230 lines
7.0 KiB
Python
230 lines
7.0 KiB
Python
import os
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import json
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import time
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import torch.nn as nn
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from pathlib import Path
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from tqdm import tqdm
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from torch.nn.utils import clip_grad_norm_
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from torch.optim.lr_scheduler import LRScheduler
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from typing import Callable, List, Optional, Protocol
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from khaosz.parallel import only_on_rank
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from khaosz.trainer.metric_util import (
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grad_max,
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grad_min,
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grad_norm,
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grad_mean,
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grad_std,
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grad_nan_num
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)
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from khaosz.data.checkpoint import Checkpoint
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from khaosz.trainer.train_context import TrainContext
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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 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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_ = context
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clip_grad_norm_(context.model.parameters(), self.max_grad_norm)
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class SchedulerCallback(TrainCallback):
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"""
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Scheduler callback for trainer.
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"""
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def __init__(self):
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self.scheduler: LRScheduler = None
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def on_train_begin(self, context: TrainContext):
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for group in context.optimizer.param_groups:
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if "initial_lr" not in group:
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group["initial_lr"] = group["lr"]
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self.scheduler = context.scheduler
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def on_batch_end(self, context: TrainContext):
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_ = context
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if self.scheduler:
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self.scheduler.step()
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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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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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):
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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.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(self.save_dir, f"epoch_{context.epoch}_iter_{context.iteration}")
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state_dict = self.state_dict_fn(context.model) if self.state_dict_fn else context.model.state_dict()
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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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)
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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_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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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__(self, num_epoch: int):
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self.num_epoch = num_epoch
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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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)
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@only_on_rank(0)
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def on_batch_end(self, context: TrainContext):
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self.progress_bar.set_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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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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class StepMonitorCallback(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.step_num = 0
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self.last_save_step = 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': lambda ctx: ctx.loss,
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'lr': lambda ctx: ctx.optimizer.param_groups[-1]['lr'],
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'grad_norm': lambda ctx: grad_norm(ctx.model),
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'grad_std': lambda ctx: grad_std(ctx.model),
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'grad_max': lambda ctx: grad_max(ctx.model),
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'grad_min': lambda ctx: grad_min(ctx.model),
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'grad_mean': lambda ctx: grad_mean(ctx.model),
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'grad_nan_num': lambda ctx: grad_nan_num(ctx.model)
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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-%d %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_step_end(self, context):
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if self.step_num % 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 self.step_num - self.last_save_step >= self.save_interval:
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self._save_log(context.epoch, context.iteration)
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self.last_save_step = self.step_num
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self.step_num += 1
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def on_train_end(self, context):
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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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