[Model] Move `vision_feature_select_strategy` into `resolve_visual_encoder_outputs` (#25938)

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

tests/models/test_vision.py

@@ -18,7 +18,7 @@ from vllm.utils import get_open_port, update_environment_variables
 
 
 @pytest.mark.parametrize(
-    ("feature_sample_layers", "num_layers_loaded", "max_possible_layers",
+    ("select_layers", "num_layers_loaded", "max_possible_layers",
      "expected_features"),
     [
         # All layers loaded
@@ -28,8 +28,8 @@ from vllm.utils import get_open_port, update_environment_variables
         ([1, 10], 10, 20, [1, 10]),
         ([-20, -11], 10, 20, [1, 10]),
     ])
-def test_resolve_visual_encoder_outputs(feature_sample_layers,
-                                        num_layers_loaded, max_possible_layers,
+def test_resolve_visual_encoder_outputs(select_layers, num_layers_loaded,
+                                        max_possible_layers,
                                         expected_features):
     """
     Test that offsets are correctly handled for vision feature layers.
@@ -39,9 +39,10 @@ def test_resolve_visual_encoder_outputs(feature_sample_layers,
     ]
     output_tensor = resolve_visual_encoder_outputs(
         encoder_outputs=encoder_outputs,
-        feature_sample_layers=feature_sample_layers,
         post_layer_norm=None,
-        max_possible_layers=max_possible_layers)
+        select_layers=select_layers,
+        max_possible_layers=max_possible_layers,
+    )
     assert torch.equal(torch.tensor(expected_features), output_tensor)
 
 

vllm/model_executor/models/aya_vision.py

@@ -27,7 +27,6 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                         PromptUpdateDetails)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
-from vllm.utils.jsontree import json_map_leaves
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
@@ -350,30 +349,12 @@ class AyaVisionForConditionalGeneration(nn.Module, SupportsMultiModal,
         self,
         vision_tower: SiglipVisionModel,
         pixel_values: torch.Tensor,
-        **kwargs,
     ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
-        target_dtype: torch.dtype = \
-            vision_tower.get_input_embeddings().weight.dtype
-        image_features: Union[torch.Tensor, tuple[torch.Tensor, ...]] = \
-            vision_tower(pixel_values.to(dtype=target_dtype), **kwargs)
-
-        def select_features(leaf: torch.Tensor):
-            return self._select_image_features(
-                leaf,
-                strategy=self.config.vision_feature_select_strategy,
+        return vision_tower(
+            pixel_values.to(dtype=vision_tower.dtype),
+            feature_select_strategy=self.config.vision_feature_select_strategy,
         )
 
-        return json_map_leaves(select_features, image_features)
-
-    def _select_image_features(self, image_features: torch.Tensor, *,
-                               strategy: str) -> torch.Tensor:
-        if strategy == "default":
-            return image_features[:, 1:]
-        elif strategy == "full":
-            return image_features
-
-        raise ValueError(f"Unexpected select feature strategy: {strategy}")
-
     def _process_image_input(self, image_input: AyaVisionImagePixelInputs,
                              **kwargs) -> list[torch.Tensor]:
         assert self.vision_tower is not None

vllm/model_executor/models/clip.py

@@ -19,7 +19,8 @@ from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.interfaces import SupportsQuant
 
-from .vision import VisionEncoderInfo, resolve_visual_encoder_outputs
+from .vision import (VisionEncoderInfo, VisionFeatureSelectStrategy,
+                     resolve_visual_encoder_outputs)
 
 
 class CLIPEncoderInfo(VisionEncoderInfo[CLIPVisionConfig]):
@@ -308,24 +309,29 @@ class CLIPVisionTransformer(nn.Module):
     def forward(
         self,
         pixel_values: torch.Tensor,
-        feature_sample_layers: Optional[list[int]] = None,
+        *,
+        select_layers: Optional[list[int]] = None,
+        feature_select_strategy: Optional[VisionFeatureSelectStrategy] = None,
     ) -> torch.Tensor:
 
         hidden_states = self.embeddings(pixel_values)
         hidden_states = self.pre_layrnorm(hidden_states)
 
-        return_all_hidden_states = feature_sample_layers is not None
-
         # Produces either the last layer output or all of the hidden states,
-        # depending on if we have feature_sample_layers or not
+        # depending on if we have select_layers or not
         encoder_outputs = self.encoder(
             inputs_embeds=hidden_states,
-            return_all_hidden_states=return_all_hidden_states)
+            return_all_hidden_states=select_layers is not None,
+        )
 
         # Handle post-norm (if applicable) and stacks feature layers if needed
         encoder_outputs = resolve_visual_encoder_outputs(
-            encoder_outputs, feature_sample_layers, self.post_layernorm,
-            self.config.num_hidden_layers)
+            encoder_outputs,
+            self.post_layernorm,
+            select_layers=select_layers,
+            max_possible_layers=self.config.num_hidden_layers,
+            feature_select_strategy=feature_select_strategy,
+        )
 
         return encoder_outputs
 
@@ -355,9 +361,14 @@ class CLIPVisionModel(nn.Module, SupportsQuant):
     def forward(
         self,
         pixel_values: torch.Tensor,
-        feature_sample_layers: Optional[list[int]] = None,
+        select_layers: Optional[list[int]] = None,
+        feature_select_strategy: Optional[VisionFeatureSelectStrategy] = None,
     ) -> torch.Tensor:
-        return self.vision_model(pixel_values, feature_sample_layers)
+        return self.vision_model(
+            pixel_values,
+            select_layers=select_layers,
+            feature_select_strategy=feature_select_strategy,
+        )
 
     @property
     def device(self):

vllm/model_executor/models/llava.py

@@ -33,7 +33,6 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                         PromptUpdateDetails)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
-from vllm.utils.jsontree import json_map_leaves
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 from .clip import CLIPVisionModel
@@ -604,16 +603,6 @@ class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
 
         raise AssertionError("This line should be unreachable.")
 
-    def _select_image_features(self, image_features: torch.Tensor, *,
-                               strategy: str) -> torch.Tensor:
-        # Copied from https://github.com/huggingface/transformers/blob/39c3c0a72af6fbda5614dde02ff236069bb79827/src/transformers/models/llava/modeling_llava.py#L421  # noqa
-        if strategy == "default":
-            return image_features[:, 1:]
-        elif strategy == "full":
-            return image_features
-
-        raise ValueError(f"Unexpected select feature strategy: {strategy}")
-
     def _image_pixels_to_features(
         self,
         vision_tower: Union[CLIPVisionModel, SiglipVisionModel,
@@ -622,17 +611,11 @@ class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
     ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
         # NOTE: we skip the step to select the vision feature layer since
         # this is already done inside the vision tower
-        image_features: Union[torch.Tensor, tuple[torch.Tensor, ...]] = \
-            vision_tower(pixel_values)
-
-        def select_features(leaf: torch.Tensor):
-            return self._select_image_features(
-                leaf,
-                strategy=self.config.vision_feature_select_strategy,
+        return vision_tower(
+            pixel_values,
+            feature_select_strategy=self.config.vision_feature_select_strategy,
         )
 
-        return json_map_leaves(select_features, image_features)
-
     def _process_image_pixels(
         self,
         inputs: Union[LlavaImagePixelInputs, PixtralHFImagePixelInputs],

vllm/model_executor/models/llava_next.py

@@ -235,12 +235,12 @@ class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal,
         # Determine the layer up to which we will initialize the vision tower
         if isinstance(vision_feature_layer, int):
             vision_hidden_size = config.vision_config.hidden_size
-            self.feature_sample_layers = None
+            self.select_layers = None
         # Used for multimodal granite models to control encoder outputs
         elif isinstance(vision_feature_layer, (list, tuple)):
             vision_hidden_size = config.vision_config.hidden_size * len(
                 vision_feature_layer)
-            self.feature_sample_layers = vision_feature_layer
+            self.select_layers = vision_feature_layer
         else:
             raise TypeError(
                 f"vision_layer_feature type: {type(vision_feature_layer)}"
@@ -312,30 +312,17 @@ class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal,
 
         raise AssertionError("This line should be unreachable.")
 
-    def _select_image_features(self, image_features: torch.Tensor, *,
-                               strategy: str) -> torch.Tensor:
-        # Copied from https://github.com/huggingface/transformers/blob/39c3c0a72af6fbda5614dde02ff236069bb79827/src/transformers/models/llava/modeling_llava.py#L421  # noqa
-        if strategy == "default":
-            return image_features[:, 1:]
-        elif strategy == "full":
-            return image_features
-
-        raise ValueError(f"Unexpected select feature strategy: {strategy}")
-
     def _image_pixels_to_features(
         self,
         vision_tower: Union[CLIPVisionModel, SiglipVisionModel],
         pixel_values: torch.Tensor,
     ) -> torch.Tensor:
-
         # NOTE: we skip the step to select the vision feature layer since
         # this is already done inside the vision tower
-        image_features = vision_tower(
-            pixel_values, feature_sample_layers=self.feature_sample_layers)
-
-        return self._select_image_features(
-            image_features,
-            strategy=self.config.vision_feature_select_strategy,
+        return vision_tower(
+            pixel_values,
+            select_layers=self.select_layers,
+            feature_select_strategy=self.config.vision_feature_select_strategy,
         )
 
     # Based on: https://github.com/haotian-liu/LLaVA/blob/main/llava/model/llava_arch.py

vllm/model_executor/models/llava_next_video.py

@@ -349,27 +349,16 @@ class LlavaNextVideoForConditionalGeneration(nn.Module, SupportsMultiModal,
                                              "w": expected_w,
                                          })
 
-    def _select_image_features(self, image_features: torch.Tensor, *,
-                               strategy: str) -> torch.Tensor:
-        if strategy == "default":
-            return image_features[:, 1:]
-        elif strategy == "full":
-            return image_features
-
-        raise ValueError(f"Unexpected select feature strategy: {strategy}")
-
     def _video_pixels_to_features(
         self,
         vision_tower: Union[CLIPVisionModel, SiglipVisionModel],
         pixel_values: torch.Tensor,
     ) -> torch.Tensor:
-
         # NOTE: we skip the step to select the vision feature layer since
         # this is already done inside the vision tower
-        image_features = vision_tower(pixel_values)
-        image_features = self._select_image_features(
-            image_features,
-            strategy=self.config.vision_feature_select_strategy,
+        image_features = vision_tower(
+            pixel_values,
+            feature_select_strategy=self.config.vision_feature_select_strategy,
         )
         image_features = self.vision_resampler(image_features)
         image_features = self.multi_modal_projector(image_features)

vllm/model_executor/models/llava_onevision.py

@@ -577,27 +577,16 @@ class LlavaOnevisionForConditionalGeneration(nn.Module, SupportsMultiModal,
 
         return mm_input_by_modality
 
-    def _select_image_features(self, image_features: torch.Tensor, *,
-                               strategy: str) -> torch.Tensor:
-        if strategy == "default":
-            return image_features[:, 1:]
-        elif strategy == "full":
-            return image_features
-
-        raise ValueError(f"Unexpected select feature strategy: {strategy}")
-
     def _image_pixels_to_features(
         self,
         vision_tower: Union[CLIPVisionModel, SiglipVisionModel],
         pixel_values: torch.Tensor,
     ) -> torch.Tensor:
-
         # NOTE: we skip the step to select the vision feature layer since
         # this is already done inside the vision tower
-        image_features = vision_tower(pixel_values)
-        return self._select_image_features(
-            image_features,
-            strategy=self.config.vision_feature_select_strategy,
+        return vision_tower(
+            pixel_values,
+            feature_select_strategy=self.config.vision_feature_select_strategy,
         )
 
     # Based on: https://github.com/haotian-liu/LLaVA/blob/main/llava/model/llava_arch.py
@@ -750,13 +739,11 @@ class LlavaOnevisionForConditionalGeneration(nn.Module, SupportsMultiModal,
         vision_tower: Union[CLIPVisionModel, SiglipVisionModel],
         pixel_values: torch.Tensor,
     ) -> torch.Tensor:
-
         # NOTE: we skip the step to select the vision feature layer since
         # this is already done inside the vision tower
-        video_features = vision_tower(pixel_values)
-        video_features = self._select_image_features(
-            video_features,
-            strategy=self.config.vision_feature_select_strategy,
+        video_features = vision_tower(
+            pixel_values,
+            feature_select_strategy=self.config.vision_feature_select_strategy,
         )
         video_features = self.multi_modal_projector(video_features)
         video_features = self.apply_pooling(video_features)

vllm/model_executor/models/minimax_vl_01.py

@@ -17,7 +17,6 @@ from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import MultiModalFieldConfig
 from vllm.sequence import IntermediateTensors
-from vllm.utils.jsontree import json_map_leaves
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 from .clip import CLIPVisionModel
@@ -221,15 +220,6 @@ class MiniMaxVL01ForConditionalGeneration(nn.Module, SupportsMultiModal,
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def _select_image_features(self, image_features: torch.Tensor, *,
-                               strategy: str) -> torch.Tensor:
-        if strategy == "default":
-            return image_features[:, 1:]
-        elif strategy == "full":
-            return image_features
-
-        raise ValueError(f"Unexpected select feature strategy: {strategy}")
-
     def _image_pixels_to_features(
         self,
         vision_tower: Union[CLIPVisionModel, SiglipVisionModel,
@@ -238,16 +228,10 @@ class MiniMaxVL01ForConditionalGeneration(nn.Module, SupportsMultiModal,
     ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
         # NOTE: we skip the step to select the vision feature layer since
         # this is already done inside the vision tower
-        image_features: tuple[torch.Tensor, ...] = \
-            tuple(vision_tower(p) for p in pixel_values)
-
-        def select_features(leaf: torch.Tensor):
-            return self._select_image_features(
-                leaf,
-                strategy=self.config.vision_feature_select_strategy,
-            )
-
-        return json_map_leaves(select_features, image_features)
+        feature_select_strategy = self.config.vision_feature_select_strategy
+        return tuple(
+            vision_tower(p, feature_select_strategy=feature_select_strategy)
+            for p in pixel_values)
 
     # adapted from https://huggingface.co/MiniMaxAI/MiniMax-VL-01/blob/main/modeling_minimax_vl_01.py#L616-L631
     def pack_image_features(self, image_features: list[torch.Tensor],

vllm/model_executor/models/pixtral.py

@@ -51,7 +51,8 @@ from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
 from .utils import flatten_bn, init_vllm_registered_model, maybe_prefix
-from .vision import VisionEncoderInfo, resolve_visual_encoder_outputs
+from .vision import (VisionEncoderInfo, VisionFeatureSelectStrategy,
+                     resolve_visual_encoder_outputs)
 
 try:
     from xformers import ops as xops
@@ -1218,7 +1219,9 @@ class PixtralHFVisionModel(nn.Module):
     def forward(
         self,
         pixel_values: list[torch.Tensor],
-        feature_sample_layers: Optional[list[int]] = None,
+        *,
+        select_layers: Optional[list[int]] = None,
+        feature_select_strategy: Optional[VisionFeatureSelectStrategy] = None,
     ) -> tuple[torch.Tensor, ...]:
         """
         Args:
@@ -1226,7 +1229,7 @@ class PixtralHFVisionModel(nn.Module):
                 in pixel_values. This means it will be a list of tensors
                 because multiple requests batched can have multiple images,
                 each with their own shape potentially
-            feature_sample_layers: Layer indices whose features should be
+            select_layers: Layer indices whose features should be
                 concatenated and used as the visual encoder output. If none
                 are provided, the last layer is used.
 
@@ -1267,15 +1270,20 @@ class PixtralHFVisionModel(nn.Module):
                 [p.shape[-2] * p.shape[-1] for p in patch_embeds_list],
                 patch_embeds)
 
-        return_all_hidden_states = feature_sample_layers is not None
         out = self.transformer(
             patch_embeds,
             attention_mask,
             position_embedding,
-            return_all_hidden_states=return_all_hidden_states)
+            return_all_hidden_states=select_layers is not None,
+        )
 
-        out = resolve_visual_encoder_outputs(out, feature_sample_layers, None,
-                                             self.config.num_hidden_layers)
+        out = resolve_visual_encoder_outputs(
+            out,
+            None,
+            select_layers=select_layers,
+            max_possible_layers=self.config.num_hidden_layers,
+            feature_select_strategy=feature_select_strategy,
+        )
 
         # squeeze dim 0 and split into separate tensors for each image
         return torch.split(out.squeeze(0), embed_sizes)

vllm/model_executor/models/siglip.py

@@ -23,7 +23,8 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader, maybe_remap_kv_scale_name)
 
-from .vision import VisionEncoderInfo, resolve_visual_encoder_outputs
+from .vision import (VisionEncoderInfo, VisionFeatureSelectStrategy,
+                     resolve_visual_encoder_outputs)
 
 
 class SiglipEncoderInfo(VisionEncoderInfo[SiglipVisionConfig]):
@@ -415,28 +416,31 @@ class SiglipVisionTransformer(nn.Module):
     def forward(
         self,
         pixel_values: torch.Tensor,
-        interpolate_pos_encoding: bool = True,
-        feature_sample_layers: Optional[list[int]] = None,
+        *,
+        interpolate_pos_encoding: bool = False,
+        select_layers: Optional[list[int]] = None,
+        feature_select_strategy: Optional[VisionFeatureSelectStrategy] = None,
     ) -> torch.Tensor:
-
         hidden_states = self.embeddings(
             pixel_values,
             interpolate_pos_encoding=interpolate_pos_encoding,
         )
 
-        return_all_hidden_states = feature_sample_layers is not None
-
         # Produces either the last layer output or all of the hidden states,
-        # depending on if we have feature_sample_layers or not
+        # depending on if we have select_layers or not
         encoder_outputs = self.encoder(
             inputs_embeds=hidden_states,
-            return_all_hidden_states=return_all_hidden_states,
+            return_all_hidden_states=select_layers is not None,
         )
 
         # Handle post-norm (if applicable) and stacks feature layers if needed
         encoder_outputs = resolve_visual_encoder_outputs(
-            encoder_outputs, feature_sample_layers, self.post_layernorm,
-            self.config.num_hidden_layers)
+            encoder_outputs,
+            self.post_layernorm,
+            select_layers=select_layers,
+            max_possible_layers=self.config.num_hidden_layers,
+            feature_select_strategy=feature_select_strategy,
+        )
 
         # TODO: add this back when pooled_output is used in inference.
         # if self.use_head:
@@ -471,16 +475,22 @@ class SiglipVisionModel(nn.Module):
     def get_input_embeddings(self) -> nn.Module:
         return self.vision_model.embeddings.patch_embedding
 
+    @property
+    def dtype(self):
+        return self.get_input_embeddings().weight.dtype
+
     def forward(
         self,
         pixel_values: torch.Tensor,
         interpolate_pos_encoding: bool = False,
-        feature_sample_layers: Optional[list[int]] = None,
+        select_layers: Optional[list[int]] = None,
+        feature_select_strategy: Optional[VisionFeatureSelectStrategy] = None,
     ) -> torch.Tensor:
         return self.vision_model(
             pixel_values=pixel_values,
             interpolate_pos_encoding=interpolate_pos_encoding,
-            feature_sample_layers=feature_sample_layers,
+            select_layers=select_layers,
+            feature_select_strategy=feature_select_strategy,
         )
 
     def load_weights(self, weights: Iterable[tuple[str,

vllm/model_executor/models/tarsier.py

@@ -33,7 +33,6 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                         PromptReplacement, PromptUpdate)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
-from vllm.utils.jsontree import json_map_leaves
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 from .clip import CLIPVisionModel
@@ -476,31 +475,17 @@ class TarsierForConditionalGeneration(nn.Module, SupportsMultiModal,
 
         raise AssertionError("This line should be unreachable.")
 
-    def _select_image_features(self, image_features: torch.Tensor, *,
-                               strategy: str) -> torch.Tensor:
-        if strategy == "default":
-            return image_features[:, 1:]
-        elif strategy == "full":
-            return image_features
-        raise ValueError(f"Unexpected select feature strategy: {strategy}")
-
     def _image_pixels_to_features(
         self,
         vision_tower: Union[CLIPVisionModel, SiglipVisionModel],
         pixel_values: Union[torch.Tensor, list[torch.Tensor]],
     ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
         # From vLLM LLaVA, vision tower output handling
-        image_hidden_states: Union[torch.Tensor, tuple[torch.Tensor, ...]] = \
-            vision_tower(pixel_values)
-
-        def select_features_fn(leaf: torch.Tensor):
-            return self._select_image_features(
-                leaf,
-                strategy=self.config.vision_feature_select_strategy,
+        return vision_tower(
+            pixel_values,
+            feature_select_strategy=self.config.vision_feature_select_strategy,
         )
 
-        return json_map_leaves(select_features_fn, image_hidden_states)
-
     def _add_tarsier_split_tokens(
             self, projected_image_features: torch.Tensor) -> torch.Tensor:
         """

vllm/model_executor/models/vision.py

@@ -4,10 +4,12 @@
 import itertools
 import math
 from abc import ABC, abstractmethod
-from typing import Final, Generic, Literal, Optional, Protocol, TypeVar, Union
+from typing import (Callable, Final, Generic, Literal, Optional, Protocol,
+                    TypeVar, Union)
 
 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,
@@ -86,11 +88,39 @@ def get_vit_attn_backend(head_size: int, dtype: torch.dtype) -> _Backend:
     return current_platform.get_vit_attn_backend(head_size, dtype)
 
 
+VisionFeatureSelectStrategy = Union[
+    Literal["class", "default", "full"],
+    Callable[[torch.Tensor], torch.Tensor],
+]
+
+
+def _get_vision_feature_selector(
+    strategy: VisionFeatureSelectStrategy,
+) -> Callable[[torch.Tensor], torch.Tensor]:
+    if callable(strategy):
+        return strategy
+
+    # https://github.com/huggingface/transformers/blob/cd74917ffc3e8f84e4a886052c5ab32b7ac623cc/src/transformers/models/clip/modeling_clip.py#L762
+    if strategy == "class":
+        return lambda feats: feats[:, 0, :]
+
+    # https://github.com/huggingface/transformers/blob/4a02bc7004285bdb12cc033e87ad2578ce2fa900/src/transformers/models/llava/modeling_llava.py#L196
+    if strategy == "default":
+        return lambda feats: feats[:, 1:, :]
+
+    if strategy == "full":
+        return lambda feats: feats
+
+    assert_never(strategy)
+
+
 def resolve_visual_encoder_outputs(
     encoder_outputs: Union[torch.Tensor, list[torch.Tensor]],
-    feature_sample_layers: Optional[list[int]],
     post_layer_norm: Optional[torch.nn.LayerNorm],
-    max_possible_layers: int,
+    *,
+    select_layers: Optional[list[int]] = None,
+    max_possible_layers: Optional[int] = None,
+    feature_select_strategy: Optional[VisionFeatureSelectStrategy] = None,
 ) -> torch.Tensor:
     """Given the outputs a visual encoder module that may correspond to the
     output of the last layer, or a list of hidden states to be stacked,
@@ -98,17 +128,32 @@ def resolve_visual_encoder_outputs(
 
     Args:
         encoder_outputs: Output of encoder's last layer or all hidden states.
-        feature_sample_layers: Optional layer indices to grab from the encoder
-            outputs; if provided, encoder outputs must be a list.
         post_layer_norm: Post norm to apply to the output of the encoder.
+        select_layers: Optional layer indices to grab from the encoder
+            outputs; if provided, encoder outputs must be a list.
         max_possible_layers: Total layers in the fully loaded visual encoder.
-
+        feature_select_strategy: Defines how to select the hidden states
+            from each layer.
     """
-    if feature_sample_layers is None:
+    if select_layers is None:
+        if not isinstance(encoder_outputs, torch.Tensor):
+            raise ValueError("Expected only a single encoder output when "
+                             "`select_layers` is not provided")
+
+        if feature_select_strategy is not None:
+            select_features = _get_vision_feature_selector(
+                feature_select_strategy)
+            encoder_outputs = select_features(encoder_outputs)
+
         if post_layer_norm is not None:
             return post_layer_norm(encoder_outputs)
+
         return encoder_outputs
 
+    if max_possible_layers is None:
+        raise ValueError("`max_possible_layers` must be provided "
+                         "alongside `select_layers`")
+
     # Get the hidden states corresponding to the layer indices.
     # Negative values are relative to the full visual encoder,
     # so offset them depending on how many layers were loaded.
@@ -120,13 +165,18 @@ def resolve_visual_encoder_outputs(
     hs_pool = [
         encoder_outputs[layer_idx]
         if layer_idx >= 0 else encoder_outputs[layer_idx + offset]
-        for layer_idx in feature_sample_layers
+        for layer_idx in select_layers
     ]
 
+    if feature_select_strategy is not None:
+        select_features = _get_vision_feature_selector(feature_select_strategy)
+        hs_pool = [select_features(hs) for hs in hs_pool]
+
     # Apply post-norm on the final hidden state if we are using it
-    uses_last_layer = feature_sample_layers[-1] in (len(hs_pool) - 1, -1)
+    uses_last_layer = select_layers[-1] in (max_possible_layers - 1, -1)
     if post_layer_norm is not None and uses_last_layer:
-        hs_pool[-1] = post_layer_norm(encoder_outputs)
+        hs_pool[-1] = post_layer_norm(hs_pool[-1])
+
     return torch.cat(hs_pool, dim=-1)