Internal change

mesh_tensorflow/transformer/utils.py

@@ -1903,7 +1903,8 @@ def auto_train_steps(batch_size,
 
 
 @gin.configurable
-def get_checkpoint_iterator(checkpoint_step, model_dir, skip_until=0):
+def get_checkpoint_iterator(checkpoint_step, model_dir, skip_until=0,
+                            stop_after=None):
   """Get an iterable of checkpoint paths from a provided checkpoint step(s).
 
   Args:
@@ -1917,6 +1918,9 @@ def get_checkpoint_iterator(checkpoint_step, model_dir, skip_until=0):
     model_dir: str, directory to look for checkpoints in.
     skip_until: an integer - for "all" or "None" behavior, filter out
       checkpoint numbers that are <= skip_until.
+    stop_after: an optional integer - for "None behavior, if specified
+      stop after finding a checkpoint number that is >= stop_at. When a
+      checkpoint number == stop_at is found, it is yielded before exiting.
 
   Returns:
     An iterable which yields checkpoint paths.
@@ -1957,7 +1961,18 @@ def _filter_fn(p):
     return filter(_filter_fn,
                   [_get_checkpoint_path(s) for s in sorted(list(ckpt_steps))])
   elif checkpoint_step is None:
-    return filter(_filter_fn, tf.train.checkpoints_iterator(model_dir))
+    checkpoints_iterator = filter(
+        _filter_fn, tf.train.checkpoints_iterator(model_dir))
+    if stop_after is not None:
+      def _generate_checkpoints():
+        for p in checkpoints_iterator:
+          step = get_step_from_checkpoint_path(p)
+          if step <= stop_after:
+            yield p
+          if step >= stop_after:
+            break
+      return _generate_checkpoints()
+    return checkpoints_iterator
   elif isinstance(checkpoint_step, int):
     return [_get_checkpoint_path(_get_closest_checkpoint(checkpoint_step))]
   else: