m53297601
/
Paddle
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Add sequence_project_functor

Browse Source
revert-4814-Add_sequence_project_op
chengduoZH 7 years ago
parent ce960575cd
commit 0ab2c436ae
4 changed files with 234 additions and 0 deletions
Show Stats Download Patch File Download Diff File

2
paddle/operators/math/CMakeLists.txt
Unescape View File

@ -7,6 +7,7 @@ if(WITH_GPU)
nv_library(cross_entropy SRCS cross_entropy.cc cross_entropy.cu DEPS operator)
nv_library(pooling SRCS pooling.cc pooling.cu DEPS device_context)
nv_library(vol2col SRCS vol2col.cc vol2col.cu DEPS device_context)
nv_library(sequence_project SRCS sequence_project.cc sequence_project.cu DEPS device_context)
else()
cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context operator)
cc_library(selected_rows_functor SRCS selected_rows_functor.cc DEPS selected_rows math_function)
@ -14,6 +15,7 @@ else()
cc_library(cross_entropy SRCS cross_entropy.cc DEPS operator)
cc_library(pooling SRCS pooling.cc DEPS device_context)
cc_library(vol2col SRCS vol2col.cc DEPS device_context)
nv_library(sequence_project SRCS sequence_project.cc DEPS device_context)
endif()
cc_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)

26
paddle/operators/math/sequence_project.cc
Unescape View File

@ -0,0 +1,26 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/operators/math/sequence_project.h"
namespace paddle {
namespace operators {
namespace math {
template class SequenceProjectFunctor<platform::CPUPlace, float>;
template class SequenceProjectFunctor<platform::CPUPlace, double>;
} // namespace math
} // namespace operators
} // namespace paddle

28
paddle/operators/math/sequence_project.cu
Unescape View File

@ -0,0 +1,28 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#define EIGEN_USE_GPU
#include "paddle/operators/math/sequence_project.h"
namespace paddle {
namespace operators {
namespace math {
template class SequenceProjectFunctor<platform::GPUPlace, float>;
template class SequenceProjectFunctor<platform::GPUPlace, double>;
} // namespace math
} // namespace operators
} // namespace paddle

178
paddle/operators/math/sequence_project.h
Unescape View File

@ -0,0 +1,178 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/tensor.h"
#include "paddle/operators/math/im2col.h"
#include "paddle/operators/math/math_function.h"
namespace paddle {
namespace operators {
namespace math {
// template <typename T, int MajorType = Eigen::RowMajor,
// typename IndexType = Eigen::DenseIndex>
// using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
/*
* \brief Converts the feature data of four dimensions(CDHW) into a colData of
* seven dimensions in the Vol2ColFunctor calculation,
* And in the Col2VolFunctor calculation, it is reversed.
*
* \param volData Vol data.
* \param volShape The shape of volData,
* [input_channels, input_depth, input_height, input_width].
* \param colData Column data.
* \param colShape The shape of colData.
*
* The shape of colData is:
* [input_channels, filter_depth, filter_height, filter_width, output_depth,
* output_height, output_width]
* So, it is easy to reshape into a convolution matrix for convolution
* calculation based on matrix multiplication.
* The shape of convolution matrix is [height, width], where the height is equal
* input_channels * filter_depth * filter_height * filter_width, and the width
* is equal output_depth * output_height * output_width.
*
* Reshape:
* shape of colData shape of convolution matrix
* [input_channels,
* filter_depth,
* filter_height,
* filter_width, ======> [height, width]
* output_depth,
* output_height,
* output_width]
*
* \note The caller needs to ensure that volShape.inputChannels is equal to
* colShape.inputChannels.
*/
template <typename Place, typename T>
class SequenceProjectFunctor {
public:
void operator()(const platform::DeviceContext& context,
const framework::LoDTensor*& in,
const framework::LoDTensor* padding_data,
framework::LoDTensor* col, bool padding_trainable,
int context_start, int context_length, int context_stride,
int up_pad, int down_pad) {
auto lod_level_0 = in->lod()[0];
paddle::operators::math::Im2ColFunctor<
paddle::operators::math::ColFormat::kOCF, Place, float>
im2col_ocf;
int input_row_begin, input_row_end;
int sequence_height, sequence_width;
sequence_width = in->dims()[1];
for (int i = 0; i < static_cast<int>(lod_level_0.size()) - 1; ++i) {
input_row_begin = (context_start > 0)
? static_cast<int>(lod_level_0[i]) + context_start
: static_cast<int>(lod_level_0[i]);
input_row_end = static_cast<int>(lod_level_0[i + 1]);
framework::Tensor out_t =
col->Slice(static_cast<int>(lod_level_0[i]),
static_cast<int>(lod_level_0[i + 1]));
sequence_height = static_cast<int>(out_t.dims()[0]);
std::vector<int64_t> output_shape(
{sequence_height, 1, 1, context_length,
sequence_width}); // output_height, output_width,
// input_channels, filter_height, filter_width
out_t.Resize(framework::make_ddim(output_shape));
if (input_row_begin < input_row_end) {
framework::Tensor in_t = in->Slice(input_row_begin, input_row_end);
std::vector<int64_t> input_shape(
{1, input_row_end - input_row_begin,
sequence_width}); // input_channels, input_height, input_width
in_t.Resize(framework::make_ddim(input_shape));
im2col_ocf(context, in_t, out_t,
/*stride_height*/ context_stride, /*stride_width*/ 0, up_pad,
down_pad);
}
if (padding_trainable) {
// add up trainable data
out_t.Resize(framework::make_ddim(
{sequence_height * context_length, sequence_width}));
if (up_pad > 0) { // add up pad
int padding_rows = std::min(
up_pad, static_cast<int>(lod_level_0[i + 1] - lod_level_0[i]));
for (int k = 0; k < padding_rows; ++k) {
int padding_size =
k + context_length < up_pad ? context_length : up_pad - k;
framework::Tensor out_t_sub = out_t.Slice(
k * context_length, k * context_length + padding_size);
framework::Tensor w_sub = padding_data->Slice(k, k + padding_size);
// in this block, using EigenVector<T>::Flatten is ok too.
auto out_t_sub_e = EigenMatrix<T>::From(out_t_sub);
auto w_sub_e = EigenMatrix<T>::From(w_sub);
out_t_sub_e.device(*context.GetEigenDevice<Place>()) = w_sub_e;
}
}
if (down_pad > 0) { // add down pad
int down_pad_begin_row =
std::max(0,
(sequence_height - context_start - context_length) + 1) +
1;
int padding_begin = std::max(0, context_start - sequence_height);
int padding_size =
sequence_height - context_start >= context_length
? 1
: context_length - (sequence_height - context_start);
if (context_start >= sequence_height) padding_size = context_length;
int padding_idx = padding_begin;
for (int t = 0; t + down_pad_begin_row <= sequence_height;
++t, ++padding_size) {
if (context_start >= sequence_height) padding_size = context_length;
if (padding_size > context_length) {
padding_size = context_length;
padding_idx++;
}
if (padding_begin > 0 || sequence_height == context_start)
padding_idx = padding_begin + t;
framework::Tensor out_t_sub = out_t.Slice(
(down_pad_begin_row + t) * context_length - padding_size,
(down_pad_begin_row + t) * context_length);
framework::Tensor w_sub = padding_data->Slice(
up_pad + padding_idx, up_pad + padding_idx + padding_size);
auto out_t_sub_e = EigenMatrix<T>::From(out_t_sub);
auto w_sub_e = EigenMatrix<T>::From(w_sub);
out_t_sub_e.device(*context.GetEigenDevice<Place>()) = w_sub_e;
}
}
}
out_t.Resize(framework::make_ddim(
{sequence_height, context_length * sequence_width}));
}
}
};
} // namespace math
} // namespace operators
} // namespace paddle
Write Preview
Loading…
Cancel
Save