Split forward_decode_paged into host bookkeeping (decode_prepare +
ids/pos upload + advance_seq_len) and a pure-GPU decode_core. The
paged-KV and sparse-MoE designs already read every per-step variable
(block table, context lens, expert ids) from stable-address device
buffers, so decode_core captures as-is.
GptOssDecodeGraph captures lazily on the second decode step (the
first eager step warms cuBLAS) after a "retained warmup": the step
runs once with the allocator quarantine on, stocking the pool with a
dedicated block for every allocation so the capture itself never
pool-misses (a cudaMalloc while capturing is illegal — and the
capture's own quarantine is what would otherwise starve the pool).
NCCL all-reduces capture cleanly; TP=2 replays in lockstep.
Wired into tp_engine, bench-gpt-oss, and xserv-chat via
GraphedGptOssDecoder (batch>1 falls back to eager;
XSERV_DECODE_GRAPH=0 disables). Greedy tokens identical to eager.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Add --prompt to override the fixed prompt, and two teacher-forced
diagnostics: --forced runs prefill over prompt+oracle ids and reports
per-position top-1 agreement; --forced-decode walks the oracle trajectory
through the decode path with per-position agreement bucketed by position,
to localize long-context decode divergence from the reference.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
