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Merge pull request PaddlePaddle#12824 from JiayiFeng/dev_sequence_pad…
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…ding_op

Sequence pad op
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@JiayiFeng
JiayiFeng authored Aug 28, 2018
2 parents 0ee6fed + 7e0c9f5 commit 7b84c58
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Showing 12 changed files with 743 additions and 271 deletions.
1 change: 1 addition & 0 deletions paddle/fluid/API.spec
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Expand Up @@ -113,6 +113,7 @@ paddle.fluid.layers.beam_search_decode ArgSpec(args=['ids', 'scores', 'beam_size
paddle.fluid.layers.conv2d_transpose ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None))
paddle.fluid.layers.conv3d_transpose ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None))
paddle.fluid.layers.sequence_expand ArgSpec(args=['x', 'y', 'ref_level', 'name'], varargs=None, keywords=None, defaults=(-1, None))
paddle.fluid.layers.sequence_pad ArgSpec(args=['x', 'pad_value', 'maxlen'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.lstm_unit ArgSpec(args=['x_t', 'hidden_t_prev', 'cell_t_prev', 'forget_bias', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(0.0, None, None, None))
paddle.fluid.layers.reduce_sum ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None))
paddle.fluid.layers.reduce_mean ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None))
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1 change: 1 addition & 0 deletions paddle/fluid/operators/CMakeLists.txt
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Expand Up @@ -291,6 +291,7 @@ op_library(unsqueeze_op DEPS reshape_op)
op_library(squeeze_op DEPS reshape_op)
op_library(extract_rows_op DEPS memory)
op_library(flatten_op DEPS reshape_op)
op_library(sequence_pad_op DEPS sequence_padding)
op_library(unstack_op DEPS stack_op)

if (WITH_GPU)
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200 changes: 97 additions & 103 deletions paddle/fluid/operators/math/sequence_padding.cc
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Expand Up @@ -18,128 +18,122 @@ namespace paddle {
namespace operators {
namespace math {

template <typename T>
void CopyValidData(framework::Tensor* dst_tensor,
const framework::Tensor* src_tensor,
const framework::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->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>
class PaddingLoDTensorFunctor<platform::CPUDeviceContext, T> {
public:
void operator()(const platform::CPUDeviceContext& context,
const framework::LoDTensor& seq, framework::Tensor* padding,
bool norm_by_times) {
auto lod = seq.lod();
PADDLE_ENFORCE_GT(lod.size(), 0UL,
"The LoD of LoDTensor seq should not be null.");

const size_t level = 0;
framework::LoD abs_offset_lod = framework::ToAbsOffset(lod);

auto seq_dims = seq.dims();
PADDLE_ENFORCE_EQ(seq_dims[0],
static_cast<int64_t>(abs_offset_lod[level].back()),
"The first dimension of LoDTensor seq should be "
"equal to the sum of all sequences's length.");

auto padding_dims = padding->dims();
PADDLE_ENFORCE_EQ(padding_dims.size(), 3UL,
"The input padding should be a 3-D Tensor of shape "
"[max_sequence_length, num_sequences, sequence_width].");

const int64_t max_sequence_length = MaximumSequenceLength(lod, level);
PADDLE_ENFORCE_EQ(padding_dims[0], max_sequence_length,
"The first dimension of Tensor padding should be the "
"maximum length of all sequences in LoDTensor seq.");

const int64_t num_sequences = abs_offset_lod[level].size() - 1;
PADDLE_ENFORCE_EQ(padding_dims[1], num_sequences,
"The second dimension of Tensor padding should be the "
"number of sequences in LoDTensor seq.");

const int64_t sequence_width = seq.numel() / seq_dims[0];
PADDLE_ENFORCE_EQ(padding_dims[2], sequence_width,
"The third dimension of Tensor padding should be the "
"width of sequence in LoDTensor seq.");

const T* seq_data = seq.data<T>();
T* padding_data = padding->data<T>();
for (int64_t i = 0; i < max_sequence_length; ++i) {
for (int64_t j = 0; j < num_sequences; ++j) {
int64_t start_pos = abs_offset_lod[level][j];
int64_t sequence_length = abs_offset_lod[level][j + 1] - start_pos;
if (i < sequence_length) {
// i > 0 => sequence_length > 0
T scale =
norm_by_times ? (1.0f / static_cast<T>(sequence_length)) : 1.0f;
for (int64_t k = 0; k < sequence_width; ++k) {
padding_data[(i * num_sequences + j) * sequence_width + k] =
seq_data[(start_pos + i) * sequence_width + k] * scale;
}
} else {
memset(padding_data + (i * num_sequences + j) * sequence_width, 0,
sequence_width * sizeof(T));
}
const framework::LoDTensor& seq_tensor,
framework::LoDTensor* pad_tensor,
const framework::LoDTensor& 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 = framework::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->data<T>();
const T* pad_value_data = pad_value.data<T>();
if (pad_value.numel() == 1) {
for (int i = 0; i < pad_tensor->numel(); ++i) {
pad_data[i] = *pad_value_data;
}
} 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<platform::CPUDeviceContext, T> {
public:
void operator()(const platform::CPUDeviceContext& context,
framework::LoDTensor* seq, const framework::Tensor& padding,
bool norm_by_times) {
auto lod = seq->lod();
PADDLE_ENFORCE_GT(lod.size(), 0UL,
"The LoD of LoDTensor seq should not be null.");

const size_t level = 0;
framework::LoD abs_offset_lod = framework::ToAbsOffset(lod);

auto seq_dims = seq->dims();
PADDLE_ENFORCE_EQ(seq_dims[0],
static_cast<int64_t>(abs_offset_lod[level].back()),
"The first dimension of LoDTensor seq should be "
"equal to the sum of all sequences's length.");

auto padding_dims = padding.dims();
PADDLE_ENFORCE_EQ(padding_dims.size(), 3UL,
"The input padding should be a 3-D Tensor of shape "
"[max_sequnece_length, num_sequences, sequence_width].");

const int64_t max_sequence_length = MaximumSequenceLength(lod, level);
PADDLE_ENFORCE_EQ(padding_dims[0], max_sequence_length,
"The first dimension of Tensor padding should be "
"the maximum length of all sequences in LoDTensor seq.");

const int64_t num_sequences = abs_offset_lod[level].size() - 1;
PADDLE_ENFORCE_EQ(padding_dims[1], num_sequences,
"The second dimension of Tensor padding should be "
"the number of sequences in LoDTensor seq.");

const int64_t sequence_width = seq->numel() / seq_dims[0];
PADDLE_ENFORCE_EQ(padding_dims[2], sequence_width,
"The third dimension of Tensor padding should be the "
"width of sequence in LoDTensor seq.");

const T* padding_data = padding.data<T>();
T* seq_data = seq->data<T>();
for (int64_t i = 0; i < num_sequences; ++i) {
int64_t start_pos = abs_offset_lod[level][i];
int64_t sequence_length = abs_offset_lod[level][i + 1] - start_pos;
for (int64_t j = 0; j < sequence_length; ++j) {
// sequence_width > j > 0
T scale =
norm_by_times ? (1.0f / static_cast<T>(sequence_length)) : 1.0f;
for (int64_t k = 0; k < sequence_width; ++k) {
seq_data[(start_pos + j) * sequence_width + k] =
padding_data[(j * num_sequences + i) * sequence_width + k] *
scale;
}
}
const framework::LoDTensor& pad_tensor,
framework::LoDTensor* seq_tensor, int pad_seq_len = -1,
int lod_level = 0, bool norm_by_times = false,
const PadLayout layout = kBatchLengthWidth) {
auto seq_offsets = framework::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<platform::CPUDeviceContext, int>;
template class PaddingLoDTensorFunctor<platform::CPUDeviceContext, int64_t>;
template class PaddingLoDTensorFunctor<platform::CPUDeviceContext, float>;
template class PaddingLoDTensorFunctor<platform::CPUDeviceContext, double>;

template class UnpaddingLoDTensorFunctor<platform::CPUDeviceContext, int>;
template class UnpaddingLoDTensorFunctor<platform::CPUDeviceContext, int64_t>;
template class UnpaddingLoDTensorFunctor<platform::CPUDeviceContext, float>;
template class UnpaddingLoDTensorFunctor<platform::CPUDeviceContext, double>;

} // namespace math
} // namespace operators
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