Replace per_device with per_replica and PerDevice with PerReplica, because the...

Replace per_device with per_replica and PerDevice with PerReplica, because the PerDevice concept was renamed and doesn't exist anymore. (#6693)

* Replace per_device with per_replica and PerDevice with PerReplica, because the PerReplica concept was renamed and doesn't exist anymore.

official/recommendation/data_pipeline.py

@@ -246,7 +246,7 @@ class DatasetManager(object):
     to the TPU through a StreamingFilesDataset.
 
     Args:
-      batch_size: The per-device batch size of the dataset.
+      batch_size: The per-replica batch size of the dataset.
       epochs_between_evals: How many epochs worth of data to yield.
         (Generator mode only.)
     """

official/resnet/resnet_run_loop.py

@@ -608,7 +608,7 @@ def resnet_main(
     return input_function(
         is_training=True,
         data_dir=flags_obj.data_dir,
-        batch_size=distribution_utils.per_device_batch_size(
+        batch_size=distribution_utils.per_replica_batch_size(
             flags_obj.batch_size, flags_core.get_num_gpus(flags_obj)),
         num_epochs=num_epochs,
         dtype=flags_core.get_tf_dtype(flags_obj),
@@ -620,7 +620,7 @@ def resnet_main(
     return input_function(
         is_training=False,
         data_dir=flags_obj.data_dir,
-        batch_size=distribution_utils.per_device_batch_size(
+        batch_size=distribution_utils.per_replica_batch_size(
             flags_obj.batch_size, flags_core.get_num_gpus(flags_obj)),
         num_epochs=1,
         dtype=flags_core.get_tf_dtype(flags_obj))

official/transformer/transformer_main.py

@@ -564,7 +564,7 @@ def run_transformer(flags_obj):
       else params["default_batch_size"]))
 
   if not params["use_tpu"]:
-    params["batch_size"] = distribution_utils.per_device_batch_size(
+    params["batch_size"] = distribution_utils.per_replica_batch_size(
         params["batch_size"], num_gpus)
 
   schedule_manager = schedule.Manager(

official/utils/misc/distribution_utils.py

@@ -151,7 +151,7 @@ def get_distribution_strategy(distribution_strategy="default",
       "Unrecognized Distribution Strategy: %r" % distribution_strategy)
 
 
-def per_device_batch_size(batch_size, num_gpus):
+def per_replica_batch_size(batch_size, num_gpus):
   """For multi-gpu, batch-size must be a multiple of the number of GPUs.
 
 

official/utils/misc/distribution_utils_test.py

@@ -45,20 +45,20 @@ class GetDistributionStrategyTest(tf.test.TestCase):
       self.assertIn('GPU', device)
 
 
-class PerDeviceBatchSizeTest(tf.test.TestCase):
-  """Tests for per_device_batch_size."""
+class PerReplicaBatchSizeTest(tf.test.TestCase):
+  """Tests for per_replica_batch_size."""
 
   def test_batch_size(self):
     self.assertEquals(
-        distribution_utils.per_device_batch_size(147, num_gpus=0), 147)
+        distribution_utils.per_replica_batch_size(147, num_gpus=0), 147)
     self.assertEquals(
-        distribution_utils.per_device_batch_size(147, num_gpus=1), 147)
+        distribution_utils.per_replica_batch_size(147, num_gpus=1), 147)
     self.assertEquals(
-        distribution_utils.per_device_batch_size(147, num_gpus=7), 21)
+        distribution_utils.per_replica_batch_size(147, num_gpus=7), 21)
 
   def test_batch_size_with_remainder(self):
     with self.assertRaises(ValueError):
-        distribution_utils.per_device_batch_size(147, num_gpus=5)
+        distribution_utils.per_replica_batch_size(147, num_gpus=5)
 
 
 if __name__ == "__main__":

research/deep_speech/deep_speech.py

@@ -257,16 +257,16 @@ def run_deep_speech(_):
       model_dir=flags_obj.model_dir,
       batch_size=flags_obj.batch_size)
 
-  per_device_batch_size = distribution_utils.per_device_batch_size(
+  per_replica_batch_size = distribution_utils.per_replica_batch_size(
       flags_obj.batch_size, num_gpus)
 
   def input_fn_train():
     return dataset.input_fn(
-        per_device_batch_size, train_speech_dataset)
+        per_replica_batch_size, train_speech_dataset)
 
   def input_fn_eval():
     return dataset.input_fn(
-        per_device_batch_size, eval_speech_dataset)
+        per_replica_batch_size, eval_speech_dataset)
 
   total_training_cycle = (flags_obj.train_epochs //
                           flags_obj.epochs_between_evals)