def _create_new_module()

in src/peft/tuners/lora/model.py [0:0]

• 46 lines of code
• 12 McCabe index (conditional complexity)

    def _create_new_module(lora_config, adapter_name, target, **kwargs):
        # Collect dispatcher functions to decide what backend to use for the replaced LoRA layer. The order matters,
        # because the first match is always used. Therefore, the default layers should be checked last.
        dispatchers = []

        if lora_config._custom_modules:
            # Experimental custom LoRA module support. Allows users to pass a custom mapping for unsupported layer
            # types by impelementing their own LoRA layers.
            def dynamic_dispatch_func(target, adapter_name, lora_config, **kwargs):
                new_module = None

                if isinstance(target, BaseTunerLayer):
                    target_base_layer = target.get_base_layer()
                else:
                    target_base_layer = target

                for key, custom_cls in lora_config._custom_modules.items():
                    if isinstance(target_base_layer, key):
                        new_module = custom_cls(target, adapter_name, **kwargs)
                        break

                return new_module

            dispatchers.append(dynamic_dispatch_func)

        # avoid eager bnb import
        if is_bnb_available():
            from .bnb import dispatch_bnb_8bit

            dispatchers.append(dispatch_bnb_8bit)

        if is_bnb_4bit_available():
            from .bnb import dispatch_bnb_4bit

            dispatchers.append(dispatch_bnb_4bit)

        dispatchers.extend(
            [
                dispatch_eetq,
                dispatch_aqlm,
                dispatch_awq,
                dispatch_gptq,
                dispatch_hqq,
                dispatch_inc,
                dispatch_torchao,
                dispatch_megatron,
                dispatch_default,
            ]
        )

        new_module = None
        for dispatcher in dispatchers:
            new_module = dispatcher(target, adapter_name, lora_config=lora_config, **kwargs)
            if new_module is not None:  # first match wins
                break

        if new_module is None:
            # no module could be matched
            raise ValueError(
                f"Target module {target} is not supported. Currently, only the following modules are supported: "
                "`torch.nn.Linear`, `torch.nn.Embedding`, `torch.nn.Conv1d`, `torch.nn.Conv2d`, `torch.nn.Conv3d`, "
                "`transformers.pytorch_utils.Conv1D`, `torch.nn.MultiheadAttention.`."
            )

        return new_module