Sagemaker Model Parallel tensoboard writing fix (#10403)

* Added tb fix

* Removed local rank condition

* Updated reference to args

src/transformers/sagemaker/trainer_sm.py

@@ -71,11 +71,21 @@ if is_smdistributed_available():
 
 class SageMakerTrainer(Trainer):
     def __init__(self, args=None, **kwargs):
+        self.is_model_parallel_enabled = is_smdistributed_available() and args.mp_parameters != ""
         super().__init__(args=args, **kwargs)
-        self.is_model_parallel_enabled = is_smdistributed_available() and self.args.mp_parameters != ""
         if self.is_model_parallel_enabled and self.args.gradient_accumulation_steps != 1:
             raise ValueError("Gradient accumulation is not supported when model parallel is enabled.")
 
+    def is_world_process_zero(self) -> bool:
+        """
+        Whether or not this process is the global main process (when training in a distributed fashion on several
+        machines, this is only going to be :obj:`True` for one process).
+        """
+        if self.is_model_parallel_enabled:
+            return smp.rank() == 0 and smp.local_rank() == 0 and smp.mp_rank() == 0 and smp.dp_rank() == 0
+        else:
+            return super.is_world_process_zero()
+
     def _get_train_sampler(self):
         if self.is_model_parallel_enabled:
             if self.args.group_by_length: