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Commit 92a0945d authored by Chao Liu's avatar Chao Liu
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convnd_fwd fp16 example

parent 63914743
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README.md
View file @ 92a0945d
......@@ -10,7 +10,7 @@ rocm/tensorflow:rocm5.1-tf2.6-dev \
/bin/bash
```
# Install the new rocm-cmake version
# Install newer version of rocm-cmake
https://github.com/RadeonOpenCompute/rocm-cmake
## Build
......@@ -54,6 +54,7 @@ make install
```
## Using CK as pre-built kernel library
Instructions for using CK as a pre-built kernel library are under ```client_example/```
## Caveat
### Kernel Timing and Verification
......
example/09_convnd_fwd/CMakeLists.txt
View file @ 92a0945d
......@@ -3,7 +3,3 @@ add_example_executable(example_convnd_fwd_xdl_int8 convnd_fwd_xdl_int8.cpp)
add_example_executable(example_convnd_fwd_xdl_fp16 convnd_fwd_xdl_fp16.cpp)
# FIXME: re-enable this exampe as test when SWDEV-335738 is fixed
add_example_executable_no_testing(example_convnd_fwd_xdl_fp64 convnd_fwd_xdl_fp64.cpp)
target_link_libraries(example_convnd_fwd_xdl_fp64 PRIVATE conv_util)
target_link_libraries(example_convnd_fwd_xdl_fp32 PRIVATE conv_util)
target_link_libraries(example_convnd_fwd_xdl_int8 PRIVATE conv_util)
target_link_libraries(example_convnd_fwd_xdl_fp16 PRIVATE conv_util)
example/09_convnd_fwd/convnd_fwd_xdl_fp16.cpp
View file @ 92a0945d
......@@ -8,17 +8,17 @@
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_convnd_fwd_xdl_nhwc_kyxc_nhwk.hpp"
#include "ck/tensor_operation/gpu/device/device_convnd_fwd_nwc_kxc_nwk_xdl.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/conv_util.hpp"
#include "ck/library/host_tensor/device_memory.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.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_conv_fwd.hpp"
namespace {
#include "parse_conv_parameter.hpp"
using InDataType = ck::half_t;
using WeiDataType = ck::half_t;
......@@ -39,13 +39,8 @@ using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto ConvFwdDefault =
ck::tensor_operation::device::ConvolutionForwardSpecialization::Default;
using DeviceConvFwdBasePtr =
ck::tensor_operation::device::DeviceConvFwdPtr<InElementOp, WeiElementOp, OutElementOp>;
template <ck::index_t NumDimSpatial>
using DeviceConvNDFwdInstance = ck::tensor_operation::device::
DeviceConvNDFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<
// clang-format off
using DeviceConvNDFwdInstance = ck::tensor_operation::device::DeviceConvNdFwdNwcKxcNwk_Xdl<
InDataType, //
WeiDataType, //
OutDataType, //
......@@ -90,107 +85,14 @@ using ReferenceConvNDFwdInstance = ck::tensor_operation::host::ReferenceConvFwd<
OutElementOp,
NumDimSpatial>;
DeviceConvFwdBasePtr get_conv_instance(int num_dim_spatial)
{
switch(num_dim_spatial)
{
case 3: {
return std::make_unique<DeviceConvNDFwdInstance<3>>();
}
case 2: {
return std::make_unique<DeviceConvNDFwdInstance<2>>();
}
case 1: {
return std::make_unique<DeviceConvNDFwdInstance<1>>();
}
default: {
throw std::runtime_error("Unsupported number of spatial dimensions provided!");
}
}
}
void print_use_msg()
{
std::cout << "arg1: verification (0=no, 1=yes)\n"
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
<< "arg3: time kernel (0=n0, 1=yes)\n"
<< "arg4: N spatial dimensions (default 2)\n"
<< "Following arguments (depending on number of spatial dims):\n"
<< " N, K, C, \n"
<< " <filter spatial dimensions>, (ie Y, X for 2D)\n"
<< " <input image spatial dimensions>, (ie Hi, Wi for 2D)\n"
<< " <strides>, (ie Sy, Sx for 2D)\n"
<< " <dilations>, (ie Dy, Dx for 2D)\n"
<< " <left padding>, (ie LeftPy, LeftPx for 2D)\n"
<< " <right padding>, (ie RightPy, RightPx for 2D)\n"
<< std::endl;
}
ck::utils::conv::ConvParams parse_conv_params(int num_dim_spatial, int argc, char* argv[])
{
// (N, K, C) + num_dim_spatial * 6 (filter, input, strides, dilations, pad left, pad right)
int conv_args = 3 + num_dim_spatial * 6;
int cmdline_nargs = conv_args + 5;
if(cmdline_nargs != argc)
{
print_use_msg();
exit(0);
}
ck::utils::conv::ConvParams params;
int arg_idx = 5;
params.num_dim_spatial_ = num_dim_spatial;
params.N_ = std::stoi(argv[arg_idx++]);
params.K_ = std::stoi(argv[arg_idx++]);
params.C_ = std::stoi(argv[arg_idx++]);
params.filter_spatial_lengths_.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i)
{
params.filter_spatial_lengths_[i] = std::stoi(argv[arg_idx++]);
}
params.input_spatial_lengths_.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i)
{
params.input_spatial_lengths_[i] = std::stoi(argv[arg_idx++]);
}
params.conv_filter_strides_.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i)
{
params.conv_filter_strides_[i] = std::stoi(argv[arg_idx++]);
}
params.conv_filter_dilations_.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i)
{
params.conv_filter_dilations_[i] = std::stoi(argv[arg_idx++]);
}
params.input_left_pads_.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i)
{
params.input_left_pads_[i] = std::stoi(argv[arg_idx++]);
}
params.input_right_pads_.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i)
{
params.input_right_pads_[i] = std::stoi(argv[arg_idx++]);
}
return params;
}
} // anonymous namespace
int main(int argc, char* argv[])
{
using namespace ck::utils::conv;
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
bool time_kernel = true;
int num_dim_spatial = 2;
ck::utils::conv::ConvParams params;
ck::tensor_operation::device::ConvParams params;
if(argc >= 5)
{
......@@ -205,29 +107,25 @@ int main(int argc, char* argv[])
params = parse_conv_params(num_dim_spatial, argc, argv);
}
std::vector<std::size_t> input_dims{static_cast<std::size_t>(params.N_),
static_cast<std::size_t>(params.C_)};
input_dims.insert(std::end(input_dims),
std::begin(params.input_spatial_lengths_),
std::end(params.input_spatial_lengths_));
std::vector<std::size_t> filter_dims{static_cast<std::size_t>(params.K_),
static_cast<std::size_t>(params.C_)};
filter_dims.insert(std::end(filter_dims),
std::begin(params.filter_spatial_lengths_),
std::end(params.filter_spatial_lengths_));
auto f_nchw_host_tensor_descriptor =
[](ck::index_t n, ck::index_t c, std::vector<ck::index_t> spatial_lengths) {
std::vector<std::size_t> nhwc_lengths{static_cast<std::size_t>(n),
static_cast<std::size_t>(c)};
nhwc_lengths.insert(
nhwc_lengths.begin() + 1, spatial_lengths.begin(), spatial_lengths.end());
const std::vector<ck::index_t>& output_spatial_lengths = params.GetOutputSpatialLengths();
std::vector<std::size_t> output_dims{static_cast<std::size_t>(params.N_),
static_cast<std::size_t>(params.K_)};
output_dims.insert(std::end(output_dims),
std::begin(output_spatial_lengths),
std::end(output_spatial_lengths));
return transpose_host_tensor_descriptor_given_new2old(
HostTensorDescriptor(nhwc_lengths), std::vector<std::size_t>({0, 3, 1, 2}));
};
Tensor<InDataType> input(get_input_host_tensor_descriptor(input_dims, num_dim_spatial));
Tensor<WeiDataType> weights(get_filters_host_tensor_descriptor(filter_dims, num_dim_spatial));
Tensor<OutDataType> host_output(get_output_host_tensor_descriptor(output_dims, num_dim_spatial));
Tensor<OutDataType> device_output(get_output_host_tensor_descriptor(output_dims, num_dim_spatial));
Tensor<InDataType> input(
f_nchw_host_tensor_descriptor(params.N_, params.C_, params.input_spatial_lengths_));
Tensor<InDataType> weights(
f_nchw_host_tensor_descriptor(params.K_, params.C_, params.filter_spatial_lengths_));
Tensor<InDataType> host_output(
f_nchw_host_tensor_descriptor(params.N_, params.K_, params.GetOutputSpatialLengths()));
Tensor<InDataType> device_output(
f_nchw_host_tensor_descriptor(params.N_, params.K_, params.GetOutputSpatialLengths()));
std::cout << "input: " << input.mDesc << std::endl;
std::cout << "weights: " << weights.mDesc << std::endl;
......@@ -253,10 +151,9 @@ int main(int argc, char* argv[])
wei_device_buf.ToDevice(weights.mData.data());
// do GEMM
auto conv = get_conv_instance(num_dim_spatial);
auto invoker = conv->MakeInvokerPointer();
auto argument =
conv->MakeArgumentPointer(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
auto conv = DeviceConvNDFwdInstance<2>{};
auto invoker = conv.MakeInvoker();
auto argument = conv.MakeArgument(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
params.N_,
......@@ -264,7 +161,7 @@ int main(int argc, char* argv[])
params.C_,
params.input_spatial_lengths_,
params.filter_spatial_lengths_,
output_spatial_lengths,
params.GetOutputSpatialLengths(),
params.conv_filter_strides_,
params.conv_filter_dilations_,
params.input_left_pads_,
......@@ -273,29 +170,22 @@ int main(int argc, char* argv[])
WeiElementOp{},
OutElementOp{});
if(!conv->IsSupportedArgument(argument.get()))
if(!conv.IsSupportedArgument(argument))
{
throw std::runtime_error(
"wrong! device_conv with the specified compilation parameters does "
"not support this Conv problem");
}
float ave_time = invoker->Run(argument.get(), StreamConfig{nullptr, time_kernel});
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = get_flops(
params.N_, params.C_, params.K_, params.filter_spatial_lengths_, output_spatial_lengths);
std::size_t num_btype = get_btype<InDataType, WeiDataType, OutDataType>(
params.N_,
params.C_,
params.K_,
params.input_spatial_lengths_,
params.filter_spatial_lengths_,
output_spatial_lengths);
std::size_t flop = params.GetFlops();
std::size_t num_btype = params.GetByte<InDataType, WeiDataType, OutDataType>();
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
<< conv->GetTypeString() << std::endl;
<< conv.GetTypeString() << std::endl;
if(do_verification)
{
......@@ -315,22 +205,27 @@ int main(int argc, char* argv[])
ref_invoker.Run(ref_argument);
out_device_buf.FromDevice(device_output.mData.data());
return ck::utils::check_err(
host_output.mData, device_output.mData, "Error: incorrect results!", 1e-5f, 1e-4f) ? 0 : 1;
return ck::utils::check_err(host_output.mData,
device_output.mData,
"Error: incorrect results!",
1e-5f,
1e-4f)
? 0
: 1;
};
switch(num_dim_spatial)
{
case 3: {
auto ref_conv = ReferenceConvNDFwdInstance<3>();
case 1: {
auto ref_conv = ReferenceConvNDFwdInstance<1>();
return verify_f(ref_conv);
}
case 2: {
auto ref_conv = ReferenceConvNDFwdInstance<2>();
return verify_f(ref_conv);
}
case 1: {
auto ref_conv = ReferenceConvNDFwdInstance<1>();
case 3: {
auto ref_conv = ReferenceConvNDFwdInstance<3>();
return verify_f(ref_conv);
}
default: {
......
example/09_convnd_fwd/parse_conv_parameter.hpp 0 → 100644
View file @ 92a0945d
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include <iostream>
#include "ck/ck.hpp"
#include "ck/library/utility/convolution_parameter.hpp"
ck::tensor_operation::device::ConvParams
parse_conv_params(int num_dim_spatial, int arg_idx, char* const argv[])
{
ck::tensor_operation::device::ConvParams params;
params.num_dim_spatial_ = num_dim_spatial;
params.N_ = std::stoi(argv[arg_idx++]);
params.K_ = std::stoi(argv[arg_idx++]);
params.C_ = std::stoi(argv[arg_idx++]);
params.filter_spatial_lengths_.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i)
{
params.filter_spatial_lengths_[i] = std::stoi(argv[arg_idx++]);
}
params.input_spatial_lengths_.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i)
{
params.input_spatial_lengths_[i] = std::stoi(argv[arg_idx++]);
}
params.conv_filter_strides_.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i)
{
params.conv_filter_strides_[i] = std::stoi(argv[arg_idx++]);
}
params.conv_filter_dilations_.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i)
{
params.conv_filter_dilations_[i] = std::stoi(argv[arg_idx++]);
}
params.input_left_pads_.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i)
{
params.input_left_pads_[i] = std::stoi(argv[arg_idx++]);
}
params.input_right_pads_.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i)
{
params.input_right_pads_[i] = std::stoi(argv[arg_idx++]);
}
return params;
}
void print_use_msg()
{
std::cout << "arg1: verification (0=no, 1=yes)\n"
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
<< "arg3: time kernel (0=n0, 1=yes)\n"
<< "arg4: N spatial dimensions (default 2)\n"
<< "Following arguments (depending on number of spatial dims):\n"
<< " N, K, C, \n"
<< " <filter spatial dimensions>, (ie Y, X for 2D)\n"
<< " <input image spatial dimensions>, (ie Hi, Wi for 2D)\n"
<< " <strides>, (ie Sy, Sx for 2D)\n"
<< " <dilations>, (ie Dy, Dx for 2D)\n"
<< " <left padding>, (ie LeftPy, LeftPx for 2D)\n"
<< " <right padding>, (ie RightPy, RightPx for 2D)\n"
<< std::endl;
}
example/CMakeLists.txt
View file @ 92a0945d
......@@ -8,7 +8,7 @@ add_custom_target(examples)
function(add_example_executable EXAMPLE_NAME FILE_NAME)
message("adding example ${EXAMPLE_NAME}")
add_executable(${EXAMPLE_NAME} ${FILE_NAME})
target_link_libraries(${EXAMPLE_NAME} PRIVATE host_tensor)
target_link_libraries(${EXAMPLE_NAME} PRIVATE utility)
add_test(NAME ${EXAMPLE_NAME} COMMAND $<TARGET_FILE:${EXAMPLE_NAME}> ${ARGN})
add_dependencies(examples ${EXAMPLE_NAME})
add_dependencies(check ${EXAMPLE_NAME})
......@@ -17,7 +17,7 @@ endfunction(add_example_executable EXAMPLE_NAME)
function(add_example_executable_no_testing EXAMPLE_NAME FILE_NAME)
message("adding example ${EXAMPLE_NAME}")
add_executable(${EXAMPLE_NAME} ${FILE_NAME})
target_link_libraries(${EXAMPLE_NAME} PRIVATE host_tensor)
target_link_libraries(${EXAMPLE_NAME} PRIVATE utility)
add_dependencies(examples ${EXAMPLE_NAME})
endfunction(add_example_executable_no_testing EXAMPLE_NAME)
......
include/ck/tensor_operation/gpu/device/device_conv_fwd.hpp
View file @ 92a0945d
......@@ -12,7 +12,14 @@ namespace ck {
namespace tensor_operation {
namespace device {
template <typename InElementwiseOperation,
template <ck::index_t NumDimSpatial,
typename InLayout,
typename WeiLayout,
typename OutLayout,
typename InDataType,
typename WeiDataType,
typename OutDataType,
typename InElementwiseOperation,
typename WeiElementwiseOperation,
typename OutElementwiseOperation>
struct DeviceConvFwd : public BaseOperator
......@@ -38,11 +45,28 @@ struct DeviceConvFwd : public BaseOperator
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
};
template <typename InElementwiseOperation,
#if 0
template <ck::index_t NumDimSpatial,
typename InLayout,
typename WeiLayout,
typename OutLayout,
typename InDataType,
typename WeiDataType,
typename OutDataType,
typename InElementwiseOperation,
typename WeiElementwiseOperation,
typename OutElementwiseOperation>
using DeviceConvFwdPtr = std::unique_ptr<
DeviceConvFwd<InElementwiseOperation, WeiElementwiseOperation, OutElementwiseOperation>>;
using DeviceConvFwdPtr = std::unique_ptr<DeviceConvFwd<NumDimSpatial,
InLayout,
WeiLayout,
OutLayout,
InDataType,
WeiDataType,
OutDataType,
InElementwiseOperation,
WeiElementwiseOperation,
OutElementwiseOperation>>;
#endif
} // namespace device
} // namespace tensor_operation
......
include/ck/tensor_operation/gpu/device/device_convnd_fwd_xdl_nhwc_kyxc_nhwk.hpp → include/ck/tensor_operation/gpu/device/device_convnd_fwd_nwc_kxc_nwk_xdl.hpp
View file @ 92a0945d
......@@ -27,10 +27,10 @@ namespace device {
// @brief Device Convolution operation.
//
// Supports:
// @li Inputs with up to 3 spatial dimentions
// @li Input tensor in NHWC data format
// @li Weight tensor in KYXC data format
// @li Output tensor in NHWK data format
// @li Forward convolution with up to 3 spatial dimentions
// @li Input tensor in NWC data format
// @li Weight tensor in KXC data format
// @li Output tensor in NWK data format
//
// 1D:
// out[N, Wo, K] = in[N, Wi, C] * wei[K, X, C]
......@@ -73,10 +73,28 @@ template <typename InDataType,
bool BBlockLdsAddExtraN,
ck::index_t CThreadTransferSrcDstVectorDim,
ck::index_t CThreadTransferDstScalarPerVector>
struct DeviceConvNDFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
: public DeviceConvFwd<InElementwiseOperation, WeiElementwiseOperation, OutElementwiseOperation>
struct DeviceConvNdFwdNwcKxcNwk_Xdl
: public DeviceConvFwd<NumDimSpatial,
ck::tuple_element_t<NumDimSpatial - 1,
ck::Tuple<ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::NDHWC>>,
ck::tuple_element_t<NumDimSpatial - 1,
ck::Tuple<ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::KZYXC>>,
ck::tuple_element_t<NumDimSpatial - 1,
ck::Tuple<ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::NDHWC>>,
InDataType,
WeiDataType,
OutDataType,
InElementwiseOperation,
WeiElementwiseOperation,
OutElementwiseOperation>
{
using DeviceOp = DeviceConvNDFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K;
using DeviceOp = DeviceConvNdFwdNwcKxcNwk_Xdl;
using ADataType = InDataType;
using BDataType = WeiDataType;
......@@ -1027,7 +1045,7 @@ struct DeviceConvNDFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
auto str = std::stringstream();
// clang-format off
str << "DeviceConvNDFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K"
str << "DeviceConvNdFwdNwcKxcNwk_Xdl"
<< "<"
<< BlockSize << ", "
<< MPerBlock << ", "
......
include/ck/tensor_operation/gpu/device/device_gemm.hpp
View file @ 92a0945d
......@@ -46,25 +46,6 @@ struct DeviceGemm : public BaseOperator
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
};
template <typename ALayout,
typename BLayout,
typename CLayout,
typename ADataType,
typename BDataType,
typename CDataType,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CElementwiseOperation>
using DeviceGemmPtr = std::unique_ptr<DeviceGemm<ALayout,
BLayout,
CLayout,
ADataType,
BDataType,
CDataType,
AElementwiseOperation,
BElementwiseOperation,
CElementwiseOperation>>;
template <typename AElementwiseOperation,
typename BElementwiseOperation,
typename CElementwiseOperation>
......
library/CMakeLists.txt
View file @ 92a0945d
add_subdirectory(src/tensor_operation_instance/gpu)
add_subdirectory(src/host_tensor)
add_subdirectory(src/utility)
library/include/ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp
View file @ 92a0945d
......@@ -8,7 +8,7 @@
#include <sstream>
#include "ck/tensor_operation/gpu/device/device_base.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/utility/host_tensor.hpp"
namespace ck {
namespace tensor_operation {
......@@ -267,7 +267,10 @@ struct ReferenceConvFwd : public device::BaseOperator
return true;
}
bool IsSupportedArgument(const device::BaseArgument*) override { return true; }
bool IsSupportedArgument(const device::BaseArgument*) override
{
return NumDimSpatial >= 1 && NumDimSpatial <= 3;
}
static auto MakeArgument(const Tensor<InDataType>& input,
const Tensor<WeiDataType>& weight,
......
library/include/ck/library/utility/check_err.hpp
View file @ 92a0945d
......@@ -13,8 +13,11 @@
#include <type_traits>
#include <vector>
#include "ck/ck.hpp"
#include "ck/utility/data_type.hpp"
#include "ck/library/utility/io.hpp"
namespace ck {
namespace utils {
......@@ -201,10 +204,3 @@ check_err(const std::vector<T>& out,
} // namespace utils
} // namespace ck
template <typename T>
std::ostream& operator<<(std::ostream& os, const std::vector<T>& v)
{
std::copy(std::begin(v), std::end(v), std::ostream_iterator<T>(os, " "));
return os;
}
library/include/ck/library/utility/conv_util.hpp deleted 100644 → 0
View file @ 63914743
This diff is collapsed.
library/include/ck/library/utility/convolution_parameter.hpp 0 → 100644
View file @ 92a0945d
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <cstdlib>
#include <numeric>
#include <iterator>
#include <vector>
#include "ck/ck.hpp"
#include "ck/library/utility/io.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
struct ConvParams
{
ConvParams();
ConvParams(ck::index_t n_dim,
ck::index_t n_batch,
ck::index_t n_out_channels,
ck::index_t n_in_channels,
const std::vector<ck::index_t>& filters_len,
const std::vector<ck::index_t>& input_len,
const std::vector<ck::index_t>& strides,
const std::vector<ck::index_t>& dilations,
const std::vector<ck::index_t>& left_pads,
const std::vector<ck::index_t>& right_pads);
ck::index_t num_dim_spatial_;
ck::index_t N_;
ck::index_t K_;
ck::index_t C_;
std::vector<ck::index_t> filter_spatial_lengths_;
std::vector<ck::index_t> input_spatial_lengths_;
std::vector<ck::index_t> output_spatial_lengths_;
std::vector<ck::index_t> conv_filter_strides_;
std::vector<ck::index_t> conv_filter_dilations_;
std::vector<ck::index_t> input_left_pads_;
std::vector<ck::index_t> input_right_pads_;
std::vector<ck::index_t> GetOutputSpatialLengths() const;
std::size_t GetFlops() const;
template <typename InDataType, typename WeiDataType, typename OutDataType>
std::size_t GetByte() const
{
// sizeof(InDataType) * (N * C * <input spatial lengths product>) +
// sizeof(WeiDataType) * (K * C * <filter spatial lengths product>) +
// sizeof(OutDataType) * (N * K * <output spatial lengths product>);
return sizeof(InDataType) * (N_ * C_ *
std::accumulate(std::begin(input_spatial_lengths_),
std::end(input_spatial_lengths_),
static_cast<std::size_t>(1),
std::multiplies<std::size_t>())) +
sizeof(WeiDataType) * (K_ * C_ *
std::accumulate(std::begin(filter_spatial_lengths_),
std::end(filter_spatial_lengths_),
static_cast<std::size_t>(1),
std::multiplies<std::size_t>())) +
sizeof(OutDataType) * (N_ * K_ *
std::accumulate(std::begin(output_spatial_lengths_),
std::end(output_spatial_lengths_),
static_cast<std::size_t>(1),
std::multiplies<std::size_t>()));
}
};
} // namespace device
} // namespace tensor_operation
} // namespace ck
std::ostream& operator<<(std::ostream& os, const ck::tensor_operation::device::ConvParams& p);
library/include/ck/library/host_tensor/host_tensor.hpp → library/include/ck/library/utility/host_tensor.hpp
View file @ 92a0945d
......@@ -122,6 +122,22 @@ struct HostTensorDescriptor
std::vector<std::size_t> mStrides;
};
template <typename New2Old>
HostTensorDescriptor transpose_host_tensor_descriptor_given_new2old(const HostTensorDescriptor& a,
const New2Old& new2old)
{
std::vector<std::size_t> new_lengths(a.GetNumOfDimension());
std::vector<std::size_t> new_strides(a.GetNumOfDimension());
for(std::size_t i = 0; i < a.GetNumOfDimension(); i++)
{
new_lengths[i] = a.GetLengths()[new2old[i]];
new_strides[i] = a.GetStrides()[new2old[i]];
}
return HostTensorDescriptor(new_lengths, new_strides);
}
struct joinable_thread : std::thread
{
template <typename... Xs>
......
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