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[Model] Use context managers for encoder- and LM-only mode (#32605)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2026-01-20 11:43:38 +08:00
committed by GitHub
parent 6c01ffb897
commit 4753f3bf69
21 changed files with 290 additions and 353 deletions
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25
vllm/model_executor/models/pixtral.py
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@@ -398,13 +398,14 @@ class PixtralForConditionalGeneration(
self.vision_args = VisionEncoderArgs(**vision_args)
# init MistralForCausalLM
self.language_model = init_vllm_registered_model(
vllm_config=vllm_config,
hf_config=config.text_config,
prefix=maybe_prefix(prefix, "language_model"),
)
with self._mark_language_model(vllm_config):
self.language_model = init_vllm_registered_model(
vllm_config=vllm_config,
hf_config=config.text_config,
prefix=maybe_prefix(prefix, "language_model"),
)
if multimodal_config.get_limit_per_prompt("image"):
with self._mark_tower_model(vllm_config, "image"):
self.vision_encoder = VisionTransformer(self.vision_args)
self.pre_mm_projector_norm = (
RMSNorm(self.vision_args.hidden_size, eps=1e-5)
@@ -423,11 +424,6 @@ class PixtralForConditionalGeneration(
self.vision_language_adapter = VisionLanguageAdapter(
self.vision_args, dim=config.text_config.hidden_size
)
else:
self.vision_encoder = None
self.pre_mm_projector_norm = None
self.patch_merger = None
self.vision_language_adapter = None
self.make_empty_intermediate_tensors = (
self.language_model.make_empty_intermediate_tensors
@@ -449,10 +445,6 @@ class PixtralForConditionalGeneration(
self,
image_input: PixtralImagePixelInputs,
) -> tuple[torch.Tensor, ...]:
assert (
self.vision_encoder is not None and self.vision_language_adapter is not None
)
images = image_input["images"]
image_features = self.vision_encoder(images)
feature_sizes = [image_feature.shape[0] for image_feature in image_features]
@@ -477,9 +469,6 @@ class PixtralForConditionalGeneration(
image_embeds = torch.split(image_embeds, feature_sizes)
return image_embeds
def get_language_model(self) -> torch.nn.Module:
return self.language_model
def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
image_input = self._parse_and_validate_image_input(**kwargs)
if image_input is None: