Move to using 'initializer' from 'initialize' to be more consistent with the tf.data APIs.

PiperOrigin-RevId: 289922514
Change-Id: I2f82cade789d707f287b9915d9856e2683aaa9f6

tensorflow/python/distribute/collective_all_reduce_strategy_test.py

@@ -357,7 +357,7 @@ class CollectiveAllReduceStrategyTestBase(
          self.cached_session(config=config,
                              target=master_target) as sess:
       iterator = distribution.make_input_fn_iterator(input_fn)
-      sess.run(iterator.initialize())
+      sess.run(iterator.initializer)
 
       for expected_value in expected_values:
         next_element = iterator.get_next()
@@ -375,7 +375,7 @@ class CollectiveAllReduceStrategyTestBase(
 
       # After re-initializing the iterator, should be able to iterate again.
       if test_reinitialize:
-        sess.run(iterator.initialize())
+        sess.run(iterator.initializer)
 
         for expected_value in expected_values:
           next_element = iterator.get_next()

tensorflow/python/distribute/distribute_lib.py

@@ -128,6 +128,7 @@ from tensorflow.python.platform import tf_logging
 from tensorflow.python.training.tracking import base as trackable
 from tensorflow.python.util import nest
 from tensorflow.python.util import tf_contextlib
+from tensorflow.python.util.deprecation import deprecated
 from tensorflow.python.util.tf_export import tf_export
 from tensorflow.tools.docs import doc_controls
 
@@ -2285,13 +2286,24 @@ class _DefaultDistributionExtended(StrategyExtendedV1):
     def get_next(self):
       return self._iterator.get_next()
 
+    @deprecated(None, "Use the iterator's `initializer` property instead.")
     def initialize(self):
+      """Initialize underlying iterators.
+
+      Returns:
+        A list of any initializer ops that should be run.
+      """
       if eager_context.executing_eagerly():
         self._iterator = self._dataset.make_one_shot_iterator()
         return []
       else:
         return [self._iterator.initializer]
 
+    @property
+    def initializer(self):
+      """Returns a list of ops that initialize the iterator."""
+      return self.initialize()
+
   # TODO(priyag): Delete this once all strategies use global batch size.
   @property
   def _global_batch_size(self):

tensorflow/python/distribute/input_lib.py

@@ -45,6 +45,7 @@ from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops.ragged import ragged_tensor
 from tensorflow.python.util import nest
+from tensorflow.python.util.deprecation import deprecated
 
 
 def get_distributed_dataset(dataset,
@@ -348,8 +349,7 @@ class DistributedIterator(object):
 class DistributedIteratorV1(DistributedIterator):
   """Input Iterator for tf.data.DatasetV1."""
 
-  # TODO(anjalisridhar): Move to using `initializer` instead to be consistent
-  # with tf.data iterator APIs.
+  @deprecated(None, "Use the iterator's `initializer` property instead.")
   def initialize(self):
     """Initialze underlying iterators.
 
@@ -360,6 +360,7 @@ class DistributedIteratorV1(DistributedIterator):
 
   @property
   def initializer(self):
+    """Returns a list of ops that initialize the iterator."""
     return self.initialize()
 
   # TODO(priyag): Remove when we switch to using `MultiDeviceIterator` for TPUs.

tensorflow/python/distribute/input_lib_test.py

@@ -171,9 +171,7 @@ class DistributedIteratorTestBase(test.TestCase):
     if iteration_type == "get_next":
       evaluate = lambda x: sess.run(x) if sess else self.evaluate(x)
       if isinstance(iterator, input_lib.DistributedIteratorV1):
-        evaluate(control_flow_ops.group(iterator.initialize()))
-      else:
-        evaluate(control_flow_ops.group(iterator._initializer))
+        evaluate(control_flow_ops.group(iterator.initializer))
 
       for expected_value in expected_values:
         next_element = iterator.get_next()
@@ -192,7 +190,7 @@ class DistributedIteratorTestBase(test.TestCase):
 
       # After re-initializing the iterator, should be able to iterate again.
       if isinstance(iterator, input_lib.DistributedIteratorV1):
-        evaluate(control_flow_ops.group(iterator.initialize()))
+        evaluate(control_flow_ops.group(iterator.initializer))
       else:
         evaluate(control_flow_ops.group(iterator._initializer))
 

tensorflow/python/distribute/keras_metrics_test.py

@@ -101,7 +101,7 @@ class KerasMetricsTest(test.TestCase, parameterized.TestCase):
               metric, args=(iterator.get_next(),)))
       batches_per_update = distribution.num_replicas_in_sync
 
-      self.evaluate(iterator.initialize())
+      self.evaluate(iterator.initializer)
       self.evaluate([v.initializer for v in metric.variables])
 
       batches_consumed = 0

tensorflow/python/distribute/metrics_v1_test.py

@@ -124,7 +124,7 @@ class MetricsV1Test(test.TestCase, parameterized.TestCase):
         # replace "distribution.num_replicas_in_sync" with "1".
         batches_per_update = distribution.num_replicas_in_sync
 
-      self.evaluate(iterator.initialize())
+      self.evaluate(iterator.initializer)
       self.evaluate(variables.local_variables_initializer())
 
       batches_consumed = 0

tensorflow/python/distribute/minimize_loss_test.py

@@ -65,7 +65,7 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase):
 
   def _get_iterator(self, strategy, input_fn):
     iterator = strategy.make_input_fn_iterator(lambda _: input_fn())
-    self.evaluate(iterator.initialize())
+    self.evaluate(iterator.initializer)
     return iterator
 
   @combinations.generate(

tensorflow/python/distribute/mirrored_variable_test.py

@@ -216,7 +216,7 @@ class MirroredVariableCreationTest(test.TestCase):
 
     iterator = distribution.make_input_fn_iterator(
         lambda _: dataset_ops.Dataset.from_tensors([[1.]]).repeat(10))
-    self.evaluate(iterator.initialize())
+    self.evaluate(iterator.initializer)
     features = iterator.get_next()
 
     with distribution.scope():

tensorflow/python/distribute/parameter_server_strategy_test.py

@@ -536,7 +536,7 @@ class ParameterServerStrategyTestBase(
          self.cached_session(config=config,
                              target=master_target) as sess:
       iterator = distribution.make_input_fn_iterator(input_fn)
-      sess.run(iterator.initialize())
+      sess.run(iterator.initializer)
 
       for expected_value in expected_values:
         next_element = iterator.get_next()
@@ -554,7 +554,7 @@ class ParameterServerStrategyTestBase(
 
       # After re-initializing the iterator, should be able to iterate again.
       if test_reinitialize:
-        sess.run(iterator.initialize())
+        sess.run(iterator.initializer)
 
         for expected_value in expected_values:
           next_element = iterator.get_next()

tensorflow/python/distribute/step_fn.py

@@ -55,7 +55,7 @@ class StandardInputStep(Step):
     self._iterator = distribution.make_input_fn_iterator(lambda _: dataset_fn())
 
   def initialize(self):
-    return self._iterator.initialize()
+    return self._iterator.initializer
 
 
 class StandardSingleLossStep(StandardInputStep):

tensorflow/python/distribute/strategy_test_lib.py

@@ -344,7 +344,7 @@ class DistributionTestBase(test.TestCase):
                               test_reinitialize=True,
                               ignore_order=False):
     evaluate = lambda x: sess.run(x) if sess else self.evaluate(x)
-    evaluate(iterator.initialize())
+    evaluate(iterator.initializer)
 
     for expected_value in expected_values:
       next_element = iterator.get_next()
@@ -362,7 +362,7 @@ class DistributionTestBase(test.TestCase):
 
     # After re-initializing the iterator, should be able to iterate again.
     if test_reinitialize:
-      evaluate(iterator.initialize())
+      evaluate(iterator.initializer)
 
       for expected_value in expected_values:
         next_element = iterator.get_next()
@@ -414,7 +414,7 @@ class DistributionTestBase(test.TestCase):
       ds = ds.batch(batch_size, drop_remainder=drop_remainder)
       i = strategy.make_dataset_iterator(ds)
 
-      self.evaluate(i.initialize())
+      self.evaluate(i.initializer)
 
       def run_and_concatenate(strategy, i):
         x, y = strategy.experimental_run(lambda z: z, i)

tensorflow/python/keras/distribute/distributed_training_utils.py

@@ -591,7 +591,7 @@ def get_iterator(dataset, distribution_strategy):
 
 def initialize_iterator(iterator, distribution_strategy):
   with distribution_strategy.scope():
-    init_op = control_flow_ops.group(iterator.initialize())
+    init_op = control_flow_ops.group(iterator.initializer)
     if not context.executing_eagerly():
       K.get_session((init_op,)).run(init_op)