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.
318 lines
13 KiB
318 lines
13 KiB
/* 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/operators/math/im2col.h"
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
namespace math {
|
|
|
|
using Tensor = framework::Tensor;
|
|
using LoDTensor = framework::LoDTensor;
|
|
template <typename T, int MajorType = Eigen::RowMajor,
|
|
typename IndexType = Eigen::DenseIndex>
|
|
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
|
|
|
|
/*
|
|
* \brief Context projection concatenates features in adjacent time-steps in
|
|
* a sequence. The i-th row of the output is the concatenation of
|
|
* context_length rows of the input. The context_length rows are the
|
|
* consecutive rows from the i+shift_start row.
|
|
* ContextProjectGradFunctor is the inverse process of ContextProjectFunctor.
|
|
*
|
|
* \param in Input data.
|
|
* \param Shape The shape of Input data:
|
|
* [mini-batch, input_hidden_size].
|
|
*
|
|
* \param padding_data Padding data.
|
|
* \param Shape The shape of Padding data:
|
|
* [up_pad + down_pad, input_hidden_size].
|
|
*
|
|
* \param col Col data.
|
|
* \param Shape The shape of Col data:
|
|
* [mini-batch, context_length * input_hidden_size].
|
|
*
|
|
* For a mini-batch of 2 variable lengths sentences, containing 3, and 1
|
|
* time-steps:
|
|
*
|
|
* Assumed input (X) is a [4, M, N] float LoDTensor, and X->lod()[0] = [0, 3,
|
|
* 4].
|
|
* Besides, for the sake of simplicity, we assume M=1 and N=2.
|
|
*
|
|
* X = [[a1, a2;
|
|
* b1, b2;
|
|
* c1, c2]
|
|
* [d1, d2]]
|
|
*
|
|
* This is to say that input (X) has 4 words and the dimension of each word
|
|
* representation is 2.
|
|
*
|
|
* - Case1:
|
|
* If context_start is -1 and padding_trainable is false, we use zero to pad
|
|
* instead of learned weight to pad,
|
|
* and the context_length is 3, the output (Out) is:
|
|
*
|
|
* Out =[[0, 0, a1, a2, b1, b2;
|
|
* a1, a2, b1, b2, c1, c2;
|
|
* b1, b2, c1, c2, 0, 0 ]
|
|
* [0, 0, d1, d2, 0, 0 ]]
|
|
*
|
|
* - Case2:
|
|
* If context_start is -1 and padding_trainable is true, we use learned weight
|
|
* to pad,
|
|
* and the context_length is 3, the output (Out) is:
|
|
*
|
|
* Out = [[w1, w2, a1, a2, b1, b2;
|
|
* a1, a2, b1, b2, c1, c2;
|
|
* b1, b2, c1, c2, w3, w4]
|
|
* [w1, w2, d1, d2, w3, w4]]
|
|
*
|
|
*/
|
|
|
|
template <typename Place, typename T>
|
|
class ContextProjectFunctor {
|
|
public:
|
|
void operator()(const platform::DeviceContext& context, const LoDTensor& in,
|
|
const Tensor& padding_data, bool padding_trainable,
|
|
const int context_start, const int context_length,
|
|
const int context_stride, const int up_pad,
|
|
const int down_pad, Tensor* col) {
|
|
auto lod_level_0 = in.lod()[0];
|
|
|
|
math::Im2ColFunctor<math::ColFormat::kOCF, Place, float> im2col_ocf;
|
|
|
|
std::vector<int> dilation({1, 1});
|
|
std::vector<int> padding({up_pad, 0, down_pad, 0});
|
|
std::vector<int> stride({context_stride, 1});
|
|
|
|
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]);
|
|
|
|
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]);
|
|
|
|
if (input_row_begin < input_row_end) {
|
|
Tensor in_t = in.Slice(input_row_begin, input_row_end);
|
|
|
|
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));
|
|
|
|
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, dilation, stride, padding, &out_t);
|
|
out_t.Resize({sequence_height, context_length * sequence_width});
|
|
}
|
|
}
|
|
if (padding_trainable) {
|
|
for (int i = 0; i < static_cast<int>(lod_level_0.size()) - 1; ++i) {
|
|
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]);
|
|
|
|
// add up trainable data
|
|
out_t.Resize({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;
|
|
Tensor out_t_sub = out_t.Slice(k * context_length,
|
|
k * context_length + padding_size);
|
|
Tensor w_sub = padding_data.Slice(k, k + 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;
|
|
}
|
|
}
|
|
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;
|
|
|
|
Tensor out_t_sub = out_t.Slice(
|
|
(down_pad_begin_row + t) * context_length - padding_size,
|
|
(down_pad_begin_row + t) * context_length);
|
|
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({sequence_height, context_length * sequence_width});
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
template <typename Place, typename T>
|
|
class ContextProjectGradFunctor {
|
|
public:
|
|
void operator()(const platform::DeviceContext& context, const LoDTensor& in,
|
|
bool padding_trainable, const int context_start,
|
|
const int context_length, const int context_stride,
|
|
const int up_pad, const int down_pad, bool pad_grad,
|
|
bool input_grad, Tensor* padding_data, Tensor* col) {
|
|
auto lod_level_0 = in.lod()[0];
|
|
|
|
math::Col2ImFunctor<math::ColFormat::kOCF, Place, float> col2im_ocf;
|
|
|
|
std::vector<int> dilation({1, 1});
|
|
std::vector<int> padding({up_pad, 0, down_pad, 0});
|
|
std::vector<int> stride({context_stride, 1});
|
|
|
|
int input_row_begin, input_row_end;
|
|
int sequence_height, sequence_width;
|
|
sequence_width = in.dims()[1];
|
|
|
|
if (input_grad) {
|
|
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]);
|
|
|
|
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]);
|
|
|
|
if (input_row_begin < input_row_end) {
|
|
Tensor in_t = in.Slice(input_row_begin, input_row_end);
|
|
|
|
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));
|
|
|
|
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));
|
|
|
|
col2im_ocf(context, out_t, dilation, stride, padding, &in_t);
|
|
out_t.Resize({sequence_height, context_length * sequence_width});
|
|
}
|
|
}
|
|
}
|
|
if (pad_grad) {
|
|
if (padding_trainable) {
|
|
for (int i = 0; i < static_cast<int>(lod_level_0.size()) - 1; ++i) {
|
|
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]);
|
|
out_t.Resize({sequence_height * context_length, sequence_width});
|
|
|
|
if (up_pad > 0) {
|
|
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;
|
|
Tensor out_t_sub = out_t.Slice(k * context_length,
|
|
k * context_length + padding_size);
|
|
Tensor w_sub = padding_data->Slice(k, k + padding_size);
|
|
auto out_t_sub_e = EigenMatrix<T>::From(out_t_sub);
|
|
auto w_sub_e = EigenMatrix<T>::From(w_sub);
|
|
w_sub_e.device(*context.GetEigenDevice<Place>()) =
|
|
w_sub_e + out_t_sub_e;
|
|
}
|
|
}
|
|
if (down_pad > 0) {
|
|
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;
|
|
|
|
Tensor out_t_sub = out_t.Slice(
|
|
(down_pad_begin_row + t) * context_length - padding_size,
|
|
(down_pad_begin_row + t) * context_length);
|
|
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);
|
|
w_sub_e.device(*context.GetEigenDevice<Place>()) =
|
|
w_sub_e + out_t_sub_e;
|
|
}
|
|
}
|
|
out_t.Resize({sequence_height, context_length * sequence_width});
|
|
}
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
} // namespace math
|
|
} // namespace operators
|
|
} // namespace paddle
|