laoyao0822andOmX
4342de0463
Synchronous CP shared KV full-hit and partial-current paths can now use the tai-kernel CUDA IPC slot-dense materialize path instead of first copying local owner pages and then running a dense CP all-reduce. This keeps the async prefetch pipeline unchanged while routing the safer synchronous runtime path through descriptor-driven owner-page reads. The runtime builds descriptors from the existing page-aligned slot contract, caches peer pointer tables for long-lived KV/index buffers, and falls back with explicit warnings if the tai-kernel IPC capability is missing. Unit coverage locks offset handle exchange, descriptor construction, and both MLA/index full and partial-current calls. Constraint: Async prefetch currently has unresolved scheduling contention with GEMM/MoE and current-layer KV collectives, so this commit intentionally does not wire IPC into prefetch. Constraint: tai-kernel must provide offset-aware CUDA IPC symbols from af9fb67. Rejected: Replace prefetch all-reduce in this slice | Nsight shows prefetch timing and communicator contention need a separate scheduling design. Rejected: Fail-fast on missing tai IPC immediately | remote ETE still needs to validate production deployment capability before removing the warning fallback. Confidence: medium Scope-risk: moderate Directive: Keep prefetch and synchronous materialize decisions separate until prefetch has a low-SM/copy-engine schedule. Related: tai-kernel af9fb67 Tested: git diff --check; remote runtime/unit evidence recorded in docs/advanced_features/nsa_prefill_cp_page_aligned_cache_contract.md. Not-tested: Fresh GLM5 ETE after this commit; async prefetch IPC path. Co-authored-by: OmX <omx@oh-my-codex.dev>
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| 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 |
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| 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 |
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