vllm.lora.layers.replicated_linear ¶

class ReplicatedLinearWithLoRA(BaseLinearLayerWithLoRA):
    def __init__(self, base_layer: ReplicatedLinear) -> None:
        super().__init__(
            base_layer,
        )
        # To ensure interface compatibility, set to 1 always.
        self.output_size = self.base_layer.output_size
        self.n_slices = 1

    def forward(
        self, input_: torch.Tensor
    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor | None]:
        """Forward of ReplicatedLinearWithLoRA

        Args:
            input_: Tensor whose last dimension is `input_size`.

        Returns:
            - output
            - bias
        """
        bias = self.base_layer.bias if not self.base_layer.skip_bias_add else None

        # Matrix multiply.
        output = self.apply(input_, bias)

        output_bias = self.base_layer.bias if self.base_layer.skip_bias_add else None

        if not self.base_layer.return_bias:
            return output

        return output, output_bias

    def apply(self, x: torch.Tensor, bias: torch.Tensor | None = None) -> torch.Tensor:
        # ReplicatedLinear subclasses such as GateLinear override forward() to
        # dispatch custom kernels and/or adjust the output dtype. Apply LoRA on
        # top of the actual base-layer output instead of bypassing that path.
        return self._apply_base_forward(x)

    # ReplicatedLinear should always be replaced, regardless of the fully
    # sharded LoRAs setting, because it is, by definition, copied per GPU.
    @classmethod
    def can_replace_layer(
        cls,
        source_layer: nn.Module,
        lora_config: LoRAConfig,
        packed_modules_list: list,
        model_config: PretrainedConfig | None = None,
    ) -> bool:
        return isinstance(source_layer, maybe_get_oot_by_class(ReplicatedLinear))

    def slice_lora_a(
        self, lora_a: torch.Tensor | list[torch.Tensor | None]
    ) -> torch.Tensor | list[torch.Tensor | None]:
        """Slice lora a if splitting for tensor parallelism."""
        return lora_a

    def slice_lora_b(
        self, lora_b: torch.Tensor | list[torch.Tensor | None]
    ) -> torch.Tensor | list[torch.Tensor | None]:
        """Slice lora b if splitting with tensor parallelism."""
        return lora_b