86412bb174
Phase 2 of the D→P sync feature (Phase 1 in dc4867c verified the
underlying RDMA link in isolation). This commit wires that link into
each SGLang worker's scheduler so D and P can exchange session KV
without going through the PD prefill pipeline.
New module:
third_party/sglang/python/sglang/srt/disaggregation/snapshot/
controller.py — SnapshotLinkController owns one mooncake transfer
engine per worker, pre-registers all kv_pool layer
buffers, and exposes prepare_receive() and
push_session_kv() APIs. Receive bookkeeping via
a session_id → SnapshotIngestRecord side-table.
Three RPC types added to io_struct.py and full plumbing wired through:
SnapshotPrepareReceiveReqInput/Output P-side alloc + return layout
SnapshotDumpReqInput/Output D-side read kv_pool + RDMA push
SnapshotFinalizeIngestReqInput/Output P-side radix tree insert
Files touched:
managers/io_struct.py 3 new ReqInput/ReqOutput pairs
managers/tokenizer_communicator_mixin.py 3 communicators, 3 awaitables
managers/scheduler.py init controller + 3 handlers
entrypoints/http_server.py 3 HTTP endpoints under /_snapshot
Activation: set SGLANG_SNAPSHOT_LINK_ENABLE=1 (and
SGLANG_SNAPSHOT_LINK_HOST / _PORT / _IB_DEVICE) per worker. Controller
init is opt-in and defaults off, so production PD pipeline is
untouched.
Subsequent work (Phase 3): agentic-pd-hybrid orchestration in
_invoke_kvcache_seeded_router to call prepare_receive on P, dump on
D-old, finalize_ingest on P, then trigger the existing P→D' transfer
which will now hit P's radix cache (skipping re-prefill).
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。