[Misc] Factor out common _apply_feature_select_strategy (#26003)

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

Author: DarkLight1337

vllm/model_executor/models/llava.py

@@ -41,7 +41,7 @@
 from .siglip import SiglipVisionModel
 from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
                     init_vllm_registered_model, maybe_prefix)
-from .vision import get_vision_encoder_info
+from .vision import get_num_selected_vision_tokens, get_vision_encoder_info
 
 
 class LlavaImagePixelInputs(TensorSchema):
@@ -147,19 +147,6 @@ def get_hf_processor(self, **kwargs: object) -> LlavaLikeProcessor:
     def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
         return {"image": None}
 
-    def _apply_feature_select_strategy(
-        self,
-        strategy: str,
-        encoder_num_image_tokens: int,
-    ) -> int:
-        if strategy == "default":
-            return encoder_num_image_tokens - 1
-        if strategy == "full":
-            return encoder_num_image_tokens
-
-        msg = f"Unexpected feature select strategy: {strategy!r}"
-        raise NotImplementedError(msg)
-
     def get_num_image_tokens(
         self,
         *,
@@ -169,12 +156,12 @@ def get_num_image_tokens(
         hf_config = self.get_hf_config()
         vision_encoder_info = self.get_vision_encoder_info()
 
-        return self._apply_feature_select_strategy(
-            hf_config.vision_feature_select_strategy,
+        return get_num_selected_vision_tokens(
             vision_encoder_info.get_num_image_tokens(
                 image_width=image_width,
                 image_height=image_height,
             ),
+            hf_config.vision_feature_select_strategy,
         )
 
     def get_image_size_with_most_features(self) -> ImageSize:

vllm/model_executor/models/llava_next.py

@@ -27,6 +27,7 @@
 from .siglip import SiglipVisionModel
 from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
                     init_vllm_registered_model, maybe_prefix)
+from .vision import get_num_selected_vision_tokens
 
 
 class LlavaNextImagePixelInputs(TensorSchema):
@@ -95,12 +96,12 @@ def get_num_image_tokens(
         hf_config = self.get_hf_config()
         vision_encoder_info = self.get_vision_encoder_info()
 
-        base_feature_size = self._apply_feature_select_strategy(
-            hf_config.vision_feature_select_strategy,
+        base_feature_size = get_num_selected_vision_tokens(
             vision_encoder_info.get_num_image_tokens(
                 image_width=image_width,
                 image_height=image_height,
             ),
+            hf_config.vision_feature_select_strategy,
         )
 
         num_patch_height, num_patch_width = get_anyres_image_grid_shape(

vllm/model_executor/models/tarsier.py

@@ -40,7 +40,8 @@
 from .siglip import SiglipVisionModel
 from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
                     maybe_prefix)
-from .vision import VisionEncoderInfo, get_vision_encoder_info
+from .vision import (VisionEncoderInfo, get_num_selected_vision_tokens,
+                     get_vision_encoder_info)
 
 
 class TarsierImagePixelInputs(TensorSchema):
@@ -201,18 +202,6 @@ def get_hf_processor(self, **kwargs: object) -> TarsierProcessor:
     def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
         return {"image": None}
 
-    def _apply_feature_select_strategy(
-        self,
-        strategy: str,
-        encoder_num_image_tokens: int,
-    ) -> int:
-        if strategy == "default":
-            return encoder_num_image_tokens - 1
-        if strategy == "full":
-            return encoder_num_image_tokens
-        msg = f"Unexpected feature select strategy: {strategy!r}"
-        raise NotImplementedError(msg)
-
     def get_num_image_tokens(
         self,
         *,
@@ -221,21 +210,21 @@ def get_num_image_tokens(
     ) -> int:
         hf_config = self.get_hf_config()
         vision_encoder_info = self.get_vision_encoder_info()
-        num_projected_patches = self._apply_feature_select_strategy(
-            hf_config.vision_feature_select_strategy,
+        num_projected_patches = get_num_selected_vision_tokens(
             vision_encoder_info.get_num_image_tokens(
                 image_width=image_width,
                 image_height=image_height,
             ),
+            hf_config.vision_feature_select_strategy,
         )
         if num_projected_patches <= 0:
             default_size = self.get_image_size_with_most_features()
-            num_projected_patches_default = self._apply_feature_select_strategy(
-                hf_config.vision_feature_select_strategy,
+            num_projected_patches_default = get_num_selected_vision_tokens(
                 vision_encoder_info.get_num_image_tokens(
                     image_width=default_size.width,
                     image_height=default_size.height,
                 ),
+                hf_config.vision_feature_select_strategy,
             )
             if num_projected_patches_default <= 0:
                 raise ValueError(

vllm/model_executor/models/vision.py

@@ -9,7 +9,6 @@
 
 import torch
 from transformers import PretrainedConfig
-from typing_extensions import assert_never
 
 from vllm.distributed import (get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size,
@@ -22,9 +21,13 @@
 _C = TypeVar("_C", bound=PretrainedConfig)
 
 
+class _RootConfig(Protocol[_C]):
+    vision_config: _C
+
+
 class VisionEncoderInfo(ABC, Generic[_C]):
 
-    def __init__(self, hf_config: _C) -> None:
+    def __init__(self, hf_config: _RootConfig[_C]) -> None:
         super().__init__()
 
         self.hf_config = hf_config
@@ -95,7 +98,7 @@ def get_vit_attn_backend(head_size: int, dtype: torch.dtype) -> _Backend:
 
 
 def _get_vision_feature_selector(
-    strategy: VisionFeatureSelectStrategy,
+    strategy: Union[VisionFeatureSelectStrategy, str],
 ) -> Callable[[torch.Tensor], torch.Tensor]:
     if callable(strategy):
         return strategy
@@ -111,7 +114,28 @@ def _get_vision_feature_selector(
     if strategy == "full":
         return lambda feats: feats
 
-    assert_never(strategy)
+    raise ValueError(f"Unexpected feature select strategy: {strategy!r}")
+
+
+def get_num_selected_vision_tokens(
+    num_vision_tokens: int,
+    strategy: Union[VisionFeatureSelectStrategy, str],
+) -> int:
+    if callable(strategy):
+        dummy_features = torch.empty(1, num_vision_tokens, 64)  # [B, L, D]
+        dummy_selected_features = strategy(dummy_features)
+        return dummy_selected_features.shape[1]
+
+    if strategy == "class":
+        return 1
+
+    if strategy == "default":
+        return num_vision_tokens - 1
+
+    if strategy == "full":
+        return num_vision_tokens
+
+    raise ValueError(f"Unexpected feature select strategy: {strategy!r}")
 
 
 def resolve_visual_encoder_outputs(