feat: add LLaVA-OneVision2 chat model wrapper

[yiyexy]

Summary

Add a chat-style inference wrapper for LLaVA-OneVision2, registered as llava_onevision2 (chat class llava_onevision2_chat). Targets the released checkpoint lmms-lab-encoder/LLaVA-OneVision2-8B-Instruct.

The model is loaded via AutoModelForImageTextToText.from_pretrained(..., trust_remote_code=True) so that the bundled remote code (modeling_llava_onevision2.py, processing_llava_onevision2.py, video_processing_llava_onevision2.py) is used — preserving patch_positions, the RoPE block layout, frame sampling + smart_resize, and per-frame timestamp expansion exactly as during training.

Changes

• New lmms_eval/models/chat/llava_onevision2.py — the wrapper:
  • Video frames are pre-fetched via qwen_vl_utils.fetch_video (soft dep via optional_import) with fps / min_pixels / max_pixels / max_frames knobs.
  • Builds a per-frame chat content list of <t seconds> text + image PIL pairs (timestamp_decimals configurable).
  • Feeds PIL frames via images=... (not videos=...) to take the image-processor branch the model was trained on.
  • Supports multi-GPU sharding (device_map=auto/balanced/...) and logs throughput via log_metrics().
• Modified lmms_eval/models/__init__.py — register llava_onevision2 in AVAILABLE_CHAT_TEMPLATE_MODELS.
• New examples/models/llava_onevision2.sh — accelerate launch example (MLVU-dev best config).
• Modified docs/advanced/throughput_metrics.md — listed llava_onevision2 as a backend logging throughput metrics.

Usage

pip install qwen-vl-utils

accelerate launch --num_processes=8 -m lmms_eval \
    --model=llava_onevision2 \
    --model_args=pretrained=lmms-lab-encoder/LLaVA-OneVision2-8B-Instruct,attn_implementation=flash_attention_2,messages_format=timestamp,fps=1,max_num_frames=384,min_pixels=102400,max_pixels=102400 \
    --tasks=mlvu_dev \
    --batch_size=1

Checklist

• pre-commit run passes (black --line-length=240, isort)
• Smoke-tested locally on the target checkpoint
• Soft-imports qwen_vl_utils (clear install hint if missing)
• No hardcoded local paths / internal env vars