With Feistel gone, deterministic and legacy modes were both just torch.randperm and the
deterministic path strictly dominated (reproducible across ranks via the (seed, epoch) seed,
no accelerate generator sync, resumable). Collapse to a single path and drop the redundant
flag:
- remove the `deterministic` and `generator` constructor args, `_iter_default`, and
`_require_deterministic`; `set_epoch` / `state_dict` / `load_state_dict` are now unconditional
- remove the `deterministic_sampler` train config field and the legacy generator branch in
lerobot_train.py (non-streaming map datasets always use the sampler)
- drop the now-obsolete generator/legacy tests
Note: removes the `generator` kwarg from EpisodeAwareSampler (back-compat break vs main); the
order is now a pure function of (seed, epoch), so no cross-rank RNG sync is needed.
Co-authored-by: Cursor <cursoragent@cursor.com>
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