Return default strategy from get_distribution_strategy when given "off".

Before, it returned None. But almost every use of get_distribution_strategy() assumes an actual strategy is returned and crashes when None is returned. Returning the default strategy fixes these issues and is equivalent to using no strategy, as the default strategy is always in effect when no other strategy is used.

PiperOrigin-RevId: 380951055

Author: reedwm

official/common/distribute_utils.py

@@ -102,8 +102,10 @@ def get_distribution_strategy(distribution_strategy="mirrored",
     distribution_strategy: a string specifying which distribution strategy to
       use. Accepted values are "off", "one_device", "mirrored",
       "parameter_server", "multi_worker_mirrored", and "tpu" -- case
-      insensitive. "off" means not to use Distribution Strategy; "tpu" means to
-      use TPUStrategy using `tpu_address`.
+      insensitive. "tpu" means to use TPUStrategy using `tpu_address`.
+      "off" means to use the default strategy which is obtained from
+      tf.distribute.get_strategy (for details on the default strategy, see
+      https://www.tensorflow.org/guide/distributed_training#default_strategy).
     num_gpus: Number of GPUs to run this model.
     all_reduce_alg: Optional. Specifies which algorithm to use when performing
       all-reduce. For `MirroredStrategy`, valid values are "nccl" and
@@ -141,7 +143,8 @@ def get_distribution_strategy(distribution_strategy="mirrored",
     if num_gpus > 1:
       raise ValueError("When {} GPUs are specified, distribution_strategy "
                        "flag cannot be set to `off`.".format(num_gpus))
-    return None
+    # Return the default distribution strategy.
+    return tf.distribute.get_strategy()
 
   if distribution_strategy == "tpu":
     # When tpu_address is an empty string, we communicate with local TPUs.

official/common/distribute_utils_test.py

@@ -43,7 +43,7 @@ def test_mirrored_strategy(self):
 
   def test_no_strategy(self):
     ds = distribute_utils.get_distribution_strategy('off')
-    self.assertIsNone(ds)
+    self.assertIs(ds, tf.distribute.get_strategy())
 
   def test_invalid_strategy(self):
     with self.assertRaisesRegexp(