- tests/test_metrics.py asserts the new linear-interp _percentile against
hand-computed expected values (single value, two-value interpolation,
endpoints, numpy-equivalent linear default, on-integer rank).
- tests/test_proxy_pick.py exercises InstanceState LRU eviction and
move-to-end on hit, plus session-affinity stickiness, the overload
fallback, the active_p_offloads penalty, and lmetric scoring. The
proxy is loaded by file path with stub fastapi/uvicorn/httpx modules
so the suite runs without the FastAPI server deps installed.
- pyproject.toml gets a hatchling wheel target and a [tool.pytest]
section so `uv run --extra dev pytest` works out of the box.
Systematic study of prefill-decode disaggregation for agentic LLM workloads
using production GLM-5.1 coder trace (2.1M requests, 71B input tokens).
Key findings:
- Cache-aware routing improves TPOT p90 by 15% and APC from 20.8% to 44.7%
without PD separation, matching PD-Sep's decode isolation benefit
- PD separation adds +72% TTFT overhead (KV transfer) with no TPOT gain
when using the same cache-aware scheduler
- Prefill remains compute-bound even at 95% KV cache reuse (AI >1000x
vs decode AI <2), but absolute FLOPs drop 71% from cache hits
- For agentic MoE workloads, cache-aware routing > PD separation
Infrastructure:
- Trace sampler preserving session structure + hash_ids for prefix sharing
- Async trace replayer with streaming TTFT/TPOT/E2E measurement
- Unified cache-aware + token-level load-balanced global scheduler proxy
supporting both PD-colocated and PD-disaggregated (Mooncake/RDMA) modes
- vLLM 0.18.1 scheduler patch for KV transfer abort race condition
- Roofline analysis tool for prefill/decode compute characterization
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
