9a9893e571
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.
Registered Tests
Tests under this directory are auto-discovered by run_suite.py via CI registration decorators.
Where Should I Put My New Test?
No server / engine launch required
| What you're testing | Directory | Requires |
|---|---|---|
| Component logic in isolation (cache, scheduler, config, parser, etc.) | unit/<module>/ |
CPU or GPU |
| CUDA kernel correctness | kernels/ |
GPU |
Server / engine launch required (E2E)
| What you're testing | Directory | Requires |
|---|---|---|
| Model inference correctness | models/, 4-gpu-models/, 8-gpu-models/ |
GPU |
| Feature-specific (OpenAI API, LoRA, speculative, distributed, VLM, etc.) | openai_server/, lora/, spec/, distributed/, ... |
GPU |
| Benchmarks (performance, accuracy, stress) | benchmark/ |
GPU |
| Platform-specific | amd/, ascend/ |
Vendor GPU |
See unit/README.md for unit test conventions.