def get_delta_weight_bone()

in src/peft/tuners/bone/layer.py [0:0]

• 43 lines of code
• 10 McCabe index (conditional complexity)

    def get_delta_weight_bone(self, adapter, orig_weight, re: bool = False) -> 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.bone_block[adapter].device
        dtype = self.bone_block[adapter].dtype
        # In case users wants to merge the adapter weights that are in
        # (b)float16 while being on CPU, we need to cast the weights to float32, perform the merge and then cast back to
        # (b)float16 because some CPUs have slow bf16/fp16 matmuls.
        cast_to_fp32 = device.type == "cpu" and (dtype == torch.float16 or dtype == torch.bfloat16)

        weight_bone = self.bone_block[adapter]

        if cast_to_fp32:
            weight_bone = weight_bone.float()

        in_features = orig_weight.size(-1)
        r = weight_bone.size(0)
        if in_features % r != 0:
            last_size = in_features % r
            n_block = in_features // r
            n_block_size = n_block * r

            if re:
                orig_weight[:, :n_block_size] = (
                    (orig_weight[:, :n_block_size].reshape(-1, n_block, r).permute(1, 2, 0) - weight_bone)
                    .permute(2, 0, 1)
                    .reshape(*orig_weight[:, :n_block_size].shape)
                )
                orig_weight[:, n_block_size:] = (
                    orig_weight[:, n_block_size:] - (weight_bone.transpose(0, 1))[:, :last_size]
                )
            else:
                orig_weight[:, :n_block_size] = (
                    (orig_weight[:, :n_block_size].reshape(-1, n_block, r).permute(1, 2, 0) + weight_bone)
                    .permute(2, 0, 1)
                    .reshape(*orig_weight[:, :n_block_size].shape)
                )
                orig_weight[:, n_block_size:] = (
                    orig_weight[:, n_block_size:] + (weight_bone.transpose(0, 1))[:, :last_size]
                )
            output_tensor = orig_weight

        else:
            if re:
                w = orig_weight.reshape(-1, orig_weight.size(1) // r, r).permute(1, 2, 0) - weight_bone
                output_tensor = w.permute(2, 0, 1).reshape(*orig_weight.shape)
            else:
                w = orig_weight.reshape(-1, orig_weight.size(1) // r, r).permute(1, 2, 0) + weight_bone
                output_tensor = w.permute(2, 0, 1).reshape(*orig_weight.shape)

        if cast_to_fp32:
            output_tensor = output_tensor.to(dtype=dtype)

            # cast back the weights
            self.bone_block[adapter].data = weight_bone.to(dtype)

        return output_tensor