// 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 "paddle/fluid/framework/ir/fuse_pass_base.h" #include "paddle/fluid/framework/ir/graph.h" #include "paddle/fluid/framework/ir/graph_pattern_detector.h" namespace paddle { namespace framework { namespace ir { /* * Map variable name to tensor of scaling factors scaling it to MAX=1.0. * bool denotes whether quantization of the variable should be done to unsigned * type. */ using VarQuantScale = std::unordered_map>; /* * Quantize all supported operators. */ class CPUQuantizePass : public FusePassBase { public: virtual ~CPUQuantizePass() {} protected: void ApplyImpl(ir::Graph* graph) const override; void QuantizeConv(Graph* graph, bool with_residual_data = false) const; void QuantizePool(Graph* graph) const; void QuantizeInput(Graph* g, Node* op, Node* input, std::string input_name, double scale_to_one, bool is_unsigned, std::string scale_attr_name = "") const; void DequantizeOutput(Graph* g, Node* op, Node* output, std::string output_name, double scale_to_one, bool is_unsigned, std::string scale_attr_name = "") const; const std::string name_scope_{"quantize"}; }; } // namespace ir } // namespace framework } // namespace paddle