fix the ckpt bugs when using DDP (#769)

colossalai/trainer/hooks/_checkpoint_hook.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python
 # -*- encoding: utf-8 -*-
-
+import torch
 from colossalai.logging import get_dist_logger
 
 from colossalai.registry import HOOKS
@@ -15,7 +15,12 @@ class SaveCheckpointHook(BaseHook):
 
     Args:
        interval (int, optional): Number of epochs between saving the checkpoint, defaults to 1.
+            if save_by_iter is True, this arg refers to the number of iters between saving.
        checkpoint_dir (str, optional): File name to save the checkpoint, defaults to None.
+       model (torch.nn.Module, Optional): The model to save, defaults to None. When not passing,
+            'trainer.engine.model' will be used. We encourage you to pass the model in it to avoid some
+            unexpected bugs, especially when using **DDP**.
+       save_by_iter (bool, optional): Whether saving the checkpoint by iter, default to False.
        priority (int, optional): Priority in the printing, hooks with small priority will be printed in front
             defaults to 10. If different hooks share same priority, the order of printing would
             depend on the hooks order in the hook list.
@@ -24,10 +29,14 @@ class SaveCheckpointHook(BaseHook):
     def __init__(self,
                  interval: int = 1,
                  checkpoint_dir: str = None,
+                 model: torch.nn.Module = None,
+                 save_by_iter: bool = False,
                  priority: int = 10):
         super().__init__(priority=priority)
         self.interval = interval
         self.checkpoint_dir = checkpoint_dir
+        self.model = model
+        self.save_by_iter = save_by_iter
         self.logger = get_dist_logger()
 
         # get lr scheduler from the LRSchedulerHook before train
@@ -39,6 +48,24 @@ class SaveCheckpointHook(BaseHook):
             if isinstance(hook, LRSchedulerHook):
                 self._lr_scheduler = hook.lr_scheduler
                 break
+        self.model = self.model if self.model is not None else trainer.engine.model
+
+    
+    def after_train_iter(self, trainer, output, label, loss):
+        """Saves the model after a training iter.
+        """
+        # save by interval
+        if self.save_by_iter and trainer.cur_step % self.interval == 0:
+            save_checkpoint(self.checkpoint_dir,
+                            trainer.cur_epoch,
+                            self.model,
+                            trainer.engine.optimizer,
+                            self._lr_scheduler)
+            self.logger.info(
+                f'checkpoint for iteration {trainer.cur_step} is saved to {self.checkpoint_dir}', ranks=[0])
+        else:
+            pass
+
 
     def after_train_epoch(self, trainer):
         """Saves the model after a training epoch.
@@ -47,7 +74,7 @@ class SaveCheckpointHook(BaseHook):
         if trainer.cur_epoch % self.interval == 0:
             save_checkpoint(self.checkpoint_dir,
                             trainer.cur_epoch,
-                            trainer.engine.model,
+                            self.model,
                             trainer.engine.optimizer,
                             self._lr_scheduler)
             self.logger.info(