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[Misc] Gemma3ForConditionalGeneration supports LoRA #14797

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Merged
merged 2 commits into from
Mar 14, 2025
Merged

[Misc] Gemma3ForConditionalGeneration supports LoRA #14797

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b874221
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jeejeelee Mar 14, 2025
223fea2
Fix
jeejeelee Mar 14, 2025
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17 changes: 14 additions & 3 deletions vllm/model_executor/models/gemma3_mm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@
from vllm.logger import init_logger
from vllm.model_executor.layers.layernorm import GemmaRMSNorm
from vllm.model_executor.layers.sampler import SamplerOutput
from vllm.model_executor.models.module_mapping import MultiModelKeys
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import MultiModalFieldConfig, MultiModalKwargs
Expand All @@ -23,7 +24,8 @@
from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
from vllm.sequence import IntermediateTensors

from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
SupportsMultiModal, SupportsPP)
from .siglip import SiglipVisionModel
from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
maybe_prefix, merge_multimodal_embeddings)
Expand Down Expand Up @@ -371,8 +373,8 @@ def forward(self, vision_outputs: torch.Tensor):
@MULTIMODAL_REGISTRY.register_processor(Gemma3MultiModalProcessor,
info=Gemma3ProcessingInfo,
dummy_inputs=Gemma3DummyInputsBuilder)
class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal,
SupportsPP):
class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
SupportsLoRA):
packed_modules_mapping = {
"qkv_proj": [
"q_proj",
Expand Down Expand Up @@ -614,3 +616,12 @@ def load_weights(self, weights: Iterable[Tuple[str,
torch.Tensor]]) -> Set[str]:
loader = AutoWeightsLoader(self)
return loader.load_weights(weights)

def get_mm_mapping(self) -> MultiModelKeys:
"""
Get the module prefix in multimodal models
"""
return MultiModelKeys.from_string_field(
language_model="language_model",
connector="multi_modal_projector",
tower_model="vision_tower")