refactor deepspeed setup devices (#9880)

src/transformers/training_args.py

@@ -535,6 +535,20 @@ class TrainingArguments:
             self.local_rank = dist.get_local_rank()
             device = torch.device("cuda", self.local_rank)
             self._n_gpu = 1
+        elif self.deepspeed:
+            # deepspeed performs its own DDP internally, and requires the program to be started with:
+            # deepspeed  ./program.py
+            # rather than:
+            # python -m torch.distributed.launch --nproc_per_node=2 ./program.py
+            from .integrations import is_deepspeed_available
+
+            if not is_deepspeed_available():
+                raise ImportError("--deepspeed requires deepspeed: `pip install deepspeed`.")
+            import deepspeed
+
+            deepspeed.init_distributed()
+            device = torch.device("cuda", self.local_rank)
+            self._n_gpu = 1
         elif self.local_rank == -1:
             # if n_gpu is > 1 we'll use nn.DataParallel.
             # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
@@ -549,20 +563,6 @@ class TrainingArguments:
         else:
             # Here, we'll use torch.distributed.
             # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
-            #
-            # deepspeed performs its own DDP internally, and requires the program to be started with:
-            # deepspeed  ./program.py
-            # rather than:
-            # python -m torch.distributed.launch --nproc_per_node=2 ./program.py
-            if self.deepspeed:
-                from .integrations import is_deepspeed_available
-
-                if not is_deepspeed_available():
-                    raise ImportError("--deepspeed requires deepspeed: `pip install deepspeed`.")
-                import deepspeed
-
-                deepspeed.init_distributed()
-            else:
             torch.distributed.init_process_group(backend="nccl")
             device = torch.device("cuda", self.local_rank)
             self._n_gpu = 1