98 lines
3.3 KiB
Python
98 lines
3.3 KiB
Python
from dataclasses import dataclass, field, fields
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from typing import Optional, Self, TYPE_CHECKING
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from torch.optim import Optimizer
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from torch.utils.data import DataLoader
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from khaosz.core.parameter import Checkpoint
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from khaosz.trainer.data_util import RandomSampler
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if TYPE_CHECKING:
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from khaosz.trainer.trainer import Trainer
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@dataclass
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class TrainContext:
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dataloader: DataLoader = field(default=None)
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optimizer: Optimizer = field(default=None)
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sampler: RandomSampler = field(default=None)
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epoch: int = field(default=0)
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current_iter: int = field(default=0)
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loss: float = field(default=0.0)
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checkpoint: Checkpoint = field(default=None)
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def asdict(self) -> dict:
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return {field.name: getattr(self, field.name)
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for field in fields(self)}
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class TrainContextBuilder:
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def __init__(self, trainer: 'Trainer'):
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self.trainer = trainer
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self._context = TrainContext(
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dataloader=None,
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optimizer=None,
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sampler=None,
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epoch=0,
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current_iter=0,
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loss=0.0,
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checkpoint=None
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)
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def with_checkpoint(self, checkpoint: Optional[Checkpoint]) -> Self:
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if checkpoint is None:
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checkpoint = Checkpoint(
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model=self.trainer.parameter.model,
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tokenizer=self.trainer.parameter.tokenizer,
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config=self.trainer.parameter.config,
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sampler_state=None,
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optimizer_state=None,
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loss_list=[]
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)
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self._context.checkpoint = checkpoint
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return self
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def with_sampler(self) -> Self:
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seed = self.trainer.train_config.random_seed
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sampler = RandomSampler(
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data_source=self.trainer.train_config.dataset,
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seed=seed
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)
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if self._context.checkpoint and self._context.checkpoint.sampler_state:
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sampler.load_state_dict(self._context.checkpoint.sampler_state)
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self._context.sampler = sampler
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self._context.epoch = sampler.epoch
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self._context.current_iter = sampler.current_iter
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if self._context.checkpoint:
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self._context.checkpoint.sampler_state = sampler.state_dict()
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return self
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def with_optimizer(self) -> Self:
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optimizer = self.trainer.train_config.optimizer
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if self._context.checkpoint and self._context.checkpoint.optimizer_state:
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optimizer.load_state_dict(self._context.checkpoint.optimizer_state)
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self._context.optimizer = optimizer
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if self._context.checkpoint:
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self._context.checkpoint.optimizer_state = optimizer.state_dict()
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return self
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def with_dataloader(self) -> Self:
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dataloader = DataLoader(
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self.trainer.train_config.dataset,
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batch_size=self.trainer.train_config.batch_size,
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sampler=self._context.sampler,
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num_workers=self.trainer.train_config.num_workers,
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pin_memory=self.trainer.train_config.pin_memory,
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prefetch_factor=self.trainer.train_config.prefetch_factor
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)
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self._context.dataloader = dataloader
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return self
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def build(self) -> TrainContext:
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return self._context |