Make StreamingDataset state_dict() more flexible (#90)

* Dataset.state_dict(num_samples, from_beginning).

* Elaborate on docstring.

* Add note.

* Rearg.

streaming/base/dataloader.py

@@ -72,7 +72,8 @@ def state_dict(self) -> Optional[Dict[str, Any]]:
         """
         if isinstance(self.dataset, StreamingDataset):
             world = World()
-            return self.dataset.state_dict(self.num_samples_yielded * world.num_ranks)
+            num_samples = self.num_samples_yielded * world.num_ranks
+            return self.dataset.state_dict(num_samples, False)
         return None
 
     def load_state_dict(self, obj: Dict[str, Any]) -> None:
@@ -84,7 +85,7 @@ def load_state_dict(self, obj: Dict[str, Any]) -> None:
             obj (Dict[str, Any]): The state.
         """
         if isinstance(self.dataset, StreamingDataset):
-            return self.dataset.load_state_dict(obj)
+            self.dataset.load_state_dict(obj)
 
     def __del__(self) -> None:
         """Terminate the workers during cleanup."""

streaming/base/dataset.py

@@ -666,23 +666,32 @@ def __iter__(self) -> Iterator[Dict[str, Any]]:
         for sample_id in self._each_sample(sample_ids):
             yield self[sample_id]
 
-    def state_dict(self, sample_in_epoch: int) -> Dict[str, Any]:
+    def state_dict(self, num_samples: int, from_beginning: bool) -> Dict[str, Any]:
         """Get a dict containing training state (called from non-worker process).
 
         This is called on rank zero.
 
+        Our stock StreamingDataLoader counts samples from start of training (from_beginning=false).
+        However, if you are always counting from the start of the epoch, set from_beginning=true.
+
         Args:
-            sample_in_epoch (int): The number of samples processed so far in the current epoch.
+            num_samples (int): The number of samples processed so far in the current epoch.
+            from_beginning (int): Whether we are counting samples from the start of this epoch, or
+                the start of just this potentially resumed training run this epoch.
 
         Returns:
             Dict[str, Any]: The state.
         """
         world = World()
         epoch = self.next_epoch - 1
         epoch, offset = self._resume(world, epoch)
+        if from_beginning:
+            sample_in_epoch = num_samples
+        else:
+            sample_in_epoch = offset + num_samples
         return {
             'epoch': epoch,
-            'sample_in_epoch': offset + sample_in_epoch,
+            'sample_in_epoch': sample_in_epoch,
             'num_canonical_nodes': self.num_canonical_nodes,
             'shuffle_seed': self.shuffle_seed
         }