Drop the Feistel permutation (and its SplitMix64 hash / cycle-walking) in favor of a
torch.randperm seeded from (seed, epoch). The deterministic mode keeps its key properties
- data order is a pure function of (seed, epoch), so it reproduces on every rank with no
global-RNG synchronization, and
- state_dict / load_state_dict still resume sample-exactly, now by regenerating the epoch's
permutation and slicing from the saved offset.
Construction stays O(num_episodes) (only episode boundaries are stored, never a per-frame
index list). The trade-off vs Feistel: the per-epoch shuffle is again O(num_frames) memory
(the randperm tensor) and no longer O(1)-seekable, in exchange for ~30 fewer LOC and a truly
uniform shuffle. Tests updated: the trillion-frame O(1) test is replaced with a
boundary-storage check and a scale resume-exactness test.
Co-authored-by: Cursor <cursoragent@cursor.com>
list(sampler) calls PyObject_LengthHint -> __len__ (the full 10**12 epoch length) and
preallocates that many slots before iterating, OOMing even though the resumed epoch only
yields 3 frames. Collect through the iterator (no length hint) so the test exercises the
real O(1) seek/drain instead of CPython's list growth heuristic.
deterministic=True is now the class default as well as the training
default; the legacy RNG path requires an explicit deterministic=False
(the train script's non-deterministic branch passes it). Docstrings and
inline comments slimmed down across the changed files.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Instead of a parallel DeterministicEpisodeAwareSampler class, extend the
existing EpisodeAwareSampler with a deterministic=True mode (seeded
Feistel permutation, epoch auto-advance, state_dict/load_state_dict).
The default mode is behavior-identical: same torch.randperm consumption
and the same generator contract accelerate synchronizes; the O(N) Python
index list is replaced by O(num_episodes) boundary arrays in both modes,
with `indices` kept as a back-compat property. Passing a generator
together with deterministic=True is rejected, and the state/seek methods
raise outside deterministic mode.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Add a sampler that never materializes frame indices: it stores only
per-episode boundaries (numpy, a few bytes per episode) and maps logical
positions to frame indices on the fly with searchsorted. Shuffling uses a
seeded Feistel permutation over [0, num_frames) (cycle-walking to the
exact domain), so the data order is a pure function of (seed, epoch):
- no RNG state to synchronize across distributed ranks,
- constant memory and zero epoch-boundary cost at any dataset size,
- O(1) seek to any position, enabling sample-exact resume.
Opt in with --deterministic_sampler=true. On resume, lerobot-train maps
the checkpointed step back to (epoch, start_index) via
compute_sampler_state and continues at the exact sample where the run
left off (up to accelerate's even_batches padding at epoch boundaries).
The shuffle is pseudo-random rather than a true uniform permutation, the
standard trade-off in large-scale training loaders.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
In distributed training, accelerate can only synchronize the shuffle
permutation across ranks when the sampler exposes a generator attribute.
EpisodeAwareSampler shuffled via the global torch RNG, so disjoint batch
shards relied on every rank's global CPU RNG staying in lockstep forever;
any rank-asymmetric RNG consumption (e.g. eval rollouts on the main
process only) silently desynced the permutations and ranks trained on
overlapping/missing samples.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
pepijn