You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
103 lines
3.8 KiB
103 lines
3.8 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_concat_op.h"
|
|
#include <vector>
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
class SeqConcatOpMaker : public framework::OpProtoAndCheckerMaker {
|
|
public:
|
|
void Make() override {
|
|
AddInput("X", "The inputs of sequence concat op").AsDuplicable();
|
|
AddOutput("Out", "The output of sequence concat op");
|
|
AddComment(
|
|
"Sequence Concat Op\n"
|
|
"It will concat LoD tensors by its sequence information.\n"
|
|
"For example:\n"
|
|
" LoD of X1 = [0, 3, 7]\n"
|
|
" LoD of X2 = [0, 7, 9]\n"
|
|
" Result LoD is [0, (3+7), (7+9)]\n"
|
|
" i.e.[0, 10, 16]\n");
|
|
}
|
|
};
|
|
|
|
class SeqConcatShapeInferer : public framework::InferShapeBase {
|
|
public:
|
|
void operator()(framework::InferShapeContext *context) const override {
|
|
PADDLE_ENFORCE(context->HasInputs("X"),
|
|
"Input(X) of Sequence Concat Op should not be null.");
|
|
PADDLE_ENFORCE(context->HasOutput("Out"),
|
|
"Output(Out) of Sequence Concat Op should not be null.");
|
|
|
|
PADDLE_ENFORCE_GT(context->Inputs("X").size(), 1,
|
|
"The number of input sequences is at least two.");
|
|
auto x_dims = context->GetInputsDim("X");
|
|
int64_t batch_size = 0;
|
|
int64_t feature_size = 0;
|
|
std::vector<int64_t> out_dims;
|
|
for (auto &x_dim : x_dims) {
|
|
if (out_dims.empty()) {
|
|
out_dims = framework::vectorize(x_dim);
|
|
}
|
|
batch_size += x_dim[0];
|
|
if (feature_size == 0) {
|
|
feature_size = framework::product(x_dim) / x_dim[0];
|
|
} else {
|
|
PADDLE_ENFORCE_EQ(
|
|
feature_size, framework::product(x_dim) / x_dim[0],
|
|
"Inputs of sequence concat must have same feature size");
|
|
}
|
|
}
|
|
if (batch_size < 0) {
|
|
batch_size = -1; // Normalize batch size for compile time.
|
|
}
|
|
out_dims[0] = batch_size;
|
|
context->SetOutputDim("Out", framework::make_ddim(out_dims));
|
|
if (!context->IsRuntime()) { // Runtime LoD infershape will be computed
|
|
// in Kernel.
|
|
context->ShareLoD("X", "Out");
|
|
}
|
|
}
|
|
};
|
|
|
|
class SeqConcatGradShapeInferer : public framework::InferShapeBase {
|
|
public:
|
|
void operator()(framework::InferShapeContext *context) const override {
|
|
context->SetOutputsDim(framework::GradVarName("X"),
|
|
context->GetInputsDim("X"));
|
|
}
|
|
};
|
|
} // namespace operators
|
|
} // namespace paddle
|
|
|
|
namespace op = paddle::operators;
|
|
|
|
REGISTER_OPERATOR(sequence_concat, paddle::framework::OperatorWithKernel,
|
|
op::SeqConcatOpMaker, op::SeqConcatShapeInferer,
|
|
paddle::framework::DefaultGradOpDescMaker<false>);
|
|
template <typename T>
|
|
using Kernel = op::SeqConcatKernel<paddle::platform::CPUDeviceContext, T>;
|
|
REGISTER_OP_CPU_KERNEL(sequence_concat, Kernel<float>, Kernel<double>,
|
|
Kernel<int64_t>);
|
|
|
|
REGISTER_OPERATOR(sequence_concat_grad, paddle::framework::OperatorWithKernel,
|
|
op::SeqConcatGradShapeInferer);
|
|
template <typename T>
|
|
using GradKernel =
|
|
op::SeqConcatGradKernel<paddle::platform::CPUDeviceContext, T>;
|
|
REGISTER_OP_CPU_KERNEL(sequence_concat_grad, GradKernel<float>,
|
|
GradKernel<double>, GradKernel<int64_t>);
|