wehub-resource-sync
131 lines
4.7 KiB
C++
131 lines
4.7 KiB
C++
/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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/framework/op_registry.h"
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#include "paddle/fluid/operators/assign_op.h"
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#include "paddle/fluid/operators/select_op_helper.h"
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#include "paddle/phi/core/platform/device_context.h"
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namespace paddle {
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namespace framework {
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class InferShapeContext;
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class OpDesc;
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class Scope;
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class Variable;
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} // namespace framework
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namespace imperative {
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class OpBase;
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} // namespace imperative
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} // namespace paddle
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namespace paddle {
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namespace operators {
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// SelectOutputOp has one input, one integer mask and multiple outputs. It
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// selects one output specified by the mask and copy the input to it.
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class SelectOutputOp : public framework::OperatorBase {
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public:
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SelectOutputOp(const std::string &type,
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const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: OperatorBase(type, inputs, outputs, attrs) {}
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private:
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void RunImpl(const framework::Scope &scope,
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const phi::Place &dev_place) const override {
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phi::DeviceContextPool &pool = phi::DeviceContextPool::Instance();
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auto &dev_ctx = *pool.Get(dev_place);
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auto &mask = scope.FindVar(Input("Mask"))->Get<DenseTensor>();
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size_t output_branch = static_cast<size_t>(GetBranchNumber(mask));
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const std::vector<std::string> &out_names = Outputs("Out");
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PADDLE_ENFORCE_LT(
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output_branch,
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out_names.size(),
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common::errors::InvalidArgument(
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"Input 'Mask' in SelectOutputOp is invalid. "
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"'Mask' must be less than the size of output vector 'Out'. "
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"But received Mask = %d, Out's size = %d.",
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output_branch,
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out_names.size()));
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const framework::Variable *x = scope.FindVar(Input("X"));
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framework::Variable *selected_out = scope.FindVar(out_names[output_branch]);
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if (nullptr != selected_out) {
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framework::VisitVarType(*x, AssignFunctor(selected_out, dev_ctx));
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}
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}
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};
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class SelectOutputOpProtoMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X",
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"The input DenseTensor or phi::DenseTensorArray or SelectedRows.");
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AddInput("Mask", "Tensor with numel 1 specifying which branch to output");
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AddOutput("Out",
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"The output can contains multiple variables. The output of "
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"selected branch will be same as input. We do nothing for "
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"variables in other branch")
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.AsDuplicable();
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// TODO(huihuangzheng): decide whether to add support for lod level
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// Because this op is blocking whole control flow. I am implementing MVP
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// (minimal viable product) here.
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AddComment(R"DOC(
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Split input variable into one output branch. The mask is an integer tensor to
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specify which output branch should copy the input.
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)DOC");
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}
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};
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class SelectOutputInferShape : public framework::InferShapeBase {
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public:
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void operator()(framework::InferShapeContext *context) const override {
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OP_INOUT_CHECK(context->HasInput("X"), "Input", "X", "SelectOutput");
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OP_INOUT_CHECK(context->HasInput("Mask"), "Input", "Mask", "SelectOutput");
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OP_INOUT_CHECK(context->HasOutputs("Out", /*allow_null=*/true),
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"Output",
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"Out",
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"SelectOutput");
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}
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};
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template <typename T>
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class SelectOutputGradMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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protected:
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void Apply(GradOpPtr<T> grad_op) const override {
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grad_op->SetType("select_input");
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grad_op->SetInput("Mask", this->Input("Mask"));
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grad_op->SetInput("X", this->OutputGrad("Out"));
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grad_op->SetOutput("Out", this->InputGrad("X"));
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grad_op->SetAttrMap(this->Attrs());
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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 ops = paddle::operators;
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REGISTER_OPERATOR(select_output,
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ops::SelectOutputOp,
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ops::SelectOutputOpProtoMaker,
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ops::SelectOutputInferShape,
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ops::SelectOutputGradMaker<paddle::framework::OpDesc>,
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ops::SelectOutputGradMaker<paddle::imperative::OpBase>);
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