forked from mindspore-Ecosystem/mindspore
70 lines
2.5 KiB
C++
70 lines
2.5 KiB
C++
/**
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* Copyright 2022 Huawei Technologies Co., Ltd
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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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*/
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#ifndef MINDSPORE_INCLUDE_API_CFG_H
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#define MINDSPORE_INCLUDE_API_CFG_H
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#include <cstddef>
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#include <string>
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#include <vector>
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#include <memory>
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#include "include/api/data_type.h"
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#include "include/api/dual_abi_helper.h"
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#include "include/api/types.h"
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namespace mindspore {
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constexpr int iter_th = 1000;
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class MixPrecisionCfg {
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public:
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MixPrecisionCfg() {
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this->dynamic_loss_scale_ = false;
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this->loss_scale_ = 128.0f;
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this->keep_batchnorm_fp32_ = true;
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this->num_of_not_nan_iter_th_ = iter_th;
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}
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MixPrecisionCfg(const MixPrecisionCfg &rhs) {
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this->dynamic_loss_scale_ = rhs.dynamic_loss_scale_;
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this->loss_scale_ = rhs.loss_scale_;
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this->keep_batchnorm_fp32_ = rhs.keep_batchnorm_fp32_;
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this->num_of_not_nan_iter_th_ = rhs.num_of_not_nan_iter_th_;
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}
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~MixPrecisionCfg() = default;
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bool dynamic_loss_scale_ = false; /**< Enable/disable dynamic loss scale during mix precision training */
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float loss_scale_; /**< Initial loss scale factor */
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bool keep_batchnorm_fp32_ = true; /**< Keep batch norm in FP32 while training */
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uint32_t num_of_not_nan_iter_th_; /**< a threshold for modifying loss scale when dynamic loss scale is enabled */
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bool is_raw_mix_precision_ = false; /**< Is mix precision model export from mindspore */
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};
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class TrainCfg {
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public:
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TrainCfg() = default;
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TrainCfg(const TrainCfg &rhs) {
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this->loss_name_ = rhs.loss_name_;
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this->mix_precision_cfg_ = rhs.mix_precision_cfg_;
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this->accumulate_gradients_ = rhs.accumulate_gradients_;
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}
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~TrainCfg() = default;
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OptimizationLevel optimization_level_ = kO0;
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std::vector<std::string> loss_name_ = {
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"loss_fct", "_loss_fn", "SigmoidCrossEntropy"}; /**< Set part of the name that identify a loss kernel */
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MixPrecisionCfg mix_precision_cfg_; /**< Mix precision configuration */
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bool accumulate_gradients_ = false;
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};
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} // namespace mindspore
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#endif // MINDSPORE_INCLUDE_API_CFG_H
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