Merge pull request #170 from PanQiWei/temporarily_replace_prepare_inp…

…uts_for_generation Replace base_model's function temporarily
huggingface · Mar 14, 2023 · 3b3fc47 · 3b3fc47
2 parents 321cbd6 + 644d68e
commit 3b3fc47
Showing 1 changed file with 67 additions and 43 deletions.
diff --git a/src/peft/peft_model.py b/src/peft/peft_model.py
@@ -513,7 +513,6 @@ class PeftModelForCausalLM(PeftModel):
     def __init__(self, model, peft_config: PeftConfig):
         super().__init__(model, peft_config)
         self.base_model_prepare_inputs_for_generation = self.base_model.prepare_inputs_for_generation
-        self.base_model.prepare_inputs_for_generation = self.prepare_inputs_for_generation
 
     def forward(
         self,
@@ -576,28 +575,38 @@ def forward(
             return self.base_model(inputs_embeds=inputs_embeds, **kwargs)
 
     def generate(self, **kwargs):
-        if not isinstance(self.peft_config, PromptLearningConfig):
-            return self.base_model.generate(**kwargs)
+        self.base_model.prepare_inputs_for_generation = self.prepare_inputs_for_generation
+        try:
+            if not isinstance(self.peft_config, PromptLearningConfig):
+                outputs = self.base_model.generate(**kwargs)
+            else:
+                if "input_ids" not in kwargs:
+                    raise ValueError("input_ids must be provided for Peft model generation")
+                if kwargs.get("attention_mask", None) is not None:
+                    # concat prompt attention mask
+                    prefix_attention_mask = torch.ones(
+                        kwargs["input_ids"].shape[0], self.peft_config.num_virtual_tokens
+                    ).to(kwargs["input_ids"].device)
+                    kwargs["attention_mask"] = torch.cat((prefix_attention_mask, kwargs["attention_mask"]), dim=1)
+
+                if kwargs.get("position_ids", None) is not None:
+                    warnings.warn(
+                        "Position ids are not supported for parameter efficient tuning. Ignoring position ids."
+                    )
+                    kwargs["position_ids"] = None
+                if kwargs.get("token_type_ids", None) is not None:
+                    warnings.warn(
+                        "Token type ids are not supported for parameter efficient tuning. Ignoring token type ids"
+                    )
+                    kwargs["token_type_ids"] = None
+
+                outputs = self.base_model.generate(**kwargs)
+        except:
+            self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation
+            raise
         else:
-            if "input_ids" not in kwargs:
-                raise ValueError("input_ids must be provided for Peft model generation")
-            if kwargs.get("attention_mask", None) is not None:
-                # concat prompt attention mask
-                prefix_attention_mask = torch.ones(
-                    kwargs["input_ids"].shape[0], self.peft_config.num_virtual_tokens
-                ).to(kwargs["input_ids"].device)
-                kwargs["attention_mask"] = torch.cat((prefix_attention_mask, kwargs["attention_mask"]), dim=1)
-
-            if kwargs.get("position_ids", None) is not None:
-                warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
-                kwargs["position_ids"] = None
-            if kwargs.get("token_type_ids", None) is not None:
-                warnings.warn(
-                    "Token type ids are not supported for parameter efficient tuning. Ignoring token type ids"
-                )
-                kwargs["token_type_ids"] = None
-
-            return self.base_model.generate(**kwargs)
+            self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation
+            return outputs
 
     def prepare_inputs_for_generation(self, *args, **kwargs):
         model_kwargs = self.base_model_prepare_inputs_for_generation(*args, **kwargs)
@@ -641,13 +650,9 @@ class PeftModelForSeq2SeqLM(PeftModel):
     def __init__(self, model, peft_config: PeftConfig):
         super().__init__(model, peft_config)
         self.base_model_prepare_inputs_for_generation = self.base_model.prepare_inputs_for_generation
-        self.base_model.prepare_inputs_for_generation = self.prepare_inputs_for_generation
         self.base_model_prepare_encoder_decoder_kwargs_for_generation = (
             self.base_model._prepare_encoder_decoder_kwargs_for_generation
         )
-        self.base_model._prepare_encoder_decoder_kwargs_for_generation = (
-            self._prepare_encoder_decoder_kwargs_for_generation
-        )
 
     def forward(
         self,
@@ -740,24 +745,43 @@ def forward(
                 )
 
     def generate(self, **kwargs):
-        if not isinstance(self.peft_config, PromptLearningConfig):
-            return self.base_model.generate(**kwargs)
-        else:
-            if "input_ids" not in kwargs:
-                raise ValueError("input_ids must be provided for Peft model generation")
-            if kwargs.get("position_ids", None) is not None:
-                warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
-                kwargs["position_ids"] = None
-            if kwargs.get("token_type_ids", None) is not None:
-                warnings.warn(
-                    "Token type ids are not supported for parameter efficient tuning. Ignoring token type ids"
-                )
-                kwargs["token_type_ids"] = None
-
-            if self.peft_config.peft_type == PeftType.PREFIX_TUNING:
-                return self.base_model.generate(**kwargs)
+        self.base_model.prepare_inputs_for_generation = self.prepare_inputs_for_generation
+        self.base_model._prepare_encoder_decoder_kwargs_for_generation = (
+            self._prepare_encoder_decoder_kwargs_for_generation
+        )
+        try:
+            if not isinstance(self.peft_config, PromptLearningConfig):
+                outputs = self.base_model.generate(**kwargs)
             else:
-                raise NotImplementedError
+                if "input_ids" not in kwargs:
+                    raise ValueError("input_ids must be provided for Peft model generation")
+                if kwargs.get("position_ids", None) is not None:
+                    warnings.warn(
+                        "Position ids are not supported for parameter efficient tuning. Ignoring position ids."
+                    )
+                    kwargs["position_ids"] = None
+                if kwargs.get("token_type_ids", None) is not None:
+                    warnings.warn(
+                        "Token type ids are not supported for parameter efficient tuning. Ignoring token type ids"
+                    )
+                    kwargs["token_type_ids"] = None
+
+                if self.peft_config.peft_type == PeftType.PREFIX_TUNING:
+                    outputs = self.base_model.generate(**kwargs)
+                else:
+                    raise NotImplementedError
+        except:
+            self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation
+            self.base_model._prepare_encoder_decoder_kwargs_for_generation = (
+                self.base_model_prepare_encoder_decoder_kwargs_for_generation
+            )
+            raise
+        else:
+            self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation
+            self.base_model._prepare_encoder_decoder_kwargs_for_generation = (
+                self.base_model_prepare_encoder_decoder_kwargs_for_generation
+            )
+            return outputs
 
     def prepare_inputs_for_generation(self, *args, **kwargs):
         model_kwargs = self.base_model_prepare_inputs_for_generation(*args, **kwargs)