2dfe22ab20
Implements the design in docs/SNAPSHOT_STORE_REFACTOR_ZH.md to fix
the alloc-failed death loop that killed D→P in E4-v4/v5 (167 sync
attempts, 0 OK because P's kv_pool was busy with its own prefill).
Mechanism change:
OLD prepare_receive: token_to_kv_pool_allocator.alloc(N) — 90%+ failure
NEW prepare_receive: SnapshotBufAllocator.alloc(slab_bytes) carves a
range from an 8 GB GPU buffer dedicated to
snapshot reception, decoupled from kv_pool
OLD finalize_ingest: just radix.insert with pre-alloc'd slots
NEW finalize_ingest: kv_pool.alloc NOW + GPU memcpy snapshot_buf →
k_buffer/v_buffer + radix.insert
Wire schema changed (clean break, no back-compat):
PrepareReceiveReqOutput swaps k/v_base_ptrs + slot_indices for
snapshot_buf_base_ptr + k/v_layer_offsets +
num_tokens
DumpReqInput swaps target_k/v_base_ptrs + target_slot_indices
for target_snapshot_buf_base +
target_k/v_layer_offsets
FinalizeIngestReqInput drops slot_indices (P resolves at ingest)
Controller adds:
SnapshotBufAllocator: first-fit free-list with 4 KB alignment
ingest_snapshot_into_kvpool: GPU→GPU copy + radix insert
Configurable buffer size via SGLANG_SNAPSHOT_LINK_BUF_BYTES env
(default 8 GB, scales down to 1 GB if alloc fails).
Removed runtime leak-check accommodation since prepare_receive no
longer touches kv_pool.
Total: ~365 LOC including alloc helper; smoke-test verification next.
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。