fix monkey patch for synthetic data for resnet keras model.

PiperOrigin-RevId: 263854996

official/utils/misc/distribution_utils.py

@@ -205,38 +205,64 @@ class SyntheticDataset(object):
   """A dataset that generates synthetic data on each device."""
 
   def __init__(self, dataset, split_by=1):
-    self._input_data = {}
     # dataset.take(1) doesn't have GPU kernel.
     with tf.device('device:CPU:0'):
       tensor = tf.data.experimental.get_single_element(dataset.take(1))
     flat_tensor = tf.nest.flatten(tensor)
     variable_data = []
-    self._initializers = []
+    initializers = []
     for t in flat_tensor:
       rebatched_t = tf.split(t, num_or_size_splits=split_by, axis=0)[0]
       assert rebatched_t.shape.is_fully_defined(), rebatched_t.shape
-      v = tf.compat.v1.get_local_variable(self.random_name(),
+      v = tf.compat.v1.get_local_variable(self._random_name(),
                                           initializer=rebatched_t)
       variable_data.append(v)
-      self._initializers.append(v.initializer)
-    self._input_data = tf.nest.pack_sequence_as(tensor, variable_data)
+      initializers.append(v.initializer)
+    input_data = tf.nest.pack_sequence_as(tensor, variable_data)
+    self._iterator = SyntheticIterator(input_data, initializers)
+
+  def _random_name(self, size=10, chars=string.ascii_uppercase + string.digits):
+    return ''.join(random.choice(chars) for _ in range(size))
+
+  def __iter__(self):
+    return self._iterator
+
+  def make_one_shot_iterator(self):
+    return self._iterator
+
+  def make_initializable_iterator(self):
+    return self._iterator
+
+
+class SyntheticIterator(object):
+  """A dataset that generates synthetic data on each device."""
+
+  def __init__(self, input_data, initializers):
+    self._input_data = input_data
+    self._initializers = initializers
 
   def get_next(self):
     return self._input_data
 
+  def next(self):
+    return self.__next__()
+
+  def __next__(self):
+    try:
+      return self.get_next()
+    except tf.errors.OutOfRangeError:
+      raise StopIteration
+
   def initialize(self):
     if tf.executing_eagerly():
       return tf.no_op()
     else:
       return self._initializers
 
-  def random_name(self, size=10, chars=string.ascii_uppercase + string.digits):
-    return ''.join(random.choice(chars) for _ in range(size))
-
 
 def _monkey_patch_dataset_method(strategy):
   """Monkey-patch `strategy`'s `make_dataset_iterator` method."""
-  def make_dataset_iterator(self, dataset):
+  def make_dataset(self, dataset):
     tf.compat.v1.logging.info('Using pure synthetic data.')
     with self.scope():
       if self.extended._global_batch_size:  # pylint: disable=protected-access
@@ -244,13 +270,21 @@ def _monkey_patch_dataset_method(strategy):
       else:
         return SyntheticDataset(dataset)
 
-  strategy.org_make_dataset_iterator = strategy.make_dataset_iterator
-  strategy.make_dataset_iterator = make_dataset_iterator
+  def make_iterator(self, dataset):
+    dist_dataset = make_dataset(self, dataset)
+    return iter(dist_dataset)
+
+  strategy.orig_make_dataset_iterator = strategy.make_dataset_iterator
+  strategy.make_dataset_iterator = make_iterator
+  strategy.orig_distribute_dataset = strategy.experimental_distribute_dataset
+  strategy.experimental_distribute_dataset = make_dataset
 
 
 def _undo_monkey_patch_dataset_method(strategy):
-  if hasattr(strategy, 'org_make_dataset_iterator'):
-    strategy.make_dataset_iterator = strategy.org_make_dataset_iterator
+  if hasattr(strategy, 'orig_make_dataset_iterator'):
+    strategy.make_dataset_iterator = strategy.orig_make_dataset_iterator
+  if hasattr(strategy, 'orig_distribute_dataset'):
+    strategy.make_dataset_iterator = strategy.orig_distribute_dataset
 
 
 def set_up_synthetic_data():