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strip extra debug statements

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This commit is contained in:
Roland Tannous 2026-02-14 19:23:51 +00:00
commit be3934860f
1 changed files with 10 additions and 23 deletions
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33
studio/backend/core/inference/inference.py
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@ -177,31 +177,23 @@ class InferenceBackend:
return False
model = self.models[base_model_name].get("model")
print(f"[DEBUG] revert_to_base_model called. Model type BEFORE: {model.__class__.__name__}")
print(f"[DEBUG] Is PeftModel? {isinstance(model, (PeftModel, PeftModelForCausalLM))}")
print(f"[DEBUG] Has peft_config? {hasattr(model, 'peft_config')}, keys={list(getattr(model, 'peft_config', {}).keys())}")
try:
# Step 1: Unload the adapter weights. This returns the base model object.
# This step is only necessary if the model is currently a PeftModel instance.
# Step 1: Unload the adapter weights if model is a PeftModel.
if isinstance(model, (PeftModel, PeftModelForCausalLM)):
print("[DEBUG] Model IS a PeftModel. Calling model.unload()...")
logger.info(f"Unloading LoRA adapters from '{base_model_name}'...")
unwrapped_base_model = model.unload()
self.models[base_model_name]["model"] = unwrapped_base_model
model = unwrapped_base_model # Continue with the unwrapped model
print(f"[DEBUG] Model type AFTER unload: {model.__class__.__name__}")
else:
print(f"[DEBUG] Model is NOT a PeftModel, skipping unload.")
model = unwrapped_base_model
# Step 2: Clear any lingering peft_config from the unwrapped model.
# After model.unload(), the base model may still carry a peft_config
# attribute (with 'default' key). Removing it entirely ensures
# load_adapter() won't warn about "multiple adapters".
# attribute. Removing it ensures PeftModel.from_pretrained() gets
# a clean base model without "multiple adapters" warnings.
if hasattr(model, 'peft_config'):
print(f"[DEBUG] Clearing lingering peft_config: {list(model.peft_config.keys())}")
del model.peft_config
print(f"[DEBUG] Model type FINAL: {model.__class__.__name__}, has peft_config={hasattr(model, 'peft_config')}. Reverted to clean base state.")
logger.info(f"Model '{base_model_name}' reverted to clean base state.")
return True
except Exception as e:
@ -209,7 +201,6 @@ class InferenceBackend:
import traceback
logger.error(traceback.format_exc())
return False
pass
def activate_lora_adapter(self, base_model_name: str, lora_path: str) -> Tuple[bool, Optional[str]]:
"""
@ -218,24 +209,20 @@ class InferenceBackend:
"""
model = self.models[base_model_name].get("model")
adapter_name_to_load = lora_path.split("/")[-1].replace(".", "_")
print(f"[DEBUG] activate_lora_adapter called. base_model_name='{base_model_name}', lora_path='{lora_path}'")
print(f"[DEBUG] adapter_name_to_load='{adapter_name_to_load}'")
print(f"[DEBUG] Model type BEFORE: {model.__class__.__name__}")
try:
# Use PeftModel.from_pretrained to wrap the clean base model with the adapter.
# This is the correct approach after model.unload() + del peft_config.
print(f"[DEBUG] Calling PeftModel.from_pretrained(model, '{lora_path}', adapter_name='{adapter_name_to_load}')...")
logger.info(f"Loading LoRA adapter '{adapter_name_to_load}' from '{lora_path}'...")
model = PeftModel.from_pretrained(model, lora_path, adapter_name=adapter_name_to_load)
self.models[base_model_name]["model"] = model
print(f"[DEBUG] Model type AFTER: {model.__class__.__name__}")
print(f"[DEBUG] activate_lora_adapter SUCCESS.")
logger.info(f"LoRA adapter '{adapter_name_to_load}' activated successfully.")
return True, adapter_name_to_load
except Exception as e:
print(f"[DEBUG] activate_lora_adapter FAILED: {e}")
logger.error(f"Failed to activate LoRA adapter '{adapter_name_to_load}': {e}")
import traceback
traceback.print_exc()
logger.error(traceback.format_exc())
return False, None
def load_adapter(self, base_model_name: str, adapter_path: str, adapter_name: str = None) -> bool: