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[Model] Move vision_feature_select_strategy into resolve_visual_encoder_outputs (#25938)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2025-09-30 19:24:57 +08:00
committed by GitHub
parent ef6e0e7132
commit d7e34b4210
12 changed files with 155 additions and 179 deletions
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31
vllm/model_executor/models/clip.py
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@@ -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):