9ccd853066
After an independent Opus-agent forensic audit, the previous "(c) 增量 fetch (工程量较大，未实现)" line in V2_DEEP_ANALYSIS §4.2 was understating the gap. The audit confirmed: - No D->P KV transfer code exists in the framework at any layer (agentic_pd_hybrid orchestration, vendored SGLang disaggregation, or mooncake transport). - Mooncake MooncakeKVManager has a hard role split: PREFILL = sender, DECODE = receiver-only loop. `add_transfer_request` asserts the disaggregation_mode is PREFILL. - The BaseKVSender / BaseKVReceiver abstraction has no bidirectional slot. - session_aware_cache.release_session only calls kv_pool_allocator.free() on eviction -- no serialization, no outbound network call. - _commit_prefill_backup_residency is only called from the seed/reseed path (_invoke_kvcache_seeded_router). direct-to-D path never updates P-side backup state. - "capacity-backup" policy semantics: it only skips the close on P after reseed -- the backup is the seed-time static snapshot, never refreshed by D-side append-prefill activity. V2_DEEP_ANALYSIS §4.2: - Decomposed the 3-7s reseed cost into the P-side re-prefill segment (1.5-3s, dominant) and the P->D mooncake transfer segment (1.5-4s). - Quantified the realistic effect of enabling RDMA: only the transfer segment shrinks, reseed reduces to 1.7-3.2s, TTFT p99 ~0.7s, still loses to DP's 0.43s. - Replaced the throwaway "(c) incremental fetch" line with a full paragraph explaining what D->P sync would require, why it's the largest engineering gap, and that the blocker is SGLang's radix-tree single-producer assumption, not the network layer. KVC_ROUTER_ALGORITHM §9: - Refined Open Question 3 (RDMA) to clarify it only helps the transfer segment, not the re-prefill segment. - Added Open Question 4: D->P incremental KV sync as the central future-work contribution gap, with cited evidence for why it doesn't currently exist. 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。