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7 Commits
| Author | SHA1 | Message | Date | |
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 Pepijn
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234c768dfb |
feat(datasets): deterministic, resumable shuffling for EpisodeAwareSampler (#3769)
* fix(datasets): expose a generator on EpisodeAwareSampler for distributed shuffle sync 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> * fix(train): seed sampler generator and gate dataset download per node - Pass a generator seeded with cfg.seed to EpisodeAwareSampler so accelerator.prepare registers it as the synchronized RNG and the shuffle order is reproducible. - Gate the initial make_dataset call on is_local_main_process instead of is_main_process: the global main process only exists on node 0, so on every other node all local ranks were downloading the dataset and building the Arrow cache concurrently. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * feat(datasets): add DeterministicEpisodeAwareSampler with O(1) memory and sample-exact resume 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> * refactor(datasets): fold deterministic mode into EpisodeAwareSampler 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> * feat(train): enable deterministic_sampler by default Deterministic data order (sample-exact resume, no cross-rank RNG sync, O(1) sampler memory) is now the default for map-style training; set deterministic_sampler=false to restore the legacy RNG-based shuffle. Streaming datasets ignore the flag (the sampler path only applies to map-style datasets), replacing the previous hard validation error so streaming configs keep working with the new default. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * feat(datasets): default EpisodeAwareSampler to deterministic mode and trim comments 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> * test(sampler): drain resumed trillion-frame sampler via iter() to avoid list() prealloc 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. * fix(datasets): guard Feistel cycle-walking loop against non-convergence Replace the unbounded while True in EpisodeAwareSampler._permute with a bounded for loop capped at _MAX_CYCLE_WALK_STEPS (100) and raise RuntimeError if the cycle-walk fails to land in [0, num_frames). The loop is expected to converge in <4 steps on the chosen power-of-two domain, so the bound is a safety net that should never trip in practice but prevents a pathological infinite loop. https://claude.ai/code/session_01HQ15tFrBsHYScjGWosEv22 * fix(datasets): make deterministic-sampler resume robust to world-size changes compute_sampler_state mapped a checkpointed step back to (epoch, start_index) using the *current* num_processes, but the number of sampler positions a step consumes scales with the world size that produced it. Resuming on a different GPU count therefore landed on the wrong epoch/offset, silently re-seeing or skipping data. Record num_processes in training_step.json at checkpoint time and feed the checkpoint's value into compute_sampler_state on resume, so the data order resumes at the right position regardless of the new world size. Warn when the world size changed (the global offset is correct, but per-rank sample-exactness needs the same topology). Old checkpoints without the field fall back to the current world size. Also document compute_sampler_state's assumptions explicitly: num_processes / batch_size must match the checkpointing run, and accelerate's even_batches=True padding is mirrored by the ceil(... / num_processes) term. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Co-authored-by: Cursor <cursoragent@cursor.com> * style: apply ruff-format to lerobot_train.py Collapse the compute_sampler_state(...) call onto one line so the ruff-format pre-commit hook passes (fixes the failing CI check). Co-authored-by: Cursor <cursoragent@cursor.com> * refactor(datasets): use seeded torch.randperm instead of Feistel in EpisodeAwareSampler 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> * refactor(datasets): make EpisodeAwareSampler always deterministic 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> * fix(datasets): address sampler review (batch_size resume guard + docs) - Record batch_size in training_step.json alongside num_processes and feed the checkpoint's value into compute_sampler_state on resume; warn when it differs (per-rank sample-exactness needs the same batch size). - Document the set_epoch vs __iter__ auto-advance coupling on EpisodeAwareSampler (callers should rely on exactly one mechanism per run). - Note the broadened (reproducibility-breaking) sampler guard and the no-generator distributed sharding correctness in lerobot_train.py. - Add load_training_batch_size + parallel tests. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(train): download dataset once on the global main process Gate the training dataset download on the global is_main_process (download once to the shared dataset root, barrier, then every other rank reads the already-populated copy) instead of per-node is_local_main_process. LeRobotDataset skips its snapshot_download when try_load() succeeds, so no rank re-downloads. Assumes the dataset root / HF cache is on storage shared across nodes. Co-authored-by: Cursor <cursoragent@cursor.com> * chore(datasets): trim sampler comment and drop duplicate tests Remove the verbose dataloader-guard comment and the two EpisodeAwareSampler tests that duplicated existing validation/warning coverage (no coverage loss). Co-authored-by: Cursor <cursoragent@cursor.com> --------- Co-authored-by: Claude Fable 5 <noreply@anthropic.com> Co-authored-by: Cursor <cursoragent@cursor.com> |
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Steven Palma
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df0763a2bc | feat(dependencies): minimal default tag install (#3362) | ||
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githubnemo
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e670ac5daf |
Add basic PEFT support to train script + record module (#1411)
* Add basic support for PEFT adapter methods This changes adds support for training policies with much less parameters by applying adapter methods such as LoRA on specific parts of the policies and therefore possibly higher learning rates / batch sizes. To make this as accessible as possible I thought it useful to provide defaults for `target_modules` and `modules_to_save`. Currently only SmolVLA has such defaults but when we agree that this change is useful I will set out to generate more such defaults. While the user can override these settings, they are expected to only change the peft_method, rank and init_type parameters. * Implement loading of PEFT adapters Loading a PEFT adapter is currently done by initializing a policy with default config and then applying the adapter on the resulting model. This has the obvious drawback that any configurations done during training are not applied in the adapted model. Currently the `use_peft` attribute of `PreTrainedConfig` is only set during loading to signal the following code that it has to deal with a PEFT adapter. However we could imagine a scenario where this is already set at training time and stored alongside the adapter. * Store policy config alongside PEFT checkpoint Before this change the PEFT-wrapped policy did not save the policy's config alongside the adapter config / weights which prevented us from changing the policy config. Now the policy config is saved both in full training and PEFT training. This change makes loading the PEFT policy adapter much easier as well. * Add default config for ACT * Support targets like `all-linear` * Formatting * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix failing tests * Remove PEFT compatibility changes in config We'll wait for the PEFT release that fixes this for good. * Remove `use_peft` parameter from training script Instead we make the PEFT config optional which has the same effect. * Log adapter config to WandB * Better documentation for CLI arguments * Don't unload & merge the PEFT model This can make things hard when using quantized layers (user expects quantized base layers with unquantized adapters for example, merging defaults to upcast the layers leading to higher memory). * Correct way of identifying when to save config * Add CLI end-to-end tests Currently there don't seem to be any way to test the CLI commands. Since this change mostly happens in those I thought it best to add a way to test these commands end-to-end. More integrated commands like `lerobot-record` need patching but standalone commands like training seem to work fine. * Update default targets Removed ACT since it doesn't make sense to fine-tune ACT without having it pretrained beforehand. SmolVLA and Pi0/0.5 are much more senseful targets. * Clean up loading code - Centralized instantiation of the PEFT wrapper in `make_policy` for inference (e.g. in `lerobot-record`) - Training a PEFT policy also sets `cfg.use_peft` so that all inference code loading the policy can rely on that attribute to identify if PEFT loading is needed - Modified RTC example to also include PEFT policies. Mostly because this is an example I'm currently exploring. * Make sure push_to_hub works Since PEFT only wraps `push_to_hub` and not `push_model_to_hub`, the reference to `self` in `policy.push_model_to_hub` is the unwrapped policy which, of course, doesn't know anything about PEFT. To make the upload process aware of PEFT, we pass the unwrapped policy down to `push_model_to_hub` as a kwarg. This is not ideal but I think it is the best way for now. * formatting * Warn when encountering from-scratch-training * Revamp pretrained model loading There were quite a few factors that convinced me that the status quo is able to load pretrained models from the PEFT adapter config but in fact that didn't work. This commit fixes the following things: - policies wrapped in PEFT will now have a `name_or_path` attribute containing the name or path of the pretrained model we're fine-tuning - we further assume that SmolVLA without `pretrained_path` and `load_vlm_weights==False` must be an user-side error - we assume that using PEFT on from-scratch-policies must be an user-side-error * Make it possible to unset policy features This is necessary to train pre-trained policies on new datasets so that the features are inferred from the new dataset and not from the pretrained policy. * Use correct loading for PEFT in RTC example * Make it possible to use PeftModels in eval * Add test checking that PEFT actually reduces params * Adapt state/action projections instead of full-finetuning There doesn't seem to be a benefit to fully fine-tune these layers over just adapting them, so we do that instead. * Disallow PEFT training on non-pretrained policies At first I thought it would make sense to have this feature in case you want to fine-tune a pre-trained section but in the end it makes more trouble than it's worth. It's still possible to allow this in the future when a concrete need arises. * Add basic documentation * Formatting * Add peft as extra dependency, mark tests Fast tests currently fail because of the missing dependency. * Fix pre-commit issues * Add walx <> peft conflict for uv * Exclude peft from pi install for now --------- Co-authored-by: nemo <git@ningu.net> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> |
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Steven Palma
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6c28ef894a | chore(docs): add missing license headers (#2140) | ||
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Steven Palma
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7cf04a5ec3 | chore: move constants to utils (#2016) | ||
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Simon Alibert
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d4ee470b00 |
Package folder structure (#1417)
* Move files * Replace imports & paths * Update relative paths * Update doc symlinks * Update instructions paths * Fix imports * Update grpc files * Update more instructions * Downgrade grpc-tools * Update manifest * Update more paths * Update config paths * Update CI paths * Update bandit exclusions * Remove walkthrough section |
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Simon Alibert
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974028bd28 |
Organize test folders (#856)
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> |