[Feature]: Add TRTLLM-gen FMHA Dense paged GQA generation cubins for P16

[elwhyjay]

🚀 The feature, motivation and pitch

FlashInfer is adding non-causal TRTLLM-gen paged GQA decode(flashinfer-ai/flashinfer#3629). That path selects TRTLLM-gen FMHA generation with PagedKv layout and Dense mask. For page size 16, TensorRT-LLM does not currently ship matching precompiled FMHA cubins or Dense metadata rows.

Requested coverage:

• qkvLayout = PagedKv
• maskType = Dense
• kernelType = Generation
• numTokensPerPage = 16
• headDimQk = headDimV in {64, 128, 256}
• tileSizeQ in {8, 16}
• tileSizeKv = 128
• Blackwell SM100/SM103, matching the existing TRTLLM-gen FMHA cubin set

Why P16 matters:

• numTokensPerPage is selected from the physical paged KV cache layout.
• Changing the downstream test to page size 32 selects a different FMHA metadata key and does not validate the P16 runtime path.
• FlashInfer supports page size 16 paged KV caches in its public decode path. Non-causal GQA should not require changing the cache layout only to match the currently shipped cubin set.

There is also a metadata issue for existing Dense-named P32 generation entries: the function names contain PagedKvDense, but the rows are indexed with maskType=Causal. Please index those rows as Dense when refreshing the metadata.

Alternatives

FlashInfer can keep the new non-causal GQA tests as xfail, but it cannot remove the xfail or validate the Dense runtime path until TensorRT-LLM ships the P16 Dense cubins and metadata.

Changing the downstream tests to page size 32 would only test a different runtime shape and would not validate the P16 path used by the reported case.

Additional context

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