Format and warning

src/transformers/modeling_utils.py

@@ -5022,16 +5022,15 @@ def tensor_parallel(self, device_mesh):
             device_mesh (`torch.distributed.DeviceMesh`):
                 The device mesh to use for tensor parallelism.
         """
+
         # Tensor parallelize a nn.Module based on the `_tp_plan` attribute of the module.
         # No op if `_tp_plan` attribute does not exist under the module.
         # This is a helper function to be used with `model.apply` to recursively
         # parallelize a model.
         def tplize(mod: torch.nn.Module) -> None:
             tp_plan = getattr(mod, "_tp_plan", None)
             if tp_plan:
-                logger.debug(
-                    f"Applying tensor parallel to {mod.__class__.__name__}: {tp_plan}"
-                )
+                logger.debug(f"Applying tensor parallel to {mod.__class__.__name__}: {tp_plan}")
                 # In model configs, we use a neutral type (string) to specify
                 # parallel styles, here we translate them into torch TP types.
                 # Using tree_map because `tp_plan` is a dict.

src/transformers/models/llama/modeling_llama.py

@@ -813,6 +813,8 @@ def __init__(self, config: LlamaConfig):
 
         self.gradient_checkpointing = False
         self._tp_plan = config._base_model_tp_plan
+        if config.pretraining_tp != 1:
+            logger.warn("`pretraining_tp` is deprecated, please use `tensor_parallel` method instead.")
         # Initialize weights and apply final processing
         self.post_init()
 

src/transformers/pytorch_utils.py

@@ -340,9 +340,7 @@ def translate_to_torch_parallel_style(style: str):
     types.
     """
     if not isinstance(style, str):
-        raise ValueError(
-            f"Unsupported parallel style type {type(style)}, expected str"
-        )
+        raise ValueError(f"Unsupported parallel style type {type(style)}, expected str")
 
     if style == "colwise":
         return ColwiseParallel()