Inductor was specialising create_block_mask on the first prefill shape
it saw (warmup with 16 prompts -> small total L). When round-0 prefill
ran at bs=64 with a much larger packed L, the cached triton block-mask
kernels launched with the wrong shape constants and hit CUDA illegal
memory access inside the document_causal mask construction, even
though the fused GEMMs and graph-captured decode path were fine.
torch.compile(create_block_mask, dynamic=True) keeps L as a runtime
arg so the same kernels work across the warmup and full-batch prefill
shapes.
Add the drift-verification and end-to-end rollout stats for the fused
addmm path: base bit-identical across 10 cycles, and bs=64 + LoRA +
capture_cudagraph + decode_kernel_options reaches 5057 tok/s median,
5224 tok/s best on B200 at 52 GB peak -- within noise of the prior
5785 tok/s baseline. Variance is round-0/1 warmup (3298, 3607 tok/s)
rather than steady-state (4940, 5082, 5057 tok/s).
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