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103 lines
3.8 KiB
103 lines
3.8 KiB
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/fluid/operators/sequence_concat_op.h"
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#include <vector>
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namespace paddle {
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namespace operators {
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class SeqConcatOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X", "The inputs of sequence concat op").AsDuplicable();
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AddOutput("Out", "The output of sequence concat op");
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AddComment(
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"Sequence Concat Op\n"
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"It will concat LoD tensors by its sequence information.\n"
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"For example:\n"
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" LoD of X1 = [0, 3, 7]\n"
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" LoD of X2 = [0, 7, 9]\n"
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" Result LoD is [0, (3+7), (7+9)]\n"
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" i.e.[0, 10, 16]\n");
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}
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};
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class SeqConcatShapeInferer : public framework::InferShapeBase {
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public:
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void operator()(framework::InferShapeContext *context) const override {
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PADDLE_ENFORCE(context->HasInputs("X"),
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"Input(X) of Sequence Concat Op should not be null.");
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PADDLE_ENFORCE(context->HasOutput("Out"),
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"Output(Out) of Sequence Concat Op should not be null.");
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PADDLE_ENFORCE_GT(context->Inputs("X").size(), 1,
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"The number of input sequences is at least two.");
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auto x_dims = context->GetInputsDim("X");
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int64_t batch_size = 0;
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int64_t feature_size = 0;
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std::vector<int64_t> out_dims;
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for (auto &x_dim : x_dims) {
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if (out_dims.empty()) {
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out_dims = framework::vectorize(x_dim);
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}
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batch_size += x_dim[0];
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if (feature_size == 0) {
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feature_size = framework::product(x_dim) / x_dim[0];
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} else {
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PADDLE_ENFORCE_EQ(
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feature_size, framework::product(x_dim) / x_dim[0],
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"Inputs of sequence concat must have same feature size");
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}
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}
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if (batch_size < 0) {
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batch_size = -1; // Normalize batch size for compile time.
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}
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out_dims[0] = batch_size;
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context->SetOutputDim("Out", framework::make_ddim(out_dims));
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if (!context->IsRuntime()) { // Runtime LoD infershape will be computed
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// in Kernel.
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context->ShareLoD("X", "Out");
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}
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}
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};
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class SeqConcatGradShapeInferer : public framework::InferShapeBase {
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public:
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void operator()(framework::InferShapeContext *context) const override {
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context->SetOutputsDim(framework::GradVarName("X"),
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context->GetInputsDim("X"));
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace op = paddle::operators;
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REGISTER_OPERATOR(sequence_concat, paddle::framework::OperatorWithKernel,
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op::SeqConcatOpMaker, op::SeqConcatShapeInferer,
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paddle::framework::DefaultGradOpDescMaker<false>);
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template <typename T>
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using Kernel = op::SeqConcatKernel<paddle::platform::CPUDeviceContext, T>;
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REGISTER_OP_CPU_KERNEL(sequence_concat, Kernel<float>, Kernel<double>,
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Kernel<int64_t>);
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REGISTER_OPERATOR(sequence_concat_grad, paddle::framework::OperatorWithKernel,
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op::SeqConcatGradShapeInferer);
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template <typename T>
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using GradKernel =
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op::SeqConcatGradKernel<paddle::platform::CPUDeviceContext, T>;
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REGISTER_OP_CPU_KERNEL(sequence_concat_grad, GradKernel<float>,
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GradKernel<double>, GradKernel<int64_t>);
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