67d52346de
Use tai-kernel for direct page_first_direct per-layer H2D load across MHA, MLA, and NSA indexer pools. This keeps SGLang off the sgl-kernel cudaMemcpyBatchAsync path that crashes on CUDA 13 while preserving fail-fast behavior when the required TAI op is unavailable. Constraint: remote CUDA 13 stack crashes in sgl-kernel PF->LF direct load via cuMemcpyBatchAsync_v2 Rejected: Silent fallback to sgl-kernel or Python loop | fallbacks would hide either a crash-prone ABI path or a large performance regression Confidence: high Scope-risk: moderate Directive: page_first_direct direct load must remain fail-fast if tai_kernel.nsa_prefill.transfer_kv_per_layer_direct_pf_lf is missing Tested: remote g0034 PYTHONPATH=python pytest -q test/registered/unit/managers/test_hicache_controller_cp.py: 55 passed, 3 warnings Tested: remote g0034 CUDA smoke for MLATokenToKVPoolHost.load_to_device_per_layer with direct/page_first_direct passed Not-tested: full SGLang ETE server after the final commit
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.