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[3/N] Initialize MM components in context managers (I-L) (#32650)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2026-01-20 18:21:56 +08:00
committed by GitHub
parent 8be263c3fb
commit 7f1bcd18ff
10 changed files with 203 additions and 389 deletions
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35
vllm/model_executor/models/internvl.py
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@@ -1092,22 +1092,24 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA)
self.downsample_ratio = config.downsample_ratio
self.ps_version = config.ps_version
self.llm_arch_name = config.text_config.architectures[0]
self.is_mono = self.llm_arch_name == "InternLM2VEForCausalLM"
self.vision_model = self._init_vision_model(
config,
quant_config=quant_config,
is_mono=self.is_mono,
prefix=maybe_prefix(prefix, "vision_model"),
)
llm_arch_name = config.text_config.architectures[0]
self.is_mono = llm_arch_name == "InternLM2VEForCausalLM"
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_tower_model(vllm_config, {"image", "video"}):
self.vision_model = self._init_vision_model(
config,
quant_config=quant_config,
is_mono=self.is_mono,
prefix=maybe_prefix(prefix, "vision_model"),
)
self.mlp1 = self._init_mlp1(config)
self.mlp1 = self._init_mlp1(config)
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"),
)
self.img_context_token_id = None
self.video_context_token_id = None
@@ -1281,8 +1283,6 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA)
):
return image_input["data"]
assert self.vision_model is not None
image_embeds = self.extract_feature(image_input["pixel_values_flat"])
num_patches = image_input["num_patches"]
@@ -1325,9 +1325,6 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA)
else:
self.visual_token_mask = None
def get_language_model(self) -> torch.nn.Module:
return self.language_model
def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
if not modalities: