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

docs/source/models/lora.rst

@@ -4,6 +4,9 @@ Using LoRA adapters
 ===================
 
 This document shows you how to use `LoRA adapters <https://arxiv.org/abs/2106.09685>`_ with vLLM on top of a base model.
+
+LoRA adapters can be used with any vLLM model that implements :class:`~vllm.model_executor.models.interfaces.SupportsLoRA`.
+
 Adapters can be efficiently served on a per request basis with minimal overhead. First we download the adapter(s) and save
 them locally with
 

vllm/lora/lora.py

@@ -2,6 +2,7 @@ from typing import List, Optional
 from typing import Sequence as GenericSequence
 
 import torch
+import torch.types
 
 from vllm.utils import is_pin_memory_available
 
@@ -64,7 +65,7 @@ class LoRALayerWeights:
             output_dim: int,
             rank: int,
             dtype: torch.dtype,
-            device: torch.device,
+            device: torch.types.Device,
             embeddings_tensor_dim: Optional[int] = None) -> "LoRALayerWeights":
         pin_memory = str(device) == "cpu" and is_pin_memory_available()
         lora_a = torch.zeros([input_dim, rank],

vllm/lora/models.py

@@ -18,6 +18,7 @@ from vllm.lora.layers import (BaseLayerWithLoRA,
 from vllm.lora.lora import LoRALayerWeights, PackedLoRALayerWeights
 from vllm.lora.utils import (from_layer, from_layer_logits_processor,
                              parse_fine_tuned_lora_name, replace_submodule)
+from vllm.model_executor.models.interfaces import SupportsLoRA
 from vllm.utils import LRUCache, is_pin_memory_available
 
 logger = init_logger(__name__)
@@ -363,7 +364,7 @@ class LoRAModelManager:
 
     def __init__(
         self,
-        model: nn.Module,
+        model: SupportsLoRA,
         max_num_seqs: int,
         max_num_batched_tokens: int,
         vocab_size: int,
@@ -411,7 +412,7 @@ class LoRAModelManager:
         # embeddings_indices
         self.indices_len: List[Optional[int]] = [None] * 4
 
-        self.model: nn.Module = model
+        self.model = model
         if hasattr(self.model, "supported_lora_modules"):
             self.supported_lora_modules = copy.deepcopy(
                 self.model.supported_lora_modules)
@@ -428,7 +429,6 @@ class LoRAModelManager:
         self._active_loras: Dict[int, None] = {}
         self._last_mapping: Optional[LoRAMapping] = None
         self._create_lora_modules()
-        self.model.lora_manager = self
 
     @property
     def capacity(self) -> int:

vllm/model_executor/model_loader/loader.py

@@ -32,7 +32,8 @@ from vllm.model_executor.model_loader.weight_utils import (
     filter_duplicate_safetensors_files, filter_files_not_needed_for_inference,
     get_quant_config, initialize_dummy_weights, np_cache_weights_iterator,
     pt_weights_iterator, safetensors_weights_iterator)
-from vllm.model_executor.models.vlm_base import VisionLanguageModelBase
+from vllm.model_executor.models.interfaces import (supports_lora,
+                                                   supports_vision)
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.utils import is_tpu
 
@@ -64,12 +65,15 @@ def _get_quantization_config(
 
 
 def _get_model_initialization_kwargs(
-        model_class: Type[nn.Module], lora_config: Optional[LoRAConfig],
-        vision_language_config: Optional[VisionLanguageConfig]
+    model_class: Type[nn.Module],
+    lora_config: Optional[LoRAConfig],
+    vlm_config: Optional[VisionLanguageConfig],
 ) -> Dict[str, Any]:
     """Get extra kwargs for model initialization."""
     extra_kwargs: Dict[str, Any] = {}
-    if hasattr(model_class, "supported_lora_modules"):
+
+    if supports_lora(model_class):
+        # lora_config=None is used to disable LoRA
         extra_kwargs["lora_config"] = lora_config
     elif lora_config:
         raise ValueError(
@@ -77,13 +81,15 @@ def _get_model_initialization_kwargs(
             "but LoRA is enabled. Support for this model may "
             "be added in the future. If this is important to you, "
             "please open an issue on github.")
-    elif issubclass(model_class, VisionLanguageModelBase):
-        if vision_language_config is None:
+
+    if supports_vision(model_class):
+        if vlm_config is None:
             raise ValueError("Provide `image_input_type` and other vision "
                              "related configurations through LLM entrypoint "
                              "or engine arguments.")
 
-        extra_kwargs["vision_language_config"] = vision_language_config
+        extra_kwargs["vlm_config"] = vlm_config
+
     return extra_kwargs
 
 

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)

vllm/model_executor/models/chatglm.py

@@ -28,6 +28,8 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import SamplerOutput
 from vllm.transformers_utils.configs import ChatGLMConfig
 
+from .interfaces import SupportsLoRA
+
 
 class GLMAttention(nn.Module):
 
@@ -322,7 +324,9 @@ class ChatGLMModel(nn.Module):
         return hidden_states
 
 
-class ChatGLMForCausalLM(nn.Module):
+class ChatGLMForCausalLM(nn.Module, SupportsLoRA):
+    supports_lora = True
+
     packed_modules_mapping = {
         "query_key_value": ["query_key_value"],
         "dense_h_to_4h": ["dense_h_to_4h"]
@@ -345,7 +349,10 @@ class ChatGLMForCausalLM(nn.Module):
         lora_config: Optional[LoRAConfig] = None,
     ):
         super().__init__()
-        self.config: ChatGLMConfig = config
+
+        self.config = config
+        self.lora_config = lora_config
+
         self.quant_config = quant_config
         self.max_position_embeddings = getattr(config, "max_sequence_length",
                                                8192)

vllm/model_executor/models/decilm.py

@@ -26,7 +26,7 @@
 from typing import Iterable, Optional, Tuple
 
 import torch
-from transformers import PretrainedConfig
+from transformers import LlamaConfig
 
 from vllm.config import CacheConfig, LoRAConfig
 from vllm.model_executor.layers.quantization.base_config import (
@@ -55,7 +55,7 @@ class DeciLMForCausalLM(LlamaForCausalLM):
 
     def __init__(
         self,
-        config: Optional[PretrainedConfig] = None,
+        config: LlamaConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,

vllm/model_executor/models/gemma.py

@@ -41,6 +41,8 @@ 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
+
 logger = init_logger(__name__)
 
 
@@ -288,7 +290,9 @@ class GemmaModel(nn.Module):
         return hidden_states
 
 
-class GemmaForCausalLM(nn.Module):
+class GemmaForCausalLM(nn.Module, SupportsLoRA):
+    supports_lora = True
+
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",
@@ -319,9 +323,11 @@ class GemmaForCausalLM(nn.Module):
         quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ) -> None:
-        del lora_config  # Unused.
         super().__init__()
+
         self.config = config
+        self.lora_config = lora_config
+
         self.quant_config = quant_config
         self.model = GemmaModel(config, cache_config, quant_config)
         self.logits_processor = LogitsProcessor(config.vocab_size)

vllm/model_executor/models/gpt_bigcode.py

@@ -41,6 +41,8 @@ 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
+
 
 class GPTBigCodeAttention(nn.Module):
 
@@ -230,7 +232,9 @@ class GPTBigCodeModel(nn.Module):
         return hidden_states
 
 
-class GPTBigCodeForCausalLM(nn.Module):
+class GPTBigCodeForCausalLM(nn.Module, SupportsLoRA):
+    supports_lora = True
+
     packed_modules_mapping = {"c_attn": ["c_attn"]}
 
     supported_lora_modules = ["c_fc", "c_proj", "wte", "lm_head", "c_attn"]
@@ -250,7 +254,10 @@ class GPTBigCodeForCausalLM(nn.Module):
         lora_config: Optional[LoRAConfig] = None,
     ):
         super().__init__()
+
         self.config = config
+        self.lora_config = lora_config
+
         self.quant_config = quant_config
         self.transformer = GPTBigCodeModel(config, cache_config, quant_config,
                                            lora_config)

vllm/model_executor/models/interfaces.py

@@ -0,0 +1,130 @@
+from typing import (ClassVar, Dict, List, Literal, Optional, Protocol, Type,
+                    Union, overload, runtime_checkable)
+
+from typing_extensions import TypeGuard
+
+from vllm.config import LoRAConfig, VisionLanguageConfig
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+@runtime_checkable
+class SupportsVision(Protocol):
+    """The interface required for all vision language models (VLMs)."""
+
+    supports_vision: ClassVar[Literal[True]]
+
+    def __init__(self, *, vlm_config: VisionLanguageConfig) -> None:
+        ...
+
+
+# We can't use runtime_checkable with ClassVar for issubclass checks
+# so we need to treat the class as an instance and use isinstance instead
+@runtime_checkable
+class _SupportsVisionType(Protocol):
+    supports_vision: Literal[True]
+
+    def __call__(self, *, vlm_config: VisionLanguageConfig) -> None:
+        ...
+
+
+@overload
+def supports_vision(model: Type[object]) -> TypeGuard[Type[SupportsVision]]:
+    ...
+
+
+@overload
+def supports_vision(model: object) -> TypeGuard[SupportsVision]:
+    ...
+
+
+def supports_vision(
+    model: Union[Type[object], object],
+) -> Union[TypeGuard[Type[SupportsVision]], TypeGuard[SupportsVision]]:
+    if isinstance(model, type):
+        return isinstance(model, _SupportsVisionType)
+
+    return isinstance(model, SupportsVision)
+
+
+@runtime_checkable
+class SupportsLoRA(Protocol):
+    """The interface required for all models that support LoRA."""
+
+    supports_lora: ClassVar[Literal[True]]
+
+    packed_modules_mapping: ClassVar[Dict[str, List[str]]]
+    supported_lora_modules: ClassVar[List[str]]
+    embedding_modules: ClassVar[Dict[str, str]]
+    embedding_padding_modules: ClassVar[List[str]]
+
+    # lora_config is None when LoRA is not enabled
+    def __init__(self, *, lora_config: Optional[LoRAConfig] = None) -> None:
+        ...
+
+
+# We can't use runtime_checkable with ClassVar for issubclass checks
+# so we need to treat the class as an instance and use isinstance instead
+@runtime_checkable
+class _SupportsLoRAType(Protocol):
+    supports_lora: Literal[True]
+
+    packed_modules_mapping: Dict[str, List[str]]
+    supported_lora_modules: List[str]
+    embedding_modules: Dict[str, str]
+    embedding_padding_modules: List[str]
+
+    def __call__(self, *, lora_config: Optional[LoRAConfig] = None) -> None:
+        ...
+
+
+@overload
+def supports_lora(model: Type[object]) -> TypeGuard[Type[SupportsLoRA]]:
+    ...
+
+
+@overload
+def supports_lora(model: object) -> TypeGuard[SupportsLoRA]:
+    ...
+
+
+def supports_lora(
+    model: Union[Type[object], object],
+) -> Union[TypeGuard[Type[SupportsLoRA]], TypeGuard[SupportsLoRA]]:
+    result = _supports_lora(model)
+
+    if not result:
+        lora_attrs = (
+            "packed_modules_mapping",
+            "supported_lora_modules",
+            "embedding_modules",
+            "embedding_padding_modules",
+        )
+        missing_attrs = tuple(attr for attr in lora_attrs
+                              if not hasattr(model, attr))
+
+        if getattr(model, "supports_lora", False):
+            if missing_attrs:
+                logger.warning(
+                    "The model (%s) sets `supports_lora=True`, "
+                    "but is missing LoRA-specific attributes: %s",
+                    model,
+                    missing_attrs,
+                )
+        else:
+            if not missing_attrs:
+                logger.warning(
+                    "The model (%s) contains all LoRA-specific attributes, "
+                    "but does not set `supports_lora=True`.", model)
+
+    return result
+
+
+def _supports_lora(
+    model: Union[Type[object], object],
+) -> Union[TypeGuard[Type[SupportsLoRA]], TypeGuard[SupportsLoRA]]:
+    if isinstance(model, type):
+        return isinstance(model, _SupportsLoRAType)
+
+    return isinstance(model, SupportsLoRA)

vllm/model_executor/models/llama.py

@@ -49,6 +49,8 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import SamplerOutput
 from vllm.utils import is_hip, print_warning_once
 
+from .interfaces import SupportsLoRA
+
 
 class LlamaMLP(nn.Module):
 
@@ -296,7 +298,9 @@ class LlamaModel(nn.Module):
         return hidden_states
 
 
-class LlamaForCausalLM(nn.Module):
+class LlamaForCausalLM(nn.Module, SupportsLoRA):
+    supports_lora = True
+
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",
@@ -336,7 +340,10 @@ class LlamaForCausalLM(nn.Module):
         lora_config: Optional[LoRAConfig] = None,
     ) -> None:
         super().__init__()
+
         self.config = config
+        self.lora_config = lora_config
+
         self.model = LlamaModel(config,
                                 cache_config,
                                 quant_config,

vllm/model_executor/models/llava.py

@@ -20,7 +20,7 @@ from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.image import get_dummy_image_data
 from vllm.sequence import SamplerOutput
 
-from .vlm_base import VisionLanguageModelBase
+from .interfaces import SupportsVision
 
 _KEYS_TO_MODIFY_MAPPING = {
     "language_model.lm_head": "lm_head",
@@ -86,18 +86,21 @@ LlavaImageInputs = Union[LlavaImagePixelInputs, LlavaImageFeatureInputs]
 @MULTIMODAL_REGISTRY.register_image_feature_input()
 @MULTIMODAL_REGISTRY.register_image_pixel_input()
 @MULTIMODAL_REGISTRY.register_dummy_data(get_dummy_image_data)
-class LlavaForConditionalGeneration(VisionLanguageModelBase):
+class LlavaForConditionalGeneration(nn.Module, SupportsVision):
+
+    supports_vision = True
 
     def __init__(self,
                  config: LlavaConfig,
-                 vision_language_config: VisionLanguageConfig,
+                 vlm_config: VisionLanguageConfig,
                  cache_config: Optional[CacheConfig] = None,
                  quant_config: Optional[QuantizationConfig] = None) -> None:
-        super().__init__(vision_language_config)
+        super().__init__()
 
         self.config = config
+        self.vlm_config = vlm_config
 
-        if self.vision_language_config.image_input_type == (
+        if self.vlm_config.image_input_type == (
                 VisionLanguageConfig.ImageInputType.PIXEL_VALUES):
             self.vision_tower = CLIPVisionModel(config.vision_config)
         else:
@@ -122,11 +125,10 @@ class LlavaForConditionalGeneration(VisionLanguageModelBase):
         self.sampler = Sampler()
 
     def _validate_image_data(self, data: torch.Tensor) -> torch.Tensor:
-        if list(data.shape[1:]) != list(
-                self.vision_language_config.image_input_shape[1:]):
+        if list(data.shape[1:]) != list(self.vlm_config.image_input_shape[1:]):
             raise ValueError(
                 f"The expected image tensor shape is batch dimension plus "
-                f"{self.vision_language_config.image_input_shape[1:]}. "
+                f"{self.vlm_config.image_input_shape[1:]}. "
                 f"You supplied {data.shape}. "
                 f"If you are using vLLM's entrypoint, make sure your "
                 f"supplied image input is consistent with "
@@ -139,7 +141,7 @@ class LlavaForConditionalGeneration(VisionLanguageModelBase):
         pixel_values = kwargs.pop("pixel_values", None)
         image_features = kwargs.pop("image_features", None)
 
-        expected_input_type = self.vision_language_config.image_input_type
+        expected_input_type = self.vlm_config.image_input_type
         ImageInputType = VisionLanguageConfig.ImageInputType
 
         if expected_input_type == ImageInputType.PIXEL_VALUES:
@@ -273,7 +275,7 @@ class LlavaForConditionalGeneration(VisionLanguageModelBase):
 
             inputs_embeds = merge_vision_embeddings(
                 input_ids, inputs_embeds, vision_embeddings,
-                self.vision_language_config.image_token_id)
+                self.vlm_config.image_token_id)
 
             input_ids = None
         else:

vllm/model_executor/models/llava_next.py

@@ -25,8 +25,8 @@ from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalData
 from vllm.multimodal.image import ImagePixelData, get_dummy_image_data
 from vllm.sequence import SamplerOutput, SequenceData
 
+from .interfaces import SupportsVision
 from .llava import LlavaMultiModalProjector, merge_vision_embeddings
-from .vlm_base import VisionLanguageModelBase
 
 logger = init_logger(__name__)
 
@@ -106,19 +106,21 @@ def _image_pixel_processor(
 
 @MULTIMODAL_REGISTRY.register_image_pixel_input(_image_pixel_processor)
 @MULTIMODAL_REGISTRY.register_dummy_data(_get_dummy_image_data)
-class LlavaNextForConditionalGeneration(VisionLanguageModelBase):
+class LlavaNextForConditionalGeneration(nn.Module, SupportsVision):
+
+    supports_vision = True
 
     def __init__(self,
                  config: LlavaNextConfig,
-                 vision_language_config: VisionLanguageConfig,
+                 vlm_config: VisionLanguageConfig,
                  cache_config: Optional[CacheConfig] = None,
                  quant_config: Optional[QuantizationConfig] = None) -> None:
-        super().__init__(vision_language_config)
+        super().__init__()
 
-        # Update the type annotation from that of its superclass
         self.config = config
+        self.vlm_config = vlm_config
 
-        if self.vision_language_config.image_input_type == (
+        if self.vlm_config.image_input_type == (
                 VisionLanguageConfig.ImageInputType.PIXEL_VALUES):
             self.vision_tower = CLIPVisionModel(config=config.vision_config)
         else:
@@ -146,7 +148,7 @@ class LlavaNextForConditionalGeneration(VisionLanguageModelBase):
             torch.empty(config.text_config.hidden_size))
 
     def _validate_image_pixels(self, data: torch.Tensor) -> torch.Tensor:
-        _, num_channels, _, _ = self.vision_language_config.image_input_shape
+        _, num_channels, _, _ = self.vlm_config.image_input_shape
 
         # Note that this is different from that of vLLM vision_language_config
         # since the image is resized by the HuggingFace preprocessor
@@ -177,7 +179,7 @@ class LlavaNextForConditionalGeneration(VisionLanguageModelBase):
         image_sizes = kwargs.pop("image_sizes", None)
         image_features = kwargs.pop("image_features", None)
 
-        expected_input_type = self.vision_language_config.image_input_type
+        expected_input_type = self.vlm_config.image_input_type
         ImageInputType = VisionLanguageConfig.ImageInputType
 
         if expected_input_type == ImageInputType.PIXEL_VALUES:
@@ -386,7 +388,7 @@ class LlavaNextForConditionalGeneration(VisionLanguageModelBase):
 
             inputs_embeds = merge_vision_embeddings(
                 input_ids, inputs_embeds, vision_embeddings,
-                self.vision_language_config.image_token_id)
+                self.vlm_config.image_token_id)
 
             input_ids = None
         else:

vllm/model_executor/models/minicpm.py

@@ -26,6 +26,7 @@ from typing import Any, Dict, Iterable, List, Optional, Tuple
 
 import torch
 from torch import nn
+from transformers import PretrainedConfig
 
 from vllm.attention import Attention, AttentionMetadata
 from vllm.config import CacheConfig, LoRAConfig
@@ -51,6 +52,8 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.sequence import SamplerOutput
 
+from .interfaces import SupportsLoRA
+
 
 class MiniCPMMoE(nn.Module):
     """A tensor-parallel MoE implementation that shards each expert
@@ -388,7 +391,9 @@ class MiniCPMModel(nn.Module):
         return hidden_states
 
 
-class MiniCPMForCausalLM(nn.Module):
+class MiniCPMForCausalLM(nn.Module, SupportsLoRA):
+    supports_lora = True
+
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",
@@ -418,13 +423,16 @@ class MiniCPMForCausalLM(nn.Module):
 
     def __init__(
         self,
-        config,
+        config: PretrainedConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ) -> None:
         super().__init__()
+
         self.config = config
+        self.lora_config = lora_config
+
         self.num_experts = getattr(self.config, "num_experts", 0)
         self.quant_config = quant_config
         self.model = MiniCPMModel(config,

vllm/model_executor/models/mixtral.py

@@ -54,6 +54,8 @@ from vllm.model_executor.utils import set_weight_attrs
 from vllm.sequence import SamplerOutput
 from vllm.utils import print_warning_once
 
+from .interfaces import SupportsLoRA
+
 
 class MixtralMoE(nn.Module):
     """A tensor-parallel MoE implementation for Mixtral that shards each expert
@@ -472,7 +474,9 @@ class MixtralModel(nn.Module):
         return hidden_states
 
 
-class MixtralForCausalLM(nn.Module):
+class MixtralForCausalLM(nn.Module, SupportsLoRA):
+    supports_lora = True
+
     fall_back_to_pt_during_load = False
 
     packed_modules_mapping = {
@@ -504,7 +508,10 @@ class MixtralForCausalLM(nn.Module):
         lora_config: Optional[LoRAConfig] = None,
     ) -> None:
         super().__init__()
+
         self.config = config
+        self.lora_config = lora_config
+
         self.model = MixtralModel(config,
                                   cache_config,
                                   quant_config,

vllm/model_executor/models/phi.py

@@ -39,7 +39,7 @@ from typing import Iterable, List, Optional, Tuple
 
 import torch
 from torch import nn
-from transformers import PretrainedConfig
+from transformers import PhiConfig
 
 from vllm.attention import Attention, AttentionMetadata
 from vllm.config import CacheConfig, LoRAConfig
@@ -59,11 +59,13 @@ 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
+
 
 class PhiAttention(nn.Module):
 
     def __init__(self,
-                 config: PretrainedConfig,
+                 config: PhiConfig,
                  cache_config: Optional[CacheConfig] = None,
                  quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
@@ -131,7 +133,7 @@ class PhiAttention(nn.Module):
 class PhiMLP(nn.Module):
 
     def __init__(self,
-                 config: PretrainedConfig,
+                 config: PhiConfig,
                  quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
 
@@ -160,7 +162,7 @@ class PhiMLP(nn.Module):
 class PhiLayer(nn.Module):
 
     def __init__(self,
-                 config: PretrainedConfig,
+                 config: PhiConfig,
                  cache_config: Optional[CacheConfig] = None,
                  quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
@@ -192,7 +194,7 @@ class PhiLayer(nn.Module):
 class PhiModel(nn.Module):
 
     def __init__(self,
-                 config: PretrainedConfig,
+                 config: PhiConfig,
                  cache_config: Optional[CacheConfig] = None,
                  quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
@@ -229,7 +231,9 @@ class PhiModel(nn.Module):
         return hidden_states
 
 
-class PhiForCausalLM(nn.Module):
+class PhiForCausalLM(nn.Module, SupportsLoRA):
+    supports_lora = True
+
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",
@@ -250,14 +254,16 @@ class PhiForCausalLM(nn.Module):
 
     def __init__(
         self,
-        config: PretrainedConfig,
+        config: PhiConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ):
-        del lora_config  # Unused.
         super().__init__()
+
         self.config = config
+        self.lora_config = lora_config
+
         self.quant_config = quant_config
 
         self.model = PhiModel(config, cache_config, quant_config)

vllm/model_executor/models/qwen2.py

@@ -48,6 +48,8 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import SamplerOutput
 from vllm.utils import print_warning_once
 
+from .interfaces import SupportsLoRA
+
 
 class Qwen2MLP(nn.Module):
 
@@ -263,7 +265,9 @@ class Qwen2Model(nn.Module):
         return hidden_states
 
 
-class Qwen2ForCausalLM(nn.Module):
+class Qwen2ForCausalLM(nn.Module, SupportsLoRA):
+    supports_lora = True
+
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",
@@ -293,7 +297,6 @@ class Qwen2ForCausalLM(nn.Module):
         quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ) -> None:
-        del lora_config
         # TODO (@robertgshaw2): see if this can be moved out
         if (cache_config.sliding_window is not None
                 and hasattr(config, "max_window_layers")):
@@ -307,7 +310,10 @@ class Qwen2ForCausalLM(nn.Module):
                              ))
 
         super().__init__()
+
         self.config = config
+        self.lora_config = lora_config
+
         self.quant_config = quant_config
         self.model = Qwen2Model(config, cache_config, quant_config)
 

vllm/model_executor/models/vlm_base.py

@@ -1,12 +0,0 @@
-from torch import nn
-
-from vllm.config import VisionLanguageConfig
-
-
-class VisionLanguageModelBase(nn.Module):
-    """Base class for all vision language models (VLMs)."""
-
-    def __init__(self, vision_language_config: VisionLanguageConfig) -> None:
-        super().__init__()
-
-        self.vision_language_config = vision_language_config

vllm/model_executor/models/xverse.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
+
 
 class XverseMLP(nn.Module):
 
@@ -266,7 +268,9 @@ class XverseModel(nn.Module):
         return hidden_states
 
 
-class XverseForCausalLM(nn.Module):
+class XverseForCausalLM(nn.Module, SupportsLoRA):
+    supports_lora = True
+
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",
@@ -299,10 +303,13 @@ class XverseForCausalLM(nn.Module):
         config: PretrainedConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
-        lora_config=None,
+        lora_config: Optional[LoRAConfig] = None,
     ) -> None:
         super().__init__()
+
         self.config = config
+        self.lora_config = lora_config
+
         self.quant_config = quant_config
         self.model = XverseModel(config, cache_config, quant_config)
         self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)

vllm/worker/model_runner.py

@@ -22,6 +22,7 @@ from vllm.lora.worker_manager import LRUCacheWorkerLoRAManager
 from vllm.model_executor import SamplingMetadata
 from vllm.model_executor.model_loader import get_model
 from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
+from vllm.model_executor.models.interfaces import supports_lora
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.sampling_params import SamplingParams
 from vllm.sequence import SamplerOutput, SequenceData, SequenceGroupMetadata
@@ -225,14 +226,8 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
                     self.model_memory_usage / float(2**30))
 
         if self.lora_config:
-            assert hasattr(self.model, "supported_lora_modules"
-                           ) and self.model.supported_lora_modules, (
-                               "Model does not support LoRA")
-            assert hasattr(
-                self.model,
-                "embedding_modules"), "Model does not have embedding_modules"
-            assert hasattr(self.model, "embedding_padding_modules"
-                           ), "Model does not have embedding_padding_modules"
+            assert supports_lora(self.model), "Model does not support LoRA"
+
             self.lora_manager = LRUCacheWorkerLoRAManager(
                 self.scheduler_config.max_num_seqs,
                 self.scheduler_config.max_num_batched_tokens,