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114 lines
4.3 KiB
114 lines
4.3 KiB
/* 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/memory/memcpy.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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namespace paddle {
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namespace operators {
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// SelectInputOp takes multiple inputs and uses an integer mask to select
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// one input to output. It is used in control flow.
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class SelectInputOp : public framework::OperatorBase {
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public:
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SelectInputOp(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 platform::Place &dev_place) const override {
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platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
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auto &dev_ctx = *pool.Get(dev_place);
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auto &mask = scope.FindVar(Input("Mask"))->Get<framework::LoDTensor>();
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size_t output_branch = static_cast<size_t>(GetBranchNumber(mask));
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const std::vector<std::string> &x_names = Inputs("X");
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PADDLE_ENFORCE_LT(
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output_branch, x_names.size(),
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platform::errors::InvalidArgument(
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"Input 'Mask' in SelectInputOp is invalid. "
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"'Mask' must be less than the size of input vector 'X'. "
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"But received Mask = %d, X's size = %d.",
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output_branch, x_names.size()));
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const framework::Variable *selected_x =
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scope.FindVar(x_names[output_branch]);
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framework::Variable *out = scope.FindVar(Output("Out"));
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framework::VisitVarType(*selected_x, AssignFunctor(out, dev_ctx));
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}
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};
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class SelectInputOpProtoMaker : 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 LoDTensors or LoDTensorArray or SelectedRows. All "
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"inputs must have same variable type")
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.AsDuplicable();
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AddInput("Mask",
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"A integer tensor with numel 1 specifying which input to output");
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AddOutput(
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"Out",
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"The merged output. The variable type of output must be same as X");
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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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Merge branches of LoDTensor into a single Output with a mask integer
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specifying the output branchi.
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)DOC");
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}
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};
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class SelectInputInferShape : 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->HasInputs("X"), "Input", "X", "SelectInput");
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OP_INOUT_CHECK(context->HasInput("Mask"), "Input", "Mask", "SelectInput");
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OP_INOUT_CHECK(context->HasOutput("Out"), "Output", "Out", "SelectInput");
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}
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};
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template <typename T>
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class SelectInputGradMaker : 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_output");
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grad_op->SetInput("X", this->OutputGrad("Out"));
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grad_op->SetInput("Mask", this->Input("Mask"));
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grad_op->SetOutput("Out",
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this->InputGrad("X", /* drop_empty_grad */ false));
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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_input, ops::SelectInputOp,
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ops::SelectInputOpProtoMaker, ops::SelectInputInferShape,
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ops::SelectInputGradMaker<paddle::framework::OpDesc>,
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ops::SelectInputGradMaker<paddle::imperative::OpBase>);
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