261 lines
7.6 KiB
Python
261 lines
7.6 KiB
Python
import json
|
|
import os
|
|
import time
|
|
from pathlib import Path
|
|
from typing import Callable, List, Optional, Protocol, runtime_checkable
|
|
|
|
import torch.nn as nn
|
|
from torch.nn.utils import clip_grad_norm_
|
|
from tqdm import tqdm
|
|
|
|
from astrai.serialization import Checkpoint
|
|
from astrai.parallel import only_on_rank
|
|
from astrai.trainer.metric_util import (
|
|
ctx_get_grad_max,
|
|
ctx_get_grad_mean,
|
|
ctx_get_grad_min,
|
|
ctx_get_grad_nan_num,
|
|
ctx_get_grad_norm,
|
|
ctx_get_grad_std,
|
|
ctx_get_loss,
|
|
ctx_get_lr,
|
|
)
|
|
from astrai.trainer.train_context import TrainContext
|
|
from astrai.factory import BaseFactory
|
|
|
|
|
|
@runtime_checkable
|
|
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 CallbackFactory(BaseFactory[TrainCallback]):
|
|
"""Factory for registering and creating training callbacks.
|
|
|
|
Example:
|
|
@CallbackFactory.register("my_callback")
|
|
class MyCallback(TrainCallback):
|
|
...
|
|
|
|
callback = CallbackFactory.create("my_callback", **kwargs)
|
|
"""
|
|
|
|
@classmethod
|
|
def _validate_component(cls, callback_cls: type) -> None:
|
|
"""Validate that the callback class inherits from TrainCallback."""
|
|
if not issubclass(callback_cls, TrainCallback):
|
|
raise TypeError(f"{callback_cls.__name__} must inherit from TrainCallback")
|
|
|
|
|
|
@CallbackFactory.register("gradient_clipping")
|
|
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)
|
|
|
|
|
|
@CallbackFactory.register("scheduler")
|
|
class SchedulerCallback(TrainCallback):
|
|
"""
|
|
Scheduler callback for trainer.
|
|
"""
|
|
|
|
def __init__(self):
|
|
pass
|
|
|
|
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"]
|
|
|
|
def on_batch_end(self, context: TrainContext):
|
|
if context.scheduler:
|
|
context.scheduler.step()
|
|
|
|
|
|
@CallbackFactory.register("checkpoint")
|
|
class CheckpointCallback(TrainCallback):
|
|
"""
|
|
Checkpoint callback for trainer.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
save_dir: str,
|
|
interval: int,
|
|
weight_only: bool = False,
|
|
state_dict_fn: Optional[Callable[[nn.Module], dict]] = None,
|
|
):
|
|
self.save_dir = save_dir
|
|
self.interval = interval
|
|
self.weight_only = weight_only
|
|
self.state_dict_fn = state_dict_fn
|
|
self.last_ckpt_iter = 0
|
|
|
|
@only_on_rank(0)
|
|
def _save_checkpoint(self, context: TrainContext):
|
|
save_path = os.path.join(
|
|
self.save_dir, f"epoch_{context.epoch}_iter_{context.iteration}"
|
|
)
|
|
state_dict = (
|
|
self.state_dict_fn(context.model)
|
|
if self.state_dict_fn
|
|
else context.model.state_dict()
|
|
)
|
|
|
|
context.checkpoint = Checkpoint(
|
|
state_dict=state_dict, epoch=context.epoch, iteration=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)
|
|
|
|
|
|
@CallbackFactory.register("progress_bar")
|
|
class ProgressBarCallback(TrainCallback):
|
|
"""
|
|
Progress bar callback for trainer.
|
|
"""
|
|
|
|
def __init__(self, num_epoch: int):
|
|
self.num_epoch = num_epoch
|
|
self.progress_bar: tqdm = None
|
|
|
|
@only_on_rank(0)
|
|
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,
|
|
)
|
|
|
|
@only_on_rank(0)
|
|
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)
|
|
|
|
@only_on_rank(0)
|
|
def on_epoch_end(self, context: TrainContext):
|
|
_ = context
|
|
if self.progress_bar:
|
|
self.progress_bar.close()
|
|
|
|
|
|
@CallbackFactory.register("metric_logger")
|
|
class MetricLoggerCallback(TrainCallback):
|
|
def __init__(
|
|
self,
|
|
log_dir: str,
|
|
save_interval: int,
|
|
log_interval: int = 10,
|
|
metrics: List[str] = None,
|
|
):
|
|
self.last_log_iter = 0
|
|
self.save_interval = save_interval
|
|
self.log_interval = log_interval
|
|
self.metrics = metrics or ["loss", "lr"]
|
|
|
|
self.log_dir = Path(log_dir) if log_dir else Path.cwd() / "logs"
|
|
self.log_dir.mkdir(parents=True, exist_ok=True)
|
|
|
|
self.log_cache = []
|
|
|
|
self._metric_funcs = {
|
|
"loss": ctx_get_loss,
|
|
"lr": ctx_get_lr,
|
|
"grad_norm": ctx_get_grad_norm,
|
|
"grad_std": ctx_get_grad_std,
|
|
"grad_max": ctx_get_grad_max,
|
|
"grad_min": ctx_get_grad_min,
|
|
"grad_mean": ctx_get_grad_mean,
|
|
"grad_nan_num": ctx_get_grad_nan_num,
|
|
}
|
|
|
|
def _get_log_data(self, context: TrainContext):
|
|
return {
|
|
"timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
|
|
"epoch": context.epoch,
|
|
"iter": context.iteration,
|
|
**{m: self._metric_funcs[m](context) for m in self.metrics},
|
|
}
|
|
|
|
@only_on_rank(0)
|
|
def _add_log(self, log_data):
|
|
self.log_cache.append(log_data)
|
|
|
|
@only_on_rank(0)
|
|
def _save_log(self, epoch, iter):
|
|
log_file = self.log_dir / f"epoch_{epoch}_iter_{iter}_metric.jsonl"
|
|
|
|
with open(log_file, "w") as f:
|
|
for log in self.log_cache:
|
|
f.write(json.dumps(log) + "\n")
|
|
|
|
def on_batch_end(self, context):
|
|
if context.iteration % self.log_interval == 0:
|
|
log_data = self._get_log_data(context)
|
|
self._add_log(log_data)
|
|
|
|
if context.iteration - self.last_log_iter >= self.save_interval:
|
|
self._save_log(context.epoch, context.iteration)
|
|
self.last_log_iter = context.iteration
|
|
|
|
def on_train_end(self, context):
|
|
if context.iteration != self.last_log_iter:
|
|
self._save_log(context.epoch, context.iteration)
|
|
|
|
def on_error(self, context):
|
|
self._save_log(context.epoch, context.iteration)
|