Simplify pipelines logic, requiring adapter weights to be merged in

optimum/graphcore/pipelines/__init__.py

@@ -212,19 +212,13 @@ def list_tasks() -> List[str]:
     return sorted([*{*SUPPORTED_TASKS, *TASK_ALIASES}])
 
 
-def is_generation_model(model):
-    if isinstance(model, PeftModel):
-        model = model.get_base_model()
-    return isinstance(model, IPUGenerationMixin) or isinstance(model, WhisperForConditionalGeneration)
-
-
 def get_poplar_executor(
     task: str,
-    model: Union[PreTrainedModel, PeftModel],
+    model: PreTrainedModel,
     ipu_config: Union[IPUConfig, str, dict] = None,
     fp16: bool = True,
     for_generation: bool = False,
-) -> Union[PreTrainedModel, PeftModel]:
+) -> PreTrainedModel:
     ipu_config_arg = ipu_config
 
     if isinstance(ipu_config, str):
@@ -247,7 +241,7 @@ def get_poplar_executor(
         ipu_config.enable_half_partials = False
     try:
         model = to_pipelined(model, ipu_config, force=False)
-        if model.config.is_encoder_decoder and is_generation_model(model):
+        if model.config.is_encoder_decoder and isinstance(model, IPUGenerationMixin):
             if "use_cache" not in parallelize_kwargs and model.__class__ in MODELS_SUPPORTING_KV_CACHE:
                 parallelize_kwargs["use_cache"] = True
             model.parallelize(for_generation=for_generation, **parallelize_kwargs)
@@ -290,13 +284,8 @@ def check_model_type(self, supported_models: Union[List[str], dict]):
                 supported_models_names.append(model.__name__)
         supported_models = supported_models_names
 
-    if isinstance(self.model, PeftModel):
-        model_class_name = self.model.get_base_model().__class__.__name__
-    elif isinstance(self.model, poptorch.PoplarExecutor):
-        model = self.model._user_model
-        if isinstance(model, PeftModel):
-            model = model.get_base_model()
-        model_class_name = model.__class__.__bases__[0].__name__
+    if isinstance(self.model, poptorch.PoplarExecutor):
+        model_class_name = self.model._user_model.__class__.__bases__[0].__name__
     elif isinstance(self.model, IPUGenerationMixin):
         model_class_name = self.model.__class__.__bases__[0].__name__
     else:
@@ -387,21 +376,26 @@ def pipeline(
                 break
             except ValueError:
                 continue
-    elif isinstance(model, (PreTrainedModel, PeftModel)):
+    elif isinstance(model, PeftModel):
+        raise TypeError(
+            "Instead of providing `model` as an instance of `PeftModel`, please call `merge_and_unload()` if LoRA "
+            "or equivalent to obtain the original `PreTrainedModel` back with adapter weights merged in."
+        )
+    elif isinstance(model, PreTrainedModel):
         if tokenizer is None and load_tokenizer:
             raise ValueError("If you pass a model as a PreTrainedModel, you must pass a tokenizer as well")
         if feature_extractor is None and load_feature_extractor:
             raise ValueError("If you pass a model as a PreTrainedModel, you must pass a feature extractor as well")
 
     for_generation = targeted_task in SUPPORTED_GENERATION_TASKS
-    if isinstance(model, (PreTrainedModel, PeftModel)):
+    if isinstance(model, PreTrainedModel):
         if ipu_config is None:
             ipu_config = SUPPORTED_TASKS[targeted_task]["default"]["ipu_config"]
 
         parallelize_kwargs = parallelize_kwargs or {}
         # Task of automatic speech recognition is a bit of an edge case where it separates into CTC (not generation) and seq2seq (generation).
         # This check will do for now.
-        for_generation |= is_generation_model(model)
+        for_generation |= isinstance(model, WhisperForConditionalGeneration)
         model = get_poplar_executor(
             targeted_task, model, ipu_config=ipu_config, fp16=fp16, for_generation=for_generation, **parallelize_kwargs
         )
@@ -453,7 +447,7 @@ def new_forward(self, model_inputs, *args, **kwargs):
                 if compiled_bs != input_bs:
                     poplar_executor.destroy()
 
-        if isinstance(self.model, poptorch.PoplarExecutor) or is_generation_model(self.model):
+        if isinstance(self.model, poptorch.PoplarExecutor) or isinstance(self.model, IPUGenerationMixin):
             if fp16:
                 # Support fp16
                 for key, input in model_inputs.items():