[Trainer] Add optional communication backends for torch.distributed when using GPU (#22247)

Update training_args.py

src/transformers/training_args.py

@@ -1641,7 +1641,10 @@ class TrainingArguments:
             # Here, we'll use torch.distributed.
             # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
             if not torch.distributed.is_initialized():
-                torch.distributed.init_process_group(backend="nccl", timeout=self.ddp_timeout_delta)
+                if self.xpu_backend and self.xpu_backend in ("mpi", "gloo"):
+                    torch.distributed.init_process_group(backend=self.xpu_backend, timeout=self.ddp_timeout_delta)
+                else:
+                    torch.distributed.init_process_group(backend="nccl", timeout=self.ddp_timeout_delta)
             device = torch.device("cuda", self.local_rank)
             self._n_gpu = 1