[9/N][Attention] Support adaptive verification in FlashMLA Sparse

[MatthewBonanni]

Summary

• allow HiSparse-backed FlashMLA Sparse to capture nonuniform adaptive-verification batches
• preserve packed variable-length decode rows as one MQA batch instead of inventing uniform request boundaries
• derive request rows from device metadata without a device-to-host synchronization

This is split from the core HiSparse PR because FlashMLA Sparse does not support adaptive verification on main; HiSparse can otherwise use the existing FlashInfer Sparse MLA support.

Duplicate work

I searched open PRs for FlashMLA Sparse adaptive-verification support and found no duplicate. #52988 concerns Kimi-K3 rather than FlashMLA Sparse or HiSparse.

Validation

• pre-commit run --files tests/v1/attention/test_sparse_mla_backends.py vllm/model_executor/layers/attention/sparse_mla_attention.py vllm/v1/attention/backends/mla/flashmla_sparse.py
• python -m pytest -q tests/v1/attention/test_sparse_mla_backends.py::test_hisparse_fp8_decode_preserves_packed_varlen_rows tests/v1/attention/test_sparse_mla_backends.py::test_flashmla_hisparse_reports_varlen_cudagraph_support tests/v1/attention/test_sparse_mla_backends.py::test_hisparse_flashmla_reorders_full_speculative_window_as_decode
  • 3 passed on one B300 GPU
• GLM TP4 end-to-end smoke with FULL_AND_PIECEWISE and forced adaptive rejection:
  • 25 requests completed
  • 7,105 drafted tokens, 0 accepted
  • 0 errors

AI assistance was used. Every changed line and the validation results were reviewed by the human submitter.