def save_mutated_as_lora()

in optimum/neuron/peft/peft_model.py [0:0]

• 35 lines of code
• 8 McCabe index (conditional complexity)

        def save_mutated_as_lora(peft_config, path_initial_model_for_weight_conversion, output_state_dict, kwargs):
            if peft_config.use_rslora and (peft_config.rank_pattern or peft_config.alpha_pattern):
                msg = (
                    "Passing `path_initial_model_for_weight_conversion` to `save_pretrained` is not supported when "
                    "using `rank_pattern` or `alpha_pattern` at the same time as `use_rslora=True`."
                )
                raise ValueError(msg)

            if not any(
                str(peft_config.init_lora_weights).lower().startswith(prefix) for prefix in ["pissa", "olora", "true"]
            ):
                warnings.warn(
                    "`path_initial_model_for_weight_conversion` only works for converting a PiSSA or OLoRA adapter to "
                    "a LoRA adapter"
                )
            initial_adapter_name = os.path.basename(path_initial_model_for_weight_conversion)
            try:
                self.load_adapter(
                    os.path.dirname(path_initial_model_for_weight_conversion),
                    subfolder=initial_adapter_name,
                    adapter_name=initial_adapter_name,
                )
                is_pissa = str(self.peft_config[initial_adapter_name].init_lora_weights).lower().startswith("pissa")
                is_olora = str(self.peft_config[initial_adapter_name].init_lora_weights).lower() == "olora"
                if is_pissa or is_olora:
                    raise ValueError(
                        "The `init_lora_weights` parameter of the initial adapter should be set to `True`. "
                        "Otherwise, `self.load_adapter` will subtract the decomposed values again based on the "
                        "residual model."
                    )
                output_state_dict = self.base_model.subtract_mutated_init(
                    output_state_dict, initial_adapter_name, kwargs
                )
            finally:
                self.delete_adapter(initial_adapter_name)
            return output_state_dict