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Commit 648f1f13 authored by Adam Osewski's avatar Adam Osewski
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Merge remote-tracking branch 'origin/develop' into aosewski/gemm_tile_loop

parents 4e5190f5 cb538740
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Showing with 615 additions and 150 deletions
library/src/tensor_operation_instance/gpu/normalization/CMakeLists.txt
View file @ 648f1f13
set(DEVICE_NORMALIZATION_INSTANCES)
if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
list(APPEND DEVICE_NORMALIZATION_INSTANCES device_layernorm2d_f16_instance.cpp
list(APPEND DEVICE_NORMALIZATION_INSTANCES
device_layernorm2d_f16_instance.cpp
device_layernorm4d_f16_instance.cpp
device_groupnorm_f16_instance.cpp
device_groupnorm_swish_f16_instance.cpp
device_groupnorm_swish_f16_f32_f32_f16_instance.cpp)
endif()
if(DTYPES MATCHES "fp32" OR NOT DEFINED DTYPES)
list(APPEND DEVICE_NORMALIZATION_INSTANCES device_layernorm2d_f32_instance.cpp
device_groupnorm_swish_f16_f32_f32_f16_instance.cpp
device_layernorm2d_f32_instance.cpp
device_layernorm4d_f32_instance.cpp
device_groupnorm_f32_instance.cpp
device_groupnorm_swish_f32_instance.cpp)
endif()
add_instance_library(device_normalization_instance ${DEVICE_NORMALIZATION_INSTANCES})
library/src/tensor_operation_instance/gpu/pool3d_fwd/CMakeLists.txt
View file @ 648f1f13
set(DEVICE_POOL3D_FWD_INSTANCES)
if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
list(APPEND DEVICE_POOL3D_FWD_INSTANCES device_avg_pool3d_fwd_ndhwc_f16_instance.cpp
device_max_pool3d_fwd_ndhwc_f16_instance.cpp)
endif()
if(DTYPES MATCHES "bf16" OR NOT DEFINED DTYPES)
list(APPEND DEVICE_POOL3D_FWD_INSTANCES device_avg_pool3d_fwd_ndhwc_bf16_instance.cpp
device_max_pool3d_fwd_ndhwc_bf16_instance.cpp)
endif()
if(DTYPES MATCHES "fp32" OR NOT DEFINED DTYPES)
list(APPEND DEVICE_POOL3D_FWD_INSTANCES device_avg_pool3d_fwd_ndhwc_f32_instance.cpp
device_max_pool3d_fwd_ndhwc_f32_instance.cpp)
endif()
list(APPEND DEVICE_POOL3D_FWD_INSTANCES device_avg_pool3d_fwd_ndhwc_f16_instance.cpp
device_max_pool3d_fwd_ndhwc_f16_instance.cpp
device_avg_pool3d_fwd_ndhwc_f32_instance.cpp
device_max_pool3d_fwd_ndhwc_f32_instance.cpp
device_avg_pool3d_fwd_ndhwc_bf16_instance.cpp
device_max_pool3d_fwd_ndhwc_bf16_instance.cpp)
add_instance_library(device_pool3d_fwd_instance ${DEVICE_POOL3D_FWD_INSTANCES})
library/src/tensor_operation_instance/gpu/quantization/CMakeLists.txt
View file @ 648f1f13
if(DTYPES MATCHES "int8" OR NOT DEFINED DTYPES)
set(CONV2D_PERLAYER_QUANT_SRC conv2d_fwd/device_conv2d_xdl_perlayer_quantization_int8_instance.cpp)
set(CONV2D_PERCHANNEL_QUANT_SRC conv2d_fwd/device_conv2d_xdl_perchannel_quantization_int8_instance.cpp)
set(CONV2D_BIAS_PERLAYER_QUANT_SRC conv2d_fwd/device_conv2d_xdl_bias_perlayer_quantization_int8_instance.cpp)
......@@ -10,17 +8,16 @@ set(GEMM_QUANT_SRC
gemm/device_gemm_quantization_xdl_c_shuffle_i8_i8_i8_mk_kn_mn_instance.cpp
gemm/device_gemm_quantization_xdl_c_shuffle_i8_i8_i8_mk_nk_mn_instance.cpp
)
if(DL_KERNELS)
list(APPEND CONV2D_PERLAYER_QUANT_SRC conv2d_fwd/device_conv2d_dl_perlayer_quantization_int8_instance.cpp)
list(APPEND CONV2D_PERCHANNEL_QUANT_SRC conv2d_fwd/device_conv2d_dl_perchannel_quantization_int8_instance.cpp)
list(APPEND CONV2D_BIAS_PERLAYER_QUANT_SRC conv2d_fwd/device_conv2d_dl_bias_perlayer_quantization_int8_instance.cpp)
list(APPEND CONV2D_BIAS_PERCHANNEL_QUANT_SRC conv2d_fwd/device_conv2d_dl_bias_perchannel_quantization_int8_instance.cpp)
list(APPEND GEMM_QUANT_SRC
gemm/device_gemm_quantization_dl_c_shuffle_i8_i8_i8_km_kn_mn_instance.cpp
gemm/device_gemm_quantization_dl_c_shuffle_i8_i8_i8_km_nk_mn_instance.cpp
gemm/device_gemm_quantization_dl_c_shuffle_i8_i8_i8_mk_kn_mn_instance.cpp
gemm/device_gemm_quantization_dl_c_shuffle_i8_i8_i8_mk_nk_mn_instance.cpp)
endif()
list(APPEND CONV2D_PERLAYER_QUANT_SRC conv2d_fwd/device_conv2d_dl_perlayer_quantization_int8_instance.cpp)
list(APPEND CONV2D_PERCHANNEL_QUANT_SRC conv2d_fwd/device_conv2d_dl_perchannel_quantization_int8_instance.cpp)
list(APPEND CONV2D_BIAS_PERLAYER_QUANT_SRC conv2d_fwd/device_conv2d_dl_bias_perlayer_quantization_int8_instance.cpp)
list(APPEND CONV2D_BIAS_PERCHANNEL_QUANT_SRC conv2d_fwd/device_conv2d_dl_bias_perchannel_quantization_int8_instance.cpp)
list(APPEND GEMM_QUANT_SRC
gemm/device_gemm_quantization_dl_c_shuffle_i8_i8_i8_km_kn_mn_instance.cpp
gemm/device_gemm_quantization_dl_c_shuffle_i8_i8_i8_km_nk_mn_instance.cpp
gemm/device_gemm_quantization_dl_c_shuffle_i8_i8_i8_mk_kn_mn_instance.cpp
gemm/device_gemm_quantization_dl_c_shuffle_i8_i8_i8_mk_nk_mn_instance.cpp)
add_instance_library(device_quantization_instance
${CONV2D_PERLAYER_QUANT_SRC}
......@@ -29,4 +26,3 @@ add_instance_library(device_quantization_instance
${CONV2D_BIAS_PERCHANNEL_QUANT_SRC}
${GEMM_QUANT_SRC}
)
endif()
\ No newline at end of file
library/src/tensor_operation_instance/gpu/softmax/CMakeLists.txt
View file @ 648f1f13
set(DEVICE_SOFTMAX_INSTANCES)
if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
list(APPEND DEVICE_SOFTMAX_INSTANCES device_softmax_f16_f16_instance_rank3_reduce1.cpp
list(APPEND DEVICE_SOFTMAX_INSTANCES
device_softmax_f16_f16_instance_rank3_reduce1.cpp
device_softmax_f16_f16_instance_rank3_reduce2.cpp
device_softmax_f16_f16_instance_rank3_reduce3.cpp
device_softmax_f16_f16_instance_rank4_reduce1.cpp
device_softmax_f16_f16_instance_rank4_reduce2.cpp
device_softmax_f16_f16_instance_rank4_reduce3.cpp
device_softmax_f16_f16_instance_rank4_reduce4.cpp)
endif()
if(DTYPES MATCHES "fp32" OR NOT DEFINED DTYPES)
list(APPEND DEVICE_SOFTMAX_INSTANCES device_softmax_f32_f32_instance_rank3_reduce1.cpp
device_softmax_f16_f16_instance_rank4_reduce4.cpp
device_softmax_f32_f32_instance_rank3_reduce1.cpp
device_softmax_f32_f32_instance_rank3_reduce2.cpp
device_softmax_f32_f32_instance_rank3_reduce3.cpp
device_softmax_f32_f32_instance_rank4_reduce1.cpp
device_softmax_f32_f32_instance_rank4_reduce2.cpp
device_softmax_f32_f32_instance_rank4_reduce3.cpp
device_softmax_f32_f32_instance_rank4_reduce4.cpp)
endif()
add_instance_library(device_softmax_instance ${DEVICE_SOFTMAX_INSTANCES})
profiler/README.md
View file @ 648f1f13
......@@ -185,7 +185,7 @@ GB/s: 69.2301
```
Note: This kernel use atomic add, this will cause output buffer to be accumulated multiple times, causing verification failure. To work around it, do not use CK's own timer and do verification at the same time.
## Profile image to column kernels
## Profile image to column/column to image kernels
```bash
# arg1: tensor operation (" OP_NAME ": " OP_DESC ")
# arg2: data type (0: Input fp32, Weight fp32, Output fp32
......@@ -197,6 +197,7 @@ Note: This kernel use atomic add, this will cause output buffer to be accumulate
# arg5: initialization (0: no init, 1: integer value, 2: decimal value)
# arg6: print tensor value (0: no; 1: yes)
# arg7: time kernel (0: no, 1: yes)
# arg8: operation type (0: ImageToColumn, 1: ColumnToImage)
# Following arguments (depending on number of spatial dims):
# Number of spatial dimensions (1=Conv1d, 2=Conv2d, 3=Conv3d)
# G, N, K, C,
......@@ -207,8 +208,8 @@ Note: This kernel use atomic add, this will cause output buffer to be accumulate
# <left padding>, (ie LeftPy, LeftPx for 2D)
# <right padding>, (ie RightPy, RightPx for 2D)
################ op datatype layout verify init log time Ndims G N K C Y X Hi Wi Sy Sx Dy Dx LeftPy LeftPx RightPy RightPx
./bin/ckProfiler image_to_column 0 0 1 1 0 1 2 1 256 1 512 3 3 28 28 1 1 1 1 0 0 0 0
################ op datatype layout verify init log time opType Ndims G N K C Y X Hi Wi Sy Sx Dy Dx LeftPy LeftPx RightPy RightPx
./bin/ckProfiler conv_tensor_rearrange 0 0 0 1 0 1 0 2 1 256 1 512 3 3 28 28 1 1 1 1 0 0 0 0
```
......@@ -222,3 +223,4 @@ name: DeviceImageToColumn<128, 32, 64, 4>
avg_time: 3.12326
GB/s: 2042.59
```
Note: Column to image kernel adds to the output memory, this will cause output buffer to be accumulated multiple times, causing verification failure. To work around it, do not use CK's own timer and do verification at the same time.
profiler/include/profiler/profile_image_to_column_impl.hpp → profiler/include/profiler/profile_conv_tensor_rearrange_impl.hpp
View file @ 648f1f13
......@@ -9,9 +9,11 @@
#include <limits>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_image_to_column.hpp"
#include "ck/tensor_operation/gpu/device/device_conv_tensor_rearrange.hpp"
#include "ck/tensor_operation/gpu/device/conv_tensor_rearrange_op.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_image_to_column_impl.hpp"
#include "ck/library/tensor_operation_instance/gpu/image_to_column.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_column_to_image_impl.hpp"
#include "ck/library/tensor_operation_instance/gpu/conv_tensor_rearrange.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
......@@ -19,22 +21,88 @@
#include "ck/library/utility/convolution_parameter.hpp"
#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_image_to_column.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_column_to_image.hpp"
namespace ck {
namespace profiler {
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using namespace conv_tensor_rearrange_op;
template <typename InputDataType, typename ConvTensorRearrangeOp>
Tensor<InputDataType> create_input(const HostTensorDescriptor& image_desc,
const HostTensorDescriptor& gemm_desc)
{
if constexpr(std::is_same_v<ConvTensorRearrangeOp, ImageToColumn>)
{
Tensor<InputDataType> input(image_desc);
return input;
}
else if constexpr(std::is_same_v<ConvTensorRearrangeOp, ColumnToImage>)
{
Tensor<InputDataType> input(gemm_desc);
return input;
}
else
{
throw std::runtime_error("Unsupported op!");
}
}
template <typename OutputDataType, typename ConvTensorRearrangeOp>
Tensor<OutputDataType> create_output(const HostTensorDescriptor& image_desc,
const HostTensorDescriptor& gemm_desc)
{
if constexpr(std::is_same_v<ConvTensorRearrangeOp, ImageToColumn>)
{
Tensor<OutputDataType> output(gemm_desc);
return output;
}
else if constexpr(std::is_same_v<ConvTensorRearrangeOp, ColumnToImage>)
{
Tensor<OutputDataType> output(image_desc);
return output;
}
else
{
throw std::runtime_error("Unsupported op!");
}
}
template <index_t NDimSpatial,
typename InputLayout,
typename InputDataType,
typename OutputDataType,
typename ConvTensorRearrangeOp>
static auto make_ref_op()
{
if constexpr(std::is_same_v<ConvTensorRearrangeOp, ImageToColumn>)
{
return ck::tensor_operation::host::
ReferenceImageToColumn<NDimSpatial, InputLayout, InputDataType, OutputDataType>{};
}
else if constexpr(std::is_same_v<ConvTensorRearrangeOp, ColumnToImage>)
{
return ck::tensor_operation::host::
ReferenceColumnToImage<NDimSpatial, InputLayout, InputDataType, OutputDataType>{};
}
else
{
throw std::runtime_error("Unsupported op!");
}
}
template <index_t NDimSpatial,
typename InputLayout,
typename InputDataType,
typename OutputDataType>
bool profile_image_to_column_impl(int do_verification,
int init_method,
bool do_log,
bool time_kernel,
const ck::utils::conv::ConvParam& conv_param)
typename OutputDataType,
typename ConvTensorRearrangeOp>
bool profile_conv_tensor_rearrange_impl(int do_verification,
int init_method,
bool do_log,
bool time_kernel,
const ck::utils::conv::ConvParam& conv_param)
{
const ck::index_t NDoHoWo =
conv_param.N_ *
......@@ -45,16 +113,16 @@ bool profile_image_to_column_impl(int do_verification,
ck::accumulate_n<ck::index_t>(
conv_param.filter_spatial_lengths_.begin(), NDimSpatial, 1, std::multiplies<>());
const auto in_desc =
const auto image_desc =
ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InputLayout>(
conv_param);
const auto out_desc = HostTensorDescriptor({NDoHoWo, CZYX});
const auto gemm_desc = HostTensorDescriptor({NDoHoWo, CZYX});
std::array<ck::index_t, NDimSpatial> input_spatial_lengths{};
std::array<ck::index_t, NDimSpatial> filter_spatial_lengths{};
std::array<ck::index_t, NDimSpatial> output_spatial_lengths{};
std::array<ck::index_t, NDimSpatial + 3> input_g_n_c_wis_strides{};
std::array<ck::index_t, 2> output_m_k_strides{};
std::array<ck::index_t, NDimSpatial + 3> image_g_n_c_wis_strides{};
std::array<ck::index_t, 2> gemm_m_k_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
std::array<ck::index_t, NDimSpatial> input_left_pads{};
......@@ -65,16 +133,19 @@ bool profile_image_to_column_impl(int do_verification,
copy(conv_param.input_spatial_lengths_, input_spatial_lengths);
copy(conv_param.filter_spatial_lengths_, filter_spatial_lengths);
copy(conv_param.output_spatial_lengths_, output_spatial_lengths);
copy(in_desc.GetStrides(), input_g_n_c_wis_strides);
copy(out_desc.GetStrides(), output_m_k_strides);
copy(image_desc.GetStrides(), image_g_n_c_wis_strides);
copy(gemm_desc.GetStrides(), gemm_m_k_strides);
copy(conv_param.conv_filter_strides_, conv_filter_strides);
copy(conv_param.conv_filter_dilations_, conv_filter_dilations);
copy(conv_param.input_left_pads_, input_left_pads);
copy(conv_param.input_right_pads_, input_right_pads);
Tensor<InputDataType> input(in_desc);
Tensor<OutputDataType> host_output(out_desc);
Tensor<OutputDataType> device_output(out_desc);
Tensor<InputDataType> input =
create_input<InputDataType, ConvTensorRearrangeOp>(image_desc, gemm_desc);
Tensor<OutputDataType> device_output =
create_output<OutputDataType, ConvTensorRearrangeOp>(image_desc, gemm_desc);
Tensor<OutputDataType> host_output =
create_output<OutputDataType, ConvTensorRearrangeOp>(image_desc, gemm_desc);
std::cout << "input: " << input.mDesc << std::endl;
std::cout << "output: " << host_output.mDesc << std::endl;
......@@ -94,17 +165,21 @@ bool profile_image_to_column_impl(int do_verification,
// run reference op
if(do_verification)
{
auto ref_image_to_column = ck::tensor_operation::host::
ReferenceImageToColumn<NDimSpatial, InputLayout, InputDataType, OutputDataType>{};
auto ref_conv_tensor_rearrange = make_ref_op<NDimSpatial,
InputLayout,
InputDataType,
OutputDataType,
ConvTensorRearrangeOp>();
auto ref_invoker = ref_image_to_column.MakeInvoker();
auto ref_argument = ref_image_to_column.MakeArgument(input,
host_output,
conv_param.filter_spatial_lengths_,
conv_param.conv_filter_strides_,
conv_param.conv_filter_dilations_,
conv_param.input_left_pads_,
conv_param.input_right_pads_);
auto ref_invoker = ref_conv_tensor_rearrange.MakeInvoker();
auto ref_argument =
ref_conv_tensor_rearrange.MakeArgument(input,
host_output,
conv_param.filter_spatial_lengths_,
conv_param.conv_filter_strides_,
conv_param.conv_filter_dilations_,
conv_param.input_left_pads_,
conv_param.input_right_pads_);
// init host output to zero
host_output.SetZero();
......@@ -112,8 +187,11 @@ bool profile_image_to_column_impl(int do_verification,
ref_invoker.Run(ref_argument);
}
using DeviceOp = ck::tensor_operation::device::
DeviceImageToColumn<NDimSpatial, InputLayout, InputDataType, OutputDataType>;
using DeviceOp = ck::tensor_operation::device::DeviceConvTensorRearrange<NDimSpatial,
InputLayout,
InputDataType,
OutputDataType,
ConvTensorRearrangeOp>;
// get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
......@@ -139,8 +217,8 @@ bool profile_image_to_column_impl(int do_verification,
input_spatial_lengths,
filter_spatial_lengths,
output_spatial_lengths,
input_g_n_c_wis_strides,
output_m_k_strides,
image_g_n_c_wis_strides,
gemm_m_k_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
......
profiler/include/profiler/profile_gemm_impl.hpp
View file @ 648f1f13
......@@ -223,6 +223,12 @@ int profile_gemm_impl(int do_verification,
{
std::cout << "Best Perf for datatype = int8";
}
#if defined CK_ENABLE_FP8
else if constexpr(is_same<CDataType, f8_t>::value)
{
std::cout << "Best Perf for datatype = fp8";
}
#endif
if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value)
{
......
profiler/src/CMakeLists.txt
View file @ 648f1f13
......@@ -28,7 +28,7 @@ set(PROFILER_SOURCES
profile_contraction_bilinear.cpp
profile_contraction_scale.cpp
profile_grouped_conv_bwd_data.cpp
profile_image_to_column.cpp
profile_conv_tensor_rearrange.cpp
)
if(DL_KERNELS)
list(APPEND PROFILER_SOURCES profile_batched_gemm_multi_d.cpp)
......@@ -84,6 +84,7 @@ target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_max_pool_bwd_instanc
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_bwd_data_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_bwd_data_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_image_to_column_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_column_to_image_instance)
if(DL_KERNELS)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_multi_d_instance)
endif()
......
profiler/src/profile_image_to_column.cpp → profiler/src/profile_conv_tensor_rearrange.cpp
View file @ 648f1f13
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "profiler/profile_image_to_column_impl.hpp"
#include "profiler/profile_conv_tensor_rearrange_impl.hpp"
#include "profiler_operation_registry.hpp"
namespace {
enum struct RearrangeOp
{
ImageToColumn, // 0
ColumnToImage, // 1
};
enum struct ConvLayout
{
NHWC, // 0
......@@ -24,8 +30,8 @@ enum struct DataType
INT8_INT8, // 3
};
#define OP_NAME "image_to_column"
#define OP_DESC "Image To Column"
#define OP_NAME "conv_tensor_rearrange"
#define OP_DESC "Conv Tensor Rearrange"
static void print_helper_msg()
{
......@@ -41,16 +47,17 @@ static void print_helper_msg()
<< "arg5: initialization (0: no init, 1: integer value, 2: decimal value)\n"
<< "arg6: print tensor value (0: no; 1: yes)\n"
<< "arg7: time kernel (0: no, 1: yes)\n"
<< "arg8: operation type (0: ImageToColumn, 1: ColumnToImage)\n"
<< ck::utils::conv::get_conv_param_parser_helper_msg() << std::endl;
// clang-format on
}
} // namespace
int profile_image_to_column(int argc, char* argv[])
int profile_conv_tensor_rearrange(int argc, char* argv[])
{
// 8 for control, 1 for num_dim_spatial
if(argc < 9)
// 9 for control, 1 for num_dim_spatial
if(argc < 10)
{
print_helper_msg();
return 1;
......@@ -62,16 +69,17 @@ int profile_image_to_column(int argc, char* argv[])
const int init_method = std::stoi(argv[5]);
const bool do_log = std::stoi(argv[6]);
const bool time_kernel = std::stoi(argv[7]);
const int num_dim_spatial = std::stoi(argv[8]);
const auto rearrange_op = static_cast<RearrangeOp>(std::stoi(argv[8]));
const int num_dim_spatial = std::stoi(argv[9]);
// 8 for control, 1 for num_dim_spatial, 4 for G/N/K/C, and 6 * num_dim_spatial
if(argc != 8 + 1 + 4 + 6 * num_dim_spatial)
// 9 for control, 1 for num_dim_spatial, 4 for G/N/K/C, and 6 * num_dim_spatial
if(argc != 9 + 1 + 4 + 6 * num_dim_spatial)
{
print_helper_msg();
return 1;
}
const auto params = ck::utils::conv::parse_conv_param(num_dim_spatial, 9, argv);
const auto params = ck::utils::conv::parse_conv_param(num_dim_spatial, 10, argv);
using F32 = float;
using F16 = ck::half_t;
......@@ -79,12 +87,17 @@ int profile_image_to_column(int argc, char* argv[])
using INT8 = int8_t;
using namespace ck::tensor_layout::convolution;
using namespace ck::conv_tensor_rearrange_op;
constexpr auto I1 = ck::Number<1>{};
constexpr auto I2 = ck::Number<2>{};
constexpr auto I3 = ck::Number<3>{};
auto profile = [&](auto num_dim_spatial_tmp, auto in_layout, auto in_type, auto out_type) {
auto profile = [&](auto num_dim_spatial_tmp,
auto in_layout,
auto in_type,
auto out_type,
auto rearrange_op_type) {
constexpr ck::index_t NDimSpatial = num_dim_spatial_tmp.value;
using InLayout = decltype(in_layout);
......@@ -92,78 +105,147 @@ int profile_image_to_column(int argc, char* argv[])
using InDataType = decltype(in_type);
using OutDataType = decltype(out_type);
using Op = decltype(rearrange_op_type);
bool pass = ck::profiler::
profile_image_to_column_impl<NDimSpatial, InLayout, InDataType, OutDataType>(
profile_conv_tensor_rearrange_impl<NDimSpatial, InLayout, InDataType, OutDataType, Op>(
do_verification, init_method, do_log, time_kernel, params);
return pass ? 0 : 1;
};
// NHWC
if(layout == ConvLayout::NHWC)
// Image To Column
if(rearrange_op == RearrangeOp::ImageToColumn)
{
if(num_dim_spatial == 1)
// NHWC
if(layout == ConvLayout::NHWC)
{
if(data_type == DataType::F32_F32)
{
return profile(I1, GNWC{}, F32{}, F32{});
}
else if(data_type == DataType::F16_F16)
if(num_dim_spatial == 1)
{
return profile(I1, GNWC{}, F16{}, F16{});
if(data_type == DataType::F32_F32)
{
return profile(I1, GNWC{}, F32{}, F32{}, ImageToColumn{});
}
else if(data_type == DataType::F16_F16)
{
return profile(I1, GNWC{}, F16{}, F16{}, ImageToColumn{});
}
else if(data_type == DataType::BF16_BF16)
{
return profile(I1, GNWC{}, BF16{}, BF16{}, ImageToColumn{});
}
else if(data_type == DataType::INT8_INT8)
{
return profile(I1, GNWC{}, INT8{}, INT8{}, ImageToColumn{});
}
}
else if(data_type == DataType::BF16_BF16)
else if(num_dim_spatial == 2)
{
return profile(I1, GNWC{}, BF16{}, BF16{});
if(data_type == DataType::F32_F32)
{
return profile(I2, GNHWC{}, F32{}, F32{}, ImageToColumn{});
}
else if(data_type == DataType::F16_F16)
{
return profile(I2, GNHWC{}, F16{}, F16{}, ImageToColumn{});
}
else if(data_type == DataType::BF16_BF16)
{
return profile(I2, GNHWC{}, BF16{}, BF16{}, ImageToColumn{});
}
else if(data_type == DataType::INT8_INT8)
{
return profile(I2, GNHWC{}, INT8{}, INT8{}, ImageToColumn{});
}
}
else if(data_type == DataType::INT8_INT8)
else if(num_dim_spatial == 3)
{
return profile(I1, GNWC{}, INT8{}, INT8{});
if(data_type == DataType::F32_F32)
{
return profile(I3, GNDHWC{}, F32{}, F32{}, ImageToColumn{});
}
else if(data_type == DataType::F16_F16)
{
return profile(I3, GNDHWC{}, F16{}, F16{}, ImageToColumn{});
}
else if(data_type == DataType::BF16_BF16)
{
return profile(I3, GNDHWC{}, BF16{}, BF16{}, ImageToColumn{});
}
else if(data_type == DataType::INT8_INT8)
{
return profile(I3, GNDHWC{}, INT8{}, INT8{}, ImageToColumn{});
}
}
}
else if(num_dim_spatial == 2)
{
if(data_type == DataType::F32_F32)
{
return profile(I2, GNHWC{}, F32{}, F32{});
}
else if(data_type == DataType::F16_F16)
{
return profile(I2, GNHWC{}, F16{}, F16{});
}
else if(data_type == DataType::BF16_BF16)
{
return profile(I2, GNHWC{}, BF16{}, BF16{});
}
else if(data_type == DataType::INT8_INT8)
{
return profile(I2, GNHWC{}, INT8{}, INT8{});
}
}
else if(num_dim_spatial == 3)
}
else if(rearrange_op == RearrangeOp::ColumnToImage)
{
// NHWC
if(layout == ConvLayout::NHWC)
{
if(data_type == DataType::F32_F32)
if(num_dim_spatial == 1)
{
return profile(I3, GNDHWC{}, F32{}, F32{});
if(data_type == DataType::F32_F32)
{
return profile(I1, GNWC{}, F32{}, F32{}, ColumnToImage{});
}
else if(data_type == DataType::F16_F16)
{
return profile(I1, GNWC{}, F16{}, F16{}, ColumnToImage{});
}
else if(data_type == DataType::BF16_BF16)
{
return profile(I1, GNWC{}, BF16{}, BF16{}, ColumnToImage{});
}
else if(data_type == DataType::INT8_INT8)
{
return profile(I1, GNWC{}, INT8{}, INT8{}, ColumnToImage{});
}
}
else if(data_type == DataType::F16_F16)
else if(num_dim_spatial == 2)
{
return profile(I3, GNDHWC{}, F16{}, F16{});
if(data_type == DataType::F32_F32)
{
return profile(I2, GNHWC{}, F32{}, F32{}, ColumnToImage{});
}
else if(data_type == DataType::F16_F16)
{
return profile(I2, GNHWC{}, F16{}, F16{}, ColumnToImage{});
}
else if(data_type == DataType::BF16_BF16)
{
return profile(I2, GNHWC{}, BF16{}, BF16{}, ColumnToImage{});
}
else if(data_type == DataType::INT8_INT8)
{
return profile(I2, GNHWC{}, INT8{}, INT8{}, ColumnToImage{});
}
}
else if(data_type == DataType::BF16_BF16)
else if(num_dim_spatial == 3)
{
return profile(I3, GNDHWC{}, BF16{}, BF16{});
}
else if(data_type == DataType::INT8_INT8)
{
return profile(I3, GNDHWC{}, INT8{}, INT8{});
if(data_type == DataType::F32_F32)
{
return profile(I3, GNDHWC{}, F32{}, F32{}, ColumnToImage{});
}
else if(data_type == DataType::F16_F16)
{
return profile(I3, GNDHWC{}, F16{}, F16{}, ColumnToImage{});
}
else if(data_type == DataType::BF16_BF16)
{
return profile(I3, GNDHWC{}, BF16{}, BF16{}, ColumnToImage{});
}
else if(data_type == DataType::INT8_INT8)
{
return profile(I3, GNDHWC{}, INT8{}, INT8{}, ColumnToImage{});
}
}
}
}
std::cout << "this data_type & layout is not implemented" << std::endl;
return 1;
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_image_to_column);
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_conv_tensor_rearrange);
profiler/src/profile_gemm.cpp
View file @ 648f1f13
......@@ -23,6 +23,7 @@ enum struct GemmDataType
F16_F16_F16, // 1
BF16_BF16_BF16, // 2
INT8_INT8_INT8, // 3
F8_F8_F8, // 4
};
#define OP_NAME "gemm"
......@@ -31,7 +32,7 @@ enum struct GemmDataType
static void print_helper_msg()
{
std::cout << "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"
<< "arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8)\n"
<< "arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8; 4: fp8)\n"
<< "arg3: matrix layout (0: A[m, k] * B[k, n] = C[m, n];\n"
<< " 1: A[m, k] * B[n, k] = C[m, n];\n"
<< " 2: A[k, m] * B[k, n] = C[m, n];\n"
......@@ -76,6 +77,9 @@ int profile_gemm(int argc, char* argv[])
using INT8 = int8_t;
using INT32 = int32_t;
#endif
#ifdef CK_ENABLE_FP8
using F8 = ck::f8_t;
#endif
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
......@@ -194,6 +198,24 @@ int profile_gemm(int argc, char* argv[])
{
return profile(Col{}, Col{}, Row{}, INT8{}, INT8{}, INT32{}, INT8{});
}
#endif
#ifdef CK_ENABLE_FP8
else if(data_type == GemmDataType::F8_F8_F8 && layout == GemmMatrixLayout::MK_KN_MN)
{
return profile(Row{}, Row{}, Row{}, F8{}, F8{}, F32{}, F8{});
}
else if(data_type == GemmDataType::F8_F8_F8 && layout == GemmMatrixLayout::MK_NK_MN)
{
return profile(Row{}, Col{}, Row{}, F8{}, F8{}, F32{}, F8{});
}
else if(data_type == GemmDataType::F8_F8_F8 && layout == GemmMatrixLayout::KM_KN_MN)
{
return profile(Col{}, Row{}, Row{}, F8{}, F8{}, F32{}, F8{});
}
else if(data_type == GemmDataType::F8_F8_F8 && layout == GemmMatrixLayout::KM_NK_MN)
{
return profile(Col{}, Col{}, Row{}, F8{}, F8{}, F32{}, F8{});
}
#endif
else
{
......
test/CMakeLists.txt
View file @ 648f1f13
......@@ -9,26 +9,121 @@ add_custom_target(tests)
function(add_test_executable TEST_NAME)
message("adding test ${TEST_NAME}")
add_executable(${TEST_NAME} ${ARGN})
add_test(NAME ${TEST_NAME} COMMAND $<TARGET_FILE:${TEST_NAME}>)
add_dependencies(tests ${TEST_NAME})
add_dependencies(check ${TEST_NAME})
rocm_install(TARGETS ${TEST_NAME} COMPONENT tests)
set(result 1)
if(DEFINED DTYPES)
foreach(source IN LISTS ARGN)
set(test 0)
foreach(type IN LISTS DTYPES)
if(type MATCHES "fp16")
set(type1 "_f16")
elseif(type MATCHES "fp32")
set(type1 "_f32")
elseif(type MATCHES "fp8")
set(type1 "_f8")
elseif(type MATCHES "bf16")
set(type1 "_b16")
elseif(type MATCHES "fp64")
set(type1 "_f64")
elseif(type MATCHES "int8")
set(type1 "_i8")
endif()
if("${source}" MATCHES "${type}" OR "${source}" MATCHES "${type1}")
#if filename matches any selected type, exit type loop and do no exclude the file from the list
set(test 0)
break()
elseif((source MATCHES "fp8" OR source MATCHES "fp32" OR source MATCHES "fp64" OR source MATCHES "bf16" OR source MATCHES "int8" OR source MATCHES "fp16" OR
source MATCHES "_f8" OR source MATCHES "_f32" OR source MATCHES "_f64" OR source MATCHES "_i8" OR source MATCHES "_f16" OR source MATCHES "_b16") AND
NOT(source MATCHES type OR source MATCHES type1))
#if filename contains a type which doesn't match any selected type, mark it for removal
set(test 1)
endif()
endforeach()
if(test EQUAL 1)
message("removing test ${source} ")
list(REMOVE_ITEM ARGN "${source}")
endif()
endforeach()
endif()
foreach(source IN LISTS ARGN)
if(NOT DEFINED DL_KERNELS AND source MATCHES "_dl")
message("removing dl test ${source} ")
list(REMOVE_ITEM ARGN "${source}")
endif()
endforeach()
#only continue if there are some source files left on the list
if(ARGN)
add_executable(${TEST_NAME} ${ARGN})
add_test(NAME ${TEST_NAME} COMMAND $<TARGET_FILE:${TEST_NAME}>)
add_dependencies(tests ${TEST_NAME})
add_dependencies(check ${TEST_NAME})
rocm_install(TARGETS ${TEST_NAME} COMPONENT tests)
set(result 0)
endif()
#message("add_test returns ${result}")
return(PROPAGATE result)
endfunction(add_test_executable TEST_NAME)
include(GoogleTest)
function(add_gtest_executable TEST_NAME)
message("adding gtest ${TEST_NAME}")
add_executable(${TEST_NAME} ${ARGN})
add_dependencies(tests ${TEST_NAME})
add_dependencies(check ${TEST_NAME})
set(result 1)
if(DEFINED DTYPES)
foreach(source IN LISTS ARGN)
set(test 0)
foreach(type IN LISTS DTYPES)
if(type MATCHES "fp16")
set(type1 "_f16")
elseif(type MATCHES "fp32")
set(type1 "_f32")
elseif(type MATCHES "fp8")
set(type1 "_f8")
elseif(type MATCHES "bf16")
set(type1 "_b16")
elseif(type MATCHES "fp64")
set(type1 "_f64")
elseif(type MATCHES "int8")
set(type1 "_i8")
endif()
if("${source}" MATCHES "${type}" OR "${source}" MATCHES "${type1}")
#if filename matches any selected type, exit type loop and do no exclude the file from the list
set(test 0)
break()
elseif((source MATCHES "fp8" OR source MATCHES "fp32" OR source MATCHES "fp64" OR source MATCHES "bf16" OR source MATCHES "int8" OR source MATCHES "fp16" OR
source MATCHES "_f8" OR source MATCHES "_f32" OR source MATCHES "_f64" OR source MATCHES "_i8" OR source MATCHES "_f16" OR source MATCHES "_b16") AND
NOT(source MATCHES type OR source MATCHES type1))
#if filename contains a type which doesn't match any selected type, mark it for removal
set(test 1)
endif()
endforeach()
if(test EQUAL 1)
message("removing gtest ${source} ")
list(REMOVE_ITEM ARGN "${source}")
endif()
endforeach()
endif()
foreach(source IN LISTS ARGN)
if(NOT DEFINED DL_KERNELS AND source MATCHES "_dl")
message("removing dl test ${source} ")
list(REMOVE_ITEM ARGN "${source}")
endif()
endforeach()
#only continue if there are some source files left on the list
if(ARGN)
add_executable(${TEST_NAME} ${ARGN})
add_dependencies(tests ${TEST_NAME})
add_dependencies(check ${TEST_NAME})
# suppress gtest warnings
target_compile_options(${TEST_NAME} PRIVATE -Wno-global-constructors -Wno-undef)
target_link_libraries(${TEST_NAME} PRIVATE gtest_main)
add_test(NAME ${TEST_NAME} COMMAND $<TARGET_FILE:${TEST_NAME}>)
rocm_install(TARGETS ${TEST_NAME} COMPONENT tests)
# suppress gtest warnings
target_compile_options(${TEST_NAME} PRIVATE -Wno-global-constructors -Wno-undef)
target_link_libraries(${TEST_NAME} PRIVATE gtest_main)
add_test(NAME ${TEST_NAME} COMMAND $<TARGET_FILE:${TEST_NAME}>)
rocm_install(TARGETS ${TEST_NAME} COMPONENT tests)
set(result 0)
endif()
#message("add_gtest returns ${result}")
return(PROPAGATE result)
endfunction(add_gtest_executable TEST_NAME)
add_subdirectory(magic_number_division)
......@@ -60,7 +155,7 @@ add_subdirectory(contraction)
add_subdirectory(pool)
add_subdirectory(batched_gemm_multi_d)
add_subdirectory(grouped_convnd_bwd_data)
add_subdirectory(image_to_column)
add_subdirectory(conv_tensor_rearrange)
if(GPU_TARGETS MATCHES "gfx11")
add_subdirectory(wmma_op)
endif()
test/batched_gemm/CMakeLists.txt
View file @ 648f1f13
......@@ -2,25 +2,21 @@ list(APPEND gpu_list gfx908 gfx90a gfx940 gfx941 gfx942)
set(target 0)
foreach(gpu IN LISTS GPU_TARGETS)
if(gpu IN_LIST gpu_list AND target EQUAL 0)
if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
add_test_executable(test_batched_gemm_fp16 batched_gemm_fp16.cpp)
target_link_libraries(test_batched_gemm_fp16 PRIVATE utility)
target_link_libraries(test_batched_gemm_fp16 PRIVATE device_batched_gemm_instance)
add_test_executable(test_batched_gemm_fp16 batched_gemm_fp16.cpp)
if(result EQUAL 0)
target_link_libraries(test_batched_gemm_fp16 PRIVATE utility device_batched_gemm_instance)
endif()
if(DTYPES MATCHES "fp32" OR NOT DEFINED DTYPES)
add_test_executable(test_batched_gemm_fp32 batched_gemm_fp32.cpp)
target_link_libraries(test_batched_gemm_fp32 PRIVATE utility)
target_link_libraries(test_batched_gemm_fp32 PRIVATE device_batched_gemm_instance)
add_test_executable(test_batched_gemm_fp32 batched_gemm_fp32.cpp)
if(result EQUAL 0)
target_link_libraries(test_batched_gemm_fp32 PRIVATE utility device_batched_gemm_instance)
endif()
if(DTYPES MATCHES "bf16" OR NOT DEFINED DTYPES)
add_test_executable(test_batched_gemm_bf16 batched_gemm_bf16.cpp)
target_link_libraries(test_batched_gemm_bf16 PRIVATE utility)
target_link_libraries(test_batched_gemm_bf16 PRIVATE device_batched_gemm_instance)
add_test_executable(test_batched_gemm_bf16 batched_gemm_bf16.cpp)
if(result EQUAL 0)
target_link_libraries(test_batched_gemm_bf16 PRIVATE utility device_batched_gemm_instance)
endif()
if(DTYPES MATCHES "int8" OR NOT DEFINED DTYPES)
add_test_executable(test_batched_gemm_int8 batched_gemm_int8.cpp)
target_link_libraries(test_batched_gemm_int8 PRIVATE utility)
target_link_libraries(test_batched_gemm_int8 PRIVATE device_batched_gemm_instance)
add_test_executable(test_batched_gemm_int8 batched_gemm_int8.cpp)
if(result EQUAL 0)
target_link_libraries(test_batched_gemm_int8 PRIVATE utility device_batched_gemm_instance)
endif()
set(target 1)
endif()
......
test/batched_gemm_gemm/CMakeLists.txt
View file @ 648f1f13
......@@ -2,12 +2,12 @@ list(APPEND gpu_list gfx908 gfx90a gfx940 gfx941 gfx942)
set(target 0)
foreach(gpu IN LISTS GPU_TARGETS)
if(gpu IN_LIST gpu_list AND target EQUAL 0)
if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
add_custom_target(test_batched_gemm_gemm)
add_gtest_executable(test_batched_gemm_gemm_fp16 test_batched_gemm_gemm_fp16.cpp)
add_custom_target(test_batched_gemm_gemm)
add_gtest_executable(test_batched_gemm_gemm_fp16 test_batched_gemm_gemm_fp16.cpp)
if(result EQUAL 0)
target_link_libraries(test_batched_gemm_gemm_fp16 PRIVATE utility device_batched_gemm_gemm_instance)
add_dependencies(test_batched_gemm_gemm test_batched_gemm_gemm_fp16)
set(target 1)
endif()
endif()
endif()
endforeach()
\ No newline at end of file
test/batched_gemm_multi_d/CMakeLists.txt
View file @ 648f1f13
if(DL_KERNELS)
add_gtest_executable(test_batched_gemm_multi_d test_batched_gemm_multi_d.cpp)
add_gtest_executable(test_batched_gemm_multi_d test_batched_gemm_multi_d_dl.cpp)
if(result EQUAL 0)
target_link_libraries(test_batched_gemm_multi_d PRIVATE utility device_batched_gemm_multi_d_instance)
endif()
test/batched_gemm_reduce/CMakeLists.txt
View file @ 648f1f13
......@@ -2,10 +2,9 @@ list(APPEND gpu_list gfx908 gfx90a gfx940 gfx941 gfx942)
set(target 0)
foreach(gpu IN LISTS GPU_TARGETS)
if(gpu IN_LIST gpu_list AND target EQUAL 0)
if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
add_test_executable(test_batched_gemm_reduce_fp16 batched_gemm_reduce_fp16.cpp)
target_link_libraries(test_batched_gemm_reduce_fp16 PRIVATE utility)
target_link_libraries(test_batched_gemm_reduce_fp16 PRIVATE device_batched_gemm_reduce_instance)
add_test_executable(test_batched_gemm_reduce_fp16 batched_gemm_reduce_fp16.cpp)
if(result EQUAL 0)
target_link_libraries(test_batched_gemm_reduce_fp16 PRIVATE utility device_batched_gemm_reduce_instance)
set(target 1)
endif()
endif()
......
test/batched_gemm_softmax_gemm/CMakeLists.txt
View file @ 648f1f13
......@@ -2,12 +2,12 @@ list(APPEND gpu_list gfx908 gfx90a gfx940 gfx941 gfx942)
set(target 0)
foreach(gpu IN LISTS GPU_TARGETS)
if(gpu IN_LIST gpu_list AND target EQUAL 0)
if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
add_custom_target(test_batched_gemm_softmax_gemm)
add_gtest_executable(test_batched_gemm_softmax_gemm_fp16 test_batched_gemm_softmax_gemm_fp16.cpp)
target_link_libraries(test_batched_gemm_softmax_gemm_fp16 PRIVATE utility device_batched_gemm_softmax_gemm_instance)
add_dependencies(test_batched_gemm_softmax_gemm test_batched_gemm_softmax_gemm_fp16)
set(target 1)
endif()
add_custom_target(test_batched_gemm_softmax_gemm)
add_gtest_executable(test_batched_gemm_softmax_gemm_fp16 test_batched_gemm_softmax_gemm_fp16.cpp)
if(result EQUAL 0)
target_link_libraries(test_batched_gemm_softmax_gemm_fp16 PRIVATE utility device_batched_gemm_softmax_gemm_instance)
add_dependencies(test_batched_gemm_softmax_gemm test_batched_gemm_softmax_gemm_fp16)
set(target 1)
endif()
endif()
endforeach()
\ No newline at end of file
test/batched_gemm_softmax_gemm_permute/CMakeLists.txt
View file @ 648f1f13
......@@ -2,25 +2,28 @@ list(APPEND gpu_list gfx908 gfx90a gfx940 gfx941 gfx942)
set(target 0)
foreach(gpu IN LISTS GPU_TARGETS)
if(gpu IN_LIST gpu_list AND target EQUAL 0)
if(DTYPES MATCHES "fp16" OR DTYPES MATCHES "bf16" OR NOT DEFINED DTYPES)
add_custom_target(test_batched_gemm_softmax_gemm_permute)
endif()
if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
add_gtest_executable(test_batched_gemm_softmax_gemm_permute_fp16 test_batched_gemm_softmax_gemm_permute_fp16.cpp)
add_gtest_executable(test_batched_gemm_bias_softmax_gemm_permute_fp16 test_batched_gemm_bias_softmax_gemm_permute_fp16.cpp)
target_link_libraries(test_batched_gemm_softmax_gemm_permute_fp16 PRIVATE utility device_batched_gemm_softmax_gemm_permute_instance)
target_link_libraries(test_batched_gemm_bias_softmax_gemm_permute_fp16 PRIVATE utility device_batched_gemm_softmax_gemm_permute_instance)
add_dependencies(test_batched_gemm_softmax_gemm_permute test_batched_gemm_softmax_gemm_permute_fp16)
add_dependencies(test_batched_gemm_softmax_gemm_permute test_batched_gemm_bias_softmax_gemm_permute_fp16)
endif()
if(DTYPES MATCHES "bf16" OR NOT DEFINED DTYPES)
add_gtest_executable(test_batched_gemm_softmax_gemm_permute_bf16 test_batched_gemm_softmax_gemm_permute_bf16.cpp)
add_gtest_executable(test_batched_gemm_bias_softmax_gemm_permute_bf16 test_batched_gemm_bias_softmax_gemm_permute_bf16.cpp)
target_link_libraries(test_batched_gemm_softmax_gemm_permute_bf16 PRIVATE utility device_batched_gemm_softmax_gemm_permute_instance)
target_link_libraries(test_batched_gemm_bias_softmax_gemm_permute_bf16 PRIVATE utility device_batched_gemm_softmax_gemm_permute_instance)
add_dependencies(test_batched_gemm_softmax_gemm_permute test_batched_gemm_softmax_gemm_permute_bf16)
add_dependencies(test_batched_gemm_softmax_gemm_permute test_batched_gemm_bias_softmax_gemm_permute_bf16)
endif()
add_custom_target(test_batched_gemm_softmax_gemm_permute)
add_gtest_executable(test_batched_gemm_softmax_gemm_permute_fp16 test_batched_gemm_softmax_gemm_permute_fp16.cpp)
if(result EQUAL 0)
target_link_libraries(test_batched_gemm_softmax_gemm_permute_fp16 PRIVATE utility device_batched_gemm_softmax_gemm_permute_instance)
add_dependencies(test_batched_gemm_softmax_gemm_permute test_batched_gemm_softmax_gemm_permute_fp16)
endif()
add_gtest_executable(test_batched_gemm_bias_softmax_gemm_permute_fp16 test_batched_gemm_bias_softmax_gemm_permute_fp16.cpp)
if(result EQUAL 0)
target_link_libraries(test_batched_gemm_bias_softmax_gemm_permute_fp16 PRIVATE utility device_batched_gemm_softmax_gemm_permute_instance)
add_dependencies(test_batched_gemm_softmax_gemm_permute test_batched_gemm_bias_softmax_gemm_permute_fp16)
endif()
add_gtest_executable(test_batched_gemm_softmax_gemm_permute_bf16 test_batched_gemm_softmax_gemm_permute_bf16.cpp)
if(result EQUAL 0)
target_link_libraries(test_batched_gemm_softmax_gemm_permute_bf16 PRIVATE utility device_batched_gemm_softmax_gemm_permute_instance)
add_dependencies(test_batched_gemm_softmax_gemm_permute test_batched_gemm_softmax_gemm_permute_bf16)
endif()
add_gtest_executable(test_batched_gemm_bias_softmax_gemm_permute_bf16 test_batched_gemm_bias_softmax_gemm_permute_bf16.cpp)
if(result EQUAL 0)
target_link_libraries(test_batched_gemm_bias_softmax_gemm_permute_bf16 PRIVATE utility device_batched_gemm_softmax_gemm_permute_instance)
add_dependencies(test_batched_gemm_softmax_gemm_permute test_batched_gemm_bias_softmax_gemm_permute_bf16)
endif()
set(target 1)
endif()
endforeach()
\ No newline at end of file
test/batchnorm/batchnorm_bwd_rank_4.cpp
View file @ 648f1f13
......@@ -70,10 +70,23 @@ class TestBatchNormBwdRank4 : public ::testing::Test
}
};
using KernelTypes = ::testing::Types<std::tuple<F16, F32, F32, F32, F16, F32, F32>,
std::tuple<F32, F32, F32, F32, F32, F32, F32>,
std::tuple<BF16, F32, F32, F32, BF16, F32, F32>,
std::tuple<F64, F64, F64, F64, F64, F64, F64>>;
using KernelTypes = ::testing::Types<
#ifdef CK_ENABLE_FP16
std::tuple<F16, F32, F32, F32, F16, F32, F32>
#endif
#ifdef CK_ENABLE_FP32
,
std::tuple<F32, F32, F32, F32, F32, F32, F32>
#endif
#ifdef CK_ENABLE_BF16
,
std::tuple<BF16, F32, F32, F32, BF16, F32, F32>
#endif
#ifdef CK_ENABLE_FP64
,
std::tuple<F64, F64, F64, F64, F64, F64, F64>
#endif
>;
TYPED_TEST_SUITE(TestBatchNormBwdRank4, KernelTypes);
......
test/batchnorm/batchnorm_fwd_rank_4.cpp
View file @ 648f1f13
......@@ -87,10 +87,23 @@ class TestBatchNormFwdRank4 : public ::testing::Test
}
};
using KernelTypes = ::testing::Types<std::tuple<F16, F16, F32, F16, F16, F32>,
std::tuple<F32, F32, F32, F32, F32, F32>,
std::tuple<BF16, BF16, F32, BF16, BF16, F32>,
std::tuple<F64, F64, F64, F64, F64, F64>>;
using KernelTypes = ::testing::Types<
#ifdef CK_ENABLE_FP16
std::tuple<F16, F16, F32, F16, F16, F32>
#endif
#ifdef CK_ENABLE_FP32
,
std::tuple<F32, F32, F32, F32, F32, F32>
#endif
#ifdef CK_ENABLE_BF16
,
std::tuple<BF16, BF16, F32, BF16, BF16, F32>
#endif
#ifdef CK_ENABLE_FP64
,
std::tuple<F64, F64, F64, F64, F64, F64>
#endif
>;
TYPED_TEST_SUITE(TestBatchNormFwdRank4, KernelTypes);
......
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