Add an offline benchmark that reuses PrefillAdder to model how L1 cached tokens, L2 HiCache hits, and extend tokens shape CP shared-KV prefill batch admission. The tool makes scheduler stop reasons and fake L2 load-back capacity pressure observable without starting a model.
Constraint: The benchmark must stay CPU/offline and avoid depending on CUDA execution or live services.
Constraint: L2 cached tokens are modeled as host_hit_length, so successful load-back both increases prefix_len and consumes fake L1 capacity.
Rejected: Build an ETE benchmark first | too slow for isolating scheduler admission behavior.
Rejected: Reimplement scheduler logic from scratch | would drift from PrefillAdder semantics.
Confidence: high
Scope-risk: narrow
Directive: Treat duration_us as Python admission overhead only; it is not an ETE latency metric.
Tested: Remote pytest test/registered/unit/managers/test_prefill_scheduler_admission_bench.py: 4 passed as part of 6 targeted tests.
Tested: Remote synthetic benchmark run with --cp-max-total-cached-tokens showed second 4096-token cached request stopped with OTHER.
Not-tested: Real traffic trace import from production logs.
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