c5c30a3f50
CP shared KV materialization repeatedly rebuilt the same logical-page slot remaps and page inverse metadata for each layer. Cache the token and paged remap metadata on the forward batch so MLA KV, index K/scale, and prefetch paths can reuse the layer-independent mapping while still materializing layer-specific data through the existing tai/torch runtime paths. Constraint: Only mapping metadata is batch-scoped; dense KV/index contents remain layer-specific and are not reused. Rejected: Cache fully materialized dense KV/index buffers | would add large per-layer memory residency and invalidation complexity. Confidence: medium Scope-risk: moderate Directive: Do not assume this removes materialize or CP all-reduce cost; profile tai fallback logs and Nsight kernels before attributing E2E gains or losses. Tested: git diff --check Tested: remote g0034 container PYTHONPATH=python python3 -m pytest test/registered/unit/mem_cache/test_cp_shared_kv_runtime.py -q (52 passed, 5 warnings) Not-tested: Full GLM-5 disaggregated E2E performance run
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.