CP shared KV now avoids the PyTorch sort/search remap for the single-request current-only path by deriving compact rows from page-level inverse mapping. The same change keeps sort NVTX attribution gated and splits high-frequency MoE sort markers behind a separate env var so profiling does not perturb normal runs.
Decode-side disaggregation prealloc also avoids rebuilding large token index tensors and records finer allocation timing, while compute-owner allocation/free tests cover the shared-KV page-lane behavior.
Constraint: The runtime tree used for validation is the remote /sgl-workspace/sglang-tai mount, which is not itself a Git repository, so these tracked files were synchronized into the local repo before commit.
Rejected: Keep torch.sort/searchsorted for current remap | it emits ATen/CCCL radixSortKVInPlace kernels in the attention hot path.
Rejected: Enable MoE sort NVTX under the generic sort env | the MoE preprocess sort is too frequent and can make profiling look like a hang.
Confidence: medium
Scope-risk: moderate
Directive: Do not reintroduce token-level torch.sort/searchsorted in CP shared-KV current remap without profiling the attention hot path under Nsight.
Tested: Remote container py_compile for modified runtime files; git diff --cached --check.
Not-tested: Full multi-node GLM5 PD throughput/profile rerun after the page-inverse current remap.
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