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[2/N] Initialize MM components in context managers (E-H) (#32641)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2026-01-20 16:12:56 +08:00
committed by GitHub
parent 148117ea2e
commit e1a34c3a5d
12 changed files with 161 additions and 189 deletions
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58
vllm/model_executor/models/gemma3n_mm.py
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@@ -503,31 +503,35 @@ class Gemma3nForConditionalGeneration(
self.multimodal_config = multimodal_config
self.vocab_size = config.text_config.vocab_size
self.vision_tower = AutoModel.from_config(config=config.vision_config)
self.audio_tower = AutoModel.from_config(config=config.audio_config)
self.embed_vision = Gemma3nMultimodalEmbedder(
config.vision_config, config.text_config
)
self.embed_audio = Gemma3nMultimodalEmbedder(
config.audio_config, config.text_config
)
with self._mark_tower_model(vllm_config, "image"):
self.vision_tower = AutoModel.from_config(config=config.vision_config)
self.embed_vision = Gemma3nMultimodalEmbedder(
config.vision_config, config.text_config
)
self.language_model: nn.Module = init_vllm_registered_model(
vllm_config=vllm_config,
hf_config=config.text_config,
prefix=maybe_prefix(prefix, "language_model"),
architectures=["Gemma3nForCausalLM"],
)
self.language_model = cast(Gemma3nForCausalLM, self.language_model)
# NOTE (NickLucche) In order to be compatible with cudagraph, the
# buffer needs to be consistent, so we pre-allocate here.
self.per_layer_embeddings = torch.zeros(
vllm_config.scheduler_config.max_num_batched_tokens,
self.config.text_config.num_hidden_layers,
self.config.text_config.hidden_size_per_layer_input,
device=self.language_model.model.embed_tokens.weight.device,
dtype=self.language_model.model.embed_tokens.weight.dtype,
)
with self._mark_tower_model(vllm_config, "audio"):
self.audio_tower = AutoModel.from_config(config=config.audio_config)
self.embed_audio = Gemma3nMultimodalEmbedder(
config.audio_config, config.text_config
)
with self._mark_language_model(vllm_config):
self.language_model: Gemma3nForCausalLM = init_vllm_registered_model(
vllm_config=vllm_config,
hf_config=config.text_config,
prefix=maybe_prefix(prefix, "language_model"),
architectures=["Gemma3nForCausalLM"],
)
# NOTE (NickLucche) In order to be compatible with cudagraph, the
# buffer needs to be consistent, so we pre-allocate here.
self.per_layer_embeddings = torch.zeros(
vllm_config.scheduler_config.max_num_batched_tokens,
self.config.text_config.num_hidden_layers,
self.config.text_config.hidden_size_per_layer_input,
device=self.language_model.model.embed_tokens.weight.device,
dtype=self.language_model.model.embed_tokens.weight.dtype,
)
def _parse_and_validate_image_input(
self, **kwargs: object
@@ -583,8 +587,6 @@ class Gemma3nForConditionalGeneration(
self,
image_input: Gemma3nImageInputs,
) -> list[torch.Tensor]:
assert self.vision_tower is not None
pixel_values = image_input["pixel_values"]
vision_outputs = self.vision_tower(
pixel_values=pixel_values, do_pooling=False, return_dict=True
@@ -609,7 +611,6 @@ class Gemma3nForConditionalGeneration(
self,
audio_input: Gemma3nAudioInputs,
) -> list[torch.Tensor]:
assert self.audio_tower is not None
# Run on padded features to enable batching
input_features = audio_input["input_features_padded"].squeeze(1)
input_features_mask = audio_input["input_features_mask"].squeeze(1)
@@ -651,9 +652,6 @@ class Gemma3nForConditionalGeneration(
# Return a list of embeddings instead of a batched tensor
return audio_features.unbind(0)
def get_language_model(self) -> torch.nn.Module:
return self.language_model
def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
mm_input_by_modality = self._parse_and_validate_multimodal_inputs(**kwargs)
if mm_input_by_modality is None: