/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. 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. */ #pragma once #include "glog/logging.h" #include "paddle/framework/eigen.h" #include "paddle/framework/operator.h" #include "paddle/framework/ddim.h" #include "paddle/operators/gather.h" #include namespace paddle { namespace operators { using namespace paddle::framework; template class CondOp final : public OperatorBase { public: /** * InferShape must be called before Run. */ void InferShape(const std::shared_ptr& scope) const override; // Set True Block void set_truenet(std::unique_ptr net) { sub_net_op_[0] = std::move(net); } // Set False Block void set_falsenet(std::unique_ptr net) { sub_net_op_[1] = std::move(net); } virtual void Run(const std::shared_ptr& scope, const platform::DeviceContext& dev_ctx) const override { auto* cond = context.Input("Cond"); // Step 1: get the true/false index at runtime // _index[0]: vector, contains all index for cond[i] == true // _index[1]: vector, contains all index for cond[i] == false for(int i = 0; i < 2; ++i) _index[i].clear(); for(int i = 0; i < cond->dims()[0]; ++i) { if (cond->data()[i]) _index[0].push_back(i); else _index[1].push_back(i); } // put _index[0] and _index[1] into two tensors // tensor_index[0] and tensor_index[1] framework::DDim dim_ = paddle::framework::make_ddim({0}); for(int i = 0; i < 2; ++i) { dim_[0] = _index[i].size(); int* tmp_ = _index[i]->mutable_data(dim_, CPUPlace()); tensor_index[i]->Resize(dim_); memcpy(tmp_, index_[i], dim_[0] * sizeof(int)); } // Step 2: collect data by calling gather for (int i = 0; i < 2; ++i) { // i= 0/i for True and False branches respectively for (auto& input : GetAttr>("sub_inputs")) { auto var_name = input.second; // find Tensor Tensor* Tensor_parent = scope.FindVar(var_name)->GetMutable(); Tensor* Tensor_child = sub_scope_[i].FindVar(var_name)->GetMutable(); Gather(dev_ctx.GetPlace(), tensor_parent, tensor_index[i], tensor_child); } } // Step 3: run for (int i = 0; i < 2; ++i) sub_net_op_[i]->Run(sub_scope_[i], dev_ctx); // Step 4: merge output results for (int i = 0; i < 2; ++i) { // i= 0/i for True and False branches respectively for (auto& output : GetAttr>("sub_outputs")) { auto var_name = output.second; // find Tensor Tensor* Tensor_parent = scope.FindVar(var_name)->GetMutable(); Tensor* Tensor_child = sub_scope_[i].FindVar(var_name)->GetMutable(); ScatterUpdate(dev_ctx.GetPlace(), tensor_child, tensor_index[i], tensor_parent); } } } private: // sub_scope_[0]: true scope // sub_scope_[1]: false scope std::vector sub_scope_; // sub_net_op_[0]: subnet_t // sub_net_op_[1]: subnet_f std::vector> sub_net_op_; // tensor_index[0]: True_index tensor // tensor_index[1]: False_index; std::vector tensor_index; // _index[0]: True_index; // _index[1]: False_index; vector > _index; }; /* class CondGradientOp final : public OperatorBase { public: void Init() override; virtual void InferShape(const std::shared_ptr& scope) const override; virtual void Run(const std::shared_ptr& scope, const platform::DeviceContext& dev_ctx) const override; };*/ } // namespace operators } // namespace paddle