5769b63082
Shared KV now relies on page-aligned CP metadata and compute-owner page allocation so persistent MLA KV and NSA index shards can be written by the rank that computed them. The compatibility read path keeps the dense full-view contract for existing topk and attention kernels, but removes duplicated prev/next index materialize, adds optional tai materialize integration, and tightens tests/docs around the fallback boundaries. Constraint: Decode remains non-CP while prefill CP owns the shared-KV changes Constraint: Existing attention/topk kernels still expect dense full-view KV/index inputs Rejected: Change attention kernels to read owner-sharded KV directly | larger semantic change reserved for later phases Rejected: Merge index K/scale storage with MLA KV storage | would couple topk and attention cache lifecycles before materialize overhead is isolated Confidence: medium Scope-risk: broad Directive: Do not remove fallback logging or debug-gated assertions without reproducing long-context chunked/radix-hit paths Tested: git diff --check --cached Not-tested: Local pytest/runtime server verification not run in this commit step per current workflow constraints
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.