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Commit b89a88b5 authored by Adam Osewski's avatar Adam Osewski
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Merge branch 'develop' into wavelet_model

parents 41d5fca7 43c898f6
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Showing with 1368 additions and 101 deletions
profiler/include/profile_batched_gemm_softmax_gemm_impl.hpp
View file @ b89a88b5
......@@ -142,13 +142,21 @@ bool profile_batched_gemm_softmax_gemm_impl(bool do_verification,
std::cout << "b1_g_n_o: " << b1_g_n_o.mDesc << std::endl;
std::cout << "c_g_m_o: " << c_g_m_o_host_result.mDesc << std::endl;
std::srand(1); // work around test flakiness
switch(init_method)
{
case 0: break;
case 1:
a_g_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
b0_g_k_n.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-5, 5});
b1_g_n_o.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-5, 5});
// Still unsure whether this kind of deterministic floating point accurary issue is expected
// or not. May want to try exact same approach as the GPU kernel in the host reference
// GEMM+Softmax+GEMM function to see if the accuracy discrepancy goes away. Until then,
// shrink the input value range as it is less likely to produce errors of around ~1e-3.
// a_g_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
// b0_g_k_n.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-5, 5});
// b1_g_n_o.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-5, 5});
a_g_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
b0_g_k_n.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 2});
b1_g_n_o.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 2});
break;
case 2:
a_g_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
......
profiler/include/profile_normalization_impl.hpp
View file @ b89a88b5
......@@ -6,25 +6,36 @@
#include <iomanip>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/device_softmax.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/convolution_parameter.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/library/reference_tensor_operation/cpu/reference_softmax.hpp"
#include "ck/tensor_operation/gpu/device/device_softmax.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/utility/data_type.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_softmax_f16_f16_rank3_instances(std::vector<DeviceNormalizationPtr>&);
void add_device_softmax_f16_f16_rank4_instances(std::vector<DeviceNormalizationPtr>&);
namespace {
using F16 = ck::half_t;
using F32 = float;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
} // namespace
void add_device_softmax_f32_f32_rank3_instances(std::vector<DeviceNormalizationPtr>&);
void add_device_softmax_f32_f32_rank4_instances(std::vector<DeviceNormalizationPtr>&);
void add_device_softmax_f16_f16_rank3_instances(
std::vector<DeviceSoftmaxPtr<F16, F32, F16, PassThrough, PassThrough, 3>>&);
void add_device_softmax_f16_f16_rank4_instances(
std::vector<DeviceSoftmaxPtr<F16, F32, F16, PassThrough, PassThrough, 4>>&);
void add_device_softmax_f32_f32_rank3_instances(
std::vector<DeviceSoftmaxPtr<F32, F32, F32, PassThrough, PassThrough, 3>>&);
void add_device_softmax_f32_f32_rank4_instances(
std::vector<DeviceSoftmaxPtr<F32, F32, F32, PassThrough, PassThrough, 4>>&);
} // namespace instance
} // namespace device
......@@ -57,7 +68,7 @@ template <> std::string type_to_string<int8_t>() { return "int8"; }
template <> std::string type_to_string<int32_t>() { return "int32"; }
// clang-format on
template <typename InDataType, typename AccDataType, typename OutDataType>
template <typename InDataType, typename AccDataType, typename OutDataType, index_t Rank>
void profile_normalization_impl(int do_verification,
int init_method,
bool do_log,
......@@ -69,6 +80,11 @@ void profile_normalization_impl(int do_verification,
AccDataType beta,
NormType norm_type)
{
if(Rank != in_length.size())
{
throw std::runtime_error("Input tensor rank is different from template argument Rank!");
}
Tensor<InDataType> in = in_strides.empty() ? Tensor<InDataType>(in_length)
: Tensor<InDataType>(in_length, in_strides);
Tensor<OutDataType> out(in.mDesc);
......@@ -99,30 +115,31 @@ void profile_normalization_impl(int do_verification,
std::vector<index_t> i_in_lengths(in.mDesc.GetLengths().begin(), in.mDesc.GetLengths().end());
std::vector<index_t> i_in_strides(in.mDesc.GetStrides().begin(), in.mDesc.GetStrides().end());
// add device normalization instances
std::vector<tensor_operation::device::DeviceNormalizationPtr> instances;
// add device softmax instances
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using DeviceOpPtr = tensor_operation::device::
DeviceSoftmaxPtr<InDataType, AccDataType, OutDataType, PassThrough, PassThrough, Rank>;
std::vector<DeviceOpPtr> instances;
if(norm_type == NormType::SOFTMAX)
{
if constexpr(is_same<InDataType, half_t>::value && is_same<OutDataType, half_t>::value &&
is_same<AccDataType, float>::value)
{
if(in_length.size() == 3)
if constexpr(Rank == 3)
tensor_operation::device::instance::add_device_softmax_f16_f16_rank3_instances(
instances);
if(in_length.size() == 4)
else if constexpr(Rank == 4)
tensor_operation::device::instance::add_device_softmax_f16_f16_rank4_instances(
instances);
}
else if constexpr(is_same<InDataType, float>::value && is_same<OutDataType, float>::value &&
is_same<AccDataType, float>::value)
{
if(in_length.size() == 3)
if constexpr(Rank == 3)
tensor_operation::device::instance::add_device_softmax_f32_f32_rank3_instances(
instances);
if(in_length.size() == 4)
else if constexpr(Rank == 4)
tensor_operation::device::instance::add_device_softmax_f32_f32_rank4_instances(
instances);
}
......@@ -137,6 +154,8 @@ void profile_normalization_impl(int do_verification,
float best_avg_time = std::numeric_limits<float>::max();
float best_gb_per_sec = 0;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
for(auto& inst_ptr : instances)
{
// Is this user's responsibility to check if problem mismatches kernel instance (ie. rank 3
......@@ -153,7 +172,9 @@ void profile_normalization_impl(int do_verification,
&alpha,
&beta,
in_dev.GetDeviceBuffer(),
out_dev.GetDeviceBuffer());
out_dev.GetDeviceBuffer(),
PassThrough{},
PassThrough{});
if(!inst_ptr->IsSupportedArgument(argument_ptr.get()))
{
......
profiler/src/profile_batched_gemm_add_relu_gemm_add.cpp 0 → 100644
View file @ b89a88b5
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "profiler/include/profile_batched_gemm_add_relu_gemm_add_impl.hpp"
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
int profile_batched_gemm_add_relu_gemm_add(int argc, char* argv[])
{
enum struct GemmMatrixLayout
{
MK_NK_MN_NO_MO_MO, // 0
MK_NK_MN_ON_MO_MO, // 1
};
enum struct GemmDataType
{
F32_F32_F32_F32_F32_F32, // 0
F16_F16_F16_F16_F16_F16, // 1
};
GemmDataType data_type = GemmDataType::F16_F16_F16_F16_F16_F16;
GemmMatrixLayout layout = GemmMatrixLayout::MK_NK_MN_NO_MO_MO;
bool do_verification = true;
int init_method = 1;
bool do_log = 0;
bool time_kernel = false;
// GEMM shape
ck::index_t M = 1024;
ck::index_t N = 1024;
ck::index_t K = 64;
ck::index_t O = 128;
ck::index_t BatchCount = 4;
ck::index_t StrideA0 = -1;
ck::index_t StrideB0 = -1;
ck::index_t StrideD0 = -1;
ck::index_t StrideB1 = -1;
ck::index_t StrideD1 = -1;
ck::index_t StrideE1 = -1;
ck::index_t BatchStrideA0 = -1;
ck::index_t BatchStrideB0 = -1;
ck::index_t BatchStrideD0 = -1;
ck::index_t BatchStrideB1 = -1;
ck::index_t BatchStrideD1 = -1;
ck::index_t BatchStrideE1 = -1;
if(argc == 8)
{
data_type = static_cast<GemmDataType>(std::stoi(argv[2]));
layout = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
do_verification = std::stoi(argv[4]);
init_method = std::stoi(argv[5]);
do_log = std::stoi(argv[6]);
time_kernel = std::stoi(argv[7]);
}
else if(argc == 13)
{
data_type = static_cast<GemmDataType>(std::stoi(argv[2]));
layout = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
do_verification = std::stoi(argv[4]);
init_method = std::stoi(argv[5]);
do_log = std::stoi(argv[6]);
time_kernel = std::stoi(argv[7]);
M = std::stoi(argv[8]);
N = std::stoi(argv[9]);
K = std::stoi(argv[10]);
O = std::stoi(argv[11]);
BatchCount = std::stoi(argv[12]);
}
else if(argc == 25)
{
data_type = static_cast<GemmDataType>(std::stoi(argv[2]));
layout = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
do_verification = std::stoi(argv[4]);
init_method = std::stoi(argv[5]);
do_log = std::stoi(argv[6]);
time_kernel = std::stoi(argv[7]);
M = std::stoi(argv[8]);
N = std::stoi(argv[9]);
K = std::stoi(argv[10]);
O = std::stoi(argv[11]);
BatchCount = std::stoi(argv[12]);
StrideA0 = std::stoi(argv[13]);
StrideB0 = std::stoi(argv[14]);
StrideD0 = std::stoi(argv[15]);
StrideB1 = std::stoi(argv[16]);
StrideD1 = std::stoi(argv[17]);
StrideE1 = std::stoi(argv[18]);
BatchStrideA0 = std::stoi(argv[19]);
BatchStrideB0 = std::stoi(argv[20]);
BatchStrideD0 = std::stoi(argv[21]);
BatchStrideB1 = std::stoi(argv[22]);
BatchStrideD1 = std::stoi(argv[23]);
BatchStrideE1 = std::stoi(argv[24]);
}
else
{
printf("arg1: tensor operation (batched_gemm_add_relu_gemm_add: "
"Batched_GEMM+Add+Relu+Gemm+Add)\n");
printf("arg2: data type (1: fp16)\n");
printf("arg3: matrix layout (0: Relu(A0[m, k] * B0[n, k] + D0[m, n]) * B1[n, o] + D1[m, o] "
"= E1[m, o]; 1: Relu(A0[m, k] * B0[n, k] + D0[m, n]) * B1[o, n] + D1[m, o] = "
"E1[m, o];)\n");
printf("arg4: verification (0: no; 1: yes)\n");
printf("arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n");
printf("arg6: print tensor value (0: no; 1: yes)\n");
printf("arg7: time kernel (0=no, 1=yes)\n");
printf("arg8 to 12: M, N, K, O, Batch\n");
printf("arg13 to 18: StrideA0, StrideB0, StrideD0, StrideB1, StrideD1, StrideE1\n");
printf("arg19 to 24: BatchStrideA0, BatchStrideB0, BatchStrideD0, BatchStrideB1, "
"BatchStrideD1, BatchStrideE1 \n");
exit(1);
}
if(data_type == GemmDataType::F16_F16_F16_F16_F16_F16 &&
layout == GemmMatrixLayout::MK_NK_MN_NO_MO_MO)
{
ck::profiler::profile_batched_gemm_add_relu_gemm_add_impl<Row, // A0Layout,
Col, // B0Layout,
ck::Tuple<Row>, // D0sLayout,
Row, // B1Layout,
ck::Tuple<Row>, // D1sLayout,
Row, // E1Layout,
F16, // A0DataType,
F16, // B0DataType,
ck::Tuple<F16>, // D0DataType,
F16, // B1DataType,
ck::Tuple<F16>, // D1sDataType
F16> // E1DataType,
(do_verification,
init_method,
do_log,
time_kernel,
M,
N,
K,
O,
BatchCount,
StrideA0,
StrideB0,
StrideD0,
StrideB1,
StrideD1,
StrideE1,
BatchStrideA0,
BatchStrideB0,
BatchStrideD0,
BatchStrideB1,
BatchStrideD1,
BatchStrideE1);
}
else if(data_type == GemmDataType::F16_F16_F16_F16_F16_F16 &&
layout == GemmMatrixLayout::MK_NK_MN_ON_MO_MO)
{
ck::profiler::profile_batched_gemm_add_relu_gemm_add_impl<Row, // A0Layout,
Col, // B0Layout,
ck::Tuple<Row>, // D0sLayout,
Col, // B1Layout,
ck::Tuple<Row>, // D1sLayout,
Row, // E1Layout,
F16, // A0DataType,
F16, // B0DataType,
ck::Tuple<F16>, // D0DataType,
F16, // B1DataType,
ck::Tuple<F16>, // D1sDataType
F16> // E1DataType,
(do_verification,
init_method,
do_log,
time_kernel,
M,
N,
K,
O,
BatchCount,
StrideA0,
StrideB0,
StrideD0,
StrideB1,
StrideD1,
StrideE1,
BatchStrideA0,
BatchStrideB0,
BatchStrideD0,
BatchStrideB1,
BatchStrideD1,
BatchStrideE1);
}
else
{
throw std::runtime_error("wrong! this data_type & layout is not implemented");
}
return 0;
}
profiler/src/profile_batched_gemm_gemm.cpp 0 → 100644
View file @ b89a88b5
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "profiler/include/profile_batched_gemm_gemm_impl.hpp"
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
int profile_batched_gemm_gemm(int argc, char* argv[])
{
enum struct GemmMatrixLayout
{
MK_NK_NO_MO, // 0
MK_NK_ON_MO, // 0
};
enum struct GemmDataType
{
F32_F32_F32_F32, // 0
F16_F16_F16_F16, // 1
};
GemmDataType data_type = GemmDataType::F16_F16_F16_F16;
GemmMatrixLayout layout = GemmMatrixLayout::MK_NK_NO_MO;
bool do_verification = true;
int init_method = 1;
bool do_log = 0;
bool time_kernel = false;
// GEMM shape
ck::index_t M = 1024;
ck::index_t N = 1024;
ck::index_t K = 64;
ck::index_t O = 128;
ck::index_t BatchCount = 4;
ck::index_t StrideA0 = -1;
ck::index_t StrideB0 = -1;
ck::index_t StrideB1 = -1;
ck::index_t StrideE1 = -1;
ck::index_t BatchStrideA0 = -1;
ck::index_t BatchStrideB0 = -1;
ck::index_t BatchStrideB1 = -1;
ck::index_t BatchStrideE1 = -1;
if(argc == 8)
{
data_type = static_cast<GemmDataType>(std::stoi(argv[2]));
layout = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
do_verification = std::stoi(argv[4]);
init_method = std::stoi(argv[5]);
do_log = std::stoi(argv[6]);
time_kernel = std::stoi(argv[7]);
}
else if(argc == 13)
{
data_type = static_cast<GemmDataType>(std::stoi(argv[2]));
layout = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
do_verification = std::stoi(argv[4]);
init_method = std::stoi(argv[5]);
do_log = std::stoi(argv[6]);
time_kernel = std::stoi(argv[7]);
M = std::stoi(argv[8]);
N = std::stoi(argv[9]);
K = std::stoi(argv[10]);
O = std::stoi(argv[11]);
BatchCount = std::stoi(argv[12]);
}
else if(argc == 21)
{
data_type = static_cast<GemmDataType>(std::stoi(argv[2]));
layout = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
do_verification = std::stoi(argv[4]);
init_method = std::stoi(argv[5]);
do_log = std::stoi(argv[6]);
time_kernel = std::stoi(argv[7]);
M = std::stoi(argv[8]);
N = std::stoi(argv[9]);
K = std::stoi(argv[10]);
O = std::stoi(argv[11]);
BatchCount = std::stoi(argv[12]);
StrideA0 = std::stoi(argv[13]);
StrideB0 = std::stoi(argv[14]);
StrideB1 = std::stoi(argv[15]);
StrideE1 = std::stoi(argv[16]);
BatchStrideA0 = std::stoi(argv[17]);
BatchStrideB0 = std::stoi(argv[18]);
BatchStrideB1 = std::stoi(argv[19]);
BatchStrideE1 = std::stoi(argv[20]);
}
else
{
printf("arg1: tensor operation (batched_gemm_gemm: Batched_GEMM+Gemm)\n");
printf("arg2: data type (1: fp16)\n");
printf("arg3: matrix layout (0: Relu(A0[m, k] * B0[n, k] + D0[m, n]) * B1[n, o] + D1[m, o] "
"= E1[m, o]; 1: Relu(A0[m, k] * B0[n, k] + D0[m, n]) * B1[o, n] + D1[m, o] = E1[m, "
"o];)\n");
printf("arg4: verification (0: no; 1: yes)\n");
printf("arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n");
printf("arg6: print tensor value (0: no; 1: yes)\n");
printf("arg7: time kernel (0=no, 1=yes)\n");
printf("arg8 to 12: M, N, K, O, Batch\n");
printf("arg13 to 16: StrideA0, StrideB0, StrideB1, StrideE1\n");
printf("arg17 to 20: BatchStrideA0, BatchStrideB0, BatchStrideB1, BatchStrideE1 \n");
exit(1);
}
if(data_type == GemmDataType::F16_F16_F16_F16 && layout == GemmMatrixLayout::MK_NK_NO_MO)
{
ck::profiler::profile_batched_gemm_gemm_impl<F16, // A0DataType,
F16, // B0DataType,
F16, // B1DataType,
F16, // E1DataType,
Row, // A0Layout,
Col, // B0Layout,
Row, // B1Layout,
Row> // E1Layout,
(do_verification,
init_method,
do_log,
time_kernel,
M,
N,
K,
O,
BatchCount,
StrideA0,
StrideB0,
StrideB1,
StrideE1,
BatchStrideA0,
BatchStrideB0,
BatchStrideB1,
BatchStrideE1);
}
else if(data_type == GemmDataType::F16_F16_F16_F16 && layout == GemmMatrixLayout::MK_NK_ON_MO)
{
ck::profiler::profile_batched_gemm_gemm_impl<F16, // A0DataType,
F16, // B0DataType,
F16, // B1DataType,
F16, // E1DataType,
Row, // A0Layout,
Col, // B0Layout,
Col, // B1Layout,
Row> // E1Layout,
(do_verification,
init_method,
do_log,
time_kernel,
M,
N,
K,
O,
BatchCount,
StrideA0,
StrideB0,
StrideB1,
StrideE1,
BatchStrideA0,
BatchStrideB0,
BatchStrideB1,
BatchStrideE1);
}
else
{
throw std::runtime_error("wrong! this data_type & layout is not implemented");
}
return 0;
}
profiler/src/profile_normalization.cpp
View file @ b89a88b5
......@@ -50,7 +50,7 @@ struct ArgParser
void print_help()
{
std::cout << "arg1: tensor operation (layernorm/batchnorm/softmax)\n"
std::cout << "arg1: tensor operation (batchnorm/softmax)\n"
<< "arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8)\n"
<< "arg3: verification (0: no; 1: yes)\n"
<< "arg4: initialization (0: no init; 1: integer value; 2: decimal value)\n"
......@@ -91,31 +91,73 @@ int profile_normalization(int argc, char* argv[])
arg_parser.long_opts["alpha"].empty() ? 1 : arg_parser.long_opts["alpha"][0];
const index_t beta = arg_parser.long_opts["beta"].empty() ? 0 : arg_parser.long_opts["beta"][0];
if(data_type == NormDataType::F16_F16)
if(length.size() == 3)
{
ck::profiler::profile_normalization_impl<ck::half_t, float, ck::half_t>(do_verification,
init_method,
do_log,
time_kernel,
length,
stride,
reduce,
float(alpha),
float(beta),
norm_type);
if(data_type == NormDataType::F16_F16)
{
ck::profiler::profile_normalization_impl<ck::half_t, float, ck::half_t, 3>(
do_verification,
init_method,
do_log,
time_kernel,
length,
stride,
reduce,
float(alpha),
float(beta),
norm_type);
}
else if(data_type == NormDataType::F32_F32)
{
ck::profiler::profile_normalization_impl<float, float, float, 3>(do_verification,
init_method,
do_log,
time_kernel,
length,
stride,
reduce,
float(alpha),
float(beta),
norm_type);
}
else
{
throw std::runtime_error("not implemented yet");
}
}
else if(data_type == NormDataType::F32_F32)
else if(length.size() == 4)
{
ck::profiler::profile_normalization_impl<float, float, float>(do_verification,
init_method,
do_log,
time_kernel,
length,
stride,
reduce,
float(alpha),
float(beta),
norm_type);
if(data_type == NormDataType::F16_F16)
{
ck::profiler::profile_normalization_impl<ck::half_t, float, ck::half_t, 4>(
do_verification,
init_method,
do_log,
time_kernel,
length,
stride,
reduce,
float(alpha),
float(beta),
norm_type);
}
else if(data_type == NormDataType::F32_F32)
{
ck::profiler::profile_normalization_impl<float, float, float, 4>(do_verification,
init_method,
do_log,
time_kernel,
length,
stride,
reduce,
float(alpha),
float(beta),
norm_type);
}
else
{
throw std::runtime_error("not implemented yet");
}
}
else
{
......
profiler/src/profiler.cpp
View file @ b89a88b5
......@@ -10,6 +10,8 @@ int profile_gemm_add_add_fastgelu(int, char*[]);
int profile_gemm_reduce(int, char*[]);
int profile_gemm_bias_add_reduce(int, char*[]);
int profile_batched_gemm(int, char*[]);
int profile_batched_gemm_gemm(int, char*[]);
int profile_batched_gemm_add_relu_gemm_add(int, char*[]);
int profile_batched_gemm_reduce(int, char*[]);
int profile_grouped_gemm(int, char*[]);
int profile_conv_fwd(int, char*[]);
......@@ -32,6 +34,8 @@ static void print_helper_message()
" gemm_reduce: GEMM+Reduce\n"
" gemm_bias_add_reduce: GEMM+Bias+Add+Reduce\n"
" batched_gemm: Batched GEMM\n"
" batched_gemm_gemm: Batched+GEMM+GEMM\n"
" batched_gemm_add_relu_gemm_add: Batched+GEMM+bias+gelu+GEMM+bias\n"
" batched_gemm_reduce: Batched GEMM+Reduce\n"
" grouped_gemm: Grouped GEMM\n"
" conv_fwd: Convolution Forward\n"
......@@ -80,6 +84,14 @@ int main(int argc, char* argv[])
{
return profile_batched_gemm(argc, argv);
}
else if(strcmp(argv[1], "batched_gemm_gemm") == 0)
{
return profile_batched_gemm_gemm(argc, argv);
}
else if(strcmp(argv[1], "batched_gemm_add_relu_gemm_add") == 0)
{
return profile_batched_gemm_add_relu_gemm_add(argc, argv);
}
else if(strcmp(argv[1], "batched_gemm_reduce") == 0)
{
return profile_batched_gemm_reduce(argc, argv);
......
script/clang-format-overwrite.sh
View file @ b89a88b5
#find . -name deps -prune -o -name build -prune -o -iname '*.h' -o -iname '*.hpp' -o -iname '*.cpp' -o -iname '*.h.in' -o -iname '*.hpp.in' -o -iname '*.cpp.in' -o -iname '*.cl' -o -iname '*.cuh' -o -iname '*.cu' | xargs -n 1 -P 16 -I{} -t sh -c 'clang-format-10 -i -style=file {}'
git status --porcelain | awk '$1 != "D" && (match($2, "\\.cpp|hpp")) {print $2}' | xargs -n 1 -P 16 -I{} -t sh -c 'clang-format-10 -i -style=file {}'
#find . -name deps -prune -o -name build -prune -o -iname '*.h' -o -iname '*.hpp' -o -iname '*.cpp' -o -iname '*.h.in' -o -iname '*.hpp.in' -o -iname '*.cpp.in' -o -iname '*.cl' -o -iname '*.cuh' -o -iname '*.cu' -o -iname '*.inc' | xargs -n 1 -P 16 -I{} -t sh -c 'clang-format-10 -i -style=file {}'
git status --porcelain | awk '$1 != "D" && (match($2, "\\.cpp|hpp|inc")) {print $2}' | xargs -n 1 -P 16 -I{} -t sh -c 'clang-format-10 -i -style=file {}'
script/process_perf_data.py
View file @ b89a88b5
......@@ -127,11 +127,16 @@ def parse_logfile(logfile):
lst=line.split()
res.append(lst[1])
#parse all other performance tests:
elif 'resnet50' or 'batched_gemm' or 'grouped_gemm' or 'conv_bwd_data' or 'gemm_bilinear' or 'reduction' in logfile:
elif 'resnet50' in logfile or 'batched_gemm' in logfile or 'grouped_gemm' in logfile or 'conv_bwd_data' in logfile or 'gemm_bilinear' in logfile or 'reduction' in logfile:
for line in open(logfile):
if 'Best Perf' in line:
lst=line.split()
res.append(lst[4])
elif 'onnx_gemm' in logfile or 'splitK_gemm' in logfile:
for line in open(logfile):
if 'Best Perf' in line:
lst=line.split()
res.append(lst[33])
return res
......@@ -281,6 +286,14 @@ def main():
for i in range(1,50):
testlist.append("Layer%i"%i)
table_name="ck_resnet50_N256_tflops"
if 'onnx_gemm' in filename:
for i in range(1,len(results)+1):
testlist.append("Test%i"%i)
table_name="ck_onnx_gemm_tflops"
if 'splitK_gemm' in filename:
for i in range(1,len(results)+1):
testlist.append("Test%i"%i)
table_name="ck_splitK_gemm_tflops"
tflops_base = get_baseline(table_name,conn)
store_new_test_result(table_name, results, testlist, branch_name, node_id, gpu_arch, compute_units, rocm_vers, hip_vers, environment, conn)
......
script/process_qa_data.sh
View file @ b89a88b5
......@@ -2,8 +2,8 @@
#
# in order to run this script you'd need the following python packages:
pip3 install --upgrade pip
pip3 install sqlalchemy pymysql pandas sshtunnel
#pip3 install --upgrade pip
#pip3 install sqlalchemy pymysql pandas sshtunnel
# you would also need to set up some environment variables in order to
# post your new test results to the database and compare them to the baseline
......@@ -20,3 +20,5 @@ python3 process_perf_data.py perf_conv_fwd_"$gpu_arch".log
python3 process_perf_data.py perf_conv_bwd_data_"$gpu_arch".log
python3 process_perf_data.py perf_gemm_bilinear_"$gpu_arch".log
python3 process_perf_data.py perf_reduction_"$gpu_arch".log
python3 process_perf_data.py perf_splitK_gemm_"$gpu_arch".log
python3 process_perf_data.py perf_onnx_gemm_"$gpu_arch".log
script/profile_onnx_gemm.sh 0 → 100755
View file @ b89a88b5
#!/bin/bash
## GPU visibility
export HIP_VISIBLE_DEVICES=0
DRIVER="../build/bin/ckProfiler"
echo $DRIVER
OP=$1
DATATYPE=$2
LAYOUT=$3
VERIFY=$4
INIT=$5
LOG=$6
TIME=$7
# GEMM kernel benchmarks used by ONNX
######## op datatype layout verify init log time M___ N___ K___ StrideA StrideB StrideC
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 384 768 768 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 384 768 2304 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 384 768 3072 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 384 3072 768 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 384 1024 1024 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 384 1024 3072 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 384 1024 4096 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 384 4096 1024 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 24576 768 768 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 24576 768 2304 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 24576 768 3072 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 24576 3072 768 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 24576 1024 1024 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 24576 1024 3072 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 24576 1024 4096 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 24576 4096 1024 -1 -1 -1
script/profile_splitK_gemm.sh 0 → 100755
View file @ b89a88b5
#!/bin/bash
## GPU visibility
export HIP_VISIBLE_DEVICES=0
DRIVER="../build/bin/ckProfiler"
echo $DRIVER
OP=$1
DATATYPE=$2
LAYOUT=$3
VERIFY=$4
INIT=$5
LOG=$6
TIME=$7
KBatch=$8
# 120 CU
######## op datatype layout verify init log time M___ N___ K___ StrideA StrideB StrideC KBatch_
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 960 1024 1024 -1 -1 -1 $KBatch
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 960 2048 2048 -1 -1 -1 $KBatch
# 104 CU
######## op datatype layout verify init log time M___ N___ K___ StrideA StrideB StrideC KBatch_
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 832 1024 1024 -1 -1 -1 $KBatch
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 832 2048 2048 -1 -1 -1 $KBatch
# 110 CU
######## op datatype layout verify init log time M___ N___ K___ StrideA StrideB StrideC KBatch_
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 1280 1408 1024 -1 -1 -1 $KBatch
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 1280 2816 2048 -1 -1 -1 $KBatch
# testing different strides
######## op datatype layout verify init log time M___ N___ K___ StrideA StrideB StrideC KBatch_
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 1024 1024 1024 1024 1024 1024 $KBatch
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2048 2048 2048 2048 2048 2048 $KBatch
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 1024 1024 1024 1056 1056 1056 $KBatch
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2048 2048 2048 2080 2080 2080 $KBatch
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 1024 1024 1024 1088 1088 1088 $KBatch
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2048 2048 2048 2112 2112 2112 $KBatch
script/run_full_performance_tests.sh
View file @ b89a88b5
......@@ -40,85 +40,103 @@ function print_log_header(){
#run gemm tests
export gemm_log="perf_gemm_${gpu_arch}.log"
print_log_header $gemm_log $env_type $branch $host_name
./profile_gemm.sh gemm 0 0 $verify 1 0 1 | tee -a $gemm_log
./profile_gemm.sh gemm 1 0 $verify 1 0 1 | tee -a $gemm_log
./profile_gemm.sh gemm 2 0 $verify 1 0 1 | tee -a $gemm_log
./profile_gemm.sh gemm 3 0 $verify 1 0 1 | tee -a $gemm_log
./profile_gemm.sh gemm 0 1 $verify 1 0 1 | tee -a $gemm_log
./profile_gemm.sh gemm 1 1 $verify 1 0 1 | tee -a $gemm_log
./profile_gemm.sh gemm 2 1 $verify 1 0 1 | tee -a $gemm_log
./profile_gemm.sh gemm 3 1 $verify 1 0 1 | tee -a $gemm_log
./profile_gemm.sh gemm 0 2 $verify 1 0 1 | tee -a $gemm_log
./profile_gemm.sh gemm 1 2 $verify 1 0 1 | tee -a $gemm_log
./profile_gemm.sh gemm 2 2 $verify 1 0 1 | tee -a $gemm_log
./profile_gemm.sh gemm 3 2 $verify 1 0 1 | tee -a $gemm_log
./profile_gemm.sh gemm 0 3 $verify 1 0 1 | tee -a $gemm_log
./profile_gemm.sh gemm 1 3 $verify 1 0 1 | tee -a $gemm_log
./profile_gemm.sh gemm 2 3 $verify 1 0 1 | tee -a $gemm_log
./profile_gemm.sh gemm 3 3 $verify 1 0 1 | tee -a $gemm_log
./profile_gemm.sh gemm 0 0 $verify 1 0 1 2>&1 | tee -a $gemm_log
./profile_gemm.sh gemm 1 0 $verify 1 0 1 2>&1 | tee -a $gemm_log
./profile_gemm.sh gemm 2 0 $verify 1 0 1 2>&1 | tee -a $gemm_log
./profile_gemm.sh gemm 3 0 $verify 1 0 1 2>&1 | tee -a $gemm_log
./profile_gemm.sh gemm 0 1 $verify 1 0 1 2>&1 | tee -a $gemm_log
./profile_gemm.sh gemm 1 1 $verify 1 0 1 2>&1 | tee -a $gemm_log
./profile_gemm.sh gemm 2 1 $verify 1 0 1 2>&1 | tee -a $gemm_log
./profile_gemm.sh gemm 3 1 $verify 1 0 1 2>&1 | tee -a $gemm_log
./profile_gemm.sh gemm 0 2 $verify 1 0 1 2>&1 | tee -a $gemm_log
./profile_gemm.sh gemm 1 2 $verify 1 0 1 2>&1 | tee -a $gemm_log
./profile_gemm.sh gemm 2 2 $verify 1 0 1 2>&1 | tee -a $gemm_log
./profile_gemm.sh gemm 3 2 $verify 1 0 1 2>&1 | tee -a $gemm_log
./profile_gemm.sh gemm 0 3 $verify 1 0 1 2>&1 | tee -a $gemm_log
./profile_gemm.sh gemm 1 3 $verify 1 0 1 2>&1 | tee -a $gemm_log
./profile_gemm.sh gemm 2 3 $verify 1 0 1 2>&1 | tee -a $gemm_log
./profile_gemm.sh gemm 3 3 $verify 1 0 1 2>&1 | tee -a $gemm_log
#run batched_gemm tests
export batched_gemm_log="perf_batched_gemm_${gpu_arch}.log"
print_log_header $batched_gemm_log $env_type $branch $host_name
./profile_batched_gemm.sh batched_gemm 0 0 $verify 1 0 1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 0 1 $verify 1 0 1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 0 2 $verify 1 0 1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 0 3 $verify 1 0 1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 1 0 $verify 1 0 1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 1 1 $verify 1 0 1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 1 2 $verify 1 0 1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 1 3 $verify 1 0 1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 2 0 $verify 1 0 1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 2 1 $verify 1 0 1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 2 2 $verify 1 0 1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 2 3 $verify 1 0 1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 3 0 $verify 1 0 1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 3 1 $verify 1 0 1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 3 2 $verify 1 0 1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 3 3 $verify 1 0 1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 0 0 $verify 1 0 1 2>&1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 0 1 $verify 1 0 1 2>&1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 0 2 $verify 1 0 1 2>&1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 0 3 $verify 1 0 1 2>&1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 1 0 $verify 1 0 1 2>&1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 1 1 $verify 1 0 1 2>&1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 1 2 $verify 1 0 1 2>&1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 1 3 $verify 1 0 1 2>&1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 2 0 $verify 1 0 1 2>&1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 2 1 $verify 1 0 1 2>&1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 2 2 $verify 1 0 1 2>&1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 2 3 $verify 1 0 1 2>&1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 3 0 $verify 1 0 1 2>&1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 3 1 $verify 1 0 1 2>&1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 3 2 $verify 1 0 1 2>&1 | tee -a $batched_gemm_log
./profile_batched_gemm.sh batched_gemm 3 3 $verify 1 0 1 2>&1 | tee -a $batched_gemm_log
#run grouped_gemm tests
export grouped_gemm_log="perf_grouped_gemm_${gpu_arch}.log"
print_log_header $grouped_gemm_log $env_type $branch $host_name
./profile_grouped_gemm.sh grouped_gemm 1 0 $verify 1 0 1 | tee -a $grouped_gemm_log
./profile_grouped_gemm.sh grouped_gemm 1 1 $verify 1 0 1 | tee -a $grouped_gemm_log
./profile_grouped_gemm.sh grouped_gemm 1 2 $verify 1 0 1 | tee -a $grouped_gemm_log
./profile_grouped_gemm.sh grouped_gemm 1 3 $verify 1 0 1 | tee -a $grouped_gemm_log
./profile_grouped_gemm.sh grouped_gemm 1 0 $verify 1 0 1 2>&1 | tee -a $grouped_gemm_log
./profile_grouped_gemm.sh grouped_gemm 1 1 $verify 1 0 1 2>&1 | tee -a $grouped_gemm_log
./profile_grouped_gemm.sh grouped_gemm 1 2 $verify 1 0 1 2>&1 | tee -a $grouped_gemm_log
./profile_grouped_gemm.sh grouped_gemm 1 3 $verify 1 0 1 2>&1 | tee -a $grouped_gemm_log
#run GEMM+Bilinear tests
export gemm_bilinear_log="perf_gemm_bilinear_${gpu_arch}.log"
print_log_header $gemm_bilinear_log $env_type $branch $host_name
./profile_gemm_bilinear.sh gemm_bilinear 1 0 $verify 1 0 1 | tee -a $gemm_bilinear_log
./profile_gemm_bilinear.sh gemm_bilinear 1 1 $verify 1 0 1 | tee -a $gemm_bilinear_log
./profile_gemm_bilinear.sh gemm_bilinear 1 2 $verify 1 0 1 | tee -a $gemm_bilinear_log
./profile_gemm_bilinear.sh gemm_bilinear 1 3 $verify 1 0 1 | tee -a $gemm_bilinear_log
./profile_gemm_bilinear.sh gemm_bilinear 1 0 $verify 1 0 1 2>&1 | tee -a $gemm_bilinear_log
./profile_gemm_bilinear.sh gemm_bilinear 1 1 $verify 1 0 1 2>&1 | tee -a $gemm_bilinear_log
./profile_gemm_bilinear.sh gemm_bilinear 1 2 $verify 1 0 1 2>&1 | tee -a $gemm_bilinear_log
./profile_gemm_bilinear.sh gemm_bilinear 1 3 $verify 1 0 1 2>&1 | tee -a $gemm_bilinear_log
#run conv_fwd tests
export conv_fwd_log="perf_conv_fwd_${gpu_arch}.log"
print_log_header $conv_fwd_log $env_type $branch $host_name
./profile_conv_fwd.sh conv_fwd 0 1 $verify 1 0 1 256 | tee -a $conv_fwd_log
./profile_conv_fwd.sh conv_fwd 1 1 $verify 1 0 1 256 | tee -a $conv_fwd_log
./profile_conv_fwd.sh conv_fwd 2 1 $verify 1 0 1 256 | tee -a $conv_fwd_log
./profile_conv_fwd.sh conv_fwd 3 1 $verify 1 0 1 256 | tee -a $conv_fwd_log
./profile_conv_fwd.sh conv_fwd 0 1 $verify 1 0 1 256 2>&1 | tee -a $conv_fwd_log
./profile_conv_fwd.sh conv_fwd 1 1 $verify 1 0 1 256 2>&1 | tee -a $conv_fwd_log
./profile_conv_fwd.sh conv_fwd 2 1 $verify 1 0 1 256 2>&1 | tee -a $conv_fwd_log
./profile_conv_fwd.sh conv_fwd 3 1 $verify 1 0 1 256 2>&1 | tee -a $conv_fwd_log
#run conv_bwd_data tests
export conv_bwd_data_log="perf_conv_bwd_data_${gpu_arch}.log"
print_log_header $conv_bwd_data_log $env_type $branch $host_name
./profile_conv_bwd_data.sh conv_bwd_data 0 1 $verify 1 0 1 256 | tee -a $conv_bwd_data_log
./profile_conv_bwd_data.sh conv_bwd_data 1 1 $verify 1 0 1 256 | tee -a $conv_bwd_data_log
./profile_conv_bwd_data.sh conv_bwd_data 2 1 $verify 1 0 1 256 | tee -a $conv_bwd_data_log
./profile_conv_bwd_data.sh conv_bwd_data 3 1 $verify 1 0 1 256 | tee -a $conv_bwd_data_log
./profile_conv_bwd_data.sh conv_bwd_data 0 1 $verify 1 0 1 256 2>&1 | tee -a $conv_bwd_data_log
./profile_conv_bwd_data.sh conv_bwd_data 1 1 $verify 1 0 1 256 2>&1 | tee -a $conv_bwd_data_log
./profile_conv_bwd_data.sh conv_bwd_data 2 1 $verify 1 0 1 256 2>&1 | tee -a $conv_bwd_data_log
./profile_conv_bwd_data.sh conv_bwd_data 3 1 $verify 1 0 1 256 2>&1 | tee -a $conv_bwd_data_log
#run resnet50 tests
export resnet256_log="perf_resnet50_N256_${gpu_arch}.log"
print_log_header $resnet256_log $env_type $branch $host_name
./profile_resnet50.sh conv_fwd_bias_relu 1 1 1 1 $verify 1 0 1 256 | tee -a $resnet256_log
./profile_resnet50.sh conv_fwd_bias_relu 1 1 1 1 $verify 1 0 1 256 2>&1 | tee -a $resnet256_log
export resnet4_log="perf_resnet50_N4_${gpu_arch}.log"
print_log_header $resnet4_log $env_type $branch $host_name
./profile_resnet50.sh conv_fwd_bias_relu 1 1 1 1 $verify 1 0 1 4 | tee -a $resnet4_log
./profile_resnet50.sh conv_fwd_bias_relu 1 1 1 1 $verify 1 0 1 4 2>&1 | tee -a $resnet4_log
#run reduction tests
export reduction_log="perf_reduction_${gpu_arch}.log"
print_log_header $reduction_log $env_type $branch $host_name
./profile_reduce_with_index.sh $verify 2 10 --half | tee -a $reduction_log
./profile_reduce_no_index.sh $verify 2 10 --half | tee -a $reduction_log
./profile_reduce_with_index.sh $verify 2 10 --half 2>&1 | tee -a $reduction_log
./profile_reduce_no_index.sh $verify 2 10 --half 2>&1 | tee -a $reduction_log
#run splitK_gemm tests
export splitK_gemm_log="perf_splitK_gemm_${gpu_arch}.log"
print_log_header $splitK_gemm_log $env_type $branch $host_name
./profile_splitK_gemm.sh gemm_splitk 0 0 $verify 1 0 1 4 2>&1 | tee -a $splitK_gemm_log
./profile_splitK_gemm.sh gemm_splitk 0 1 $verify 1 0 1 4 2>&1 | tee -a $splitK_gemm_log
./profile_splitK_gemm.sh gemm_splitk 0 2 $verify 1 0 1 4 2>&1 | tee -a $splitK_gemm_log
./profile_splitK_gemm.sh gemm_splitk 0 3 $verify 1 0 1 4 2>&1 | tee -a $splitK_gemm_log
./profile_splitK_gemm.sh gemm_splitk 1 0 $verify 1 0 1 4 2>&1 | tee -a $splitK_gemm_log
./profile_splitK_gemm.sh gemm_splitk 1 1 $verify 1 0 1 4 2>&1 | tee -a $splitK_gemm_log
./profile_splitK_gemm.sh gemm_splitk 1 2 $verify 1 0 1 4 2>&1 | tee -a $splitK_gemm_log
./profile_splitK_gemm.sh gemm_splitk 1 3 $verify 1 0 1 4 2>&1 | tee -a $splitK_gemm_log
#run ONNX gemm tests
export onnx_log="perf_onnx_gemm_${gpu_arch}.log"
print_log_header $onnx_log $env_type $branch $host_name
./profile_onnx_gemm.sh gemm 0 0 $verify 1 0 1 2>&1 | tee -a $onnx_log
./profile_onnx_gemm.sh gemm 1 0 $verify 1 0 1 2>&1 | tee -a $onnx_log
test/CMakeLists.txt
View file @ b89a88b5
......@@ -51,3 +51,4 @@ add_subdirectory(grouped_convnd_fwd)
add_subdirectory(block_to_ctile_map)
add_subdirectory(softmax)
add_subdirectory(layernorm)
add_subdirectory(data_type)
test/batched_gemm_gemm/test_batched_gemm_gemm_fp16.cpp
View file @ b89a88b5
......@@ -11,7 +11,8 @@ class TestBatchedGemmGemmFP16 : public TestBatchedGemmGemm<Tuple>
// clang-format off
using KernelTypes = ::testing::Types<
std::tuple<F16, F16, F16, F16, Row, Col, Row, Row>
std::tuple<F16, F16, F16, F16, Row, Col, Row, Row>,
std::tuple<F16, F16, F16, F16, Row, Col, Col, Row>
>;
// clang-format on
......@@ -19,6 +20,73 @@ TYPED_TEST_SUITE(TestBatchedGemmGemmFP16, KernelTypes);
TYPED_TEST(TestBatchedGemmGemmFP16, Test_FP16) { this->Run(); }
TYPED_TEST(TestBatchedGemmGemmFP16, Test_FP16_PadM)
{
this->lengths_ = std::vector<std::vector<int>>{
{136, 128, 32, 128, 1},
};
this->Run();
}
TYPED_TEST(TestBatchedGemmGemmFP16, Test_FP16_PadN)
{
this->lengths_ = std::vector<std::vector<int>>{
{128, 136, 32, 128, 1},
};
this->Run();
}
TYPED_TEST(TestBatchedGemmGemmFP16, Test_FP16_PadK)
{
this->lengths_ = std::vector<std::vector<int>>{
{128, 128, 40, 128, 1},
{128, 128, 136, 128, 1},
};
this->Run();
}
TYPED_TEST(TestBatchedGemmGemmFP16, Test_FP16_PadO)
{
this->lengths_ = std::vector<std::vector<int>>{
{128, 128, 32, 136, 1},
};
this->Run();
}
TYPED_TEST(TestBatchedGemmGemmFP16, Test_FP16_OddM)
{
this->lengths_ = std::vector<std::vector<int>>{
{129, 128, 32, 128, 1},
};
this->Run();
}
TYPED_TEST(TestBatchedGemmGemmFP16, Test_FP16_OddN)
{
this->lengths_ = std::vector<std::vector<int>>{
{128, 129, 32, 128, 1},
};
this->Run();
}
TYPED_TEST(TestBatchedGemmGemmFP16, Test_FP16_OddK)
{
this->lengths_ = std::vector<std::vector<int>>{
{128, 128, 33, 128, 1},
{128, 128, 129, 128, 1},
};
this->Run();
}
// If kernel B1Layout is RowMajor, expect not to support odd O size
TYPED_TEST(TestBatchedGemmGemmFP16, Test_FP16_OddO)
{
this->lengths_ = std::vector<std::vector<int>>{
{128, 128, 32, 129, 1},
};
this->Run();
}
TYPED_TEST(TestBatchedGemmGemmFP16, DISABLED_Bench_FP16)
{
this->lengths_ = std::vector<std::vector<int>>{
......@@ -37,3 +105,45 @@ TYPED_TEST(TestBatchedGemmGemmFP16, DISABLED_Bench_FP16)
this->verify_ = false;
this->Run();
}
using ck::tensor_operation::device::GemmSpecialization;
TEST(TestBatchedGemmGemmInterface, GemmSpecializationSizeMatch)
{
int P = 120; // requires padding
int Q = 128; // do not require padding
// IsSupported(M, N, K, O)
// clang-format off
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::Default>{}.IsSupported(Q, Q, Q, Q));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MPadding>{}.IsSupported(P, Q, Q, Q));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::NPadding>{}.IsSupported(Q, P, Q, Q));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::KPadding>{}.IsSupported(Q, Q, P, Q));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNPadding>{}.IsSupported(P, P, Q, Q));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MKPadding>{}.IsSupported(P, Q, P, Q));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::NKPadding>{}.IsSupported(Q, P, P, Q));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKPadding>{}.IsSupported(P, P, P, Q));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::OPadding>{}.IsSupported(Q, Q, Q, P));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MOPadding>{}.IsSupported(P, Q, Q, P));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::NOPadding>{}.IsSupported(Q, P, Q, P));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::KOPadding>{}.IsSupported(Q, Q, P, P));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNOPadding>{}.IsSupported(P, P, Q, P));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MKOPadding>{}.IsSupported(P, Q, P, P));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::NKOPadding>{}.IsSupported(Q, P, P, P));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKOPadding>{}.IsSupported(P, P, P, P));
// clang-format on
}
TEST(TestBatchedGemmGemmInterface, GemmSpecializationSizeMismatch)
{
// IsSupported(M, N, K, O)
// clang-format off
EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::Default>{}.IsSupported(128, 128, 120, 128));
EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKPadding>{}.IsSupported(128, 128, 128, 120));
// Kernel can't support odd K size because SrcVectorDim == KDim and must satisfy SizeKRaw % ABSrcScalarPerVector == 0
EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKOPadding>{}.IsSupported(128, 128, 129, 128));
EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKOPadding>{}.IsSupported(128, 128, 130, 128));
// Kernel can't support odd O size because SrcVectorDim == ODim and must satisfy SizeORaw % B1SrcScalarPerVector == 0
EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKOPadding>{}.IsSupported(128, 128, 128, 129));
// clang-format on
}
test/batched_gemm_gemm/test_batched_gemm_gemm_util.hpp
View file @ b89a88b5
......@@ -4,8 +4,12 @@
#include <iostream>
#include <vector>
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_batched_gemm_gemm_xdl_cshuffle.hpp"
#include "profiler/include/profile_batched_gemm_gemm_impl.hpp"
using ck::tensor_operation::device::GemmSpecialization;
template <ck::index_t N>
using I = ck::Number<N>;
......@@ -66,3 +70,120 @@ struct TestBatchedGemmGemm : public ::testing::Test
}
}
};
template <GemmSpecialization GemmSpec>
struct DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128
{
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ALayout = Row;
using B0Layout = Col;
using B1Layout = Row;
using CLayout = Row;
using ADataType = F16;
using B0DataType = F16;
using B1DataType = F16;
using AccDataType = float;
using CShuffleDataType = float;
using CDataType = F16;
using AElementOp = PassThrough;
using B0ElementOp = PassThrough;
using Acc0ElementOp = PassThrough;
using B1ElementOp = PassThrough;
using CElementOp = PassThrough;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
// static constexpr auto GemmSpec = std::tuple_element_t<0, Tuple>::value;
using DeviceGemmGemmInstance = ck::tensor_operation::device::DeviceBatchedGemmGemm_Xdl_CShuffle<
ALayout,
B0Layout,
B1Layout,
CLayout,
ADataType,
B0DataType,
B1DataType,
CDataType,
AccDataType,
CShuffleDataType,
AElementOp,
B0ElementOp,
Acc0ElementOp,
B1ElementOp,
CElementOp,
GemmSpec,
1,
256,
128, // MPerBlock
128, // NPerBlock
32, // KPerBlock
128, // Gemm1NPerBlock
32, // Gemm1KPerBlock
8, // AK1
8, // BK1
2, // B1K1
32, // MPerXDL
32, // NPerXDL
1, // MXdlPerWave
4, // NXdlPerWave
4, // Gemm1NXdlPerWave
S<4, 64, 1>, // ABlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 64, 1>, // BBlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<8, 32, 1>, // B1BlockTransfer
S<0, 2, 1>,
S<0, 2, 1>,
1,
4,
2,
false,
1, // CShuffleMXdlPerWavePerShuffle
2, // CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8>; // CShuffleBlockTransferScalarPerVector_NPerBlock
bool IsSupported(int M, int N, int K, int O)
{
auto gemm = DeviceGemmGemmInstance{};
auto invoker = gemm.MakeInvoker();
auto argument = gemm.MakeArgument(static_cast<ADataType*>(nullptr),
static_cast<B0DataType*>(nullptr),
static_cast<B1DataType*>(nullptr),
static_cast<CDataType*>(nullptr),
M,
N,
K,
O,
0, // BatchCount
0, // StrideA
0, // StrideB0
0, // StrideB1
0, // StrideC
0, // BatchStrideA
0, // BatchStrideB0
0, // BatchStrideB1
0, // BatchStrideC
PassThrough{}, // a_element_op
PassThrough{}, // b0_element_op
PassThrough{}, // acc0_element_op
PassThrough{}, // b1_element_op
PassThrough{}); // c_element_op
return gemm.IsSupportedArgument(argument);
}
};
test/batched_gemm_softmax_gemm/test_batched_gemm_softmax_gemm_fp16.cpp
View file @ b89a88b5
......@@ -19,6 +19,73 @@ TYPED_TEST_SUITE(TestBatchedGemmSoftmaxGemmFP16, KernelTypes);
TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, Test_FP16) { this->Run(); }
TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, Test_FP16_PadM)
{
this->lengths_ = std::vector<std::vector<int>>{
{136, 128, 32, 128, 1},
};
this->Run();
}
TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, Test_FP16_PadN)
{
this->lengths_ = std::vector<std::vector<int>>{
{128, 136, 32, 128, 1},
};
this->Run();
}
TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, Test_FP16_PadK)
{
this->lengths_ = std::vector<std::vector<int>>{
{128, 128, 40, 128, 1},
{128, 128, 136, 128, 1},
};
this->Run();
}
TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, Test_FP16_PadO)
{
this->lengths_ = std::vector<std::vector<int>>{
{128, 128, 32, 136, 1},
};
this->Run();
}
TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, Test_FP16_OddM)
{
this->lengths_ = std::vector<std::vector<int>>{
{129, 128, 32, 128, 1},
};
this->Run();
}
TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, Test_FP16_OddN)
{
this->lengths_ = std::vector<std::vector<int>>{
{128, 129, 32, 128, 1},
};
this->Run();
}
TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, Test_FP16_OddK)
{
this->lengths_ = std::vector<std::vector<int>>{
{128, 128, 33, 128, 1},
{128, 128, 129, 128, 1},
};
this->Run();
}
// If kernel B1Layout is RowMajor, expect not to support odd O size
TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, Test_FP16_OddO)
{
this->lengths_ = std::vector<std::vector<int>>{
{128, 128, 32, 129, 1},
};
this->Run();
}
TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, DISABLED_Bench_FP16)
{
this->lengths_ = std::vector<std::vector<int>>{
......@@ -37,3 +104,58 @@ TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, DISABLED_Bench_FP16)
this->verify_ = false;
this->Run();
}
using ck::tensor_operation::device::GemmSpecialization;
// TODO: enable KPadding tests when it is implemented
TEST(TestBatchedGemmSoftmaxGemmInterface, GemmSpecializationSizeMatch)
{
int P = 120; // requires padding
int Q = 128; // do not require padding
// IsSupported(M, N, K, O)
// clang-format off
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::Default>{}.IsSupported(Q, Q, Q, Q));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MPadding>{}.IsSupported(P, Q, Q, Q));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::NPadding>{}.IsSupported(Q, P, Q, Q));
// EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::KPadding>{}.IsSupported(Q, Q, P, Q));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNPadding>{}.IsSupported(P, P, Q, Q));
// EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MKPadding>{}.IsSupported(P, Q, P, Q));
// EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::NKPadding>{}.IsSupported(Q, P, P, Q));
// EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKPadding>{}.IsSupported(P, P, P, Q));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::OPadding>{}.IsSupported(Q, Q, Q, P));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MOPadding>{}.IsSupported(P, Q, Q, P));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::NOPadding>{}.IsSupported(Q, P, Q, P));
// EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::KOPadding>{}.IsSupported(Q, Q, P, P));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNOPadding>{}.IsSupported(P, P, Q, P));
// EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MKOPadding>{}.IsSupported(P, Q, P, P));
// EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::NKOPadding>{}.IsSupported(Q, P, P, P));
// EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKOPadding>{}.IsSupported(P, P, P, P));
// clang-format on
}
TEST(TestBatchedGemmSoftmaxGemmInterface, GemmSpecializationSizeMismatch)
{
// IsSupported(M, N, K, O)
// clang-format off
EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::Default>{}.IsSupported(128, 128, 120, 128));
// EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKPadding>{}.IsSupported(128, 128, 128, 120));
// Kernel can't support odd K size because SrcVectorDim == KDim and must satisfy SizeKRaw % ABSrcScalarPerVector == 0
// EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKOPadding>{}.IsSupported(128, 128, 129, 128));
// EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKOPadding>{}.IsSupported(128, 128, 130, 128));
// Kernel can't support odd O size because SrcVectorDim == ODim and must satisfy SizeORaw % B1SrcScalarPerVector == 0
// EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKOPadding>{}.IsSupported(128, 128, 128, 129));
// clang-format on
}
TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, AdhocTest)
{
this->lengths_ = std::vector<std::vector<int>>{
{49, 49, 64, 64, 24},
{64, 49, 64, 64, 24},
{1020, 1020, 64, 128, 24},
{576, 576, 64, 64, 24},
};
this->bench_ = true;
this->Run();
}
test/batched_gemm_softmax_gemm/test_batched_gemm_softmax_gemm_util.hpp
View file @ b89a88b5
......@@ -4,7 +4,10 @@
#include <iostream>
#include <vector>
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_batched_gemm_softmax_gemm_xdl_cshuffle.hpp"
#include "profiler/include/profile_batched_gemm_softmax_gemm_impl.hpp"
using ck::tensor_operation::device::GemmSpecialization;
template <ck::index_t N>
using I = ck::Number<N>;
......@@ -66,3 +69,121 @@ struct TestBatchedGemmSoftmaxGemm : public ::testing::Test
}
}
};
template <GemmSpecialization GemmSpec>
struct DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128
{
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ALayout = Row;
using B0Layout = Col;
using B1Layout = Row;
using CLayout = Row;
using ADataType = F16;
using B0DataType = F16;
using B1DataType = F16;
using AccDataType = float;
using CShuffleDataType = float;
using CDataType = F16;
using AElementOp = PassThrough;
using B0ElementOp = PassThrough;
using Acc0ElementOp = PassThrough;
using B1ElementOp = PassThrough;
using CElementOp = PassThrough;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
// static constexpr auto GemmSpec = std::tuple_element_t<0, Tuple>::value;
using DeviceGemmGemmInstance =
ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemm_Xdl_CShuffle<
ALayout,
B0Layout,
B1Layout,
CLayout,
ADataType,
B0DataType,
B1DataType,
CDataType,
AccDataType,
CShuffleDataType,
AElementOp,
B0ElementOp,
Acc0ElementOp,
B1ElementOp,
CElementOp,
GemmSpec,
1,
256,
128, // MPerBlock
128, // NPerBlock
32, // KPerBlock
128, // Gemm1NPerBlock
32, // Gemm1KPerBlock
8, // AK1
8, // BK1
2, // B1K1
32, // MPerXDL
32, // NPerXDL
1, // MXdlPerWave
4, // NXdlPerWave
4, // Gemm1NXdlPerWave
S<4, 64, 1>, // ABlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 64, 1>, // BBlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<8, 32, 1>, // B1BlockTransfer
S<0, 2, 1>,
S<0, 2, 1>,
1,
4,
2,
false,
1, // CShuffleMXdlPerWavePerShuffle
2, // CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8>; // CShuffleBlockTransferScalarPerVector_NPerBlock
bool IsSupported(int M, int N, int K, int O)
{
auto gemm = DeviceGemmGemmInstance{};
auto invoker = gemm.MakeInvoker();
auto argument = gemm.MakeArgument(static_cast<ADataType*>(nullptr),
static_cast<B0DataType*>(nullptr),
static_cast<B1DataType*>(nullptr),
static_cast<CDataType*>(nullptr),
M,
N,
K,
O,
0, // BatchCount
0, // StrideA
0, // StrideB0
0, // StrideB1
0, // StrideC
0, // BatchStrideA
0, // BatchStrideB0
0, // BatchStrideB1
0, // BatchStrideC
PassThrough{}, // a_element_op
PassThrough{}, // b0_element_op
PassThrough{}, // acc0_element_op
PassThrough{}, // b1_element_op
PassThrough{}); // c_element_op
return gemm.IsSupportedArgument(argument);
}
};
test/data_type/CMakeLists.txt 0 → 100644
View file @ b89a88b5
if (USE_BITINT_EXTENSION_INT4)
add_gtest_executable(test_int4 int4.cpp)
target_link_libraries(test_int4 PRIVATE utility)
endif()
test/data_type/int4.cpp 0 → 100644
View file @ b89a88b5
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <bitset>
#include <cinttypes>
#include <cstdint>
#include <iomanip>
#include "gtest/gtest.h"
#include <hip/hip_runtime.h>
#include "ck/host_utility/hip_check_error.hpp"
#include "ck/utility/data_type.hpp"
#include "ck/utility/math_v2.hpp"
#include "ck/utility/get_id.hpp"
#include "ck/library/utility/device_memory.hpp"
using ck::int4_t;
TEST(Int4, BaseArithmetic)
{
int4_t a{1};
int4_t b{-2};
EXPECT_EQ(a + a, int4_t{2});
EXPECT_EQ(a - a, int4_t{0});
EXPECT_EQ(a + b, int4_t{-1});
EXPECT_EQ(a - b, int4_t{3});
EXPECT_EQ(a * a, int4_t{1});
EXPECT_EQ(a * b, int4_t{-2});
EXPECT_EQ(b * b, int4_t{4});
EXPECT_EQ(a / b, int4_t{0});
a = int4_t{4};
EXPECT_EQ(a / b, int4_t{-2});
b = int4_t{2};
EXPECT_EQ(a % b, int4_t{0});
}
TEST(Int4, NumericLimits)
{
EXPECT_EQ(ck::NumericLimits<int4_t>::Min(), int4_t{-8});
EXPECT_EQ(ck::NumericLimits<int4_t>::Max(), int4_t{7});
EXPECT_EQ(ck::NumericLimits<int4_t>::Lowest(), int4_t{-8});
}
TEST(Int4, MathOpsV2)
{
int4_t a{4};
int4_t b{-5};
EXPECT_EQ(ck::math::abs(a), int4_t{4});
EXPECT_EQ(ck::math::abs(b), int4_t{5});
EXPECT_FALSE(ck::math::isnan(b));
}
namespace {
__global__ void copy(const int4_t* src, std::int8_t* dst, ck::index_t N)
{
ck::index_t tid = ck::get_thread_global_1d_id();
const int8_t* src_i8 = reinterpret_cast<const int8_t*>(src);
if(tid < N)
{
for(ck::index_t i = tid; i < N; i += ck::get_grid_size())
{
dst[i] = src_i8[i];
}
}
}
__global__ void copy_with_static_cast(const int4_t* src, std::int8_t* dst, ck::index_t N)
{
ck::index_t tid = ck::get_thread_global_1d_id();
if(tid < N)
{
for(ck::index_t i = tid; i < N; i += ck::get_grid_size())
{
dst[i] = static_cast<std::int8_t>(src[i]);
}
}
}
} // anonymous namespace
TEST(Int4, CopyAsI8PositiveValue)
{
constexpr std::size_t SIZE = 100;
std::vector<int4_t> h_src_i4(SIZE, 7);
std::vector<std::int8_t> h_src_i8(SIZE, 7);
std::vector<std::int8_t> h_dst_i8(SIZE, 0);
DeviceMem d_src_i4(h_src_i4.size() * sizeof(int4_t));
DeviceMem d_dst_i8(h_dst_i8.size() * sizeof(std::int8_t));
d_src_i4.SetZero();
d_dst_i8.SetZero();
d_src_i4.ToDevice(h_src_i4.data());
copy<<<1, 64>>>(reinterpret_cast<const int4_t*>(d_src_i4.GetDeviceBuffer()),
reinterpret_cast<std::int8_t*>(d_dst_i8.GetDeviceBuffer()),
SIZE);
hip_check_error(hipDeviceSynchronize());
d_dst_i8.FromDevice(h_dst_i8.data());
for(std::size_t i = 0; i < SIZE; ++i)
{
EXPECT_EQ(h_src_i8[i], h_dst_i8[i]);
}
}
TEST(Int4, DISABLED_CopyAsI8NegativeValue)
{
constexpr std::size_t SIZE = 32;
std::vector<int4_t> h_src_i4(SIZE, -8);
std::vector<std::int8_t> h_src_i8(SIZE, -8);
std::vector<std::int8_t> h_dst_i8(SIZE, 0);
DeviceMem d_src_i4(h_src_i4.size() * sizeof(int4_t));
DeviceMem d_dst_i8(h_dst_i8.size() * sizeof(std::int8_t));
d_src_i4.SetZero();
d_dst_i8.SetZero();
d_src_i4.ToDevice(h_src_i4.data());
copy<<<1, 64>>>(reinterpret_cast<const int4_t*>(d_src_i4.GetDeviceBuffer()),
reinterpret_cast<std::int8_t*>(d_dst_i8.GetDeviceBuffer()),
SIZE);
hip_check_error(hipDeviceSynchronize());
d_dst_i8.FromDevice(h_dst_i8.data());
for(std::size_t i = 0; i < SIZE; ++i)
{
EXPECT_EQ(h_src_i8[i], h_dst_i8[i]);
}
}
TEST(Int4, CopyAsI8NegativeValueStaticCast)
{
constexpr std::size_t SIZE = 32;
std::vector<int4_t> h_src_i4(SIZE, -8);
std::vector<std::int8_t> h_src_i8(SIZE, -8);
std::vector<std::int8_t> h_dst_i8(SIZE, 0);
DeviceMem d_src_i4(h_src_i4.size() * sizeof(int4_t));
DeviceMem d_dst_i8(h_dst_i8.size() * sizeof(std::int8_t));
d_src_i4.SetZero();
d_dst_i8.SetZero();
d_src_i4.ToDevice(h_src_i4.data());
copy_with_static_cast<<<1, 64>>>(reinterpret_cast<const int4_t*>(d_src_i4.GetDeviceBuffer()),
reinterpret_cast<std::int8_t*>(d_dst_i8.GetDeviceBuffer()),
SIZE);
hip_check_error(hipDeviceSynchronize());
d_dst_i8.FromDevice(h_dst_i8.data());
for(std::size_t i = 0; i < SIZE; ++i)
{
EXPECT_EQ(h_src_i8[i], h_dst_i8[i]);
}
}
TEST(Int4, DISABLED_BitwiseRepresentation)
{
using bit8_t = std::bitset<8>;
int4_t a_i4{3};
std::int8_t a_i8 = *reinterpret_cast<std::int8_t*>(&a_i4);
std::int8_t b_i8{3};
#if 0
std::cout << std::hex << std::showbase << static_cast<int32_t>(a_i8)
<< ", " << static_cast<int32_t>(b_i8) << std::endl;
#endif
EXPECT_EQ(bit8_t{static_cast<std::uint64_t>(a_i8)}, bit8_t{static_cast<std::uint64_t>(b_i8)});
a_i4 = int4_t{-3};
a_i8 = *reinterpret_cast<std::int8_t*>(&a_i4);
b_i8 = std::int8_t{-3};
#if 0
std::cout << std::hex << std::showbase << static_cast<int32_t>(a_i8)
<< ", " << static_cast<int32_t>(b_i8) << std::endl;
#endif
EXPECT_EQ(bit8_t{static_cast<std::uint64_t>(a_i8)}, bit8_t{static_cast<std::uint64_t>(b_i8)});
}
TEST(Int4, BitwiseRepresentationStaticCast)
{
using bit8_t = std::bitset<8>;
int4_t a_i4{3};
std::int8_t a_i8 = static_cast<std::int8_t>(a_i4);
std::int8_t b_i8{3};
#if 0
std::cout << std::hex << std::showbase << static_cast<int32_t>(a_i8)
<< ", " << static_cast<int32_t>(b_i8) << std::endl;
#endif
EXPECT_EQ(bit8_t{static_cast<std::uint64_t>(a_i8)}, bit8_t{static_cast<std::uint64_t>(b_i8)});
a_i4 = int4_t{-3};
a_i8 = static_cast<std::int8_t>(a_i4);
b_i8 = std::int8_t{-3};
#if 0
std::cout << std::hex << std::showbase << static_cast<int32_t>(a_i8)
<< ", " << static_cast<int32_t>(b_i8) << std::endl;
#endif
EXPECT_EQ(bit8_t{static_cast<std::uint64_t>(a_i8)}, bit8_t{static_cast<std::uint64_t>(b_i8)});
}
test/gemm_split_k/gemm_split_k.cpp
View file @ b89a88b5
......@@ -8,7 +8,6 @@
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm_xdl_splitk.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/gemm_splitk.hpp"
......
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