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The bs>1 partial-current materialize issued one sum-all-reduce per request span per buffer per layer (48 collectives per F-layer at bs=12, 2880 per batch, 419ms + 49ms launch gaps in the production trace), all inline on the compute stream. The data is a partitioned gather, not a reduction: every byte has exactly one producer. compose_v2 (SGLANG_CP_SHARED_KV_COMPOSE_V2, default on) replaces this with: - fast path: one tai-kernel CUDA-IPC slot-dense gather covering ALL prefix spans (full-range descriptors, -1 sentinels zero-fill current slots and replace the dense zero-fill) + ONE collective over the compact current pages (uint8 byte view; exact because every byte is writer-exclusive). - fallback (no peer IPC): local materialize of all prefix spans + ONE whole-buffer sum-all-reduce (rows are still writer-exclusive pre-reduce). IPC capability is decided once by the cached peer-pointer probe; after a successful probe a failing gather raises (no per-call try/except). cp_shared_kv_compose.py adds the per-batch ComposePlan descriptor cache (layer-invariant, keyed on the slot_logical_pages identity) and the CpComposeArena with tier-S carve discipline (deterministic bump, layer- parity halves; default off) so the Step B symmetric-memory conversion is a registration flip. Microbenchmark (g0034 8xH200, traced 12-req batch, per batch): per-span 214ms -> fused AR 119ms -> IPC prefix + compact current 84ms; symm target 61ms. Validation: 143 unit tests incl. v2-contract twins, legacy siblings and rank-merged simulations under both paths; mem_cache dir 432 passed; 8-rank GPU byte-exactness vs legacy with real NCCL + IPC (test/manual/ test_cp_shared_kv_compose_v2_8rank.py) passed with no fallback markers. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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.