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1 : // SPDX-License-Identifier: Apache-2.0
2 : /**
3 : * Copyright (C) 2021 Parichay Kapoor <pk.kapoor@samsung.com>
4 : *
5 : * @file optimizer_wrapped.h
6 : * @date 10 December 2021
7 : * @brief This is Optimizer Wrapped interface class
8 : * @see https://github.com/nnstreamer/nntrainer
9 : * @author Parichay Kapoor <pk.kapoor@samsung.com>
10 : * @bug No known bugs except for NYI items
11 : *
12 : * @details wraps the optimizer and learning rate scheduler together
13 : */
14 :
15 : #ifndef __OPTIMIZER_WRAPPER_H__
16 : #define __OPTIMIZER_WRAPPER_H__
17 :
18 : #if __cplusplus
19 :
20 : #include <string>
21 : #include <vector>
22 :
23 : #include <common_properties.h>
24 : #include <lr_scheduler.h>
25 : #include <optimizer.h>
26 : #include <optimizer_devel.h>
27 :
28 : namespace nntrainer {
29 :
30 : using OptimizerCore = nntrainer::Optimizer;
31 :
32 : /**
33 : * @class Optimizer Base class for optimizers
34 : * @brief Base class for all optimizers
35 : */
36 : class OptimizerWrapped : public ml::train::Optimizer {
37 : public:
38 : /**
39 : * @brief Constructor of OptimizerWrapped class
40 : * @param opt optimizer to wrap
41 : *
42 : */
43 : OptimizerWrapped(std::unique_ptr<OptimizerCore> &&opt);
44 :
45 : /**
46 : * @brief Destructor of Optimizer Class
47 : */
48 1526 : ~OptimizerWrapped() = default;
49 :
50 : /**
51 : * Support all the interface requirements by ml::train::Optimizer
52 : */
53 :
54 : /**
55 : * @brief get Optimizer Type
56 : * @retval Optimizer type
57 : */
58 : const std::string getType() const override;
59 :
60 : /**
61 : * @brief Default allowed properties
62 : * Available for all optimizers
63 : * - learning_rate : float
64 : *
65 : * Available for SGD and Adam optimizers
66 : * - decay_rate : float,
67 : * - decay_steps : float,
68 : *
69 : * Available for Adam optimizer
70 : * - beta1 : float,
71 : * - beta2 : float,
72 : * - epsilon : float,
73 : */
74 :
75 : /**
76 : * @brief set Optimizer Parameters
77 : * @param[in] values Optimizer Parameter list
78 : * @details This function accepts vector of properties in the format -
79 : * { std::string property_name, void * property_val, ...}
80 : */
81 : void setProperty(const std::vector<std::string> &values) override;
82 :
83 : /**
84 : * @brief Set the Learning Rate Scheduler object
85 : *
86 : * @param lrs the learning rate scheduler object
87 : */
88 : int setLearningRateScheduler(
89 : std::shared_ptr<ml::train::LearningRateScheduler> lrs) override;
90 :
91 : /**
92 : * Support all the interface requirements by nntrainer::Optimizer
93 : */
94 :
95 : /**
96 : * @brief get Learning Rate for the given iteration
97 : * @param[in] iteration Iteration for the learning rate
98 : * @retval Learning rate in double
99 : * @detail the return value of this function and getLearningRate() must
100 : * match for iteration == 0.
101 : */
102 : double getLearningRate(size_t iteration);
103 :
104 : /**
105 : * @brief apply gradient to weight
106 : * @param[in] context Optimizer context
107 : */
108 : void applyGradient(RunOptimizerContext &context);
109 :
110 : /**
111 : * @brief this function helps exporting the optimizer in a predefined format,
112 : * while workarounding issue caused by templated function type eraser
113 : *
114 : * @param exporter exporter that contains exporting logic
115 : * @param method enum value to identify how it should be exported to
116 : */
117 : void exportTo(Exporter &exporter,
118 : const ml::train::ExportMethods &method) const;
119 :
120 : /**
121 : * @brief finalize optimizer.
122 : */
123 : void finalize();
124 :
125 : /**
126 : * @brief Read Training optimizer parameters from file
127 : * @param[in] file input stream file
128 : */
129 : void read(std::ifstream &file);
130 :
131 : /**
132 : * @brief Save Training optimizer parameters from file
133 : * @param[in] file output stream file
134 : */
135 : void save(std::ofstream &file);
136 :
137 : /**
138 : * @brief Get dimension of extra variables if the optimizer needs any.
139 : * @param dim Dimension of tensor to be added as a optimizer variable
140 : * @return Vector of dimensions
141 : */
142 : std::vector<TensorDim> getOptimizerVariableDim(const TensorDim &dim);
143 :
144 : /**
145 : * @brief Get the Learning Rate Scheduler object
146 : *
147 : * @return the learning rate scheduler object
148 : */
149 : nntrainer::LearningRateScheduler *getLearningRateScheduler();
150 :
151 : private:
152 : std::unique_ptr<OptimizerCore> optimizer; /**< the underlying optimizer */
153 : std::shared_ptr<nntrainer::LearningRateScheduler>
154 : lr_sched; /**< the underlying learning rate scheduler */
155 :
156 : /** @todo remove DecayRate, DecaySteps*/
157 : std::tuple<props::LearningRate, props::DecayRate, props::DecaySteps>
158 : props; /**< lr scheduler props for backward compatibility */
159 : };
160 :
161 : /**
162 : * @brief Optimizer wrapped creator with constructor for optimizer
163 : *
164 : * @params[in] type Type of the optimizer to be constructed
165 : * @params[in] properties Properties of the optimizer
166 : */
167 : std::unique_ptr<OptimizerWrapped>
168 : createOptimizerWrapped(const ml::train::OptimizerType &type,
169 : const std::vector<std::string> &properties = {});
170 :
171 : /**
172 : * @brief Optimizer wrapped creator with constructor for optimizer
173 : *
174 : * @params[in] type Type of the optimizer to be constructed
175 : * @params[in] properties Properties of the optimizer
176 : */
177 : std::unique_ptr<OptimizerWrapped>
178 : createOptimizerWrapped(const std::string &type,
179 : const std::vector<std::string> &properties = {});
180 :
181 : /**
182 : * @brief Optimizer wrapped creator with constructor for optimizer
183 : *
184 : * @params[in] type Type of the optimizer to be constructed
185 : * @params[in] properties Properties of the optimizer
186 : */
187 : std::unique_ptr<OptimizerWrapped>
188 : createOptimizerWrapped(std::unique_ptr<OptimizerCore> &&opt,
189 : const std::vector<std::string> &properties = {});
190 :
191 : } // namespace nntrainer
192 :
193 : #endif // __cpluscplus
194 : #endif // __OPTIMIZER_WRAPPER_H__
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