74194e660a
Add v4 sweep results and post-mortem analysis showing:
- direct-to-D path: 54.3% (1P7D) / 58.0% (2P6D) of requests now use
KVC cleanly. P50=0.5s and TTFT P50=0.043s; this path beats baseline
8DP across the board (P50 -24%, TTFT P50 -54%, TTFT P90 -79%).
- Overall vs baseline (errors+truncated excluded):
v4 2P6D P50=0.85s vs baseline 0.66s (28% slower).
Reason is not errors -- 35% of requests still hit
fallback-large-append-session-cap, where capacity-based
cap = usable_tokens / target_tokens evaluates to 1-2 (not 16)
for large agentic inputs.
- 9-10% errors on KVC variants are mooncake TCP transfer timeouts,
not SGLang logic bugs. Prefill log shows
"Failed to send kv chunk ... 32s timeout ... session not alive".
Errors concentrate in turn>=31 (large inputs) after run >44.8%.
Track:
- docs/KVC_DEBUG_JOURNEY_V1_TO_V4.md: append v4 results table,
per-mode breakdown, and error root cause.
- scripts/analysis/{analyze_v3,analyze_v4,analyze_errors,compare_no_error}.py
- outputs/qwen3-30b-tp1-v{3,4}*/exp*_summary.json (force-added,
small JSON; metrics.jsonl excluded due to size).
- outputs/qwen3-30b-tp1-v{3,4}*/sweep_results.txt
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。