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Change LoDTensor to DenseTensor #10611

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Change LoDTensor to DenseTensor #10611

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14 changes: 7 additions & 7 deletions lite/backends/arm/math/sequence2batch.h
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Original file line number Diff line number Diff line change
Expand Up @@ -66,7 +66,7 @@ class CopyMatrixRowsFunctor {
};

template <typename T>
class LoDTensor2BatchFunctor {
class DenseTensor2BatchFunctor {
// Calculate the length of each sequence and
// sort sequence index by the length.
// example: sequences = {s0, s1, s2}
Expand All @@ -89,7 +89,7 @@ class LoDTensor2BatchFunctor {
if (!is_cal_batch_lod) {
auto lods = batch->lod();
CHECK_GT(lods.size(), 2UL)
<< "The LoD of LoDTensor should inlcude at least 2-level "
<< "The LoD of DenseTensor should inlcude at least 2-level "
"sequence information.";
CHECK_EQ(lods[1].size(), static_cast<size_t>(lod_tensor.dims()[0]))
<< "The LoD information should be consistent with the dims.";
Expand Down Expand Up @@ -141,12 +141,12 @@ class LoDTensor2BatchFunctor {
batch_lods.emplace_back(std::vector<uint64_t>{0});
batch_lods.emplace_back(std::vector<uint64_t>{0});

// batch_lods[0] is the start positions for batch LoDTensor
// batch_lods[0] is the start positions for batch DenseTensor
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] is the raw index in the input DenseTensor
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] is the sort order for the input DenseTensor.
batch_lods[2].resize(seq_info.size());

auto batch_starts = batch_lods[0].data();
Expand Down Expand Up @@ -179,12 +179,12 @@ class LoDTensor2BatchFunctor {
};

template <typename T>
class Batch2LoDTensorFunctor {
class Batch2DenseTensorFunctor {
public:
void operator()(const Tensor& batch, Tensor* lod_tensor) const {
auto in_lod = batch.lod();
CHECK_GT(in_lod.size(), 2UL)
<< "The LoD of LoDTensor should inlcude at least 2-level "
<< "The LoD of DenseTensor should inlcude at least 2-level "
"sequence information.";
CHECK_EQ(in_lod[1].size(), static_cast<size_t>(lod_tensor->dims()[0]))
<< "The LoD information should be consistent with the dims.";
Expand Down
20 changes: 10 additions & 10 deletions lite/backends/host/math/sequence_padding.cc
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Expand Up @@ -82,7 +82,7 @@ static void fast_mem_init(void* dest,
}

template <typename T>
class PaddingLoDTensorFunctor<lite::TargetType::kHost, T> {
class PaddingDenseTensorFunctor<lite::TargetType::kHost, T> {
public:
void operator()(const lite::Context<lite::TargetType::kHost>& context,
const lite::Tensor& seq_tensor,
Expand Down Expand Up @@ -135,7 +135,7 @@ class PaddingLoDTensorFunctor<lite::TargetType::kHost, T> {
};

template <typename T>
class UnpaddingLoDTensorFunctor<lite::TargetType::kHost, T> {
class UnpaddingDenseTensorFunctor<lite::TargetType::kHost, T> {
public:
void operator()(const lite::Context<lite::TargetType::kHost>& context,
const lite::Tensor& pad_tensor,
Expand Down Expand Up @@ -170,15 +170,15 @@ class UnpaddingLoDTensorFunctor<lite::TargetType::kHost, T> {
}
};

template class PaddingLoDTensorFunctor<lite::TargetType::kHost, int>;
template class PaddingLoDTensorFunctor<lite::TargetType::kHost, int64_t>;
template class PaddingLoDTensorFunctor<lite::TargetType::kHost, float>;
template class PaddingLoDTensorFunctor<lite::TargetType::kHost, double>;
template class PaddingDenseTensorFunctor<lite::TargetType::kHost, int>;
template class PaddingDenseTensorFunctor<lite::TargetType::kHost, int64_t>;
template class PaddingDenseTensorFunctor<lite::TargetType::kHost, float>;
template class PaddingDenseTensorFunctor<lite::TargetType::kHost, double>;

template class UnpaddingLoDTensorFunctor<lite::TargetType::kHost, int>;
template class UnpaddingLoDTensorFunctor<lite::TargetType::kHost, int64_t>;
template class UnpaddingLoDTensorFunctor<lite::TargetType::kHost, float>;
template class UnpaddingLoDTensorFunctor<lite::TargetType::kHost, double>;
template class UnpaddingDenseTensorFunctor<lite::TargetType::kHost, int>;
template class UnpaddingDenseTensorFunctor<lite::TargetType::kHost, int64_t>;
template class UnpaddingDenseTensorFunctor<lite::TargetType::kHost, float>;
template class UnpaddingDenseTensorFunctor<lite::TargetType::kHost, double>;

} // namespace math
} // namespace host
Expand Down
9 changes: 5 additions & 4 deletions lite/backends/host/math/sequence_padding.h
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Expand Up @@ -57,7 +57,7 @@ inline static void CheckDims(const lite::DDim& seq_tensor_dims,
}

/*
* \brief Padding/Unpadding LoDTensor to/from normal Tensor of the shape
* \brief Padding/Unpadding DenseTensor to/from normal Tensor of the shape
* [max_sequence_length, num_sequences, sequence_width].
*
* Padding sequence:
Expand All @@ -72,7 +72,8 @@ inline static void CheckDims(const lite::DDim& seq_tensor_dims,
* 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
* \param seq DenseTensor 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.
Expand All @@ -83,7 +84,7 @@ inline static void CheckDims(const lite::DDim& seq_tensor_dims,
* \note transposition is also done in this functor.
*/
template <lite::TargetType Target, typename T>
class PaddingLoDTensorFunctor {
class PaddingDenseTensorFunctor {
public:
void operator()(const lite::Context<Target>& context,
const lite::Tensor& seq_tensor,
Expand All @@ -96,7 +97,7 @@ class PaddingLoDTensorFunctor {
};

template <lite::TargetType Target, typename T>
class UnpaddingLoDTensorFunctor {
class UnpaddingDenseTensorFunctor {
public:
void operator()(const lite::Context<Target>& context,
const lite::Tensor& pad_tensor,
Expand Down
3 changes: 2 additions & 1 deletion lite/backends/nnadapter/nnadapter/include/nnadapter/nnadapter.h
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Expand Up @@ -2273,7 +2273,8 @@ typedef enum {
* bounding boxes.
* In this case, input bboxes should be the first case with shape [N,
* M, 4/8/16/24/32].
* 2. A 2-D LoDTensor with shape [M, C]. M is the number of bbox, C is the
* 2. A 2-D DenseTensor with shape [M, C]. M is the number of bbox, C is
* the
* class number.
* In this case, input bboxes should be the second case with shape [M,
* C, 4].
Expand Down
2 changes: 1 addition & 1 deletion lite/backends/x86/math/context_project.h
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Expand Up @@ -48,7 +48,7 @@ namespace math {
* 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,
* Assumed input (X) is a [4, M, N] float DenseTensor, and X->lod()[0] = [0, 3,
* 4].
* Besides, for the sake of simplicity, we assume M=1 and N=2.
*
Expand Down
8 changes: 4 additions & 4 deletions lite/backends/x86/math/sequence2batch.cc
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Expand Up @@ -54,10 +54,10 @@ class CopyMatrixRowsFunctor<lite::TargetType::kX86, T> {
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>;
template class DenseTensor2BatchFunctor<lite::TargetType::kX86, float>;
template class DenseTensor2BatchFunctor<lite::TargetType::kX86, double>;
template class Batch2DenseTensorFunctor<lite::TargetType::kX86, float>;
template class Batch2DenseTensorFunctor<lite::TargetType::kX86, double>;

} // namespace math
} // namespace x86
Expand Down
14 changes: 7 additions & 7 deletions lite/backends/x86/math/sequence2batch.h
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Expand Up @@ -42,7 +42,7 @@ class CopyMatrixRowsFunctor {
};

template <lite::TargetType Target, typename T>
class LoDTensor2BatchFunctor {
class DenseTensor2BatchFunctor {
// Calculate the length of each sequence and
// sort sequence index by the length.
// example: sequences = {s0, s1, s2}
Expand All @@ -67,7 +67,7 @@ class LoDTensor2BatchFunctor {
if (!is_cal_batch_lod) {
auto lods = batch->lod();
CHECK_GT(lods.size(), 2UL)
<< "The LoD of LoDTensor should inlcude at least 2-level "
<< "The LoD of DenseTensor should inlcude at least 2-level "
"sequence information.";
CHECK_EQ(lods[1].size(), static_cast<size_t>(lod_tensor.dims()[0]))
<< "The LoD information should be consistent with the dims.";
Expand Down Expand Up @@ -119,12 +119,12 @@ class LoDTensor2BatchFunctor {
LoD* batch_lods = batch->mutable_lod();
batch_lods->resize(3);

// batch_lods[0] is the start positions for batch LoDTensor
// batch_lods[0] is the start positions for batch DenseTensor
int max_seqlen = seq_info[0].length;
batch_lods->at(0).resize(static_cast<size_t>(max_seqlen + 1));
// batch_lods[1] is the raw index in the input LoDTensor
// batch_lods[1] is the raw index in the input DenseTensor
batch_lods->at(1).resize(static_cast<size_t>(lod_tensor.dims()[0]));
// batch_lods[2] is the sort order for the input LoDTensor.
// batch_lods[2] is the sort order for the input DenseTensor.
batch_lods->at(2).resize(seq_info.size());

auto* batch_starts = batch_lods->at(0).data();
Expand Down Expand Up @@ -156,14 +156,14 @@ class LoDTensor2BatchFunctor {
};

template <lite::TargetType Target, typename T>
class Batch2LoDTensorFunctor {
class Batch2DenseTensorFunctor {
public:
void operator()(const lite::Context<Target>& context,
const lite::Tensor& batch,
lite::Tensor* lod_tensor) const {
auto in_lod = batch.lod();
CHECK_GT(in_lod.size(), 2UL)
<< "The LoD of LoDTensor should inlcude at least 2-level "
<< "The LoD of DenseTensor should inlcude at least 2-level "
"sequence information.";
CHECK_EQ(in_lod[1].size(), static_cast<size_t>(lod_tensor->dims()[0]))
<< "The LoD information should be consistent with the dims.";
Expand Down
4 changes: 2 additions & 2 deletions lite/backends/x86/math/sequence_scale.cc
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Expand Up @@ -21,7 +21,7 @@ namespace x86 {
namespace math {

template <typename T>
class ScaleLoDTensorFunctor<lite::TargetType::kX86, T> {
class ScaleDenseTensorFunctor<lite::TargetType::kX86, T> {
public:
void operator()(const lite::Context<lite::TargetType::kX86>& context,
const T* scales,
Expand All @@ -43,7 +43,7 @@ class ScaleLoDTensorFunctor<lite::TargetType::kX86, T> {
}
};

template class ScaleLoDTensorFunctor<lite::TargetType::kX86, float>;
template class ScaleDenseTensorFunctor<lite::TargetType::kX86, float>;

} // namespace math
} // namespace x86
Expand Down
5 changes: 3 additions & 2 deletions lite/backends/x86/math/sequence_scale.h
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Expand Up @@ -36,7 +36,8 @@ namespace math {

*
* \param context Device context of this functor.
* \param seq LoDTensor which is stored in sequence format, the shape
* \param seq DenseTensor 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.
Expand All @@ -46,7 +47,7 @@ namespace math {
*/

template <lite::TargetType Target, typename T>
class ScaleLoDTensorFunctor {
class ScaleDenseTensorFunctor {
public:
void operator()(const lite::Context<Target>& context,
const T* scales,
Expand Down
8 changes: 4 additions & 4 deletions lite/kernels/arm/gru_compute.cc
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Expand Up @@ -83,7 +83,7 @@ void GRUComputeRun(const operators::GRUParam& param,
bit_length = param.bit_length;
}

lite::arm::math::LoDTensor2BatchFunctor<float> to_batch;
lite::arm::math::DenseTensor2BatchFunctor<float> to_batch;
to_batch(*input, batch_gate, true, param.is_reverse);

if (bias) {
Expand Down Expand Up @@ -164,7 +164,7 @@ void GRUComputeRun(const operators::GRUParam& param,
}
gru_value.prev_out_value = gru_value.output_value;
}
lite::arm::math::Batch2LoDTensorFunctor<float> to_seq;
lite::arm::math::Batch2DenseTensorFunctor<float> to_seq;
*(batch_hidden->mutable_lod()) = batch_gate->lod();
to_seq(*batch_hidden, hidden);
}
Expand Down Expand Up @@ -211,7 +211,7 @@ void GRUCompute<PRECISION(kFP16)>::Run() {
hidden->mutable_data<float16_t>();
memset(batch_gate_data, 0, batch_gate->numel() * sizeof(float16_t));

lite::arm::math::LoDTensor2BatchFunctor<float16_t> to_batch;
lite::arm::math::DenseTensor2BatchFunctor<float16_t> to_batch;
to_batch(*input, batch_gate, true, param.is_reverse);

if (bias) {
Expand Down Expand Up @@ -267,7 +267,7 @@ void GRUCompute<PRECISION(kFP16)>::Run() {

gru_value.prev_out_value = gru_value.output_value;
}
lite::arm::math::Batch2LoDTensorFunctor<float16_t> to_seq;
lite::arm::math::Batch2DenseTensorFunctor<float16_t> to_seq;
*(batch_hidden->mutable_lod()) = batch_gate->lod();
to_seq(*batch_hidden, hidden);
}
Expand Down
4 changes: 2 additions & 2 deletions lite/kernels/arm/lstm_compute.cc
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Expand Up @@ -57,7 +57,7 @@ void LSTMComputeRun(const operators::LstmParam& param,
memset(cell_ptr, 0, cell_out->numel() * sizeof(float));

bool is_reverse = param.is_reverse;
lite::arm::math::LoDTensor2BatchFunctor<float> to_batch;
lite::arm::math::DenseTensor2BatchFunctor<float> to_batch;
to_batch(*input, batch_gate, true, is_reverse);

auto in_dims = input->dims();
Expand Down Expand Up @@ -247,7 +247,7 @@ void LSTMComputeRun(const operators::LstmParam& param,
lstm_value.prev_state_value = lstm_value.state_value;
}

lite::arm::math::Batch2LoDTensorFunctor<float> to_seq;
lite::arm::math::Batch2DenseTensorFunctor<float> to_seq;
auto* lod_hidden = batch_hidden.mutable_lod();
*lod_hidden = batch_gate->lod();
to_seq(batch_hidden, hidden_out);
Expand Down
2 changes: 1 addition & 1 deletion lite/kernels/arm/slice_compute.cc
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Expand Up @@ -28,7 +28,7 @@ void DealTensorArray(const operators::SliceParam& param,
const std::vector<int64_t>& ends,
bool out_is_array) {
auto in_array = param.XTensorList;
// If the input is LoDTensorArray, the rank of input is 1.
// If the input is DenseTensorArray, the rank of input is 1.
int64_t in_size = in_array->size();
int64_t start = starts[0] < 0 ? (starts[0] + in_size) : starts[0];
int64_t end = ends[0] < 0 ? (ends[0] + in_size) : ends[0];
Expand Down
38 changes: 19 additions & 19 deletions lite/kernels/host/beam_search_decode_compute.cc
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Expand Up @@ -21,8 +21,8 @@ namespace lite {
namespace kernels {
namespace host {

using LoDTensor = lite::Tensor;
using LoDTensorArray = std::vector<lite::Tensor>;
using DenseTensor = lite::Tensor;
using DenseTensorArray = std::vector<lite::Tensor>;

// all the lod have 2 levels.
// The first is source level, the second is sentence level.
Expand Down Expand Up @@ -52,15 +52,15 @@ struct BeamSearchDecoder {
* with word score.
* Param:
* sentence_vector_list: sentence_vector for each source sentence.
* id_tensor: result LoDTensor for sentences of id.
* score_tensor: result LoDTensor for sentences of score.
* id_tensor: result DenseTensor for sentences of id.
* score_tensor: result DenseTensor for sentences of score.
* reverse: whether ids of sentence in sentence_vector_list is reversed
* sort_by_score: whether to sort hypotheses of each sentence by scores.
*/
void ConvertSentenceVectorToLodTensor(
std::vector<SentenceVector<T>> sentence_vector_list,
LoDTensor* id_tensor,
LoDTensor* score_tensor,
DenseTensor* id_tensor,
DenseTensor* score_tensor,
bool reverse = true,
bool sort_by_score = true) const {
size_t src_num = sentence_vector_list.size();
Expand Down Expand Up @@ -129,12 +129,12 @@ struct BeamSearchDecoder {

/**
* Gather the hypotheses for each source sentence by backtrace though the
* LoDTensorArray step_ids whose lods reserve the path in the tree.
* DenseTensorArray step_ids whose lods reserve the path in the tree.
*/
void Backtrace(const LoDTensorArray& step_ids,
const LoDTensorArray& step_scores,
LoDTensor* id_tensor,
LoDTensor* score_tensor) const {
void Backtrace(const DenseTensorArray& step_ids,
const DenseTensorArray& step_scores,
DenseTensor* id_tensor,
DenseTensor* score_tensor) const {
CHECK(!step_ids.empty()) << "step num should be larger than 0";
CHECK_EQ(step_ids.size(), step_scores.size())
<< "step_ids and step_scores should be the same";
Expand Down Expand Up @@ -211,10 +211,10 @@ struct BeamSearchDecoder {
};

struct BeamSearchDecodeFunctor {
BeamSearchDecodeFunctor(const LoDTensorArray& step_ids,
const LoDTensorArray& step_scores,
LoDTensor* id_tensor,
LoDTensor* score_tensor,
BeamSearchDecodeFunctor(const DenseTensorArray& step_ids,
const DenseTensorArray& step_scores,
DenseTensor* id_tensor,
DenseTensor* score_tensor,
size_t beam_size,
int end_id)
: beam_size_(beam_size),
Expand All @@ -233,10 +233,10 @@ struct BeamSearchDecodeFunctor {

size_t beam_size_;
int end_id_;
const LoDTensorArray& step_ids_;
const LoDTensorArray& step_scores_;
LoDTensor* id_tensor_;
LoDTensor* score_tensor_;
const DenseTensorArray& step_ids_;
const DenseTensorArray& step_scores_;
DenseTensor* id_tensor_;
DenseTensor* score_tensor_;
};

template <>
Expand Down
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