support manifold in average_checkpoint.py

Summary: use PathManager to support averaging checkpoints.

Reviewed By: myleott

Differential Revision: D20725346

fbshipit-source-id: 44b91f8652826da72c82087f8fbab7ae7d179423

scripts/average_checkpoints.py

@@ -10,6 +10,8 @@
 import os
 import re
 
+from fairseq.file_io import PathManager
+
 
 def average_checkpoints(inputs):
     """Loads checkpoints from inputs and returns a model with averaged weights.
@@ -27,13 +29,14 @@ def average_checkpoints(inputs):
     new_state = None
     num_models = len(inputs)
 
-    for f in inputs:
-        state = torch.load(
-            f,
-            map_location=(
-                lambda s, _: torch.serialization.default_restore_location(s, 'cpu')
-            ),
-        )
+    for fpath in inputs:
+        with PathManager.open(fpath, 'rb') as f:
+            state = torch.load(
+                f,
+                map_location=(
+                    lambda s, _: torch.serialization.default_restore_location(s, 'cpu')
+                ),
+            )
         # Copies over the settings from the first checkpoint
         if new_state is None:
             new_state = state
@@ -74,7 +77,7 @@ def last_n_checkpoints(paths, n, update_based, upper_bound=None):
         pt_regexp = re.compile(r'checkpoint_\d+_(\d+)\.pt')
     else:
         pt_regexp = re.compile(r'checkpoint(\d+)\.pt')
-    files = os.listdir(path)
+    files = PathManager.ls(path)
 
     entries = []
     for f in files:
@@ -135,7 +138,8 @@ def main():
         print('averaging checkpoints: ', args.inputs)
 
     new_state = average_checkpoints(args.inputs)
-    torch.save(new_state, args.output)
+    with PathManager.open(args.output, 'wb') as f:
+        torch.save(new_state, f)
     print('Finished writing averaged checkpoint to {}.'.format(args.output))