aboutsummaryrefslogtreecommitdiff
path: root/libs/utils.py
blob: c237a77bebe85f56083de8eba83e22861476a839 (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
import dataclasses
import os
import random
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Iterable, Callable

import torch
from torch import nn
from torch.backends import cudnn
from torch.utils.data import Dataset, DataLoader, RandomSampler
from torch.utils.tensorboard import SummaryWriter

from libs.logging import CSV_EPOCH_LOGGER, CSV_BATCH_LOGGER, BaseBatchLogRecord, BaseEpochLogRecord, Loggers, \
    init_csv_logger, csv_logger, tensorboard_logger
from libs.schedulers import LinearWarmupAndCosineAnneal, LinearLR


@dataclass
class BaseConfig:
    @dataclass
    class DatasetConfig:
        dataset: str

    @dataclass
    class DataLoaderConfig:
        batch_size: int
        num_workers: int

    @dataclass
    class OptimConfig:
        optim: str
        lr: float

    @dataclass
    class SchedConfig:
        sched: None
        warmup_iters: int

    dataset_config: DatasetConfig
    dataloader_config: DataLoaderConfig
    optim_config: OptimConfig
    sched_config: SchedConfig

    @staticmethod
    def _config_from_args(args, dcls):
        return dcls(**{f.name: getattr(args, f.name)
                       for f in dataclasses.fields(dcls)})

    @classmethod
    def from_args(cls, args):
        dataset_config = cls._config_from_args(args, cls.DatasetConfig)
        dataloader_config = cls._config_from_args(args, cls.DataLoaderConfig)
        optim_config = cls._config_from_args(args, cls.OptimConfig)
        sched_config = cls._config_from_args(args, cls.SchedConfig)

        return cls(dataset_config, dataloader_config, optim_config, sched_config)


class Trainer(ABC):
    def __init__(
            self,
            seed: int,
            checkpoint_dir: str,
            device: torch.device,
            inf_mode: bool,
            num_iters: int,
            config: BaseConfig,
    ):
        self._args = locals()
        self._set_seed(seed)

        train_set, test_set = self._prepare_dataset(config.dataset_config)
        train_loader, test_loader = self._create_dataloader(
            train_set, test_set, inf_mode, config.dataloader_config
        )

        models = self._init_models(config.dataset_config.dataset)
        models = {n: m.to(device) for n, m in models}
        optims = dict(self._configure_optimizers(models.items(), config.optim_config))
        last_metrics = self._auto_load_checkpoint(
            checkpoint_dir, inf_mode, **(models | optims)
        )

        if last_metrics is None:
            last_step = -1
        elif isinstance(last_metrics, BaseEpochLogRecord):
            last_step = last_metrics.epoch
        elif isinstance(last_metrics, BaseBatchLogRecord):
            last_step = last_metrics.global_batch
        else:
            raise NotImplementedError(f"Unknown log type: '{type(last_metrics)}'")
        scheds = dict(self._configure_scheduler(
            optims.items(), last_step, num_iters, config.sched_config,
        ))

        self.restore_iter = last_step + 1
        self.num_iters = num_iters
        self.train_loader = train_loader
        self.test_loader = test_loader
        self.models = models
        self.optims = optims
        self.scheds = scheds
        self._inf_mode = inf_mode
        self._checkpoint_dir = checkpoint_dir

        self._custom_init_fn(config)

    @dataclass
    class BatchLogRecord(BaseBatchLogRecord):
        pass

    @dataclass
    class EpochLogRecord(BaseEpochLogRecord):
        pass

    @staticmethod
    def _set_seed(seed):
        if seed in {-1, None, ''}:
            cudnn.benchmark = True
        else:
            random.seed(seed)
            torch.manual_seed(seed)
            cudnn.deterministic = True

    def init_logger(self, log_dir):
        csv_batch_log_fname = os.path.join(log_dir, 'batch-log.csv')
        csv_batch_logger = init_csv_logger(
            name=CSV_BATCH_LOGGER,
            filename=csv_batch_log_fname,
            metric_names=[f.name for f in dataclasses.fields(self.BatchLogRecord)]
        )
        csv_epoch_logger = None
        if not self._inf_mode:
            csv_epoch_log_fname = os.path.join(log_dir, 'epoch-log.csv')
            csv_epoch_logger = init_csv_logger(
                name=CSV_EPOCH_LOGGER,
                filename=csv_epoch_log_fname,
                metric_names=[f.name for f in dataclasses.fields(self.EpochLogRecord)]
            )
        tb_logger = SummaryWriter(os.path.join(log_dir, 'runs'))

        return Loggers(csv_batch_logger, csv_epoch_logger, tb_logger)

    def dump_args(self, exclude=frozenset()) -> dict:
        return {k: v for k, v in self._args.items() if k not in {'self'} | exclude}

    @staticmethod
    @abstractmethod
    def _prepare_dataset(dataset_config: BaseConfig.DatasetConfig) -> tuple[Dataset, Dataset]:
        train_set = Dataset()
        test_set = Dataset()
        return train_set, test_set

    @staticmethod
    def _create_dataloader(
            train_set: Dataset, test_set: Dataset,
            inf_mode: bool, dataloader_config: BaseConfig.DataLoaderConfig
    ) -> tuple[DataLoader, DataLoader]:
        if inf_mode:
            inf_sampler = RandomSampler(train_set,
                                        replacement=True,
                                        num_samples=int(1e20))
            train_loader = DataLoader(train_set,
                                      sampler=inf_sampler,
                                      batch_size=dataloader_config.batch_size,
                                      num_workers=dataloader_config.num_workers)
        else:
            train_loader = DataLoader(train_set,
                                      shuffle=True,
                                      batch_size=dataloader_config.batch_size,
                                      num_workers=dataloader_config.num_workers)
        test_loader = DataLoader(test_set,
                                 shuffle=False,
                                 batch_size=dataloader_config.batch_size,
                                 num_workers=dataloader_config.num_workers)

        return train_loader, test_loader

    @staticmethod
    @abstractmethod
    def _init_models(dataset: str) -> Iterable[tuple[str, torch.nn.Module]]:
        model = torch.nn.Module()
        yield 'model_name', model

    @staticmethod
    @abstractmethod
    def _configure_optimizers(
            models: Iterable[tuple[str, torch.nn.Module]],
            optim_config: BaseConfig.OptimConfig
    ) -> Iterable[tuple[str, torch.optim.Optimizer]]:
        for model_name, model in models:
            optim = torch.optim.Optimizer([model.state_dict()], {})
            yield f"{model_name}_optim", optim

    def _auto_load_checkpoint(
            self,
            checkpoint_dir: str,
            inf_mode: bool,
            **modules
    ) -> None | BaseEpochLogRecord | BaseEpochLogRecord:
        if not os.path.exists(checkpoint_dir):
            return None
        checkpoint_files = os.listdir(checkpoint_dir)
        if not checkpoint_files:
            return None
        iter2checkpoint = {int(os.path.splitext(checkpoint_file)[0]): checkpoint_file
                           for checkpoint_file in checkpoint_files}
        restore_iter = max(iter2checkpoint.keys())
        latest_checkpoint = iter2checkpoint[restore_iter]
        checkpoint = torch.load(os.path.join(checkpoint_dir, latest_checkpoint))
        for module_name in modules.keys():
            module_state_dict = checkpoint[f"{module_name}_state_dict"]
            module = modules[module_name]
            if isinstance(module, nn.Module):
                module.load_state_dict(module_state_dict)
            else:
                module.data = module_state_dict

        last_metrics = {k: v for k, v in checkpoint.items()
                        if not k.endswith('state_dict')}
        if inf_mode:
            last_metrics = self.BatchLogRecord(**last_metrics)
        else:
            last_metrics = self.EpochLogRecord(**last_metrics)

        return last_metrics

    @staticmethod
    def _configure_scheduler(
            optims: Iterable[tuple[str, torch.optim.Optimizer]],
            last_step: int, num_iters: int, sched_config: BaseConfig.SchedConfig,
    ) -> Iterable[tuple[str, torch.optim.lr_scheduler._LRScheduler]
                  | tuple[str, None]]:
        for optim_name, optim in optims:
            if sched_config.sched == 'warmup-anneal':
                scheduler = LinearWarmupAndCosineAnneal(
                    optim,
                    warm_up=sched_config.warmup_iters / num_iters,
                    T_max=num_iters,
                    last_epoch=last_step,
                )
            elif sched_config.sched == 'linear':
                scheduler = LinearLR(
                    optim,
                    num_epochs=num_iters,
                    last_epoch=last_step
                )
            elif sched_config.sched in {None, '', 'const'}:
                scheduler = None
            else:
                raise NotImplementedError(f"Unimplemented scheduler: '{sched_config.sched}'")

            yield f"{optim_name}_sched", scheduler

    def _custom_init_fn(self, config: BaseConfig):
        pass

    @staticmethod
    @csv_logger
    @tensorboard_logger
    def log(loggers: Loggers, metrics: BaseBatchLogRecord | BaseEpochLogRecord):
        return loggers, metrics

    def save_checkpoint(self, metrics: BaseEpochLogRecord | BaseBatchLogRecord):
        os.makedirs(self._checkpoint_dir, exist_ok=True)
        checkpoint_name = os.path.join(self._checkpoint_dir, f"{metrics.epoch:06d}.pt")
        models_state_dict = {f"{model_name}_state_dict": model.state_dict()
                             if isinstance(model, nn.Module) else model.data
                             for model_name, model in self.models.items()}
        optims_state_dict = {f"{optim_name}_state_dict": optim.state_dict()
                             for optim_name, optim in self.optims.items()}
        checkpoint = metrics.__dict__ | models_state_dict | optims_state_dict
        torch.save(checkpoint, checkpoint_name)

    @abstractmethod
    def train(self, num_iters: int, loss_fn: Callable, logger: Loggers, device: torch.device):
        pass

    @abstractmethod
    def eval(self, loss_fn: Callable, device: torch.device):
        pass