// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. // // 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 #include #include #include #include #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/framework/op_desc.h" #include "paddle/fluid/framework/variable.h" #include "paddle/fluid/lite/core/context.h" #include "paddle/fluid/lite/core/kernel.h" #include "paddle/fluid/lite/core/scope.h" namespace paddle { namespace lite { using any_t = boost::variant; using anys_t = std::map; // For registry factory. struct Registry { void Touch() {} }; namespace mir { class Node; class SSAGraph; } class OpInfo; /** * The base class of an light-weight operators, currently just used in inference * to eliminate overhead of some operations in current framework. * * The Operator are designed as follows: * - it can has some members to hold the argument and some other computation * resources, * - it should act like a function call, no more logic included. */ class OpLite : public Registry { public: // The strategies to pick a kernel from candidates. enum class KernelStrategy { // Return the user specified one. kStatic = 0, // Specify the expected kernel externally. kSpecified, // Run each kernel to evaluate and get the best kernel. kRuntime, }; OpLite() = default; OpLite(const std::string &type) : op_type_(type) {} OpLite(std::unique_ptr &&x, const std::vector &valid_places) : op_context_(std::move(x)), valid_places_(valid_places) {} void SetValidPlaces(const std::vector &places) { valid_places_ = places; } const std::vector &valid_places() const { return valid_places_; } // Check the shape. virtual bool CheckShape() const { return true; } // Inference the outputs' shape. virtual bool InferShape() const { return true; } // Run this operator. virtual bool Run(); bool Attach(const framework::OpDesc &opdesc, lite::Scope *scope); const std::shared_ptr &op_info() const { return op_info_; } std::shared_ptr &mutable_op_info() { return op_info_; } // Human-readable information. virtual std::string DebugString() const = 0; const Place &kernel_place() const { return kernel_place_; } void PickKernel(const std::vector &valid_places, KernelStrategy kernel_strategy = KernelStrategy::kStatic); virtual ~OpLite() = default; protected: // Attach it with the runtime environment. virtual bool AttachImpl(const framework::OpDesc &opdesc, lite::Scope *scope) = 0; // Specify the kernel to run by default. This will specify the value of // `kernel_place_`. virtual void StaticPickKernel(const std::vector &valid_targets) { auto kernels = CreateKernels(valid_targets); kernel_ = std::move(kernels.front()); } // Wait until all the inputs' events are ready. void SyncInputEvents() {} // Record the output events, and that will tell all the dependent operators // some inputs are ready. void RecordOutputEvents() {} // Create all the kernels for the valid targets. std::vector> CreateKernels( const std::vector &places, const std::string &kernel_type = ""); friend class mir::Node; friend class mir::SSAGraph; protected: std::unique_ptr op_context_; std::unique_ptr kernel_; std::string op_type_; std::vector valid_places_; Place kernel_place_{TARGET(kHost), PRECISION(kFloat)}; std::shared_ptr op_info_; }; /* * Operator Information, such as some description. It will be shared by all the * kernels of the same operator. */ class OpInfo { public: void Build(const framework::OpDesc &desc) { ExtractInputsAndOutputs(desc); CollectInputAndOutputArgnames(desc); CollectArguments(desc); } const std::list &input_names() const { return input_names_; } const std::list &output_names() const { return output_names_; } const std::map> &input_argument() { return input_argument_; } const std::map> &output_argument() { return output_argument_; } const std::list &input_argnames() const { return input_argnames_; } const std::list &output_argnames() const { return output_argnames_; } private: void ExtractInputsAndOutputs(const framework::OpDesc &opdesc) { for (const auto &item : opdesc.Inputs()) { for (const auto &x : item.second) { input_names_.push_back(x); } } for (const auto &item : opdesc.Outputs()) { for (const auto &x : item.second) { output_names_.push_back(x); } } } void CollectInputAndOutputArgnames(const framework::OpDesc &opdesc) { for (const auto &item : opdesc.InputNames()) { input_argnames_.push_back(item); } for (const auto &item : opdesc.OutputNames()) { output_argnames_.push_back(item); } } void CollectArguments(const framework::OpDesc &opdesc) { for (const auto &item : opdesc.Inputs()) { for (auto &x : item.second) { input_argument_[item.first].push_back(x); } } for (const auto &item : opdesc.Outputs()) { for (auto &x : item.second) { output_argument_[item.first].push_back(x); } } } private: std::list input_names_; std::list output_names_; std::list input_argnames_; std::list output_argnames_; std::map> input_argument_; std::map> output_argument_; }; } // namespace lite } // namespace paddle