Zhuoyuan
8 years ago
parent
c3d684efc4
commit
adfef243d2
@ -1,56 +0,0 @@
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#include "paddle/operators/switch_op.h"
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namespace paddle {
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namespace operators {
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void CondOp::InferShape(const std::shared_ptr<Scope>& scope) const {
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// Create two Nets
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// Create two scopes
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for (int i = 0; i < 2; ++i)
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sub_scope.push_back(scope.NewScope());
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for (int i = 0; i < 2; ++i)
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sub_net_op_[i].InferShape(sub_scope[i]);
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for (int i = 0; i < 2; ++i)
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tensor_index = new Tensor();
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for (int i = 0; i < 2; ++i)
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_index.push_back(vector<int>());
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for (int i = 0; i < 2; ++i)
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{
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// for (auto& input : net_op_[i]->Inputs()) {
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for (auto& input : GetAttr<std::vector<std::string>>("True_inputs")) {
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auto var_name = input.second;
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// Create a new tensor in sub-scope for input-type tensor
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sub_scope[i]->NewVar(var_name)->GetMutable<Tensor>();
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}
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}
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}
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class CondOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
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public:
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CondOpProtoAndCheckerMaker(OpProto *proto, OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("Cond", "The condition, which is a bool vector");
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AddInput("Xs", "Inputs of Subnets");
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AddAttr<std::vector<std::string>>("sub_inputs", "Inputs of the Whole Op, net op and so forth");
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AddAttr<std::vector<std::string>>("sub_outputs", "True Outputs needs merge");
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AddOutput("Outs", "The output of cond op");
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AddComment(R"DOC(
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Sample dependent Cond Operator:
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The equation is: Out[i] = subnet_t[i], if Cond[i] == true
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Out[i] = subnet_t[i], if Cond[i] == false
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)DOC");
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}
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};
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} // namespace operators
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} // namespace paddle
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REGISTER_OP_WITHOUT_GRADIENT(cond_op,
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paddle::operators::CondOp,
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paddle::operators::CondOpProtoAndCheckerMaker);
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@ -1,131 +0,0 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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#pragma once
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#include "glog/logging.h"
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#include "paddle/framework/eigen.h"
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#include "paddle/framework/operator.h"
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#include "paddle/framework/ddim.h"
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#include "paddle/operators/gather.h"
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#include <vector>
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namespace paddle {
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namespace operators {
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using namespace paddle::framework;
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template <typename Place, typename T>
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class CondOp final : public OperatorBase {
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public:
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/**
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* InferShape must be called before Run.
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*/
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void InferShape(const std::shared_ptr<Scope>& scope) const override;
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// Set True Block
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void set_truenet(std::unique_ptr<OperatorBase> net) {
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sub_net_op_[0] = std::move(net);
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}
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// Set False Block
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void set_falsenet(std::unique_ptr<OperatorBase> net) {
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sub_net_op_[1] = std::move(net);
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}
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virtual void Run(const std::shared_ptr<Scope>& scope,
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const platform::DeviceContext& dev_ctx) const override {
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auto* cond = context.Input<Tensor>("Cond");
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// Step 1: get the true/false index at runtime
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// _index[0]: vector<int>, contains all index for cond[i] == true
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// _index[1]: vector<int>, contains all index for cond[i] == false
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for(int i = 0; i < 2; ++i)
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_index[i].clear();
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for(int i = 0; i < cond->dims()[0]; ++i) {
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if (cond->data<bool>()[i])
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_index[0].push_back(i);
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else
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_index[1].push_back(i);
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}
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// put _index[0] and _index[1] into two tensors
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// tensor_index[0] and tensor_index[1]
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framework::DDim dim_ = paddle::framework::make_ddim({0});
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for(int i = 0; i < 2; ++i) {
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dim_[0] = _index[i].size();
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int* tmp_ = _index[i]->mutable_data<int>(dim_, CPUPlace());
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tensor_index[i]->Resize(dim_);
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memcpy(tmp_, index_[i], dim_[0] * sizeof(int));
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}
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// Step 2: collect data by calling gather
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for (int i = 0; i < 2; ++i) {
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// i= 0/i for True and False branches respectively
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for (auto& input : GetAttr<std::vector<std::string>>("sub_inputs")) {
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auto var_name = input.second;
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// find Tensor
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Tensor* Tensor_parent = scope.FindVar(var_name)->GetMutable<Tensor>();
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Tensor* Tensor_child = sub_scope_[i].FindVar(var_name)->GetMutable<Tensor>();
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Gather<T>(dev_ctx.GetPlace(), tensor_parent, tensor_index[i], tensor_child);
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}
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}
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// Step 3: run
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for (int i = 0; i < 2; ++i)
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sub_net_op_[i]->Run(sub_scope_[i], dev_ctx);
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// Step 4: merge output results
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for (int i = 0; i < 2; ++i) {
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// i= 0/i for True and False branches respectively
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for (auto& output : GetAttr<std::vector<std::string>>("sub_outputs")) {
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auto var_name = output.second;
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// find Tensor
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Tensor* Tensor_parent = scope.FindVar(var_name)->GetMutable<Tensor>();
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Tensor* Tensor_child = sub_scope_[i].FindVar(var_name)->GetMutable<Tensor>();
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ScatterUpdate<T>(dev_ctx.GetPlace(), tensor_child, tensor_index[i], tensor_parent);
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}
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}
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}
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private:
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// sub_scope_[0]: true scope
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// sub_scope_[1]: false scope
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std::vector<Scope*> sub_scope_;
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// sub_net_op_[0]: subnet_t
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// sub_net_op_[1]: subnet_f
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std::vector<std::unique_ptr<framework::OperatorBase>> sub_net_op_;
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// tensor_index[0]: True_index tensor
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// tensor_index[1]: False_index;
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std::vector<Tensor*> tensor_index;
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// _index[0]: True_index;
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// _index[1]: False_index;
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vector<vector<int> > _index;
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};
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/*
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class CondGradientOp final : public OperatorBase {
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public:
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void Init() override;
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virtual void InferShape(const std::shared_ptr<Scope>& scope) const override;
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virtual void Run(const std::shared_ptr<Scope>& scope,
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const platform::DeviceContext& dev_ctx) const override;
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};*/
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} // namespace operators
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} // namespace paddle
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