5eac9b4f6b8ff54ca8215b30eb0f3357ef0909b5
The old filter `if row.latency_s is not None` accepted SGLang's fast input-length-aborts (latency_s ~ 0.08s, finish_reason='abort/BadRequest') as if they were successful zero-cost requests. This deflated mean/p50 of any run where the model rejected oversized inputs. Impact on existing comparisons (ts=1 4-run validation + v2): KVC v2 has 40 aborts + 5 ReadTimeouts (was reported as just 5); DP 4w has 67 aborts (was reported as 5). Both runs have abort behavior; the asymmetry (40 vs 67) is purely from SGLang's mem-fraction-derived max-input-len: KVC decode-only worker gets ~10 GB free GPU mem -> max-input=92098, DP fused worker gets ~9 GB -> max-input=87811, because DP also needs chunked-prefill workspace. The KVC-vs-DP latency-win direction holds and widens slightly under the fixed filter (lat mean delta: -0.8% -> -1.4%); see V2_DEEP_ANALYSIS_ZH §4.3 for the recomputed table. Changes: - metrics.py: new _is_failed_request(row) helper; latency/ttft/tpot stats now exclude both errors and aborts. New summary fields abort_count and failure_count expose the counts directly. - scripts/analysis/recompute_summary.py: re-derives summary.json from existing metrics.jsonl using the fixed code, with optional --diff against the old buggy summary for inspection. 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。
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