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

Signed-off-by: DarkLight1337 <[email protected]>

.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 @@ def __init__(
         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 @@
 """
 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 @@
 from vllm.model_executor.models import (is_embedding_model,
                                         is_text_generation_model,
                                         supports_multimodal)
-# yapf conflicts with isort for this block
-# yapf: disable
-from vllm.model_executor.models.registry import (_CROSS_ENCODER_MODELS,
-                                                 _EMBEDDING_MODELS,
-                                                 _MULTIMODAL_MODELS,
+from vllm.model_executor.models.adapters import as_embedding_model
+from vllm.model_executor.models.registry import (_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
-    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}
-        assert is_embedding_model(model_cls) is (model_arch
-                                                 in embedding_models)
-
-        assert supports_multimodal(model_cls) is (model_arch
-                                                  in _MULTIMODAL_MODELS)
+        return  # Ignore these models which do not have a unified format
+
+    if (model_arch in _TEXT_GENERATION_MODELS
+            or model_arch in _MULTIMODAL_MODELS):
+        assert is_text_generation_model(model_cls)
+
+    # All vLLM models should be convertible to an embedding model
+    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.sequence import IntermediateTensors
+from vllm.config import VllmConfig
+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 @@ def forward(
         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 @@ def forward(
 
         # 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"),
-    ("intfloat/e5-mistral-7b-instruct", "generate"),
+    ("Qwen/Qwen2.5-Math-RM-72B", "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 @@ def _resolve_task(
             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 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,
-                        resolve_mm_processor_kwargs)
+from vllm.utils import (ClassRegistry, get_allowed_kwarg_only_overrides,
+                        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],
-                                                  DummyDataFactory] = {}
-        self._dummy_encoder_factories_by_model_type: Dict[
-            Type[nn.Module], DummyDataFactory] = {}
-        self._input_processors_by_model_type: Dict[Type[nn.Module],
-                                                   InputProcessor] = {}
+        self._dummy_factories_by_model_type = \
+            ClassRegistry[nn.Module, DummyDataFactory]()
+        self._dummy_encoder_factories_by_model_type = \
+            ClassRegistry[nn.Module, DummyDataFactory]()
+        self._input_processors_by_model_type = \
+            ClassRegistry[nn.Module, InputProcessor]()
 
     def _default_dummy_data_factory(
         self,

vllm/model_executor/layers/pooler.py

@@ -60,9 +60,7 @@ def from_config_with_defaults(
         softmax: bool,
         step_tag_id: Optional[int] = None,
         returned_token_ids: Optional[List[int]] = None,
-    ) -> Optional["Pooler"]:
-        if pooler_config is None:
-            return None
+    ) -> "Pooler":
         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 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 @@ def load_model(self, vllm_config: VllmConfig) -> nn.Module:
             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]:
-    architectures = getattr(model_config.hf_config, "architectures", [])
+    model_config: ModelConfig,
+    *,
+    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: