in peft finetune, only the trainable parameters need to be saved (#27825)

to reduce the storage size and also save the time of checkpoint saving while using deepspeed for training
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>

src/transformers/trainer.py

@@ -212,6 +212,10 @@ if is_accelerate_available():
         from accelerate.utils import DeepSpeedSchedulerWrapper
 
 
+def _is_peft_model(model):
+    return is_peft_available() and isinstance(model, PeftModel)
+
+
 if TYPE_CHECKING:
     import optuna
 
@@ -398,13 +402,12 @@ class Trainer:
                     " to `True` to avoid any unexpected behavior such as device placement mismatching."
                 )
 
-        _is_peft_model = is_peft_available() and isinstance(model, PeftModel)
         _is_quantized_and_base_model = getattr(model, "is_quantized", False) and not getattr(
             model, "_hf_peft_config_loaded", False
         )
 
         # At this stage the model is already loaded
-        if _is_quantized_and_base_model and not _is_peft_model:
+        if _is_quantized_and_base_model and not _is_peft_model(model):
             raise ValueError(
                 "You cannot perform fine-tuning on purely quantized models. Please attach trainable adapters on top of"
                 " the quantized model to correctly perform fine-tuning. Please see: https://huggingface.co/docs/transformers/peft"
@@ -619,7 +622,7 @@ class Trainer:
         """
         unwrapped_model = unwrap_model(model)
 
-        if is_peft_available() and isinstance(unwrapped_model, PeftModel):
+        if _is_peft_model(unwrapped_model):
             embeddings = unwrapped_model.base_model.model.get_input_embeddings()
         else:
             embeddings = unwrapped_model.get_input_embeddings()
@@ -640,7 +643,7 @@ class Trainer:
 
         unwrapped_model = unwrap_model(model)
 
-        if is_peft_available() and isinstance(unwrapped_model, PeftModel):
+        if _is_peft_model(unwrapped_model):
             embeddings = unwrapped_model.base_model.model.get_input_embeddings()
         else:
             embeddings = unwrapped_model.get_input_embeddings()
@@ -696,7 +699,7 @@ class Trainer:
         if self._signature_columns is None:
             # Inspect model forward signature to keep only the arguments it accepts.
             model_to_inspect = self.model
-            if is_peft_available() and isinstance(self.model, PeftModel):
+            if _is_peft_model(self.model):
                 model_to_inspect = self.model.get_base_model()
             signature = inspect.signature(model_to_inspect.forward)
             self._signature_columns = list(signature.parameters.keys())
@@ -2114,7 +2117,7 @@ class Trainer:
                 self._issue_warnings_after_load(load_result)
 
         # Load adapters following PR # 24096
-        elif is_peft_available() and isinstance(model, PeftModel):
+        elif _is_peft_model(model):
             # If train a model using PEFT & LoRA, assume that adapter have been saved properly.
             if hasattr(model, "active_adapter") and hasattr(model, "load_adapter"):
                 if os.path.exists(resume_from_checkpoint):
@@ -2177,7 +2180,7 @@ class Trainer:
                     state_dict["_smp_is_partial"] = False
                     load_result = model.load_state_dict(state_dict, strict=True)
             else:
-                if is_peft_available() and isinstance(model, PeftModel):
+                if _is_peft_model(model):
                     # If train a model using PEFT & LoRA, assume that adapter have been saved properly.
                     if hasattr(model, "active_adapter") and hasattr(model, "load_adapter"):
                         if os.path.exists(best_adapter_model_path) or os.path.exists(best_safe_adapter_model_path):
@@ -2453,6 +2456,12 @@ class Trainer:
         elif self.is_deepspeed_enabled:
             # under zero3 model file itself doesn't get saved since it's bogus! Unless deepspeed
             # config `stage3_gather_16bit_weights_on_model_save` is True
+            accept_exclude_frozen_parameters = "exclude_frozen_parameters" in set(
+                inspect.signature(self.model_wrapped.save_checkpoint).parameters.keys()
+            )
+            if accept_exclude_frozen_parameters and _is_peft_model(self.model):
+                self.model_wrapped.save_checkpoint(output_dir, exclude_frozen_parameters=True)
+            else:
                 self.model_wrapped.save_checkpoint(output_dir)
         elif self.is_fsdp_enabled:
             # save fsdp specific ckpt for resuming from ckpt
@@ -2766,7 +2775,7 @@ class Trainer:
 
         if labels is not None:
             unwrapped_model = unwrap_model(model)
-            if is_peft_available() and isinstance(unwrapped_model, PeftModel):
+            if _is_peft_model(unwrapped_model):
                 model_name = unwrapped_model.base_model.model._get_name()
             else:
                 model_name = unwrapped_model._get_name()