227 lines
7.3 KiB
Python
227 lines
7.3 KiB
Python
import os
|
|
import json
|
|
import time
|
|
|
|
from pathlib import Path
|
|
from tqdm import tqdm
|
|
from torch.nn.utils import clip_grad_norm_
|
|
from torch.optim.lr_scheduler import LRScheduler
|
|
from typing import List, Optional, Protocol, TYPE_CHECKING
|
|
|
|
from khaosz.trainer.metric_util import (
|
|
grad_max,
|
|
grad_min,
|
|
grad_norm,
|
|
grad_mean,
|
|
grad_std,
|
|
grad_nan_num
|
|
)
|
|
from khaosz.trainer.checkpoint import Checkpoint
|
|
|
|
if TYPE_CHECKING:
|
|
from khaosz.trainer.train_context import TrainContext
|
|
|
|
|
|
class TrainCallback(Protocol):
|
|
"""
|
|
Callback interface for trainer.
|
|
"""
|
|
|
|
def on_train_begin(self, context: 'TrainContext'):
|
|
""" Called at the beginning of training. """
|
|
|
|
def on_train_end(self, context: 'TrainContext'):
|
|
""" Called at the end of training. """
|
|
|
|
def on_epoch_begin(self, context: 'TrainContext'):
|
|
""" Called at the beginning of each epoch. """
|
|
|
|
def on_epoch_end(self, context: 'TrainContext'):
|
|
""" Called at the end of each epoch. """
|
|
|
|
def on_step_begin(self, context: 'TrainContext'):
|
|
""" Called at the beginning of each step. """
|
|
|
|
def on_step_end(self, context: 'TrainContext'):
|
|
""" Called at the end of each step."""
|
|
|
|
def on_batch_begin(self, context: 'TrainContext'):
|
|
""" Called at the beginning of each batch. """
|
|
|
|
def on_batch_end(self, context: 'TrainContext'):
|
|
""" Called at the end of each batch. """
|
|
|
|
def on_error(self, context: 'TrainContext'):
|
|
""" Called when an error occurs during training. """
|
|
|
|
|
|
class GradientClippingCallback(TrainCallback):
|
|
"""
|
|
Gradient clipping callback for trainer.
|
|
"""
|
|
def __init__(self, max_grad_norm: float):
|
|
self.max_grad_norm = max_grad_norm
|
|
|
|
def on_step_begin(self, context: 'TrainContext'):
|
|
_ = context
|
|
clip_grad_norm_(context.model.parameters(), self.max_grad_norm)
|
|
|
|
|
|
class SchedulerCallback(TrainCallback):
|
|
"""
|
|
Scheduler callback for trainer.
|
|
"""
|
|
def __init__(self, scheduler: LRScheduler):
|
|
self.scheduler: LRScheduler = scheduler
|
|
|
|
def on_train_begin(self, context: 'TrainContext'):
|
|
for group in context.optimizer.param_groups:
|
|
if "initial_lr" not in group:
|
|
group["initial_lr"] = group["lr"]
|
|
|
|
self.scheduler = context.scheduler
|
|
|
|
def on_batch_end(self, context: 'TrainContext'):
|
|
_ = context
|
|
if self.scheduler:
|
|
self.scheduler.step()
|
|
|
|
|
|
class CheckpointCallback(TrainCallback):
|
|
"""
|
|
Checkpoint callback for trainer.
|
|
"""
|
|
def __init__(self, interval: int, save_dir: str):
|
|
self.interval = interval
|
|
self.save_dir = save_dir
|
|
self.last_ckpt_iter = 0
|
|
|
|
def _save_checkpoint(self, context: 'TrainContext'):
|
|
save_path = os.path.join(self.save_dir, f"epoch_{context.epoch}iter_{context.iteration}")
|
|
context.checkpoint = Checkpoint(
|
|
context.optimizer.state_dict(),
|
|
context.scheduler.state_dict(),
|
|
context.epoch,
|
|
context.iteration
|
|
)
|
|
context.checkpoint.save(save_path)
|
|
self.last_ckpt_iter = context.iteration
|
|
|
|
def on_batch_end(self, context: 'TrainContext'):
|
|
if context.iteration - self.last_ckpt_iter >= self.interval:
|
|
self._save_checkpoint(context)
|
|
|
|
def on_train_end(self, context: 'TrainContext'):
|
|
if context.iteration != self.last_ckpt_iter:
|
|
self._save_checkpoint(context)
|
|
|
|
def on_error(self, context: 'TrainContext'):
|
|
self._save_checkpoint(context)
|
|
|
|
|
|
class ProgressBarCallback(TrainCallback):
|
|
"""
|
|
Progress bar callback for trainer.
|
|
"""
|
|
def __init__(self, num_epoch: int):
|
|
self.num_epoch = num_epoch
|
|
self.progress_bar: tqdm = None
|
|
|
|
def on_epoch_begin(self, context: 'TrainContext'):
|
|
self.progress_bar = tqdm(
|
|
context.dataloader,
|
|
desc=f"Epoch {context.epoch+1}/{self.num_epoch}",
|
|
dynamic_ncols=True
|
|
)
|
|
|
|
def on_batch_end(self, context: 'TrainContext'):
|
|
self.progress_bar.set_postfix({
|
|
"loss": f"{context.loss:.4f}",
|
|
"lr": f"{context.optimizer.param_groups[-1]['lr']:.2e}"
|
|
})
|
|
self.progress_bar.update(1)
|
|
|
|
def on_epoch_end(self, context: 'TrainContext'):
|
|
_ = context
|
|
if self.progress_bar:
|
|
self.progress_bar.close()
|
|
|
|
|
|
class StepMonitorCallback(TrainCallback):
|
|
"""
|
|
Customizable logger callback for trainer.
|
|
|
|
This callback provides flexible logging capabilities for training metrics,
|
|
supporting multiple log formats and custom log handlers.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
log_dir: Optional[str] = None,
|
|
log_interval: int = 100,
|
|
metrics: Optional[List[str]] = None
|
|
):
|
|
"""
|
|
Args:
|
|
log_dir: Directory to save log files. If None, logs won't be saved to file.
|
|
log_interval: Log every N steps
|
|
metrics: List of metrics to log. Supported: ['loss', 'lr', 'grad_norm', 'grad_std',
|
|
grad_max', 'grad_min', 'grad_mean', 'grad_nan_num']
|
|
custom_handlers: List of custom log handler functions
|
|
json_log: Whether to save logs in JSON format
|
|
"""
|
|
|
|
self.log_dir = Path(log_dir) if log_dir else Path(os.getcwd()) / "logs"
|
|
self.log_interval = log_interval
|
|
self.metrics = metrics or ['loss', 'lr']
|
|
self.step_num = 0
|
|
|
|
self.log_dir.mkdir(parents=True, exist_ok=True)
|
|
|
|
def _handle_info(self, context: 'TrainContext'):
|
|
""" Logs training information to console and file. """
|
|
|
|
log_data = {
|
|
"timestamp": time.strftime('%Y-%m-%d %H:%M:%S'),
|
|
"epoch": context.epoch,
|
|
"iter": context.iteration,
|
|
"metrics": self.metrics,
|
|
}
|
|
|
|
for metric in self.metrics:
|
|
if metric == 'loss':
|
|
log_data[metric] = context.loss
|
|
elif metric == 'lr':
|
|
log_data[metric] = context.optimizer.param_groups[-1]['lr']
|
|
elif metric == 'grad_norm':
|
|
log_data[metric] = grad_norm(context.model)
|
|
elif metric == 'grad_std':
|
|
log_data[metric] = grad_std(context.model)
|
|
elif metric == 'grad_max':
|
|
log_data[metric] = grad_max(context.model)
|
|
elif metric == 'grad_min':
|
|
log_data[metric] = grad_min(context.model)
|
|
elif metric == 'grad_mean':
|
|
log_data[metric] = grad_mean(context.model)
|
|
elif metric == 'grad_nan_num':
|
|
log_data[metric] = grad_nan_num(context.model)
|
|
else:
|
|
raise ValueError(f"Invalid metric: {metric}")
|
|
|
|
return log_data
|
|
|
|
def _handle_log(self, context: 'TrainContext'):
|
|
""" Logs training information to console and file. """
|
|
log_data = self._handle_info(context)
|
|
try:
|
|
log_file = self.log_dir / f"log_epoch_{context.epoch}_iter_{context.iteration}.json"
|
|
with open(log_file, 'a') as f:
|
|
json.dump(log_data, f, indent=4)
|
|
except Exception:
|
|
raise
|
|
|
|
def on_step_end(self, context: 'TrainContext'):
|
|
if self.step_num % self.log_interval == 0:
|
|
self._handle_log(context)
|
|
|
|
self.step_num += 1 |