You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
121 lines
4.0 KiB
121 lines
4.0 KiB
/* Copyright (c) 2018 paddlepaddle Authors. All Rights Reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License. */
|
|
|
|
#include "paddle/fluid/operators/math/concat.h"
|
|
#include <vector>
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
namespace math {
|
|
|
|
/*
|
|
* All tensors' dimension should be the same and the values of
|
|
* each dimension must be the same, except the axis dimension.
|
|
*/
|
|
template <typename T>
|
|
class ConcatFunctor<platform::CPUDeviceContext, T> {
|
|
public:
|
|
void operator()(const platform::CPUDeviceContext& context,
|
|
const std::vector<framework::Tensor>& input, const int axis,
|
|
framework::Tensor* output) {
|
|
// TODO(zcd): Add input data validity checking
|
|
int num = input.size();
|
|
|
|
int rows = 1;
|
|
auto dim_0 = input[0].dims();
|
|
for (int i = 0; i < axis; ++i) {
|
|
rows *= dim_0[i];
|
|
}
|
|
int out_rows = rows, out_cols = 0;
|
|
|
|
std::vector<int64_t> input_cols(input.size());
|
|
for (int i = 0; i < num; ++i) {
|
|
int t_cols = input[i].numel() / rows;
|
|
out_cols += t_cols;
|
|
input_cols[i] = t_cols;
|
|
}
|
|
auto cpu_place = boost::get<platform::CPUPlace>(context.GetPlace());
|
|
|
|
// computation
|
|
for (int k = 0; k < out_rows; ++k) {
|
|
T* dst_ptr = output->data<T>() + k * out_cols;
|
|
int col_idx = 0;
|
|
for (int j = 0; j < num; ++j) {
|
|
int col_len = input_cols[j];
|
|
const T* src_prt = input[j].data<T>() + k * col_len;
|
|
memory::Copy(cpu_place, dst_ptr + col_idx, cpu_place, src_prt,
|
|
sizeof(T) * col_len);
|
|
col_idx += col_len;
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
/*
|
|
* All tensors' dimension should be the same and the values of
|
|
* each dimension must be the same, except the axis dimension.
|
|
*/
|
|
template <typename T>
|
|
class ConcatGradFunctor<platform::CPUDeviceContext, T> {
|
|
public:
|
|
void operator()(const platform::CPUDeviceContext& context,
|
|
const framework::Tensor& input, const int axis,
|
|
std::vector<framework::Tensor>* outputs) {
|
|
// TODO(zcd): Add input data validity checking
|
|
int num = outputs->size();
|
|
|
|
int input_rows = 1;
|
|
auto dim_0 = outputs->at(0).dims();
|
|
for (int i = 0; i < axis; ++i) {
|
|
input_rows *= dim_0[i];
|
|
}
|
|
int input_cols = 0;
|
|
|
|
std::vector<int64_t> output_cols(outputs->size());
|
|
for (int i = 0; i < num; ++i) {
|
|
int t_cols = outputs->at(i).numel() / input_rows;
|
|
input_cols += t_cols;
|
|
output_cols[i] = t_cols;
|
|
}
|
|
auto cpu_place = boost::get<platform::CPUPlace>(context.GetPlace());
|
|
|
|
// computation
|
|
for (int k = 0; k < input_rows; ++k) {
|
|
const T* src_ptr = input.data<T>() + k * input_cols;
|
|
int col_idx = 0;
|
|
for (int j = 0; j < num; ++j) {
|
|
int col_len = output_cols[j];
|
|
T* dst_ptr = outputs->at(j).data<T>() + k * col_len;
|
|
memory::Copy(cpu_place, dst_ptr, cpu_place, src_ptr + col_idx,
|
|
sizeof(T) * col_len);
|
|
col_idx += col_len;
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
template class ConcatFunctor<platform::CPUDeviceContext, int>;
|
|
template class ConcatFunctor<platform::CPUDeviceContext, int64_t>;
|
|
template class ConcatFunctor<platform::CPUDeviceContext, float>;
|
|
template class ConcatFunctor<platform::CPUDeviceContext, double>;
|
|
|
|
template class ConcatGradFunctor<platform::CPUDeviceContext, int>;
|
|
template class ConcatGradFunctor<platform::CPUDeviceContext, int64_t>;
|
|
template class ConcatGradFunctor<platform::CPUDeviceContext, float>;
|
|
template class ConcatGradFunctor<platform::CPUDeviceContext, double>;
|
|
|
|
} // namespace math
|
|
} // namespace operators
|
|
} // namespace paddle
|