99 lines
3.7 KiB
Python
99 lines
3.7 KiB
Python
import logging
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from typing import Optional, List
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from khaosz.config import TrainConfig
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from khaosz.trainer.train_callback import (
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TrainCallback,
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ProgressBarCallback,
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CheckpointCallback,
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GradientClippingCallback,
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SchedulerCallback
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)
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from khaosz.trainer.train_context import TrainContext, TrainContextBuilder
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from khaosz.trainer.checkpoint import Checkpoint
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from khaosz.parallel.setup import spawn_parallel_fn
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logger = logging.getLogger(__name__)
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class Trainer:
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def __init__(
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self,
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train_config: TrainConfig,
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callbacks: Optional[List[TrainCallback]] = None
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):
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self.train_config = train_config
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self.callbacks = callbacks or self._get_default_callbacks()
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def _get_default_callbacks(self) -> List[TrainCallback]:
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train_config = self.train_config
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return [
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ProgressBarCallback(train_config.n_epoch),
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CheckpointCallback(train_config.checkpoint_dir, train_config.checkpoint_interval),
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GradientClippingCallback(train_config.max_grad_norm),
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SchedulerCallback(),
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]
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def _build_context(self, checkpoint: Optional[Checkpoint]) -> TrainContext:
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return (TrainContextBuilder(self.train_config)
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.with_checkpoint(checkpoint)
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.with_dataloader()
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.with_strategy()
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.build())
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def _call_callbacks(self, method_name: str, context: TrainContext):
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for callback in self.callbacks:
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method = getattr(callback, method_name, None)
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if method:
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method(context)
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def train(self, checkpoint: Optional[Checkpoint] = None):
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config = self.train_config
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spawn_parallel_fn(
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self._train_impl,
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backend=config.backend,
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world_size=config.nprocs,
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master_addr=config.master_addr,
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master_port=config.master_port,
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checkpoint=checkpoint
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)
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def _train_impl(self, checkpoint: Optional[Checkpoint] = None) -> Checkpoint:
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context = self._build_context(checkpoint)
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self._call_callbacks('on_train_begin', context)
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try:
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context.model.train()
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# 1.epoch
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for epoch in range(context.epoch, self.train_config.n_epoch):
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context.epoch = epoch
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self._call_callbacks('on_epoch_begin', context)
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for batch in context.dataloader:
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if context.iteration % self.train_config.accumulation_steps == 0:
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# 2. step
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self._call_callbacks('on_step_begin', context)
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context.optimizer.step()
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context.optimizer.zero_grad()
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self._call_callbacks('on_step_end', context)
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# 3. batch
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self._call_callbacks('on_batch_begin', context)
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loss = context.strategy(batch)
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context.loss = loss.item()
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context.iteration += 1
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# to make the loss normalized by accumulation steps
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stand_batch = self.train_config.accumulation_steps * self.train_config.nprocs
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stand_loss = loss / stand_batch
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stand_loss.backward()
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self._call_callbacks('on_batch_end', context)
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self._call_callbacks('on_epoch_end', context)
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except Exception as e:
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logger.error(f"Training failed: {str(e)}", exc_info=True)
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self._call_callbacks('on_error', context)
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raise
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finally:
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self._call_callbacks('on_train_end', context) |