Gahow Wang 110bd68000 docs(failures): consolidated 5-mode failure taxonomy
Consolidates failure modes scattered across V2_DEEP_ANALYSIS,
E1_E2_RESULTS, E3_FINDINGS, KVC_EVICTION_GRANULARITY,
REAL_ALI_KVC_EXPERIMENT into a single lookup table with
five fields per mode: symptom → root cause → trigger →
current mitigation → real fix.

Five modes covered:
  A. Mooncake "instance not alive" cascade
     — E2 80%-failure pathology; admission no-space →
       seed burst → heartbeat drop → batch abort
  B. Cold-D / overlap-pinning
     — shared boilerplate hash pins all sessions to a
       subset of D's; load_floor_bonus is a patch, the
       real fix is exclusive_overlap redefinition
  C. Evict storm (session-level eviction)
     — release_session frees 38–88K tokens in one shot;
       fix is BLOCK_LEVEL_EVICTION_DESIGN
  C'. Reseed storm (turn-1 concurrent seeds)
     — startup-phase mooncake burst; fix is per-D
       pending-seed budget, frequency drops after C
  D. Streaming-session correction invariant crash (E3)
     — schedule_batch.py:1646 landmine, hotfixed by
       986f351, root-fix is removing the correction
       path entirely (BLOCK_LEVEL_EVICTION §3.7)

Each mode has a forensic link back to the original
experiment doc that surfaced it.

§6 adds a diagnostic cheat sheet: "if you see X, look at Y."
§7 wires every mode to a roadmap item — Milestone 1 should
graduate §1–§4 to "mitigated" and eliminate §5.

INDEX_ZH gets a new §1.6 section linking this and the
SGLang patch inventory.

No code change. Reading dependency for anyone debugging
a sweep or writing paper §Limitations.
2026-05-13 00:43:58 +08:00
2026-04-24 12:17:40 +00:00

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

新加入的合作者:先看 docs/INDEX_ZH.md,按"我是谁"选 3 篇必读文档。 项目当前进度、薄弱点、路线图总览见 docs/AUDIT_AND_ROADMAP_ZH.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 defaultstickykv-aware 路由策略。
  • 支持 pd-disaggregationkvcache-centricpd-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 metricsrequest-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 SGLang v0.5.10 snapshot。
  • 不提交 outputs/、日志、__pycache__、虚拟环境。

单元测试(无 GPU

算法层policies、Algorithm 1 / Theorem 1有 pure-Python 单测,跑测试不需要 GPU、不需要 SGLang

uv sync --group test
uv run pytest

详见 tests/README.md

评测脚本

docs/EVALUATION_PROTOCOL_ZH.md 跑数据后:

# M3: 按 turn_id / input_length / overlap_ratio / append_tokens 分桶
scripts/analysis/stratified.py outputs/<run>/request-metrics.jsonl

# M2: paired-on-same-trial bootstrap 95% CI
scripts/analysis/paired_compare.py \
    --baseline outputs/run-dp/request-metrics.jsonl \
    --candidate outputs/run-kvc/request-metrics.jsonl
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