c01d6101d6
Standalone reference document capturing the v2 reseed slow-path forensic
audit before opening the feat/d-to-p-sync branch. Designed to be quoted
directly by future paper drafts and to prevent the team from re-relitigating
the same questions verbally.
Contents:
§1. The three team-member challenges that disproved "capacity-backup will
save the slow path" (each with code citation and verdict):
1) P pool can't fit all backups -- replay.py:1618-1620 caps backup
count at 1 for sessions with ~50K peak input.
2) P's backup is a stale snapshot -- 49K of direct-to-D append work
never flows through P. _commit_prefill_backup_residency
(replay.py:1483) is only called from seed/reseed paths;
direct-to-D path (replay.py:2719) never touches P-side state.
3) When D evicts, old KV is freed directly (no D->P dump).
session_aware_cache.release_session only calls
kv_pool_allocator.free().
§2. End-to-end reseed timeline (t=0 to t=4550ms) with code citations
showing exactly where each component sits. P-side re-prefill =
1.5-3s, mooncake transfer = 1.5-4s, both contributing 50/50 to
total reseed cost.
§3. Table of "looks like D->P but isn't" code locations -- every
candidate found during forensic search ruled out with line citations.
§4. Specification of what D->P incremental sync would require:
mooncake bidirectional roles (~400 LOC), D-side append commit hook
(easy), P-side radix tree multi-producer extension (the real blocker),
agentic-pd-hybrid replay.py hooks. Estimated 1-2 weeks engineering.
§5. Confirmation via `git ls-remote origin --refs` that author has NOT
secretly implemented D->P on another branch -- only main + this
working branch exist on the server.
§6. Roadmap for the upcoming feat/d-to-p-sync branch.
Appendices: code position crosswalk, related commits, paper section
suggestions.
This document is referenced by V2_DEEP_ANALYSIS_ZH §4.2 and by
KVC_ROUTER_ALGORITHM §9 Open Question 4.
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。