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[Model] Enable LoRA support for tower and connector in LLaVA (#31513)

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Signed-off-by: Jay Hemnani <jayhemnani9910@gmail.com>
Co-authored-by: Jay Hemnani <jayhemnani9910@gmail.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Jay Hemnani
2026-01-01 19:32:39 -08:00
committed by GitHub
parent ea53ca5e85
commit 5ac55eb30f
2 changed files with 37 additions and 3 deletions
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2
docs/models/supported_models.md
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@@ -699,7 +699,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
| `LightOnOCRForConditionalGeneration` | LightOnOCR-1B | T + I<sup>+</sup> | `lightonai/LightOnOCR-1B`, etc | ✅︎ | ✅︎ |
| `Llama4ForConditionalGeneration` | Llama 4 | T + I<sup>+</sup> | `meta-llama/Llama-4-Scout-17B-16E-Instruct`, `meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8`, `meta-llama/Llama-4-Maverick-17B-128E-Instruct`, etc. | ✅︎ | ✅︎ |
| `Llama_Nemotron_Nano_VL` | Llama Nemotron Nano VL | T + I<sup>E+</sup> | `nvidia/Llama-3.1-Nemotron-Nano-VL-8B-V1` | ✅︎ | ✅︎ |
| `LlavaForConditionalGeneration` | LLaVA-1.5, Pixtral (HF Transformers) | T + I<sup>E+</sup> | `llava-hf/llava-1.5-7b-hf`, `TIGER-Lab/Mantis-8B-siglip-llama3` (see note), `mistral-community/pixtral-12b`, etc. | | ✅︎ |
| `LlavaForConditionalGeneration` | LLaVA-1.5, Pixtral (HF Transformers) | T + I<sup>E+</sup> | `llava-hf/llava-1.5-7b-hf`, `TIGER-Lab/Mantis-8B-siglip-llama3` (see note), `mistral-community/pixtral-12b`, etc. | ✅︎ | ✅︎ |
| `LlavaNextForConditionalGeneration` | LLaVA-NeXT | T + I<sup>E+</sup> | `llava-hf/llava-v1.6-mistral-7b-hf`, `llava-hf/llava-v1.6-vicuna-7b-hf`, etc. | | ✅︎ |
| `LlavaNextVideoForConditionalGeneration` | LLaVA-NeXT-Video | T + V | `llava-hf/LLaVA-NeXT-Video-7B-hf`, etc. | | ✅︎ |
| `LlavaOnevisionForConditionalGeneration` | LLaVA-Onevision | T + I<sup>+</sup> + V<sup>+</sup> | `llava-hf/llava-onevision-qwen2-7b-ov-hf`, `llava-hf/llava-onevision-qwen2-0.5b-ov-hf`, etc. | | ✅︎ |

38
vllm/model_executor/models/llava.py
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@@ -51,7 +51,13 @@ from vllm.sequence import IntermediateTensors
from vllm.utils.tensor_schema import TensorSchema, TensorShape
from .clip import CLIPVisionModel
from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
from .interfaces import (
MultiModalEmbeddings,
SupportsLoRA,
SupportsMultiModal,
SupportsPP,
)
from .module_mapping import MultiModelKeys
from .pixtral import PixtralHFEncoderInfo, PixtralHFVisionModel
from .siglip import SiglipVisionModel
from .utils import (
@@ -505,7 +511,9 @@ def init_vision_tower_for_llava(
info=_build_llava_or_pixtral_hf_info,
dummy_inputs=LlavaDummyInputsBuilder,
)
class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
class LlavaForConditionalGeneration(
nn.Module, SupportsLoRA, SupportsMultiModal, SupportsPP
):
packed_modules_mapping = {
"qkv_proj": ["q_proj", "k_proj", "v_proj"],
"gate_up_proj": ["gate_proj", "up_proj"],
@@ -734,6 +742,32 @@ class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
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",
)
def get_num_mm_encoder_tokens(
self,
num_image_tokens: int,
) -> int:
# LLaVA's vision encoder outputs one token per patch without
# spatial merging or pixel shuffle
return num_image_tokens
def get_num_mm_connector_tokens(
self,
num_vision_tokens: int,
) -> int:
# LLaVA's MLP projector outputs the same number of tokens
# as it receives from the vision encoder (1:1 mapping)
return num_vision_tokens
class MantisProcessingInfo(LlavaProcessingInfo):
def get_hf_processor(self, **kwargs: object):