[Model] Replace embedding models with pooling adapter (#10769)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

.buildkite/test-pipeline.yaml

@@ -334,7 +334,6 @@ steps:
   commands:
     - pytest -v -s models/decoder_only/language -m 'core_model or quant_model'
     - pytest -v -s models/embedding/language -m core_model
-    - pytest -v -s models/embedding/vision_language -m core_model
 
 - label: Language Models Test (Extended) # 50min
   optional: true
@@ -346,7 +345,6 @@ steps:
   commands:
     - pytest -v -s models/decoder_only/language -m 'not core_model and not quant_model'
     - pytest -v -s models/embedding/language -m 'not core_model'
-    - pytest -v -s models/embedding/vision_language -m 'not core_model'
 
 - label: Multi-Modal Models Test (Standard) # 26min
   #mirror_hardwares: [amd]
@@ -359,6 +357,7 @@ steps:
   commands:
     - pytest -v -s models/decoder_only/audio_language -m 'core_model or quant_model'
     - pytest -v -s --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m 'core_model or quant_model'
+    - pytest -v -s models/embedding/vision_language -m core_model
     - pytest -v -s models/encoder_decoder/language -m core_model
     - pytest -v -s models/encoder_decoder/vision_language -m core_model
 
@@ -376,6 +375,7 @@ steps:
     # https://github.com/huggingface/transformers/issues/34307
     - pytest -v -s models/decoder_only/vision_language/test_phi3v.py
     - pytest -v -s --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m 'not core_model and not quant_model'
+    - pytest -v -s models/embedding/vision_language -m 'not core_model'
     - pytest -v -s models/encoder_decoder/language -m 'not core_model'
     - pytest -v -s models/encoder_decoder/vision_language -m 'not core_model'
 

docs/source/models/supported_models.rst

@@ -357,7 +357,7 @@ Text Embedding
     - ✅︎
   * - :code:`Qwen2Model`, :code:`Qwen2ForCausalLM`
     - Qwen2-based
-    - :code:`ssmits/Qwen2-7B-Instruct-embed-base`, :code:`Alibaba-NLP/gte-Qwen2-7B-instruct` (see note), etc.
+    - :code:`ssmits/Qwen2-7B-Instruct-embed-base` (see note), :code:`Alibaba-NLP/gte-Qwen2-7B-instruct` (see note), etc.
     - ✅︎
     - ✅︎
   * - :code:`RobertaModel`, :code:`RobertaForMaskedLM`
@@ -378,6 +378,10 @@ Text Embedding
 .. tip::
   You can override the model's pooling method by passing :code:`--override-pooler-config`.
 
+.. note::
+  :code:`ssmits/Qwen2-7B-Instruct-embed-base` has an improperly defined Sentence Transformers config.
+  You should manually set mean pooling by passing :code:`--override-pooler-config '{"pooling_type": "MEAN"}'`.
+
 .. note::
   Unlike base Qwen2, :code:`Alibaba-NLP/gte-Qwen2-7B-instruct` uses bi-directional attention.
   You can set :code:`--hf-overrides '{"is_causal": false}'` to change the attention mask accordingly.
@@ -397,12 +401,21 @@ Reward Modeling
     - Example HF Models
     - :ref:`LoRA <lora>`
     - :ref:`PP <distributed_serving>`
+  * - :code:`LlamaForCausalLM`
+    - Llama-based
+    - :code:`peiyi9979/math-shepherd-mistral-7b-prm`, etc.
+    - ✅︎
+    - ✅︎
   * - :code:`Qwen2ForRewardModel`
     - Qwen2-based
     - :code:`Qwen/Qwen2.5-Math-RM-72B`, etc.
     - ✅︎
     - ✅︎
 
+.. important::
+  For process-supervised reward models such as :code:`peiyi9979/math-shepherd-mistral-7b-prm`, the pooling config should be set explicitly,
+  e.g.: :code:`--override-pooler-config '{"pooling_type": "STEP", "step_tag_id": 123, "returned_token_ids": [456, 789]}'`.
+
 .. note::
     As an interim measure, these models are supported in both offline and online inference via Embeddings API.
 

tests/conftest.py

@@ -263,7 +263,6 @@ class HfRunner:
         dtype: str = "half",
         *,
         model_kwargs: Optional[Dict[str, Any]] = None,
-        is_embedding_model: bool = False,
         is_sentence_transformer: bool = False,
         is_cross_encoder: bool = False,
         skip_tokenizer_init: bool = False,

tests/models/embedding/language/test_embedding.py

@@ -4,6 +4,8 @@ Run `pytest tests/models/embedding/language/test_embedding.py`.
 """
 import pytest
 
+from vllm.config import PoolerConfig
+
 from ..utils import check_embeddings_close
 
 
@@ -33,6 +35,9 @@ def test_models(
     dtype: str,
 ) -> None:
     vllm_extra_kwargs = {}
+    if model == "ssmits/Qwen2-7B-Instruct-embed-base":
+        vllm_extra_kwargs["override_pooler_config"] = \
+            PoolerConfig(pooling_type="MEAN")
     if model == "Alibaba-NLP/gte-Qwen2-7B-instruct":
         vllm_extra_kwargs["hf_overrides"] = {"is_causal": False}
 

tests/models/test_registry.py

@@ -6,11 +6,8 @@ import torch.cuda
 from vllm.model_executor.models import (is_embedding_model,
                                         is_text_generation_model,
                                         supports_multimodal)
-# yapf conflicts with isort for this block
+from vllm.model_executor.models.adapters import as_embedding_model
-# yapf: disable
+from vllm.model_executor.models.registry import (_MULTIMODAL_MODELS,
-from vllm.model_executor.models.registry import (_CROSS_ENCODER_MODELS,
-                                                 _EMBEDDING_MODELS,
-                                                 _MULTIMODAL_MODELS,
                                                  _SPECULATIVE_DECODING_MODELS,
                                                  _TEXT_GENERATION_MODELS,
                                                  ModelRegistry)
@@ -26,18 +23,18 @@ def test_registry_imports(model_arch):
     model_cls, _ = ModelRegistry.resolve_model_cls(model_arch)
 
     if model_arch in _SPECULATIVE_DECODING_MODELS:
-        pass  # Ignore these models which do not have a unified format
+        return  # Ignore these models which do not have a unified format
-    else:
-        assert is_text_generation_model(model_cls) is (
-            model_arch in _TEXT_GENERATION_MODELS
-            or model_arch in _MULTIMODAL_MODELS)
 
-        embedding_models = {**_EMBEDDING_MODELS, **_CROSS_ENCODER_MODELS}
+    if (model_arch in _TEXT_GENERATION_MODELS
-        assert is_embedding_model(model_cls) is (model_arch
+            or model_arch in _MULTIMODAL_MODELS):
-                                                 in embedding_models)
+        assert is_text_generation_model(model_cls)
 
-        assert supports_multimodal(model_cls) is (model_arch
+    # All vLLM models should be convertible to an embedding model
-                                                  in _MULTIMODAL_MODELS)
+    embed_model = as_embedding_model(model_cls)
+    assert is_embedding_model(embed_model)
+
+    if model_arch in _MULTIMODAL_MODELS:
+        assert supports_multimodal(model_cls)
 
 
 @fork_new_process_for_each_test

tests/plugins/vllm_add_dummy_model/vllm_add_dummy_model/my_gemma_embedding.py

@@ -1,13 +1,34 @@
-from typing import List, Optional, Union
+from typing import Iterable, List, Optional, Tuple, Union
 
 import torch
+import torch.nn as nn
 
 from vllm.attention import AttentionMetadata
-from vllm.model_executor.models.gemma2 import Gemma2EmbeddingModel
+from vllm.config import VllmConfig
-from vllm.sequence import IntermediateTensors
+from vllm.model_executor.layers.pooler import Pooler, PoolingType
+from vllm.model_executor.models.gemma2 import Gemma2Model
+from vllm.model_executor.models.utils import WeightsMapper, maybe_prefix
+from vllm.model_executor.pooling_metadata import PoolingMetadata
+from vllm.sequence import IntermediateTensors, PoolerOutput
 
 
-class MyGemma2Embedding(Gemma2EmbeddingModel):
+class MyGemma2Embedding(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        self.model = Gemma2Model(vllm_config=vllm_config,
+                                 prefix=maybe_prefix(prefix, "model"))
+
+        self._pooler = Pooler.from_config_with_defaults(
+            vllm_config.model_config.pooler_config,
+            pooling_type=PoolingType.LAST,
+            normalize=True,
+            softmax=False,
+        )
+
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
 
     def forward(
         self,
@@ -18,7 +39,7 @@ class MyGemma2Embedding(Gemma2EmbeddingModel):
         intermediate_tensors: Optional[IntermediateTensors] = None,
         inputs_embeds: Optional[torch.Tensor] = None,
     ) -> Union[torch.Tensor, IntermediateTensors]:
-        hidden_states = super().forward(
+        hidden_states = self.model(
             input_ids,
             positions,
             kv_caches,
@@ -32,3 +53,17 @@ class MyGemma2Embedding(Gemma2EmbeddingModel):
 
         # Return all-zero embeddings
         return torch.zeros_like(hidden_states)
+
+    def pooler(
+        self,
+        hidden_states: torch.Tensor,
+        pooling_metadata: PoolingMetadata,
+    ) -> Optional[PoolerOutput]:
+        return self._pooler(hidden_states, pooling_metadata)
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={"model.": ""})
+        weights = hf_to_vllm_mapper.apply(weights)
+        weights = ((name, data) for name, data in weights
+                   if not name.startswith("lm_head."))
+        return self.model.load_weights(weights)

tests/test_config.py

@@ -26,8 +26,7 @@ def test_auto_task(model_id, expected_task):
 
 
 @pytest.mark.parametrize(("model_id", "bad_task"), [
-    ("facebook/opt-125m", "embedding"),
+    ("Qwen/Qwen2.5-Math-RM-72B", "generate"),
-    ("intfloat/e5-mistral-7b-instruct", "generate"),
 ])
 def test_incorrect_task(model_id, bad_task):
     with pytest.raises(ValueError, match=r"does not support the .* task"):

vllm/config.py

@@ -370,6 +370,31 @@ class ModelConfig:
             selected_task = next(iter(supported_tasks_lst))
 
             if len(supported_tasks) > 1:
+                suffix_to_preferred_task: List[Tuple[str, _Task]] = [
+                    # Hardcode the models that are exceptions
+                    ("AquilaModel", "generate"),
+                    ("ChatGLMModel", "generate"),
+                    # Other models follow this pattern
+                    ("ForCausalLM", "generate"),
+                    ("ForConditionalGeneration", "generate"),
+                    ("ChatModel", "generate"),
+                    ("LMHeadModel", "generate"),
+                    ("EmbeddingModel", "embedding"),
+                    ("RewardModel", "embedding"),
+                    ("ForSequenceClassification", "embedding"),
+                ]
+                info, arch = ModelRegistry.inspect_model_cls(architectures)
+
+                for suffix, pref_task in suffix_to_preferred_task:
+                    if arch.endswith(suffix) and pref_task in supported_tasks:
+                        selected_task = pref_task
+                        break
+                else:
+                    if (arch.endswith("Model")
+                            and info.architecture.endswith("ForCausalLM")
+                            and "embedding" in supported_tasks):
+                        selected_task = "embedding"
+
                 logger.info(
                     "This model supports multiple tasks: %s. "
                     "Defaulting to '%s'.", supported_tasks, selected_task)

vllm/inputs/registry.py

@@ -11,8 +11,8 @@ from typing_extensions import TypeVar, assert_never
 from vllm.logger import init_logger
 from vllm.transformers_utils.processor import cached_get_processor
 from vllm.transformers_utils.tokenizer import AnyTokenizer
-from vllm.utils import (get_allowed_kwarg_only_overrides, print_warning_once,
+from vllm.utils import (ClassRegistry, get_allowed_kwarg_only_overrides,
-                        resolve_mm_processor_kwargs)
+                        print_warning_once, resolve_mm_processor_kwargs)
 
 from .data import ProcessorInputs, SingletonInputs
 from .parse import is_encoder_decoder_inputs
@@ -136,12 +136,12 @@ class InputRegistry:
     """
 
     def __init__(self) -> None:
-        self._dummy_factories_by_model_type: Dict[Type[nn.Module],
+        self._dummy_factories_by_model_type = \
-                                                  DummyDataFactory] = {}
+            ClassRegistry[nn.Module, DummyDataFactory]()
-        self._dummy_encoder_factories_by_model_type: Dict[
+        self._dummy_encoder_factories_by_model_type = \
-            Type[nn.Module], DummyDataFactory] = {}
+            ClassRegistry[nn.Module, DummyDataFactory]()
-        self._input_processors_by_model_type: Dict[Type[nn.Module],
+        self._input_processors_by_model_type = \
-                                                   InputProcessor] = {}
+            ClassRegistry[nn.Module, InputProcessor]()
 
     def _default_dummy_data_factory(
         self,

vllm/model_executor/layers/pooler.py

@@ -60,9 +60,7 @@ class Pooler(nn.Module):
         softmax: bool,
         step_tag_id: Optional[int] = None,
         returned_token_ids: Optional[List[int]] = None,
-    ) -> Optional["Pooler"]:
+    ) -> "Pooler":
-        if pooler_config is None:
-            return None
         return cls(
             pooling_type=PoolingType[pooler_config.pooling_type]
             if pooler_config.pooling_type is not None else pooling_type,

vllm/model_executor/model_loader/loader.py

@@ -9,6 +9,7 @@ import itertools
 import json
 import math
 import os
+import warnings
 from abc import ABC, abstractmethod
 from contextlib import contextmanager
 from typing import Any, Dict, Generator, Iterable, List, Optional, Tuple, cast
@@ -97,22 +98,31 @@ def device_loading_context(module: torch.nn.Module,
 logger = init_logger(__name__)
 
 
-def _initialize_model(vllm_config: VllmConfig, prefix: str = "") -> nn.Module:
+def _initialize_model(
+    vllm_config: VllmConfig,
+    *,
+    prefix: str = "",
+    architectures: Optional[list[str]] = None,
+) -> nn.Module:
     """Initialize a model with the given configurations."""
     model_config = vllm_config.model_config
-    model_class, _ = get_model_architecture(model_config)
+    model_class, _ = get_model_architecture(model_config,
+                                            architectures=architectures)
+
     signatures = inspect.signature(model_class.__init__)
     all_params = [param.name for param in signatures.parameters.values()]
     if "vllm_config" in all_params and "prefix" in all_params:
         # new-style model class
         with set_current_vllm_config(vllm_config):
             return model_class(vllm_config=vllm_config, prefix=prefix)
+
     msg = ("vLLM model class should accept `vllm_config` and `prefix` as "
            "input arguments. Possibly you have an old-style model class"
            " registered from out of tree and it is used for new vLLM version. "
            "Check https://docs.vllm.ai/en/latest/design/arch_overview.html "
            "for the design and update the model class accordingly.")
-    logger.warning(msg)
+    warnings.warn(msg, DeprecationWarning, stacklevel=2)
+
     logger.warning(
         "Trying to guess the arguments for old-style model class %s",
         model_class,
@@ -356,7 +366,7 @@ class DefaultModelLoader(BaseModelLoader):
             weights_to_load = {name for name, _ in model.named_parameters()}
             loaded_weights = model.load_weights(
                 self._get_all_weights(model_config, model))
-            # We only enable strict check for non-quantiized models
+            # We only enable strict check for non-quantized models
             # that have loaded weights tracking currently.
             if model_config.quantization is None and loaded_weights is not None:
                 weights_not_loaded = weights_to_load - loaded_weights

vllm/model_executor/model_loader/utils.py

@@ -1,12 +1,13 @@
 """Utilities for selecting and loading models."""
 import contextlib
-from typing import Tuple, Type
+from typing import Optional, Tuple, Type
 
 import torch
 from torch import nn
 
 from vllm.config import ModelConfig
 from vllm.model_executor.models import ModelRegistry
+from vllm.model_executor.models.adapters import as_embedding_model
 
 
 @contextlib.contextmanager
@@ -19,8 +20,13 @@ def set_default_torch_dtype(dtype: torch.dtype):
 
 
 def get_model_architecture(
-        model_config: ModelConfig) -> Tuple[Type[nn.Module], str]:
+    model_config: ModelConfig,
-    architectures = getattr(model_config.hf_config, "architectures", [])
+    *,
+    architectures: Optional[list[str]] = None,
+) -> Tuple[Type[nn.Module], str]:
+    if architectures is None:
+        architectures = getattr(model_config.hf_config, "architectures", [])
+
     # Special handling for quantized Mixtral.
     # FIXME(woosuk): This is a temporary hack.
     mixtral_supported = [
@@ -32,7 +38,11 @@ def get_model_architecture(
             and "MixtralForCausalLM" in architectures):
         architectures = ["QuantMixtralForCausalLM"]
 
-    return ModelRegistry.resolve_model_cls(architectures)
+    model_cls, arch = ModelRegistry.resolve_model_cls(architectures)
+    if model_config.task == "embedding":
+        model_cls = as_embedding_model(model_cls)
+
+    return model_cls, arch
 
 
 def get_architecture_class_name(model_config: ModelConfig) -> str:

vllm/model_executor/models/adapters.py

@@ -0,0 +1,98 @@
+from collections.abc import Iterable
+from typing import Any, TypeVar
+
+import torch
+import torch.nn as nn
+
+from .interfaces_base import VllmModelForEmbedding, is_embedding_model
+
+_T = TypeVar("_T", bound=type[nn.Module])
+
+
+def as_embedding_model(cls: _T) -> _T:
+    """Subclass an existing vLLM model to support embeddings."""
+    # Avoid modifying existing embedding models
+    if is_embedding_model(cls):
+        return cls
+
+    # Lazy import
+    from vllm.config import VllmConfig
+    from vllm.model_executor.layers.pooler import (Pooler, PoolerOutput,
+                                                   PoolingType)
+    from vllm.model_executor.pooling_metadata import PoolingMetadata
+
+    from .utils import AutoWeightsLoader, WeightsMapper
+
+    class ModelForEmbedding(cls, VllmModelForEmbedding):
+
+        def __init__(
+            self,
+            *,
+            vllm_config: "VllmConfig",
+            prefix: str = "",
+            **kwargs: Any,
+        ) -> None:
+            super().__init__(vllm_config=vllm_config, prefix=prefix, **kwargs)
+
+            # These are not used in embedding models
+            for attr in ("lm_head", "logits_processor"):
+                if hasattr(self, attr):
+                    delattr(self, attr)
+
+            pooler_config = vllm_config.model_config.pooler_config
+            assert pooler_config is not None
+
+            # If the model already defines a pooler instance, don't overwrite it
+            if not getattr(self, "_pooler", None):
+                self._pooler = Pooler.from_config_with_defaults(
+                    pooler_config,
+                    pooling_type=PoolingType.LAST,
+                    normalize=True,
+                    softmax=False,
+                )
+
+        def pooler(
+            self,
+            hidden_states: torch.Tensor,
+            pooling_metadata: PoolingMetadata,
+        ) -> PoolerOutput:
+            return self._pooler(hidden_states, pooling_metadata)
+
+        def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+            # TODO: Support uninitialized params tracking
+
+            # We have deleted this attribute, so don't load it
+            weights = ((name, data) for name, data in weights
+                       if not name.startswith("lm_head."))
+
+            # If `*ForCausalLM` defines `load_weights` on the inner model
+            # and there are no other inner modules with parameters,
+            # we support loading from both `*Model` and `*ForCausalLM`
+            if hasattr(self, "model") and hasattr(self.model, "load_weights"):
+                # Whether only `self.model` contains parameters
+                model_is_only_param = all(
+                    name == "model" or next(child.parameters(), None) is None
+                    for name, child in self.named_children())
+
+                if model_is_only_param:
+                    mapper = WeightsMapper(orig_to_new_prefix={"model.": ""})
+                    weights = mapper.apply(weights)
+
+                    self.model.load_weights(weights)
+                    return
+
+            # For most other models
+            if hasattr(cls, "load_weights"):
+                cls.load_weights(self, weights)  # type: ignore
+            # Fallback
+            else:
+                loader = AutoWeightsLoader(self)
+                loader.load_weights(weights)
+
+    ModelForEmbedding.__name__ = cls.__name__ \
+        .removesuffix("ForCausalLM") \
+        .removesuffix("ForConditionalGeneration") \
+        .removesuffix("ChatModel") \
+        .removesuffix("LMHeadModel") + "ForEmbedding"
+
+    return ModelForEmbedding  # type: ignore

vllm/model_executor/models/blip2.py

@@ -512,9 +512,10 @@ class Blip2ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
         )
 
         self.language_model = init_vllm_registered_model(
-            config.text_config,
             vllm_config=vllm_config,
-            prefix=maybe_prefix(prefix, "language_model"))
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
 
         self.make_empty_intermediate_tensors = (
             self.language_model.make_empty_intermediate_tensors)

vllm/model_executor/models/gemma2.py

@@ -30,19 +30,17 @@ from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
-from vllm.model_executor.layers.pooler import Pooler, PoolingType
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
-from vllm.model_executor.pooling_metadata import PoolingMetadata
 from vllm.model_executor.sampling_metadata import SamplingMetadata
-from vllm.sequence import IntermediateTensors, PoolerOutput
+from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsLoRA, SupportsPP
-from .utils import (AutoWeightsLoader, WeightsMapper, extract_layer_index,
+from .utils import (AutoWeightsLoader, extract_layer_index,
                     is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
@@ -455,55 +453,3 @@ class Gemma2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
                            if self.config.tie_word_embeddings else None),
         )
         return loader.load_weights(weights)
-
-
-class Gemma2EmbeddingModel(nn.Module, SupportsPP):
-    """
-    A model that uses Gemma2 with additional embedding functionalities.
-
-    This class encapsulates the Gemma2Model and provides an interface for
-    embedding operations and customized pooling functions.
-
-    Attributes:
-        model: An instance of Gemma2Model used for forward operations.
-        _pooler: An instance of Pooler used for pooling operations.
-    """
-
-    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
-        super().__init__()
-
-        self.model = Gemma2Model(vllm_config=vllm_config,
-                                 prefix=maybe_prefix(prefix, "model"))
-        self._pooler = Pooler.from_config_with_defaults(
-            vllm_config.model_config.pooler_config,
-            pooling_type=PoolingType.LAST,
-            normalize=True,
-            softmax=False)
-        self.make_empty_intermediate_tensors = (
-            self.model.make_empty_intermediate_tensors)
-
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor],
-        positions: torch.Tensor,
-        kv_caches: List[torch.Tensor],
-        attn_metadata: AttentionMetadata,
-        intermediate_tensors: Optional[IntermediateTensors] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-    ) -> Union[torch.Tensor, IntermediateTensors]:
-        return self.model(input_ids, positions, kv_caches, attn_metadata,
-                          intermediate_tensors, inputs_embeds)
-
-    def pooler(
-        self,
-        hidden_states: torch.Tensor,
-        pooling_metadata: PoolingMetadata,
-    ) -> Optional[PoolerOutput]:
-        return self._pooler(hidden_states, pooling_metadata)
-
-    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
-        hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={"model.": ""})
-        weights = hf_to_vllm_mapper.apply(weights)
-        weights = ((name, data) for name, data in weights
-                   if not name.startswith("lm_head."))
-        self.model.load_weights(weights)

vllm/model_executor/models/internvl.py

@@ -474,9 +474,10 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP):
         )
 
         self.language_model = init_vllm_registered_model(
-            config.text_config,
             vllm_config=vllm_config,
-            prefix=maybe_prefix(prefix, "language_model"))
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
 
         self.mlp1 = self._init_mlp1(config)
 

vllm/model_executor/models/llama.py

@@ -37,7 +37,6 @@ from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
-from vllm.model_executor.layers.pooler import Pooler, PoolingType
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
     get_compressed_tensors_cache_scale)
@@ -47,14 +46,13 @@ 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, kv_cache_scales_loader, maybe_remap_kv_scale_name)
-from vllm.model_executor.pooling_metadata import PoolingMetadata
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.platforms import current_platform
-from vllm.sequence import IntermediateTensors, PoolerOutput
+from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsLoRA, SupportsPP
-from .utils import (AutoWeightsLoader, PPMissingLayer, WeightsMapper,
+from .utils import (AutoWeightsLoader, PPMissingLayer, extract_layer_index,
-                    extract_layer_index, is_pp_missing_parameter,
+                    is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
 
@@ -511,11 +509,12 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         config = vllm_config.model_config.hf_config
         quant_config = vllm_config.quant_config
         lora_config = vllm_config.lora_config
-        pooler_config = vllm_config.model_config.pooler_config
         self.config = config
         self.lora_config = lora_config
 
-        self.model = self._init_model(vllm_config=vllm_config, prefix=prefix)
+        self.model = self._init_model(vllm_config=vllm_config,
+                                      prefix=maybe_prefix(prefix, "model"))
+
         if get_pp_group().is_last_rank:
             self.unpadded_vocab_size = config.vocab_size
             if lora_config:
@@ -544,13 +543,9 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
             self.sampler = get_sampler()
         else:
             self.lm_head = PPMissingLayer()
+
         self.make_empty_intermediate_tensors = (
             self.model.make_empty_intermediate_tensors)
-        self._pooler = Pooler.from_config_with_defaults(
-            pooler_config,
-            pooling_type=PoolingType.STEP,
-            normalize=False,
-            softmax=False)
 
     def _init_model(self, vllm_config: VllmConfig, prefix: str = ""):
         return LlamaModel(vllm_config=vllm_config, prefix=prefix)
@@ -581,14 +576,6 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
                                        sampling_metadata)
         return logits
 
-    def pooler(
-        self,
-        hidden_states: torch.Tensor,
-        pooling_metadata: PoolingMetadata,
-    ) -> Optional[PoolerOutput]:
-        logits = self.compute_logits(hidden_states, None)
-        return self._pooler(logits, pooling_metadata)
-
     def sample(self, logits: torch.Tensor,
                sampling_metadata: SamplingMetadata) -> Optional[SamplerOutput]:
         next_tokens = self.sampler(logits, sampling_metadata)
@@ -639,78 +626,3 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
                 name = name.replace(item, mapping[item])
 
         return name, loaded_weight
-
-
-class LlamaEmbeddingModel(nn.Module, SupportsLoRA, SupportsPP):
-    """
-    A model that uses Llama with additional embedding functionalities.
-
-    This class encapsulates the LlamaModel and provides an interface for
-    embedding operations and customized pooling functions.
-
-    Attributes:
-        model: An instance of LlamaModel used for forward operations.
-        _pooler: An instance of Pooler used for pooling operations.
-    """
-    packed_modules_mapping = {
-        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
-        "gate_up_proj": ["gate_proj", "up_proj"]
-    }
-
-    # LoRA specific attributes
-    supported_lora_modules = [
-        "qkv_proj", "o_proj", "gate_up_proj", "down_proj", "embed_tokens"
-    ]
-    embedding_modules = {
-        "embed_tokens": "input_embeddings",
-    }
-    embedding_padding_modules = []
-
-    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
-        super().__init__()
-
-        pooler_config = vllm_config.model_config.pooler_config
-
-        self.model = LlamaModel(vllm_config=vllm_config,
-                                prefix=maybe_prefix(prefix, "model"))
-        self._pooler = Pooler.from_config_with_defaults(
-            pooler_config,
-            pooling_type=PoolingType.LAST,
-            normalize=True,
-            softmax=False)
-        self.make_empty_intermediate_tensors = (
-            self.model.make_empty_intermediate_tensors)
-
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor],
-        positions: torch.Tensor,
-        kv_caches: List[torch.Tensor],
-        attn_metadata: AttentionMetadata,
-        intermediate_tensors: Optional[IntermediateTensors] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-    ) -> Union[torch.Tensor, IntermediateTensors]:
-        return self.model(input_ids, positions, kv_caches, attn_metadata,
-                          intermediate_tensors, inputs_embeds)
-
-    def pooler(
-        self,
-        hidden_states: torch.Tensor,
-        pooling_metadata: PoolingMetadata,
-    ) -> Optional[PoolerOutput]:
-        return self._pooler(hidden_states, pooling_metadata)
-
-    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
-        hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={"model.": ""})
-        weights = hf_to_vllm_mapper.apply(weights)
-        weights = ((name, data) for name, data in weights
-                   if not name.startswith("lm_head."))
-        self.model.load_weights(weights)
-
-    def load_kv_cache_scales(self, quantization_param_path: str) -> None:
-        self.model.load_kv_cache_scales(quantization_param_path)
-
-    # LRUCacheWorkerLoRAManager instantiation requires model config.
-    @property
-    def config(self):
-        return self.model.config

vllm/model_executor/models/llava.py

@@ -319,9 +319,10 @@ class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
             projector_hidden_act=config.projector_hidden_act)
 
         self.language_model = init_vllm_registered_model(
-            config.text_config,
             vllm_config=vllm_config,
-            prefix=maybe_prefix(prefix, "language_model"))
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
 
         self.make_empty_intermediate_tensors = (
             self.language_model.make_empty_intermediate_tensors)

vllm/model_executor/models/llava_next.py

@@ -14,13 +14,11 @@ from vllm.attention import AttentionMetadata
 from vllm.config import VllmConfig
 from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, DummyData,
                          InputContext)
-from vllm.model_executor.layers.pooler import Pooler, PoolingType
 from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
-from vllm.model_executor.pooling_metadata import PoolingMetadata
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import NestedTensors
-from vllm.sequence import IntermediateTensors, PoolerOutput
+from vllm.sequence import IntermediateTensors
 from vllm.utils import is_list_of
 
 from .clip import (CLIPVisionModel, dummy_image_for_clip,
@@ -286,7 +284,6 @@ class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal,
         super().__init__()
         config = vllm_config.model_config.hf_config
         quant_config = vllm_config.quant_config
-        pooler_config = vllm_config.model_config.pooler_config
         multimodal_config = vllm_config.model_config.multimodal_config
 
         vision_feature_layer = config.vision_feature_layer
@@ -321,17 +318,11 @@ class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal,
             projector_hidden_act=config.projector_hidden_act)
 
         self.language_model = init_vllm_registered_model(
-            config.text_config,
             vllm_config=vllm_config,
-            prefix=maybe_prefix(prefix, "language_model"))
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
 
-        # The same model class supports both language generation and embedding
-        # because the architecture name is the same
-        self._pooler = Pooler.from_config_with_defaults(
-            pooler_config,
-            pooling_type=PoolingType.LAST,
-            normalize=True,
-            softmax=False)
         self.make_empty_intermediate_tensors = (
             self.language_model.make_empty_intermediate_tensors)
 
@@ -678,13 +669,6 @@ class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal,
     ) -> Optional[SamplerOutput]:
         return self.language_model.sample(logits, sampling_metadata)
 
-    def pooler(
-        self,
-        hidden_states: torch.Tensor,
-        pooling_metadata: PoolingMetadata,
-    ) -> Optional[PoolerOutput]:
-        return self._pooler(hidden_states, pooling_metadata)
-
     def load_weights(self, weights: Iterable[Tuple[str,
                                                    torch.Tensor]]) -> Set[str]:
         loader = AutoWeightsLoader(self)

vllm/model_executor/models/llava_next_video.py

@@ -275,9 +275,10 @@ class LlavaNextVideoForConditionalGeneration(nn.Module, SupportsMultiModal,
             text_hidden_size=config.text_config.hidden_size,
             projector_hidden_act=config.projector_hidden_act)
         self.language_model = init_vllm_registered_model(
-            config.text_config,
             vllm_config=vllm_config,
-            prefix=maybe_prefix(prefix, "language_model"))
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
 
         self.make_empty_intermediate_tensors = (
             self.language_model.model.make_empty_intermediate_tensors)

vllm/model_executor/models/llava_onevision.py

@@ -422,9 +422,10 @@ class LlavaOnevisionForConditionalGeneration(nn.Module, SupportsMultiModal,
             prefix=maybe_prefix(prefix, "vision_tower"))
         self.multi_modal_projector = LlavaOnevisionMultiModalProjector(config)
         self.language_model = init_vllm_registered_model(
-            config.text_config,
             vllm_config=vllm_config,
-            prefix=maybe_prefix(prefix, "language_model"))
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
         self.image_newline = nn.Parameter(
             torch.empty(config.text_config.hidden_size))
 

vllm/model_executor/models/paligemma.py

@@ -151,9 +151,10 @@ class PaliGemmaForConditionalGeneration(nn.Module, SupportsMultiModal,
         self.quant_config = quant_config
         config.text_config.architectures = ["GemmaForCausalLM"]
         self.language_model = init_vllm_registered_model(
-            config.text_config,
             vllm_config=vllm_config,
-            prefix=maybe_prefix(prefix, "language_model"))
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
         logit_scale = getattr(config, "logit_scale", 1.0)
         self.language_model.logits_processor.scale *= logit_scale
 

vllm/model_executor/models/phi3v.py

@@ -29,24 +29,22 @@ from vllm.config import ModelConfig, VllmConfig
 from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, DummyData,
                          InputContext, token_inputs)
 from vllm.logger import init_logger
-from vllm.model_executor.layers.pooler import Pooler, PoolingType
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding)
 from vllm.model_executor.models.clip import CLIPVisionModel
-from vllm.model_executor.models.llama import LlamaForCausalLM
-from vllm.model_executor.pooling_metadata import PoolingMetadata
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import NestedTensors, PlaceholderRange
 from vllm.multimodal.utils import cached_get_tokenizer, repeat_and_pad_token
-from vllm.sequence import IntermediateTensors, PoolerOutput
+from vllm.sequence import IntermediateTensors
 from vllm.utils import is_list_of
 
 from .clip import dummy_image_for_clip, dummy_seq_data_for_clip
 from .interfaces import SupportsMultiModal, SupportsPP
-from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, maybe_prefix,
+from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
+                    init_vllm_registered_model, maybe_prefix,
                     merge_multimodal_embeddings)
 
 logger = init_logger(__name__)
@@ -536,7 +534,6 @@ class Phi3VForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
         super().__init__()
         config = vllm_config.model_config.hf_config
         quant_config = vllm_config.quant_config
-        pooler_config = vllm_config.model_config.pooler_config
         multimodal_config = vllm_config.model_config.multimodal_config
         self.config = config
         self.multimodal_config = multimodal_config
@@ -556,18 +553,17 @@ class Phi3VForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
             quant_config,
             prefix=maybe_prefix(prefix, "model.vision_embed_tokens"))
 
-        # The prefix is empty intentionally because default prefix of
+        self.language_model = init_vllm_registered_model(
-        # LlamaForCausalLM is "model"
+            vllm_config=vllm_config,
-        self.language_model = LlamaForCausalLM(vllm_config=vllm_config,
+            # The prefix is empty intentionally because default prefix of
-                                               prefix="")
+            # LlamaForCausalLM is "model"
+            prefix="",
+            # We don't directly initialize vLLM's LlamaForCausalLM so we
+            # can automatically apply embedding wrapper if this model is
+            # initialized as an embedding model
+            architectures=["LlamaForCausalLM"],
+        )
 
-        # The same model class supports both language generation and embedding
-        # because the architecture name is the same
-        self._pooler = Pooler.from_config_with_defaults(
-            pooler_config,
-            pooling_type=PoolingType.LAST,
-            normalize=True,
-            softmax=False)
         self.make_empty_intermediate_tensors = (
             self.language_model.make_empty_intermediate_tensors)
 
@@ -739,13 +735,6 @@ class Phi3VForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
     ) -> Optional[SamplerOutput]:
         return self.language_model.sample(logits, sampling_metadata)
 
-    def pooler(
-        self,
-        hidden_states: torch.Tensor,
-        pooling_metadata: PoolingMetadata,
-    ) -> Optional[PoolerOutput]:
-        return self._pooler(hidden_states, pooling_metadata)
-
     def load_weights(self, weights: Iterable[Tuple[str,
                                                    torch.Tensor]]) -> Set[str]:
         hf_to_vllm_mapper = WeightsMapper(

vllm/model_executor/models/pixtral.py

@@ -172,9 +172,10 @@ class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal,
 
         # init MistralForCausalLM
         self.language_model = init_vllm_registered_model(
-            config.text_config,
             vllm_config=vllm_config,
-            prefix=maybe_prefix(prefix, "language_model"))
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
 
         self.vision_encoder = VisionTransformer(self.vision_args)
         self.vision_language_adapter = VisionLanguageAdapter(

vllm/model_executor/models/qwen2.py

@@ -31,6 +31,7 @@ from vllm.attention import Attention, AttentionMetadata, AttentionType
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
@@ -55,6 +56,8 @@ from .utils import (AutoWeightsLoader, PPMissingLayer, WeightsMapper,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
 
+logger = init_logger(__name__)
+
 
 class Qwen2MLP(nn.Module):
 
@@ -433,7 +436,6 @@ class Qwen2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         config = vllm_config.model_config.hf_config
         quant_config = vllm_config.quant_config
         lora_config = vllm_config.lora_config
-        pooler_config = vllm_config.model_config.pooler_config
 
         self.config = config
         self.lora_config = lora_config
@@ -454,14 +456,6 @@ class Qwen2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = get_sampler()
 
-        # The same model class supports both language generation and embedding
-        # because the architecture name is the same
-        self._pooler = Pooler.from_config_with_defaults(
-            pooler_config,
-            pooling_type=PoolingType.LAST,
-            normalize=True,
-            softmax=False)
-
         self.make_empty_intermediate_tensors = (
             self.model.make_empty_intermediate_tensors)
 
@@ -499,13 +493,6 @@ class Qwen2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         next_tokens = self.sampler(logits, sampling_metadata)
         return next_tokens
 
-    def pooler(
-        self,
-        hidden_states: torch.Tensor,
-        pooling_metadata: PoolingMetadata,
-    ) -> Optional[PoolerOutput]:
-        return self._pooler(hidden_states, pooling_metadata)
-
     def load_weights(self, weights: Iterable[Tuple[str,
                                                    torch.Tensor]]) -> Set[str]:
         loader = AutoWeightsLoader(
@@ -553,6 +540,15 @@ class Qwen2EmbeddingModel(nn.Module, SupportsLoRA, SupportsPP):
         self.model = Qwen2Model(vllm_config=vllm_config,
                                 prefix=maybe_prefix(prefix, "model"))
 
+        # TODO: Replace this model class with for_embedding(Qwen2ForCausalLM),
+        # after changing the default pooling method
+        if pooler_config.pooling_type is None:
+            logger.warning(
+                "This embedding model will default to last-token pooling in "
+                "an upcoming version. To avoid breaking changes, you should "
+                "pass `--override-pooler-config '{\"pooling_type\": \"MEAN\"}'`"
+                " explicitly.")
+
         self._pooler = Pooler.from_config_with_defaults(
             pooler_config,
             pooling_type=PoolingType.MEAN,

vllm/model_executor/models/qwen2_vl.py

@@ -50,7 +50,6 @@ from vllm.model_executor.layers.activation import QuickGELU
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
-from vllm.model_executor.layers.pooler import Pooler, PoolingType
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.quantization.gptq import GPTQConfig
 from vllm.model_executor.layers.quantization.gptq_marlin import (
@@ -59,14 +58,13 @@ from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.qwen2 import Qwen2Model
-from vllm.model_executor.pooling_metadata import PoolingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.image import cached_get_image_processor
 from vllm.multimodal.inputs import (MultiModalData, MultiModalDataDict,
                                     MultiModalKwargs, NestedTensors)
 from vllm.multimodal.utils import cached_get_tokenizer
 from vllm.platforms import _Backend
-from vllm.sequence import IntermediateTensors, PoolerOutput, SequenceData
+from vllm.sequence import IntermediateTensors, SequenceData
 from vllm.transformers_utils.config import uses_mrope
 from vllm.transformers_utils.processor import cached_get_processor
 
@@ -1070,7 +1068,6 @@ class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal,
         config = vllm_config.model_config.hf_config
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
-        pooler_config = vllm_config.model_config.pooler_config
         multimodal_config = vllm_config.model_config.multimodal_config
         assert not cache_config.enable_prefix_caching, \
             "Qwen2-VL currently does not support prefix caching"
@@ -1102,11 +1099,7 @@ class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal,
 
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = get_sampler()
-        self._pooler = Pooler.from_config_with_defaults(
+
-            pooler_config,
-            pooling_type=PoolingType.LAST,
-            normalize=True,
-            softmax=False)
         self.make_empty_intermediate_tensors = (
             make_empty_intermediate_tensors_factory(
                 ["hidden_states", "residual"], config.hidden_size))
@@ -1361,13 +1354,6 @@ class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal,
         next_tokens = self.sampler(logits, sampling_metadata)
         return next_tokens
 
-    def pooler(
-        self,
-        hidden_states: torch.Tensor,
-        pooling_metadata: PoolingMetadata,
-    ) -> Optional[PoolerOutput]:
-        return self._pooler(hidden_states, pooling_metadata)
-
     def load_weights(self, weights: Iterable[Tuple[str,
                                                    torch.Tensor]]) -> Set[str]:
         stacked_params_mapping = [

vllm/model_executor/models/registry.py

@@ -20,6 +20,7 @@ import torch.nn as nn
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
 
+from .adapters import as_embedding_model
 from .interfaces import (has_inner_state, is_attention_free,
                          supports_cross_encoding, supports_multimodal,
                          supports_pp)
@@ -107,15 +108,15 @@ _EMBEDDING_MODELS = {
     "RobertaForMaskedLM": ("roberta", "RobertaEmbeddingModel"),
     "XLMRobertaModel": ("roberta", "RobertaEmbeddingModel"),
     "DeciLMForCausalLM": ("decilm", "DeciLMForCausalLM"),
-    "Gemma2Model": ("gemma2", "Gemma2EmbeddingModel"),
+    "Gemma2Model": ("gemma2", "Gemma2ForCausalLM"),
     "GlmForCausalLM": ("glm", "GlmForCausalLM"),
-    "LlamaModel": ("llama", "LlamaEmbeddingModel"),
+    "LlamaModel": ("llama", "LlamaForCausalLM"),
     **{
         # Multiple models share the same architecture, so we include them all
         k: (mod, arch) for k, (mod, arch) in _TEXT_GENERATION_MODELS.items()
         if arch == "LlamaForCausalLM"
     },
-    "MistralModel": ("llama", "LlamaEmbeddingModel"),
+    "MistralModel": ("llama", "LlamaForCausalLM"),
     "Phi3ForCausalLM": ("phi3", "Phi3ForCausalLM"),
     "Qwen2Model": ("qwen2", "Qwen2EmbeddingModel"),
     "Qwen2ForCausalLM": ("qwen2", "Qwen2ForCausalLM"),
@@ -125,7 +126,7 @@ _EMBEDDING_MODELS = {
     # [Multimodal]
     "LlavaNextForConditionalGeneration": ("llava_next", "LlavaNextForConditionalGeneration"),  # noqa: E501
     "Phi3VForCausalLM": ("phi3v", "Phi3VForCausalLM"),
-    "Qwen2VLForConditionalGeneration": ("qwen2_vl", "Qwen2VLForConditionalGeneration") # noqa: E501,
+    "Qwen2VLForConditionalGeneration": ("qwen2_vl", "Qwen2VLForConditionalGeneration"),  # noqa: E501
 }
 
 _CROSS_ENCODER_MODELS = {
@@ -208,6 +209,7 @@ _ROCM_PARTIALLY_SUPPORTED_MODELS: Dict[str, str] = {
 
 @dataclass(frozen=True)
 class _ModelInfo:
+    architecture: str
     is_text_generation_model: bool
     is_embedding_model: bool
     supports_cross_encoding: bool
@@ -218,9 +220,19 @@ class _ModelInfo:
 
     @staticmethod
     def from_model_cls(model: Type[nn.Module]) -> "_ModelInfo":
+        is_embedding_model_ = is_embedding_model(model)
+        if not is_embedding_model_:
+            try:
+                as_embedding_model(model)
+            except Exception:
+                pass
+            else:
+                is_embedding_model_ = True
+
         return _ModelInfo(
+            architecture=model.__name__,
             is_text_generation_model=is_text_generation_model(model),
-            is_embedding_model=is_embedding_model(model),
+            is_embedding_model=is_embedding_model_,
             supports_cross_encoding=supports_cross_encoding(model),
             supports_multimodal=supports_multimodal(model),
             supports_pp=supports_pp(model),
@@ -399,13 +411,13 @@ class _ModelRegistry:
     def inspect_model_cls(
         self,
         architectures: Union[str, List[str]],
-    ) -> _ModelInfo:
+    ) -> Tuple[_ModelInfo, str]:
         architectures = self._normalize_archs(architectures)
 
         for arch in architectures:
             model_info = self._try_inspect_model_cls(arch)
             if model_info is not None:
-                return model_info
+                return (model_info, arch)
 
         return self._raise_for_unsupported(architectures)
 
@@ -426,39 +438,50 @@ class _ModelRegistry:
         self,
         architectures: Union[str, List[str]],
     ) -> bool:
-        return self.inspect_model_cls(architectures).is_text_generation_model
+        model_cls, _ = self.inspect_model_cls(architectures)
+        return model_cls.is_text_generation_model
 
     def is_embedding_model(
         self,
         architectures: Union[str, List[str]],
     ) -> bool:
-        return self.inspect_model_cls(architectures).is_embedding_model
+        model_cls, _ = self.inspect_model_cls(architectures)
+        return model_cls.is_embedding_model
 
     def is_cross_encoder_model(
         self,
         architectures: Union[str, List[str]],
     ) -> bool:
-        return self.inspect_model_cls(architectures).supports_cross_encoding
+        model_cls, _ = self.inspect_model_cls(architectures)
+        return model_cls.supports_cross_encoding
 
     def is_multimodal_model(
         self,
         architectures: Union[str, List[str]],
     ) -> bool:
-        return self.inspect_model_cls(architectures).supports_multimodal
+        model_cls, _ = self.inspect_model_cls(architectures)
+        return model_cls.supports_multimodal
 
     def is_pp_supported_model(
         self,
         architectures: Union[str, List[str]],
     ) -> bool:
-        return self.inspect_model_cls(architectures).supports_pp
+        model_cls, _ = self.inspect_model_cls(architectures)
+        return model_cls.supports_pp
 
-    def model_has_inner_state(self, architectures: Union[str,
+    def model_has_inner_state(
-                                                         List[str]]) -> bool:
+        self,
-        return self.inspect_model_cls(architectures).has_inner_state
+        architectures: Union[str, List[str]],
+    ) -> bool:
+        model_cls, _ = self.inspect_model_cls(architectures)
+        return model_cls.has_inner_state
 
-    def is_attention_free_model(self, architectures: Union[str,
+    def is_attention_free_model(
-                                                           List[str]]) -> bool:
+        self,
-        return self.inspect_model_cls(architectures).is_attention_free
+        architectures: Union[str, List[str]],
+    ) -> bool:
+        model_cls, _ = self.inspect_model_cls(architectures)
+        return model_cls.is_attention_free
 
 
 ModelRegistry = _ModelRegistry({

vllm/model_executor/models/ultravox.py

@@ -360,9 +360,10 @@ class UltravoxModel(nn.Module, SupportsMultiModal, SupportsPP):
                 ))
         self.multi_modal_projector = UltravoxProjector(config)
         self.language_model = init_vllm_registered_model(
-            config.text_config,
             vllm_config=vllm_config,
-            prefix=maybe_prefix(prefix, "language_model"))
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
         if config.text_model_id is not None:
             # this prefix is not for initialization, but for loading weights
             # note the trailing dot

vllm/model_executor/models/utils.py

@@ -173,8 +173,15 @@ class AutoWeightsLoader:
             module_load_weights = getattr(module, "load_weights", None)
             if callable(module_load_weights):
                 loaded_params = module_load_weights(weights)
-                yield from map(lambda x: self._get_qualname(base_prefix, x),
+                if loaded_params is None:
-                               loaded_params)
+                    logger.warning(
+                        "Unable to collect loaded parameters "
+                        "for module %s", module)
+                else:
+                    yield from map(
+                        lambda x: self._get_qualname(base_prefix, x),
+                        loaded_params,
+                    )
 
         child_modules = dict(module.named_children())
         child_params = dict(module.named_parameters(recurse=False))
@@ -232,17 +239,24 @@ class AutoWeightsLoader:
 
 
 def init_vllm_registered_model(
-    hf_config: PretrainedConfig,
     vllm_config: VllmConfig,
+    *,
     prefix: str = "",
+    hf_config: Optional[PretrainedConfig] = None,
+    architectures: Optional[list[str]] = None,
 ) -> nn.Module:
     """
     Helper function to initialize an inner model registered to vLLM,
     based on the arguments passed to the outer vLLM model.
     """
     from vllm.model_executor.model_loader.loader import _initialize_model
-    vllm_config = vllm_config.with_hf_config(hf_config)
+
-    return _initialize_model(vllm_config, prefix)
+    if hf_config is not None:
+        vllm_config = vllm_config.with_hf_config(hf_config)
+
+    return _initialize_model(vllm_config=vllm_config,
+                             prefix=prefix,
+                             architectures=architectures)
 
 
 @overload

vllm/multimodal/base.py

@@ -7,7 +7,7 @@ from torch import nn
 
 from vllm.inputs import InputContext
 from vllm.logger import init_logger
-from vllm.utils import (get_allowed_kwarg_only_overrides,
+from vllm.utils import (ClassRegistry, get_allowed_kwarg_only_overrides,
                         resolve_mm_processor_kwargs)
 
 if TYPE_CHECKING:
@@ -54,8 +54,8 @@ class MultiModalPlugin(ABC):
     """
 
     def __init__(self) -> None:
-        self._input_mappers: Dict[Type[nn.Module], MultiModalInputMapper] = {}
+        self._input_mappers = ClassRegistry[nn.Module, MultiModalInputMapper]()
-        self._max_mm_tokens: Dict[Type[nn.Module], MultiModalTokensCalc] = {}
+        self._max_mm_tokens = ClassRegistry[nn.Module, MultiModalTokensCalc]()
 
     @abstractmethod
     def get_data_key(self) -> str:

vllm/multimodal/registry.py

@@ -9,6 +9,7 @@ from typing_extensions import TypeAlias
 from vllm.inputs import InputProcessingContext
 from vllm.logger import init_logger
 from vllm.transformers_utils.tokenizer import AnyTokenizer
+from vllm.utils import ClassRegistry
 
 from .audio import AudioPlugin
 from .base import MultiModalInputMapper, MultiModalPlugin, MultiModalTokensCalc
@@ -62,8 +63,8 @@ class MultiModalRegistry:
             plugins: Sequence[MultiModalPlugin] = DEFAULT_PLUGINS) -> None:
         self._plugins = {p.get_data_key(): p for p in plugins}
 
-        self._processor_factories: Dict[Type[nn.Module],
+        self._processor_factories = ClassRegistry[nn.Module,
-                                        MultiModalProcessorFactory] = {}
+                                                  MultiModalProcessorFactory]()
 
         # This is used for non-multimodal models
         self._disabled_limits_per_plugin = {k: 0 for k in self._plugins}

vllm/utils.py

@@ -20,7 +20,7 @@ import uuid
 import warnings
 import weakref
 from asyncio import FIRST_COMPLETED, AbstractEventLoop, Future, Task
-from collections import defaultdict
+from collections import UserDict, defaultdict
 from collections.abc import Iterable, Mapping
 from functools import lru_cache, partial, wraps
 from platform import uname
@@ -1517,13 +1517,13 @@ class AtomicCounter:
 
 
 # Adapted from: https://stackoverflow.com/a/47212782/5082708
-class LazyDict(Mapping, Generic[T]):
+class LazyDict(Mapping[str, T], Generic[T]):
 
     def __init__(self, factory: Dict[str, Callable[[], T]]):
         self._factory = factory
         self._dict: Dict[str, T] = {}
 
-    def __getitem__(self, key) -> T:
+    def __getitem__(self, key: str) -> T:
         if key not in self._dict:
             if key not in self._factory:
                 raise KeyError(key)
@@ -1540,6 +1540,22 @@ class LazyDict(Mapping, Generic[T]):
         return len(self._factory)
 
 
+class ClassRegistry(UserDict[type[T], _V]):
+
+    def __getitem__(self, key: type[T]) -> _V:
+        for cls in key.mro():
+            if cls in self.data:
+                return self.data[cls]
+
+        raise KeyError(key)
+
+    def __contains__(self, key: object) -> bool:
+        if not isinstance(key, type):
+            return False
+
+        return any(cls in self.data for cls in key.mro())
+
+
 def weak_ref_tensor(tensor: torch.Tensor) -> torch.Tensor:
     """
     Create a weak reference to a tensor.