[Model] Add base class for LoRA-supported models (#5018)

vllm/model_executor/models/baichuan.py

@@ -45,6 +45,8 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import SamplerOutput
 
+from .interfaces import SupportsLoRA
+
 
 def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
     closest_power_of_2 = 2**math.floor(math.log2(total_num_heads))
@@ -292,7 +294,9 @@ class BaiChuanModel(nn.Module):
         return hidden_states
 
 
-class BaiChuanBaseForCausalLM(nn.Module):
+class BaiChuanBaseForCausalLM(nn.Module, SupportsLoRA):
+    supports_lora = True
+
     packed_modules_mapping = {
         "W_pack": ["W_pack"],
         "gate_up_proj": [
@@ -312,14 +316,17 @@ class BaiChuanBaseForCausalLM(nn.Module):
 
     def __init__(
         self,
-        config,
+        config: PretrainedConfig,
         position_embedding: str,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ):
         super().__init__()
+
         self.config = config
+        self.lora_config = lora_config
+
         self.quant_config = quant_config
         self.model = BaiChuanModel(config, position_embedding, cache_config,
                                    quant_config)