Repository: huggingface/transformers
Pull request: Pass state dict

# What does this PR do?

FSDP + PEFT (lora) learning process crashes (`Watchdog caught collective operation timeout`) when trainer tries to get peft state dict (via **get_peft_model_state_dict** function from [huggingface/peft](https://github.com/huggingface/peft/blob/5cdade973eb345b0a8aeda52f3c2fb4199d0a028/src/peft/utils/save_and_load.py#L51)) and doesn't provide it with model's state dict. Therefore the function tries to get this data on its own and freezes because it has no information about FSDP

This PR addresses this issue.


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Changed files:
filename: src/transformers/integrations/peft.py
patch: @@ -446,7 +446,7 @@ def active_adapter(self) -> str:
 
         return self.active_adapters()[0]
 
-    def get_adapter_state_dict(self, adapter_name: Optional[str] = None) -> dict:
+    def get_adapter_state_dict(self, adapter_name: Optional[str] = None, state_dict: Optional[dict] = None) -> dict:
         """
         If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
         official documentation: https://huggingface.co/docs/peft
@@ -457,6 +457,10 @@ def get_adapter_state_dict(self, adapter_name: Optional[str] = None) -> dict:
         Args:
             adapter_name (`str`, *optional*):
                 The name of the adapter to get the state dict from. If no name is passed, the active adapter is used.
+            state_dict (nested dictionary of `torch.Tensor`, *optional*)
+                The state dictionary of the model. Will default to `self.state_dict()`, but can be used if special
+                precautions need to be taken when recovering the state dictionary of a model (like when using model
+                parallelism).
         """
         check_peft_version(min_version=MIN_PEFT_VERSION)
 
@@ -468,7 +472,7 @@ def get_adapter_state_dict(self, adapter_name: Optional[str] = None) -> dict:
         if adapter_name is None:
             adapter_name = self.active_adapters()[0]
 
-        adapter_state_dict = get_peft_model_state_dict(self, adapter_name=adapter_name)
+        adapter_state_dict = get_peft_model_state_dict(self, state_dict=state_dict, adapter_name=adapter_name)
         return adapter_state_dict
 
     def _dispatch_accelerate_model(

filename: src/transformers/modeling_utils.py
patch: @@ -3359,7 +3359,7 @@ def save_pretrained(
                 logger.info(
                     "Detected adapters on the model, saving the model in the PEFT format, only adapter weights will be saved."
                 )
-                state_dict = model_to_save.get_adapter_state_dict()
+                state_dict = model_to_save.get_adapter_state_dict(state_dict=state_dict)
 
                 if save_peft_format:
                     logger.info(