未验证 提交 54101ef0 编写于 作者: H huzhiqiang 提交者: GitHub

add x86 math:sequence_scale,sequence_padding,sequence2batch,sequence_pooling. test=develop (#1884)

add x86 math:sequence_scale,sequence_padding,sequence2batch,sequence_pooling. test=develop (#1884)
上级 1ee60474
...@@ -37,10 +37,10 @@ math_library(math_function DEPS blas) ...@@ -37,10 +37,10 @@ math_library(math_function DEPS blas)
math_library(maxouting) math_library(maxouting)
math_library(pooling) math_library(pooling)
# math_library(selected_rows_functor DEPS selected_rows math_function blas) # math_library(selected_rows_functor DEPS selected_rows math_function blas)
# math_library(sequence2batch) math_library(sequence2batch)
# math_library(sequence_padding) math_library(sequence_padding)
# math_library(sequence_pooling DEPS math_function jit_kernel_helper) math_library(sequence_pooling DEPS math_function jit_kernel_helper)
# math_library(sequence_scale) math_library(sequence_scale)
math_library(softmax DEPS math_function jit_kernel_helper) math_library(softmax DEPS math_function jit_kernel_helper)
math_library(beam_search DEPS math_function) math_library(beam_search DEPS math_function)
# #
......
/* Copyright (c) 2016 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 "lite/x86/math/sequence2batch.h"
namespace paddle {
namespace lite {
namespace x86 {
namespace math {
template <typename T>
class CopyMatrixRowsFunctor<lite::TargetType::kX86, T> {
public:
void operator()(const lite::Context<lite::TargetType::kX86>& context,
const lite::Tensor& src,
std::vector<size_t> index_lod,
lite::Tensor* dst,
bool is_src_index) {
size_t* index = index_lod.data();
auto src_dims = src.dims();
auto dst_dims = dst->dims();
PADDLE_ENFORCE_EQ(
src_dims.size(), 2UL, "The src must be matrix with rank 2.");
PADDLE_ENFORCE_EQ(
dst_dims.size(), 2UL, "The dst must be matrix with rank 2.");
PADDLE_ENFORCE_EQ(
src_dims[1], dst_dims[1], "The width of src and dst must be same.");
auto height = dst_dims[0];
auto width = dst_dims[1];
auto* src_data = src.data<T>();
auto* dst_data = dst->mutable_data<T>();
const int sz = width * sizeof(T);
if (is_src_index) {
for (int i = 0; i < height; ++i) {
memcpy(dst_data + i * width, src_data + index[i] * width, sz);
}
} else {
for (int i = 0; i < height; ++i) {
memcpy(dst_data + index[i] * width, src_data + i * width, sz);
}
}
}
};
template class CopyMatrixRowsFunctor<lite::TargetType::kX86, float>;
template class CopyMatrixRowsFunctor<lite::TargetType::kX86, double>;
template class LoDTensor2BatchFunctor<lite::TargetType::kX86, float>;
template class LoDTensor2BatchFunctor<lite::TargetType::kX86, double>;
template class Batch2LoDTensorFunctor<lite::TargetType::kX86, float>;
template class Batch2LoDTensorFunctor<lite::TargetType::kX86, double>;
} // namespace math
} // namespace x86
} // namespace lite
} // namespace paddle
/* Copyright (c) 2016 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. */
#pragma once
#include <algorithm>
#include <vector>
#include "lite/core/context.h"
#include "lite/core/tensor.h"
#include "lite/fluid/eigen.h"
#include "lite/fluid/lod.h"
#include "lite/utils/paddle_enforce.h"
namespace paddle {
namespace lite {
namespace x86 {
namespace math {
template <typename T,
int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = lite::fluid::EigenMatrix<T, MajorType, IndexType>;
template <lite::TargetType Target, typename T>
class CopyMatrixRowsFunctor {
public:
// If is_src_index is true,
// copy the indexed rows of input src to the output dst.
// If is_src_index is false,
// copy the input src to the indexed rows of output dst.
// The indexed rows are based on the input index.
void operator()(const lite::Context<Target>& context,
const lite::Tensor& src,
std::vector<size_t> index_lod,
lite::Tensor* dst,
bool is_src_index);
};
template <lite::TargetType Target, typename T>
class LoDTensor2BatchFunctor {
// Calculate the length of each sequence and
// sort sequence index by the length.
// example: sequences = {s0, s1, s2}
// s0: 0 0 0 0, s1: 1 1 1 1 1, s2: 2 2 2
// seq_info[3] = {(4, 5, 1), (0, 4, 0), (9, 3, 2)}
//
struct SeqInfo {
SeqInfo(int start, int length, int seq_idx)
: start(start), length(length), seq_idx(seq_idx) {}
int start;
int length;
int seq_idx;
};
public:
void operator()(const lite::Context<Target>& context,
const lite::Tensor& lod_tensor,
lite::Tensor* batch,
bool is_cal_batch_lod,
bool is_reverse = false) const {
if (!is_cal_batch_lod) {
auto lods = batch->lod();
PADDLE_ENFORCE_GT(lods.size(),
2UL,
"The LoD of LoDTensor should inlcude at least 2-level "
"sequence information.");
PADDLE_ENFORCE_EQ(
lods[1].size(),
static_cast<size_t>(lod_tensor.dims()[0]),
"The LoD information should be consistent with the dims.");
CopyMatrixRowsFunctor<Target, T> to_batch;
to_batch(context, lod_tensor, lods[1], batch, true);
return;
}
auto lods = lod_tensor.lod();
PADDLE_ENFORCE_EQ(lods.size(), 1UL, "Only support one level sequence now.");
const auto& lod = lods[0];
std::vector<SeqInfo> seq_info;
for (size_t seq_id = 0; seq_id < lod.size() - 1; ++seq_id) {
int length = lod[seq_id + 1] - lod[seq_id];
seq_info.emplace_back(lod[seq_id], length, seq_id);
}
std::sort(seq_info.begin(), seq_info.end(), [](SeqInfo a, SeqInfo b) {
return a.length > b.length;
});
// Calculate the start position of each batch.
// example: sequences = {s0, s1, s2}
// s0: 0 0 0 0, s1: 1 1 1 1 1, s2: 2 2 2
// max_seqlen = 5,
// batchIndex = {b0, b1, b2, b3, b4}
// b0: 1 0 2, b1: 1 0 2, b2: 1 0 2, b3: 1 0, b4: 1
// batch_start_positions[6] = {0, 3, 6, 9, 11, 12}
// batch_start_positions[0] = len(b0)
// batch_start_positions[1] = len(b0) + len(b1)
// batch_start_positions[2] = len(b0) + len(b1) + len(b2)
// ...
// seq2batch_idx[12] = {4, 0, 9,
// 5, 1, 10,
// 6, 2, 11,
// 7, 3,
// 8}
// seq_order = {1, 0, 2}, the sort order.
// where 1 is the second sequence,
// 0 is the first sequence,
// 2 is the third sequence.
// The max_seqlen represents batch size after rearranging the
// input LodTensor. It is also the maximum length of input sequence.
lite::LoD batch_lods;
batch_lods.emplace_back(std::vector<size_t>{0});
batch_lods.emplace_back(std::vector<size_t>{0});
batch_lods.emplace_back(std::vector<size_t>{0});
// batch_lods[0] is the start positions for batch LoDTensor
int max_seqlen = seq_info[0].length;
batch_lods[0].resize(static_cast<size_t>(max_seqlen + 1));
// batch_lods[1] is the raw index in the input LoDTensor
batch_lods[1].resize(static_cast<size_t>(lod_tensor.dims()[0]));
// batch_lods[2] is the sort order for the input LoDTensor.
batch_lods[2].resize(seq_info.size());
size_t* batch_starts = batch_lods[0].data();
size_t* seq2batch_idx = batch_lods[1].data();
batch_starts[0] = 0;
for (int n = 0; n < max_seqlen; n++) {
auto batch_id = static_cast<int>(batch_starts[n]);
for (size_t i = 0; i < seq_info.size(); ++i) {
int seq_len = seq_info[i].length;
int start = seq_info[i].start;
if (n < seq_len) {
seq2batch_idx[batch_id] =
is_reverse ? start + seq_len - 1 - n : start + n;
batch_id++;
} else {
break;
}
}
batch_starts[n + 1] = static_cast<size_t>(batch_id);
}
size_t* seq_order = batch_lods[2].data();
for (size_t i = 0; i < seq_info.size(); ++i) {
seq_order[i] = seq_info[i].seq_idx;
}
batch->set_lod(batch_lods);
CopyMatrixRowsFunctor<Target, T> to_batch;
to_batch(context, lod_tensor, batch_lods[1], batch, true);
}
};
template <lite::TargetType Target, typename T>
class Batch2LoDTensorFunctor {
public:
void operator()(const lite::Context<Target>& context,
const lite::Tensor& batch,
lite::Tensor* lod_tensor) const {
auto in_lod = batch.lod();
PADDLE_ENFORCE_GT(in_lod.size(),
2UL,
"The LoD of LoDTensor should inlcude at least 2-level "
"sequence information.");
PADDLE_ENFORCE_EQ(
in_lod[1].size(),
static_cast<size_t>(lod_tensor->dims()[0]),
"The LoD information should be consistent with the dims.");
CopyMatrixRowsFunctor<Target, T> to_seq;
to_seq(context, batch, in_lod[1], lod_tensor, false);
}
};
} // namespace math
} // namespace x86
} // namespace lite
} // namespace paddle
/* Copyright (c) 2016 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 "lite/x86/math/sequence_padding.h"
namespace paddle {
namespace lite {
namespace x86 {
namespace math {
template <typename T>
void CopyValidData(lite::Tensor* dst_tensor,
const lite::Tensor* src_tensor,
const std::vector<size_t>& seq_offsets,
int pad_seq_len,
int step_width,
bool norm_by_len,
CopyType type,
PadLayout layout) {
int seq_num = seq_offsets.size() - 1;
const T* src_data = src_tensor->data<T>();
T* dst_data = dst_tensor->mutable_data<T>();
int seq_cpy_gap = step_width;
int pad_cpy_gap =
layout == kBatchLengthWidth ? step_width : seq_num * step_width;
for (int seq_idx = 0; seq_idx < seq_num; ++seq_idx) {
int valid_seq_len = seq_offsets[seq_idx + 1] - seq_offsets[seq_idx];
PADDLE_ENFORCE_GE(
pad_seq_len,
valid_seq_len,
"The padded sequence length can not be less than its original length.");
int seq_data_offset = seq_offsets[seq_idx] * step_width;
int pad_data_offset = layout == kBatchLengthWidth
? seq_idx * pad_seq_len * step_width
: seq_idx * step_width;
float scale = 1.0f / static_cast<float>(valid_seq_len);
for (int step_idx = 0; step_idx < valid_seq_len; ++step_idx) {
const T* src =
src_data + (type == kSeqToPad ? seq_data_offset : pad_data_offset);
T* dst =
dst_data + (type == kSeqToPad ? pad_data_offset : seq_data_offset);
memcpy(dst, src, step_width * sizeof(T));
if (norm_by_len) {
for (int i = 0; i < step_width; ++i) {
*(dst + i) *= scale;
}
}
seq_data_offset += seq_cpy_gap;
pad_data_offset += pad_cpy_gap;
}
}
}
template <typename T>
static void fast_mem_init(void* dest,
size_t dest_size,
const T* src,
size_t num_bytes) {
if (dest == nullptr || dest_size == 0 || src == nullptr) return;
memcpy(dest, src, num_bytes);
dest_size *= num_bytes;
while (dest_size > num_bytes) {
size_t remaining = dest_size - num_bytes;
size_t count = (remaining > num_bytes) ? num_bytes : remaining;
memcpy((unsigned char*)dest + num_bytes, dest, count);
num_bytes += count;
}
}
template <typename T>
class PaddingLoDTensorFunctor<lite::TargetType::kX86, T> {
public:
void operator()(const lite::Context<lite::TargetType::kX86>& context,
const lite::Tensor& seq_tensor,
lite::Tensor* pad_tensor,
const lite::Tensor& pad_value,
int pad_seq_len = -1,
int lod_level = 0,
bool norm_by_times = false,
const PadLayout layout = kBatchLengthWidth) {
auto seq_lod = seq_tensor.lod();
const auto seq_offsets = lite::fluid::ToAbsOffset(seq_lod)[lod_level];
const auto& seq_tensor_dims = seq_tensor.dims();
const auto& pad_tensor_dims = pad_tensor->dims();
if (pad_seq_len == -1) {
pad_seq_len = MaximumSequenceLength(seq_offsets);
}
int step_width = seq_tensor.numel() / seq_tensor_dims[0];
CheckDims(seq_tensor_dims,
pad_tensor_dims,
seq_offsets,
pad_seq_len,
step_width,
layout);
PADDLE_ENFORCE(pad_value.numel() == 1 || pad_value.numel() == step_width,
"The numel of 'pad_value' can only be 1 or be equal to the "
"'step_width'.");
// fill padding value
T* pad_data = pad_tensor->mutable_data<T>();
const T* pad_value_data = pad_value.data<T>();
if (pad_value.numel() == 1) {
fast_mem_init<T>(
pad_data, pad_tensor->numel(), pad_value_data, sizeof(T));
} else {
for (int i = 0; i < pad_tensor->numel(); i += step_width) {
memcpy(pad_data + i, pad_value_data, step_width * sizeof(T));
}
}
CopyValidData<T>(pad_tensor,
&seq_tensor,
seq_offsets,
pad_seq_len,
step_width,
norm_by_times,
kSeqToPad,
layout);
}
};
template <typename T>
class UnpaddingLoDTensorFunctor<lite::TargetType::kX86, T> {
public:
void operator()(const lite::Context<lite::TargetType::kX86>& context,
const lite::Tensor& pad_tensor,
lite::Tensor* seq_tensor,
int pad_seq_len = -1,
int lod_level = 0,
bool norm_by_times = false,
const PadLayout layout = kBatchLengthWidth) {
auto seq_offsets = lite::fluid::ToAbsOffset(seq_tensor->lod())[lod_level];
const auto& seq_tensor_dims = seq_tensor->dims();
const auto& pad_tensor_dims = pad_tensor.dims();
if (pad_seq_len == -1) {
pad_seq_len = MaximumSequenceLength(seq_offsets);
}
int step_width = seq_tensor->numel() / seq_tensor_dims[0];
CheckDims(seq_tensor_dims,
pad_tensor_dims,
seq_offsets,
pad_seq_len,
step_width,
layout);
CopyValidData<T>(seq_tensor,
&pad_tensor,
seq_offsets,
pad_seq_len,
step_width,
norm_by_times,
kPadToSeq,
layout);
}
};
template class PaddingLoDTensorFunctor<lite::TargetType::kX86, int>;
template class PaddingLoDTensorFunctor<lite::TargetType::kX86, int64_t>;
template class PaddingLoDTensorFunctor<lite::TargetType::kX86, float>;
template class PaddingLoDTensorFunctor<lite::TargetType::kX86, double>;
template class UnpaddingLoDTensorFunctor<lite::TargetType::kX86, int>;
template class UnpaddingLoDTensorFunctor<lite::TargetType::kX86, int64_t>;
template class UnpaddingLoDTensorFunctor<lite::TargetType::kX86, float>;
template class UnpaddingLoDTensorFunctor<lite::TargetType::kX86, double>;
} // namespace math
} // namespace x86
} // namespace lite
} // namespace paddle
/* Copyright (c) 2016 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. */
#pragma once
#include <algorithm>
#include <vector>
#include "lite/core/context.h"
#include "lite/core/tensor.h"
#include "lite/fluid/lod.h"
#include "lite/utils/paddle_enforce.h"
namespace paddle {
namespace lite {
namespace x86 {
namespace math {
enum PadLayout { kBatchLengthWidth = 0, kLengthBatchWidth };
enum CopyType { kSeqToPad, kPadToSeq };
inline static size_t MaximumSequenceLength(
const std::vector<size_t>& seq_offset) {
size_t seq_num = seq_offset.size() - 1;
size_t max_seq_len = 0;
for (size_t i = 0; i < seq_num; ++i) {
max_seq_len = std::max(max_seq_len, seq_offset[i + 1] - seq_offset[i]);
}
return max_seq_len;
}
inline static void CheckDims(const lite::DDim& seq_tensor_dims,
const lite::DDim& pad_tensor_dims,
const std::vector<size_t>& seq_offset,
int64_t padded_seq_len,
int64_t step_width,
const PadLayout& layout) {
PADDLE_ENFORCE_EQ(static_cast<size_t>(seq_tensor_dims[0]),
seq_offset.back(),
"Value of 1st dimension of the sequence tensor should be "
"equal to sum of lengths of all sequences.");
PADDLE_ENFORCE(seq_tensor_dims.size() + 1 == pad_tensor_dims.size() ||
seq_tensor_dims.size() == pad_tensor_dims.size(),
"pad_tensor's rank should be 1 greater than seq_tensor's "
"rank, or be equal with it.");
}
/*
* \brief Padding/Unpadding LoDTensor to/from normal Tensor of the shape
* [max_sequence_length, num_sequences, sequence_width].
*
* Padding sequence:
* padding[i] = seq[lod[level][i]]
* Unpadding sequence:
* seq[lod[level][i]] = padding[i]
*
* All sequences will be padded to the same length and stored in a transposed
* shape.
* Example:
* seq (s0, s0, s0, s0; s1, s1; s2, s2, s2; s3)
* padding (s0, s1, s2, s3; s0, s1, s2, 0; s0, 0, s2, 0; s0, 0, 0, 0)
*
* \param context device context of this functor.
* \param seq LoDTensor which is stored in sequence format, the shape
* is [total_sequence_length, sequence_width] where
* total_sequence_length is the sum of all sequences'
* length.
* \param padding Tensor which is padded to the same length, the shape is
* [max_sequence_length, num_sequences, sequence_width].
* \param norm_by_times whether dividing sequence's length.
*
* \note transposition is also done in this functor.
*/
template <lite::TargetType Target, typename T>
class PaddingLoDTensorFunctor {
public:
void operator()(const lite::Context<Target>& context,
const lite::Tensor& seq_tensor,
lite::Tensor* pad_tensor,
const lite::Tensor& pad_value,
int pad_seq_len = -1,
int lod_level = 0,
bool norm_by_times = false,
const PadLayout layout = kBatchLengthWidth);
};
template <lite::TargetType Target, typename T>
class UnpaddingLoDTensorFunctor {
public:
void operator()(const lite::Context<Target>& context,
const lite::Tensor& pad_tensor,
lite::Tensor* seq_tensor,
int pad_seq_len = -1,
int lod_level = 0,
bool norm_by_times = false,
const PadLayout layout = kBatchLengthWidth);
};
} // namespace math
} // namespace x86
} // namespace lite
} // namespace paddle
/* Copyright (c) 2016 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 "lite/x86/math/sequence_scale.h"
#include "lite/fluid/lod.h"
namespace paddle {
namespace lite {
namespace x86 {
namespace math {
template <typename T>
class ScaleLoDTensorFunctor<lite::TargetType::kX86, T> {
public:
void operator()(const lite::Context<lite::TargetType::kX86>& context,
const T* scales,
lite::Tensor* seq) {
const size_t level = 0;
auto lod = seq->lod();
const size_t num_seq = lod[level].size() - 1;
size_t seq_width = seq->dims()[1];
lite::LoD abs_offset_lod = lite::fluid::ToAbsOffset(lod);
T* seq_data = seq->mutable_data<T>(lite::TargetType::kX86);
for (size_t i = 0; i < num_seq; ++i) {
for (size_t j = lod[level][i] * seq_width;
j < lod[level][i + 1] * seq_width;
++j) {
seq_data[j] *= scales[i];
}
}
}
};
template class ScaleLoDTensorFunctor<lite::TargetType::kX86, float>;
} // namespace math
} // namespace x86
} // namespace lite
} // namespace paddle
/* Copyright (c) 2016 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. */
#pragma once
#include "lite/core/context.h"
#include "lite/core/tensor.h"
namespace paddle {
namespace lite {
namespace x86 {
namespace math {
/*
* \brief Scale a sequence.
*
* All sequences will be padded to the same length and stored in a transposed
* shape.
* Example:
* Given:
* seq = (s0, s0, s0, s0; s1, s1; s2, s2, s2; s3)
* scales = (2, 3, 4, 5)
* then:
* result = (2*s0, 2*s0, 2*s0, 2*s0; 3*s1, 3*s1; 4*s2, 4*s2, 4*s2; 5*s3)
*
* \param context Device context of this functor.
* \param seq LoDTensor which is stored in sequence format, the shape
* is [total_sequence_length, sequence_width] where
* total_sequence_length is the sum of all sequences'
* length.
* \param scales Array<T>. The i-th sequence will be scaled by scales[i].
* \param num_seq Number of sequence
*
*/
template <lite::TargetType Target, typename T>
class ScaleLoDTensorFunctor {
public:
void operator()(const lite::Context<Target>& context,
const T* scales,
lite::Tensor* seq);
};
} // namespace math
} // namespace x86
} // namespace lite
} // namespace paddle
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