[V1]SupportsV0Only protocol for model definitions (#13959)

Signed-off-by: Roger Wang <ywang@roblox.com>

vllm/config.py

@@ -1039,6 +1039,11 @@ class ModelConfig:
     def runner_type(self) -> RunnerType:
         return _TASK_RUNNER[self.task]
 
+    @property
+    def is_v1_compatible(self) -> bool:
+        architectures = getattr(self.hf_config, "architectures", [])
+        return ModelRegistry.is_v1_compatible(architectures)
+
 
 class CacheConfig:
     """Configuration for the KV cache.

vllm/model_executor/models/__init__.py

@@ -1,8 +1,9 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from .interfaces import (HasInnerState, SupportsLoRA, SupportsMultiModal,
-                         SupportsPP, has_inner_state, supports_lora,
-                         supports_multimodal, supports_pp)
+                         SupportsPP, SupportsV0Only, has_inner_state,
+                         supports_lora, supports_multimodal, supports_pp,
+                         supports_v0_only)
 from .interfaces_base import (VllmModelForPooling, VllmModelForTextGeneration,
                               is_pooling_model, is_text_generation_model)
 from .registry import ModelRegistry
@@ -21,4 +22,6 @@ __all__ = [
     "supports_multimodal",
     "SupportsPP",
     "supports_pp",
+    "SupportsV0Only",
+    "supports_v0_only",
 ]

vllm/model_executor/models/bamba.py

@@ -32,7 +32,8 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 from vllm.utils import LayerBlockType
 
-from .interfaces import HasInnerState, IsHybrid, SupportsLoRA, SupportsPP
+from .interfaces import (HasInnerState, IsHybrid, SupportsLoRA, SupportsPP,
+                         SupportsV0Only)
 from .utils import (is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
@@ -366,7 +367,7 @@ class BambaModel(nn.Module):
 
 
 class BambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
-                       IsHybrid):
+                       IsHybrid, SupportsV0Only):
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",

vllm/model_executor/models/bart.py

@@ -43,6 +43,7 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
+from .interfaces import SupportsV0Only
 from .utils import maybe_prefix
 
 logger = logging.get_logger(__name__)
@@ -776,7 +777,7 @@ class BartModel(nn.Module):
         return decoder_outputs
 
 
-class BartForConditionalGeneration(nn.Module):
+class BartForConditionalGeneration(nn.Module, SupportsV0Only):
     base_model_prefix = "model"
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):

vllm/model_executor/models/bert.py

@@ -26,7 +26,7 @@ from vllm.sequence import IntermediateTensors, PoolerOutput
 from vllm.transformers_utils.config import (
     get_cross_encoder_activation_function)
 
-from .interfaces import SupportsCrossEncoding
+from .interfaces import SupportsCrossEncoding, SupportsV0Only
 from .utils import WeightsMapper, maybe_prefix
 
 
@@ -385,7 +385,7 @@ class BertModel(nn.Module):
         return loaded_params
 
 
-class BertEmbeddingModel(nn.Module):
+class BertEmbeddingModel(nn.Module, SupportsV0Only):
     """A model that uses Bert to provide embedding functionalities.
 
    This class encapsulates the BertModel and provides an interface for

vllm/model_executor/models/florence2.py

@@ -29,7 +29,7 @@ from vllm.multimodal.processing import (BaseProcessingInfo,
 from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
 from vllm.sequence import IntermediateTensors
 
-from .interfaces import SupportsMultiModal
+from .interfaces import SupportsMultiModal, SupportsV0Only
 from .utils import AutoWeightsLoader, flatten_bn, merge_multimodal_embeddings
 
 
@@ -651,7 +651,7 @@ class Florence2LanguageModel(nn.Module):
         return decoder_outputs
 
 
-class Florence2LanguageForConditionalGeneration(nn.Module):
+class Florence2LanguageForConditionalGeneration(nn.Module, SupportsV0Only):
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()

vllm/model_executor/models/gritlm.py

@@ -19,6 +19,8 @@ from vllm.sequence import (IntermediateTensors, PoolerOutput,
                            PoolingSequenceGroupOutput)
 from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
 
+from .interfaces import SupportsV0Only
+
 logger = init_logger(__name__)
 
 
@@ -177,7 +179,7 @@ class GritLMPooler(nn.Module):
         return PoolerOutput(outputs=pooled_outputs)
 
 
-class GritLM(LlamaForCausalLM):
+class GritLM(LlamaForCausalLM, SupportsV0Only):
     """This class implements the embedding model for parasail-ai/GritLM-7B-vllm.
 
     The class inherits from LlamaForCausalLM and provides a custom pooling

vllm/model_executor/models/interfaces.py

@@ -498,3 +498,29 @@ def supports_transcription(
         return isinstance(model, SupportsTranscription)
 
     return isinstance(model, SupportsTranscription)
+
+
+@runtime_checkable
+class SupportsV0Only(Protocol):
+    """Models with this interface are not compatible with V1 vLLM."""
+
+    supports_v0_only: ClassVar[Literal[True]] = True
+
+
+@overload
+def supports_v0_only(model: Type[object]) -> TypeIs[Type[SupportsV0Only]]:
+    ...
+
+
+@overload
+def supports_v0_only(model: object) -> TypeIs[SupportsV0Only]:
+    ...
+
+
+def supports_v0_only(
+    model: Union[Type[object], object],
+) -> Union[TypeIs[Type[SupportsV0Only]], TypeIs[SupportsV0Only]]:
+    if isinstance(model, type):
+        return isinstance(model, SupportsV0Only)
+
+    return isinstance(model, SupportsV0Only)

vllm/model_executor/models/jamba.py

@@ -30,7 +30,8 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors, PoolerOutput
 from vllm.utils import LayerBlockType
 
-from .interfaces import HasInnerState, IsHybrid, SupportsLoRA, SupportsPP
+from .interfaces import (HasInnerState, IsHybrid, SupportsLoRA, SupportsPP,
+                         SupportsV0Only)
 from .utils import (is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
@@ -353,7 +354,7 @@ class JambaModel(nn.Module):
 
 
 class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
-                       IsHybrid):
+                       IsHybrid, SupportsV0Only):
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",

vllm/model_executor/models/mamba.py

@@ -19,7 +19,8 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
     DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.interfaces import (HasInnerState,
-                                                   IsAttentionFree, SupportsPP)
+                                                   IsAttentionFree, SupportsPP,
+                                                   SupportsV0Only)
 from vllm.model_executor.models.mamba_cache import (MambaCacheManager,
                                                     MambaCacheParams)
 from vllm.model_executor.sampling_metadata import SamplingMetadata
@@ -155,7 +156,8 @@ class MambaModel(nn.Module):
         return hidden_states
 
 
-class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree, SupportsPP):
+class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree, SupportsPP,
+                       SupportsV0Only):
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         config = vllm_config.model_config.hf_config

vllm/model_executor/models/mamba2.py

@@ -22,7 +22,8 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
     DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.interfaces import (HasInnerState,
-                                                   IsAttentionFree)
+                                                   IsAttentionFree,
+                                                   SupportsV0Only)
 from vllm.model_executor.models.mamba_cache import (MambaCacheManager,
                                                     MambaCacheParams)
 from vllm.model_executor.sampling_metadata import SamplingMetadata
@@ -174,7 +175,8 @@ class Mamba2Model(nn.Module):
         return hidden_states
 
 
-class Mamba2ForCausalLM(nn.Module, HasInnerState, IsAttentionFree):
+class Mamba2ForCausalLM(nn.Module, HasInnerState, IsAttentionFree,
+                        SupportsV0Only):
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         config = vllm_config.model_config.hf_config

vllm/model_executor/models/minicpmv.py

@@ -63,7 +63,8 @@ from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
 
 from .idefics2_vision_model import Idefics2VisionTransformer
-from .interfaces import SupportsLoRA, SupportsMultiModal, SupportsPP
+from .interfaces import (SupportsLoRA, SupportsMultiModal, SupportsPP,
+                         SupportsV0Only)
 from .utils import AutoWeightsLoader, maybe_prefix
 
 CPU_DEVICE = torch.device("cpu")
@@ -804,7 +805,8 @@ class MiniCPMVMultiModalProcessor(BaseMultiModalProcessor[_I]):
         return result
 
 
-class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
+class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP,
+                        SupportsV0Only):
     """
     The abstract class of MiniCPMV can only be inherited, but cannot be
     instantiated.

vllm/model_executor/models/mllama.py

@@ -63,7 +63,7 @@ from vllm.multimodal.processing import (BaseProcessingInfo,
 from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
 
 from .clip import CLIPMLP
-from .interfaces import SupportsMultiModal
+from .interfaces import SupportsMultiModal, SupportsV0Only
 from .llama import LlamaDecoderLayer, LlamaMLP
 from .utils import maybe_prefix
 
@@ -1128,7 +1128,8 @@ class MllamaForCausalLM(nn.Module):
 @MULTIMODAL_REGISTRY.register_processor(MllamaMultiModalProcessor,
                                         info=MllamaProcessingInfo,
                                         dummy_inputs=MllamaDummyInputsBuilder)
-class MllamaForConditionalGeneration(nn.Module, SupportsMultiModal):
+class MllamaForConditionalGeneration(nn.Module, SupportsMultiModal,
+                                     SupportsV0Only):
     packed_modules_mapping = {
         "qkv_proj": ["q_proj", "k_proj", "v_proj"],
         "gate_up_proj": ["gate_proj", "up_proj"]

vllm/model_executor/models/paligemma.py

@@ -18,7 +18,7 @@ from vllm.multimodal.inputs import NestedTensors
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
 
-from .interfaces import SupportsMultiModal, SupportsPP
+from .interfaces import SupportsMultiModal, SupportsPP, SupportsV0Only
 from .siglip import (SiglipVisionModel, dummy_image_for_siglip,
                      dummy_seq_data_for_siglip, get_max_siglip_image_tokens)
 from .utils import (AutoWeightsLoader, init_vllm_registered_model,
@@ -136,7 +136,7 @@ class PaliGemmaMultiModalProjector(nn.Module):
 @INPUT_REGISTRY.register_dummy_data(dummy_data_for_paligemma)
 @INPUT_REGISTRY.register_input_processor(input_processor_for_paligemma)
 class PaliGemmaForConditionalGeneration(nn.Module, SupportsMultiModal,
-                                        SupportsPP):
+                                        SupportsPP, SupportsV0Only):
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",

vllm/model_executor/models/prithvi_geospatial_mae.py

@@ -25,7 +25,8 @@ from transformers import BatchFeature
 from vllm.config import VllmConfig
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.interfaces import (IsAttentionFree,
-                                                   SupportsMultiModal)
+                                                   SupportsMultiModal,
+                                                   SupportsV0Only)
 from vllm.model_executor.models.utils import AutoWeightsLoader
 from vllm.model_executor.pooling_metadata import PoolingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
@@ -111,7 +112,8 @@ class PrithviGeoSpatialMAEMultiModalProcessor(BaseMultiModalProcessor):
     PrithviGeoSpatialMAEMultiModalProcessor,
     info=PrithviGeoSpatialMAEProcessingInfo,
     dummy_inputs=PrithviGeoSpatialMAEInputBuilder)
-class PrithviGeoSpatialMAE(nn.Module, IsAttentionFree, SupportsMultiModal):
+class PrithviGeoSpatialMAE(nn.Module, IsAttentionFree, SupportsMultiModal,
+                           SupportsV0Only):
     """ Prithvi Masked Autoencoder"""
 
     def _instantiate_model(self, config: dict) -> Optional[nn.Module]:

vllm/model_executor/models/qwen2_rm.py

@@ -17,7 +17,7 @@ from vllm.model_executor.layers.pooler import Pooler, PoolingType, SimplePooler
 from vllm.model_executor.pooling_metadata import PoolingMetadata
 from vllm.sequence import IntermediateTensors, PoolerOutput
 
-from .interfaces import SupportsLoRA, SupportsPP
+from .interfaces import SupportsLoRA, SupportsPP, SupportsV0Only
 from .qwen2 import Qwen2Model
 from .utils import AutoWeightsLoader, maybe_prefix
 
@@ -33,7 +33,8 @@ class ReLU(nn.Module):
         return self.activation(input)
 
 
-class Qwen2RewardBaseModel(nn.Module, SupportsLoRA, SupportsPP):
+class Qwen2RewardBaseModel(nn.Module, SupportsLoRA, SupportsPP,
+                           SupportsV0Only):
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",

vllm/model_executor/models/registry.py

@@ -22,7 +22,7 @@ from vllm.logger import init_logger
 
 from .interfaces import (has_inner_state, is_attention_free, is_hybrid,
                          supports_cross_encoding, supports_multimodal,
-                         supports_pp, supports_transcription)
+                         supports_pp, supports_transcription, supports_v0_only)
 from .interfaces_base import is_text_generation_model
 
 logger = init_logger(__name__)
@@ -228,6 +228,7 @@ class _ModelInfo:
     is_attention_free: bool
     is_hybrid: bool
     supports_transcription: bool
+    supports_v0_only: bool
 
     @staticmethod
     def from_model_cls(model: Type[nn.Module]) -> "_ModelInfo":
@@ -241,7 +242,9 @@ class _ModelInfo:
             has_inner_state=has_inner_state(model),
             is_attention_free=is_attention_free(model),
             is_hybrid=is_hybrid(model),
-            supports_transcription=supports_transcription(model))
+            supports_transcription=supports_transcription(model),
+            supports_v0_only=supports_v0_only(model),
+        )
 
 
 class _BaseRegisteredModel(ABC):
@@ -504,6 +507,13 @@ class _ModelRegistry:
         model_cls, _ = self.inspect_model_cls(architectures)
         return model_cls.supports_transcription
 
+    def is_v1_compatible(
+        self,
+        architectures: Union[str, List[str]],
+    ) -> bool:
+        model_cls, _ = self.inspect_model_cls(architectures)
+        return not model_cls.supports_v0_only
+
 
 ModelRegistry = _ModelRegistry({
     model_arch:

vllm/model_executor/models/roberta.py

@@ -19,7 +19,7 @@ from vllm.sequence import IntermediateTensors, PoolerOutput
 from vllm.transformers_utils.config import (
     get_cross_encoder_activation_function)
 
-from .interfaces import SupportsCrossEncoding
+from .interfaces import SupportsCrossEncoding, SupportsV0Only
 
 
 def roberta_task_weights_filter(
@@ -191,7 +191,8 @@ class RobertaEmbeddingModel(BertEmbeddingModel):
         assert len(loaded), "Unable to load RobertaEmbeddingModel"
 
 
-class RobertaForSequenceClassification(nn.Module, SupportsCrossEncoding):
+class RobertaForSequenceClassification(nn.Module, SupportsCrossEncoding,
+                                       SupportsV0Only):
     """A model that uses Roberta to provide embedding functionalities.
 
    This class encapsulates the BertModel and provides an interface for

vllm/model_executor/models/whisper.py

@@ -34,7 +34,8 @@ from vllm.multimodal.processing import (BaseProcessingInfo,
                                         PromptReplacement, PromptUpdate)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
 
-from .interfaces import SupportsMultiModal, SupportsTranscription
+from .interfaces import (SupportsMultiModal, SupportsTranscription,
+                         SupportsV0Only)
 from .utils import (AutoWeightsLoader, WeightsMapper, cast_overflow_tensors,
                     make_layers)
 
@@ -643,7 +644,7 @@ class WhisperMultiModalProcessor(
                                         info=WhisperProcessingInfo,
                                         dummy_inputs=WhisperDummyInputsBuilder)
 class WhisperForConditionalGeneration(nn.Module, SupportsTranscription,
-                                      SupportsMultiModal):
+                                      SupportsMultiModal, SupportsV0Only):
     packed_modules_mapping = {
         "self_attn.qkv_proj": [
             "self_attn.q_proj",