adf357b02c
When chunked prefill is active, CP shared-KV bs>1 cannot consume more extend tokens than the current chunk budget. If the CP-specific extend-token limit is omitted, default it to rem_chunk_tokens so scheduler admission reflects the reachable chunk capacity. The request-count and cached-token knobs keep their None-as-unlimited behavior. Constraint: CP bs>1 batching must not advertise a larger extend batch than chunked prefill can execute. Rejected: Require users to always set --cp-shared-kv-prefill-max-total-extend-tokens | the safe default is already available from chunked prefill state. Rejected: Default batch request or cached-token limits | those are policy knobs and None should remain unlimited. Confidence: high Scope-risk: narrow Directive: Keep --cp-shared-kv-prefill-max-total-extend-tokens as min(user_limit, chunk_budget) when both exist. Tested: Local py_compile for schedule_policy.py and test_prefill_adder.py. Tested: Remote g0034 cjy-glm5-new targeted prefill_adder tests: 2 passed. Not-tested: Full ETE scheduler batching distribution after defaulting the extend limit. Co-authored-by: OmX <omx@oh-my-codex.dev>
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.