[Bugfix][MTP][Sparse MLA] Allow sparse MLA with MTP to run with FULL cudagraphs

[MatthewBonanni]

Purpose

DeepseekV32IndexerMetadataBuilder currently only reports support for UNIFORM_SINGLE_TOKEN_DECODE. Therefore, when running a sparse MLA model with MTP, FULL cudagraphs are never captured.

In reality, the deepGEMM kernel fp8_paged_mqa_logits supports MTP with num_speculative_tokens=1 (i.e. next_n = 2).

This PR changes the reported support to UNIFORM_BATCH and adds an explicit error for num_speculative_tokens > 1, rather than letting the kernel itself crash.

Test Plan

vllm serve deepseek-ai/DeepSeek-V3.2 \
    -tp 8 -ep \
    --speculative-config '{"method": "mtp", "num_speculative_tokens": 1}' \
    --no-enable-prefix-caching

with

wget https://raw.githubusercontent.com/hemingkx/Spec-Bench/main/data/spec_bench/question.jsonl
vllm bench serve \
    --dataset-name spec_bench \
    --dataset-path question.jsonl \
    --spec-bench-output-len 1024 \
    --seed 42 \
    --ignore-eos \
    --temperature 0 \
    --skip-chat-template

Test Result

Main:

PR:

============ Serving Benchmark Result ============
Successful requests:                     1000      
Failed requests:                         0         
Benchmark duration (s):                  234.50    
Total input tokens:                      269392    
Total generated tokens:                  1023063   
Request throughput (req/s):              4.26      
Output token throughput (tok/s):         4362.81   
Peak output token throughput (tok/s):    7000.00   
Peak concurrent requests:                1000.00   
Total token throughput (tok/s):          5511.62   
---------------Time to First Token----------------
Mean TTFT (ms):                          41407.32  
Median TTFT (ms):                        35815.64  
P99 TTFT (ms):                           95279.58  
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          156.45    
Median TPOT (ms):                        154.36    
P99 TPOT (ms):                           192.94    
---------------Inter-token Latency----------------
Mean ITL (ms):                           305.20    
Median ITL (ms):                         145.98    
P99 ITL (ms):                            3598.89   
---------------Speculative Decoding---------------
Acceptance rate (%):                     95.25     
Acceptance length:                       1.95      
Drafts:                                  523691    
Draft tokens:                            523691    
Accepted tokens:                         498803    
Per-position acceptance (%):
  Position 0:                            95.25     
==================================================

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Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
• (Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.

[gemini-code-assist]

Code Review

This pull request correctly enables FULL cudagraph support for sparse MLA models with MTP by changing _cudagraph_support to UNIFORM_BATCH. It also adds a necessary safeguard to prevent crashes by raising a ValueError for unsupported num_speculative_tokens > 1, which is a limitation of the fp8_paged_mqa_logits kernel. The changes are well-implemented, improving both functionality and robustness. The code is clean and the logic is sound.