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未验证 提交 6cdaa371 编写于 作者: 柠 柠檬味~ 提交者： GitHub
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DenseTensor (#48419)

上级 2a3ddce0
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内联 并排
Showing with 195 addition and 203 deletion
paddle/fluid/operators/sequence_ops/sequence_conv_op.cc
浏览文件 @ 6cdaa371
......@@ -145,30 +145,31 @@ class SequenceConvOpMaker : public framework::OpProtoAndCheckerMaker {
void Make() override {
AddInput(
"X",
"(LoDTensor) the input(X) is a LodTensor, which supports "
"(phi::DenseTensor) the input(X) is a LodTensor, which supports "
"variable-time length input sequence. The underlying tensor in "
"this LoDTensor is a matrix with shape (T, N), where T is the "
"this phi::DenseTensor is a matrix with shape (T, N), where T is the "
"total time steps in this mini-batch and N is the input_hidden_size.");
AddInput("PaddingData",
"(Tensor, optional) the input(PaddingData) is an optional "
"parameter, and it is learnable. "
"This is a tensor with shape (P, N), where P is the "
"top_pad + bottom_pad, N is the input_hidden_size. In order to "
"ensure the equal length of sequence before and after "
"convolution, it is necessary to fill the top and bottom of each "
"sequence according to context_length, context_stride and "
"context_start")
AddInput(
"PaddingData",
"(phi::DenseTensor, optional) the input(PaddingData) is an optional "
"parameter, and it is learnable. "
"This is a tensor with shape (P, N), where P is the "
"top_pad + bottom_pad, N is the input_hidden_size. In order to "
"ensure the equal length of sequence before and after "
"convolution, it is necessary to fill the top and bottom of each "
"sequence according to context_length, context_stride and "
"context_start")
.AsDispensable();
AddInput(
"Filter",
"(Tensor) the input(Filter) is an learnable parameter."
"(phi::DenseTensor) the input(Filter) is an learnable parameter."
"This is a tensor with shape (K, M), where K is the "
"context_length * input_hidden_size, M is the output feature size.");
AddOutput(
"Out",
"(LoDTensor) the output(Out) is a LodTensor, which support "
"(phi::DenseTensor) the output(Out) is a LodTensor, which support "
"variable-time length output sequence. The underlying tensor in "
"this LoDTensor is a matrix with shape (T, M), where, T is the "
"this phi::DenseTensor is a matrix with shape (T, M), where, T is the "
"total time steps in this mini-batch, M is the output feature size.");
AddAttr<bool>("paddingTrainable",
......
paddle/fluid/operators/sequence_ops/sequence_conv_op.h
浏览文件 @ 6cdaa371
......@@ -22,15 +22,12 @@ limitations under the License. */
namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
template <typename DeviceContext, typename T>
class SequenceConvKernel : 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");
auto filter = *context.Input<phi::DenseTensor>("Filter");
out->mutable_data<T>(context.GetPlace());
......@@ -40,11 +37,11 @@ class SequenceConvKernel : public framework::OpKernel<T> {
int context_stride = context.Attr<int>("contextStride");
bool padding_trainable = context.Attr<bool>("paddingTrainable");
PADDLE_ENFORCE_EQ(
in->lod().empty(),
false,
platform::errors::InvalidArgument("Input(X) Tensor of SequenceConvOp "
"does not contain LoD information."));
PADDLE_ENFORCE_EQ(in->lod().empty(),
false,
platform::errors::InvalidArgument(
"Input(X) phi::DenseTensor of SequenceConvOp "
"does not contain LoD information."));
PADDLE_ENFORCE_EQ(
in->lod().size(),
1UL,
......@@ -64,7 +61,7 @@ class SequenceConvKernel : public framework::OpKernel<T> {
framework::DDim col_shape = {in->dims()[0],
context_length * sequence_width};
Tensor col;
phi::DenseTensor col;
col.mutable_data<T>(col_shape, context.GetPlace());
// Because if padding_trainable is false, padding data should be zeros.
phi::funcs::SetConstant<DeviceContext, T> set_zero;
......@@ -92,13 +89,14 @@ template <typename DeviceContext, typename T>
class SequenceConvGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in_g = context.Output<LoDTensor>(framework::GradVarName("X"));
auto* out_g = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto* in_g = context.Output<phi::DenseTensor>(framework::GradVarName("X"));
auto* out_g =
context.Input<phi::DenseTensor>(framework::GradVarName("Out"));
auto* filter_g =
context.Output<phi::DenseTensor>(framework::GradVarName("Filter"));
auto* padding_data_g =
context.Output<phi::DenseTensor>(framework::GradVarName("PaddingData"));
auto* in = context.Input<LoDTensor>("X");
auto* in = context.Input<phi::DenseTensor>("X");
auto* filter = context.Input<phi::DenseTensor>("Filter");
int context_start = context.Attr<int>("contextStart");
......@@ -125,7 +123,7 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
// use col_shape in the im2col calculation
framework::DDim col_shape = {in->dims()[0],
sequence_width * context_length};
Tensor col;
phi::DenseTensor col;
if (in_g || filter_g || (padding_trainable && padding_data_g)) {
col.mutable_data<T>(col_shape, context.GetPlace());
......@@ -159,7 +157,7 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
padding_data_g->mutable_data<T>(context.GetPlace());
set_zero(dev_ctx, padding_data_g, static_cast<T>(0));
LoDTensor* input = const_cast<LoDTensor*>(in);
phi::DenseTensor* input = const_cast<phi::DenseTensor*>(in);
seq_project_grad_functor(dev_ctx,
*input,
padding_trainable,
......@@ -178,8 +176,8 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
filter_g->mutable_data<T>(context.GetPlace());
set_zero(dev_ctx, filter_g, static_cast<T>(0));
Tensor filter_grad = *filter_g;
LoDTensor out_grad = *out_g;
phi::DenseTensor filter_grad = *filter_g;
phi::DenseTensor out_grad = *out_g;
const phi::DenseTensor* padding_data = nullptr;
if (padding_trainable) {
......
paddle/fluid/operators/sequence_ops/sequence_conv_op_xpu.cc
浏览文件 @ 6cdaa371
......@@ -19,14 +19,13 @@ limitations under the License. */
namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
template <typename DeviceContext, typename T>
class SequenceConvXPUKernel : 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");
auto filter = *context.Input<phi::DenseTensor>("Filter");
out->mutable_data<T>(context.GetPlace());
......@@ -36,11 +35,11 @@ class SequenceConvXPUKernel : public framework::OpKernel<T> {
int context_stride = context.Attr<int>("contextStride");
bool padding_trainable = context.Attr<bool>("paddingTrainable");
PADDLE_ENFORCE_EQ(
in->lod().empty(),
false,
platform::errors::InvalidArgument("Input(X) Tensor of SequenceConvOp "
"does not contain LoD information."));
PADDLE_ENFORCE_EQ(in->lod().empty(),
false,
platform::errors::InvalidArgument(
"Input(X) phi::DenseTensor of SequenceConvOp "
"does not contain LoD information."));
PADDLE_ENFORCE_EQ(
in->lod().size(),
1UL,
......@@ -159,11 +158,12 @@ template <typename DeviceContext, typename T>
class SequenceConvGradXPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in_g = context.Output<LoDTensor>(framework::GradVarName("X"));
auto* out_g = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto* in_g = context.Output<phi::DenseTensor>(framework::GradVarName("X"));
auto* out_g =
context.Input<phi::DenseTensor>(framework::GradVarName("Out"));
auto* filter_g =
context.Output<phi::DenseTensor>(framework::GradVarName("Filter"));
auto* in = context.Input<LoDTensor>("X");
auto* in = context.Input<phi::DenseTensor>("X");
auto* filter = context.Input<phi::DenseTensor>("Filter");
int context_start = context.Attr<int>("contextStart");
......@@ -171,11 +171,11 @@ class SequenceConvGradXPUKernel : public framework::OpKernel<T> {
int context_stride = context.Attr<int>("contextStride");
bool padding_trainable = context.Attr<bool>("paddingTrainable");
PADDLE_ENFORCE_EQ(
in->lod().empty(),
false,
platform::errors::InvalidArgument("Input(X) Tensor of SequenceConvOp "
"does not contain LoD information."));
PADDLE_ENFORCE_EQ(in->lod().empty(),
false,
platform::errors::InvalidArgument(
"Input(X) phi::DenseTensor of SequenceConvOp "
"does not contain LoD information."));
PADDLE_ENFORCE_EQ(
in->lod().size(),
1UL,
......
paddle/fluid/operators/sequence_ops/sequence_enumerate_op.cc
浏览文件 @ 6cdaa371
......@@ -36,11 +36,11 @@ class SequenceEnumerateOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(2-D LoDTensor with the 2nd dimension equal to 1) "
"Input LoDTensor of SequenceEnumerate operator.");
"(2-D phi::DenseTensor with the 2nd dimension equal to 1) "
"Input phi::DenseTensor of SequenceEnumerate operator.");
AddOutput("Out",
"(2-D LoDTensor with the 2nd dimension equal to win_size) "
"Output LoDTensor of SequenceEnumerate operator.");
"(2-D phi::DenseTensor with the 2nd dimension equal to win_size) "
"Output phi::DenseTensor of SequenceEnumerate operator.");
AddAttr<int>("win_size", "(int) The enumerate sequence window size.")
.AddCustomChecker([](const int& win_size) {
PADDLE_ENFORCE_GE(win_size,
......
paddle/fluid/operators/sequence_ops/sequence_enumerate_op.cu
浏览文件 @ 6cdaa371
......@@ -21,7 +21,6 @@
namespace paddle {
namespace operators {
using phi::PADDLE_CUDA_NUM_THREADS;
using LoDTensor = phi::DenseTensor;
template <typename T>
__global__ void CalcOutPut(const T* in_data,
......@@ -52,8 +51,8 @@ template <typename T>
class SequenceEnumerateOpCUDAKernel : 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 win_size = context.Attr<int>("win_size");
int pad_value = context.Attr<int>("pad_value");
......
paddle/fluid/operators/sequence_ops/sequence_enumerate_op.h
浏览文件 @ 6cdaa371
......@@ -18,14 +18,13 @@
namespace paddle {
namespace operators {
using LoDTensor = phi::DenseTensor;
template <typename DeviceContext, typename T>
class SequenceEnumerateKernel : 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 win_size = context.Attr<int>("win_size");
auto pad_value = static_cast<T>(context.Attr<int>("pad_value"));
......@@ -33,7 +32,7 @@ class SequenceEnumerateKernel : public framework::OpKernel<T> {
in->lod().empty(),
false,
platform::errors::InvalidArgument(
"Input(X) Tensor of SequenceEnumerateOp does not contain "
"Input(X) phi::DenseTensor of SequenceEnumerateOp does not contain "
"LoD information."));
auto in_dims = in->dims();
......
paddle/fluid/operators/sequence_ops/sequence_erase_op.cc
浏览文件 @ 6cdaa371
......@@ -27,20 +27,21 @@ class SequenceEraseOp : public framework::OperatorWithKernel {
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "SequenceErase");
OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "SequenceErase");
auto x_dims = ctx->GetInputDim("X");
PADDLE_ENFORCE(x_dims.size() == 2 && x_dims[1] == 1,
platform::errors::InvalidArgument(
"Input(X) of SequenceEraseOp should be a 2-D LoDTensor "
"with the 2nd dimension equal to 1,"
"but received size %d with the 2nd dimension %d.",
x_dims.size(),
x_dims[1]));
PADDLE_ENFORCE(
x_dims.size() == 2 && x_dims[1] == 1,
platform::errors::InvalidArgument(
"Input(X) of SequenceEraseOp should be a 2-D phi::DenseTensor "
"with the 2nd dimension equal to 1,"
"but received size %d with the 2nd dimension %d.",
x_dims.size(),
x_dims[1]));
ctx->SetOutputDim("Out", x_dims);
// The output LoDTensor's lod_level should be input X's lod_level.
// The output phi::DenseTensor's lod_level should be input X's lod_level.
// 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 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.
// 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.
if (!ctx->IsRuntime()) {
ctx->SetLoDLevel("Out", ctx->GetLoDLevel("X"));
}
......@@ -51,11 +52,11 @@ class SequenceEraseOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(2-D LoDTensor with the 2nd dim. equal to 1) "
"Input LoDTensor of SequenceEraseOp.");
"(2-D phi::DenseTensor with the 2nd dim. equal to 1) "
"Input phi::DenseTensor of SequenceEraseOp.");
AddOutput("Out",
"(2-D LoDTensor with the 2nd dim. equal to 1) "
"Output LoDTensor of SequenceEraseOp.");
"(2-D phi::DenseTensor with the 2nd dim. equal to 1) "
"Output phi::DenseTensor of SequenceEraseOp.");
AddAttr<std::vector<int>>("tokens",
"(vector<int>) Tokens need to be erased from "
"input sequences.");
......@@ -64,7 +65,7 @@ Sequence Erase Operator.
Sequence erase operator erases tokens specified by Attr(tokens) from the input
sequences Input(X), and outputs the remaining data and modifies the LoD
information at the same time. For example, given a 2-D LoDTensor
information at the same time. For example, given a 2-D phi::DenseTensor
X = [[2, 2, 6, 1, 3, 9, 6, 1, 0, 1]]^T
......@@ -77,7 +78,7 @@ operation, the three sequences become
X1' = [[6]]^T, X2' = [[1, 9]]^T and X3' = [[6, 1, 0, 1]]^T.
Hence the LoDTensor Output(Out) should be
Hence the phi::DenseTensor Output(Out) should be
Out = [[6, 1, 9, 6, 1, 0, 1]]^T,
......
paddle/fluid/operators/sequence_ops/sequence_erase_op.cu
浏览文件 @ 6cdaa371
......@@ -21,7 +21,6 @@ limitations under the License. */
namespace paddle {
namespace operators {
using phi::PADDLE_CUDA_NUM_THREADS;
using LoDTensor = phi::DenseTensor;
template <typename T>
__global__ void LabelErasedIdx(const T* in_dat,
......@@ -67,8 +66,8 @@ template <typename T>
class SequenceEraseOpCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
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");
auto lod = in->lod();
PADDLE_ENFORCE_EQ(
......
paddle/fluid/operators/sequence_ops/sequence_expand_as_op.cc
浏览文件 @ 6cdaa371
......@@ -20,8 +20,6 @@ limitations under the License. */
namespace paddle {
namespace operators {
using LoDTensor = phi::DenseTensor;
class SequenceExpandAsOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......@@ -49,8 +47,8 @@ class SequenceExpandAsOp : public framework::OperatorWithKernel {
framework::Variable* y_var =
PADDLE_GET(framework::Variable*, ctx->GetInputVarPtrs("Y")[0]);
auto& x_dim = x_var->Get<LoDTensor>().dims();
auto& y_lod = y_var->Get<LoDTensor>().lod();
auto& x_dim = x_var->Get<phi::DenseTensor>().dims();
auto& y_lod = y_var->Get<phi::DenseTensor>().lod();
PADDLE_ENFORCE_EQ(y_lod.size(),
1,
......@@ -96,13 +94,16 @@ class SequenceExpandAsOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(LoDTensor, default LoDTensor<float>) A 2-D LoDTensor whose lod "
"(phi::DenseTensor, default phi::DenseTensor<float>) A 2-D "
"phi::DenseTensor whose lod "
"level is at most 1.");
AddInput("Y",
"(LoDTensor, default LoDTensor<float>) Referred LoDTensor whose "
"(phi::DenseTensor, default phi::DenseTensor<float>) Referred "
"phi::DenseTensor whose "
"lod (specified level) is referred by Input(X).");
AddOutput("Out",
"(LodTensor, default LoDTensor<float>) Output LoDTensor which is "
"(phi::DenseTensor, default phi::DenseTensor<float>) Output "
"phi::DenseTensor which is "
"generated from Input(X) by referring lod of Input(Y).");
AddComment(R"DOC(
Sequence Expand As Operator.
......@@ -116,26 +117,26 @@ Following are cases to better explain how this works:
Case 1:
Given a 1-level LoDTensor input(X)
Given a 1-level phi::DenseTensor input(X)
X.data = [[a], [b], [c], [d]]
X.dims = [4, 1]
and input(Y)
Y.lod = [[0, 3, 6, 7, 8]]
ref_level: 0
then we get 1-level LoDTensor
then we get 1-level phi::DenseTensor
Out.lod = [[0, 3, 6, 7, 8]]
Out.data = [[a], [a], [a], [b], [b], [b], [c], [d]]
Out.dims = [8, 1]
Case 2:
Given a common Tensor input(X)
Given a common phi::DenseTensor input(X)
X.data = [[a, b], [c, d], [e, f]]
X.dims = [3, 2]
and input(Y)
Y.lod = [[0, 2, 3, 6]]
ref_level: 0
then we get a common LoDTensor
then we get a common phi::DenseTensor
Out.lod = [[0, 2, 3, 6]]
Out.data = [[a, b], [a, b] [c, d], [e, f], [e, f], [e, f]]
Out.dims = [6, 2]
......
paddle/fluid/operators/sequence_ops/sequence_expand_as_op.cu
浏览文件 @ 6cdaa371
......@@ -20,8 +20,6 @@ limitations under the License. */
namespace paddle {
namespace operators {
using LoDTensor = phi::DenseTensor;
template <typename T>
static __global__ void sequence_expand_as_kernel(const T *in_data,
const size_t *expand_offset,
......@@ -69,9 +67,9 @@ template <typename T>
struct SequenceExpandAsFunctor<phi::GPUContext, T> {
void operator()(
const phi::GPUContext &context,
const LoDTensor &x,
const phi::DenseTensor &x,
const framework::Vector<size_t> &ref_lod, /*expand referenced lod*/
LoDTensor *out) {
phi::DenseTensor *out) {
int height = x.dims()[0];
int width = phi::product(x.dims()) / height;
......@@ -99,9 +97,9 @@ struct SequenceExpandAsFunctor<phi::GPUContext, T> {
template <typename T>
struct SequenceExpandAsGradFunctor<phi::GPUContext, T> {
void operator()(const phi::GPUContext &context,
const LoDTensor &dout,
const phi::DenseTensor &dout,
const framework::Vector<size_t> &ref_lod, /*expand based lod*/
LoDTensor *dx) {
phi::DenseTensor *dx) {
int height = dx->dims()[0];
int width = phi::product(dx->dims()) / height;
......
paddle/fluid/operators/sequence_ops/sequence_expand_op.h
浏览文件 @ 6cdaa371
......@@ -22,7 +22,6 @@ limitations under the License. */
namespace paddle {
namespace operators {
using LoDTensor = phi::DenseTensor;
template <typename T,
int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
......@@ -32,30 +31,30 @@ template <typename DeviceContext, typename T>
struct SequenceExpandFunctor {
void operator()(
const DeviceContext& ctx,
const LoDTensor& x,
const phi::DenseTensor& x,
const framework::Vector<size_t>& x_lod, /*expand source lod*/
const framework::Vector<size_t>& ref_lod, /*expand referenced lod*/
LoDTensor* out);
phi::DenseTensor* out);
};
template <typename DeviceContext, typename T>
struct SequenceExpandGradFunctor {
void operator()(
const DeviceContext& ctx,
const LoDTensor& dout,
const phi::DenseTensor& dout,
const framework::Vector<size_t>& x_lod, /*expand source lod*/
const framework::Vector<size_t>& ref_lod, /*expand referenced lod*/
LoDTensor* dx);
phi::DenseTensor* dx);
};
template <typename T>
struct SequenceExpandFunctor<phi::CPUContext, T> {
void operator()(
const phi::CPUContext& context,
const LoDTensor& x,
const phi::DenseTensor& x,
const framework::Vector<size_t>& x_lod, /*expand source lod*/
const framework::Vector<size_t>& ref_lod, /*expand referenced lod*/
LoDTensor* out) {
phi::DenseTensor* out) {
int out_offset = 0;
int x_item_length = x.numel() / x.dims()[0];
auto out_data = out->data<T>();
......@@ -88,9 +87,9 @@ template <typename DeviceContext, typename T>
class SequenceExpandKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* x = context.Input<LoDTensor>("X");
auto* y = context.Input<LoDTensor>("Y");
auto* out = context.Output<LoDTensor>("Out");
auto* x = context.Input<phi::DenseTensor>("X");
auto* y = context.Input<phi::DenseTensor>("Y");
auto* out = context.Output<phi::DenseTensor>("Out");
int ref_level = context.Attr<int>("ref_level");
auto& x_lod = x->lod();
......@@ -100,7 +99,7 @@ class SequenceExpandKernel : public framework::OpKernel<T> {
y_lod.empty(),
false,
platform::errors::InvalidArgument(
"Input(Y) Tensor of SequenceExpandOp does not contain "
"Input(Y) phi::DenseTensor of SequenceExpandOp does not contain "
"LoD information."));
if (ref_level == -1) ref_level = y_lod.size() - 1;
......@@ -164,10 +163,10 @@ template <typename T>
struct SequenceExpandGradFunctor<phi::CPUContext, T> {
void operator()(
const phi::CPUContext& context,
const LoDTensor& dout,
const phi::DenseTensor& dout,
const framework::Vector<size_t>& x_lod, /*expand source lod*/
const framework::Vector<size_t>& ref_lod, /*expand referenced lod*/
LoDTensor* dx) {
phi::DenseTensor* dx) {
int dout_offset = 0;
for (size_t i = 1; i < ref_lod.size(); ++i) {
int repeat_num = ref_lod[i] - ref_lod[i - 1];
......@@ -193,10 +192,11 @@ template <typename DeviceContext, typename T>
class SequenceExpandGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* g_out = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto* x = context.Input<LoDTensor>("X");
auto* y = context.Input<LoDTensor>("Y");
auto* g_x = context.Output<LoDTensor>(framework::GradVarName("X"));
auto* g_out =
context.Input<phi::DenseTensor>(framework::GradVarName("Out"));
auto* x = context.Input<phi::DenseTensor>("X");
auto* y = context.Input<phi::DenseTensor>("Y");
auto* g_x = context.Output<phi::DenseTensor>(framework::GradVarName("X"));
int ref_level = context.Attr<int>("ref_level");
g_x->mutable_data<T>(context.GetPlace());
......
paddle/fluid/operators/sequence_ops/sequence_mask_op.cc
浏览文件 @ 6cdaa371
......@@ -82,7 +82,7 @@ class SequenceMaskOpMaker : public framework::OpProtoAndCheckerMaker {
SequenceMask Operator
This operator outputs a Mask according to Input(X) and Attr(maxlen).
Supposing Input(X) is a Tensor with shape [d_1, d_2, ..., d_n], the
Supposing Input(X) is a phi::DenseTensor with shape [d_1, d_2, ..., d_n], the
Output(Y) is a mask with shape [d_1, d_2, ..., d_n, maxlen], where:
Y(i_1, i_2, ..., i_n, j) = (j < X(i_1, i_2, ..., i_n))
......
paddle/fluid/operators/sequence_ops/sequence_mask_op.h
浏览文件 @ 6cdaa371
......@@ -28,9 +28,6 @@
namespace paddle {
namespace operators {
using LoDTensor = phi::DenseTensor;
using Tensor = phi::DenseTensor;
template <typename Tx, typename Ty>
struct SequenceMaskForRangeFunctor {
HOSTDEVICE SequenceMaskForRangeFunctor(const Tx *x, Ty *y, int maxlen)
......@@ -50,8 +47,11 @@ struct SequenceMaskForRangeFunctor {
template <typename DeviceContext, typename Tx>
struct SequenceMaskFunctor {
SequenceMaskFunctor(
const DeviceContext &ctx, const Tx *x, Tensor *y, int limits, int maxlen)
SequenceMaskFunctor(const DeviceContext &ctx,
const Tx *x,
phi::DenseTensor *y,
int limits,
int maxlen)
: ctx_(ctx), x_(x), y_(y), limits_(limits), maxlen_(maxlen) {}
template <typename Ty>
......@@ -64,15 +64,13 @@ struct SequenceMaskFunctor {
private:
const DeviceContext &ctx_;
const Tx *x_;
Tensor *y_;
phi::DenseTensor *y_;
int limits_;
int maxlen_;
};
template <typename DeviceContext, typename Tx>
class SequenceMaskKernel : public framework::OpKernel<Tx> {
using Tensor = phi::DenseTensor;
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto *x = ctx.Input<phi::DenseTensor>("X");
......
paddle/fluid/operators/sequence_ops/sequence_mask_op_npu.cc
浏览文件 @ 6cdaa371
......@@ -18,8 +18,6 @@ limitations under the License. */
namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
template <typename DeviceContext, typename T>
class SequenceMaskNPUKernel : public framework::OpKernel<T> {
public:
......@@ -58,7 +56,7 @@ class SequenceMaskNPUKernel : public framework::OpKernel<T> {
auto y_dim = phi::vectorize<int>(x->dims());
y_dim.push_back(maxlen);
Tensor cast_x;
phi::DenseTensor cast_x;
cast_x.mutable_data<int32_t>(x->dims(), ctx.GetPlace());
const auto& cast1_runner = NpuOpRunner(
"Cast",
......@@ -68,7 +66,7 @@ class SequenceMaskNPUKernel : public framework::OpKernel<T> {
ConvertToNpuDtype(framework::TransToProtoVarType(cast_x.dtype()))}});
cast1_runner.Run(dev_ctx.stream());
Tensor tmp;
phi::DenseTensor tmp;
tmp.mutable_data<int32_t>(phi::make_ddim({maxlen}), ctx.GetPlace());
NpuOpRunner range_runner;
range_runner.SetType("Range");
......@@ -78,7 +76,7 @@ class SequenceMaskNPUKernel : public framework::OpKernel<T> {
range_runner.AddOutput(tmp);
range_runner.Run(dev_ctx.stream());
Tensor expand_tmp;
phi::DenseTensor expand_tmp;
expand_tmp.mutable_data<int32_t>(phi::make_ddim(y_dim), ctx.GetPlace());
const auto& expand_runner =
NpuOpRunner("ExpandD", {tmp}, {expand_tmp}, {{"shape", y_dim}});
......@@ -87,7 +85,7 @@ class SequenceMaskNPUKernel : public framework::OpKernel<T> {
auto x_dims = phi::vectorize<int>(x->dims());
x_dims.push_back(1);
cast_x.Resize(phi::make_ddim({x_dims}));
Tensor x_tmp;
phi::DenseTensor x_tmp;
x_tmp.mutable_data<int32_t>(phi::make_ddim(y_dim), ctx.GetPlace());
const auto& tile_runner =
NpuOpRunner("TileWithAxis",
......@@ -96,7 +94,7 @@ class SequenceMaskNPUKernel : public framework::OpKernel<T> {
{{"axis", x->dims().size()}, {"tiles", maxlen}});
tile_runner.Run(dev_ctx.stream());
Tensor y_tmp;
phi::DenseTensor y_tmp;
y_tmp.mutable_data<uint8_t>(phi::make_ddim(y_dim), ctx.GetPlace());
const auto& less_runner =
NpuOpRunner("Less", {expand_tmp, x_tmp}, {y_tmp}, {});
......
paddle/fluid/operators/sequence_ops/sequence_pad_op.cc
浏览文件 @ 6cdaa371
......@@ -69,7 +69,7 @@ class SequencePadOp : public framework::OperatorWithKernel {
// run time
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::NotFound(
......@@ -145,20 +145,22 @@ class SequencePadOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(LoDTensor, default LoDTensor<float>) Input variable which "
"(phi::DenseTensor, default phi::DenseTensor<float>) Input "
"variable which "
"should contain lod information.");
AddInput("PadValue",
"(LoDTensor), this Tensor holds values that will be fill into "
"(phi::DenseTensor), this phi::DenseTensor holds values that will "
"be fill into "
"padded steps. It can be a scalar or a tensor whose shape equals "
"to time steps in sequences. If it's a scalar, it will be "
"automatically broadcasted to the shape of time step.");
AddOutput(
"Out",
"(LoDTensor) The output vairable, which contains padded sequences.");
AddOutput(
"Length",
"(LoDTensor) The output vairable, which contains the actual length of "
"sequences before padding.");
AddOutput("Out",
"(phi::DenseTensor) The output vairable, which contains padded "
"sequences.");
AddOutput("Length",
"(phi::DenseTensor) The output vairable, which contains the "
"actual length of "
"sequences before padding.");
AddAttr<int>(
"padded_length",
"The length of padded sequences. It can be set to -1 or "
......@@ -179,41 +181,41 @@ class SequencePadOpMaker : public framework::OpProtoAndCheckerMaker {
Case 1:
Given a 1-level LoDTensor input(X):
Given a 1-level phi::DenseTensor input(X):
X.lod = [[0, 2, 5]]
X.data = [a, b, c, d, e]
and Input(PadValue):
PadValue.data = [0]
and attribite 'padded_length' = 4,
then we get LoDTensor:
then we get phi::DenseTensor:
Out.data = [[a, b, 0, 0],
[c, d, e, 0]]
Length.data = [2, 3]
Case 2:
Given a 1-level LoDTensor input(X):
Given a 1-level phi::DenseTensor input(X):
X.lod = [[0, 2, 5]]
X.data = [[a1, a2], [b1, b2], [c1, c2], [d1, d2], [e1, e2]]
and Input(PadValue):
PadValue.data = [0]
and attribite 'padded_length' = -1, which mean using the length
of longest input sequence(3 in this case),
then we get LoDTensor:
then we get phi::DenseTensor:
Out.data = [[[a1, a2], [b1, b2], [0, 0]],
[[c1, c2], [d1, d2], [e1, e2]]]
Length.data = [2, 3]
Case 3:
Given a 1-level LoDTensor input(X):
Given a 1-level phi::DenseTensor input(X):
X.lod = [[0, 2, 5]]
X.data = [[a1, a2], [b1, b2], [c1, c2], [d1, d2], [e1, e2]]
and Input(PadValue):
PadValue.data = [p1, p2]
and attribite 'padded_length' = -1, which mean using the length
of longest input sequence(3 in this case),
then we get LoDTensor:
then we get phi::DenseTensor:
Out.data = [[[a1, a2], [b1, b2], [p1, p2]],
[[c1, c2], [d1, d2], [e1, e2]]]
Length.data = [2, 3]
......
paddle/fluid/operators/sequence_ops/sequence_pad_op.h
浏览文件 @ 6cdaa371
......@@ -24,25 +24,24 @@ limitations under the License. */
namespace paddle {
namespace operators {
using LoDTensor = phi::DenseTensor;
using LoD = framework::LoD;
template <typename DeviceContext, typename T>
class SequencePadOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const auto* x = ctx.Input<LoDTensor>("X");
auto* out = ctx.Output<LoDTensor>("Out");
auto* len_t = ctx.Output<LoDTensor>("Length");
const auto* x = ctx.Input<phi::DenseTensor>("X");
auto* out = ctx.Output<phi::DenseTensor>("Out");
auto* len_t = ctx.Output<phi::DenseTensor>("Length");
out->mutable_data<T>(ctx.GetPlace());
PADDLE_ENFORCE_EQ(
x->lod().empty(),
false,
platform::errors::NotFound("Input(X) Tensor of SequencePadOp does not "
"contain LoD information."));
PADDLE_ENFORCE_EQ(x->lod().empty(),
false,
platform::errors::NotFound(
"Input(X) phi::DenseTensor of SequencePadOp does not "
"contain LoD information."));
const auto* pad_value = ctx.Input<LoDTensor>("PadValue");
const auto* pad_value = ctx.Input<phi::DenseTensor>("PadValue");
int padded_length = ctx.Attr<int>("padded_length");
......@@ -56,7 +55,7 @@ class SequencePadOpKernel : public framework::OpKernel<T> {
false,
math::kBatchLengthWidth);
LoDTensor seq_len;
phi::DenseTensor seq_len;
seq_len.Resize(len_t->dims());
int64_t* len_data = seq_len.mutable_data<int64_t>(platform::CPUPlace());
for (size_t i = 1; i < x->lod()[0].size(); ++i) {
......@@ -73,9 +72,10 @@ template <typename DeviceContext, typename T>
class SequencePadGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* d_x = ctx.Output<LoDTensor>(framework::GradVarName("X"));
auto* d_x = ctx.Output<phi::DenseTensor>(framework::GradVarName("X"));
if (d_x) {
const auto* d_out = ctx.Input<LoDTensor>(framework::GradVarName("Out"));
const auto* d_out =
ctx.Input<phi::DenseTensor>(framework::GradVarName("Out"));
d_x->mutable_data<T>(ctx.GetPlace());
int padded_length = ctx.Attr<int>("padded_length");
......
paddle/fluid/operators/sequence_ops/sequence_pool_op.cc
浏览文件 @ 6cdaa371
......@@ -53,12 +53,15 @@ class SequencePoolOp : public framework::OperatorWithKernel {
class SequencePoolOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(LoDTensor) The variable-length input of SequencePoolOp");
AddOutput("Out",
"(Tensor) The output of SequencePoolOp does not contain LoD "
"information.");
AddInput("X",
"(phi::DenseTensor) The variable-length input of SequencePoolOp");
AddOutput(
"Out",
"(phi::DenseTensor) The output of SequencePoolOp does not contain LoD "
"information.");
AddOutput("MaxIndex",
"(Tensor<int>) This tensor is used for the sequence max-pooling "
"(phi::DenseTensor<int>) This tensor is used for the sequence "
"max-pooling "
"to record the max indexes.")
.AsIntermediate();
AddAttr<bool>("is_test",
......@@ -92,11 +95,11 @@ The following example explains how this works:
For a mini-batch of 3 variable-length sentences,
containing 2, 3, and 2 time-steps:
Assume X is a [7,M,N] LoDTensor, and X->lod()[0] = [0, 2, 5, 7], 7=2+3+2.
Assume X is a [7,M,N] phi::DenseTensor, and X->lod()[0] = [0, 2, 5, 7], 7=2+3+2.
Besides, for the sake of simplicity, we assume M=1 and N=1,
and the value of X = [[1, 3], [2, 4, 6], [5, 1]].
Thus, Out is a [3,1,1] Tensor without LoD information.
Thus, Out is a [3,1,1] phi::DenseTensor without LoD information.
And for different pooltype, the value of Out is as follows:
- AVERAGE: [2, 4, 3], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2
......
paddle/fluid/operators/sequence_ops/sequence_pool_op.h
浏览文件 @ 6cdaa371
......@@ -23,15 +23,12 @@ limitations under the License. */
namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
template <typename DeviceContext, typename T>
class SequencePoolKernel : 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");
std::string pooltype = context.Attr<std::string>("pooltype");
T pad_value = static_cast<T>(context.Attr<float>("pad_value"));
......@@ -39,11 +36,11 @@ class SequencePoolKernel : public framework::OpKernel<T> {
auto lod = in->lod();
auto lod_level = lod.size();
// InferShape by lod
PADDLE_ENFORCE_GT(
lod_level,
0,
platform::errors::InvalidArgument("Input(X) Tensor of SequencePoolOp "
"does not contain LoD information."));
PADDLE_ENFORCE_GT(lod_level,
0,
platform::errors::InvalidArgument(
"Input(X) phi::DenseTensor of SequencePoolOp "
"does not contain LoD information."));
PADDLE_ENFORCE_LE(lod_level,
2UL,
platform::errors::InvalidArgument(
......@@ -100,8 +97,9 @@ template <typename DeviceContext, typename T>
class SequencePoolGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* out_g = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto* in_g = context.Output<LoDTensor>(framework::GradVarName("X"));
auto* out_g =
context.Input<phi::DenseTensor>(framework::GradVarName("Out"));
auto* in_g = context.Output<phi::DenseTensor>(framework::GradVarName("X"));
std::string pooltype = context.Attr<std::string>("pooltype");
const phi::DenseTensor* index = nullptr;
if (pooltype == "MAX") {
......
paddle/fluid/operators/sequence_ops/sequence_softmax_op.h
浏览文件 @ 6cdaa371
......@@ -19,33 +19,30 @@ limitations under the License. */
namespace paddle {
namespace operators {
using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
template <typename DeviceContext, typename T>
struct SequenceSoftmaxFunctor {
void operator()(
const DeviceContext &ctx,
const LoDTensor &x,
const phi::DenseTensor &x,
const framework::Vector<size_t> &ref_lod, /*expand referenced lod*/
LoDTensor *out);
phi::DenseTensor *out);
};
template <typename DeviceContext, typename T>
struct SequenceSoftmaxGradFunctor {
void operator()(const DeviceContext &ctx,
const LoDTensor &dout,
const LoDTensor &out,
const phi::DenseTensor &dout,
const phi::DenseTensor &out,
const framework::Vector<size_t> &ref_lod, /*referenced lod*/
LoDTensor *dx);
phi::DenseTensor *dx);
};
template <typename T>
struct SequenceSoftmaxFunctor<phi::CPUContext, T> {
void operator()(const phi::CPUContext &ctx,
const LoDTensor &x,
const phi::DenseTensor &x,
const framework::Vector<size_t> &ref_lod, /*referenced lod*/
LoDTensor *out) {
phi::DenseTensor *out) {
size_t height = ref_lod.size() - 1;
const T *in_data = x.data<T>();
T *out_data = out->mutable_data<T>(ctx.GetPlace());
......@@ -65,10 +62,10 @@ struct SequenceSoftmaxFunctor<phi::CPUContext, T> {
template <typename T>
struct SequenceSoftmaxGradFunctor<phi::CPUContext, T> {
void operator()(const phi::CPUContext &ctx,
const LoDTensor &dout,
const LoDTensor &out,
const phi::DenseTensor &dout,
const phi::DenseTensor &out,
const framework::Vector<size_t> &ref_lod, /*referenced lod*/
LoDTensor *dx) {
phi::DenseTensor *dx) {
size_t height = ref_lod.size() - 1;
const T *softmax_grad_data = dout.data<T>();
......@@ -94,17 +91,17 @@ template <typename DeviceContext, typename T>
class SequenceSoftmaxKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto *x = ctx.Input<LoDTensor>("X");
auto *out = ctx.Output<LoDTensor>("Out");
auto *x = ctx.Input<phi::DenseTensor>("X");
auto *out = ctx.Output<phi::DenseTensor>("Out");
auto lod = x->lod();
auto dims = x->dims();
PADDLE_ENFORCE_EQ(
lod.empty(),
false,
platform::errors::InvalidArgument(
"Input(X) Tensor of SequenceSoftmax operator does not contain "
"LoD information."));
PADDLE_ENFORCE_EQ(lod.empty(),
false,
platform::errors::InvalidArgument(
"Input(X) phi::DenseTensor of SequenceSoftmax "
"operator does not contain "
"LoD information."));
const size_t level = lod.size() - 1;
PADDLE_ENFORCE_EQ(
......@@ -138,10 +135,10 @@ template <typename DeviceContext, typename T>
class SequenceSoftmaxGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto *out = ctx.Input<LoDTensor>("Out");
auto *out_grad = ctx.Input<LoDTensor>(framework::GradVarName("Out"));
auto *x = ctx.Input<LoDTensor>("X");
auto *x_grad = ctx.Output<LoDTensor>(framework::GradVarName("X"));
auto *out = ctx.Input<phi::DenseTensor>("Out");
auto *out_grad = ctx.Input<phi::DenseTensor>(framework::GradVarName("Out"));
auto *x = ctx.Input<phi::DenseTensor>("X");
auto *x_grad = ctx.Output<phi::DenseTensor>(framework::GradVarName("X"));
if (!x_grad) {
return;
}
......
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