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未验证 提交 91dd8a2e 编写于 作者: 张 张春乔 提交者： GitHub
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Replace LoDTensor with phi::DenseTensor in fluid\operators (#48417) 

* replace LoDTensor with phi::DenseTensor in fluid\operators

* replace LoDTensor with phi::DenseTensor in fluid\operators

* Update split_lod_tensor_op.cc

* Update warpctc_op.cc

* Update broadcast_tensors_op.cc

* Update crf_decoding_op.cc

* Update lstm_op.cc

* Update lstm_op.cc

* Update lod_reset_op.cc

* Update gru_op.cc

* Update linear_chain_crf_op.cc

* resume 2 files for confilct

* Update gru_op.cc

* Update linear_chain_crf_op.cc

* Update lstm_op.cc
上级 30315ac9
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paddle/fluid/operators/array_to_lod_tensor_op.cc
浏览文件 @ 91dd8a2e
......@@ -157,7 +157,7 @@ class ArrayToLoDTensorOp : public framework::OperatorBase {
return table_items[a].index < table_items[b].index;
});
// Build LoDTensor `out`
// Build phi::DenseTensor `out`
framework::LoD *out_lod = out->mutable_lod();
out_lod->clear();
auto prefix_lod = rank_table.coarse_lod();
......@@ -215,16 +215,18 @@ class ArrayToLoDTensorOpProtoMaker : public framework::OpProtoAndCheckerMaker {
void Make() override {
AddInput("X",
"(std::vector<LodTensor>) A vector of tensors that is going to "
"be casted to a big LoDTensor.");
"be casted to a big phi::DenseTensor.");
AddInput("RankTable",
"(LoDRankTable) RankTable provides the coarse lod information to "
"build the output LoDTensor. See "
"build the output phi::DenseTensor. See "
"'paddle/framework/lod_rank_table.h' for more details.");
AddOutput("Out", "(LoDTensor) The LoDTensor formed by input tensor array.");
AddOutput("Out",
"(phi::DenseTensor) The phi::DenseTensor formed by input tensor "
"array.");
AddComment(
R"DOC(This Op build a big LoDTensor from a std::vector<LoDTensor>
R"DOC(This Op build a big phi::DenseTensor from a std::vector<phi::DenseTensor>
and a LoDRankTable. It is supposed to be used in getting dynamic RNN's
outputs back to a normal LoDTensor. The std::vector<LoDTensor>
outputs back to a normal phi::DenseTensor. The std::vector<phi::DenseTensor>
would be the output of RNN Op and the LoDRankTable would be build
with RNN's input.)DOC");
}
......@@ -247,9 +249,9 @@ class ArrayToLoDTensorInferShape : public framework::InferShapeBase {
// detail kernel implementation.
context->SetOutputDim("Out", context->GetInputDim("X"));
// The output LoDTensor's lod_level should be input X's lod_level + 1.
// For compile-time, we call SetLoDLevel to set output's lod_level.
// For runtime, output LoDTensor's lod is determined by input X's lod and
// The output phi::DenseTensor's lod_level should be input X's lod_level
// + 1. For compile-time, we call SetLoDLevel to set output's lod_level. For
// runtime, output phi::DenseTensor's lod is determined by input X's lod and
// the level specified by input RandTable.
// We cannot get X's detail lod and RankTable's level in this function, so
// leave this work to the detail kernel implementation.
......
paddle/fluid/operators/assert_op.cc
浏览文件 @ 91dd8a2e
......@@ -41,8 +41,6 @@ const char kSummarize[] = "summarize";
namespace paddle {
namespace operators {
using LoDTensor = phi::DenseTensor;
class AssertOp : public framework::OperatorBase {
public:
AssertOp(const std::string &type,
......@@ -58,7 +56,7 @@ class AssertOp : public framework::OperatorBase {
PADDLE_ENFORCE_NOT_NULL(cond_var_ptr,
platform::errors::NotFound(
"Input(Condition) of AssertOp is not found."));
const LoDTensor &cond = cond_var_ptr->Get<LoDTensor>();
const phi::DenseTensor &cond = cond_var_ptr->Get<phi::DenseTensor>();
PADDLE_ENFORCE_EQ(
cond.dims(),
phi::make_ddim({1}),
......@@ -78,7 +76,7 @@ class AssertOp : public framework::OperatorBase {
const std::vector<std::string> &x_names = Inputs(kData);
for (const std::string &name : x_names) {
const framework::Variable *x_var_ptr = scope.FindVar(name);
const phi::DenseTensor &x_tensor = x_var_ptr->Get<LoDTensor>();
const phi::DenseTensor &x_tensor = x_var_ptr->Get<phi::DenseTensor>();
formatter.Print(x_tensor, name);
}
......
paddle/fluid/operators/assign_op.cc
浏览文件 @ 91dd8a2e
......@@ -79,16 +79,19 @@ class AssignInferVarType : public framework::VarTypeInference {
class AssignOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(LoDTensor, SelectedRows or LoDTensorArray) The input variable "
"could be LoDTensor, SelectedRows or LoDTensorArray.")
AddInput(
"X",
"(phi::DenseTensor, SelectedRows or phi::DenseTensorArray) The input "
"variable "
"could be phi::DenseTensor, SelectedRows or phi::DenseTensorArray.")
.AsDispensable();
AddOutput("Out",
"(LoDTensor, SelectedRows or LoDTensorArray) The type of output "
"(phi::DenseTensor, SelectedRows or phi::DenseTensorArray) The "
"type of output "
"is the same as input X.");
AddComment(R"DOC(Assign Operator
Out = X, when type in [LoDTensor/SelectedRows/LoDTensorArray]
Out = X, when type in [phi::DenseTensor/SelectedRows/phi::DenseTensorArray]
raise error if the type is not listed above.
)DOC");
}
......
paddle/fluid/operators/assign_pos_op.cu
浏览文件 @ 91dd8a2e
......@@ -59,13 +59,14 @@ class AssignPosCUDAKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& context) const override {
// assign pos decides which tokens should be fetched belong to specially
// counter orderingly.
auto cum_count = context.Input<LoDTensor>(
auto cum_count = context.Input<phi::DenseTensor>(
"cum_count"); // (counter number) int32 | int64
auto numbers =
context.Input<LoDTensor>("X"); // (batch_size * seq_len, topk) int32
auto numbers = context.Input<phi::DenseTensor>(
"X"); // (batch_size * seq_len, topk) int32
auto eff_num_len =
context.Input<LoDTensor>("eff_num_len"); // (sum(cum_count))
auto out = context.Output<LoDTensor>("Out"); // (cum_count) value ranges
context.Input<phi::DenseTensor>("eff_num_len"); // (sum(cum_count))
auto out =
context.Output<phi::DenseTensor>("Out"); // (cum_count) value ranges
// from 0 to batch_size *
// seq_len * topk
auto place = context.GetPlace();
......
paddle/fluid/operators/assign_pos_op.h
浏览文件 @ 91dd8a2e
......@@ -20,8 +20,6 @@ limitations under the License. */
namespace paddle {
namespace operators {
using LoDTensor = phi::DenseTensor;
template <typename T>
class AssignPosOpCPUKernel : public framework::OpKernel<T> {
public:
......
paddle/fluid/operators/attention_lstm_op.cc
浏览文件 @ 91dd8a2e
......@@ -205,10 +205,11 @@ framework::OpKernelType AttentionLSTMOp::GetExpectedKernelType(
}
void AttentionLSTMOpMaker::Make() {
AddInput("X",
"(LoDTensor) the input is a LodTensor, which support "
AddInput(
"X",
"(phi::DenseTensor) the input is a LodTensor, which support "
"variable-time length input sequence. The underlying tensor in "
"this LoDTensor is a matrix with shape (T X M), where T is the "
"this phi::DenseTensor is a matrix with shape (T X M), where T is the "
"total time steps in this mini-batch, M is the dim size of x.");
AddInput("C0",
"(Tensor) LSTM C0"
......@@ -247,11 +248,13 @@ void AttentionLSTMOpMaker::Make() {
"Note: we should add the bias of hidden and context accorindg to "
"the same gate: "
"{B_forget, B_input, B_output, B_cell}");
AddOutput("Hidden",
"(LoDTensor) (same as LSTMOp) the hidden state of LSTM operator. "
AddOutput(
"Hidden",
"(phi::DenseTensor) (same as LSTMOp) the hidden state of LSTM operator. "
"The shape is (T x D), and lod is the same with the `Input`.");
AddOutput("Cell",
"(LoDTensor) (same as LSTMOp) the cell state of LSTM operator. "
AddOutput(
"Cell",
"(phi::DenseTensor) (same as LSTMOp) the cell state of LSTM operator. "
"The shape is (T x D), and lod is the same with the `Input`.");
AddOutput("AttentionedX",
"(Tensor) shape is (T x 1), the result after X * AttentionWeight,"
......@@ -339,7 +342,7 @@ class AttentionLSTMKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
using DeviceContext = phi::CPUContext;
auto* x = ctx.Input<LoDTensor>("X");
auto* x = ctx.Input<phi::DenseTensor>("X");
auto* h0 = ctx.Input<phi::DenseTensor>("H0");
auto* c0 = ctx.Input<phi::DenseTensor>("C0");
auto* atten_w = ctx.Input<phi::DenseTensor>("AttentionWeight");
......@@ -350,8 +353,8 @@ class AttentionLSTMKernel : public framework::OpKernel<T> {
auto* lstm_w = ctx.Input<phi::DenseTensor>("LSTMWeight");
auto* lstm_b = ctx.Input<phi::DenseTensor>("LSTMBias");
auto* hidden_out = ctx.Output<LoDTensor>("Hidden");
auto* cell_out = ctx.Output<LoDTensor>("Cell");
auto* hidden_out = ctx.Output<phi::DenseTensor>("Hidden");
auto* cell_out = ctx.Output<phi::DenseTensor>("Cell");
auto* atted_x = ctx.Output<phi::DenseTensor>("AttentionedX");
auto* fc_out = ctx.Output<phi::DenseTensor>("AttentionFCOut");
auto* lstm_x = ctx.Output<phi::DenseTensor>("LSTMX");
......
paddle/fluid/operators/attention_lstm_op.h
浏览文件 @ 91dd8a2e
......@@ -18,7 +18,6 @@ limitations under the License. */
namespace paddle {
namespace operators {
using LoDTensor = phi::DenseTensor;
using Tensor = phi::DenseTensor;
class AttentionLSTMOp : public framework::OperatorWithKernel {
......
paddle/fluid/operators/batch_norm_op.cc
浏览文件 @ 91dd8a2e
......@@ -383,8 +383,8 @@ framework::OpKernelType BatchNormGradOp::GetExpectedKernelType(
const Tensor *t = nullptr;
if (var->IsType<Tensor>()) {
t = &var->Get<Tensor>();
} else if (var->IsType<LoDTensor>()) {
t = &var->Get<LoDTensor>();
} else if (var->IsType<phi::DenseTensor>()) {
t = &var->Get<phi::DenseTensor>();
}
if (t == nullptr) {
PADDLE_THROW(
......@@ -525,8 +525,8 @@ framework::OpKernelType BatchNormDoubleGradOp::GetExpectedKernelType(
const Tensor *t = nullptr;
if (var->IsType<Tensor>()) {
t = &var->Get<Tensor>();
} else if (var->IsType<LoDTensor>()) {
t = &var->Get<LoDTensor>();
} else if (var->IsType<phi::DenseTensor>()) {
t = &var->Get<phi::DenseTensor>();
}
if (t == nullptr) {
PADDLE_THROW(
......
paddle/fluid/operators/batch_norm_op.h
浏览文件 @ 91dd8a2e
......@@ -28,7 +28,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
using DataLayout = phi::DataLayout;
template <typename T>
......
paddle/fluid/operators/beam_search_decode_op.h
浏览文件 @ 91dd8a2e
......@@ -23,8 +23,8 @@ namespace operators {
struct BeamSearchDecodeFunctor {
BeamSearchDecodeFunctor(const LoDTensorArray& step_ids,
const LoDTensorArray& step_scores,
LoDTensor* id_tensor,
LoDTensor* score_tensor,
phi::DenseTensor* id_tensor,
phi::DenseTensor* score_tensor,
size_t beam_size,
int end_id)
: beam_size_(beam_size),
......@@ -119,8 +119,8 @@ struct BeamSearchDecodeFunctor {
const LoDTensorArray& step_scores_origin_;
LoDTensorArray step_ids_ = LoDTensorArray();
LoDTensorArray step_scores_ = LoDTensorArray();
LoDTensor* id_tensor_;
LoDTensor* score_tensor_;
phi::DenseTensor* id_tensor_;
phi::DenseTensor* score_tensor_;
};
template <typename DeviceContext, typename T>
......@@ -164,8 +164,10 @@ class BeamSearchDecodeOpKernel : public framework::OpKernel<T> {
int end_id = context.Attr<int>("end_id");
// prepare output
LoDTensor* sentenceIds = context.Output<LoDTensor>("SentenceIds");
LoDTensor* sentenceScores = context.Output<LoDTensor>("SentenceScores");
phi::DenseTensor* sentenceIds =
context.Output<phi::DenseTensor>("SentenceIds");
phi::DenseTensor* sentenceScores =
context.Output<phi::DenseTensor>("SentenceScores");
BeamSearchDecodeFunctor bs(
*ids, *scores, sentenceIds, sentenceScores, beam_size, end_id);
......
paddle/fluid/operators/beam_search_decode_op_def.h
浏览文件 @ 91dd8a2e
......@@ -23,7 +23,6 @@ limitations under the License. */
namespace paddle {
namespace operators {
using LoDTensor = phi::DenseTensor;
using LoDTensorArray = framework::LoDTensorArray;
// all the lod have 2 levels.
......@@ -54,15 +53,15 @@ 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 phi::DenseTensor for sentences of id.
* score_tensor: result phi::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,
phi::DenseTensor* id_tensor,
phi::DenseTensor* score_tensor,
bool reverse = true,
bool sort_by_score = true) const;
......@@ -72,8 +71,8 @@ struct BeamSearchDecoder {
*/
void Backtrace(const LoDTensorArray& step_ids,
const LoDTensorArray& step_scores,
LoDTensor* id_tensor,
LoDTensor* score_tensor) const;
phi::DenseTensor* id_tensor,
phi::DenseTensor* score_tensor) const;
size_t beam_size_;
int end_id_;
......@@ -82,8 +81,8 @@ struct BeamSearchDecoder {
template <typename T>
void BeamSearchDecoder<T>::ConvertSentenceVectorToLodTensor(
std::vector<SentenceVector<T>> sentence_vector_list,
LoDTensor* id_tensor,
LoDTensor* score_tensor,
phi::DenseTensor* id_tensor,
phi::DenseTensor* score_tensor,
bool reverse,
bool sort_by_score) const {
size_t src_num = sentence_vector_list.size();
......@@ -158,8 +157,8 @@ void BeamSearchDecoder<T>::ConvertSentenceVectorToLodTensor(
template <typename T>
void BeamSearchDecoder<T>::Backtrace(const LoDTensorArray& step_ids,
const LoDTensorArray& step_scores,
LoDTensor* id_tensor,
LoDTensor* score_tensor) const {
phi::DenseTensor* id_tensor,
phi::DenseTensor* score_tensor) const {
PADDLE_ENFORCE_NE(
step_ids.empty(),
true,
......
paddle/fluid/operators/beam_search_decode_op_test.cc
浏览文件 @ 91dd8a2e
......@@ -18,7 +18,6 @@ limitations under the License. */
using CPUPlace = paddle::platform::CPUPlace;
using LoD = paddle::framework::LoD;
using LoDTensor = phi::DenseTensor;
using LoDTensorArray = paddle::framework::LoDTensorArray;
template <typename T>
......@@ -59,7 +58,7 @@ void GenerateExample(const std::vector<size_t>& level_0,
lod.push_back(level_1);
// Ids
LoDTensor tensor_id;
phi::DenseTensor tensor_id;
tensor_id.set_lod(lod);
tensor_id.Resize({static_cast<int64_t>(data.size())});
// malloc memory
......@@ -69,7 +68,7 @@ void GenerateExample(const std::vector<size_t>& level_0,
}
// Scores
LoDTensor tensor_score;
phi::DenseTensor tensor_score;
tensor_score.set_lod(lod);
tensor_score.Resize({static_cast<int64_t>(data.size())});
// malloc memory
......@@ -124,8 +123,8 @@ void BeamSearchDecodeTestFrame() {
BeamSearchDecoder<T> helper(2, 1); // beam_size = 2, end_id = 1
LoDTensor id_tensor;
LoDTensor score_tensor;
phi::DenseTensor id_tensor;
phi::DenseTensor score_tensor;
helper.Backtrace(ids, scores, &id_tensor, &score_tensor);
LoD lod = id_tensor.lod();
......
paddle/fluid/operators/beam_search_decode_op_xpu.cc
浏览文件 @ 91dd8a2e
......@@ -62,20 +62,21 @@ class BeamSearchDecodeXPUKernel : public framework::OpKernel<T> {
int end_id = context.Attr<int>("end_id");
// prepare output
LoDTensor* sentenceIds = nullptr;
LoDTensor* sentenceScores = nullptr;
phi::DenseTensor* sentenceIds = nullptr;
phi::DenseTensor* sentenceScores = nullptr;
LoDTensor* sentenceIds_temp = context.Output<LoDTensor>("SentenceIds");
LoDTensor* sentenceScores_temp =
context.Output<LoDTensor>("SentenceScores");
phi::DenseTensor* sentenceIds_temp =
context.Output<phi::DenseTensor>("SentenceIds");
phi::DenseTensor* sentenceScores_temp =
context.Output<phi::DenseTensor>("SentenceScores");
if (platform::is_xpu_place(ids->at(0).place())) {
sentenceIds = new LoDTensor();
sentenceIds = new phi::DenseTensor();
sentenceIds->set_lod(sentenceIds_temp->lod());
}
if (platform::is_xpu_place(ids->at(0).place())) {
sentenceScores = new LoDTensor();
sentenceScores = new phi::DenseTensor();
sentenceScores->set_lod(sentenceScores_temp->lod());
}
......
paddle/fluid/operators/beam_search_decode_op_xpu.h
浏览文件 @ 91dd8a2e
......@@ -18,7 +18,7 @@ limitations under the License. */
namespace paddle {
namespace operators {
int SetMeta(const LoDTensor& srcTensor, LoDTensor* dstTensor) {
int SetMeta(const phi::DenseTensor& srcTensor, phi::DenseTensor* dstTensor) {
if (srcTensor.dtype() == paddle::experimental::DataType::INT32 ||
srcTensor.dtype() == paddle::experimental::DataType::INT64 ||
srcTensor.dtype() == paddle::experimental::DataType::FLOAT32 ||
......@@ -33,8 +33,8 @@ int SetMeta(const LoDTensor& srcTensor, LoDTensor* dstTensor) {
return xpu::Error_t::SUCCESS;
}
template <typename T>
int CopyTensorByXPU(const LoDTensor& srcTensor,
LoDTensor* dstTensor,
int CopyTensorByXPU(const phi::DenseTensor& srcTensor,
phi::DenseTensor* dstTensor,
int flag,
const Place& place) {
const T* srcData = srcTensor.template data<T>();
......@@ -67,8 +67,8 @@ int CopyTensorByXPU(const LoDTensor& srcTensor,
return xpu::Error_t::SUCCESS;
}
const int CopyTensorByType(const LoDTensor& srcTensor,
LoDTensor* dstTensor,
const int CopyTensorByType(const phi::DenseTensor& srcTensor,
phi::DenseTensor* dstTensor,
int flag,
const Place& place) {
int r = 0;
......@@ -97,8 +97,8 @@ const int CopyTensorByType(const LoDTensor& srcTensor,
struct BeamSearchDecodeXPUFunctor {
BeamSearchDecodeXPUFunctor(const LoDTensorArray& step_ids,
const LoDTensorArray& step_scores,
LoDTensor* id_tensor,
LoDTensor* score_tensor,
phi::DenseTensor* id_tensor,
phi::DenseTensor* score_tensor,
size_t beam_size,
int end_id)
: beam_size_(beam_size),
......@@ -164,8 +164,8 @@ struct BeamSearchDecodeXPUFunctor {
// scenarios.
LoDTensorArray step_ids_ = LoDTensorArray();
LoDTensorArray step_scores_ = LoDTensorArray();
LoDTensor* id_tensor_;
LoDTensor* score_tensor_;
phi::DenseTensor* id_tensor_;
phi::DenseTensor* score_tensor_;
};
} // namespace operators
......
paddle/fluid/operators/beam_search_decode_op_xpu_test.cc
浏览文件 @ 91dd8a2e
......@@ -19,7 +19,6 @@ limitations under the License. */
using CPUPlace = paddle::platform::CPUPlace;
using XPUPlace = paddle::platform::XPUPlace;
using LoD = paddle::framework::LoD;
using LoDTensor = phi::DenseTensor;
using LoDTensorArray = paddle::framework::LoDTensorArray;
template <typename T>
......@@ -67,7 +66,7 @@ void GenerateXPUExample(const std::vector<size_t>& level_0,
lod.push_back(level_1);
// Ids
LoDTensor tensor_id_cpu;
phi::DenseTensor tensor_id_cpu;
tensor_id_cpu.set_lod(lod);
tensor_id_cpu.Resize({static_cast<int64_t>(data.size())});
// malloc memory
......@@ -76,7 +75,7 @@ void GenerateXPUExample(const std::vector<size_t>& level_0,
id_cpu_ptr[i] = static_cast<int64_t>(data.at(i));
}
LoDTensor tensor_id;
phi::DenseTensor tensor_id;
const phi::DenseTensorMeta meta_data_id(paddle::experimental::DataType::INT64,
tensor_id_cpu.dims());
tensor_id.set_meta(meta_data_id);
......@@ -90,7 +89,7 @@ void GenerateXPUExample(const std::vector<size_t>& level_0,
tensor_id_cpu.numel() * sizeof(int64_t));
// Scores
LoDTensor tensor_score_cpu;
phi::DenseTensor tensor_score_cpu;
tensor_score_cpu.set_lod(lod);
tensor_score_cpu.Resize({static_cast<int64_t>(data.size())});
// malloc memory
......@@ -99,7 +98,7 @@ void GenerateXPUExample(const std::vector<size_t>& level_0,
score_cpu_ptr[i] = static_cast<T>(data.at(i));
}
LoDTensor tensor_score;
phi::DenseTensor tensor_score;
if (std::is_same<float, T>::value) {
const phi::DenseTensorMeta meta_data_score(
......@@ -178,8 +177,8 @@ void BeamSearchDecodeTestByXPUFrame() {
ASSERT_EQ(ids.size(), 5UL);
ASSERT_EQ(scores.size(), 5UL);
LoDTensor id_tensor_cpu;
LoDTensor score_tensor_cpu;
phi::DenseTensor id_tensor_cpu;
phi::DenseTensor score_tensor_cpu;
paddle::operators::BeamSearchDecodeXPUFunctor bs_xpu(
ids, scores, &id_tensor_cpu, &score_tensor_cpu, 2, 1);
......
paddle/fluid/operators/beam_search_op.cc
浏览文件 @ 91dd8a2e
......@@ -27,20 +27,24 @@ class BeamSearchOpMaker : public framework::OpProtoAndCheckerMaker {
void Make() override {
// inputs and outputs stored in proto
AddInput("pre_ids",
"(LoDTensor) The LoDTensor containing the selected ids at the "
"(phi::DenseTensor) The phi::DenseTensor containing the selected "
"ids at the "
"previous step. It should be a tensor with shape (batch_size, 1) "
"and lod `[[0, 1, ... , batch_size], [0, 1, ..., batch_size]]` at "
"the first step.");
AddInput("pre_scores",
"(LoDTensor) The LoDTensor containing the accumulated "
AddInput(
"pre_scores",
"(phi::DenseTensor) The phi::DenseTensor containing the accumulated "
"scores corresponding to the selected ids at the previous step.");
AddInput("ids",
"(LoDTensor) The LoDTensor containing the candidates ids. Its "
"(phi::DenseTensor) The phi::DenseTensor containing the "
"candidates ids. Its "
"shape should be (batch_size * beam_size, W). If not set, it will "
"be calculated out according to Input(scores) in this operator.")
.AsDispensable();
AddInput("scores",
"(LoDTensor) The LoDTensor containing the current scores "
AddInput(
"scores",
"(phi::DenseTensor) The phi::DenseTensor containing the current scores "
"corresponding to Input(ids). If Input(ids) is not nullptr, its "
"shape is the same as that of Input(ids)."
"If is_accumulated is true, Input(scores) is accumulated scores "
......@@ -49,15 +53,16 @@ class BeamSearchOpMaker : public framework::OpProtoAndCheckerMaker {
"first.");
AddOutput("selected_ids",
"A LodTensor that stores the IDs selected by beam search.");
AddOutput("selected_scores",
"A LoDTensor containing the accumulated scores corresponding to "
AddOutput(
"selected_scores",
"A phi::DenseTensor containing the accumulated scores corresponding to "
"Output(selected_ids).");
AddOutput("parent_idx",
"A Tensor preserving the selected_ids' parent index in pre_ids.")
.AsDispensable();
// Attributes stored in AttributeMap
AddAttr<int>("level", "the level of LoDTensor");
AddAttr<int>("level", "the level of phi::DenseTensor");
AddAttr<int>("beam_size", "beam size for beam search");
AddAttr<int>("end_id",
"the token id which indicates the end of a sequence");
......
paddle/fluid/operators/broadcast_tensors_op.cc
浏览文件 @ 91dd8a2e
......@@ -41,10 +41,11 @@ class BroadcastTensorsOp : public framework::OperatorWithKernel {
class BroadcastTensorsOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
AddInput(
"X",
"A Varaible list. The shape and data type of the list elements"
"should be consistent. Variable can be multi-dimensional Tensor"
"or LoDTensor, and data types can be: bool, float16, float32, "
"or phi::DenseTensor, and data types can be: bool, float16, float32, "
"float64, int32, "
"int64.")
.AsDuplicable();
......@@ -54,7 +55,7 @@ class BroadcastTensorsOpMaker : public framework::OpProtoAndCheckerMaker {
.AsDuplicable();
AddComment(
R"DOC(This OP is used to broadcast a vector of inputs
with Tensor or LoDTensor type, following broadcast semantics.)DOC");
with phi::DenseTensor type, following broadcast semantics.)DOC");
}
};
......
paddle/fluid/operators/check_memory_continue_op.cc
浏览文件 @ 91dd8a2e
......@@ -31,11 +31,12 @@ class CheckMemoryContinueOp : public framework::OperatorWithKernel {
class CheckMemoryContinueOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(vector<LoDTensor>) The input tensors.").AsDuplicable();
AddOutput("Out", "(LoDTensor) The output tensor.").AsDuplicable();
AddOutput(
"XOut",
"(vector<LoDTensor>) The output tensors which are the same as x. It is "
AddInput("X", "(vector<phi::DenseTensor>) The input tensors.")
.AsDuplicable();
AddOutput("Out", "(phi::DenseTensor) The output tensor.").AsDuplicable();
AddOutput("XOut",
"(vector<phi::DenseTensor>) The output tensors which are the "
"same as x. It is "
"used to build the graph dependency");
AddComment(R"DOC(
CheckMemoryContinue Operator.
......
paddle/fluid/operators/chunk_eval_op.h
浏览文件 @ 91dd8a2e
......@@ -23,8 +23,6 @@ limitations under the License. */
namespace paddle {
namespace operators {
using LoDTensor = phi::DenseTensor;
template <typename DeviceContext, typename T>
class ChunkEvalKernel : public framework::OpKernel<T> {
public:
......@@ -187,9 +185,9 @@ class ChunkEvalKernel : public framework::OpKernel<T> {
context.Attr<std::vector<int>>("excluded_chunk_types").begin(),
context.Attr<std::vector<int>>("excluded_chunk_types").end());
auto* inference = context.Input<LoDTensor>("Inference");
auto* inference = context.Input<phi::DenseTensor>("Inference");
auto place = inference->place();
auto* label = context.Input<LoDTensor>("Label");
auto* label = context.Input<phi::DenseTensor>("Label");
auto* precision = context.Output<phi::DenseTensor>("Precision");
auto* recall = context.Output<phi::DenseTensor>("Recall");
auto* f1 = context.Output<phi::DenseTensor>("F1-Score");
......
paddle/fluid/operators/coalesce_tensor_op.cc
浏览文件 @ 91dd8a2e
......@@ -120,7 +120,7 @@ class CoalesceTensorOpKernel : public framework::OpKernel<T> {
in_var_names.size(),
out_var_names.size()));
// Input & Output check: only support LoDTensor
// Input & Output check: only support phi::DenseTensor
bool has_not_init_in_vars = false;
for (size_t i = 0; i < in_tensors.size(); ++i) {
PADDLE_ENFORCE_NOT_NULL(
......@@ -426,17 +426,17 @@ class CoalesceTensorOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("Input",
"(vector<LoDTensor>) The input tensors of"
"(vector<phi::DenseTensor>) The input tensors of"
" coalesce_tensor operator.")
.AsDuplicable();
AddOutput("Output",
"(vector<LoDTensor>) The output "
"(vector<phi::DenseTensor>) The output "
"tensors of coalesce_tensor operator. And the address "
"of output tensors are continuous, they are sliced from the "
"tensor of FusedOutput.")
.AsDuplicable();
AddOutput("FusedOutput",
"(LoDTensor) The output tensor "
"(phi::DenseTensor) The output tensor "
"of coalesce_tensor operator. And the tensors of"
" Output is sliced from the tensor of FusedOutput.");
AddAttr<int>("dtype", "The output data type.");
......
paddle/fluid/operators/common_infer_shape_functions.cc
浏览文件 @ 91dd8a2e
......@@ -154,7 +154,7 @@ void BinaryOpBroadcastInferShape(framework::InferShapeContext *ctx) {
ctx->GetInputsVarType(y_name).front(),
framework::proto::VarType::LOD_TENSOR,
platform::errors::InvalidArgument(
"The var type of input %s should be LoDTensor, but got %s.",
"The var type of input %s should be phi::DenseTensor, but got %s.",
ctx->Inputs(y_name).front(),
ctx->GetInputsVarType(y_name).front()));
......
paddle/fluid/operators/copy_cross_scope_op.cc
浏览文件 @ 91dd8a2e
......@@ -30,7 +30,6 @@ class OpBase;
} // namespace imperative
} // namespace paddle
using LoDTensor = phi::DenseTensor;
using Tensor = phi::DenseTensor;
namespace paddle {
......@@ -64,7 +63,7 @@ class CopyCrossScopeOp : public framework::OperatorBase {
PADDLE_ENFORCE_NOT_NULL(
id_var,
platform::errors::NotFound("No variable with name %s found.", id_name));
auto id_tensor = id_var->GetMutable<LoDTensor>();
auto id_tensor = id_var->GetMutable<phi::DenseTensor>();
auto it = scope.kids().begin();
phi::DenseTensor cpu_id_tensor;
paddle::framework::TensorCopySync(
......@@ -88,8 +87,8 @@ class CopyCrossScopeOp : public framework::OperatorBase {
platform::errors::NotFound(
"No variable with name %s found in destination scope.",
x_name));
auto dst_tensor = dst_var->GetMutable<LoDTensor>();
auto main_tensor = main_var->GetMutable<LoDTensor>();
auto dst_tensor = dst_var->GetMutable<phi::DenseTensor>();
auto main_tensor = main_var->GetMutable<phi::DenseTensor>();
paddle::framework::TensorCopySync(
*dst_tensor, main_tensor->place(), main_tensor);
}
......@@ -109,8 +108,8 @@ class CopyCrossScopeOp : public framework::OperatorBase {
dst_var,
platform::errors::NotFound(
"No variable with name %s found in destination scope.", x_name));
auto src_tensor = source_var->GetMutable<LoDTensor>();
auto dst_tensor = dst_var->GetMutable<LoDTensor>();
auto src_tensor = source_var->GetMutable<phi::DenseTensor>();
auto dst_tensor = dst_var->GetMutable<phi::DenseTensor>();
paddle::framework::TensorCopySync(
*src_tensor, dst_tensor->place(), dst_tensor);
......@@ -120,7 +119,7 @@ class CopyCrossScopeOp : public framework::OperatorBase {
main_var,
platform::errors::NotFound(
"No variable with name %s found in destination scope.", x_name));
auto main_tensor = main_var->GetMutable<LoDTensor>();
auto main_tensor = main_var->GetMutable<phi::DenseTensor>();
paddle::framework::TensorCopySync(
*dst_tensor, main_tensor->place(), main_tensor);
}
......
paddle/fluid/operators/crf_decoding_op.cc
浏览文件 @ 91dd8a2e
......@@ -21,7 +21,8 @@ class CRFDecodingOpMaker : public framework::OpProtoAndCheckerMaker {
void Make() override {
AddInput(
"Emission",
"(Tensor/LoDTensor). For a LoDTensor input, its shape is [N x D] "
"(Tensor/phi::DenseTensor). For a phi::DenseTensor input, its shape is "
"[N x D] "
"where N is the total sequence length of the mini-batch and D is "
"the total tag number. While for a tensor input, its shape is "
"[B X S X D] with B the batch size and S the sequence length of each "
......@@ -39,14 +40,14 @@ class CRFDecodingOpMaker : public framework::OpProtoAndCheckerMaker {
"The data type is the same as Input(Emission).");
AddInput(
"Label",
"(Tensor/LoDTensor). The ground truth with shape "
"[N x 1] (for LoDTensor) or [B x S] (for Tensor). This input is "
"(phi::DenseTensor). The ground truth with shape "
"[N x 1] (for phi::DenseTensor) or [B x S] (for Tensor). This input is "
"optional. See more details in the operator's comments. The data type "
"is int64.")
.AsDispensable();
AddOutput(
"ViterbiPath",
"(Tensor/LoDTensor). The decoding results. What to "
"(phi::DenseTensor). The decoding results. What to "
"return changes depending on whether the Input(Label) (the ground "
"truth) is given. See more details in the operator's comment. "
"The data type is int64.");
......
paddle/fluid/operators/crf_decoding_op.h
浏览文件 @ 91dd8a2e
......@@ -24,15 +24,14 @@ namespace paddle {
namespace operators {
using framework::LoD;
using LoDTensor = phi::DenseTensor;
template <typename DeviceContext, typename T>
class CRFDecodingOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* emission_weights = ctx.Input<LoDTensor>("Emission");
auto* emission_weights = ctx.Input<phi::DenseTensor>("Emission");
auto* transition_weights = ctx.Input<phi::DenseTensor>("Transition");
auto* label = ctx.Input<LoDTensor>("Label");
auto* label = ctx.Input<phi::DenseTensor>("Label");
auto* decoded_path = ctx.Output<phi::DenseTensor>("ViterbiPath");
int64_t* path = decoded_path->mutable_data<int64_t>(platform::CPUPlace());
......
paddle/fluid/operators/ctc_align_op.cu
浏览文件 @ 91dd8a2e
......@@ -85,8 +85,8 @@ class CTCAlignOpCUDAKernel : public framework::OpKernel<T> {
platform::errors::InvalidArgument(
"CTCAlign operator CUDA kernel must use CUDAPlace "
"rather than CPUPlace."));
auto* input = ctx.Input<LoDTensor>("Input");
auto* output = ctx.Output<LoDTensor>("Output");
auto* input = ctx.Input<phi::DenseTensor>("Input");
auto* output = ctx.Output<phi::DenseTensor>("Output");
const int blank = ctx.Attr<int>("blank");
const int merge_repeated =
static_cast<int>(ctx.Attr<bool>("merge_repeated"));
......@@ -99,9 +99,9 @@ class CTCAlignOpCUDAKernel : public framework::OpKernel<T> {
auto input_dims = input->dims();
T* output_data = output->mutable_data<T>({input_dims[0], input_dims[1]},
ctx.GetPlace());
auto* input_length = ctx.Input<LoDTensor>("InputLength");
auto* input_length = ctx.Input<phi::DenseTensor>("InputLength");
const T* input_length_data = input_length->data<T>();
auto* output_length = ctx.Output<LoDTensor>("OutputLength");
auto* output_length = ctx.Output<phi::DenseTensor>("OutputLength");
T* output_length_data =
output_length->mutable_data<T>({input_dims[0], 1}, ctx.GetPlace());
PaddingMergeAndDelCudaKernel<T>
......
paddle/fluid/operators/ctc_align_op.h
浏览文件 @ 91dd8a2e
......@@ -25,14 +25,13 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
template <typename DeviceContext, typename T>
class CTCAlignKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<LoDTensor>("Input");
auto* output = ctx.Output<LoDTensor>("Output");
auto* input = ctx.Input<phi::DenseTensor>("Input");
auto* output = ctx.Output<phi::DenseTensor>("Output");
size_t blank = static_cast<size_t>(ctx.Attr<int>("blank"));
bool merge_repeated = ctx.Attr<bool>("merge_repeated");
T* output_data = output->mutable_data<T>(ctx.GetPlace());
......@@ -43,10 +42,10 @@ class CTCAlignKernel : public framework::OpKernel<T> {
if (input->lod().empty()) {
size_t padding_value =
static_cast<size_t>(ctx.Attr<int>("padding_value"));
auto* input_length = ctx.Input<LoDTensor>("InputLength");
auto* input_length = ctx.Input<phi::DenseTensor>("InputLength");
const T* input_length_data = input_length->data<T>();
auto* output_length = ctx.Output<LoDTensor>("OutputLength");
auto* output_length = ctx.Output<phi::DenseTensor>("OutputLength");
T* output_length_data = output_length->mutable_data<T>(ctx.GetPlace());
for (size_t batch_id = 0; batch_id < (unsigned)input_dims[0];
......
paddle/fluid/operators/cudnn_lstm_op.cu.cc
浏览文件 @ 91dd8a2e
......@@ -26,7 +26,6 @@ limitations under the License. */
namespace paddle {
namespace operators {
using LoDTensor = phi::DenseTensor;
using Tensor = phi::DenseTensor;
template <typename T, typename Type>
......
paddle/fluid/operators/cvm_op.cu
浏览文件 @ 91dd8a2e
......@@ -23,7 +23,6 @@ namespace operators {
using phi::PADDLE_CUDA_NUM_THREADS;
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
template <typename T>
__global__ void CvmComputeKernel(const bool use_cvm,
......@@ -87,7 +86,7 @@ template <typename T>
class CVMCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const auto* x = context.Input<LoDTensor>("X");
const auto* x = context.Input<phi::DenseTensor>("X");
const T* x_data = x->data<T>();
auto batch_size = x->dims()[0];
......@@ -95,7 +94,7 @@ class CVMCUDAKernel : public framework::OpKernel<T> {
auto item_size = numel / batch_size;
auto use_cvm = context.Attr<bool>("use_cvm");
auto* y = context.Output<LoDTensor>("Y");
auto* y = context.Output<phi::DenseTensor>("Y");
T* y_data = y->mutable_data<T>(context.GetPlace());
// for Input X do not have Lod Information.
......@@ -128,7 +127,7 @@ template <typename T>
class CVMGradCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* dx = context.Output<LoDTensor>(framework::GradVarName("X"));
auto* dx = context.Output<phi::DenseTensor>(framework::GradVarName("X"));
T* dx_data = dx->mutable_data<T>(context.GetPlace());
const phi::DenseTensor* cvm = context.Input<phi::DenseTensor>("CVM");
......
paddle/fluid/operators/cvm_op.h
浏览文件 @ 91dd8a2e
......@@ -20,7 +20,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
template <typename T>
void CvmComputeKernel(const bool use_cvm,
......@@ -61,14 +60,14 @@ template <typename T>
class CVMOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const auto* x = context.Input<LoDTensor>("X");
const auto* x = context.Input<phi::DenseTensor>("X");
const T* x_data = x->data<T>();
auto batch_size = x->dims()[0];
auto item_size = x->numel() / batch_size;
auto use_cvm = context.Attr<bool>("use_cvm");
auto* y = context.Output<LoDTensor>("Y");
auto* y = context.Output<phi::DenseTensor>("Y");
T* y_data = y->mutable_data<T>(context.GetPlace());
// for Input X do not have Lod Information.
......@@ -102,7 +101,7 @@ template <typename T>
class CVMGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* dx = context.Output<LoDTensor>(framework::GradVarName("X"));
auto* dx = context.Output<phi::DenseTensor>(framework::GradVarName("X"));
T* dx_data = dx->mutable_data<T>(context.GetPlace());
const phi::DenseTensor* cvm = context.Input<phi::DenseTensor>("CVM");
......
paddle/fluid/operators/data_norm_op.cc
浏览文件 @ 91dd8a2e
......@@ -24,7 +24,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
using DataLayout = phi::DataLayout;
template <typename T>
......@@ -487,8 +486,8 @@ class DataNormGradOp : public framework::OperatorWithKernel {
const Tensor *t = nullptr;
if (var->IsType<Tensor>()) {
t = &var->Get<Tensor>();
} else if (var->IsType<LoDTensor>()) {
t = &var->Get<LoDTensor>();
} else if (var->IsType<phi::DenseTensor>()) {
t = &var->Get<phi::DenseTensor>();
}
if (t == nullptr) {
PADDLE_THROW(platform::errors::InvalidArgument(
......
paddle/fluid/operators/data_norm_op.cu
浏览文件 @ 91dd8a2e
......@@ -27,7 +27,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
using DataLayout = phi::DataLayout;
using phi::PADDLE_CUDA_NUM_THREADS;
......
paddle/fluid/operators/deformable_psroi_pooling_op.cc
浏览文件 @ 91dd8a2e
......@@ -33,9 +33,9 @@ class DeformablePSROIPoolOpMaker : public framework::OpProtoAndCheckerMaker {
"H is height of the feature, and "
"W is the width of the feature.");
AddInput("ROIs",
"(LoDTensor), "
"(phi::DenseTensor), "
"ROIs (Regions of Interest) to pool over. "
"ROIs should be a 2-D LoDTensor of shape (num_rois, 4) "
"ROIs should be a 2-D phi::DenseTensor of shape (num_rois, 4) "
"given as [[x1, y1, x2, y2], ...]. "
"(x1, y1) is the top left coordinates, and "
"(x2, y2) is the bottom right coordinates.");
......@@ -149,7 +149,8 @@ class DeformablePSROIPoolOp : public framework::OperatorWithKernel {
rois_dims.size(),
2,
platform::errors::InvalidArgument(
"Input(ROIs) should be a 2-D LoDTensor of shape (num_rois, 4) "
"Input(ROIs) should be a 2-D phi::DenseTensor of shape (num_rois, "
"4) "
"given as [[ x1, y1, x2, y2], ...]. The rank of Input(ROIs) should "
"be 2, but received ROIs rank is:%d, ROIs shape is:[%s].",
rois_dims.size(),
......
paddle/fluid/operators/deformable_psroi_pooling_op.cu
浏览文件 @ 91dd8a2e
......@@ -40,7 +40,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
using phi::PADDLE_CUDA_NUM_THREADS;
static inline int GET_BLOCKS(const int N) {
......@@ -185,7 +184,7 @@ class DeformablePSROIPoolCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const phi::DenseTensor* input = ctx.Input<phi::DenseTensor>("Input");
const LoDTensor* rois = ctx.Input<LoDTensor>("ROIs");
const phi::DenseTensor* rois = ctx.Input<phi::DenseTensor>("ROIs");
const phi::DenseTensor* trans = ctx.Input<phi::DenseTensor>("Trans");
phi::DenseTensor* out = ctx.Output<phi::DenseTensor>("Output");
out->mutable_data<T>(ctx.GetPlace());
......@@ -486,7 +485,7 @@ class DeformablePSROIPoolGradCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const phi::DenseTensor* input = ctx.Input<phi::DenseTensor>("Input");
const LoDTensor* rois = ctx.Input<LoDTensor>("ROIs");
const phi::DenseTensor* rois = ctx.Input<phi::DenseTensor>("ROIs");
const phi::DenseTensor* trans = ctx.Input<phi::DenseTensor>("Trans");
const phi::DenseTensor* top_count = ctx.Input<phi::DenseTensor>("TopCount");
const phi::DenseTensor* output_grad =
......
paddle/fluid/operators/deformable_psroi_pooling_op.h
浏览文件 @ 91dd8a2e
......@@ -34,7 +34,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
template <typename T>
T bilinear_interp(
......@@ -80,7 +79,7 @@ void DeformablePSROIPoolForwardCPUKernel(const int count,
T* top_count,
const int batch_size,
int* roi_batch_id_data,
const LoDTensor* rois) {
const phi::DenseTensor* rois) {
for (int ix = 0; ix < count; ix++) {
int pw = ix % pooled_width;
int ph = (ix / pooled_width) % pooled_height;
......@@ -174,7 +173,7 @@ class DeformablePSROIPoolCPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<phi::DenseTensor>("Input");
auto* rois = ctx.Input<LoDTensor>("ROIs");
auto* rois = ctx.Input<phi::DenseTensor>("ROIs");
auto* trans = ctx.Input<phi::DenseTensor>("Trans");
auto* out = ctx.Output<phi::DenseTensor>("Output");
out->mutable_data<T>(ctx.GetPlace());
......@@ -316,7 +315,7 @@ void DeformablePSROIPoolBackwardAccCPUKernel(const int count,
const int channels_each_class,
const int batch_size,
int* roi_batch_id_data,
const LoDTensor* rois) {
const phi::DenseTensor* rois) {
for (int index = 0; index < count; index++) {
int pw = index % pooled_width;
int ph = (index / pooled_width) % pooled_height;
......@@ -476,7 +475,7 @@ class DeformablePSROIPoolGradCPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<phi::DenseTensor>("Input");
auto* rois = ctx.Input<LoDTensor>("ROIs");
auto* rois = ctx.Input<phi::DenseTensor>("ROIs");
auto* trans = ctx.Input<phi::DenseTensor>("Trans");
auto* top_count = ctx.Input<phi::DenseTensor>("TopCount");
auto* output_grad =
......
paddle/fluid/operators/dequeue_op.cc
浏览文件 @ 91dd8a2e
......@@ -20,7 +20,6 @@
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/operators/reader/lod_tensor_blocking_queue.h"
using LoDTensor = phi::DenseTensor;
using LoDTensorBlockingQueueHolder =
paddle::operators::reader::LoDTensorBlockingQueueHolder;
......@@ -59,7 +58,7 @@ class DequeueOp : public framework::OperatorBase {
out_var,
platform::errors::NotFound("No variable with name %s found",
out_names[i]));
auto* out_tensor = out_var->GetMutable<LoDTensor>();
auto* out_tensor = out_var->GetMutable<phi::DenseTensor>();
PADDLE_ENFORCE_NOT_NULL(
out_tensor,
platform::errors::InvalidArgument(
......
paddle/fluid/operators/detection_map_op.cc
浏览文件 @ 91dd8a2e
......@@ -103,7 +103,8 @@ class DetectionMAPOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("DetectRes",
"(LoDTensor) A 2-D LoDTensor with shape [M, 6] represents the "
"(phi::DenseTensor) A 2-D phi::DenseTensor with shape [M, 6] "
"represents the "
"detections. Each row has 6 values: "
"[label, confidence, xmin, ymin, xmax, ymax], M is the total "
"number of detect results in this mini-batch. For each instance, "
......@@ -111,7 +112,7 @@ class DetectionMAPOpMaker : public framework::OpProtoAndCheckerMaker {
"offset is N + 1, if LoD[i + 1] - LoD[i] == 0, means there is "
"no detected data.");
AddInput("Label",
"(LoDTensor) A 2-D LoDTensor represents the"
"(phi::DenseTensor) A 2-D phi::DenseTensor represents the"
"Labeled ground-truth data. Each row has 6 values: "
"[label, xmin, ymin, xmax, ymax, is_difficult] or 5 values: "
"[label, xmin, ymin, xmax, ymax], where N is the total "
......@@ -135,14 +136,16 @@ class DetectionMAPOpMaker : public framework::OpProtoAndCheckerMaker {
"current mini-batch are calculated.")
.AsDispensable();
AddInput("TruePos",
"(LoDTensor) A 2-D LoDTensor with shape [Ntp, 2], store the "
"(phi::DenseTensor) A 2-D phi::DenseTensor with shape [Ntp, 2], "
"store the "
"input true positive example of each class."
"This input is used to pass the AccumTruePos generated by the "
"previous mini-batch when the multi mini-batches cumulative "
"calculation carried out. ")
.AsDispensable();
AddInput("FalsePos",
"(LoDTensor) A 2-D LoDTensor with shape [Nfp, 2], store the "
"(phi::DenseTensor) A 2-D phi::DenseTensor with shape [Nfp, 2], "
"store the "
"input false positive example of each class."
"This input is used to pass the AccumFalsePos generated by the "
"previous mini-batch when the multi mini-batches cumulative "
......@@ -153,13 +156,15 @@ class DetectionMAPOpMaker : public framework::OpProtoAndCheckerMaker {
"positive example count of each class. It combines the input "
"input(PosCount) and the positive example count computed from "
"input(Detection) and input(Label).");
AddOutput("AccumTruePos",
"(LoDTensor) A LoDTensor with shape [Ntp', 2], store the "
AddOutput(
"AccumTruePos",
"(phi::DenseTensor) A phi::DenseTensor with shape [Ntp', 2], store the "
"true positive example of each class. It combines the "
"input(TruePos) and the true positive examples computed from "
"input(Detection) and input(Label).");
AddOutput("AccumFalsePos",
"(LoDTensor) A LoDTensor with shape [Nfp', 2], store the "
AddOutput(
"AccumFalsePos",
"(phi::DenseTensor) A phi::DenseTensor with shape [Nfp', 2], store the "
"false positive example of each class. It combines the "
"input(FalsePos) and the false positive examples computed from "
"input(Detection) and input(Label).");
......
paddle/fluid/operators/edit_distance_op.cc
浏览文件 @ 91dd8a2e
......@@ -35,11 +35,11 @@ class EditDistanceOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("Hyps",
"2-D Tensor<int64_t>, or 2-D LoDTensor<int64_t> with last "
"2-D Tensor<int64_t>, or 2-D phi::DenseTensor<int64_t> with last "
"dimension being 1. "
"The indices for hypothesis strings.");
AddInput("Refs",
"2-D Tensor<int64_t>, or 2-D LoDTensor<int64_t> with last "
"2-D Tensor<int64_t>, or 2-D phi::DenseTensor<int64_t> with last "
"dimension being 1. "
"The indices for reference strings.");
AddInput("HypsLength",
......@@ -75,7 +75,7 @@ insertion:
So the edit distance between A and B is 3.
Input(Hyps) is a 2-D Tensor or a 2-D LoDTensor consisting of all the hypothesis strings.
Input(Hyps) is a 2-D Tensor or a 2-D phi::DenseTensor consisting of all the hypothesis strings.
And the `batch_size` reference strings are arranged in order in the same way in the
Input(Refs).
......
paddle/fluid/operators/enqueue_op.cc
浏览文件 @ 91dd8a2e
......@@ -31,7 +31,6 @@ class OpBase;
} // namespace imperative
} // namespace paddle
using LoDTensor = phi::DenseTensor;
using LoDTensorBlockingQueueHolder =
paddle::operators::reader::LoDTensorBlockingQueueHolder;
......@@ -61,7 +60,7 @@ class EnqueueOp : public framework::OperatorBase {
PADDLE_ENFORCE_NOT_NULL(in_var,
platform::errors::NotFound(
"No variable with name %s found.", var_name));
auto* in_tensor = in_var->GetMutable<LoDTensor>();
auto* in_tensor = in_var->GetMutable<phi::DenseTensor>();
auto* queue_holder =
queue_holder_var->template GetMutable<LoDTensorBlockingQueueHolder>();
......
paddle/fluid/operators/fill_constant_batch_size_like_op_mlu.cc
浏览文件 @ 91dd8a2e
......@@ -32,7 +32,7 @@ class FillConstantBatchSizeLikeOpMLUKernel : public framework::OpKernel<T> {
auto *out = ctx.Output<phi::DenseTensor>("Out");
auto *in = ctx.Input<phi::DenseTensor>("Input");
if (in->lod().size() && ctx.Attr<int>("input_dim_idx") == 0) {
// set the correct batch size for the LoDTensor.
// set the correct batch size for the phi::DenseTensor.
auto odims = out->dims();
int output_dim_idx = ctx.Attr<int>("output_dim_idx");
odims[output_dim_idx] = static_cast<int>(in->lod().back().size()) - 1;
......
paddle/fluid/operators/fill_constant_batch_size_like_op_npu.cc
浏览文件 @ 91dd8a2e
......@@ -35,7 +35,7 @@ class FillConstantBatchSizeLikeOpNPUKernel : public framework::OpKernel<T> {
auto *out = ctx.Output<phi::DenseTensor>("Out");
auto *in = ctx.Input<phi::DenseTensor>("Input");
if (in->lod().size() && ctx.Attr<int>("input_dim_idx") == 0) {
// set the correct batch size for the LoDTensor.
// set the correct batch size for the phi::DenseTensor.
auto odims = out->dims();
int output_dim_idx = ctx.Attr<int>("output_dim_idx");
odims[output_dim_idx] = static_cast<int>(in->lod().back().size()) - 1;
......
paddle/fluid/operators/fill_op.cc
浏览文件 @ 91dd8a2e
......@@ -27,7 +27,7 @@ class FillOpMaker : public framework::OpProtoAndCheckerMaker {
Fill an tensor with `value` and `shape`. The type of the tensor is specify by
`dtype`.
)DOC");
AddOutput("Out", "(LoDTensor) The output tensor.");
AddOutput("Out", "(phi::DenseTensor) The output tensor.");
AddAttr<std::vector<float>>(
"value", "The float values of tensor, which are flatten in row major");
AddAttr<std::vector<int>>("shape", "The shape of output tensor");
......
paddle/fluid/operators/filter_by_instag_op.cc
浏览文件 @ 91dd8a2e
......@@ -69,16 +69,17 @@ class FilterByInstagOp : public framework::OperatorWithKernel {
class FilterByInstagOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("Ins", "(LoDTensor) embeded tensor");
AddInput("Ins_tag", "(LoDTensor) ins tag list");
AddInput("Ins", "(phi::DenseTensor) embeded tensor");
AddInput("Ins_tag", "(phi::DenseTensor) ins tag list");
AddInput("Filter_tag", "(1D Tensor) filter tag list");
AddAttr<bool>("is_lod", "is Ins with LoD info or not, default True");
AddAttr<int64_t>("out_val_if_empty",
"if the output after filter is empty, the output value")
.SetDefault(0);
AddOutput("Out", "(LoDTensor) embeded tensor filtered by instag");
AddOutput("Out", "(phi::DenseTensor) embeded tensor filtered by instag");
AddOutput("LossWeight", "(Tensor) loss weight.");
AddOutput("IndexMap", "(LoDTensor) mapping from Out rows to X1 rows");
AddOutput("IndexMap",
"(phi::DenseTensor) mapping from Out rows to X1 rows");
AddComment(R"DOC(
Filter By Instag Op
......
paddle/fluid/operators/filter_by_instag_op.cu
浏览文件 @ 91dd8a2e
......@@ -45,7 +45,6 @@ namespace operators {
using Tensor = phi::DenseTensor;
using SelectedRows = phi::SelectedRows;
using LoDTensor = phi::DenseTensor;
template <typename T>
using Vector = framework::Vector<T>;
......@@ -341,7 +340,7 @@ class FilterByInstagGPUKernel : public framework::OpKernel<T> {
// context.cuda_device_context().GetMaxThreadsPerBlock();
// X1 is global FC output
// Dim [batch size, embedding size]
const LoDTensor* x1 = context.Input<LoDTensor>("Ins");
const phi::DenseTensor* x1 = context.Input<phi::DenseTensor>("Ins");
bool is_lod = context.Attr<bool>("is_lod");
int is_x1_lod = -1;
......@@ -354,7 +353,7 @@ class FilterByInstagGPUKernel : public framework::OpKernel<T> {
size_t x1_embed_size = x1->dims()[1];
// X2 is ins tag list
// LoD [[0, Sum(ins1), Sum(ins1, ins2), ... ]]
const LoDTensor* x2 = context.Input<LoDTensor>("Ins_tag");
const phi::DenseTensor* x2 = context.Input<phi::DenseTensor>("Ins_tag");
// expected auto = const int64_t
const int64_t* x2_data = x2->data<int64_t>();
......@@ -389,7 +388,7 @@ class FilterByInstagGPUKernel : public framework::OpKernel<T> {
x1_lods.push_back(i + 1);
}
} else {
// x1_lods = context.Input<LoDTensor>("Ins")->lod()[0];
// x1_lods = context.Input<phi::DenseTensor>("Ins")->lod()[0];
// new: lod_level=0 => lod() return {}
if (x1->lod().size() != 0) { // lod_level = 1
x1_lods = x1->lod()[0];
......@@ -412,9 +411,10 @@ class FilterByInstagGPUKernel : public framework::OpKernel<T> {
// for those whose ins been dropout, set 0 for whole lines.
// otherwise, copy whole line
// Dim [local fc count, batch size, embedding size]
LoDTensor* out = context.Output<LoDTensor>("Out");
LoDTensor* map = context.Output<LoDTensor>("IndexMap");
LoDTensor* loss_weight = context.Output<LoDTensor>("LossWeight");
phi::DenseTensor* out = context.Output<phi::DenseTensor>("Out");
phi::DenseTensor* map = context.Output<phi::DenseTensor>("IndexMap");
phi::DenseTensor* loss_weight =
context.Output<phi::DenseTensor>("LossWeight");
int out_first = x1_lods.back();
......@@ -563,13 +563,15 @@ class FilterByInstagGradGPUKernel : public framework::OpKernel<T> {
auto gpu_place = context.GetPlace();
gpuStream_t current_stream = context.cuda_device_context().stream();
auto max_thread_num_per_block = 1024;
auto* output_grad = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto* x1_grad = context.Output<LoDTensor>(framework::GradVarName("Ins"));
auto* loss_weight = context.Input<LoDTensor>("LossWeight");
auto* mmap = context.Input<LoDTensor>("IndexMap");
auto* x1 = context.Input<LoDTensor>("Ins");
x1_grad->set_lod(context.Input<LoDTensor>("Ins")->lod());
auto* output_grad =
context.Input<phi::DenseTensor>(framework::GradVarName("Out"));
auto* x1_grad =
context.Output<phi::DenseTensor>(framework::GradVarName("Ins"));
auto* loss_weight = context.Input<phi::DenseTensor>("LossWeight");
auto* mmap = context.Input<phi::DenseTensor>("IndexMap");
auto* x1 = context.Input<phi::DenseTensor>("Ins");
x1_grad->set_lod(context.Input<phi::DenseTensor>("Ins")->lod());
x1_grad->Resize(x1->dims());
auto* mmap_data = mmap->data<int64_t>();
......
paddle/fluid/operators/filter_by_instag_op.h
浏览文件 @ 91dd8a2e
......@@ -31,7 +31,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using SelectedRows = phi::SelectedRows;
using LoDTensor = phi::DenseTensor;
template <typename T>
using Vector = framework::Vector<T>;
......@@ -42,12 +41,12 @@ class FilterByInstagKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& context) const override {
// X1 is global FC output
// Dim [batch size, embedding size]
auto* x1 = context.Input<LoDTensor>("Ins");
auto* x1 = context.Input<phi::DenseTensor>("Ins");
bool is_x1_lod = context.Attr<bool>("is_lod");
int64_t out_val_if_empty = context.Attr<int64_t>("out_val_if_empty");
// X2 is ins tag list
// LoD [[0, Sum(ins1), Sum(ins1, ins2), ... ]]
auto* x2 = context.Input<LoDTensor>("Ins_tag");
auto* x2 = context.Input<phi::DenseTensor>("Ins_tag");
// X3 is local fc tag list
// LoD [[0, Sum(fc1), Sum(fc1, fc2) ...]]
auto* x3 = context.Input<phi::DenseTensor>("Filter_tag");
......@@ -107,9 +106,10 @@ class FilterByInstagKernel : public framework::OpKernel<T> {
// for those whose ins been dropout, set 0 for whole lines.
// otherwise, copy whole line
// Dim [local fc count, batch size, embedding size]
LoDTensor* out = context.Output<LoDTensor>("Out");
LoDTensor* map = context.Output<LoDTensor>("IndexMap");
LoDTensor* loss_weight = context.Output<LoDTensor>("LossWeight");
phi::DenseTensor* out = context.Output<phi::DenseTensor>("Out");
phi::DenseTensor* map = context.Output<phi::DenseTensor>("IndexMap");
phi::DenseTensor* loss_weight =
context.Output<phi::DenseTensor>("LossWeight");
// expected auto = const T
auto* x1_data = x1->data<T>();
// expected auto = T
......@@ -196,12 +196,14 @@ template <typename T>
class FilterByInstagGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* output_grad = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto* x1_grad = context.Output<LoDTensor>(framework::GradVarName("Ins"));
auto* loss_weight = context.Input<LoDTensor>("LossWeight");
auto* mmap = context.Input<LoDTensor>("IndexMap");
auto* x1 = context.Input<LoDTensor>("Ins");
x1_grad->set_lod(context.Input<LoDTensor>("Ins")->lod());
auto* output_grad =
context.Input<phi::DenseTensor>(framework::GradVarName("Out"));
auto* x1_grad =
context.Output<phi::DenseTensor>(framework::GradVarName("Ins"));
auto* loss_weight = context.Input<phi::DenseTensor>("LossWeight");
auto* mmap = context.Input<phi::DenseTensor>("IndexMap");
auto* x1 = context.Input<phi::DenseTensor>("Ins");
x1_grad->set_lod(context.Input<phi::DenseTensor>("Ins")->lod());
x1_grad->Resize(x1->dims());
auto mmap_data = mmap->data<int64_t>();
// expected auto = T
......
paddle/fluid/operators/get_tensor_from_selected_rows_op.cc
浏览文件 @ 91dd8a2e
......@@ -35,10 +35,11 @@ class GetTensorFromSelectedRowsOp : public framework::OperatorWithKernel {
"but the received is %s",
ctx->Inputs("X").front(),
ctx->GetInputsVarType("X").front()));
PADDLE_ENFORCE_EQ(ctx->GetOutputsVarType("Out").front(),
PADDLE_ENFORCE_EQ(
ctx->GetOutputsVarType("Out").front(),
framework::proto::VarType::LOD_TENSOR,
platform::errors::InvalidArgument(
"The output Out(%s)'s type should be LoDTensor, "
"The output Out(%s)'s type should be phi::DenseTensor, "
"but the received is %s",
ctx->Outputs("Out").front(),
ctx->GetOutputsVarType("Out").front()));
......@@ -72,7 +73,7 @@ class GetTensorFromSelectedRowsOpProtoMaker
public:
void Make() override {
AddInput("X", "The input type is SelectedRows.");
AddOutput("Out", "The output type is LoDTensor.");
AddOutput("Out", "The output type is phi::DenseTensor.");
AddComment(
R"DOC(
GetTensorFromSelectedRows Operator
......
paddle/fluid/operators/group_norm_op.cc
浏览文件 @ 91dd8a2e
......@@ -29,7 +29,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
using DataLayout = phi::DataLayout;
class GroupNormOp : public framework::OperatorWithKernel {
......@@ -127,8 +126,8 @@ class GroupNormGradOp : public framework::OperatorWithKernel {
const Tensor *t = nullptr;
if (var->IsType<Tensor>()) {
t = &var->Get<Tensor>();
} else if (var->IsType<LoDTensor>()) {
t = &var->Get<LoDTensor>();
} else if (var->IsType<phi::DenseTensor>()) {
t = &var->Get<phi::DenseTensor>();
}
PADDLE_ENFORCE_NOT_NULL(
t,
......
paddle/fluid/operators/group_norm_op.h
浏览文件 @ 91dd8a2e
......@@ -29,7 +29,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
using DataLayout = phi::DataLayout;
template <typename DeviceContext, typename T>
......
paddle/fluid/operators/gru_op.cc
浏览文件 @ 91dd8a2e
......@@ -115,11 +115,12 @@ class GRUOp : public framework::OperatorWithKernel {
class GRUOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("Input",
"(LoDTensor) The first input is a LodTensor, which supports "
AddInput(
"Input",
"(phi::DenseTensor) The first input is a LodTensor, which supports "
"variable-time length input sequence. The underlying tensor in "
"this LoDTenosr is a matrix with shape (T X 3D), where, T is the "
"total time steps in this mini-batch, D is the hidden size.");
"this phi::DenseTensor is a matrix with shape (T X 3D), where, T is "
"the total time steps in this mini-batch, D is the hidden size.");
AddInput("H0",
"(Tensor, optional) The initial hidden state is an optional "
"input. This is a tensor with shape (N x D), where N is the "
......@@ -136,35 +137,38 @@ class GRUOpMaker : public framework::OpProtoAndCheckerMaker {
"(Tensor, optional) Bias vector with shape (1 x 3D) concating "
"bias of the update gate, reset gate and output candidate.")
.AsDispensable();
AddOutput("BatchGate",
"(LoDTensor) To compute with batches, sequence data will be "
AddOutput(
"BatchGate",
"(phi::DenseTensor) To compute with batches, sequence data will be "
"reorganized into several successive batches each containing "
"data from the same time step. The LoDTensor BatchGate contains "
"data from the same time step. The phi::DenseTensor BatchGate contains "
"the update gate, reset gate and output candidate values "
"organized in batches. The LoD size is 2. The first LoD contains "
"the batch offsets and the second LoD contains the indexes in "
"the raw sequence data.")
.AsIntermediate()
.AsExtra();
AddOutput(
"BatchResetHiddenPrev",
"(LoDTensor) The reset hidden state LoDTensor organized in batches. "
"This LoDTensor is a matrix with shape (T X D) and has the same LoD "
AddOutput("BatchResetHiddenPrev",
"(phi::DenseTensor) The reset hidden state phi::DenseTensor "
"organized in batches. "
"This phi::DenseTensor is a matrix with shape (T X D) and has "
"the same LoD "
"with `BatchGate`.")
.AsIntermediate()
.AsExtra();
AddOutput(
"BatchHidden",
"(LoDTensor) The hidden state LoDTensor organized in batches. "
"This LoDTensor is a matrix with shape (T X D) and has the same LoD "
AddOutput("BatchHidden",
"(phi::DenseTensor) The hidden state phi::DenseTensor organized "
"in batches. "
"This phi::DenseTensor is a matrix with shape (T X D) and has "
"the same LoD "
"with `BatchGate`.")
.AsIntermediate()
.AsExtra();
AddOutput(
"Hidden",
"(LoDTensor) the hidden state LoDTensor organized in sequences. "
"This LoDTensor is a matrix with shape (T X D) and has the same LoD "
"with `BatchGate`.");
AddOutput("Hidden",
"(phi::DenseTensor) the hidden state phi::DenseTensor organized "
"in sequences. "
"This phi::DenseTensor is a matrix with shape (T X D) and has "
"the same LoD with `BatchGate`.");
AddAttr<std::string>("activation",
"(string, default tanh) "
"The activation type used for output candidate {h}_t.")
......@@ -314,23 +318,24 @@ class GRUCPUKernel : public framework::OpKernel<T> {
public:
void BatchCompute(const framework::ExecutionContext& context) const {
using DeviceContext = phi::CPUContext;
using LodTensorPtr = LoDTensor*;
using LodTensorPtr = phi::DenseTensor*;
bool is_test = context.Attr<bool>("is_test");
bool origin_mode = context.Attr<bool>("origin_mode");
auto* input = context.Input<LoDTensor>("Input");
auto* input = context.Input<phi::DenseTensor>("Input");
auto* h0 = context.Input<phi::DenseTensor>("H0");
auto* weight = context.Input<phi::DenseTensor>("Weight");
const T* weight_data = weight->data<T>();
auto* bias = context.Input<phi::DenseTensor>("Bias");
auto* hidden = context.Output<LoDTensor>("Hidden");
auto* hidden = context.Output<phi::DenseTensor>("Hidden");
hidden->mutable_data<T>(context.GetPlace());
auto input_dims = input->dims();
auto hidden_dims = hidden->dims();
LodTensorPtr batch_gate, batch_reset_hidden_prev, batch_hidden;
LoDTensor batch_gate_tmp, batch_reset_hidden_prev_tmp, batch_hidden_tmp;
phi::DenseTensor batch_gate_tmp, batch_reset_hidden_prev_tmp,
batch_hidden_tmp;
if (is_test) {
batch_gate = &batch_gate_tmp;
batch_gate->Resize(input_dims);
......@@ -341,10 +346,10 @@ class GRUCPUKernel : public framework::OpKernel<T> {
batch_hidden = &batch_hidden_tmp;
batch_hidden->Resize(hidden_dims);
} else {
batch_gate = context.Output<LoDTensor>("BatchGate");
batch_hidden = context.Output<LoDTensor>("BatchHidden");
batch_gate = context.Output<phi::DenseTensor>("BatchGate");
batch_hidden = context.Output<phi::DenseTensor>("BatchHidden");
batch_reset_hidden_prev =
context.Output<LoDTensor>("BatchResetHiddenPrev");
context.Output<phi::DenseTensor>("BatchResetHiddenPrev");
}
batch_gate->mutable_data<T>(context.GetPlace());
batch_reset_hidden_prev->mutable_data<T>(context.GetPlace());
......
paddle/fluid/operators/gru_op.cu.cc
浏览文件 @ 91dd8a2e
......@@ -21,23 +21,24 @@ template <typename DeviceContext, typename T>
class GRUKernel : public framework::OpKernel<T> {
public:
void BatchCompute(const framework::ExecutionContext& context) const {
using LodTensorPtr = LoDTensor*;
using LodTensorPtr = phi::DenseTensor*;
bool is_test = context.Attr<bool>("is_test");
bool origin_mode = context.Attr<bool>("origin_mode");
auto* input = context.Input<LoDTensor>("Input");
auto* input = context.Input<phi::DenseTensor>("Input");
auto* h0 = context.Input<phi::DenseTensor>("H0");
auto* weight = context.Input<phi::DenseTensor>("Weight");
const T* weight_data = weight->data<T>();
auto* bias = context.Input<phi::DenseTensor>("Bias");
auto* hidden = context.Output<LoDTensor>("Hidden");
auto* hidden = context.Output<phi::DenseTensor>("Hidden");
hidden->mutable_data<T>(context.GetPlace());
auto input_dims = input->dims();
auto hidden_dims = hidden->dims();
LodTensorPtr batch_gate, batch_reset_hidden_prev, batch_hidden;
LoDTensor batch_gate_tmp, batch_reset_hidden_prev_tmp, batch_hidden_tmp;
phi::DenseTensor batch_gate_tmp, batch_reset_hidden_prev_tmp,
batch_hidden_tmp;
if (is_test) {
batch_gate = &batch_gate_tmp;
batch_gate->Resize(input_dims);
......@@ -48,10 +49,10 @@ class GRUKernel : public framework::OpKernel<T> {
batch_hidden = &batch_hidden_tmp;
batch_hidden->Resize(hidden_dims);
} else {
batch_gate = context.Output<LoDTensor>("BatchGate");
batch_hidden = context.Output<LoDTensor>("BatchHidden");
batch_gate = context.Output<phi::DenseTensor>("BatchGate");
batch_hidden = context.Output<phi::DenseTensor>("BatchHidden");
batch_reset_hidden_prev =
context.Output<LoDTensor>("BatchResetHiddenPrev");
context.Output<phi::DenseTensor>("BatchResetHiddenPrev");
}
batch_gate->mutable_data<T>(context.GetPlace());
batch_reset_hidden_prev->mutable_data<T>(context.GetPlace());
......
paddle/fluid/operators/gru_op.h
浏览文件 @ 91dd8a2e
......@@ -25,7 +25,6 @@ limitations under the License. */
namespace paddle {
namespace operators {
using LoDTensor = phi::DenseTensor;
using Tensor = phi::DenseTensor;
template <typename DeviceContext, typename T>
......@@ -47,15 +46,15 @@ class GRUGradKernel : public framework::OpKernel<T> {
auto* h0 = context.Input<phi::DenseTensor>("H0");
auto* weight = context.Input<phi::DenseTensor>("Weight");
const T* weight_data = weight->data<T>();
auto* batch_gate = context.Input<LoDTensor>("BatchGate");
auto* batch_gate = context.Input<phi::DenseTensor>("BatchGate");
auto* batch_reset_hidden_prev =
context.Input<LoDTensor>("BatchResetHiddenPrev");
auto* batch_hidden = context.Input<LoDTensor>("BatchHidden");
auto* hidden = context.Input<LoDTensor>("Hidden");
context.Input<phi::DenseTensor>("BatchResetHiddenPrev");
auto* batch_hidden = context.Input<phi::DenseTensor>("BatchHidden");
auto* hidden = context.Input<phi::DenseTensor>("Hidden");
auto* hidden_grad =
context.Input<LoDTensor>(framework::GradVarName("Hidden"));
context.Input<phi::DenseTensor>(framework::GradVarName("Hidden"));
auto* input_grad =
context.Output<LoDTensor>(framework::GradVarName("Input"));
context.Output<phi::DenseTensor>(framework::GradVarName("Input"));
auto* h0_grad =
context.Output<phi::DenseTensor>(framework::GradVarName("H0"));
auto* weight_grad =
......@@ -68,7 +67,8 @@ class GRUGradKernel : public framework::OpKernel<T> {
int frame_size = hidden_dims[1];
phi::funcs::LoDTensor2BatchFunctor<DeviceContext, T> to_batch;
LoDTensor batch_hidden_grad, batch_gate_grad, batch_reset_hidden_prev_grad;
phi::DenseTensor batch_hidden_grad, batch_gate_grad,
batch_reset_hidden_prev_grad;
batch_hidden_grad.mutable_data<T>(hidden_dims, context.GetPlace());
batch_gate_grad.mutable_data<T>(gate_dims, context.GetPlace());
batch_reset_hidden_prev_grad.mutable_data<T>(hidden_dims,
......
paddle/fluid/operators/hierarchical_sigmoid_op.cc
浏览文件 @ 91dd8a2e
......@@ -82,42 +82,43 @@ class HierarchicalSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(LoDTensor, required) The input tensor with shape [N, D], "
"(phi::DenseTensor, required) The input tensor with shape [N, D], "
"where N is the size of mini-batch, and D is the feature size.");
AddInput("W",
"(LoDTensor, required), The parameters of hierarchical "
"(phi::DenseTensor, required), The parameters of hierarchical "
"sigmoid operator, each of them is a 2-D tensor, the shape is"
"[K, D]. Which K is the num of non-leaf node in Path Tree");
AddInput("Label",
"(LoDTensor, required), The labels of training data. It's a"
"(phi::DenseTensor, required), The labels of training data. It's a"
"tensor with shape [N, 1].");
AddInput("PathTable",
"(LoDTensor, optional), The Path Table from root to current word"
AddInput(
"PathTable",
"(phi::DenseTensor, optional), The Path Table from root to current word"
"it should have shape like [N, L], L is the length of the Path")
.AsDispensable();
AddInput(
"PathCode",
"(LoDTensor, optional), The Code on each Node of the Path from root "
AddInput("PathCode",
"(phi::DenseTensor, optional), The Code on each Node of the Path "
"from root "
"to current word"
"it should have shape like [N, L], L is the length of the Path")
.AsDispensable();
AddInput("Bias",
"(LoDTensor, optional), The bias is a tensor with shape or "
"(phi::DenseTensor, optional), The bias is a tensor with shape or "
"[num_classes, 1]"
"[num_classes - 1, 1].")
.AsDispensable();
AddOutput(
"Out",
"(LoDTensor, required) The output of hierarchical sigmoid operator."
AddOutput("Out",
"(phi::DenseTensor, required) The output of hierarchical sigmoid "
"operator."
"The shape is [N, 1].");
AddOutput("PreOut",
"(LoDTensor, required) A intermedia 2-D tensor with shape "
"(phi::DenseTensor, required) A intermedia 2-D tensor with shape "
"[batch_size, code_length], where code_length represents the "
"maximum path length from root to leaf nodes.")
.AsIntermediate();
AddOutput(
"W_Out",
"(LoDTensor, optional) using input 'W' as Output to make it mutable"
AddOutput("W_Out",
"(phi::DenseTensor, optional) using input 'W' as Output to make "
"it mutable"
"When we are using prefetch")
.AsIntermediate();
AddAttr<AttrType>("num_classes", "(int, optional), The number of classes")
......@@ -227,7 +228,8 @@ class HierarchicalSigmoidGradOpGradVarTypeInference
auto bias_grad_var_name = framework::GradVarName("Bias");
if (ctx->HasOutput(bias_grad_var_name)) {
VLOG(3) << "hierarchical_sigmoid_grad op "
<< framework::GradVarName("Bias") << " is set to LoDTensor";
<< framework::GradVarName("Bias")
<< " is set to phi::DenseTensor";
ctx->SetOutputType(bias_grad_var_name,
framework::proto::VarType::LOD_TENSOR);
}
......@@ -241,7 +243,7 @@ class HierarchicalSigmoidGradOpGradVarTypeInference
framework::proto::VarType::SELECTED_ROWS);
} else {
VLOG(3) << "hierarchical_sigmoid_grad op " << framework::GradVarName("W")
<< " is set to LoDTensor";
<< " is set to phi::DenseTensor";
ctx->SetOutputType(w_grad_var_name,
framework::proto::VarType::LOD_TENSOR);
}
......
paddle/fluid/operators/im2sequence_op.h
浏览文件 @ 91dd8a2e
......@@ -27,7 +27,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
inline int Im2SeqOutputSize(
int input_size, int filter_size, int padding_0, int padding_1, int stride) {
......@@ -41,7 +40,7 @@ class Im2SequenceKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const phi::DenseTensor* in = ctx.Input<phi::DenseTensor>("X");
LoDTensor* out = ctx.Output<LoDTensor>("Out");
phi::DenseTensor* out = ctx.Output<phi::DenseTensor>("Out");
auto in_dim = in->dims();
int batch_size = in_dim[0];
int img_channels = in_dim[1];
......
paddle/fluid/operators/index_select_op.h
浏览文件 @ 91dd8a2e
......@@ -23,21 +23,20 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
using DDim = framework::DDim;
template <typename DeviceContext, typename T, typename IndexT = int>
void IndexSelectInner(const framework::ExecutionContext& context,
LoDTensor* input,
const LoDTensor& index,
LoDTensor* output,
phi::DenseTensor* input,
const phi::DenseTensor& index,
phi::DenseTensor* output,
int dim) {
auto input_dim = input->dims();
auto input_dim_size = input_dim.size();
auto output_dim = output->dims();
auto index_size = index.dims()[0];
LoDTensor index_cpu_copy;
phi::DenseTensor index_cpu_copy;
if (!platform::is_cpu_place(index.place())) {
framework::TensorCopySync(index, platform::CPUPlace(), &index_cpu_copy);
}
......@@ -127,9 +126,9 @@ struct IndexSelectAdd<
template <typename DeviceContext, typename T, typename IndexT = int>
void IndexSelectGradInner(const framework::ExecutionContext& context,
const LoDTensor& out_grad,
const LoDTensor& index,
LoDTensor* x_grad,
const phi::DenseTensor& out_grad,
const phi::DenseTensor& index,
phi::DenseTensor* x_grad,
int dim) {
const T* input_data = out_grad.data<T>();
const IndexT* index_data = index.data<IndexT>();
......
paddle/fluid/operators/inplace_abn_op.cc
浏览文件 @ 91dd8a2e
......@@ -147,8 +147,8 @@ class InplaceABNGradOp : public paddle::operators::BatchNormGradOp {
const phi::DenseTensor* t = nullptr;
if (var->IsType<Tensor>()) {
t = &var->Get<Tensor>();
} else if (var->IsType<LoDTensor>()) {
t = &var->Get<LoDTensor>();
} else if (var->IsType<phi::DenseTensor>()) {
t = &var->Get<phi::DenseTensor>();
}
if (t == nullptr) {
PADDLE_THROW(
......
paddle/fluid/operators/instance_norm_op.cc
浏览文件 @ 91dd8a2e
......@@ -108,8 +108,8 @@ framework::OpKernelType InstanceNormGradOp::GetExpectedKernelType(
const Tensor *t = nullptr;
if (var->IsType<Tensor>()) {
t = &var->Get<Tensor>();
} else if (var->IsType<LoDTensor>()) {
t = &var->Get<LoDTensor>();
} else if (var->IsType<phi::DenseTensor>()) {
t = &var->Get<phi::DenseTensor>();
}
if (t == nullptr) {
PADDLE_THROW(
......@@ -129,8 +129,8 @@ framework::OpKernelType InstanceNormDoubleGradOp::GetExpectedKernelType(
const Tensor *t = nullptr;
if (var->IsType<Tensor>()) {
t = &var->Get<Tensor>();
} else if (var->IsType<LoDTensor>()) {
t = &var->Get<LoDTensor>();
} else if (var->IsType<phi::DenseTensor>()) {
t = &var->Get<phi::DenseTensor>();
}
if (t == nullptr) {
PADDLE_THROW(
......
paddle/fluid/operators/instance_norm_op.h
浏览文件 @ 91dd8a2e
......@@ -23,7 +23,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
using DataLayout = phi::DataLayout;
class InstanceNormOp : public framework::OperatorWithKernel {
......
paddle/fluid/operators/label_smooth_op_mlu.cc
浏览文件 @ 91dd8a2e
......@@ -19,15 +19,14 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
template <typename T>
class LabelSmoothMLUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in_t = ctx.Input<LoDTensor>("X");
auto* in_t = ctx.Input<phi::DenseTensor>("X");
auto* dist_t = ctx.Input<phi::DenseTensor>("PriorDist");
auto* out_t = ctx.Output<LoDTensor>("Out");
auto* out_t = ctx.Output<phi::DenseTensor>("Out");
auto epsilon = ctx.Attr<float>("epsilon");
auto epsilon_gt = 1.0f - epsilon;
......
paddle/fluid/operators/label_smooth_op_npu.cc
浏览文件 @ 91dd8a2e
......@@ -19,7 +19,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
template <typename T>
void LabelSmoothMuls(const platform::Place& place,
......@@ -58,8 +57,8 @@ template <typename T>
class LabelSmoothNPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* out_t = ctx.Output<LoDTensor>("Out");
auto* in_t = ctx.Input<LoDTensor>("X");
auto* out_t = ctx.Output<phi::DenseTensor>("Out");
auto* in_t = ctx.Input<phi::DenseTensor>("X");
auto* dist_t = ctx.Input<phi::DenseTensor>("PriorDist");
auto epsilon = ctx.Attr<float>("epsilon");
......
paddle/fluid/operators/layer_norm_op.cc
浏览文件 @ 91dd8a2e
......@@ -21,7 +21,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
using DataLayout = phi::DataLayout;
class LayerNormOp : public framework::OperatorWithKernel {
......@@ -214,8 +213,8 @@ class LayerNormGradOp : public framework::OperatorWithKernel {
const Tensor *t = nullptr;
if (var->IsType<Tensor>()) {
t = &var->Get<Tensor>();
} else if (var->IsType<LoDTensor>()) {
t = &var->Get<LoDTensor>();
} else if (var->IsType<phi::DenseTensor>()) {
t = &var->Get<phi::DenseTensor>();
}
PADDLE_ENFORCE_NOT_NULL(
t, platform::errors::NotFound("Y@GRAD of LayerNorm Op is not found."));
......
paddle/fluid/operators/limit_by_capacity_op.cu
浏览文件 @ 91dd8a2e
......@@ -28,7 +28,6 @@
namespace paddle {
namespace operators {
using LoDTensor = phi::DenseTensor;
using Tensor = phi::DenseTensor;
template <typename T>
......
paddle/fluid/operators/linear_chain_crf_op.cc
浏览文件 @ 91dd8a2e
......@@ -23,23 +23,24 @@ class LinearChainCRFOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("Emission",
"(LoDTensor/Tensor<float>). When a LoDTensor input,A 2-D LoDTensor"
"(phi::DenseTensor<float>). When a phi::DenseTensor "
"input,A 2-D phi::DenseTensor"
" with shape [N x D], where N is the size of the "
"mini-batch and D is the total tag number. The unscaled emission "
"weight matrix for the linear chain CRF. When a Tensor input,"
"A Tensor with shape [N x S x D], where N is batch number,"
"S is max length of sequences, D is the total tag number."
"A LoDTensor or Tensor with type float32, float64.");
"A phi::DenseTensor with type float32, float64.");
AddInput("Transition",
"(Tensor, default Tensor<float>) A 2-D Tensor with shape "
"[(D + 2) x D]. The learnable parameter for the linear_chain_crf "
"operator. See more details in the operator's comments.");
AddInput("Label",
"(LoDTensor/Tensor<int64_t>), when a LoDTensor input, "
"(phi::DenseTensor<int64_t>), when a phi::DenseTensor input, "
"[N x 1], where N is the total element number in a mini-batch. "
"when a Tensor input, [N x S], where N is batch number. "
"S is max length of sequences. The ground truth."
"A LoDTensor or Tensor with int64.");
"A phi::DenseTensor with int64.");
AddInput("Length",
"(Tensor, default Tensor<int64_t>) A Tensor with shape "
"[M x 1], where M is the sequence number in a mini-batch."
......@@ -63,7 +64,7 @@ class LinearChainCRFOpMaker : public framework::OpProtoAndCheckerMaker {
"The exponentials of Input(Emission). This is an intermediate "
"computational result in forward computation, and will be reused in "
"backward computation."
"A LoDTensor or Tensor with type float32, float64.")
"A phi::DenseTensor with type float32, float64.")
.AsIntermediate();
AddOutput(
"TransitionExps",
......@@ -71,7 +72,7 @@ class LinearChainCRFOpMaker : public framework::OpProtoAndCheckerMaker {
"[(D + 2) x D]. The exponentials of Input(Transition). This is an "
"intermediate computational result in forward computation, and "
"will be reused in backward computation."
"A LoDTensor or Tensor with type float32, float64.")
"A phi::DenseTensor with type float32, float64.")
.AsIntermediate();
AddOutput(
"LogLikelihood",
......
paddle/fluid/operators/linear_chain_crf_op.h
浏览文件 @ 91dd8a2e
......@@ -47,7 +47,6 @@ struct ScalarMul {
};
using framework::LoD;
using LoDTensor = phi::DenseTensor;
template <typename DeviceContext, typename T>
class LinearChainCRFOpKernel : public framework::OpKernel<T> {
......@@ -114,7 +113,7 @@ class LinearChainCRFOpKernel : public framework::OpKernel<T> {
phi::funcs::set_constant(ctx.device_context(), emission_exps, 0.0);
phi::funcs::set_constant(ctx.device_context(), alpha, 0.0);
} else {
in_lod = ctx.Input<LoDTensor>("Label")->lod();
in_lod = ctx.Input<phi::DenseTensor>("Label")->lod();
PADDLE_ENFORCE_NE(in_lod.size(),
0,
platform::errors::InvalidArgument(
......@@ -286,7 +285,7 @@ class LinearChainCRFGradOpKernel : public framework::OpKernel<T> {
emission_exps_tmp.Resize(
{emission_dims[0] * emission_dims[1], emission_dims[2]});
} else {
in_lod = ctx.Input<LoDTensor>("Label")->lod();
in_lod = ctx.Input<phi::DenseTensor>("Label")->lod();
PADDLE_ENFORCE_NE(in_lod.size(),
0,
platform::errors::InvalidArgument(
......
paddle/fluid/operators/load_combine_op.cc
浏览文件 @ 91dd8a2e
......@@ -62,7 +62,7 @@ class LoadCombineOpProtoMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
LoadCombine Operator.
LoadCombine operator loads LoDTensor variables from a file, which could be
LoadCombine operator loads phi::DenseTensor variables from a file, which could be
loaded in memory already. The file should contain one or more LoDTensors
serialized using the SaveCombine operator. The
LoadCombine operator applies a deserialization strategy to appropriately load
......
paddle/fluid/operators/load_op.cc
浏览文件 @ 91dd8a2e
......@@ -37,7 +37,7 @@ class LoadOp : public framework::OperatorWithKernel {
class LoadOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddOutput("Out", "The LoDTensor / SelectedRows need to be loaded");
AddOutput("Out", "The phi::DenseTensor / SelectedRows need to be loaded");
AddAttr<bool>(
"load_as_fp16",
"If true, the tensor will be first loaded and then "
......@@ -54,7 +54,8 @@ class LoadOpProtoMaker : public framework::OpProtoAndCheckerMaker {
"(vector<int64_t>) The shape of the output")
.SetDefault({});
AddComment(
"Load operator will load a LoDTensor / SelectedRows variable from "
"Load operator will load a phi::DenseTensor / SelectedRows variable "
"from "
"disk "
"file.");
}
......
paddle/fluid/operators/load_op_npu.cc
浏览文件 @ 91dd8a2e
......@@ -54,7 +54,8 @@ class LoadOpKernel : public framework::OpKernel<T> {
LoadSelectedRows(fin, place, out_var);
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
"Load operator only supports loading LoDTensor and SelectedRows "
"Load operator only supports loading phi::DenseTensor and "
"SelectedRows "
"variable, %s has wrong type",
out_var_name));
}
......
paddle/fluid/operators/lod_rank_table_op.cc
浏览文件 @ 91dd8a2e
......@@ -52,13 +52,14 @@ class LoDRankTableOp : public framework::OperatorBase {
class LoDRankTableOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(LoDTensor) input lod tensor, must contain lod information.");
AddInput(
"X",
"(phi::DenseTensor) input lod tensor, must contain lod information.");
AddOutput("Out", "(LoDRankTable) The rank table of specific level.");
AddAttr<int>("level", "(int) the specific lod level to rank.")
.SetDefault(0)
.EqualGreaterThan(0);
AddComment(R"DOC(Create LoDRanTable by LoDTensor
AddComment(R"DOC(Create LoDRanTable by phi::DenseTensor
LoD Rank Table stores the `level` of `lod` which is ordered by sequence
length in descending order. It is useful when implement dynamic RNN and is
......
paddle/fluid/operators/lod_reset_op.cc
浏览文件 @ 91dd8a2e
......@@ -105,18 +105,20 @@ class LoDResetOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(Tensor, LoDTensor) Input variable of LoDResetOp which "
"could be a Tensor or LoDTensor, where the data of output "
"(Tensor, phi::DenseTensor) Input variable of LoDResetOp which "
"could be a Tensor or phi::DenseTensor, where the data of output "
"variable inherits from.");
AddInput("Y",
"(Tensor, LoDTensor, optional) If provided and Y is LoDTensor, "
"(phi::DenseTensor, optional) If provided and Y is "
"phi::DenseTensor, "
"lod of Input(Y) would be considered as the target lod first, "
"otherwise data of Input(Y) would be considered as the "
"target lod.")
.AsDispensable();
AddOutput("Out",
"(LoDTensor) Output variable of LoDResetOp which should be a "
"LoDTensor.");
AddOutput(
"Out",
"(phi::DenseTensor) Output variable of LoDResetOp which should be a "
"phi::DenseTensor.");
AddAttr<std::vector<int>>("target_lod",
"The target level 0 LoD from Attr().")
.SetDefault(std::vector<int>{});
......@@ -124,7 +126,7 @@ class LoDResetOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(LoDReset operator
Set LoD of `X` to a new one specified by `Y` or attribute `target_lod`. When `Y`
provided and `Y` is a LoDTensor, `Y.lod` would be considered as target LoD
provided and `Y` is a phi::DenseTensor, `Y.lod` would be considered as target LoD
first, otherwise `Y.data` would be considered as target LoD. If `Y` is not
provided, target LoD should be specified by attribute `target_lod`.
If target LoD is specified by `Y.data` or `target_lod`, only one level LoD
......@@ -132,7 +134,7 @@ is supported.
Example 1:
Given a 1-level LoDTensor input(X):
Given a 1-level phi::DenseTensor input(X):
X.lod = [[ 0, 2, 5 6 ]]
X.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
X.dims = [6, 1]
......@@ -146,7 +148,7 @@ then we get a 1-level LoDTensor:
Example 2:
Given a 1-level LoDTensor input(X):
Given a 1-level phi::DenseTensor input(X):
X.lod = [[ 0, 2, 5 6 ]]
X.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
X.dims = [6, 1]
......@@ -162,7 +164,7 @@ then we get a 1-level LoDTensor:
Example 3:
Given a 1-level LoDTensor input(X):
Given a 1-level phi::DenseTensor input(X):
X.lod = [[ 0, 2, 5 6 ]]
X.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
X.dims = [6, 1]
......
paddle/fluid/operators/lod_tensor_to_array_op.cc
浏览文件 @ 91dd8a2e
......@@ -125,8 +125,8 @@ class LoDTensorToArrayOp : public framework::OperatorBase {
PADDLE_ENFORCE_LT(
rank_level,
x.lod().size(),
platform::errors::InvalidArgument(
"Input should be a LoDTensor, and its lod_level should be at "
platform::errors::InvalidArgument("Input should be a phi::DenseTensor, "
"and its lod_level should be at "
"least %d, but given is %d.",
rank_level + 1,
x.lod().size()));
......@@ -189,14 +189,15 @@ class LoDTensorToArrayOp : public framework::OperatorBase {
class LoDTensorToArrayOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(LoDTensor), the input lod tensor is a minibatch of sequences, "
AddInput(
"X",
"(phi::DenseTensor), the input lod tensor is a minibatch of sequences, "
"and will be split to a tensor_array according to "
"Input(RankTable).");
AddInput("RankTable", "(LoDRankTable), the rank table.");
AddOutput("Out",
"(LoDTensorArray), the result tensor_array, which is actually a "
"std::vector<LoDTensor>.");
"std::vector<phi::DenseTensor>.");
AddComment(R"DOC(LoDTensorToArray operator.
Input(X) is a minibatch of sequences. Input(RankTable) stores the order of the input sequences.
The lod_tensor_to_array operator will spilt the input sequences to a tensor_array, with each
......@@ -234,9 +235,9 @@ class LoDTensorToArrayInferShape : public framework::InferShapeBase {
// kernel implementation.
context->SetOutputDim("Out", x_dim);
// The output LoDTensor's lod_level should be input X's lod_level - 1.
// For compile time, we call SetLoDLevel to set output's lod_level.
// For runtime, output LoDTensor's lod is determined by input X's lod and
// The output phi::DenseTensor's lod_level should be input X's lod_level
// - 1. For compile time, we call SetLoDLevel to set output's lod_level. For
// runtime, output phi::DenseTensor's lod is determined by input X's lod and
// the level specified by input RandTable.
// We cannot get X's detail lod and RankTable's level in this function, so
// leave this work to the detail kernel implementation.
......
paddle/fluid/operators/lookup_table_dequant_op.h
浏览文件 @ 91dd8a2e
......@@ -28,7 +28,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
using SelectedRows = phi::SelectedRows;
using DDim = framework::DDim;
......@@ -52,8 +51,8 @@ template <typename T>
class LookupTableDequantKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
auto *ids_t = context.Input<LoDTensor>("Ids"); // int tensor
auto *output_t = context.Output<LoDTensor>("Out"); // float tensor
auto *ids_t = context.Input<phi::DenseTensor>("Ids"); // int tensor
auto *output_t = context.Output<phi::DenseTensor>("Out"); // float tensor
auto *table_var = context.InputVar("W");
auto id_name = context.InputNames("Ids").front();
......@@ -66,9 +65,9 @@ class LookupTableDequantKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_GE(
table_var->Type(),
framework::VarTypeTrait<LoDTensor>::kId,
framework::VarTypeTrait<phi::DenseTensor>::kId,
platform::errors::InvalidArgument("lookup table must be LodTensor"));
auto *table_t = context.Input<LoDTensor>("W");
auto *table_t = context.Input<phi::DenseTensor>("W");
int64_t row_number = table_t->dims()[0];
int64_t quant_number = table_t->dims()[1];
int64_t row_width = (quant_number - 2) * 4;
......
paddle/fluid/operators/lookup_table_op.cc
浏览文件 @ 91dd8a2e
......@@ -212,7 +212,7 @@ class LookupTableOpGradVarTypeInference : public framework::VarTypeInference {
framework::proto::VarType::SELECTED_ROWS);
} else {
VLOG(3) << "lookup_table_grad op " << framework::GradVarName("W")
<< " is set to LoDTensor";
<< " is set to phi::DenseTensor";
ctx->SetOutputType(out_var_name, framework::proto::VarType::LOD_TENSOR);
}
ctx->SetOutputDataType(out_var_name, ctx->GetInputDataType("W"));
......
paddle/fluid/operators/lookup_table_op.cu
浏览文件 @ 91dd8a2e
......@@ -103,9 +103,9 @@ template <typename T>
class LookupTableCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
auto *table_t = context.Input<LoDTensor>("W");
auto *ids_t = context.Input<LoDTensor>("Ids");
auto *output_t = context.Output<LoDTensor>("Out");
auto *table_t = context.Input<phi::DenseTensor>("W");
auto *ids_t = context.Input<phi::DenseTensor>("Ids");
auto *output_t = context.Output<phi::DenseTensor>("Out");
int64_t padding_idx = context.Attr<int64_t>("padding_idx");
auto id_name = context.InputNames("Ids").front();
......@@ -157,9 +157,10 @@ class LookupTableGradCUDAKernel : public framework::OpKernel<T> {
// Since paddings are not trainable and fixed in forward, the gradient of
// paddings makes no sense and we don't deal with it in backward.
if (is_sparse) {
auto *ids = context.Input<LoDTensor>("Ids");
auto *table = context.Input<LoDTensor>("W");
auto *d_output = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto *ids = context.Input<phi::DenseTensor>("Ids");
auto *table = context.Input<phi::DenseTensor>("W");
auto *d_output =
context.Input<phi::DenseTensor>(framework::GradVarName("Out"));
auto *d_table =
context.Output<phi::SelectedRows>(framework::GradVarName("W"));
......@@ -209,9 +210,11 @@ class LookupTableGradCUDAKernel : public framework::OpKernel<T> {
stream);
} else {
auto ids_t = context.Input<LoDTensor>("Ids");
auto d_output_t = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto d_table_t = context.Output<LoDTensor>(framework::GradVarName("W"));
auto ids_t = context.Input<phi::DenseTensor>("Ids");
auto d_output_t =
context.Input<phi::DenseTensor>(framework::GradVarName("Out"));
auto d_table_t =
context.Output<phi::DenseTensor>(framework::GradVarName("W"));
int N = d_table_t->dims()[0];
int D = d_table_t->dims()[1];
......
paddle/fluid/operators/lookup_table_op.h
浏览文件 @ 91dd8a2e
......@@ -27,7 +27,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
using SelectedRows = phi::SelectedRows;
using DDim = framework::DDim;
......@@ -37,8 +36,8 @@ template <typename T>
class LookupTableKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
auto *ids_t = context.Input<LoDTensor>("Ids"); // int tensor
auto *output_t = context.Output<LoDTensor>("Out"); // float tensor
auto *ids_t = context.Input<phi::DenseTensor>("Ids"); // int tensor
auto *output_t = context.Output<phi::DenseTensor>("Out"); // float tensor
auto *table_var = context.InputVar("W");
auto id_name = context.InputNames("Ids").front();
......@@ -51,8 +50,8 @@ class LookupTableKernel : public framework::OpKernel<T> {
int64_t *ids = const_cast<int64_t *>(ids_t->data<int64_t>());
int64_t ids_numel = ids_t->numel();
if (table_var->IsType<LoDTensor>()) {
auto *table_t = context.Input<LoDTensor>("W");
if (table_var->IsType<phi::DenseTensor>()) {
auto *table_t = context.Input<phi::DenseTensor>("W");
int64_t row_number = table_t->dims()[0];
int64_t row_width = table_t->dims()[1];
......@@ -165,15 +164,15 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext &context) const override {
auto *table_var = context.InputVar("W");
DDim table_dim;
if (table_var->IsType<LoDTensor>()) {
table_dim = context.Input<LoDTensor>("W")->dims();
if (table_var->IsType<phi::DenseTensor>()) {
table_dim = context.Input<phi::DenseTensor>("W")->dims();
} else if (table_var->IsType<phi::SelectedRows>()) {
auto *table_t = context.Input<phi::SelectedRows>("W");
table_dim = table_t->value().dims();
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
"The parameter W of a LookupTable "
"must be either LoDTensor or SelectedRows"));
"must be either phi::DenseTensor or SelectedRows"));
}
int64_t padding_idx = context.Attr<int64_t>("padding_idx");
......@@ -181,8 +180,9 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
// Since paddings are not trainable and fixed in forward, the gradient of
// paddings makes no sense and we don't deal with it in backward.
if (is_sparse) {
auto *ids = context.Input<LoDTensor>("Ids");
auto *d_output = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto *ids = context.Input<phi::DenseTensor>("Ids");
auto *d_output =
context.Input<phi::DenseTensor>(framework::GradVarName("Out"));
auto *d_table =
context.Output<phi::SelectedRows>(framework::GradVarName("W"));
......@@ -216,9 +216,11 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
d_output_dims_2d));
memcpy(d_table_data, d_output_data, sizeof(T) * d_output->numel());
} else {
auto *ids = context.Input<LoDTensor>("Ids");
auto *d_output = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto *d_table = context.Output<LoDTensor>(framework::GradVarName("W"));
auto *ids = context.Input<phi::DenseTensor>("Ids");
auto *d_output =
context.Input<phi::DenseTensor>(framework::GradVarName("Out"));
auto *d_table =
context.Output<phi::DenseTensor>(framework::GradVarName("W"));
auto *ids_data = ids->data<int64_t>();
......
paddle/fluid/operators/lookup_table_v2_op.cc
浏览文件 @ 91dd8a2e
......@@ -156,7 +156,7 @@ class LookupTableV2OpGradVarTypeInference : public framework::VarTypeInference {
framework::proto::VarType::SELECTED_ROWS);
} else {
VLOG(3) << "lookup_table_v2_grad op " << framework::GradVarName("W")
<< " is set to LoDTensor";
<< " is set to phi::DenseTensor";
ctx->SetOutputType(out_var_name, framework::proto::VarType::LOD_TENSOR);
}
ctx->SetOutputDataType(out_var_name, ctx->GetInputDataType("W"));
......
paddle/fluid/operators/lookup_table_v2_op.h
浏览文件 @ 91dd8a2e
......@@ -28,7 +28,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
using SelectedRows = phi::SelectedRows;
using DDim = framework::DDim;
......@@ -57,7 +56,7 @@ struct LookupTableV2CPUFunctor {
template <typename IdT>
void apply() {
auto *output_t = context_.Output<LoDTensor>("Out"); // float tensor
auto *output_t = context_.Output<phi::DenseTensor>("Out"); // float tensor
auto *table_var = context_.InputVar("W");
int64_t padding_idx = context_.Attr<int64_t>("padding_idx");
......@@ -65,8 +64,8 @@ struct LookupTableV2CPUFunctor {
auto ids = CopyIdsToVector<IdT, int64_t>(*ids_t_);
auto ids_numel = static_cast<int64_t>(ids.size());
if (table_var->template IsType<LoDTensor>()) {
const auto &table_t = table_var->template Get<LoDTensor>();
if (table_var->template IsType<phi::DenseTensor>()) {
const auto &table_t = table_var->template Get<phi::DenseTensor>();
int64_t row_number = table_t.dims()[0];
int64_t row_width = table_t.dims()[1];
......@@ -168,15 +167,15 @@ struct LookupTableV2GradCPUFunctor {
void apply() {
auto *table_var = context_.InputVar("W");
DDim table_dim;
if (table_var->template IsType<LoDTensor>()) {
table_dim = context_.Input<LoDTensor>("W")->dims();
if (table_var->template IsType<phi::DenseTensor>()) {
table_dim = context_.Input<phi::DenseTensor>("W")->dims();
} else if (table_var->template IsType<phi::SelectedRows>()) {
auto *table_t = context_.Input<phi::SelectedRows>("W");
table_dim = table_t->value().dims();
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
"The parameter W of a LookupTableV2 "
"must be either LoDTensor or SelectedRows"));
"must be either phi::DenseTensor or SelectedRows"));
}
int64_t padding_idx = context_.Attr<int64_t>("padding_idx");
......@@ -188,7 +187,8 @@ struct LookupTableV2GradCPUFunctor {
// Since paddings are not trainable and fixed in forward, the gradient of
// paddings makes no sense and we don't deal with it in backward.
if (is_sparse) {
auto *d_output = context_.Input<LoDTensor>(framework::GradVarName("Out"));
auto *d_output =
context_.Input<phi::DenseTensor>(framework::GradVarName("Out"));
auto *d_table =
context_.Output<phi::SelectedRows>(framework::GradVarName("W"));
......@@ -219,8 +219,10 @@ struct LookupTableV2GradCPUFunctor {
memcpy(d_table_data, d_output_data, sizeof(T) * d_output->numel());
} else {
auto *d_output = context_.Input<LoDTensor>(framework::GradVarName("Out"));
auto *d_table = context_.Output<LoDTensor>(framework::GradVarName("W"));
auto *d_output =
context_.Input<phi::DenseTensor>(framework::GradVarName("Out"));
auto *d_table =
context_.Output<phi::DenseTensor>(framework::GradVarName("W"));
auto *ids_data = ids.data();
int64_t N = table_dim[0];
......
paddle/fluid/operators/lookup_table_v2_op_mlu.cc
浏览文件 @ 91dd8a2e
......@@ -32,7 +32,7 @@ class LookupTableV2MLUKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_EQ(
table_var->IsType<phi::DenseTensor>(),
true,
platform::errors::InvalidArgument("mlu only accept LoDTensor"));
platform::errors::InvalidArgument("mlu only accept phi::DenseTensor"));
output_t->mutable_data<T>(ctx.GetPlace());
MLUCnnlTensorDesc ids_desc(*ids_t);
......@@ -55,11 +55,12 @@ class LookupTableV2GradMLUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto *table_var = ctx.InputVar("W");
PADDLE_ENFORCE_EQ(table_var->IsType<phi::DenseTensor>(),
PADDLE_ENFORCE_EQ(
table_var->IsType<phi::DenseTensor>(),
true,
platform::errors::PermissionDenied(
"Unsupported Variable Type , idx in "
"LookupTableV2GradMLUKernel should be LoDTensor."));
"LookupTableV2GradMLUKernel should be phi::DenseTensor."));
bool is_sparse = ctx.Attr<bool>("is_sparse");
PADDLE_ENFORCE_EQ(
is_sparse,
......
paddle/fluid/operators/lookup_table_v2_op_npu.cc
浏览文件 @ 91dd8a2e
......@@ -37,7 +37,7 @@ class LookupTableV2NPUKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_EQ(
table_var->IsType<phi::DenseTensor>(),
true,
platform::errors::InvalidArgument("npu only accept LoDTensor"));
platform::errors::InvalidArgument("npu only accept phi::DenseTensor"));
output_t->mutable_data<T>(ctx.GetPlace());
int64_t padding_idx = ctx.Attr<int64_t>("padding_idx");
......
paddle/fluid/operators/lstm_op.cc
浏览文件 @ 91dd8a2e
......@@ -146,10 +146,11 @@ class LSTMOp : public framework::OperatorWithKernel {
class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("Input",
"(LoDTensor) the first input is a LodTensor, which support "
"variable-time length input sequence. The underlying tensor in "
"this LoDTensor is a matrix with shape (T X 4D), where T is the "
AddInput(
"Input",
"(phi::DenseTensor) the first input is a phi::DenseTensor, which "
"support variable-time length input sequence. The underlying tensor in "
"this phi::DenseTensor is a matrix with shape (T X 4D), where T is the "
"total time steps in this mini-batch, D is the hidden size.");
AddInput("H0",
"(Tensor, optional) the initial hidden state is an optional "
......@@ -176,15 +177,17 @@ class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
" - The shape is (1 x 7D). "
" - Bias = {b_c, b_i, b_f, b_o, W_ic, W_fc, W_oc}.");
AddOutput("Hidden",
"(LoDTensor) the hidden state of LSTM operator. "
"(phi::DenseTensor) the hidden state of LSTM operator. "
"The shape is (T x D), and lod is the same with the `Input`.");
AddOutput("Cell",
"(LoDTensor) the cell state of LSTM operator. "
"(phi::DenseTensor) the cell state of LSTM operator. "
"The shape is (T x D), and lod is the same with the `Input`.");
AddOutput("BatchGate",
"(LoDTensor) This LoDTensor contains input gate, forget gate "
AddOutput(
"BatchGate",
"(phi::DenseTensor) This phi::DenseTensor contains input gate, forget "
"gate "
"and output gate after the nonlinear computation. This "
"LoDTensor has the same shape as the reorganized input, which "
"phi::DenseTensor has the same shape as the reorganized input, which "
"is also be called batch input. The LoD size is 2. The first "
"LoD is the batch offsets and the second LoD contains the "
"indexes, which denote the position of reorganized sequence "
......@@ -192,7 +195,8 @@ class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
.AsIntermediate()
.AsExtra();
AddOutput("BatchCellPreAct",
"(LoDTensor) This LoDTensor is obtained in the forward and used "
"(phi::DenseTensor) This phi::DenseTensor is obtained in the "
"forward and used "
"in the backward.")
.AsIntermediate()
.AsExtra();
......
paddle/fluid/operators/lstm_op.h
浏览文件 @ 91dd8a2e
......@@ -24,7 +24,6 @@ limitations under the License. */
namespace paddle {
namespace operators {
using LoDTensor = phi::DenseTensor;
using Tensor = phi::DenseTensor;
template <typename DeviceContext, typename T>
......@@ -44,25 +43,25 @@ class LSTMKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
bool is_test = ctx.Attr<bool>("is_test");
auto* input = ctx.Input<LoDTensor>("Input");
auto* input = ctx.Input<phi::DenseTensor>("Input");
auto* weight = ctx.Input<phi::DenseTensor>("Weight");
auto* bias = ctx.Input<phi::DenseTensor>("Bias");
auto* hidden_t0 = ctx.Input<phi::DenseTensor>("H0");
auto* cell_t0 = ctx.Input<phi::DenseTensor>("C0");
LoDTensor* batch_gate = nullptr;
LoDTensor batch_gate_temp;
phi::DenseTensor* batch_gate = nullptr;
phi::DenseTensor batch_gate_temp;
if (is_test) {
batch_gate = &batch_gate_temp;
batch_gate->Resize(input->dims());
} else {
batch_gate = ctx.Output<LoDTensor>("BatchGate");
batch_gate = ctx.Output<phi::DenseTensor>("BatchGate");
}
batch_gate->mutable_data<T>(ctx.GetPlace());
auto* hidden_out = ctx.Output<LoDTensor>("Hidden");
auto* hidden_out = ctx.Output<phi::DenseTensor>("Hidden");
hidden_out->mutable_data<T>(ctx.GetPlace());
auto* cell_out = ctx.Output<LoDTensor>("Cell");
auto* cell_out = ctx.Output<phi::DenseTensor>("Cell");
cell_out->mutable_data<T>(ctx.GetPlace());
bool is_reverse = ctx.Attr<bool>("is_reverse");
......@@ -110,12 +109,12 @@ class LSTMKernel : public framework::OpKernel<T> {
}
// Use the local variable as here.
LoDTensor batch_hidden, batch_cell, batch_cell_pre_act_temp;
LoDTensor* batch_cell_pre_act;
phi::DenseTensor batch_hidden, batch_cell, batch_cell_pre_act_temp;
phi::DenseTensor* batch_cell_pre_act;
if (is_test) {
batch_cell_pre_act = &batch_cell_pre_act_temp;
} else {
batch_cell_pre_act = ctx.Output<LoDTensor>("BatchCellPreAct");
batch_cell_pre_act = ctx.Output<phi::DenseTensor>("BatchCellPreAct");
}
batch_hidden.mutable_data<T>(dims, ctx.GetPlace());
batch_cell.mutable_data<T>(dims, ctx.GetPlace());
......@@ -191,11 +190,11 @@ class LSTMKernel : public framework::OpKernel<T> {
phi::funcs::Batch2LoDTensorFunctor<DeviceContext, T> to_seq;
batch_hidden.set_lod(batch_gate->lod());
// restore the output hidden in LoDTensor from the batch hidden
// restore the output hidden in phi::DenseTensor from the batch hidden
to_seq(device_ctx, batch_hidden, hidden_out);
batch_cell.set_lod(batch_gate->lod());
// restore the output cell state in LoDTensor from the batch cell
// restore the output cell state in phi::DenseTensor from the batch cell
to_seq(device_ctx, batch_cell, cell_out);
}
};
......@@ -204,19 +203,20 @@ template <typename DeviceContext, typename T>
class LSTMGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<LoDTensor>("Input");
auto* input = ctx.Input<phi::DenseTensor>("Input");
auto* weight = ctx.Input<phi::DenseTensor>("Weight");
auto* bias = ctx.Input<phi::DenseTensor>("Bias");
auto* hidden_out = ctx.Input<LoDTensor>("Hidden");
auto* cell_out = ctx.Input<LoDTensor>("Cell");
auto* hidden_out = ctx.Input<phi::DenseTensor>("Hidden");
auto* cell_out = ctx.Input<phi::DenseTensor>("Cell");
auto* batch_gate = ctx.Input<LoDTensor>("BatchGate");
auto* batch_cell_pre_act = ctx.Input<LoDTensor>("BatchCellPreAct");
auto* batch_gate = ctx.Input<phi::DenseTensor>("BatchGate");
auto* batch_cell_pre_act = ctx.Input<phi::DenseTensor>("BatchCellPreAct");
auto* hidden_g = ctx.Input<LoDTensor>(framework::GradVarName("Hidden"));
auto* hidden_g =
ctx.Input<phi::DenseTensor>(framework::GradVarName("Hidden"));
auto* in_g = ctx.Output<LoDTensor>(framework::GradVarName("Input"));
auto* in_g = ctx.Output<phi::DenseTensor>(framework::GradVarName("Input"));
auto* weight_g =
ctx.Output<phi::DenseTensor>(framework::GradVarName("Weight"));
auto* bias_g = ctx.Output<phi::DenseTensor>(framework::GradVarName("Bias"));
......@@ -301,12 +301,12 @@ class LSTMGradKernel : public framework::OpKernel<T> {
to_batch(ctx, src, &dst, false);
};
LoDTensor batch_hidden, batch_hidden_g, batch_cell;
phi::DenseTensor batch_hidden, batch_hidden_g, batch_cell;
ToBatch(device_ctx, *hidden_out, out_dims, batch_hidden);
ToBatch(device_ctx, *hidden_g, out_dims, batch_hidden_g);
ToBatch(device_ctx, *cell_out, out_dims, batch_cell);
LoDTensor batch_cell_g, batch_gate_g;
phi::DenseTensor batch_cell_g, batch_gate_g;
batch_cell_g.mutable_data<T>(out_dims, ctx.GetPlace());
// TODO(qingqing) support the case output cell has gradient.
// to_batch(device_ctx, *cell_g, batch_cell_g, false);
......
paddle/fluid/operators/lstmp_op.cc
浏览文件 @ 91dd8a2e
......@@ -154,10 +154,11 @@ class LSTMPOp : public framework::OperatorWithKernel {
class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("Input",
"(LoDTensor) the input for sequence data, which supports "
AddInput(
"Input",
"(phi::DenseTensor) the input for sequence data, which supports "
"variable-time length input sequence. The underlying tensor in "
"this LoDTensor is a matrix with shape (T X 4D), where T is the "
"this phi::DenseTensor is a matrix with shape (T X 4D), where T is the "
"total time steps in this mini-batch, D is the hidden size.");
AddInput("H0",
"(Tensor, optional) the initial hidden state is an optional "
......@@ -190,28 +191,33 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
" - The shape is (1 x 7D). "
" - Bias = {b_c, b_i, b_f, b_o, W_ic, W_fc, W_oc}.");
AddOutput("Projection",
"(LoDTensor) the projection of the hidden state of LSTMP "
"(phi::DenseTensor) the projection of the hidden state of LSTMP "
"operator. The shape is (T x P), and LoD is the same with the "
"`Input`.");
AddOutput("Cell",
"(LoDTensor) the cell state of LSTMP operator. "
"(phi::DenseTensor) the cell state of LSTMP operator. "
"The shape is (T x D), and lod is the same with the `Input`.");
AddOutput("BatchGate",
"(LoDTensor) This LoDTensor contains input gate, forget gate "
"and output gate after the activations. This LoDTensor has the "
AddOutput(
"BatchGate",
"(phi::DenseTensor) This phi::DenseTensor contains input gate, forget "
"gate "
"and output gate after the activations. This phi::DenseTensor has the "
"same shape as the reorganized input, which is also be called "
"batch input. The LoD size is 2. The first-level LoD is the "
"batch offsets and the second contains the indices, which "
"denotes the position of reorganized sequence in the raw input.")
.AsIntermediate();
AddOutput("BatchCellPreAct",
"(LoDTensor) the pre-activation cell state reorganized in batch. "
"This LoDTensor is obtained in the forward and used in the "
AddOutput(
"BatchCellPreAct",
"(phi::DenseTensor) the pre-activation cell state reorganized in "
"batch. "
"This phi::DenseTensor is obtained in the forward and used in the "
"backward.")
.AsIntermediate();
AddOutput("BatchHidden",
"(LoDTensor) the hidden state reorganized in batch. "
"This LoDTensor is obtained in the forward and used in the "
AddOutput(
"BatchHidden",
"(phi::DenseTensor) the hidden state reorganized in batch. "
"This phi::DenseTensor is obtained in the forward and used in the "
"backward.")
.AsIntermediate();
AddAttr<bool>("use_peepholes",
......
paddle/fluid/operators/lstmp_op.h
浏览文件 @ 91dd8a2e
......@@ -29,7 +29,6 @@ limitations under the License. */
namespace paddle {
namespace operators {
using LoDTensor = phi::DenseTensor;
using Tensor = phi::DenseTensor;
using platform::Transform;
......@@ -107,7 +106,7 @@ class LSTMPKernel : public framework::OpKernel<T> {
}
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<LoDTensor>("Input");
auto* input = ctx.Input<phi::DenseTensor>("Input");
auto* weight = ctx.Input<phi::DenseTensor>("Weight");
auto* proj_weight = ctx.Input<phi::DenseTensor>("ProjWeight");
auto* bias = ctx.Input<phi::DenseTensor>("Bias");
......@@ -118,11 +117,11 @@ class LSTMPKernel : public framework::OpKernel<T> {
auto proj_clip = static_cast<T>(ctx.Attr<float>("proj_clip"));
auto cell_clip = static_cast<T>(ctx.Attr<float>("cell_clip"));
auto* batch_gate = ctx.Output<LoDTensor>("BatchGate");
auto* batch_gate = ctx.Output<phi::DenseTensor>("BatchGate");
batch_gate->mutable_data<T>(ctx.GetPlace());
auto* proj_out = ctx.Output<LoDTensor>("Projection");
auto* proj_out = ctx.Output<phi::DenseTensor>("Projection");
proj_out->mutable_data<T>(ctx.GetPlace());
auto* cell_out = ctx.Output<LoDTensor>("Cell");
auto* cell_out = ctx.Output<phi::DenseTensor>("Cell");
cell_out->mutable_data<T>(ctx.GetPlace());
bool is_reverse = ctx.Attr<bool>("is_reverse");
......@@ -172,10 +171,10 @@ class LSTMPKernel : public framework::OpKernel<T> {
}
// Use the local variable as here.
LoDTensor batch_proj, batch_cell;
auto* batch_cell_pre_act = ctx.Output<LoDTensor>("BatchCellPreAct");
phi::DenseTensor batch_proj, batch_cell;
auto* batch_cell_pre_act = ctx.Output<phi::DenseTensor>("BatchCellPreAct");
batch_cell_pre_act->mutable_data<T>(dims, ctx.GetPlace());
auto* batch_hidden = ctx.Output<LoDTensor>("BatchHidden");
auto* batch_hidden = ctx.Output<phi::DenseTensor>("BatchHidden");
batch_hidden->mutable_data<T>(dims, ctx.GetPlace()); // T x D
batch_proj.mutable_data<T>(proj_dims, ctx.GetPlace()); // T x P
batch_cell.mutable_data<T>(dims, ctx.GetPlace()); // T x D
......@@ -272,11 +271,11 @@ class LSTMPKernel : public framework::OpKernel<T> {
phi::funcs::Batch2LoDTensorFunctor<DeviceContext, T> to_seq;
batch_proj.set_lod(batch_gate->lod());
// restore the output hidden in LoDTensor from the batch hidden
// restore the output hidden in phi::DenseTensor from the batch hidden
to_seq(device_ctx, batch_proj, proj_out);
batch_cell.set_lod(batch_gate->lod());
// restore the output cell state in LoDTensor from the batch cell
// restore the output cell state in phi::DenseTensor from the batch cell
to_seq(device_ctx, batch_cell, cell_out);
}
};
......@@ -310,20 +309,20 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
auto* proj_weight = ctx.Input<phi::DenseTensor>("ProjWeight");
auto* bias = ctx.Input<phi::DenseTensor>("Bias");
auto* proj_out = ctx.Input<LoDTensor>("Projection");
auto* cell_out = ctx.Input<LoDTensor>("Cell");
auto* proj_out = ctx.Input<phi::DenseTensor>("Projection");
auto* cell_out = ctx.Input<phi::DenseTensor>("Cell");
auto proj_clip = static_cast<T>(ctx.Attr<float>("proj_clip"));
auto cell_clip = static_cast<T>(ctx.Attr<float>("cell_clip"));
auto* batch_gate = ctx.Input<LoDTensor>("BatchGate");
auto* batch_cell_pre_act = ctx.Input<LoDTensor>("BatchCellPreAct");
auto* batch_hidden = ctx.Input<LoDTensor>("BatchHidden");
auto* batch_gate = ctx.Input<phi::DenseTensor>("BatchGate");
auto* batch_cell_pre_act = ctx.Input<phi::DenseTensor>("BatchCellPreAct");
auto* batch_hidden = ctx.Input<phi::DenseTensor>("BatchHidden");
auto* projection_g =
ctx.Input<LoDTensor>(framework::GradVarName("Projection"));
ctx.Input<phi::DenseTensor>(framework::GradVarName("Projection"));
auto* in_g = ctx.Output<LoDTensor>(framework::GradVarName("Input"));
auto* in_g = ctx.Output<phi::DenseTensor>(framework::GradVarName("Input"));
auto* weight_g =
ctx.Output<phi::DenseTensor>(framework::GradVarName("Weight"));
auto* proj_weight_g =
......@@ -415,13 +414,13 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
to_batch(ctx, src, &dst, false);
};
LoDTensor batch_hidden_g, batch_proj, batch_proj_g, batch_cell;
phi::DenseTensor batch_hidden_g, batch_proj, batch_proj_g, batch_cell;
batch_hidden_g.mutable_data<T>(out_dims, ctx.GetPlace());
ToBatch(device_ctx, *proj_out, proj_dims, batch_proj); // T x P
ToBatch(device_ctx, *projection_g, proj_dims, batch_proj_g); // T x P
ToBatch(device_ctx, *cell_out, out_dims, batch_cell); // T x D
LoDTensor batch_cell_g, batch_gate_g;
phi::DenseTensor batch_cell_g, batch_gate_g;
batch_cell_g.mutable_data<T>(out_dims, ctx.GetPlace());
// TODO(qingqing) support the case output cell has gradient.
// to_batch(device_ctx, *cell_g, batch_cell_g, false);
......
paddle/fluid/operators/match_matrix_tensor_op.cc
浏览文件 @ 91dd8a2e
......@@ -25,7 +25,6 @@ limitations under the License. */
namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
using LoD = framework::LoD;
void MatchMatrixTensorOP::InferShape(framework::InferShapeContext* ctx) const {
......@@ -92,7 +91,7 @@ void MatchMatrixTensorOP::InferShape(framework::InferShapeContext* ctx) const {
if (ctx->IsRuntime()) {
framework::Variable* x_var =
PADDLE_GET(framework::Variable*, ctx->GetInputVarPtrs("X")[0]);
const auto& x_lod = x_var->Get<LoDTensor>().lod();
const auto& x_lod = x_var->Get<phi::DenseTensor>().lod();
PADDLE_ENFORCE_EQ(x_lod.empty(),
false,
platform::errors::InvalidArgument(
......@@ -117,7 +116,7 @@ void MatchMatrixTensorOP::InferShape(framework::InferShapeContext* ctx) const {
framework::Variable* y_var =
PADDLE_GET(framework::Variable*, ctx->GetInputVarPtrs("Y")[0]);
const auto& y_lod = y_var->Get<LoDTensor>().lod();
const auto& y_lod = y_var->Get<phi::DenseTensor>().lod();
PADDLE_ENFORCE_EQ(y_lod.empty(),
false,
platform::errors::InvalidArgument(
......@@ -213,18 +212,22 @@ void MatchMatrixTensorOpGrad::InferShape(
void MatchMatrixTensorOpMaker::Make() {
AddInput("X",
"X (LoDTensor, default LoDTensor<float>) Input variable which "
"X (phi::DenseTensor, default phi::DenseTensor<float>) Input "
"variable which "
"should contain lod information.");
AddInput("Y",
"Y (LoDTensor, default LoDTensor<float>) Input variable which "
"Y (phi::DenseTensor, default phi::DenseTensor<float>) Input "
"variable which "
"should contain lod information.");
AddInput("W", "W (Tensor), The weight of X and Y.");
AddAttr<int>("dim_t", "the dim of W").SetDefault(1);
AddOutput("Out",
"(LoDTensor, default LoDTensor<float>) Output variable which "
"(phi::DenseTensor, default phi::DenseTensor<float>) Output "
"variable which "
"is X * W * Y");
AddOutput("Tmp",
"(LoDTensor, default LoDTensor<float>) tmp variable which is "
"(phi::DenseTensor, default phi::DenseTensor<float>) tmp variable "
"which is "
"used for X * W");
AddComment(R"DOC(
Match Matrix Tensor Operator
......@@ -242,11 +245,11 @@ template <typename DeviceContext, typename T>
class CPUMatchMatrixTensorOPKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<LoDTensor>("X");
auto* y = ctx.Input<LoDTensor>("Y");
auto* x = ctx.Input<phi::DenseTensor>("X");
auto* y = ctx.Input<phi::DenseTensor>("Y");
auto* w = ctx.Input<phi::DenseTensor>("W");
auto* out = ctx.Output<LoDTensor>("Out");
auto* tmp = ctx.Output<LoDTensor>("Tmp");
auto* out = ctx.Output<phi::DenseTensor>("Out");
auto* tmp = ctx.Output<phi::DenseTensor>("Tmp");
int dim_t = ctx.Attr<int>("dim_t");
int64_t dim_in = x->dims()[1];
......@@ -322,10 +325,10 @@ template <typename DeviceContext, typename T>
class CPUMatchMatrixTensorOPGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<LoDTensor>("X");
auto* y = ctx.Input<LoDTensor>("Y");
auto* x = ctx.Input<phi::DenseTensor>("X");
auto* y = ctx.Input<phi::DenseTensor>("Y");
auto* w = ctx.Input<phi::DenseTensor>("W");
auto* tmp = ctx.Input<LoDTensor>("Tmp");
auto* tmp = ctx.Input<phi::DenseTensor>("Tmp");
int dim_t = ctx.Attr<int>("dim_t");
int64_t dim_in = x->dims()[1];
......@@ -346,9 +349,9 @@ class CPUMatchMatrixTensorOPGradKernel : public framework::OpKernel<T> {
auto* bottom_r_data = y->data<T>();
auto* bottom_l_trans_data = tmp->data<T>();
auto* d_out = ctx.Input<LoDTensor>(framework::GradVarName("Out"));
auto* d_x = ctx.Output<LoDTensor>(framework::GradVarName("X"));
auto* d_y = ctx.Output<LoDTensor>(framework::GradVarName("Y"));
auto* d_out = ctx.Input<phi::DenseTensor>(framework::GradVarName("Out"));
auto* d_x = ctx.Output<phi::DenseTensor>(framework::GradVarName("X"));
auto* d_y = ctx.Output<phi::DenseTensor>(framework::GradVarName("Y"));
Tensor tmp_grad;
tmp_grad.Resize(tmp->dims());
......
paddle/fluid/operators/memcpy_d2h_op.cc
浏览文件 @ 91dd8a2e
......@@ -83,9 +83,9 @@ class MemcpyD2HKernel {
class MemcpyD2HOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(LoDTensor) The input variable ");
AddInput("X", "(phi::DenseTensor) The input variable ");
AddOutput("Out",
"(LoDTensor) The type of output "
"(phi::DenseTensor) The type of output "
"is the same as input X.");
AddAttr<int>(
"dst_place_type",
......@@ -98,7 +98,7 @@ class MemcpyD2HOpProtoMaker : public framework::OpProtoAndCheckerMaker {
MemcpyD2H Operator.
By now, it ONLY supports the memcopy between NPUPlace/CUDAPlace <-> CUDAPinnedPlace/CPU.
You would have to update it if you want other more capacities.
Out = X, when type in [LoDTensor]
Out = X, when type in [phi::DenseTensor]
raise error if the type is not listed above.
)DOC");
}
......
paddle/fluid/operators/memcpy_h2d_op.cc
浏览文件 @ 91dd8a2e
......@@ -84,9 +84,9 @@ class MemcpyH2DKernel {
class MemcpyH2DOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(LoDTensor) The input variable ");
AddInput("X", "(phi::DenseTensor) The input variable ");
AddOutput("Out",
"(LoDTensor) The type of output "
"(phi::DenseTensor) The type of output "
"is the same as input X.");
AddAttr<int>("dst_place_type",
"Determine the dst place of tensor copy. "
......@@ -100,7 +100,7 @@ class MemcpyH2DOpProtoMaker : public framework::OpProtoAndCheckerMaker {
MemcpyD2H Operator.
By now, it ONLY supports the memcopy between CUDAPinnedPlace/CPU <-> NPUPlace/CUDAPlace.
You would have to update it if you want other more capacities.
Out = X, when type in [LoDTensor]
Out = X, when type in [phi::DenseTensor]
raise error if the type is not listed above.
)DOC");
}
......
paddle/fluid/operators/memcpy_op.cc
浏览文件 @ 91dd8a2e
......@@ -100,9 +100,9 @@ class MemcpyKernel {
class MemcpyOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(LoDTensor) The input variable ");
AddInput("X", "(phi::DenseTensor) The input variable ");
AddOutput("Out",
"(LoDTensor) The type of output "
"(phi::DenseTensor) The type of output "
"is the same as input X.");
AddAttr<int>("dst_place_type",
"Determine the dst place of tensor copy. "
......@@ -122,7 +122,7 @@ class MemcpyOpProtoMaker : public framework::OpProtoAndCheckerMaker {
NPUPlace <-> CPUPlace, and used as an internal op by Recompute-Offload.
You would have to update it if you want other more capacities.
Out = X, when type in [LoDTensor]
Out = X, when type in [phi::DenseTensor]
raise error if the type is not listed above.
)DOC");
}
......
paddle/fluid/operators/merge_lod_tensor_op.cc
浏览文件 @ 91dd8a2e
......@@ -104,7 +104,7 @@ class MergeLoDTensorOp : public framework::OperatorBase {
out_lod->clear();
size_t out_offset = 0;
// Build LoDTensor `out`
// Build phi::DenseTensor `out`
size_t in_true_idx = 0;
size_t in_false_idx = 0;
......@@ -182,18 +182,18 @@ class MergeLoDTensorOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"The input LoDTensor, contains complete lod information to "
"The input phi::DenseTensor, contains complete lod information to "
"construct the output");
AddInput("Mask", "A bool column vector which mask the input");
AddInput("InTrue", "The True branch to be merged");
AddInput("InFalse", "The False branch to be merged");
AddOutput("Out", "The merged output LoDTensor");
AddOutput("Out", "The merged output phi::DenseTensor");
AddAttr<int>("level", "(int) the specific lod level to rank.")
.SetDefault(0)
.EqualGreaterThan(0);
AddComment(
R"DOC(
Merge True and False branches of LoDTensor into a single Output,
Merge True and False branches of phi::DenseTensor into a single Output,
with a mask at certain lod level. X is used to obtain complete
lod information. Please refer to SplitLoDTensorOp.)DOC");
}
......
paddle/fluid/operators/nce_op.cc
浏览文件 @ 91dd8a2e
......@@ -300,7 +300,7 @@ class NCEOpGradVarTypeInference : public framework::VarTypeInference {
ctx->SetOutputType(weight_grad, framework::proto::VarType::SELECTED_ROWS);
} else {
VLOG(3) << "nce_op_grad op " << weight_grad << " and "
<< " is set to LoDTensor";
<< " is set to phi::DenseTensor";
ctx->SetOutputType(weight_grad, framework::proto::VarType::LOD_TENSOR);
}
ctx->SetOutputDataType(weight_grad, ctx->GetInputDataType("Input"));
......
paddle/fluid/operators/nce_op.h
浏览文件 @ 91dd8a2e
......@@ -32,7 +32,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
using SelectedRows = phi::SelectedRows;
using Sampler = math::Sampler;
using DDim = framework::DDim;
......@@ -395,15 +394,15 @@ class NCEGradKernel : public framework::OpKernel<T> {
auto *table_var = context.InputVar("Weight");
DDim table_dim;
if (table_var->IsType<LoDTensor>()) {
table_dim = context.Input<LoDTensor>("Weight")->dims();
if (table_var->IsType<phi::DenseTensor>()) {
table_dim = context.Input<phi::DenseTensor>("Weight")->dims();
} else if (table_var->IsType<phi::SelectedRows>()) {
auto *table_t = context.Input<phi::SelectedRows>("Weight");
table_dim = table_t->value().dims();
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
"The parameter Weight of a NCE_OP "
"must be either LoDTensor or SelectedRows"));
"must be either phi::DenseTensor or SelectedRows"));
}
auto d_w =
......
paddle/fluid/operators/number_count_op.cu
浏览文件 @ 91dd8a2e
......@@ -37,7 +37,6 @@ static inline int GET_BLOCKS(const int N) {
return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS;
}
using LoDTensor = phi::DenseTensor;
using Tensor = phi::DenseTensor;
template <typename T>
......@@ -86,9 +85,9 @@ template <typename T>
class NumberCountOpCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto numbers = context.Input<LoDTensor>("numbers");
auto numbers = context.Input<phi::DenseTensor>("numbers");
auto upper_range = context.Attr<int>("upper_range");
auto number_count = context.Output<LoDTensor>("Out");
auto number_count = context.Output<phi::DenseTensor>("Out");
int64_t batch_size = numbers->numel();
auto place = context.GetPlace();
......
paddle/fluid/operators/one_hot_op.cc
浏览文件 @ 91dd8a2e
......@@ -79,7 +79,8 @@ class OneHotOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(LoDTensor, LoDTensor<int>) Input variable with rank at least 2. "
"(phi::DenseTensor, phi::DenseTensor<int>) Input variable with "
"rank at least 2. "
"The last dimension of X should be 1. Each value of X is an index "
"to indicate the position.");
AddInput("depth_tensor", "(Tensor, Tensor<int>), Length of one-hot vector")
......
paddle/fluid/operators/one_hot_op.cu
浏览文件 @ 91dd8a2e
......@@ -60,13 +60,12 @@ struct OneHotOpCUDAFunctor {
}
};
using LoDTensor = phi::DenseTensor;
template <typename DeviceContext, typename T>
class OneHotCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in = context.Input<LoDTensor>("X");
auto* out = context.Output<LoDTensor>("Out");
auto* in = context.Input<phi::DenseTensor>("X");
auto* out = context.Output<phi::DenseTensor>("Out");
int depth = -1;
if (context.HasInput("depth_tensor")) {
......
paddle/fluid/operators/one_hot_op.h
浏览文件 @ 91dd8a2e
......@@ -76,14 +76,13 @@ struct OneHotOpFunctor {
}
};
using LoDTensor = phi::DenseTensor;
using Tensor = phi::DenseTensor;
template <typename DeviceContext, typename T>
class OneHotKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in = context.Input<LoDTensor>("X");
auto* out = context.Output<LoDTensor>("Out");
auto* in = context.Input<phi::DenseTensor>("X");
auto* out = context.Output<phi::DenseTensor>("Out");
int depth = context.Attr<int>("depth");
bool allow_out_of_range = context.Attr<bool>("allow_out_of_range");
if (context.HasInput("depth_tensor")) {
......
paddle/fluid/operators/one_hot_op_npu.cc
浏览文件 @ 91dd8a2e
......@@ -25,8 +25,8 @@ class OneHotNPUKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
auto& dev_ctx =
ctx.template device_context<paddle::platform::NPUDeviceContext>();
auto* in = ctx.Input<LoDTensor>("X");
auto* out = ctx.Output<LoDTensor>("Out");
auto* in = ctx.Input<phi::DenseTensor>("X");
auto* out = ctx.Output<phi::DenseTensor>("Out");
int depth = ctx.Attr<int>("depth");
if (ctx.HasInput("depth_tensor")) {
......
paddle/fluid/operators/one_hot_op_xpu.cc
浏览文件 @ 91dd8a2e
......@@ -22,15 +22,14 @@
namespace paddle {
namespace operators {
using LoDTensor = phi::DenseTensor;
using Tensor = phi::DenseTensor;
template <typename DeviceContext, typename T>
class OneHotXPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const auto* in = context.Input<LoDTensor>("X");
auto* out = context.Output<LoDTensor>("Out");
const auto* in = context.Input<phi::DenseTensor>("X");
auto* out = context.Output<phi::DenseTensor>("Out");
// get depth from attr
int depth = context.Attr<int>("depth");
......
paddle/fluid/operators/one_hot_v2_op.cc
浏览文件 @ 91dd8a2e
......@@ -52,7 +52,8 @@ class OneHotV2OpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(LoDTensor, LoDTensor<int>) Input variable with rank at least 2. "
"(phi::DenseTensor, phi::DenseTensor<int>) Input variable with "
"rank at least 2. "
"The last dimension of X should be 1. Each value of X is an index "
"to indicate the position.");
AddInput("depth_tensor", "(Tensor, Tensor<int>), Length of one-hot vector")
......
paddle/fluid/operators/one_hot_v2_op_mlu.cc
浏览文件 @ 91dd8a2e
......@@ -20,7 +20,6 @@ limitations under the License. */
namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
template <typename T>
class OneHotV2MLUKernel : public framework::OpKernel<T> {
......@@ -28,8 +27,8 @@ class OneHotV2MLUKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
auto& dev_ctx =
ctx.template device_context<paddle::platform::MLUDeviceContext>();
auto* in = ctx.Input<LoDTensor>("X");
auto* out = ctx.Output<LoDTensor>("Out");
auto* in = ctx.Input<phi::DenseTensor>("X");
auto* out = ctx.Output<phi::DenseTensor>("Out");
int depth = ctx.Attr<int>("depth");
if (ctx.HasInput("depth_tensor")) {
std::vector<int32_t> depth_data;
......
paddle/fluid/operators/one_hot_v2_op_npu.cc
浏览文件 @ 91dd8a2e
......@@ -18,7 +18,6 @@ limitations under the License. */
namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
template <typename T>
class OneHotV2NPUKernel : public framework::OpKernel<T> {
......@@ -26,8 +25,8 @@ class OneHotV2NPUKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
auto& dev_ctx =
ctx.template device_context<paddle::platform::NPUDeviceContext>();
auto* in = ctx.Input<LoDTensor>("X");
auto* out = ctx.Output<LoDTensor>("Out");
auto* in = ctx.Input<phi::DenseTensor>("X");
auto* out = ctx.Output<phi::DenseTensor>("Out");
int depth = ctx.Attr<int>("depth");
if (ctx.HasInput("depth_tensor")) {
......
paddle/fluid/operators/partial_concat_op.cu
浏览文件 @ 91dd8a2e
......@@ -23,7 +23,6 @@ namespace operators {
#define CEIL_DIV(x, y) (((x) + (y)-1) / (y))
using LoDTensor = phi::DenseTensor;
using Tensor = phi::DenseTensor;
template <class T>
......@@ -154,8 +153,8 @@ class PartialConcatGradOpCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto *out_grad = ctx.Input<phi::DenseTensor>(framework::GradVarName("Out"));
auto ins = ctx.MultiInput<LoDTensor>("X");
auto outs = ctx.MultiOutput<LoDTensor>(framework::GradVarName("X"));
auto ins = ctx.MultiInput<phi::DenseTensor>("X");
auto outs = ctx.MultiOutput<phi::DenseTensor>(framework::GradVarName("X"));
PADDLE_ENFORCE_EQ(ins[0] != nullptr,
true,
......
paddle/fluid/operators/partial_sum_op.cu
浏览文件 @ 91dd8a2e
......@@ -23,7 +23,6 @@ namespace operators {
#define CEIL_DIV(x, y) (((x) + (y)-1) / (y))
using LoDTensor = phi::DenseTensor;
using Tensor = phi::DenseTensor;
template <class T>
......@@ -153,8 +152,8 @@ class PartialSumGradOpCUDAKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext &ctx) const override {
const Tensor *out_grad =
ctx.Input<phi::DenseTensor>(framework::GradVarName("Out"));
auto ins = ctx.MultiInput<LoDTensor>("X");
auto outs = ctx.MultiOutput<LoDTensor>(framework::GradVarName("X"));
auto ins = ctx.MultiInput<phi::DenseTensor>("X");
auto outs = ctx.MultiOutput<phi::DenseTensor>(framework::GradVarName("X"));
PADDLE_ENFORCE_EQ(
ins[0] != nullptr,
......
paddle/fluid/operators/positive_negative_pair_op.h
浏览文件 @ 91dd8a2e
......@@ -20,7 +20,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
template <typename DeviceContext, typename T>
class PositiveNegativePairKernel : public framework::OpKernel<T> {
......
paddle/fluid/operators/prroi_pool_op.cc
浏览文件 @ 91dd8a2e
......@@ -20,7 +20,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
class PRROIPoolOpMaker : public framework::OpProtoAndCheckerMaker {
public:
......@@ -33,9 +32,9 @@ class PRROIPoolOpMaker : public framework::OpProtoAndCheckerMaker {
"H is the height of the input feature map, and "
"W is the width.");
AddInput("ROIs",
"(LoDTensor), "
"(phi::DenseTensor), "
"ROIs (Regions of Interest) to pool over. "
"should be a 2-D LoDTensor of shape (num_rois, 4) "
"should be a 2-D phi::DenseTensor of shape (num_rois, 4) "
"given as [(x1, y1, x2, y2), ...]. "
"where (x1, y1) is the top left coordinates, and "
"(x2, y2) is the bottom right coordinates. "
......@@ -95,13 +94,13 @@ class PRROIPoolOp : public framework::OperatorWithKernel {
rois_dims.size(),
2,
platform::errors::InvalidArgument(
"ROIs should be a 2-D LoDTensor of shape (num_rois, 4) "
"ROIs should be a 2-D phi::DenseTensor of shape (num_rois, 4) "
"given as [(x1, y1, x2, y2), ...]"));
PADDLE_ENFORCE_EQ(
rois_dims[1],
4,
platform::errors::InvalidArgument(
"ROIs should be a 2-D LoDTensor of shape (num_rois, 4) "
"ROIs should be a 2-D phi::DenseTensor of shape (num_rois, 4) "
"given as [(x1, y1, x2, y2), ...]"));
int pooled_height = ctx->Attrs().Get<int>("pooled_height");
int pooled_width = ctx->Attrs().Get<int>("pooled_width");
......
paddle/fluid/operators/prroi_pool_op.cu
浏览文件 @ 91dd8a2e
......@@ -18,7 +18,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
static constexpr int kNumCUDAThreads = 512;
static constexpr int kNumMaximumNumBlocks = 4096;
......@@ -219,7 +218,7 @@ class GPUPRROIPoolOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<phi::DenseTensor>("X");
auto* rois = ctx.Input<LoDTensor>("ROIs");
auto* rois = ctx.Input<phi::DenseTensor>("ROIs");
auto* out = ctx.Output<phi::DenseTensor>("Out");
auto pooled_height = ctx.Attr<int>("pooled_height");
......@@ -322,7 +321,7 @@ class GPUPRROIPoolGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<phi::DenseTensor>("X");
auto* rois = ctx.Input<LoDTensor>("ROIs");
auto* rois = ctx.Input<phi::DenseTensor>("ROIs");
auto* out = ctx.Input<phi::DenseTensor>("Out");
auto* output_grad =
......@@ -330,7 +329,7 @@ class GPUPRROIPoolGradOpKernel : public framework::OpKernel<T> {
auto* input_grad =
ctx.Output<phi::DenseTensor>(framework::GradVarName("X"));
auto* input_roi_grad =
ctx.Output<LoDTensor>(framework::GradVarName("ROIs"));
ctx.Output<phi::DenseTensor>(framework::GradVarName("ROIs"));
auto pooled_height = ctx.Attr<int>("pooled_height");
auto pooled_width = ctx.Attr<int>("pooled_width");
......
paddle/fluid/operators/prune_gate_by_capacity_op.cu
浏览文件 @ 91dd8a2e
......@@ -30,7 +30,6 @@ DECLARE_bool(avoid_op_randomness);
namespace paddle {
namespace operators {
using LoDTensor = phi::DenseTensor;
static constexpr int kNumCUDAThreads = 512;
static constexpr int kNumMaxinumNumBlocks = 4096;
......@@ -111,10 +110,11 @@ template <typename DeviceContext, typename T>
class PruneGateByCapacityCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* gate_idx = context.Input<LoDTensor>("GateIdx");
auto* expert_count = context.Input<LoDTensor>("ExpertCount");
// auto* expert_count_out = context.Output<LoDTensor>("ExpertCountOut");
auto* new_gate_idx = context.Output<LoDTensor>("NewGateIdx");
auto* gate_idx = context.Input<phi::DenseTensor>("GateIdx");
auto* expert_count = context.Input<phi::DenseTensor>("ExpertCount");
// auto* expert_count_out =
// context.Output<phi::DenseTensor>("ExpertCountOut");
auto* new_gate_idx = context.Output<phi::DenseTensor>("NewGateIdx");
auto* new_gate_idx_data = new_gate_idx->mutable_data<T>(context.GetPlace());
phi::DenseTensor expert_count_out;
......
paddle/fluid/operators/psroi_pool_op.cc
浏览文件 @ 91dd8a2e
......@@ -32,9 +32,9 @@ class PSROIPoolOpMaker : public framework::OpProtoAndCheckerMaker {
"H is the height of the input feature map, and "
"W is the width. The data type can be float32 or float64");
AddInput("ROIs",
"(LoDTensor), "
"(phi::DenseTensor), "
"ROIs (Regions of Interest) to pool over. "
"should be a 2-D LoDTensor of shape (num_rois, 4) "
"should be a 2-D phi::DenseTensor of shape (num_rois, 4) "
"given as [(x1, y1, x2, y2), ...]. "
"where (x1, y1) is the top left coordinates, and "
"(x2, y2) is the bottom right coordinates. "
......
paddle/fluid/operators/pull_box_extended_sparse_op.h
浏览文件 @ 91dd8a2e
......@@ -108,7 +108,6 @@ static void PushBoxExtendedSparseFunctor(
#endif
}
using LoDTensor = phi::DenseTensor;
template <typename T>
class PullBoxExtendedSparseCPUKernel : public framework::OpKernel<T> {
public:
......
paddle/fluid/operators/pull_box_sparse_op.h
浏览文件 @ 91dd8a2e
......@@ -113,7 +113,6 @@ static void PushBoxSparseFunctor(const framework::ExecutionContext &ctx) {
#endif
}
using LoDTensor = phi::DenseTensor;
template <typename T>
class PullBoxSparseKernel : public framework::OpKernel<T> {
public:
......
paddle/fluid/operators/pull_gpups_sparse_op.cu
浏览文件 @ 91dd8a2e
......@@ -19,7 +19,6 @@
namespace paddle {
namespace operators {
using phi::PADDLE_CUDA_NUM_THREADS;
using LoDTensor = phi::DenseTensor;
template <typename T>
class PullGpuPSSparseCUDAKernel : public framework::OpKernel<T> {
......
paddle/fluid/operators/pull_gpups_sparse_op.h
浏览文件 @ 91dd8a2e
......@@ -97,7 +97,6 @@ static void PushGpuPSSparseFunctor(const framework::ExecutionContext &ctx) {
#endif
}
using LoDTensor = phi::DenseTensor;
template <typename T>
class PullGpuPSSparseCPUKernel : public framework::OpKernel<T> {
public:
......
paddle/fluid/operators/py_func_op.cc
浏览文件 @ 91dd8a2e
......@@ -118,8 +118,8 @@ static void CallPythonFunc(py::object *callable,
out->ShareDataWith(*py_out_tensor);
} catch (py::cast_error &) {
PADDLE_THROW(platform::errors::InvalidArgument(
"py::cast to LoDTensor error. The %d-th output expection is "
"LoDTensor",
"py::cast to phi::DenseTensor error. The %d-th output expection is "
"phi::DenseTensor",
i));
}
}
......
paddle/fluid/operators/pyramid_hash_op.cc
浏览文件 @ 91dd8a2e
......@@ -29,7 +29,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
using LoD = framework::LoD;
class PyramidHashOpMaker : public framework::OpProtoAndCheckerMaker {
......@@ -275,12 +274,12 @@ class CPUPyramidHashOPKernel : public framework::OpKernel<T> {
}
void Compute(const framework::ExecutionContext& ctx) const override {
auto* bottom = ctx.Input<LoDTensor>("X");
auto* bottom = ctx.Input<phi::DenseTensor>("X");
auto* _blobs_0 = ctx.Input<phi::DenseTensor>("W");
auto* _blobs_1 = ctx.Input<phi::DenseTensor>("WhiteList");
auto* _blobs_2 = ctx.Input<phi::DenseTensor>("BlackList");
auto* top = ctx.Output<LoDTensor>("Out");
auto* drop_pos = ctx.Output<LoDTensor>("DropPos");
auto* top = ctx.Output<phi::DenseTensor>("Out");
auto* drop_pos = ctx.Output<phi::DenseTensor>("DropPos");
int _num_emb = ctx.Attr<int>("num_emb");
bool use_filter = ctx.Attr<bool>("use_filter");
......@@ -296,7 +295,7 @@ class CPUPyramidHashOPKernel : public framework::OpKernel<T> {
const auto& offset = bottom->lod()[0];
const auto* bottom_data_ori = bottom->data<int32_t>();
auto* buff = ctx.Output<LoDTensor>("X_Temp_Out");
auto* buff = ctx.Output<phi::DenseTensor>("X_Temp_Out");
buff->Resize(phi::make_ddim({bottom->dims()[0], bottom->dims()[1]}));
float* bottom_data = buff->mutable_data<float>(ctx.GetPlace());
for (int i = 0; i < bottom->dims()[0]; i++) {
......@@ -512,10 +511,10 @@ class CPUPyramidHashOPGradKernel : public framework::OpKernel<T> {
}
void Compute(const framework::ExecutionContext& ctx) const override {
auto* bottom = ctx.Input<LoDTensor>("X");
auto* bottom = ctx.Input<phi::DenseTensor>("X");
auto* _blobs = ctx.Input<phi::DenseTensor>("W");
auto* drop_pos = ctx.Input<LoDTensor>("DropPos");
auto* top = ctx.Input<LoDTensor>(framework::GradVarName("Out"));
auto* drop_pos = ctx.Input<phi::DenseTensor>("DropPos");
auto* top = ctx.Input<phi::DenseTensor>(framework::GradVarName("Out"));
int _num_emb = ctx.Attr<int>("num_emb");
float _lr = ctx.Attr<float>("lr");
......@@ -523,7 +522,7 @@ class CPUPyramidHashOPGradKernel : public framework::OpKernel<T> {
int _space_len = ctx.Attr<int>("space_len");
int _pyramid_layer = ctx.Attr<int>("pyramid_layer");
auto* buff = ctx.Input<LoDTensor>("X_Temp_Out");
auto* buff = ctx.Input<phi::DenseTensor>("X_Temp_Out");
auto* bottom_data = buff->data<T>();
int _slot_len = bottom->dims()[0];
......
paddle/fluid/operators/random_routing_op.cu
浏览文件 @ 91dd8a2e
......@@ -29,7 +29,6 @@ static inline int GET_BLOCKS(const int N) {
return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS;
}
using LoDTensor = phi::DenseTensor;
using Tensor = phi::DenseTensor;
template <typename T>
......@@ -54,10 +53,10 @@ template <typename T>
class RandomRoutingOpCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto topk_idx = context.Input<LoDTensor>("TopK_Idx");
auto topk_value = context.Input<LoDTensor>("TopK_Value");
auto prob = context.Input<LoDTensor>("Prob");
auto out = context.Output<LoDTensor>("Out");
auto topk_idx = context.Input<phi::DenseTensor>("TopK_Idx");
auto topk_value = context.Input<phi::DenseTensor>("TopK_Value");
auto prob = context.Input<phi::DenseTensor>("Prob");
auto out = context.Output<phi::DenseTensor>("Out");
auto place = context.GetPlace();
const auto& dev_ctx = context.template device_context<phi::GPUContext>();
......
paddle/fluid/operators/recurrent_op.cc
浏览文件 @ 91dd8a2e
......@@ -153,11 +153,12 @@ int64_t RecurrentBase::GetSequenceLength(const framework::Scope &scope) const {
PADDLE_ENFORCE_NOT_NULL(var,
platform::errors::InvalidArgument(
"RecurrentOp finds var %s is NULL", iname));
PADDLE_ENFORCE_EQ(var->IsType<phi::DenseTensor>(),
PADDLE_ENFORCE_EQ(
var->IsType<phi::DenseTensor>(),
true,
platform::errors::InvalidArgument(
"RecurrentOp only accepts LoDTensor as input but "
"input var %s is not LoDTensor",
"RecurrentOp only accepts phi::DenseTensor as input but "
"input var %s is not phi::DenseTensor",
iname));
auto &dim = var->Get<phi::DenseTensor>().dims();
if (seq_len == -1) {
......
paddle/fluid/operators/reorder_lod_tensor_by_rank_op.cc
浏览文件 @ 91dd8a2e
......@@ -37,13 +37,14 @@ class ReorderLoDTensorByRankTableOpProtoMaker
: public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(LoDTensor), the input lod tensor to be reordered according to "
AddInput(
"X",
"(phi::DenseTensor), the input lod tensor to be reordered according to "
"Input(RankTable).");
AddInput("RankTable",
"(LoDRankTable), the rank table according to which Input(X) is "
"reordered.");
AddOutput("Out", "LoDTensor, the reordered lod tensor.");
AddOutput("Out", "phi::DenseTensor, the reordered lod tensor.");
AddComment(R"DOC(ReorderLoDTensorByRankTable operator.
Input(X) is a batch of sequences. Input(RankTable) stores new orders of the
......
paddle/fluid/operators/reverse_op.cc
浏览文件 @ 91dd8a2e
......@@ -48,15 +48,15 @@ class ReverseOpVarTypeInference : public framework::VarTypeInference {
class ReverseOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "The LoDTensor to be flipped.");
AddOutput("Out", "The LoDTensor after flipping.");
AddInput("X", "The phi::DenseTensor to be flipped.");
AddOutput("Out", "The phi::DenseTensor after flipping.");
AddAttr<std::vector<int>>(
"axis", "The axises that along which order of elements is reversed.")
.SupportTensor();
AddComment(R"DOC(
Reverse Operator.
Reverse the order of elements in the input LoDTensor along given axises.
Reverse the order of elements in the input phi::DenseTensor along given axises.
Case 1:
Given
......
paddle/fluid/operators/roi_align_op.cc
浏览文件 @ 91dd8a2e
......@@ -21,7 +21,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
class ROIAlignOp : public framework::OperatorWithKernel {
public:
......@@ -73,9 +72,9 @@ class ROIAlignOpMaker : public framework::OpProtoAndCheckerMaker {
"H is the height of the feature, and "
"W is the width of the feature.");
AddInput("ROIs",
"(LoDTensor), "
"(phi::DenseTensor), "
"ROIs (Regions of Interest) to pool over. "
"should be a 2-D LoDTensor of shape (num_rois, 4)"
"should be a 2-D phi::DenseTensor of shape (num_rois, 4)"
"given as [[x1, y1, x2, y2], ...]. "
"(x1, y1) is the top left coordinates, and "
"(x2, y2) is the bottom right coordinates.");
......
paddle/fluid/operators/roi_align_op_mlu.cc
浏览文件 @ 91dd8a2e
......@@ -20,14 +20,13 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
template <typename T>
class ROIAlignOpMLUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<phi::DenseTensor>("X");
auto* rois = ctx.Input<LoDTensor>("ROIs");
auto* rois = ctx.Input<phi::DenseTensor>("ROIs");
auto* out = ctx.Output<phi::DenseTensor>("Out");
out->mutable_data<T>(ctx.GetPlace());
out->set_layout(phi::DataLayout::kNHWC);
......@@ -175,7 +174,7 @@ template <typename T>
class ROIAlignGradOpMLUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* rois = ctx.Input<LoDTensor>("ROIs");
auto* rois = ctx.Input<phi::DenseTensor>("ROIs");
auto* out_grad = ctx.Input<phi::DenseTensor>(framework::GradVarName("Out"));
auto* in_grad = ctx.Output<phi::DenseTensor>(framework::GradVarName("X"));
......
paddle/fluid/operators/roi_pool_op.cc
浏览文件 @ 91dd8a2e
......@@ -24,7 +24,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
class ROIPoolOp : public framework::OperatorWithKernel {
public:
......@@ -75,9 +74,9 @@ class ROIPoolOpMaker : public framework::OpProtoAndCheckerMaker {
"H is the height of the feature, and "
"W is the width of the feature.");
AddInput("ROIs",
"(LoDTensor), "
"(phi::DenseTensor), "
"ROIs (Regions of Interest) to pool over. "
"should be a 2-D LoDTensor of shape (num_rois, 4)"
"should be a 2-D phi::DenseTensor of shape (num_rois, 4)"
"given as [[x1, y1, x2, y2], ...]. "
"Where batch_id is the id of the data, "
"(x1, y1) is the top left coordinates, and "
......
paddle/fluid/operators/row_conv_op.cc
浏览文件 @ 91dd8a2e
......@@ -23,8 +23,6 @@ limitations under the License. */
namespace paddle {
namespace operators {
using LoDTensor = phi::DenseTensor;
template <typename T,
int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
......@@ -84,7 +82,7 @@ class RowConvOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("X",
"the input(X) is a LodTensor or tensor, LodTensor(X) supports "
"variable time-length input sequences. The underlying tensor "
"in this LoDTensor is a matrix with shape (T x N), where T "
"in this phi::DenseTensor is a matrix with shape (T x N), where T "
"is the total time steps in this mini-batch and N is the input "
"data dimension. the shape of Tensor input(X) has shape "
"(B x T x N), B is batch size;");
......@@ -142,9 +140,9 @@ template <typename T>
class RowConvKernel<phi::CPUContext, T> : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
auto *x = context.Input<LoDTensor>("X");
auto *x = context.Input<phi::DenseTensor>("X");
auto *filter = context.Input<phi::DenseTensor>("Filter");
auto *out = context.Output<LoDTensor>("Out");
auto *out = context.Output<phi::DenseTensor>("Out");
out->mutable_data<T>(context.GetPlace());
......@@ -217,10 +215,11 @@ template <typename T>
class RowConvGradKernel<phi::CPUContext, T> : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
auto *x = context.Input<LoDTensor>("X");
auto *x = context.Input<phi::DenseTensor>("X");
auto *filter = context.Input<phi::DenseTensor>("Filter");
auto *d_out = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto *dx = context.Output<LoDTensor>(framework::GradVarName("X"));
auto *d_out =
context.Input<phi::DenseTensor>(framework::GradVarName("Out"));
auto *dx = context.Output<phi::DenseTensor>(framework::GradVarName("X"));
auto *d_filter =
context.Output<phi::DenseTensor>(framework::GradVarName("Filter"));
......
paddle/fluid/operators/row_conv_op.cu
浏览文件 @ 91dd8a2e
......@@ -18,8 +18,6 @@ limitations under the License. */
namespace paddle {
namespace operators {
using LoDTensor = phi::DenseTensor;
namespace {
inline int DivUp(int x, int y) { return (x + y - 1) / y; }
......@@ -325,9 +323,9 @@ template <typename T>
class RowConvKernel<phi::GPUContext, T> : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
auto *X = context.Input<LoDTensor>("X");
auto *X = context.Input<phi::DenseTensor>("X");
auto *Filter = context.Input<phi::DenseTensor>("Filter");
auto *Out = context.Output<LoDTensor>("Out");
auto *Out = context.Output<phi::DenseTensor>("Out");
const T *in = X->data<T>();
const T *weight = Filter->data<T>();
......@@ -379,15 +377,15 @@ template <typename T>
class RowConvGradKernel<phi::GPUContext, T> : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
auto *X = context.Input<LoDTensor>("X");
auto *X = context.Input<phi::DenseTensor>("X");
auto *Filter = context.Input<phi::DenseTensor>("Filter");
auto *dOut = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto *dOut = context.Input<phi::DenseTensor>(framework::GradVarName("Out"));
const T *in = X->data<T>();
const T *weights = Filter->data<T>();
const T *dout = dOut->data<T>();
phi::DenseTensor *dX =
context.Output<LoDTensor>(framework::GradVarName("X"));
context.Output<phi::DenseTensor>(framework::GradVarName("X"));
phi::DenseTensor *dFilter =
context.Output<phi::DenseTensor>(framework::GradVarName("Filter"));
int batch_size = 0;
......
paddle/fluid/operators/run_program_op.cc
浏览文件 @ 91dd8a2e
......@@ -65,18 +65,18 @@ class RunProgramOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(vector<LoDTensor>)"
"(vector<phi::DenseTensor>)"
"The input tensors of RunProgram operator, also the feed targets "
"of loaded program.")
.AsDuplicable();
AddInput("Params",
"(vector<LoDTensor or SelecetedRows>)"
"(vector<phi::DenseTensor or SelecetedRows>)"
"The input parameter of RunProgram operator, also the parameters "
"of the loaded program.")
.AsDuplicable()
.AsDispensable();
AddOutput("Out",
"(vector<LoDTensor>)"
"(vector<phi::DenseTensor>)"
"The output tensors of RunProgram operator, also the fetch "
"targets of the loaded program.")
.AsDuplicable();
......@@ -87,7 +87,7 @@ class RunProgramOpMaker : public framework::OpProtoAndCheckerMaker {
"NOTE: Do not use Scope directly because Scope output is not "
"currently supported.");
AddOutput("DOut",
"(vector<LoDTensor>)"
"(vector<phi::DenseTensor>)"
"The output tensors for GRAD Tensors in RunProgram forward "
"operator, the forward operator contains GRAD Tensors when it "
"computes double grad.")
......
paddle/fluid/operators/run_program_op.h
浏览文件 @ 91dd8a2e
......@@ -48,25 +48,24 @@ using BlockDesc = framework::BlockDesc;
using ProgramDesc = framework::ProgramDesc;
using Variable = framework::Variable;
using LoDTensor = phi::DenseTensor;
using SelectedRows = phi::SelectedRows;
namespace details {
// all input vars should be LoDTensor & is initialized
// all input vars should be phi::DenseTensor & is initialized
static void CheckInputVarStatus(const Variable &var,
const std::string &var_name) {
PADDLE_ENFORCE_EQ(
var.IsType<LoDTensor>(),
PADDLE_ENFORCE_EQ(var.IsType<phi::DenseTensor>(),
true,
platform::errors::InvalidArgument(
"The input variable %s of "
"RunProgram(Grad)Op holds "
"wrong type. Expect type is LoDTensor, but receive type is %s.",
"wrong type. Expect type is phi::DenseTensor, but "
"receive type is %s.",
var_name,
platform::demangle(framework::ToTypeName(var.Type()))));
PADDLE_ENFORCE_EQ(
var.Get<LoDTensor>().IsInitialized(),
var.Get<phi::DenseTensor>().IsInitialized(),
true,
platform::errors::InvalidArgument("The tensor in input variable %s of "
"RunProgram(Grad)Op "
......@@ -77,17 +76,18 @@ static void CheckInputVarStatus(const Variable &var,
static void CheckOutputVarStatus(const Variable &src_var,
const Variable &dst_var,
const std::string &var_name) {
if (dst_var.IsType<LoDTensor>()) {
if (dst_var.IsType<phi::DenseTensor>()) {
PADDLE_ENFORCE_EQ(
src_var.IsType<LoDTensor>(),
src_var.IsType<phi::DenseTensor>(),
true,
platform::errors::InvalidArgument(
"The output variable %s get from "
"RunProgram(Grad)Op's internal scope holds "
"wrong type. Expect type is LoDTensor, but receive type is %s.",
"wrong type. Expect type is phi::DenseTensor, but receive type is "
"%s.",
var_name,
platform::demangle(framework::ToTypeName(src_var.Type()))));
PADDLE_ENFORCE_EQ(src_var.Get<LoDTensor>().IsInitialized(),
PADDLE_ENFORCE_EQ(src_var.Get<phi::DenseTensor>().IsInitialized(),
true,
platform::errors::InvalidArgument(
"The tensor in output variable %s get from "
......@@ -115,7 +115,7 @@ static void CheckOutputVarStatus(const Variable &src_var,
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
"The RunProgram(Grad)Op only support output "
"variable of type LoDTensor or SelectedRows, "
"variable of type phi::DenseTensor or SelectedRows, "
"but received variable %s's type is %s",
var_name,
platform::demangle(framework::ToTypeName(dst_var.Type()))));
......@@ -123,12 +123,12 @@ static void CheckOutputVarStatus(const Variable &src_var,
}
static void VariableShare(const Variable &src_var, Variable *dst_var) {
// The previous check ensures that the variable type can only be LoDTensor or
// SelectedRows.
if (src_var.IsType<LoDTensor>()) {
auto *lod_tensor = dst_var->GetMutable<LoDTensor>();
lod_tensor->ShareDataWith(src_var.Get<LoDTensor>());
lod_tensor->set_lod(src_var.Get<LoDTensor>().lod());
// The previous check ensures that the variable type can only be
// phi::DenseTensor or SelectedRows.
if (src_var.IsType<phi::DenseTensor>()) {
auto *lod_tensor = dst_var->GetMutable<phi::DenseTensor>();
lod_tensor->ShareDataWith(src_var.Get<phi::DenseTensor>());
lod_tensor->set_lod(src_var.Get<phi::DenseTensor>().lod());
} else if (src_var.IsType<phi::SelectedRows>()) {
auto *selected_rows = dst_var->GetMutable<phi::SelectedRows>();
selected_rows->mutable_value()->ShareDataWith(
......
paddle/fluid/operators/save_combine_op.cc
浏览文件 @ 91dd8a2e
......@@ -54,7 +54,7 @@ class SaveCombineOpProtoMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
SaveCombine operator
This operator will serialize and write a list of input LoDTensor variables
This operator will serialize and write a list of input phi::DenseTensor variables
to a file on disk.
)DOC");
AddAttr<bool>("overwrite",
......@@ -70,7 +70,7 @@ to a file on disk.
AddAttr<std::string>(
"file_path",
"(string)"
"The \"file_path\" where the LoDTensor variables will be saved.")
"The \"file_path\" where the phi::DenseTensor variables will be saved.")
.AddCustomChecker(
[](const std::string& path) { return !path.empty(); });
AddAttr<bool>("save_to_memory",
......
paddle/fluid/operators/save_combine_op.h
浏览文件 @ 91dd8a2e
......@@ -72,12 +72,13 @@ class SaveCombineOpKernel : public framework::OpKernel<T> {
inp_vars[i],
platform::errors::InvalidArgument("Cannot find variable %s to save.",
inp_var_names[i]));
PADDLE_ENFORCE_EQ(inp_vars[i]->IsType<phi::DenseTensor>() ||
PADDLE_ENFORCE_EQ(
inp_vars[i]->IsType<phi::DenseTensor>() ||
inp_vars[i]->IsType<framework::Vocab>(),
true,
platform::errors::InvalidArgument(
"SaveCombine operator only supports saving "
"LoDTensor or Vocab variable, %s has wrong type.",
"phi::DenseTensor or Vocab variable, %s has wrong type.",
inp_var_names[i]));
if (inp_vars[i]->IsType<phi::DenseTensor>()) {
......
paddle/fluid/operators/save_op.cc
浏览文件 @ 91dd8a2e
......@@ -40,11 +40,12 @@ class SaveOp : public framework::OperatorWithKernel {
class SaveOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(Tensor ) Input LoDTensor and SelectedRows to be saved");
AddInput("X",
"(Tensor ) Input phi::DenseTensor and SelectedRows to be saved");
AddComment(R"DOC(
Save operator
This operator will serialize and write LoDTensor / SelectedRows variable to file on disk.
This operator will serialize and write phi::DenseTensor / SelectedRows variable to file on disk.
)DOC");
AddAttr<bool>("overwrite",
"(boolean, default true)"
......
paddle/fluid/operators/save_op.h
浏览文件 @ 91dd8a2e
......@@ -64,7 +64,8 @@ class SaveOpKernel : public framework::OpKernel<T> {
SaveSelectedRows(ctx, place, input_var, filename);
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
"Save operator only supports saving LoDTensor and SelectedRows "
"Save operator only supports saving phi::DenseTensor and "
"SelectedRows "
"variable, %s has wrong type",
iname));
}
......
paddle/fluid/operators/search_compute.h
浏览文件 @ 91dd8a2e
......@@ -29,7 +29,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
using LoD = framework::LoD;
template <typename DeviceContext, typename T>
......
paddle/fluid/operators/select_input_op.cc
浏览文件 @ 91dd8a2e
......@@ -73,7 +73,7 @@ class SelectInputOpProtoMaker : public framework::OpProtoAndCheckerMaker {
// Because this op is blocking whole control flow. I am implementing MVP
// (minimal viable product) here.
AddComment(R"DOC(
Merge branches of LoDTensor into a single Output with a mask integer
Merge branches of phi::DenseTensor into a single Output with a mask integer
specifying the output branchi.
)DOC");
}
......
paddle/fluid/operators/select_output_op.cc
浏览文件 @ 91dd8a2e
......@@ -71,7 +71,9 @@ class SelectOutputOp : public framework::OperatorBase {
class SelectOutputOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "The input LoDTensor or LoDTensorArray or SelectedRows.");
AddInput(
"X",
"The input phi::DenseTensor or phi::DenseTensorArray or SelectedRows.");
AddInput("Mask", "Tensor with numel 1 specifying which branch to output");
AddOutput("Out",
"The output can contains multiple variables. The output of "
......
paddle/fluid/operators/shape_op.cc
浏览文件 @ 91dd8a2e
......@@ -47,10 +47,10 @@ class ShapeOp : public framework::OperatorWithKernel {
class ShapeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("Input", "(LoDTensor), The input tensor.");
AddOutput(
"Out",
"(LoDTensor), The shape of input tensor, the data type of the shape"
AddInput("Input", "(phi::DenseTensor), The input tensor.");
AddOutput("Out",
"(phi::DenseTensor), The shape of input tensor, the data type of "
"the shape"
" is int32_t, will be on the same device with the input Tensor.");
AddComment(R"DOC(
Shape Operator.
......
paddle/fluid/operators/shape_op_mlu.cc
浏览文件 @ 91dd8a2e
......@@ -21,7 +21,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
using SelectedRows = phi::SelectedRows;
template <typename T>
......@@ -33,7 +32,7 @@ class ShapeMLUKernel : public framework::OpKernel<T> {
if (in_var->IsType<phi::SelectedRows>()) {
in_dims = in_var->Get<phi::SelectedRows>().value().dims();
} else {
in_dims = in_var->Get<LoDTensor>().dims();
in_dims = in_var->Get<phi::DenseTensor>().dims();
}
auto* out_t = ctx.Output<phi::DenseTensor>("Out");
out_t->Resize({in_dims.size()});
......
paddle/fluid/operators/shard_index_op.cc
浏览文件 @ 91dd8a2e
......@@ -37,7 +37,8 @@ class ShardIndexOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(LoDTensor, LoDTensor<int|int64>) Input variable. Each value "
"(phi::DenseTensor, phi::DenseTensor<int|int64>) Input variable. "
"Each value "
"of X is an index.");
AddOutput(
"Out",
......
paddle/fluid/operators/shard_index_op_npu.cc
浏览文件 @ 91dd8a2e
......@@ -18,15 +18,14 @@
namespace paddle {
namespace operators {
using LoDTensor = phi::DenseTensor;
using Tensor = phi::DenseTensor;
template <typename T>
class ShardIndexNPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
VLOG(4) << "start kernel";
auto* in = context.Input<LoDTensor>("X");
auto* out = context.Output<LoDTensor>("Out");
auto* in = context.Input<phi::DenseTensor>("X");
auto* out = context.Output<phi::DenseTensor>("Out");
int index_num = context.Attr<int>("index_num");
int nshards = context.Attr<int>("nshards");
int shard_id = context.Attr<int>("shard_id");
......
paddle/fluid/operators/share_data_op.cc
浏览文件 @ 91dd8a2e
......@@ -34,7 +34,7 @@ class ShareDataOp : public framework::OperatorWithKernel {
in_type == framework::proto::VarType::SELECTED_ROWS,
true,
platform::errors::InvalidArgument(
"Type of Variable[X] must be LoDTensor or SelectedRows!"));
"Type of Variable[X] must be phi::DenseTensor or SelectedRows!"));
PADDLE_ENFORCE_EQ(
in_type,
out_type,
......
paddle/fluid/operators/shrink_rnn_memory_op.cc
浏览文件 @ 91dd8a2e
......@@ -92,12 +92,13 @@ class ShrinkRNNMemoryOp : public ArrayOp {
class ShrinkRNNMemoryOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(LoDTensor) The RNN step memory to be shrank.");
AddInput("X", "(phi::DenseTensor) The RNN step memory to be shrank.");
AddInput("RankTable", "(LoDRankTable) The lod_rank_table of dynamic RNN.");
AddInput("I",
"(LoDTensor) The step index. The RNN step memory 'X' will be "
AddInput(
"I",
"(phi::DenseTensor) The step index. The RNN step memory 'X' will be "
"shrank to match the size of the input of the index'th step.");
AddOutput("Out", "(LoDTensor) The shrank RNN step memory.");
AddOutput("Out", "(phi::DenseTensor) The shrank RNN step memory.");
AddComment(R"DOC(
This operator is used to shrink output batch of memory defined in dynamic RNN.
......
paddle/fluid/operators/shuffle_batch_op.cc
浏览文件 @ 91dd8a2e
......@@ -76,17 +76,18 @@ class ShuffleBatchOp : public framework::OperatorWithKernel {
class ShuffleBatchOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(LoDTensor) The input tensor of shuffle_batch op.");
AddInput("Seed", "(LoDTensor) The input seed tensor.");
AddInput("X", "(phi::DenseTensor) The input tensor of shuffle_batch op.");
AddInput("Seed", "(phi::DenseTensor) The input seed tensor.");
AddAttr<int>(
"startup_seed",
"If input tensor 'Seed' is not initialized, the 'startup_seed' "
"will be used to replace it. The seed after shuffle batch will "
"be saved in 'SeedOut'. ")
.SetDefault(0);
AddOutput("Out", "(LoDTensor) The output tensor of shuffle_batch op.");
AddOutput("Out",
"(phi::DenseTensor) The output tensor of shuffle_batch op.");
AddOutput("ShuffleIdx", "(Tensor) Record forword shuffle order");
AddOutput("SeedOut", "(LoDTensor) Saved new generated seed.");
AddOutput("SeedOut", "(phi::DenseTensor) Saved new generated seed.");
AddComment(R"DOC(
Shuffle Batch Operator.
......
paddle/fluid/operators/shuffle_batch_op.h
浏览文件 @ 91dd8a2e
......@@ -33,7 +33,6 @@
namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
template <typename T>
using Vector = framework::Vector<T>;
......@@ -42,11 +41,11 @@ template <typename T>
class ShuffleBatchKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
auto *x = context.Input<LoDTensor>("X");
auto *seed = context.Input<LoDTensor>("Seed");
auto *out = context.Output<LoDTensor>("Out");
auto *shuffleidx = context.Output<LoDTensor>("ShuffleIdx");
auto *seed_out = context.Output<LoDTensor>("SeedOut");
auto *x = context.Input<phi::DenseTensor>("X");
auto *seed = context.Input<phi::DenseTensor>("Seed");
auto *out = context.Output<phi::DenseTensor>("Out");
auto *shuffleidx = context.Output<phi::DenseTensor>("ShuffleIdx");
auto *seed_out = context.Output<phi::DenseTensor>("SeedOut");
auto x_embed_size = x->dims()[x->dims().size() - 1];
auto elem_size = 1;
......@@ -128,9 +127,11 @@ template <typename T>
class ShuffleBatchGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
auto *out_grad = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto *shuffleidx = context.Input<LoDTensor>("ShuffleIdx");
auto *x_grad = context.Output<LoDTensor>(framework::GradVarName("X"));
auto *out_grad =
context.Input<phi::DenseTensor>(framework::GradVarName("Out"));
auto *shuffleidx = context.Input<phi::DenseTensor>("ShuffleIdx");
auto *x_grad =
context.Output<phi::DenseTensor>(framework::GradVarName("X"));
auto embed_size = out_grad->dims()[out_grad->dims().size() - 1];
auto elem_size = 1;
......
paddle/fluid/operators/slice_op.cc
浏览文件 @ 91dd8a2e
......@@ -203,9 +203,9 @@ class SliceOpVarTypeInference : public framework::VarTypeInference {
auto not_decrease =
paddle::get<std::vector<int>>(decrease_axis).size() == 0;
if (not_decrease) {
// The default type of out is LoDTensor.
// The default type of out is phi::DenseTensor.
// However, if no axis is decreased and the type of input is not
// LoDTensor, the type of out should be the same as input.
// phi::DenseTensor, the type of out should be the same as input.
// For example, input is a LoDTensorArray and no axis is decreased, the
// output should be a LoDTensorArray.
ctx->SetOutputType(out_name, ctx->GetInputType(x_name));
......@@ -369,8 +369,8 @@ class SliceOpGradVarTypeInference : public framework::VarTypeInference {
auto d_out = framework::GradVarName("Out");
auto out = framework::GradVarName("Input");
// The types of grad_input and input should always be the same.
// The default type of out is LoDTensor, but the type of input can be
// LoDTensor or LoDTensorArray,
// The default type of out is phi::DenseTensor, but the type of input can be
// phi::DenseTensor or phi::DenseTensorArray,
// so set the type of both to be the same.
ctx->SetOutputType(out, ctx->GetInputType(x));
ctx->SetOutputDataType(out, ctx->GetInputDataType(d_out));
......
paddle/fluid/operators/split_lod_tensor_op.cc
浏览文件 @ 91dd8a2e
......@@ -145,20 +145,20 @@ class SplitLoDTensorOp : public framework::OperatorBase {
class SplitLoDTensorOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "The input LoDTensor");
AddInput("X", "The input phi::DenseTensor");
AddInput("Mask", "A bool column vector which mask the input");
AddOutput("OutTrue", "True branch of input LoDTensor");
AddOutput("OutFalse", "False branch of input LoDTensor");
AddOutput("OutTrue", "True branch of input phi::DenseTensor");
AddOutput("OutFalse", "False branch of input phi::DenseTensor");
AddAttr<int>("level", "(int) the specific lod level to split.")
.SetDefault(0)
.EqualGreaterThan(0);
AddComment(
R"DOC(
Split a LoDTensor with a Mask at certain level. The input LoDTensor
Split a phi::DenseTensor with a Mask at certain level. The input phi::DenseTensor
has 3 sequence at certain lod level. The Mask is a bool column vector,
such as [0, 1, 0] at the same level. The first and third sequence will
be send to False Output LoDTensor; whereas the second sequence will
be send to True Output LoDTensor. Please refer to MergeLoDTensorOp.)DOC");
be send to False Output phi::DenseTensor; whereas the second sequence will
be send to True Output phi::DenseTensor. Please refer to MergeLoDTensorOp.)DOC");
}
};
......
paddle/fluid/operators/split_op.cc
浏览文件 @ 91dd8a2e
......@@ -21,7 +21,6 @@ limitations under the License. */
namespace paddle {
namespace operators {
using LoDTensor = phi::DenseTensor;
using framework::Variable;
......@@ -77,11 +76,11 @@ class SplitOp : public framework::OperatorWithKernel {
const paddle::small_vector<framework::InferShapeVarPtr,
phi::kInputSmallVectorSize>
&sections_varptr_list = ctx->GetInputVarPtrs("SectionsTensorList");
std::vector<LoDTensor> sections_from_tensor;
std::vector<phi::DenseTensor> sections_from_tensor;
sections_from_tensor.reserve(sections_tensor_list_size);
for (const auto &section_varptr : sections_varptr_list) {
Variable *var = PADDLE_GET_CONST(Variable *, section_varptr);
sections_from_tensor.emplace_back(var->Get<LoDTensor>());
sections_from_tensor.emplace_back(var->Get<phi::DenseTensor>());
}
sections_final = std::move(phi::IntArray(sections_from_tensor));
} else if (!ctx->IsRuntime() && ctx->HasInputs("SectionsTensorList")) {
......
paddle/fluid/operators/sum_op.cc
浏览文件 @ 91dd8a2e
......@@ -128,10 +128,11 @@ class SumOp : public framework::OperatorWithKernel {
class SumOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
AddInput(
"X",
"A Varaible list. The shape and data type of the list elements"
"should be consistent. Variable can be multi-dimensional Tensor"
"or LoDTensor, and data types can be: float32, float64, int32, "
"or phi::DenseTensor, and data types can be: float32, float64, int32, "
"int64.")
.AsDuplicable();
AddOutput("Out",
......@@ -145,8 +146,9 @@ class SumOpMaker : public framework::OpProtoAndCheckerMaker {
"(string, default \"float32\"). Data type of mkldnn kernel")
.SetDefault("float32")
.InEnum({"float32", "bfloat16"});
AddComment(R"DOC(This OP is used to sum one or more Tensor or LoDTensor
of the input. If the input is LoDTensor, the output only
AddComment(
R"DOC(This OP is used to sum one or more Tensor or phi::DenseTensor
of the input. If the input is phi::DenseTensor, the output only
shares LoD information with the first input.)DOC");
}
};
......
paddle/fluid/operators/sum_op_mlu.cc
浏览文件 @ 91dd8a2e
......@@ -21,7 +21,6 @@ namespace operators {
using Tensor = phi::DenseTensor;
using SelectedRows = phi::SelectedRows;
using LoDTensor = phi::DenseTensor;
template <typename DeviceContext, typename T>
class SumMLUKernel : public framework::OpKernel<T> {
......
paddle/fluid/operators/sum_op_npu.cc
浏览文件 @ 91dd8a2e
......@@ -25,7 +25,6 @@ namespace operators {
using Tensor = phi::DenseTensor;
using SelectedRows = phi::SelectedRows;
using LoDTensor = phi::DenseTensor;
template <typename DeviceContext, typename T>
class SumNPUKernel : public framework::OpKernel<T> {
......
paddle/fluid/operators/svd_helper.h
浏览文件 @ 91dd8a2e
......@@ -744,7 +744,7 @@ struct DeviceIndependenceTensorOperations {
const framework::AttributeMap& attrs,
std::vector<int> out_shape,
NameOutTensor out_str = {"Out"}) {
// varialble set dims must be LoDTensor / SelectedRowTensor
// varialble set dims must be phi::DenseTensor / SelectedRowTensor
framework::Scope& local_scope = context.scope().NewScope();
framework::VariableNameMap op_outputs;
for (auto out_name : out_str) {
......
paddle/fluid/operators/tdm_child_op.h
浏览文件 @ 91dd8a2e
......@@ -29,16 +29,15 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
using DDim = framework::DDim;
using LoD = framework::LoD;
template <typename T, typename InfoT = int, typename OutT = int>
void TDMChildInner(const framework::ExecutionContext &context,
const LoDTensor &input,
const LoDTensor &tree_info,
LoDTensor *child,
LoDTensor *mask) {
const phi::DenseTensor &input,
const phi::DenseTensor &tree_info,
phi::DenseTensor *child,
phi::DenseTensor *mask) {
auto child_nums = context.Attr<int>("child_nums");
auto info_dims = tree_info.dims();
int node_nums = info_dims[0];
......@@ -114,7 +113,7 @@ class TDMChildKernel : public framework::OpKernel<T> {
auto *input_var = ctx.InputVar("X");
auto *tree_info_var = ctx.InputVar("TreeInfo");
auto &input_tensor = input_var->Get<LoDTensor>();
auto &input_tensor = input_var->Get<phi::DenseTensor>();
const auto &input_type =
framework::TransToProtoVarType(input_tensor.dtype());
bool input_type_match = input_type == framework::proto::VarType::INT32 ||
......@@ -130,7 +129,7 @@ class TDMChildKernel : public framework::OpKernel<T> {
paddle::framework::DataTypeToString(
framework::proto::VarType::INT64)));
auto &tree_info_tensor = tree_info_var->Get<LoDTensor>();
auto &tree_info_tensor = tree_info_var->Get<phi::DenseTensor>();
const auto &info_type =
framework::TransToProtoVarType(tree_info_tensor.dtype());
bool info_type_match = info_type == framework::proto::VarType::INT32 ||
......
paddle/fluid/operators/tdm_sampler_op.h
浏览文件 @ 91dd8a2e
......@@ -33,17 +33,16 @@ using Tensor = phi::DenseTensor;
using Sampler = math::Sampler;
using DDim = framework::DDim;
using LoD = framework::LoD;
using LoDTensor = phi::DenseTensor;
using LoDAndOffset = std::pair<LoD, std::pair<size_t, size_t>>;
template <typename T, typename TreeT = int, typename OutT = int>
void TDMSamplerInner(const framework::ExecutionContext &context,
const LoDTensor &input_tensor,
const LoDTensor &travel_lod_tensor,
const LoDTensor &layer_lod_tensor,
LoDTensor *out_tensor,
LoDTensor *label_tensor,
LoDTensor *mask_tensor) {
const phi::DenseTensor &input_tensor,
const phi::DenseTensor &travel_lod_tensor,
const phi::DenseTensor &layer_lod_tensor,
phi::DenseTensor *out_tensor,
phi::DenseTensor *label_tensor,
phi::DenseTensor *mask_tensor) {
auto neg_samples_num_vec =
context.Attr<std::vector<int>>("neg_samples_num_list");
auto layer_offset_lod = context.Attr<std::vector<int>>("layer_offset_lod");
......
paddle/fluid/operators/transfer_layout_op.cc
浏览文件 @ 91dd8a2e
......@@ -94,8 +94,9 @@ class TransferLayoutKernel {
class TransferLayoutOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(LoDTensor) The input Tensor");
AddOutput("Out", "(LoDTensor) The Output Tensor with desired layout");
AddInput("X", "(phi::DenseTensor) The input Tensor");
AddOutput("Out",
"(phi::DenseTensor) The Output Tensor with desired layout");
// NOTE(zhiqiu): in most case, the src_layout is not needed, the op can use
// the layout
// of input X. However, in some mkldnn kernel, the src layout computed by
......
paddle/fluid/operators/var_conv_2d_op.cc
浏览文件 @ 91dd8a2e
......@@ -25,16 +25,17 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
using LoD = framework::LoD;
void VarConv2dOpMaker::Make() {
AddInput("X",
"X (LoDTensor, default LoDTensor<float>) Input variable which "
"X (phi::DenseTensor, default phi::DenseTensor<float>) Input "
"variable which "
"should contain lod information.");
AddInput("ROW", "(LoDTensor) the row variable provides lod information");
AddInput("ROW",
"(phi::DenseTensor) the row variable provides lod information");
AddInput("COLUMN",
"(LoDTensor) the column variable provides lod information");
"(phi::DenseTensor) the column variable provides lod information");
AddInput("W", "W (Tensor), the filter.");
AddAttr<int>("InputChannel", "the input filter num").SetDefault(1);
AddAttr<int>("OutputChannel", "the output filter num").SetDefault(1);
......@@ -43,9 +44,12 @@ void VarConv2dOpMaker::Make() {
AddAttr<int>("KernelH", "the height of Kernel").SetDefault(1);
AddAttr<int>("KernelW", "the width of Kernel").SetDefault(1);
AddOutput("Out", "(LoDTensor, default LoDTensor<float>) Output variable");
AddOutput(
"Out",
"(phi::DenseTensor, default phi::DenseTensor<float>) Output variable");
AddOutput("Col",
"(LoDTensor, default LoDTensor<float>) the intermediate result "
"(phi::DenseTensor, default phi::DenseTensor<float>) the "
"intermediate result "
"variable");
AddComment(R"DOC(
......@@ -125,7 +129,7 @@ void VarConv2dOP::InferShape(framework::InferShapeContext* ctx) const {
if (ctx->IsRuntime()) {
framework::Variable* x_var =
PADDLE_GET(framework::Variable*, ctx->GetInputVarPtrs("X")[0]);
const auto& x_lod = x_var->Get<LoDTensor>().lod();
const auto& x_lod = x_var->Get<phi::DenseTensor>().lod();
PADDLE_ENFORCE_EQ(
!x_lod.empty(),
true,
......@@ -146,7 +150,7 @@ void VarConv2dOP::InferShape(framework::InferShapeContext* ctx) const {
framework::Variable* row_var =
PADDLE_GET(framework::Variable*, ctx->GetInputVarPtrs("ROW")[0]);
const auto& row_lod = row_var->Get<LoDTensor>().lod();
const auto& row_lod = row_var->Get<phi::DenseTensor>().lod();
PADDLE_ENFORCE_EQ(!row_lod.empty(),
true,
platform::errors::InvalidArgument(
......@@ -155,7 +159,7 @@ void VarConv2dOP::InferShape(framework::InferShapeContext* ctx) const {
framework::Variable* col_var =
PADDLE_GET(framework::Variable*, ctx->GetInputVarPtrs("COLUMN")[0]);
const auto& col_lod = col_var->Get<LoDTensor>().lod();
const auto& col_lod = col_var->Get<phi::DenseTensor>().lod();
PADDLE_ENFORCE_EQ(!col_lod.empty(),
true,
platform::errors::InvalidArgument(
......@@ -175,11 +179,11 @@ template <typename DeviceContext, typename T>
class CPUVarConv2dOPKernel : public framework::OpKernel<T> {
public:
void Im2Col(const framework::ExecutionContext& ctx,
const LoDTensor& input,
LoDTensor* col) const {
const phi::DenseTensor& input,
phi::DenseTensor* col) const {
int input_channel = ctx.Attr<int>("InputChannel");
auto* in_row = ctx.Input<LoDTensor>("ROW");
auto* in_col = ctx.Input<LoDTensor>("COLUMN");
auto* in_row = ctx.Input<phi::DenseTensor>("ROW");
auto* in_col = ctx.Input<phi::DenseTensor>("COLUMN");
int kernel_h = ctx.Attr<int>("KernelH");
int kernel_w = ctx.Attr<int>("KernelW");
int stride_h = ctx.Attr<int>("StrideH");
......@@ -267,12 +271,12 @@ class CPUVarConv2dOPKernel : public framework::OpKernel<T> {
}
void Compute(const framework::ExecutionContext& ctx) const override {
auto* bottom = ctx.Input<LoDTensor>("X");
auto* in_row = ctx.Input<LoDTensor>("ROW");
auto* in_col = ctx.Input<LoDTensor>("COLUMN");
auto* bottom = ctx.Input<phi::DenseTensor>("X");
auto* in_row = ctx.Input<phi::DenseTensor>("ROW");
auto* in_col = ctx.Input<phi::DenseTensor>("COLUMN");
auto* w = ctx.Input<phi::DenseTensor>("W");
auto* top = ctx.Output<LoDTensor>("Out");
auto* col = ctx.Output<LoDTensor>("Col");
auto* top = ctx.Output<phi::DenseTensor>("Out");
auto* col = ctx.Output<phi::DenseTensor>("Col");
int output_channel = ctx.Attr<int>("OutputChannel");
int input_channel = ctx.Attr<int>("InputChannel");
......@@ -390,10 +394,10 @@ template <typename DeviceContext, typename T>
class CPUVarConv2dOPGradKernel : public framework::OpKernel<T> {
public:
void Im2ColGrad(const framework::ExecutionContext& ctx, T* top_diff) const {
auto* x = ctx.Input<LoDTensor>("X");
auto* in_row = ctx.Input<LoDTensor>("ROW");
auto* in_col = ctx.Input<LoDTensor>("COLUMN");
auto* col = ctx.Input<LoDTensor>("Col");
auto* x = ctx.Input<phi::DenseTensor>("X");
auto* in_row = ctx.Input<phi::DenseTensor>("ROW");
auto* in_col = ctx.Input<phi::DenseTensor>("COLUMN");
auto* col = ctx.Input<phi::DenseTensor>("Col");
int input_channel = ctx.Attr<int>("InputChannel");
int kernel_h = ctx.Attr<int>("KernelH");
......@@ -401,7 +405,7 @@ class CPUVarConv2dOPGradKernel : public framework::OpKernel<T> {
int stride_h = ctx.Attr<int>("StrideH");
int stride_w = ctx.Attr<int>("StrideW");
auto* dx = ctx.Output<LoDTensor>(framework::GradVarName("X"));
auto* dx = ctx.Output<phi::DenseTensor>(framework::GradVarName("X"));
auto* dx_data = dx->mutable_data<T>(ctx.GetPlace());
memset(dx_data, 0.0, x->dims()[0] * x->dims()[1] * sizeof(T));
......@@ -450,18 +454,18 @@ class CPUVarConv2dOPGradKernel : public framework::OpKernel<T> {
}
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<LoDTensor>("X");
auto* x = ctx.Input<phi::DenseTensor>("X");
auto* w = ctx.Input<phi::DenseTensor>("W");
auto* col = ctx.Input<LoDTensor>("Col");
auto* out = ctx.Input<LoDTensor>("Out");
auto* col = ctx.Input<phi::DenseTensor>("Col");
auto* out = ctx.Input<phi::DenseTensor>("Out");
int output_channel = ctx.Attr<int>("OutputChannel");
int input_channel = ctx.Attr<int>("InputChannel");
int kernel_h = ctx.Attr<int>("KernelH");
int kernel_w = ctx.Attr<int>("KernelW");
auto* d_out = ctx.Input<LoDTensor>(framework::GradVarName("Out"));
auto* dx = ctx.Output<LoDTensor>(framework::GradVarName("X"));
auto* d_out = ctx.Input<phi::DenseTensor>(framework::GradVarName("Out"));
auto* dx = ctx.Output<phi::DenseTensor>(framework::GradVarName("X"));
auto* d_w = ctx.Output<phi::DenseTensor>(framework::GradVarName("W"));
Tensor col_grad;
......
paddle/fluid/operators/var_conv_2d_op.h
浏览文件 @ 91dd8a2e
......@@ -20,7 +20,6 @@ namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
using LoD = framework::LoD;
class VarConv2dOP : public framework::OperatorWithKernel {
......
paddle/fluid/operators/warpctc_op.cc
浏览文件 @ 91dd8a2e
......@@ -44,8 +44,8 @@ class WarpCTCOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("Logits",
"(2-D LoDTensor<float>) or (3-D Tensor<float>), the "
"unscaled probabilities of variable-length sequences."
"(2-D phi::DenseTensor<float>) or (3-D phi::DenseTensor<float>), "
"the unscaled probabilities of variable-length sequences."
"When is a 2-D Tensor with LoD information, "
"it's shape is [Lp, num_classes + 1], "
"where Lp is the sum of all input sequences' length "
......@@ -56,7 +56,7 @@ class WarpCTCOpMaker : public framework::OpProtoAndCheckerMaker {
"where max_logit_length is the length of the longest "
"logit sequence.");
AddInput("Label",
"(2-D LoDTensor<int>) or (2-D Tensor<int>), the "
"(2-D phi::DenseTensor<int>), the "
"ground truth of variable-length sequence. "
"When it is a 2-D Tensor with LoD information, "
"it is of the shape [Lg, 1], where Lg is th sum of "
......
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