init

vllm/lora/layers/replicated_linear.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Optional, Union
+
+import torch
+import torch.nn as nn
+from transformers import PretrainedConfig
+
+from vllm.config.lora import LoRAConfig
+from vllm.model_executor.layers.linear import ReplicatedLinear
+
+from .base_linear import BaseLinearLayerWithLoRA
+
+
+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
+    ) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[torch.Tensor]]]:
+        """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
+
+    # 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: Optional[PretrainedConfig],
+    ) -> bool:
+        return type(source_layer) is ReplicatedLinear