[Model] Add LFM2-VL model support

[tianshu-Michael-yu]

Purpose

Add support for the LFM2-VL (Liquid Foundation Model 2 Vision-Language) model family from LiquidAI.

Changes:

• Add Lfm2VLForConditionalGeneration model implementation with full multimodal processing pipeline
• Add SigLIP2 vision encoder (siglip2.py) used by LFM2-VL
• Register model in the multimodal model registry
• Fix max_seqlen type hint in MMEncoderAttention to accept int | torch.Tensor
• Add double-buffering for is_mm_embed in GPU model runner to avoid race conditions with async copies

The model supports:

• Dynamic resolution image processing with smart resize
• Hybrid architecture (Mamba + Attention)
• LoRA fine-tuning
• Pipeline parallelism

Test Plan

python -m vllm.entrypoints.openai.api_server
--model LiquidAI/LFM2-VL-3B
--trust-remote-code
--served-model-name lfm2-vl-3b
--mm-processor-cache-type shm
--async-scheduling
--compilation-config '{"max_cudagraph_capture_size": 8192, "compile_mm_encoder": true}'## Test Result

Tested with LiquidAI/LFM2-VL-450M, LFM2-VL-3B, and LFM2-VL-8B-A1B variants.

LFM2-VL-3B benchmark results (MMStar):

• vLLM spinup time: 99s
• Overall Score: 0.578

---

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[gemini-code-assist]

Code Review

This pull request adds support for the LFM2-VL model, including its SigLIP2 vision encoder. The changes are comprehensive, covering the model implementation, multimodal processing pipeline, and necessary integrations. I've identified a couple of issues. First, there's a line of dead code in the smart_resize logic within lfm2_vl.py that should be removed. More critically, the data parallelism feature for the vision encoder (mm_encoder_tp_mode=\"data\") appears to be broken due to a hardcoded use_data_parallel=False flag in siglip2.py. This prevents the vision encoder from being replicated across tensor parallel ranks as intended. My review includes detailed comments on these points.

[mergify]

Hi @tianshu-Michael-yu, the pre-commit checks have failed. Please run:

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[mgoin]

We need to add an entry to https://github.com/vllm-project/vllm/blob/main/tests/models/registry.py cc @DarkLight1337 for final review

[DarkLight1337]

Please also add this model to the Supported Models page, example scripts (e.g. examples/offline_inference/vision_language.py), as well as the test registry tests/models/registry.py.

[mergify]

Documentation preview: https://vllm--31758.org.readthedocs.build/en/31758/

[yileld]

here is the problm I meet.
When I run LFM2-VL-450M

self.projector_use_layernorm = config.projector_use_layernorm
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/transformers/configuration_utils.py", line 207, in __getattribute__
return super().__getattribute__(key)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Lfm2VlConfig' object has no attribute 'projector_use_layernorm'

if I add "projector_use_layernorm": false in config.json, still error
ValueError: There is no module or parameter named 'multi_modal_projector.layer_norm' in Lfm2VLForConditionalGeneration