43ad2fe52d
The shared-KV path now keeps more CP metadata on-device and reuses physical out-cache locations across MLA and NSA index writes, so each layer avoids repeating logical-to-physical remaps. The in-seq CP all-gather rerange path now delegates to tai-kernel when available and falls back to the existing torch split/cat path with an explicit log. This also extends the Phase8 prefetch machinery to cover shared KV materialization metadata and keeps debug/fallback behavior gated so the fast path is not polluted by diagnostic checks. Constraint: Custom CP kernels must live in tai-kernel and be imported lazily from SGLang Constraint: Decode does not use CP; these changes target NSA prefill CP in-seq-split shared KV Rejected: Recompute physical local cache locations separately for MLA and index writes | repeats the same remap work every layer Rejected: Keep the in-seq rerange Triton code inline in SGLang | duplicates kernel ownership and blocks tai-kernel reuse Confidence: medium Scope-risk: moderate Directive: Keep CP collective ordering identical across ranks; do not add rank-local fallback decisions inside shared KV materialize paths Tested: Remote g0034 container py_compile for modified SGLang/tai-kernel files; remote pytest test/registered/unit/layers/test_nsa_cp_utils.py passed with 24 tests Not-tested: Full multi-node GLM5 prefill/decode throughput after the final commit boundary
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.