`torch.distributed` group initialization for `torch_neuron` disabled when...

`torch.distributed` group initialization for `torch_neuron` disabled when `optimum-neuron` is installed (#22728)

* Make the process group initialization not happen if optimum_neuron is installed

* Add warning

* Remove list and added warning

src/transformers/training_args.py

@@ -54,8 +54,13 @@ from .utils import (
     logging,
     requires_backends,
 )
+from .utils.import_utils import is_optimum_neuron_available
 
 
+logger = logging.get_logger(__name__)
+log_levels = logging.get_log_levels_dict().copy()
+trainer_log_levels = dict(**log_levels, passive=-1)
+
 if is_torch_available():
     import torch
     import torch.distributed as dist
@@ -67,6 +72,17 @@ if is_torch_neuroncore_available(check_device=False):
     # torchrun support
     # https://github.com/pytorch/xla/pull/3609
     if os.environ.get("TORCHELASTIC_RUN_ID"):
+        if is_optimum_neuron_available():
+            logger.info(
+                "Make sure that you are performing the training with the TrainiumTrainer from optimum[neuron], this "
+                "will fail otherwise."
+            )
+        else:
+            logger.warning(
+                "Please use the TrainiumTrainer from optimum[neuron] instead of the Transformers library to perform "
+                "training on AWS Trainium instances. More information here: "
+                "https://github.com/huggingface/optimum-neuron"
+            )
             import torch_xla.distributed.xla_backend as xbn
 
             if not isinstance(torch.distributed.group.WORLD, xbn.ProcessGroupXla):
@@ -81,11 +97,6 @@ if is_sagemaker_mp_enabled():
     smp.init()
 
 
-logger = logging.get_logger(__name__)
-log_levels = logging.get_log_levels_dict().copy()
-trainer_log_levels = dict(**log_levels, passive=-1)
-
-
 def default_logdir() -> str:
     """
     Same default as PyTorch

src/transformers/utils/import_utils.py

@@ -583,6 +583,10 @@ def is_optimum_available():
     return importlib.util.find_spec("optimum") is not None
 
 
+def is_optimum_neuron_available():
+    return importlib.util.find_spec("optimum.neuron") is not None
+
+
 def is_safetensors_available():
     if is_torch_available():
         if version.parse(_torch_version) >= version.parse("1.10"):