[Feature] Add FlashInfer cuDNN backend for ViT attention

[dorhuri123]

Summary

• Adds cuDNN-accelerated attention via FlashInfer's cudnn_batch_prefill_with_kv_cache for Vision Transformer encoders
• Supports bf16 with attention masks, targeting Qwen2.5-VL and Qwen3-VL models
• Full integration for Qwen3-VL; parameter plumbing for Qwen2.5-VL (full integration in follow-up)

Key Changes

New cuDNN wrapper (vllm/v1/attention/ops/vit_attn_wrappers.py)

• flashinfer_wrapper() calls FlashInfer's cuDNN batch prefill API
• Handles 3-section cu_seqlens format (batch_offsets_qk, batch_offsets_v, batch_offsets_o)
• Registered as custom op for torch.compile compatibility

MMEncoderAttention updates (vllm/model_executor/layers/attention/mm_encoder_attention.py)

• New _forward_flashinfer() method with FlashInfer dispatch
• workspace_buffer parameter for pre-allocated cuDNN workspace (128MB)
• sequence_lengths parameter threaded through all forward methods

Qwen3-VL integration (vllm/model_executor/models/qwen3_vl.py)

• Batch bucket padding (BATCH_BUCKETS = [8, 16, 32, 64]) for cuDNN graph caching
• compute_flashinfer_cu_seqlens() computes the 3-section cu_seqlens format
• Fixed max_seqlen to 128K to avoid cuDNN recompilation
• FlashInfer added to supported backends

Platform support (vllm/platforms/cuda.py)

• FLASHINFER added to CUDA's get_supported_vit_attn_backends()

Usage

Set the ViT attention backend to FlashInfer:

--override-mm-encoder-attn-backend FLASHINFER

Credits

Based on CentML/vllm#30.

Test plan

• Verify Qwen3-VL inference with --override-mm-encoder-attn-backend FLASHINFER
• Check no regression with default backends (FLASH_ATTN, TORCH_SDPA)
• Verify cuDNN graph caching with batch bucket padding

[gemini-code-assist]

Code Review

This pull request introduces FlashInfer cuDNN backend support for Vision Transformer (ViT) attention, targeting Qwen2.5-VL and Qwen3-VL models. The changes involve adding new wrappers for FlashInfer, updating MMEncoderAttention to dispatch to the new backend, and integrating it into Qwen3-VL with batch bucket padding for cuDNN graph caching. The workspace_buffer and sequence_lengths parameters are threaded through the attention layers to support the new backend. Overall, the implementation seems to correctly integrate the FlashInfer backend. However, there are a few areas that could be improved for clarity, maintainability, and torch.compile compatibility.