOperation{};
auto b_element_op = BElementwiseOperation{};
auto c_element_op = CElementwiseOperation{};
// do GEMM
auto invoker = cgemm.MakeInvoker();
auto argument =
cgemm.MakeArgument(static_cast<ADataType*>(a_m_k_real_device_buf.GetDeviceBuffer()),
static_cast<ADataType*>(a_m_k_imag_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_k_n_real_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_k_n_imag_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_m_n_real_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_m_n_imag_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(workspace_device_buf.GetDeviceBuffer()),
M,
N,
K,
StrideA,
StrideB,
StrideC,
a_element_op,
b_element_op,
c_element_op);
if(!cgemm.IsSupportedArgument(argument))
{
throw std::runtime_error(
"wrong! device_cgemm with the specified compilation parameters does "
"not support this CGEMM problem");
}
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(8) * M * N * K;
std::size_t num_btype =
std::size_t(2) *
(sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + 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, "
<< cgemm.GetTypeString() << std::endl;
c_m_n_real_device_buf.FromDevice(c_m_n_real_device_result.mData.data());
c_m_n_imag_device_buf.FromDevice(c_m_n_imag_device_result.mData.data());
if(do_verification)
{
Tensor<CDataType> c_m_n_real_host_result(
f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<CDataType> c_m_n_imag_host_result(
f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
auto ref_cgemm = ReferenceCGemmInstance{};
auto ref_invoker = ref_cgemm.MakeInvoker();
auto ref_argument = ref_cgemm.MakeArgument(a_m_k_real,
a_m_k_imag,
b_k_n_real,
b_k_n_imag,
c_m_n_real_host_result,
c_m_n_imag_host_result,
a_element_op,
b_element_op,
c_element_op);
ref_invoker.Run(ref_argument);
bool result = true;
result = ck::utils::check_err(c_m_n_real_device_result.mData,
c_m_n_real_host_result.mData,
"Verification error: incorrect results in real part!",
1e-2f,
1e-1f);
result = result &&
ck::utils::check_err(c_m_n_imag_device_result.mData,
c_m_n_imag_host_result.mData,
"Verification error: incorrect results in imaginary part!",
1e-2f,
1e-1f);
return result ? 0 : 1;
}
return 0;
}

211
example/22_cgemm/cgemm_xdl_fp16.cpp
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@@ -2,43 +2,30 @@
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_cgemm_4gemm_xdl_cshuffle.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "cgemm_xdl_common.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_cgemm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/device_cgemm_4gemm_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ADataType = F16;
using BDataType = F16;
using CDataType = F16;
using AccDataType = F32;
using ADataType = F16;
using BDataType = F16;
using CDataType = F16;
using AccDataType = F32;
using CShuffleDataType = F32;
using ALayout = ck::tensor_layout::gemm::RowMajor;
using BLayout = ck::tensor_layout::gemm::ColumnMajor;
using CLayout = ck::tensor_layout::gemm::RowMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
using ReferenceCGemmInstance = ck::tensor_operation::host::
ReferenceCGemm<ADataType, BDataType, CDataType, PassThrough, PassThrough, PassThrough>;
// clang-format off
using DeviceCGemmInstance = ck::tensor_operation::device::DeviceCGemm_4Gemm_Xdl_CShuffle
<ALayout, // typename ALayout
@@ -48,7 +35,7 @@ using DeviceCGemmInstance = ck::tensor_operation::device::DeviceCGemm_4Gemm_Xdl_
BDataType, // typename BDataType
CDataType, // typename CDataType
AccDataType, // typename GemmAccDataType
CDataType, // typename CShuffleDataType
CShuffleDataType, // typename CShuffleDataType
PassThrough, // typename AElementwiseOperation
PassThrough, // typename BElementwiseOperation
PassThrough, // typename CElementwiseOperation
@@ -84,9 +71,6 @@ using DeviceCGemmInstance = ck::tensor_operation::device::DeviceCGemm_4Gemm_Xdl_
8>; // index_t CShuffleBlockTransferScalarPerVector_NPerBlock
// clang-format on
using ReferenceCGemmInstance = ck::tensor_operation::host::
ReferenceCGemm<ADataType, BDataType, CDataType, PassThrough, PassThrough, PassThrough>;
int main(int argc, char* argv[])
{
bool do_verification = true;
@@ -124,155 +108,24 @@ int main(int argc, char* argv[])
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: run kernel # of times (>1)\n");
printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
std::cout << "arg1: verification (0=no, 1=yes)\n"
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
<< "arg3: run kernel # of times (>1)\n"
<< "arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n"
<< std::endl;
exit(0);
}
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({stride, 1}));
}
else
{
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({1, stride}));
}
};
Tensor<ADataType> a_m_k_real(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
Tensor<ADataType> a_m_k_imag(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
Tensor<BDataType> b_k_n_real(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
Tensor<BDataType> b_k_n_imag(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
Tensor<CDataType> c_m_n_real_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<CDataType> c_m_n_imag_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
std::cout << "a_m_k_real: " << a_m_k_real.mDesc << std::endl;
std::cout << "a_m_k_imag: " << a_m_k_imag.mDesc << std::endl;
std::cout << "b_k_n_real: " << b_k_n_real.mDesc << std::endl;
std::cout << "b_k_n_imag: " << b_k_n_imag.mDesc << std::endl;
std::cout << "c_m_n_real: " << c_m_n_real_device_result.mDesc << std::endl;
std::cout << "c_m_n_imag: " << c_m_n_imag_device_result.mDesc << std::endl;
switch(init_method)
{
case 0: break;
case 1:
a_m_k_real.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
a_m_k_imag.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
b_k_n_real.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
b_k_n_imag.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
break;
default:
a_m_k_real.GenerateTensorValue(GeneratorTensor_3<ADataType>{-0.5, 0.5});
a_m_k_imag.GenerateTensorValue(GeneratorTensor_3<ADataType>{-0.5, 0.5});
b_k_n_real.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
b_k_n_imag.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
}
auto cgemm = DeviceCGemmInstance{};
DeviceMem a_m_k_real_device_buf(sizeof(ADataType) * a_m_k_real.mDesc.GetElementSpaceSize());
DeviceMem a_m_k_imag_device_buf(sizeof(ADataType) * a_m_k_imag.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_real_device_buf(sizeof(BDataType) * b_k_n_real.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_imag_device_buf(sizeof(BDataType) * b_k_n_imag.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_real_device_buf(sizeof(CDataType) *
c_m_n_real_device_result.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_imag_device_buf(sizeof(CDataType) *
c_m_n_imag_device_result.mDesc.GetElementSpaceSize());
DeviceMem workspace_device_buf(cgemm.GetWorkspaceSize(M, N, K, StrideA, StrideB, StrideC));
a_m_k_real_device_buf.ToDevice(a_m_k_real.mData.data());
a_m_k_imag_device_buf.ToDevice(a_m_k_imag.mData.data());
b_k_n_real_device_buf.ToDevice(b_k_n_real.mData.data());
b_k_n_imag_device_buf.ToDevice(b_k_n_imag.mData.data());
auto a_element_op = PassThrough{};
auto b_element_op = PassThrough{};
auto c_element_op = PassThrough{};
// do GEMM
auto invoker = cgemm.MakeInvoker();
auto argument =
cgemm.MakeArgument(static_cast<ADataType*>(a_m_k_real_device_buf.GetDeviceBuffer()),
static_cast<ADataType*>(a_m_k_imag_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_k_n_real_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_k_n_imag_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_m_n_real_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_m_n_imag_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(workspace_device_buf.GetDeviceBuffer()),
M,
N,
K,
StrideA,
StrideB,
StrideC,
a_element_op,
b_element_op,
c_element_op);
if(!cgemm.IsSupportedArgument(argument))
{
throw std::runtime_error(
"wrong! device_cgemm with the specified compilation parameters does "
"not support this CGEMM problem");
}
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(8) * M * N * K;
std::size_t num_btype =
std::size_t(2) *
(sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + 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, "
<< cgemm.GetTypeString() << std::endl;
c_m_n_real_device_buf.FromDevice(c_m_n_real_device_result.mData.data());
c_m_n_imag_device_buf.FromDevice(c_m_n_imag_device_result.mData.data());
if(do_verification)
{
Tensor<CDataType> c_m_n_real_host_result(
f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<CDataType> c_m_n_imag_host_result(
f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
auto ref_cgemm = ReferenceCGemmInstance{};
auto ref_invoker = ref_cgemm.MakeInvoker();
auto ref_argument = ref_cgemm.MakeArgument(a_m_k_real,
a_m_k_imag,
b_k_n_real,
b_k_n_imag,
c_m_n_real_host_result,
c_m_n_imag_host_result,
a_element_op,
b_element_op,
c_element_op);
ref_invoker.Run(ref_argument);
ck::utils::check_err(c_m_n_real_device_result.mData,
c_m_n_real_host_result.mData,
"Verification error: incorrect results in real part!",
1e-2f,
1e-1f);
ck::utils::check_err(c_m_n_imag_device_result.mData,
c_m_n_imag_host_result.mData,
"Verification error: incorrect results in imaginary part!",
1e-2f,
1e-1f);
}
return 0;
return run_cgemm_xdl<ADataType,
BDataType,
CDataType,
ALayout,
BLayout,
CLayout,
PassThrough,
PassThrough,
PassThrough,
DeviceCGemmInstance,
ReferenceCGemmInstance>(
M, N, K, StrideA, StrideB, StrideC, do_verification, init_method, time_kernel);
}

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example/22_cgemm/cgemm_xdl_fp32.cpp Normal file
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// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include "cgemm_xdl_common.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_cgemm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/device_cgemm_4gemm_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
using ADataType = F32;
using BDataType = F32;
using CDataType = F32;
using AccDataType = F32;
using ALayout = ck::tensor_layout::gemm::RowMajor;
using BLayout = ck::tensor_layout::gemm::ColumnMajor;
using CLayout = ck::tensor_layout::gemm::RowMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
using ReferenceCGemmInstance = ck::tensor_operation::host::
ReferenceCGemm<ADataType, BDataType, CDataType, PassThrough, PassThrough, PassThrough>;
// clang-format off
using DeviceCGemmInstance = ck::tensor_operation::device::DeviceCGemm_4Gemm_Xdl_CShuffle
<ALayout, // typename ALayout
BLayout, // typename BLayout
CLayout, // typename CLayout
ADataType, // typename ADataType
BDataType, // typename BDataType
CDataType, // typename CDataType
AccDataType, // typename GemmAccDataType
CDataType, // typename CShuffleDataType
PassThrough, // typename AElementwiseOperation
PassThrough, // typename BElementwiseOperation
PassThrough, // typename CElementwiseOperation
GemmDefault, // GemmSpecialization GemmSpec
1, // index_t NumGemmKPrefetchStage
256, // index_t BlockSize
256, // index_t MPerBlock
128, // index_t NPerBlock
16, // index_t KPerBlock
4, // index_t AK1
4, // index_t BK1
32, // index_t MPerXDL
32, // index_t NPerXDL
4, // index_t MXdlPerWave
2, // index_t NXdlPerWave
S<4, 64, 1>, // typename ABlockTransferThreadClusterLengths_AK0_M_AK1
S<1, 0, 2>, // typename ABlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // typename ABlockTransferSrcAccessOrder
2, // index_t ABlockTransferSrcVectorDim
4, // index_t ABlockTransferSrcScalarPerVector
4, // index_t ABlockTransferDstScalarPerVector_AK1
1, // index_t ABlockLdsExtraM
S<4, 64, 1>, // typename BBlockTransferThreadClusterLengths_BK0_N_BK1
S<1, 0, 2>, // typename BBlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // typename BBlockTransferSrcAccessOrder
2, // index_t BBlockTransferSrcVectorDim
4, // index_t BBlockTransferSrcScalarPerVector
4, // index_t BBlockTransferDstScalarPerVector_BK1
1, // index_t BBlockLdsExtraN
1, // index_t CShuffleMXdlPerWavePerShuffle
1, // index_t CShuffleNXdlPerWavePerShuffle
S<1, 16, 1, 16>, // typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
4>; // index_t CShuffleBlockTransferScalarPerVector_NPerBlock
// clang-format on
int main(int argc, char* argv[])
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
// CGEMM shape
ck::index_t M = 3840;
ck::index_t N = 4096;
ck::index_t K = 4096;
ck::index_t StrideA = 4096;
ck::index_t StrideB = 4096;
ck::index_t StrideC = 4096;
if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 10)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
StrideA = std::stoi(argv[7]);
StrideB = std::stoi(argv[8]);
StrideC = std::stoi(argv[9]);
}
else
{
std::cout << "arg1: verification (0=no, 1=yes)\n"
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
<< "arg3: run kernel # of times (>1)\n"
<< "arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n"
<< std::endl;
exit(0);
}
return run_cgemm_xdl<ADataType,
BDataType,
CDataType,
ALayout,
BLayout,
CLayout,
PassThrough,
PassThrough,
PassThrough,
DeviceCGemmInstance,
ReferenceCGemmInstance>(
M, N, K, StrideA, StrideB, StrideC, do_verification, init_method, time_kernel);
}

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// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include "cgemm_xdl_common.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_cgemm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/device_cgemm_4gemm_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
using ADataType = INT8;
using BDataType = INT8;
using CDataType = INT8;
using AccDataType = INT32;
using ALayout = ck::tensor_layout::gemm::RowMajor;
using BLayout = ck::tensor_layout::gemm::ColumnMajor;
using CLayout = ck::tensor_layout::gemm::RowMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
using ReferenceCGemmInstance = ck::tensor_operation::host::
ReferenceCGemm<ADataType, BDataType, CDataType, PassThrough, PassThrough, PassThrough>;
// clang-format off
using DeviceCGemmInstance = ck::tensor_operation::device::DeviceCGemm_4Gemm_Xdl_CShuffle
<ALayout, // typename ALayout
BLayout, // typename BLayout
CLayout, // typename CLayout
ADataType, // typename ADataType
BDataType, // typename BDataType
CDataType, // typename CDataType
AccDataType, // typename GemmAccDataType
CDataType, // typename CShuffleDataType
PassThrough, // typename AElementwiseOperation
PassThrough, // typename BElementwiseOperation
PassThrough, // typename CElementwiseOperation
GemmDefault, // GemmSpecialization GemmSpec
1, // index_t NumGemmKPrefetchStage
256, // index_t BlockSize
256, // index_t MPerBlock
128, // index_t NPerBlock
64, // index_t KPerBlock
16, // index_t AK1
16, // index_t BK1
32, // index_t MPerXDL
32, // index_t NPerXDL
4, // index_t MXdlPerWave
2, // index_t NXdlPerWave
S<4, 64, 1>, // typename ABlockTransferThreadClusterLengths_AK0_M_AK1
S<1, 0, 2>, // typename ABlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // typename ABlockTransferSrcAccessOrder
2, // index_t ABlockTransferSrcVectorDim
16, // index_t ABlockTransferSrcScalarPerVector
16, // index_t ABlockTransferDstScalarPerVector_AK1
1, // index_t ABlockLdsExtraM
S<4, 64, 1>, // typename BBlockTransferThreadClusterLengths_BK0_N_BK1
S<1, 0, 2>, // typename BBlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // typename BBlockTransferSrcAccessOrder
2, // index_t BBlockTransferSrcVectorDim
8, // index_t BBlockTransferSrcScalarPerVector
8, // index_t BBlockTransferDstScalarPerVector_BK1
1, // index_t BBlockLdsExtraN
1, // index_t CShuffleMXdlPerWavePerShuffle
1, // index_t CShuffleNXdlPerWavePerShuffle
S<1, 64, 1, 4>, // typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
16>; // index_t CShuffleBlockTransferScalarPerVector_NPerBlock
// clang-format on
int main(int argc, char* argv[])
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
// CGEMM shape
ck::index_t M = 3840;
ck::index_t N = 4096;
ck::index_t K = 4096;
ck::index_t StrideA = 4096;
ck::index_t StrideB = 4096;
ck::index_t StrideC = 4096;
if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 10)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
StrideA = std::stoi(argv[7]);
StrideB = std::stoi(argv[8]);
StrideC = std::stoi(argv[9]);
}
else
{
std::cout << "arg1: verification (0=no, 1=yes)\n"
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
<< "arg3: run kernel # of times (>1)\n"
<< "arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n"
<< std::endl;
exit(0);
}
return run_cgemm_xdl<ADataType,
BDataType,
CDataType,
ALayout,
BLayout,
CLayout,
PassThrough,
PassThrough,
PassThrough,
DeviceCGemmInstance,
ReferenceCGemmInstance>(
M, N, K, StrideA, StrideB, StrideC, do_verification, init_method, time_kernel);
}