[Model] Explicit default_pooling_type interface (#23736)

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

vllm/model_executor/models/bert.py

@@ -28,8 +28,8 @@ from vllm.model_executor.pooling_metadata import PoolingMetadata
 from vllm.sequence import IntermediateTensors
 from vllm.tasks import PoolingTask
 
-from .interfaces import (SupportsCrossEncoding, SupportsQuant,
+from .interfaces import SupportsCrossEncoding, SupportsQuant
-                         default_pooling_type)
+from .interfaces_base import default_pooling_type
 from .utils import AutoWeightsLoader, WeightsMapper, maybe_prefix
 
 

vllm/model_executor/models/bert_with_rope.py

@@ -27,13 +27,14 @@ from vllm.model_executor.layers.rotary_embedding import get_rope
 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.models.interfaces import (SupportsQuant,
-                                                   default_pooling_type)
 from vllm.model_executor.models.utils import WeightsMapper
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
 
+from .interfaces import SupportsQuant
+from .interfaces_base import default_pooling_type
+
 
 class BertWithRopeEmbedding(nn.Module):
 

vllm/model_executor/models/gritlm.py

@@ -20,7 +20,7 @@ from vllm.sequence import PoolerOutput
 from vllm.tasks import PoolingTask
 from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
 
-from .interfaces import default_pooling_type
+from .interfaces_base import default_pooling_type
 
 logger = init_logger(__name__)
 

vllm/model_executor/models/interfaces.py

@@ -3,7 +3,7 @@
 
 from collections.abc import Iterable, Mapping, MutableSequence
 from typing import (TYPE_CHECKING, ClassVar, Literal, Optional, Protocol,
-                    TypeVar, Union, overload, runtime_checkable)
+                    Union, overload, runtime_checkable)
 
 import numpy as np
 import torch
@@ -641,23 +641,6 @@ def supports_cross_encoding(
     return is_pooling_model(model) and _supports_cross_encoding(model)
 
 
-_T = TypeVar("_T", bound=type[torch.nn.Module])
-
-
-def default_pooling_type(pooling_type: str):
-    """Set default_pooling_type decorator. """
-
-    def func(model: _T) -> _T:
-        model.default_pooling_type = pooling_type  # type: ignore
-        return model
-
-    return func
-
-
-def get_default_pooling_type(model: Union[type[object], object]) -> str:
-    return getattr(model, "default_pooling_type", "LAST")
-
-
 class SupportsQuant:
     """The interface required for all models that support quantization."""
 

vllm/model_executor/models/interfaces_base.py

@@ -144,6 +144,17 @@ class VllmModelForPooling(VllmModel[T_co], Protocol[T_co]):
         MRO of your model class.
     """
 
+    default_pooling_type: ClassVar[str] = "LAST"
+    """
+    Indicates the
+    [vllm.model_executor.layers.pooler.PoolerConfig.pooling_type][]
+    to use by default.
+
+    You can use the
+    [vllm.model_executor.models.interfaces_base.default_pooling_type][]
+    decorator to conveniently set this field.
+    """
+
     pooler: Pooler
     """The pooler is only called on TP rank 0."""
 
@@ -165,3 +176,20 @@ def is_pooling_model(
         return False
 
     return getattr(model, "is_pooling_model", False)
+
+
+_T = TypeVar("_T", bound=type[nn.Module])
+
+
+def default_pooling_type(pooling_type: str):
+    """Decorator to set `VllmModelForPooling.default_pooling_type`."""
+
+    def func(model: _T) -> _T:
+        model.default_pooling_type = pooling_type  # type: ignore
+        return model
+
+    return func
+
+
+def get_default_pooling_type(model: Union[type[object], object]) -> str:
+    return getattr(model, "default_pooling_type", "LAST")

vllm/model_executor/models/internlm2.py

@@ -31,7 +31,8 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
-from .interfaces import SupportsLoRA, SupportsPP, default_pooling_type
+from .interfaces import SupportsLoRA, SupportsPP
+from .interfaces_base import default_pooling_type
 from .utils import (is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)

vllm/model_executor/models/modernbert.py

@@ -26,7 +26,8 @@ from vllm.model_executor.pooling_metadata import PoolingMetadata
 from vllm.sequence import IntermediateTensors
 from vllm.tasks import PoolingTask
 
-from .interfaces import SupportsCrossEncoding, default_pooling_type
+from .interfaces import SupportsCrossEncoding
+from .interfaces_base import default_pooling_type
 from .utils import WeightsMapper, maybe_prefix
 
 

vllm/model_executor/models/prithvi_geospatial_mae.py

@@ -27,9 +27,6 @@ from transformers import BatchFeature
 from vllm.config import VllmConfig
 from vllm.model_executor.layers.pooler import DispatchPooler, Pooler
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
-from vllm.model_executor.models.interfaces import (
-    IsAttentionFree, MultiModalEmbeddings, SupportsMultiModalWithRawInput,
-    default_pooling_type)
 from vllm.model_executor.models.utils import AutoWeightsLoader
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import (ImageItem, ModalityData,
@@ -43,6 +40,10 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
 
+from .interfaces import (IsAttentionFree, MultiModalEmbeddings,
+                         SupportsMultiModalWithRawInput)
+from .interfaces_base import default_pooling_type
+
 
 def _prithvi_field_config(hf_inputs: Mapping[str, torch.Tensor]):
     # This model receives in input a multi-dimensional tensor representing

vllm/model_executor/models/qwen2_rm.py

@@ -18,7 +18,8 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
 from vllm.model_executor.layers.pooler import DispatchPooler, Pooler
 from vllm.sequence import IntermediateTensors
 
-from .interfaces import SupportsLoRA, SupportsPP, default_pooling_type
+from .interfaces import SupportsLoRA, SupportsPP
+from .interfaces_base import default_pooling_type
 from .qwen2 import Qwen2Model
 from .utils import AutoWeightsLoader, maybe_prefix
 

vllm/model_executor/models/registry.py

@@ -25,11 +25,12 @@ from vllm.logger import init_logger
 from vllm.transformers_utils.dynamic_module import (
     try_get_class_from_dynamic_module)
 
-from .interfaces import (get_default_pooling_type, has_inner_state, has_noops,
+from .interfaces import (has_inner_state, has_noops, is_attention_free,
-                         is_attention_free, is_hybrid, supports_cross_encoding,
+                         is_hybrid, supports_cross_encoding,
                          supports_multimodal, supports_multimodal_raw_input,
                          supports_pp, supports_transcription, supports_v0_only)
-from .interfaces_base import is_pooling_model, is_text_generation_model
+from .interfaces_base import (get_default_pooling_type, is_pooling_model,
+                              is_text_generation_model)
 
 logger = init_logger(__name__)
 

vllm/model_executor/models/roberta.py

@@ -22,7 +22,8 @@ from vllm.model_executor.models.utils import (AutoWeightsLoader, WeightsMapper,
 from vllm.sequence import IntermediateTensors
 
 from .bert_with_rope import BertWithRope, JinaRobertaModel
-from .interfaces import SupportsCrossEncoding, default_pooling_type
+from .interfaces import SupportsCrossEncoding
+from .interfaces_base import default_pooling_type
 
 
 class RobertaEmbedding(nn.Module):