docs: T20 — Phase-2 systems-depth capstone (reframe README to two phases)

Re-conclude xtrain as TWO phases now that Phase-2 (T14–T18) is merged on main:

README.md
- Status header: "complete (T1–T13) + scaling v0–v8" → "complete — two phases"
  (Phase 1 = from-scratch stack T1–T13 + v0–v8 scaling study; Phase 2 = the five
  deferred systems-stack features T14–T18).
- Crate table: note the Phase-2 additions (fused flash-attn + repeat_kv + dropout
  in autodiff; GQA + dropout in model; grad-accum in train; process-per-GPU
  launcher in distributed).
- Build-journey section retitled Phase 1 + Phase 2; replaced the run-on T14–T18
  prose with a structured "## Phase 2" summary (5 features + honest results:
  flash = mem-not-walltime win, GQA group-sum backward, grad-accum −74% mem,
  dropout × recompute bit-exact, T17 throughput-neutral falsification).
- Engineering lessons: T17 added as the THIRD profile-first falsification;
  reinforced honest-correctness with the Phase-2 hard gates + md5 b04fc9f9.
- Doc index: doc range …14-* → …17-*; KI status line (process-per-GPU CLOSED,
  KI-4 accepted tradeoff).

docs/evolution.md
- New "三·五、Phase 2 systems-depth synthesis": ties the 5 features into the
  per-axis (算法/架构/Infra/数据) narrative + the two integration notes.

docs/known-issues.md
- KI-4 reframed as a deliberately-accepted modeling tradeoff (保 xserv closed
  loop; T19 DROPPED), not "open".
- New integration notes: (a) DDP tests need --test-threads=1 (parallel deadlock);
  (b) fresh-train md5 is non-deterministic (atomicAdd reduction order) → the valid
  determinism gate is export re-determinism, not fresh-train reproduction.
- (process-per-GPU item was already CLOSED=measured no-op in T17.)

Docs-only; no code touched.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

docs/known-issues.md

@@ -156,6 +156,20 @@ _(KI-3 已在 T13 FIXED，见上方 Fixed。本节暂无待启项。)_
 
 ## Modeling notes
 
-### KI-4 · 大词表 embedding 占比过高
+### KI-4 · 大词表 embedding 占比过高 — `接受的建模权衡（用户拍板，T19 DROPPED）`
 - gpt2 `vocab=50257` 在 dim 小时让 embed+lm_head 主导参数：v1 25.7M/34M、v2 38.6M/66.9M；core transformer 才是学习主体。
-- 后续可考虑更贴合 TinyStories 的小 vocab（会牺牲 xserv gpt2-tokenizer 复用）；或在更大 dim 下让 core 自然成为主体（继续 scaling 即可缓解占比）。
+- **决定（不是 open，是一个被接受的权衡）**：曾计划 T19 训一个更贴语料的小 vocab 来压 embed 占比，**用户拍板 DROP**——保住 **xserv gpt2-tokenizer 闭环**（项目皇冠：导出权重回流 xserv 逐 token 一致）比清理 embedding 占比更值。小 dim 下 embed 占比高=复用 gpt2 大词表的**已知、接受的代价**。
+- 缓解路径仍在：更大 dim 时 core 自然成为主体（继续 scaling 即可摊薄占比，v8 dim1024 core 226M 已主导）；若日后愿意放弃该版闭环再重启小词表（见 [`~/toc/projects/xtrain.md`](../../toc/projects/xtrain.md) T19）。
+
+---
+
+## 集成 / 测试注记（pre-existing，非回归，记账）
+
+### DDP 三测并行会争卡 deadlock → `--test-threads=1`
+- `xtrain-distributed` 的三个 DDP 测试（thread-per-GPU correctness / scaling、process-per-GPU `ddp_proc`）若被 cargo **并行**跑，会在共享的 2 卡上互相争 GPU/NCCL 资源 **deadlock**（不是数值 bug，是测试调度）。
+- **跑法**：`cargo test ... -- --test-threads=1`（或把 DDP 测试标 serial）串行跑即全绿。Phase-2 全回归 capture 均在 `--test-threads=1` 下取得。
+
+### fresh-train md5 run-to-run 不定 → 有效确定性闸门是「导出重确定性」而非「fresh-train 复现」
+- **现象**：从随机初始化全新训练（fresh-train）产出的 ckpt，其 md5 **run-to-run 不逐位复现**。
+- **根因**：反向里多处 `atomicAdd` 归约（如跨行 dK/dV、扇出累加）的浮点**加法序非确定**（GPU 原子操作完成序不固定）→ 末位 ULP 抖动逐步累积 → ckpt 字节不同。属本机/本版的已知浮点非确定性，**不是正确性回归**（loss 轨迹仍同量级收敛）。
+- **因此项目的确定性硬闸门定义为「导出（export）重确定性」**：拿**同一个已固定的 ckpt** 重新导出 HF-safetensors，md5 与 registry **逐位一致**（`b04fc9f9`，两阶段全程）——这条是确定性的、承重的；**不要求** fresh-train 字节复现。