fix 2.4.1ckpt (#3629)

Author: bigning

composer/trainer/_patch_pytorch.py

@@ -945,8 +945,7 @@ def unshard_with_sync(self):
 
 if version.parse(torch.__version__) >= version.parse('2.4.0') and version.parse(
         torch.__version__,
-) < version.parse('2.4.1'):
-    # 2.4.0 only patch
+) < version.parse('2.4.2'):
     # PyTorch issue: https://github.com/pytorch/pytorch/issues/133923
     from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE
     from typing import Mapping, Collection
@@ -1003,9 +1002,6 @@ def _traverse_obj(path: OBJ_PATH, value: STATE_DICT_ITEM) -> None:
         for key, value in state_dict.items():
             _traverse_obj((str(key),), value)
 
-if version.parse(torch.__version__) >= version.parse('2.4.0') and version.parse(
-        torch.__version__,
-) < version.parse('2.4.2'):
     # Save original FlatParamHandle.unshard to revert back to when dropping automicrobatching hooks
     from torch.distributed.fsdp._flat_param import FlatParamHandle
     original_unshard = FlatParamHandle.unshard

composer/utils/checkpoint.py

@@ -623,50 +623,41 @@ def dist_cp_load(
     load_planner: Optional[LoadPlanner] = None,
 ):
     if version.parse(torch.__version__) >= version.parse('2.4.0'):
-        if version.parse(torch.__version__) < version.parse('2.4.1'):
-            # PyTorch 2.4.0
-            from torch.distributed.checkpoint.utils import CheckpointException
-            try:
-                dist_cp.load(
-                    state_dict=state_dict,
-                    storage_reader=storage_reader,
-                    planner=load_planner,
-                )
-            except CheckpointException as e:
-                checkpoint_metadata = storage_reader.read_metadata().state_dict_metadata
-                if 'state.metadata' in checkpoint_metadata and 'state.metadata.composer_env_info.composer_version' not in checkpoint_metadata:
-                    # Torch 2.4 changed the way how state dict is flattened. It broke backward compatibility.
-                    # Torch issue: https://github.com/pytorch/pytorch/issues/133923.
-                    # We override the traverse_state_dict so that the load planner could
-                    # use the old way of flattening the state dict
-                    log.debug('Trying to load checkpointing saved before torch 2.4')
-
-                    import torch.distributed.checkpoint._nested_dict as nested_dict
-                    import torch.distributed.checkpoint._sharded_tensor_utils as sharded_tensor_util
-                    from torch.distributed.checkpoint._traverse import traverse_state_dict as traverse_2_4_0
-
-                    from composer.trainer._patch_pytorch import traverse_state_dict as backward_compatible_traverse
-
-                    nested_dict.traverse_state_dict = backward_compatible_traverse
-                    sharded_tensor_util.traverse_state_dict = backward_compatible_traverse
-
-                    dist_cp.load(
-                        state_dict=state_dict,
-                        storage_reader=storage_reader,
-                        planner=load_planner,
-                    )
-                    # Revert the override
-                    nested_dict.traverse_state_dict = traverse_2_4_0
-                    sharded_tensor_util.traverse_state_dict = traverse_2_4_0
-                else:
-                    raise e
-        else:
-            # PyTorch 2.4.1
+        from torch.distributed.checkpoint.utils import CheckpointException
+        try:
             dist_cp.load(
                 state_dict=state_dict,
                 storage_reader=storage_reader,
                 planner=load_planner,
             )
+        except CheckpointException as e:
+            checkpoint_metadata = storage_reader.read_metadata().state_dict_metadata
+            if 'state.metadata' in checkpoint_metadata and 'state.metadata.composer_env_info.composer_version' not in checkpoint_metadata:
+                # Torch 2.4 changed the way how state dict is flattened. It broke backward compatibility.
+                # Torch issue: https://github.com/pytorch/pytorch/issues/133923.
+                # We override the traverse_state_dict so that the load planner could
+                # use the old way of flattening the state dict
+                log.debug('Trying to load checkpointing saved before torch 2.4')
+
+                import torch.distributed.checkpoint._nested_dict as nested_dict
+                import torch.distributed.checkpoint._sharded_tensor_utils as sharded_tensor_util
+                from torch.distributed.checkpoint._traverse import traverse_state_dict as traverse_2_4_0
+
+                from composer.trainer._patch_pytorch import traverse_state_dict as backward_compatible_traverse
+
+                nested_dict.traverse_state_dict = backward_compatible_traverse
+                sharded_tensor_util.traverse_state_dict = backward_compatible_traverse
+
+                dist_cp.load(
+                    state_dict=state_dict,
+                    storage_reader=storage_reader,
+                    planner=load_planner,
+                )
+                # Revert the override
+                nested_dict.traverse_state_dict = traverse_2_4_0
+                sharded_tensor_util.traverse_state_dict = traverse_2_4_0
+            else:
+                raise e
     else:
         dist_cp.load_state_dict(
             state_dict=state_dict,