ViperEkura
4145d35e3c
- model: nn.Module -> model_fn 工厂函数，spawn 边界只传字符串 - Trainer.train(resume_dir=path) — Checkpoint 不再通过 pickle 传递 - TrainContextBuilder.with_resume_dir(path) — 自动检测 meta.json 分流 resume/from-scratch - CheckpointCallback: 拆分 state_dict 收集（全 rank）与磁盘写入（rank-0），修复 FSDP 死锁 - serialization: load_torch 支持 broadcast，消除 _load_extra/_load_torch_broadcast - optimizer/scheduler 恢复逻辑内联到 build()，在 executor.prepare() 之后执行 - pyproject.toml: ruff exclude build/ 避免 CI 扫描构建产物
110 lines
3.8 KiB
Python
110 lines
3.8 KiB
Python
import logging
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from typing import List, Optional
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from astrai.config import TrainConfig
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from astrai.parallel.setup import spawn_parallel_fn
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from astrai.trainer.train_callback import (
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CallbackFactory,
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TrainCallback,
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)
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from astrai.trainer.train_context import TrainContext, TrainContextBuilder
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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, train_config: TrainConfig, callbacks: Optional[List[TrainCallback]] = None
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):
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self.train_config = train_config
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default_callbacks = self._get_default_callbacks()
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self.callbacks = (
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default_callbacks + callbacks if callbacks else default_callbacks
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)
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def _get_default_callbacks(self) -> List[TrainCallback]:
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cfg = self.train_config
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callbacks = [
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CallbackFactory.create(
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"gradient_checkpointing",
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modules=cfg.gradient_checkpointing_modules,
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),
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CallbackFactory.create(
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"checkpoint",
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cfg.ckpt_dir,
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cfg.ckpt_interval,
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),
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CallbackFactory.create(
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"metric_logger",
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log_dir=cfg.log_dir,
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save_interval=cfg.ckpt_interval,
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log_interval=cfg.log_interval,
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metrics=cfg.metrics,
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),
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CallbackFactory.create("progress_bar", cfg.n_epoch),
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CallbackFactory.create("gradient_clipping", cfg.max_grad_norm),
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CallbackFactory.create("validation"),
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]
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return callbacks
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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 _trainer_loop(self, resume_dir: Optional[str] = None):
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context = (
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TrainContextBuilder(self.train_config).with_resume_dir(resume_dir).build()
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)
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executor = context.executor
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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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for epoch in range(context.epoch, context.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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self._call_callbacks("on_batch_begin", context)
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with executor.accumulate(context.model):
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loss = context.strategy(batch)
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context.loss = loss.item()
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stand_loss = loss / executor.grad_accum_steps
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executor.backward(stand_loss)
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context.iteration += 1
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self._call_callbacks("on_batch_end", context)
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if executor.sync_gradients:
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self._call_callbacks("on_optimizer_step", context)
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context.optimizer.step()
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context.optimizer.zero_grad()
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if context.scheduler:
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context.scheduler.step()
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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("Training failed: %s", 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)
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def train(self, resume_dir: Optional[str] = None):
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cfg = self.train_config
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spawn_parallel_fn(
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self._trainer_loop,
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backend=cfg.backend,
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world_size=cfg.nprocs,
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master_addr=cfg.master_addr,
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master_port=cfg.master_port,
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device_type=cfg.device_type,
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start_method=cfg.start_method,
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resume_dir=resume_dir,
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)
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