Use distributed_file_utils in ModelCheckpoint callback so the temporary file is saved in a subdirectory of user provided filepath.

PiperOrigin-RevId: 307471655
Change-Id: I107cc4f1278aa60718cd476f6cd813246bf35c05

tensorflow/python/keras/callbacks.py

@@ -27,7 +27,6 @@ import io
 import json
 import os
 import re
-import tempfile
 import time
 
 import numpy as np
@@ -1303,36 +1302,24 @@ class ModelCheckpoint(Callback):
   def _get_file_path(self, epoch, logs):
     """Returns the file path for checkpoint."""
     # pylint: disable=protected-access
-    if not self.model._in_multi_worker_mode(
-    ) or multi_worker_util.should_save_checkpoint():
     try:
       # `filepath` may contain placeholders such as `{epoch:02d}` and
       # `{mape:.2f}`. A mismatch between logged metrics and the path's
       # placeholders can cause formatting to fail.
-        return self.filepath.format(epoch=epoch + 1, **logs)
+      file_path = self.filepath.format(epoch=epoch + 1, **logs)
     except KeyError as e:
       raise KeyError('Failed to format this callback filepath: "{}". '
                      'Reason: {}'.format(self.filepath, e))
-    else:
-      # If this is multi-worker training, and this worker should not
-      # save checkpoint, we use a temp filepath to store a dummy checkpoint, so
-      # it writes to a file that will be removed at the end of `_save_model()`
-      # call. This is because the SyncOnReadVariable needs to be synced across
-      # all the workers in order to be read, and all workers need to initiate
-      # that.
-      self._temp_file_dir = tempfile.mkdtemp()
-      extension = os.path.splitext(self.filepath)[1]
-      return os.path.join(self._temp_file_dir, 'temp' + extension)
+    self._write_filepath = distributed_file_utils.write_filepath(
+        file_path, self.model.distribute_strategy)
+    return self._write_filepath
 
   def _maybe_remove_file(self):
     # Remove the checkpoint directory in multi-worker training where this worker
     # should not checkpoint. It is a dummy directory previously saved for sync
     # distributed training.
-
-    if (self.model._in_multi_worker_mode() and  # pylint: disable=protected-access
-        not multi_worker_util.should_save_checkpoint()):
-      file_io.delete_recursively(self._temp_file_dir)
-      del self._temp_file_dir
+    distributed_file_utils.remove_temp_dir_with_filepath(
+        self._write_filepath, self.model.distribute_strategy)
 
   def _get_most_recently_modified_file_matching_pattern(self, pattern):
     """Returns the most recently modified filepath matching pattern.

tensorflow/python/keras/distribute/multi_worker_callback_tf2_test.py

@@ -23,6 +23,7 @@ from absl.testing import parameterized
 
 from tensorflow.python.distribute import collective_all_reduce_strategy as collective_strategy
 from tensorflow.python.distribute import combinations
+from tensorflow.python.distribute import distributed_file_utils
 from tensorflow.python.distribute import multi_process_runner
 from tensorflow.python.distribute import multi_worker_test_base as test_base
 from tensorflow.python.keras import callbacks
@@ -106,6 +107,16 @@ class KerasCallbackMultiProcessTest(parameterized.TestCase, test.TestCase):
           training_state.checkpoint_exists(saving_filepath),
           test_base.is_chief())
 
+      # If it's chief, the model should be saved (`write_filepath` should
+      # simply return `saving_filepath`); if not, i.e. for non-chief workers,
+      # the temporary path generated by `write_filepath` should no longer
+      # contain the checkpoint that has been deleted.
+      test_obj.assertEqual(
+          training_state.checkpoint_exists(
+              distributed_file_utils.write_filepath(
+                  saving_filepath, model._distribution_strategy)),
+          test_base.is_chief())
+
     multi_process_runner.run(
         proc_model_checkpoint_saves_on_chief_but_not_otherwise,
         cluster_spec=test_base.create_cluster_spec(num_workers=2),