feat(train): checkpoint and restore per-rank RNG state for exact resume - #1026
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📝 WalkthroughWalkthroughChangesThe trainer now saves and restores per-rank torch CPU and device RNG state during checkpoint resume. Mid-epoch and end-of-epoch checkpoints use different snapshot timings. Tests verify exact replay and missing-state handling. CLI documentation describes these rules. Deterministic checkpoint resume
Merge Risk: 🟡 Moderate · up to For samplers without fast-skip support, replaying skipped batches can consume random values before the saved state is restored, causing resumed training to diverge from an uninterrupted run. This bounded correctness issue should be fixed and covered by a regression test before merging. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
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Mid-epoch resume (vllm-project#603) restores the data position, the optimizer and the scheduler, but not the random number generators. Anchor sampling (torch.randperm on the accelerator), noise augmentation and dropout keep drawing from wherever the fresh process happens to start, so a resumed run diverges from the uninterrupted one even with --num-workers 0, and interrupted/uninterrupted runs cannot be compared step for step. Every rank now writes rng_state_rank{N}.pt next to each integer-epoch checkpoint (Python, NumPy, torch CPU and the rank's accelerator generator, stored as primitives/tensors so it loads with weights_only=True). On resume the state is loaded in setup_trainer and applied once in train_epoch, after the fast-skip has been prepared and before the loader iterator draws its base seed, so the first resumed step sees exactly the generators the checkpoint saw. Checkpoints without the file resume with a warning. Tests cover the snapshot format, exact replay of torch/Python/NumPy draws across a mid-epoch resume, end-of-epoch resume, per-rank files under the distributed rank gate, and the missing-file fallback. Signed-off-by: Zihan Lin <linzihan.ai@gmail.com>
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…best Review feedback on the first revision found two real gaps: - Restore ordering. A mid-epoch checkpoint is taken while the epoch's DataLoader iterator is alive; the resumed process creates a new iterator, which draws the loader base seed from the CPU generator. Restoring the snapshot before that draw left the resumed run one draw behind the uninterrupted one (reproduced with num_workers=0). The previous test hid this by recreating the iterator on the reference side as well. The snapshot is now applied after the resumed iterator exists for mid-epoch resume, and before it for end-of-epoch resume (_epoch_iterator), and the tests compare a live iterator that continues against a fresh process that resumes, with a dataset that draws on every fetch. - save_best=True. maybe_save_checkpoint returns early under save_best and the checkpoint is written by maybe_update_best, which never saved the RNG state. End-of-epoch snapshots are now taken once at the end of the epoch loop, after validation and the best-checkpoint update, for whichever path wrote the checkpoint, which is also the point an uninterrupted run would continue from (validation draws included). Mid-epoch checkpoints keep the snapshot at save time. Every rank's write is followed by a barrier in distributed mode so a checkpoint is not visible as resumable before all rank files exist. Signed-off-by: Zihan Lin <linzihan.ai@gmail.com>
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Review: keep the change minimal. The trainer's own random draws (anchor sampling, noise augmentation, dropout) all come from torch generators, so the snapshot now holds the torch CPU generator and this rank's accelerator generator only; Python random and NumPy state are left out until something in the training step draws from them. Tests and docs updated accordingly. Signed-off-by: Zihan Lin <linzihan.ai@gmail.com>
Signed-off-by: Zihan Lin <linzihan.ai@gmail.com>
Review: a missing or unreadable RNG file and a failed restore no longer warn-and-continue; only the missing-file case (checkpoints predating this change) is tolerated, anything else raises. Resolve the device module via torch.get_device_module and gate on torch.accelerator.is_available() instead of getattr/hasattr probing. Fold the consumed-once assertion into the existing mid-epoch resume test. Signed-off-by: Zihan Lin <linzihan.ai@gmail.com>
…ady None) Signed-off-by: Zihan Lin <linzihan.ai@gmail.com>
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In `@src/speculators/train/trainer.py`:
- Around line 267-274: Update _epoch_iterator to restore the pending RNG state
after fallback replay skips skip_steps batches, before remaining batches are
processed; preserve the existing ordering for samplers supporting fast skip, and
add a regression test using _NoisyDataset with a sampler lacking fast-skip
support.
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…poch snapshot With checkpoint_freq < 1, a mid-epoch checkpoint writes rng_state_rank*.pt into the same epoch directory the end-of-epoch save reuses. The end-of-epoch snapshot is deferred until validation finishes, so in that window the directory holds end-of-epoch weights next to mid-epoch RNG state; a resume after an interruption during validation would silently replay mid-epoch random draws. Remove this rank's RNG file at both deferral sites so such a resume falls back to the documented no-RNG warning instead. Raised by review on the PR. Signed-off-by: Zihan Lin <linzihan.ai@gmail.com>
…n fallback resume When the sampler lacks the fast-skip API, _epoch_iterator returned the loader from batch 0: the training loop retrained the already-trained batches under shifted local_step numbering, and the RNG snapshot was applied before the replay, so with num_workers=0 the replayed __getitem__ calls consumed the freshly restored stream and every remaining batch drew from the wrong position. Fetch and discard the skipped batches first, then apply the snapshot - the drained calls consume random draws exactly as the original epoch did, training never sees them, and local_step stays aligned with the full epoch. Fast-skip samplers keep the existing order (restore right after iterator creation, nothing to drain). The trainer mocks in test_mid_epoch_resume now route through the real _prepare_resume_skip instead of reimplementing it, so they exercise the replay flag and the sampler slice rather than a parallel copy. Raised by review on the PR. Signed-off-by: Zihan Lin <linzihan.ai@gmail.com>
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Purpose
Mid-epoch resume restores the data position, optimizer and scheduler, but not torch RNG state. Accelerator-side anchor sampling, noise augmentation and dropout therefore diverge after resume.
Save the torch CPU and current accelerator RNG state in one per-rank file. Mid-epoch state is captured at checkpoint time; end-of-epoch state is captured after validation and any
--save-bestupdate. On resume, it is restored after creating the existing-epoch iterator for a mid-epoch checkpoint and before creating the next-epoch iterator at an epoch boundary. Checkpoints without an RNG file still resume with a warning.This covers torch RNG only. Exact replay requires
--num-workers 0, the same rank/device topology, and the remaining checkpoint guarantees discussed in #1027.Implements #1027.
Tests
Unit (
tests/unit/train/test_mid_epoch_resume.py, 14 tests): snapshot format /weights_onlyload; end-of-epoch snapshot written after validation;--save-bestcheckpoints get it; a live iterator that continues vs. a fresh process that resumes mid-epoch, with a dataset that draws on every fetch; the same at an epoch boundary; per-rank files under the distributed rank gate; missing-file fallback.Hardware, on this branch + #899 (fp32 optimizer state) + #900 (scheduler seeding) with model weights serialized as fp32 — a tiny model with dropout, AdamW, 10 steps/epoch, crash after the mid-epoch checkpoint, then resume in a fresh process and compare against the uninterrupted run:
Checklist