[MM][Perf][CG] Support ViT full CUDA graph for InternVL

[oguzhankir]

Purpose

Add ViT CUDA Graph support for InternVL models (InternVL3, InternVL2.5, InternVL2), following #38061 (Qwen3-VL). Part of #38175.

InternVL's InternVisionModel uses standard ViT attention with no rotary embeddings or variable-length metadata, so no extra buffer keys are needed.

Test Plan

• Unit tests: pytest tests/v1/cudagraph/test_encoder_cudagraph.py -v
• Added InternVL entry to tests/models/multimodal/generation/test_vit_cudagraph.py
• E2E benchmark on RTX 4090

Test Result

Unit tests: 36 passed ✅

E2E Benchmark — RTX 4090, OpenGVLab/InternVL3-2B, 2 images/req, 200 prompts @ 8 RPS:

Metric	No CG	With CG	Δ
Mean TTFT	81.82 ms	76.81 ms	↓ 6.3%
Median TTFT	74.00 ms	72.49 ms	↓ 2.0%
P99 TTFT	147.65 ms	122.11 ms	↓ 17.3%

CG config: encoder_cudagraph_token_budgets=[256,512,1024], encoder_cudagraph_max_vision_items_per_batch=4

Larger improvements expected with bigger models and more images per request.

Documentation

• Added InternVLChatModel to docs/design/cuda_graphs_multimodal.md
• Added internvl_chat to MODELS_SUPPORT_VIT_CUDA_GRAPH in examples

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[mergify]

Documentation preview: https://vllm--41759.org.readthedocs.build/en/41759/

[gemini-code-assist]

Code Review

This pull request enables encoder CUDA Graph support for the InternVLChatModel. Key changes include implementing the SupportsEncoderCudaGraph interface in the model executor, updating the multimodal CUDA graph documentation, and adding a dedicated test case for InternVL3. I have no feedback to provide.

[claude]

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

[evezhier]

I've tested this and can confirm the perf. My changes are identical.

[oguzhankir]

I've tested this and can confirm the perf. My changes are identical.

Thanks for testing and confirming! 🙏