314c4cda0e
Reviewer feedback: the original gpu_utilization figure was confusing.
"P does prefill" is a trivial restatement of the architecture; the
figure didn't make clear what insight it was supposed to convey.
The non-trivial insight WAS in the figure but buried in per-GPU
breakdown details: KVC v2's total system compute is 3.47M tokens
vs DP's 5.17M -- a 33% reduction for the same 4449-request workload.
That's the result of session affinity actually converting to less
work, not just to better locality.
Redesigned the figure to lead with that finding:
Left panel (NEW): system-wide compute as two stacked bars
- KVC: P heavy prefill (1.07M) + D append-prefill (1.39M) + decode (1.01M)
- DP: full prefill (4.17M) + decode (1.00M)
- Big "-33% total compute" badge bracketed by an arrow between the
bar tops makes the headline number unmissable
Right panel (kept, simplified): per-GPU work distribution
- Same color coding as the left panel, so the architecture story
flows from "what work the system does" to "where it happens"
- In-panel annotation boxes describe the two architectural shapes
(specialized P + light D vs uniform fused workers)
- Removed the second legend that was overlapping bars
Doc §4.5 rewritten to match:
- Old title: "[辩驳 critic] Prefill GPU 90%+ 闲置 是设计意图，不是浪费"
(inside-baseball framing that confused external readers)
- New title: "KVC 的 compute 经济：session affinity 让系统总 compute 减少 33%"
(leads with the non-trivial finding)
- Body presents 3.47M vs 5.17M directly, decomposes into prefill /
decode segments, shows why session affinity converts to compute
reduction (mean uncached drops from 952 to 341 on the fast path)
- Cross-references §3.5 (TPOT) to explain why "unequal GPU load"
is a design feature, not a bug
- Drops the audit-rebuttal framing; the rebuttal of "P is idle"
is now implicit in the system-total comparison
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。