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Paddle/paddle/fluid/operators/sequence_ops/sequence_mask_op.cc

110 lines
4.0 KiB

// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/operators/sequence_ops/sequence_mask_op.h"
#include <string>
namespace paddle {
namespace operators {
class SequenceMaskOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "SequenceMask");
OP_INOUT_CHECK(ctx->HasOutput("Y"), "Output", "Y", "SequenceMask");
int maxlen = ctx->Attrs().Get<int>("maxlen");
auto dim = framework::vectorize<int>(ctx->GetInputDim("X"));
if (ctx->HasInputs("MaxLenTensor")) {
dim.push_back(-1);
} else {
dim.push_back(maxlen > 0 ? maxlen : -1);
}
ctx->SetOutputDim("Y", framework::make_ddim(dim));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
OperatorWithKernel::IndicateVarDataType(ctx, "X"),
ctx.device_context());
}
framework::OpKernelType GetKernelTypeForVar(
const std::string& var_name, const Tensor& tensor,
const framework::OpKernelType& expected_kernel_type) const override {
if (var_name == "depth_tensor") {
return expected_kernel_type;
}
return framework::OpKernelType(expected_kernel_type.data_type_,
tensor.place(), tensor.layout());
}
};
class SequenceMaskOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "The input tensor of sequence_mask op.");
AddOutput("Y", "The output mask of sequence_mask op.");
AddInput("MaxLenTensor",
"Max length tensor"
"have higher priority than maxlen attribute")
.AsDispensable();
AddAttr<int>("maxlen",
"The maximum length of the sequence. If maxlen < 0, maxlen "
"= max(Input(X)).")
.SetDefault(-1)
.AddCustomChecker([](const int& v) {
PADDLE_ENFORCE_EQ(
v < 0 || v >= 1, true,
platform::errors::InvalidArgument(
"Attr(maxlen) must be less than 0 or larger than 1"));
});
AddAttr<int>("out_dtype", "Output data type");
AddComment(R"DOC(
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
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))
If maxlen < 0, maxlen = max(X)
)DOC");
}
};
} // namespace operators
} // namespace paddle
REGISTER_OPERATOR(
sequence_mask, paddle::operators::SequenceMaskOp,
paddle::operators::SequenceMaskOpMaker,
paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
REGISTER_OP_CPU_KERNEL(
sequence_mask,
paddle::operators::SequenceMaskKernel<paddle::platform::CPUDeviceContext,
int>,
paddle::operators::SequenceMaskKernel<paddle::platform::CPUDeviceContext,
int64_t>,
paddle::operators::SequenceMaskKernel<paddle::platform::CPUDeviceContext,
float>,
paddle::operators::SequenceMaskKernel<paddle::platform::CPUDeviceContext,
double>);