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f7e893282a docs: T21 — record DDP-dropout wiring gap + fix (known-issues / evolution / dropout doc)
- known-issues.md: new "DDP-dropout wiring" Fixed entry (gap + fix +
  regression test), with the meta-lesson that op/single-GPU unit tests can
  miss launcher-level integration gaps — only the V9-PILOT end-to-end run on
  the real launcher path exposed it.
- 17-dropout.md: annotate the DDP-combination note with the T18 wiring gap
  and its T21 fix.
- evolution.md: T21 row (Infra) recording the fix + meta-lesson.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-06-18 21:13:57 +08:00
a447631c4b test: T21 — DDP-dropout regression (live under DDP + p=0 bit-identical)
Adds ddp_dropout_is_live_and_p0_bit_identical, run via the real launcher
path (DdpContext::init + train_rank). It would have caught the original bug:

- GATE A (world=1, ONE step — the deterministic scope): a p=0 run is
  BIT-IDENTICAL (step loss + post-step params) to the no-dropout path.
  ops::dropout(p=0) is a clone no-op regardless of training mode. At world=1
  the NCCL all-reduce short-circuits; bit-identity holds for a single
  forward+backward (the engine's atomicAdd backward order compounds a few
  ULP only over many AdamW steps — the known fresh-train md5 caveat — so the
  honest bit-identity scope is one step, no optimizer-state compounding).
- GATE A2 (world=2): p=0 matches a separate no-dropout baseline within NCCL's
  run-to-run ULP noise (< 1e-6, KI-5 — the all-reduce is not bit-reproducible
  on this PCIe box). Enabling dropout=0 doesn't perturb the DDP path beyond it.
- GATE B (world=2): a p=0.2 run's loss trace DIFFERS by > 1e-3 from p=0 —
  orders of magnitude above the KI-5 noise floor. On the pre-T21 code the
  model stays in eval mode, so p=0.2 would be an identity and the trace would
  match p=0 at the noise floor — this gate fails.
- GATE C: model.is_training() == true after the run (direct proof that
  train_rank called model.train() and it survived the final-step eval).
- p>0 run is finite (no NaN/Inf).

eval_every < steps so a periodic eval fires mid-run (flipping to eval mode),
exercising the per-step model.train() restore discipline the pilot called out.
Run with --test-threads=1 like the other DDP tests (shared-GPU deadlock).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-06-18 21:13:57 +08:00

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