356 lines
11 KiB
Python
356 lines
11 KiB
Python
import json
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import logging
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import os
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import sys
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import time
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from pathlib import Path
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from typing import IO, Callable, List, Optional, Protocol, runtime_checkable
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import torch
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import torch.distributed as dist
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import torch.nn as nn
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from torch.nn.utils import clip_grad_norm_
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from torch.utils.checkpoint import checkpoint as torch_checkpoint
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from tqdm import tqdm
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from astrai.factory import BaseFactory
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from astrai.parallel import only_on_rank
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from astrai.parallel.setup import get_current_device
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from astrai.serialization import Checkpoint
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from astrai.trainer.metric_util import (
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ctx_get_grad_max,
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ctx_get_grad_mean,
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ctx_get_grad_min,
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ctx_get_grad_nan_num,
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ctx_get_grad_norm,
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ctx_get_grad_std,
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ctx_get_loss,
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ctx_get_lr,
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ctx_get_val_loss,
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)
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from astrai.trainer.train_context import TrainContext
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logger = logging.getLogger(__name__)
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@runtime_checkable
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class TrainCallback(Protocol):
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"""
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Callback interface for trainer.
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"""
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def on_train_begin(self, context: TrainContext):
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"""Called at the beginning of training."""
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def on_train_end(self, context: TrainContext):
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"""Called at the end of training."""
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def on_epoch_begin(self, context: TrainContext):
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"""Called at the beginning of each epoch."""
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def on_epoch_end(self, context: TrainContext):
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"""Called at the end of each epoch."""
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def on_step_begin(self, context: TrainContext):
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"""Called at the beginning of each step."""
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def on_step_end(self, context: TrainContext):
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"""Called at the end of each step."""
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def on_batch_begin(self, context: TrainContext):
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"""Called at the beginning of each batch."""
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def on_batch_end(self, context: TrainContext):
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"""Called at the end of each batch."""
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def on_error(self, context: TrainContext):
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"""Called when an error occurs during training."""
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class CallbackFactory(BaseFactory[TrainCallback]):
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"""Factory for registering and creating training callbacks.
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Example:
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@CallbackFactory.register("my_callback")
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class MyCallback(TrainCallback):
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...
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callback = CallbackFactory.create("my_callback", **kwargs)
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"""
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@CallbackFactory.register("gradient_clipping")
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class GradientClippingCallback(TrainCallback):
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"""
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Gradient clipping callback for trainer.
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"""
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def __init__(self, max_grad_norm: float):
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self.max_grad_norm = max_grad_norm
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def on_step_begin(self, context: TrainContext):
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clip_grad_norm_(context.model.parameters(), self.max_grad_norm)
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@CallbackFactory.register("gradient_checkpointing")
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class GradientCheckpointingCallback(TrainCallback):
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"""
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Activation checkpointing callback — trades compute for memory
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by recomputing specified module activations during the backward pass.
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Args:
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modules: Module types to apply checkpointing to.
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"""
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def __init__(self, modules: Optional[List[type]] = None):
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self.modules = tuple(modules) if modules else ()
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def _enable(self, module: nn.Module):
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if self.modules and isinstance(module, self.modules):
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fn = module.forward
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module._original_forward = fn
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module.forward = lambda *a, **kw: torch_checkpoint(
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fn, *a, use_reentrant=False, **kw
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)
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@staticmethod
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def _disable(module: nn.Module):
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if hasattr(module, "_original_forward"):
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module.forward = module._original_forward
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del module._original_forward
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def on_train_begin(self, context: TrainContext):
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context.model.apply(self._enable)
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logger.info("Gradient checkpointing enabled")
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def on_train_end(self, context: TrainContext):
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context.model.apply(self._disable)
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@CallbackFactory.register("checkpoint")
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class CheckpointCallback(TrainCallback):
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"""
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Checkpoint callback for trainer.
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"""
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extra_keys = ("optimizer", "scheduler")
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def __init__(
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self,
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save_dir: str,
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interval: int,
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weight_only: bool = False,
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state_dict_fn: Optional[Callable[[nn.Module], dict]] = None,
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save_extra_fn: Optional[Callable[["TrainContext"], dict]] = None,
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load_extra_fn: Optional[Callable[[dict, "TrainContext"], None]] = None,
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):
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self.save_dir = save_dir
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self.interval = interval
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self.weight_only = weight_only
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self.state_dict_fn = state_dict_fn
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self.save_extra_fn = save_extra_fn or CheckpointCallback.save_extra
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self.load_extra_fn = load_extra_fn or CheckpointCallback.load_extra
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self.last_ckpt_iter = 0
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@only_on_rank(0)
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def _save_checkpoint(self, context: TrainContext):
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save_path = os.path.join(
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self.save_dir, f"epoch_{context.epoch}_iter_{context.iteration}"
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)
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state_dict = (
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self.state_dict_fn(context.model)
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if self.state_dict_fn
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else context.model.state_dict()
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)
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extra = self.save_extra_fn(context)
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context.checkpoint = Checkpoint(
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state_dict=state_dict,
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epoch=context.epoch,
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iteration=context.iteration,
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extra=extra,
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meta=context.config.to_dict(),
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)
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context.checkpoint.save(save_path)
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self.last_ckpt_iter = context.iteration
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def on_train_begin(self, context: TrainContext):
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if context.checkpoint and context.checkpoint.extra:
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self.load_extra_fn(context.checkpoint.extra, context)
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def on_batch_end(self, context: TrainContext):
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if context.iteration - self.last_ckpt_iter >= self.interval:
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self._save_checkpoint(context)
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def on_train_end(self, context: TrainContext):
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if context.iteration != self.last_ckpt_iter:
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self._save_checkpoint(context)
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def on_error(self, context: TrainContext):
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self._save_checkpoint(context)
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@staticmethod
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def save_extra(context: TrainContext) -> dict:
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extra = {}
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for name in CheckpointCallback.extra_keys:
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obj = getattr(context, name, None)
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if obj:
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extra[name] = obj.state_dict()
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return extra
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@staticmethod
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def load_extra(extra: dict, context: TrainContext):
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for name in CheckpointCallback.extra_keys:
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if name in extra:
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getattr(context, name).load_state_dict(extra[name])
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@CallbackFactory.register("progress_bar")
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class ProgressBarCallback(TrainCallback):
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"""
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Progress bar callback for trainer.
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"""
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def __init__(
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self, num_epoch: int, log_interval: int = 100, file: IO[str] = sys.stdout
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):
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self.num_epoch = num_epoch
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self.log_interval = log_interval
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self.file = file
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self.progress_bar: tqdm = None
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@only_on_rank(0)
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def on_epoch_begin(self, context: TrainContext):
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self.progress_bar = tqdm(
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context.dataloader,
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desc=f"Epoch {context.epoch + 1}/{self.num_epoch}",
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dynamic_ncols=True,
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file=self.file,
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)
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@only_on_rank(0)
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def on_batch_end(self, context: TrainContext):
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postfix = {
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"loss": f"{context.loss:.4f}",
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"lr": f"{context.optimizer.param_groups[-1]['lr']:.2e}",
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}
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if context.val_loss > 0:
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postfix["val_loss"] = f"{context.val_loss:.4f}"
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self.progress_bar.set_postfix(postfix)
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self.progress_bar.update(1)
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@only_on_rank(0)
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def on_epoch_end(self, context: TrainContext):
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_ = context
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if self.progress_bar:
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self.progress_bar.close()
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@CallbackFactory.register("metric_logger")
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class MetricLoggerCallback(TrainCallback):
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def __init__(
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self,
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log_dir: str,
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save_interval: int,
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log_interval: int = 10,
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metrics: List[str] = None,
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):
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self.last_log_iter = 0
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self.save_interval = save_interval
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self.log_interval = log_interval
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self.metrics = metrics or ["loss", "lr"]
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self.log_dir = Path(log_dir) if log_dir else Path.cwd() / "logs"
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self.log_dir.mkdir(parents=True, exist_ok=True)
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self.log_cache = []
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self._metric_funcs = {
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"loss": ctx_get_loss,
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"lr": ctx_get_lr,
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"val_loss": ctx_get_val_loss,
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"grad_norm": ctx_get_grad_norm,
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"grad_std": ctx_get_grad_std,
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"grad_max": ctx_get_grad_max,
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"grad_min": ctx_get_grad_min,
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"grad_mean": ctx_get_grad_mean,
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"grad_nan_num": ctx_get_grad_nan_num,
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}
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def _get_log_data(self, context: TrainContext):
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return {
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"timestamp": time.strftime("%Y-%m-%dT%H:%M:%S"),
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"epoch": context.epoch,
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"iter": context.iteration,
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**{m: self._metric_funcs[m](context) for m in self.metrics},
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}
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@only_on_rank(0)
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def _add_log(self, log_data):
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self.log_cache.append(log_data)
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@only_on_rank(0)
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def _save_log(self, epoch, iter):
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log_file = self.log_dir / f"epoch_{epoch}_iter_{iter}_metric.jsonl"
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with open(log_file, "w") as f:
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for log in self.log_cache:
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f.write(json.dumps(log) + "\n")
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def on_batch_end(self, context):
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if context.iteration % self.log_interval == 0:
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log_data = self._get_log_data(context)
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self._add_log(log_data)
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if context.iteration - self.last_log_iter >= self.save_interval:
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self._save_log(context.epoch, context.iteration)
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self.last_log_iter = context.iteration
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def on_train_end(self, context):
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if context.iteration != self.last_log_iter:
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self._save_log(context.epoch, context.iteration)
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def on_error(self, context):
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self._save_log(context.epoch, context.iteration)
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@CallbackFactory.register("validation")
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class ValidationCallback(TrainCallback):
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def _run_validation(self, context: TrainContext):
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context.model.eval()
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total_loss = 0.0
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num_batches = 0
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with torch.no_grad():
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for batch in context.val_dataloader:
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loss = context.strategy(batch)
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total_loss += loss.item()
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num_batches += 1
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avg_loss = total_loss / max(num_batches, 1)
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if context.world_size > 1 and dist.is_initialized():
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loss_tensor = torch.tensor([avg_loss], device=get_current_device())
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dist.all_reduce(loss_tensor, op=dist.ReduceOp.AVG)
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avg_loss = loss_tensor.item()
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context.val_loss = avg_loss
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context.model.train()
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step_count = context.iteration // context.config.grad_accum_steps
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logger.info(
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f"Epoch {context.epoch + 1}, Step {step_count}, Val Loss: {avg_loss:.4f}"
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)
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def on_step_end(self, context: TrainContext):
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if context.val_dataloader is None:
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return
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cfg = context.config
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if cfg.val_step <= 0:
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return
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step_count = context.iteration // cfg.grad_accum_steps
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if step_count % cfg.val_step == 0:
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self._run_validation(context)
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