Add tpu_zone and gcp_project in training_args_tf.py (#9825)

* add tpu_zone and gcp_project in training_args_tf.py

* make style

Co-authored-by: kykim <kykim>

src/transformers/training_args_tf.py

@@ -135,6 +135,12 @@ class TFTrainingArguments(TrainingArguments):
             at the next training step under the keyword argument ``mems``.
         tpu_name (:obj:`str`, `optional`):
             The name of the TPU the process is running on.
+        tpu_zone (:obj:`str`, `optional`):
+            The zone of the TPU the process is running on. If not specified, we will attempt to automatically detect
+            from metadata.
+        gcp_project (:obj:`str`, `optional`):
+            Google Cloud Project name for the Cloud TPU-enabled project. If not specified, we will attempt to
+            automatically detect from metadata.
         run_name (:obj:`str`, `optional`):
             A descriptor for the run. Notably used for wandb logging.
         xla (:obj:`bool`, `optional`):
@@ -146,6 +152,16 @@ class TFTrainingArguments(TrainingArguments):
         metadata={"help": "Name of TPU"},
     )
 
+    tpu_zone: str = field(
+        default=None,
+        metadata={"help": "Zone of TPU"},
+    )
+
+    gcp_project: str = field(
+        default=None,
+        metadata={"help": "Name of Cloud TPU-enabled project"},
+    )
+
     poly_power: float = field(
         default=1.0,
         metadata={"help": "Power for the Polynomial decay LR scheduler."},
@@ -173,7 +189,9 @@ class TFTrainingArguments(TrainingArguments):
         else:
             try:
                 if self.tpu_name:
-                    tpu = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name)
+                    tpu = tf.distribute.cluster_resolver.TPUClusterResolver(
+                        self.tpu_name, zone=self.tpu_zone, project=self.gcp_project
+                    )
                 else:
                     tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
             except ValueError: