37e9caa431
After the critic-agent audit, V2_DEEP_ANALYSIS had drifted into an
audit-grade "5 wins / 1 loss / 3 draws" framing that mistook KVC's
deliberate design motifs (cache concentration via session affinity;
prefill-GPU idle as TTFT-stability trade-off) for "comparison
unfairness." This commit corrects the framing back to a production-
decision lens and adds a paper-track formal specification of the
router algorithm.
V2_DEEP_ANALYSIS_ZH.md changes:
- §0 TL;DR: lead with "online coding agent serving should pick
KVC 1P3D"; the only real cost is TTFT p99 long-tail (3x DP) from
the 8.3% mooncake reseed path, mitigable with real RDMA.
- §4 restructured into three buckets:
real costs (TTFT p99 tail, abort accounting now fixed),
counter-arguments to the critic (cache concentration and idle
prefill GPU are design intent, not deficits),
methodology to-do (naive-1P3D control, v2 N>=2 determinism).
- §6 replaces "5/1/3 rescoring" with production decision rationale:
KVC wins on 6 latency/TTFT metrics + lower failure rate; pays
TTFT p99 tail; lists workloads where DP would reverse the call.
- §8 decision points: D1 recommends Yes (accept v2 as milestone);
D8 added: paper motif "KVC trades P idle for TTFT stability."
KVC_ROUTER_ALGORITHM.md (new, paper-track, Chinese narrative + English
algorithm boxes / variable names / theorems for direct paper reuse):
- Problem formulation, system model, full notation
- Algorithm 1 Route: lexicographic-tuple scoring on
(overlap+alpha*sticky, sticky, -inflight, -assigned)
- Algorithm 2 Admit: D-worker autonomous admission deciding
Direct / Seed / Reseed / reject (with reason)
- Algorithm 3 Dispatch: end-to-end orchestration with reset-on-success
(the v2-specific fix that eliminates v1's self-amplifying thrashing)
- Theorem 1 (no permanent starvation) and Theorem 2 (fast-path
determinism), each with a proof sketch
- Comparison table vs vanilla pd-disagg / DP cache-aware
- Anti-patterns ("what KVC explicitly is NOT")
- Open questions for reviewers
- Suggested paper citation phrasing
- Appendix A: algorithm-step to source-file:line crosswalk
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Agentic PD Hybrid
这个项目是在 SGLang xPyD 上做一个最小实验框架，用来判断：
面向 agentic coding workload 的 session-aware / KV-cache-aware P/D routing，能不能降低端到端延迟。
更完整但仍然简洁的说明见 docs/PROJECT_OVERVIEW.md。
当前做了什么
- 启动单机 SGLang P/D 栈。
- 回放 Ali coding agent trace，并记录 request-level metrics。
- 支持
default、sticky、kv-aware路由策略。 - 支持
pd-disaggregation、kvcache-centric、pd-colo对比。 - 支持小 append、多轮 session 的 micro-benchmark trace。
- 维护了基于 SGLang
v0.5.10的本地 patch，放在third_party/sglang。
环境
统一使用 uv：
uv sync
默认模型路径：
~/models/Qwen/Qwen3-Coder-30B-A3B-Instruct
当前主要测试环境是单机 8 GPU，约束是 prefill + decode <= 8。
常用命令
生成小 append trace：
uv run agentic-pd-hybrid make-small-append-trace \
--output outputs/smoke-hotcap-30k-1k-256.jsonl \
--session-count 4 \
--turns-per-session 3 \
--initial-input-length 30000 \
--append-input-length 1000 \
--output-length 256
跑 live benchmark：
uv run agentic-pd-hybrid benchmark-live \
--trace outputs/micro-serveable-varturn-30k-1k-256-20260424T0756Z.jsonl \
--output-root outputs/live-serveable-varturn-30k-1k-256-hotcap \
--mechanism kvcache-centric \
--policy kv-aware \
--kvcache-admission-mode worker \
--prefill-workers 1 \
--decode-workers 1 \
--prefill-gpu-ids 0 \
--decode-gpu-ids 1 \
--transfer-backend mooncake \
--target-duration-s 2000 \
--session-sample-rate 1.0 \
--min-turns 2 \
--time-scale 1 \
--concurrency-limit 1000
只回放并写 metrics：
uv run agentic-pd-hybrid replay \
--trace path/to/trace.jsonl \
--policy kv-aware \
--mechanism pd-disaggregation \
--router-url http://127.0.0.1:8000 \
--output outputs/replay.jsonl
输出
每次 replay/benchmark 会写：
- request metrics：
request-metrics.jsonl - 汇总结果：
request-metrics.jsonl.summary.json
重点看：
- E2E latency
- TTFT / TPOT
- execution mode
- cached tokens
- KV transfer blocks
- error
维护约定
- 项目代码改动：
feat:/fix:/docs:。 - SGLang 改动：
feat(sglang): .../fix(sglang): ...。 third_party/sglang的基线是 clean SGLangv0.5.10snapshot。- 不提交
outputs/、日志、__pycache__、虚拟环境。