5a2fb8799c
A single self-contained reading manual designed to bring a fresh agent
(LLM or human) to current-state proficiency in 30 min of reading +
30 min of environment validation, then have them run the next round of
ablation experiments without re-litigating questions already settled.
Structure:
§0 TL;DR -- what you are inheriting in 5 lines
§1 Reading order, tiered into Must-Read / On-Demand / Archive,
with reasons for each
§2 Current-state snapshot: trace/hardware/branches + claims verified
+ hypotheses pending
§3 The three ablation experiments (E1/E2/E3) with full CLI flag
specifications and environment-validation checklist
§4 Known gotchas (8 of them) with symptoms and fixes -- the most
important section to skim before you start
§5 CLI cheatsheet: run experiments / read data / plot / git
§6 Result-analysis checklist: numbers to collect, expected ranges
§7 FAQ for likely stuck-points
§8 Anti-patterns: what NOT to do
§9 Two specific deliverables the main agent expects back
Appendix A: file location lookup table
Appendix B: commit lookup table (by intent)
Goals encoded into the doc:
- Frame "your job is ablation, not new development" -- the new agent
should not be tempted to start D->P sync work; that goes on the
feat/d-to-p-sync branch in a separate phase.
- Make abort-accounting / max-input-len / mooncake-TCP-default
pitfalls extremely visible up front so they don't get repeated.
- Provide expected-result ranges so a 2x deviation is treated as a
config check, not a "finding".
- Make the critic-vs-production framing explicit so the new agent
knows when an audit-style "MAJOR" is actually a design intent.
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。