deleted duplicate definitions
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samit
parent
89d0a98192
commit
862b4100d2
1 changed files with 0 additions and 131 deletions
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@ -268,47 +268,6 @@ class InferenceBackend:
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logger.error(traceback.format_exc())
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return False, None
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def load_adapter(self, base_model_name: str, adapter_path: str, adapter_name: str = None) -> bool:
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"""
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Load a LoRA adapter onto the base model if it's not already registered.
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This method is idempotent.
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"""
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if base_model_name not in self.models:
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logger.error(f"Base model {base_model_name} not loaded")
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return False
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model = self.models[base_model_name].get("model")
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if model is None:
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logger.error(f"Model object for {base_model_name} is None.")
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return False
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if adapter_name is None:
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adapter_name = adapter_path.split("/")[-1].replace(".", "_")
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# If we've loaded this adapter before, we don't need to do anything.
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if adapter_name in self.models[base_model_name].get("loaded_adapters", {}):
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logger.info(f"Adapter '{adapter_name}' is already registered. Skipping.")
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return True
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try:
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logger.info(f"Loading new adapter '{adapter_name}' from '{adapter_path}' onto {base_model_name}")
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# Unsloth modifies the model in-place and returns None. Do NOT re-assign.
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model.load_adapter(adapter_path, adapter_name=adapter_name)
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# Update our internal registry so we don't load it again.
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self.models[base_model_name]["loaded_adapters"][adapter_name] = adapter_path
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total_adapters = len(getattr(model, 'peft_config', {}))
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logger.info(f"Adapter '{adapter_name}' loaded successfully. (Total adapters on model: {total_adapters})")
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return True
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except Exception as e:
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logger.error(f"Failed to load adapter '{adapter_name}': {e}")
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import traceback
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logger.error(traceback.format_exc())
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return False
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pass
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def enable_adapter(self, base_model_name: str, adapter_name: str) -> bool:
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"""Enable specific adapter (for generation)"""
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if base_model_name not in self.models:
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@ -341,55 +300,6 @@ class InferenceBackend:
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logger.error(f"Failed to disable adapters: {e}")
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return False
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# In backend/inference.py
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def load_for_eval(self, lora_path: str, max_seq_length: int = 2048,
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dtype = None, load_in_4bit: bool = True,
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hf_token: Optional[str] = None) -> Tuple[bool, Optional[str], Optional[str]]:
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"""
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Prepare for eval: ensure base model and the specified adapter are loaded.
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"""
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try:
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from utils.models import ModelConfig
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lora_config = ModelConfig.from_lora_path(lora_path, hf_token)
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if not lora_config:
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return False, None, None
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base_model_name = lora_config.base_model
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# 1. Load the base model if it's not already in memory (this logic is correct)
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if base_model_name not in self.models or not self.models[base_model_name].get("model"):
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logger.info(f"Base model '{base_model_name}' not loaded, loading now.")
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base_config = ModelConfig.from_ui_selection(base_model_name, None, is_lora=False)
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if not self.load_model(base_config, max_seq_length, dtype, load_in_4bit, hf_token):
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return False, None, None
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else:
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logger.info(f"Base model '{base_model_name}' is already in memory.")
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self.active_model_name = base_model_name
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# 2. Delegate to our now-idempotent load_adapter function.
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# It will handle all cases: first adapter, or subsequent adapters.
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adapter_name = lora_path.split("/")[-1].replace(".", "_")
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adapter_success = self.load_adapter(
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base_model_name=base_model_name,
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adapter_path=lora_path,
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adapter_name=adapter_name
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)
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if not adapter_success:
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return False, base_model_name, None
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return True, base_model_name, adapter_name
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except Exception as e:
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logger.error(f"Error during load_for_eval: {e}")
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import traceback
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logger.error(traceback.format_exc())
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return False, None, None
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pass
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def load_for_eval(self, lora_path: str, max_seq_length: int = 2048,
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dtype = None, load_in_4bit: bool = True,
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hf_token: Optional[str] = None) -> Tuple[bool, Optional[str], Optional[str]]:
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@ -1272,47 +1182,6 @@ class InferenceBackend:
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"""Get name of currently loading model"""
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return next(iter(self.loading_models)) if self.loading_models else None
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def load_model_simple(self,
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model_path: str,
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hf_token: Optional[str] = None,
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max_seq_length: int = 2048,
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load_in_4bit: bool = True) -> bool:
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"""
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Simple model loading wrapper for chat interface.
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Accepts model path as string and handles ModelConfig creation internally.
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Args:
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model_path: Model name or path (e.g., "unsloth/llama-3-8b")
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hf_token: HuggingFace token for gated models
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max_seq_length: Maximum sequence length
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load_in_4bit: Whether to use 4-bit quantization
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Returns:
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bool: True if successful, False otherwise
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"""
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try:
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# Create config from string path
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config = ModelConfig.from_ui_selection(
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model_path,
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lora_path=None, # No LoRA for chat
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is_lora=False
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)
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# Call existing load_model with config
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return self.load_model(
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config=config,
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max_seq_length=max_seq_length,
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dtype=None, # Auto-detect
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load_in_4bit=load_in_4bit,
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hf_token=hf_token
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)
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except Exception as e:
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logger.error(f"Error in load_model_simple: {e}")
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return False
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def load_model_simple(self,
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model_path: str,
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hf_token: Optional[str] = None,
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