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106 lines
3.3 KiB
106 lines
3.3 KiB
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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 <cassert>
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#include <map>
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#include <memory>
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#include <string>
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#include <unordered_set>
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#include <vector>
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#include "paddle_api.h" // NOLINT
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namespace paddle {
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// Algorithms for finding scale of quantized Tensors.
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enum class ScaleAlgo {
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NONE, // Do not compute scale
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MAX, // Find scale based on the maximum absolute value
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MAX_CH, // Find scale based on the maximum absolute value per channel
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KL, // Find scale based on KL Divergence
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};
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struct MkldnnQuantizerConfig {
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MkldnnQuantizerConfig();
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/** Specify a quantization algorithm for a connection (input/output) of the
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* operator type.
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* @param op_type_name the operator's name.
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* @param conn_name name of the connection (input/output) of the operator.
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* @param algo the algorithm for computing scale.
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*/
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void SetScaleAlgo(std::string op_type_name, std::string conn_name,
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ScaleAlgo algo) {
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rules_[op_type_name][conn_name] = algo;
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}
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/** Get the quantization algorithm for a connection (input/output) of the
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* operator type.
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* @param op_type_name the operator's name.
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* @param conn_name name of the connection (input/output) of the operator.
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* @return the algorithm for computing scale.
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*/
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ScaleAlgo scale_algo(const std::string& op_type_name,
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const std::string& conn_name) const;
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/** Set the batch of data to be used for warm-up iteration.
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* @param data batch of data.
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*/
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void SetWarmupData(std::shared_ptr<std::vector<PaddleTensor>> data) {
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warmup_data_ = data;
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}
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/** Get the batch of data used for warm-up iteration.
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* @return batch of data.
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*/
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std::shared_ptr<std::vector<PaddleTensor>> warmup_data() const {
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return warmup_data_;
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}
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void SetWarmupBatchSize(int batch_size) { warmup_bs_ = batch_size; }
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int warmup_batch_size() const { return warmup_bs_; }
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void SetEnabledOpTypes(std::unordered_set<std::string> op_list) {
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enabled_op_types_ = op_list;
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}
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const std::unordered_set<std::string>& enabled_op_types() const {
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return enabled_op_types_;
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}
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void SetExcludedOpIds(std::unordered_set<int> op_ids_list) {
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excluded_op_ids_ = op_ids_list;
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}
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const std::unordered_set<int>& excluded_op_ids() const {
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return excluded_op_ids_;
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}
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void SetDefaultScaleAlgo(ScaleAlgo algo) { default_scale_algo_ = algo; }
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ScaleAlgo default_scale_algo() const { return default_scale_algo_; }
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protected:
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std::map<std::string, std::map<std::string, ScaleAlgo>> rules_;
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std::unordered_set<std::string> enabled_op_types_;
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std::unordered_set<int> excluded_op_ids_;
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std::shared_ptr<std::vector<PaddleTensor>> warmup_data_;
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int warmup_bs_{1};
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ScaleAlgo default_scale_algo_{ScaleAlgo::MAX};
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};
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} // namespace paddle
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