src/peft/tuners/lora/layer.py [678:716]:
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                    else:
                        self.lora_variant[active_adapter].merge_unsafe(self, active_adapter, base_layer.weight)

                    if self.lora_bias[active_adapter]:
                        base_layer.bias.data += self.lora_B[active_adapter].bias * self.scaling[active_adapter]

                self.merged_adapters.append(active_adapter)

    def unmerge(self) -> None:
        """
        This method unmerges all merged adapter layers from the base weights.
        """
        if not self.merged:
            warnings.warn("Already unmerged. Nothing to do.")
            return
        while len(self.merged_adapters) > 0:
            active_adapter = self.merged_adapters.pop()
            if active_adapter in self.lora_A.keys():
                weight = self.get_base_layer().weight
                if active_adapter not in self.lora_variant:  # vanilla LoRA
                    orig_dtype = weight.dtype
                    delta_weight = self.get_delta_weight(active_adapter)
                    weight.data -= delta_weight.to(orig_dtype)
                else:
                    unmerged = self.lora_variant[active_adapter].unmerge(self, active_adapter, weight)
                    weight.data = unmerged

                if self.lora_bias[active_adapter]:
                    self.get_base_layer().bias.data -= self.lora_B[active_adapter].bias * self.scaling[active_adapter]

    def get_delta_weight(self, adapter) -> torch.Tensor:
        """
        Compute the delta weight for the given adapter.

        Args:
            adapter (str):
                The name of the adapter for which the delta weight should be computed.
        """
        device = self.lora_B[adapter].weight.device
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src/peft/tuners/lora/layer.py [1209:1247]:
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                    else:
                        self.lora_variant[active_adapter].merge_unsafe(self, active_adapter, base_layer.weight)

                    if self.lora_bias[active_adapter]:
                        base_layer.bias.data += self.lora_B[active_adapter].bias * self.scaling[active_adapter]

                self.merged_adapters.append(active_adapter)

    def unmerge(self) -> None:
        """
        This method unmerges all merged adapter layers from the base weights.
        """
        if not self.merged:
            warnings.warn("Already unmerged. Nothing to do.")
            return
        while len(self.merged_adapters) > 0:
            active_adapter = self.merged_adapters.pop()
            if active_adapter in self.lora_A.keys():
                weight = self.get_base_layer().weight
                if active_adapter not in self.lora_variant:  # vanilla LoRA
                    orig_dtype = weight.dtype
                    delta_weight = self.get_delta_weight(active_adapter)
                    weight.data -= delta_weight.to(orig_dtype)
                else:
                    unmerged = self.lora_variant[active_adapter].unmerge(self, active_adapter, weight)
                    weight.data = unmerged

                if self.lora_bias[active_adapter]:
                    self.get_base_layer().bias.data -= self.lora_B[active_adapter].bias * self.scaling[active_adapter]

    def get_delta_weight(self, adapter) -> torch.Tensor:
        """
        Compute the delta weight for the given adapter.

        Args:
            adapter (str):
                The name of the adapter for which the delta weight should be computed.
        """
        device = self.lora_B[adapter].weight.device
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