99 lines
3.5 KiB
Python
99 lines
3.5 KiB
Python
import torch.nn as nn
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from torch.optim import Optimizer
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from torch.optim.lr_scheduler import LRScheduler
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from torch.utils.data import DataLoader
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from khaosz.data import ResumableDistributedSampler
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from khaosz.data.checkpoint import Checkpoint
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from khaosz.trainer.strategy import StrategyFactory, BaseStrategy
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from khaosz.config.train_config import TrainConfig
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from khaosz.parallel.setup import get_current_device, get_world_size, get_rank
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from dataclasses import dataclass, field
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from typing import Optional, Self
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@dataclass
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class TrainContext:
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model: nn.Module = field(default=None)
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strategy: BaseStrategy = field(default=None)
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dataloader: DataLoader = field(default=None)
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optimizer: Optimizer = field(default=None)
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scheduler: LRScheduler = field(default=None)
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checkpoint: Checkpoint = field(default=None)
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epoch: int = field(default=0)
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iteration: int = field(default=0)
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loss: float = field(default=0.0)
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world_size: int = field(default=1)
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rank: int = field(default=0)
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kwargs: dict = field(default_factory=dict)
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class TrainContextBuilder:
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def __init__(self, config: TrainConfig):
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self.config = config
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self._context = TrainContext(
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model=config.model,
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world_size=get_world_size(),
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rank=get_rank(),
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)
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device = get_current_device()
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self._context.model = self._context.model.to(device=device)
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if self.config.nprocs > 1:
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fn = self.config.parallel_wrapper
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self._context.model = fn(self._context.model)
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self._context.optimizer = self.config.optimizer_fn(self._context.model)
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self._context.scheduler = self.config.scheduler_fn(self._context.optimizer)
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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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state_dict=self._context.model.state_dict(),
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)
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else:
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# resume from the assigned checkpoint or assigned iteration
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self._context.epoch = max(checkpoint.epoch, self.config.start_epoch)
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self._context.iteration = max(checkpoint.iteration, self.config.start_batch)
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self._context.model.load_state_dict(checkpoint.state_dict)
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self._context.checkpoint = checkpoint
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return self
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def with_dataloader(self) -> Self:
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# fix: change batch level iteration to sample level offset
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config = self.config
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sampler_offset = self._context.iteration * config.batch_size
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resumeable_sampler = ResumableDistributedSampler(
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data_source=config.dataset,
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start_epoch=self._context.epoch,
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start_iter=sampler_offset,
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seed=config.random_seed
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)
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dataloader = DataLoader(
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config.dataset,
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batch_size=config.batch_size,
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sampler=resumeable_sampler,
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num_workers=config.num_workers,
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pin_memory=config.pin_memory,
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prefetch_factor=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 with_strategy(self) -> Self:
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self._context.strategy = StrategyFactory.load(
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model=self.config.model,
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train_type=self.config.strategy,
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device=get_current_device(),
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**self.config.extra_kwargs
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)
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return self
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def build(self) -> TrainContext:
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return self._context |