/* * Licensed to the Apache Software Foundation (ASF) under one * or more contributor license agreements. See the NOTICE file * distributed with this work for additional information * regarding copyright ownership. The ASF licenses this file * to you 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. */ /*! * Copyright (c) 2016 by Contributors * \file executor.h * \brief executor definition * \author Chuntao Hong, Zhang Chen */ #ifndef MXNET_CPP_EXECUTOR_H_ #define MXNET_CPP_EXECUTOR_H_ #include #include #include #include #include "mxnet-cpp/base.h" #include "mxnet-cpp/symbol.h" namespace mxnet { namespace cpp { class Optimizer; /*! * \brief Executor interface */ class Executor { friend class Monitor; public: Executor(const Symbol &symbol, Context context, const std::vector &arg_arrays, const std::vector &grad_arrays, const std::vector &grad_reqs, const std::vector &aux_arrays, const std::map &group_to_ctx = std::map(), Executor *shared_exec = nullptr); explicit Executor(const ExecutorHandle &h) { handle_ = h; } /*! * \brief Perform a Forward operation of Operator * After this operation, user can get the result by using function head. */ void Forward(bool is_train) { MXExecutorForward(handle_, is_train ? 1 : 0); mx_uint out_size; NDArrayHandle *out_array; CHECK_EQ(MXExecutorOutputs(handle_, &out_size, &out_array), 0); for (mx_uint i = 0; i < out_size; ++i) { outputs[i] = NDArray(out_array[i]); } } /*! * \brief Perform a Backward operation of the Operator. * This must be called after Forward. * After this operation, NDArrays specified by grad_in_args_store will be *updated accordingly. * User is allowed to pass in an empty Array if the head node is * loss function and head gradeitn is not needed. * * \param head_grads the gradient of head nodes to be backproped. */ void Backward(const std::vector &head_grads = std::vector()) { std::vector head_grads_; for (auto d : head_grads) { head_grads_.push_back(d.GetHandle()); } if (head_grads_.size() > 0) { MXExecutorBackward(handle_, head_grads_.size(), head_grads_.data()); } else { MXExecutorBackward(handle_, 0, nullptr); } } // TODO(zhangchen-qinyinghua) // To implement reshape function void Reshape(); /*! * \brief update the arguments with given learning rate and optimizer * \return the SymbolHandle */ std::string DebugStr(); /*! * \brief destructor, free the handle */ ~Executor() { MXExecutorFree(handle_); } std::vector arg_arrays; std::vector grad_arrays; std::vector aux_arrays; /*! * \brief arrays store the outputs of forward */ std::vector outputs; std::map arg_dict() { return GetDict(symbol_.ListArguments(), arg_arrays); } std::map grad_dict() { return GetDict(symbol_.ListArguments(), grad_arrays); } std::map aux_dict() { return GetDict(symbol_.ListAuxiliaryStates(), aux_arrays); } private: Executor(const Executor &e); Executor &operator=(const Executor &e); ExecutorHandle handle_; Symbol symbol_; std::map GetDict(const std::vector &names, const std::vector &arrays) { std::map ret; std::set name_set; for (const auto &s : names) { CHECK(name_set.find(s) == name_set.end()) << "Duplicate names detected, " << s; name_set.insert(s); } CHECK_EQ(name_set.size(), arrays.size()) << "names size not equal to arrays size"; for (size_t i = 0; i < names.size(); ++i) { ret[names[i]] = arrays[i]; } return ret; } }; } // namespace cpp } // namespace mxnet #endif // MXNET_CPP_EXECUTOR_H_