* refactor(dataset): enhance dataset root directory handling and introduce hub cache support
- Updated DatasetConfig and LeRobotDatasetMetadata to clarify root directory behavior and introduce a dedicated hub cache for downloads.
- Refactored LeRobotDataset and StreamingLeRobotDataset to utilize the new hub cache and improve directory management.
- Added tests to ensure correct behavior when using the hub cache and handling different revisions without a specified root directory.
* refactor(dataset): improve root directory handling in LeRobotDataset
- Updated LeRobotDataset to store the requested root path separately from the actual root path.
- Adjusted metadata loading to use the requested root, enhancing clarity and consistency in directory management.
* refactor(dataset): minor improvements for hub cache support
* chore(datasets): guard in resume + assertion test
---------
Co-authored-by: AdilZouitine <adilzouitinegm@gmail.com>
Co-authored-by: mickaelChen <mickael.chen.levinson@gmail.com>
* refactor(dataset): split reader and writer
* chore(dataset): remove proxys
* refactor(dataset): better reader & writer encapsulation
* refactor(datasets): clean API + reduce leaky implementations
* refactor(dataset): API cleaning for writer, reader and meta
* refactor(dataset): expose writer & reader + other minor improvements
* refactor(dataset): improve teardown routine
* refactor(dataset): add hf_dataset property at the facade level
* chore(dataset): add init for datasset module
* docs(dataset): add docstrings for public API of the dataset classes
* tests(dataset): add tests for new classes
* fix(dataset): remove circular dependecy
* fix(vqbet): use in-place fill_ to avoid overwriting DDP GPU buffers with CPU tensors
When VQ discretization phase completes, the code was overwriting
register_buffer('discretized') and register_buffer('freeze_codebook')
with torch.tensor(True), which is created on CPU. DDP then fails in
_sync_buffers() with: RuntimeError: No backend type associated with
device type cpu. Fix by updating the buffers in-place with .fill_(True)
so device and registration are preserved.
Made-with: Cursor
* test(vqbet): add regression test for in-place buffer update during discretization
Verifies that discretize() updates the 'discretized' and 'freeze_codebook'
registered buffers in-place (via fill_()) rather than replacing them with new
CPU tensors. The test checks data_ptr() identity and that the tensors remain
registered buffers after the call. This prevents regressions of the DDP fix.
Made-with: Cursor
* test(vqbet): add GPU regression test to verify buffers stay on CUDA after discretize()
Directly catches the original DDP failure mode: when buffers are replaced with
torch.tensor(True) they land on CPU, causing NCCL to raise 'No backend type
associated with device type cpu' in _sync_buffers(). The GPU test places the
model on cuda:0 and asserts both buffers remain on CUDA after discretization.
Made-with: Cursor
* test(vqbet): simplify to single device-check test in test_policies.py
Per reviewer feedback: remove the separate test file and replace the two
CPU/GPU tests (with data_ptr checks) with a single focused test in
tests/policies/test_policies.py that only asserts the registered buffers
remain on the model device after discretize(). Uses DEVICE from tests/utils.py
so it runs on whatever device the CI/user selects (cpu, cuda, mps).
Made-with: Cursor
* style: fix import order in test_policies.py to pass ruff/pre-commit checks
Made-with: Cursor
---------
Co-authored-by: Zhan DiJia <2476100824@example.com>
Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
* fix(ci): skip HF log in (and tests) in forks and community PRs
* chore(test): remove comment about test meant to be only run locally
* fix(tests): no hf log in decorator for xvla
* fix(test): no decorator in yield
* chore(docstrings): updating v2.1-v3.0 conversion script docstrings to match the new task label
* chore(task): renamming the default index label in the tasks DataFrame to task
* Revert "chore(docstrings): updating v2.1-v3.0 conversion script docstrings to match the new task label"
This reverts commit f55de3255278f23f18b5d955565f6768d094951d.
* chore(docstrings): updating docstrings to match dataset v3.0 architecture
* chore(format): formatting code
* fix(root): adding proper support for the root and new_root arguments
* feat(roots): adding a roots agrument for the merge operation
* chore(clean): cleaning up code
* chore(doctrings): updating doctrings with new features
* fix(repo_id): setting repo_id to None when not needed
* fix(roots/repo_ids): making mypy happy by using repo_ids and roots for merge operation
* fix(path): fixing path related issues
* fix(repo_id): fixing issues related to repo_id
* chore(doctrings): updating docstrings + fix typo
* chore(clean): cleaning code
* fix(split new_repo_id): reverting new_repo_id addition for split operation
* docs(dosctrings): completing docstrings
* fix(repo_ids/roots): improving checks for repo_ids/roots lengths
* fix(repo_ids): making repo_ids optional in MergeConfig but raise if not given
* fix(docstrings): fixing docstrings for split operation
* fix(hints): updating get_output_path hints to accept paths as strings too
* fix(y/N prompts): removing y/N prompts in lerobot_edit_dataset
* fix(merge repo_id): fixing merge operation to use new_repo_id instead of repo_id
* fix(typo): fixing typo in doctrings
* Add New featrue to lerobot_edit_datset.py that show dataset information.
* Fix to draccus error when happen give only --operation.type=info
* Updating test and documents regarding lerobot-edit-dataset info function.
* Updating documents regarding lerobot-edit-dataset extract function. option name in document is mistake.
* feat(datasets): Update to align formatting with pre-commit.(#2917)
Update to align formatting by pre-commit.
---------
Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com>
* feat(datasets): add modify_tasks function for in-place task editing
Add a new utility function to modify tasks in LeRobotDataset in-place.
This allows users to:
- Set a single task for all episodes
- Set specific tasks for individual episodes
- Combine a default task with per-episode overrides
* feat(edit-dataset): add CLI support for modify_tasks operation
Integrate the modify_tasks function into lerobot_edit_dataset CLI.
Users can now modify dataset tasks via command line:
Supports setting a default task, per-episode tasks, or both combined.
* test(datasets): add tests for modify_tasks function
Add comprehensive test coverage for the modify_tasks utility:
- Single task for all episodes
- Episode-specific task assignment
- Default task with per-episode overrides
- Error handling for missing/invalid arguments
- Verification of task_index correctness
- In-place modification behavior
- Metadata preservation
* respond to copilot review
* fix(sac): make temperature a property to fix checkpoint resume bug
Temperature was stored as a plain float and not restored after loading
a checkpoint, causing incorrect loss computations until update_temperature()
was called. Changed to a property that always computes from log_alpha,
ensuring correct behavior after checkpoint loading.
* simplify docstrings
* feat(cameras): add new read_latest() method
* fix(cameras): fix threading bug + clear state
* refactor(cameras): multiple improvements
* feat(camera): add context manager to camera base class
* chore(camera): slight modifications to opencv
* test(cameras): update opencv tests according to the changes
* refactor(cameras): reflect desing changes to realsense + deal with depth
* test(cameras): fix realsense tests accordingly to new changes
* refactor(cameras): update reachymini and zmq accordingly
* chore: wrap resource sensitive examples into a try/finally
* test(cameras): add test for new read_latest
* test(cameras): fix problem with image artifact in opencv tests
* test(cameras): fix test_read_latest_high_frequency expectations
* Apply suggestions from code review 1
Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com>
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
* chore(cameras): address feedback
* feat(cameras): add max_age_ms check in read_latest
* test(cameras): fix read_latest tests
* chore(redundancies): removing redundancies in Reachy 2 camera class
* fix(warmup): replacing the arbitrary time.sleep in by an actual warmup in the RealSense camera class
* chore(format): formatting latest changes
* chore(warning): adding a "to be implemented" warning for read_latest() in Camera base class
* chore(warning): making read_latest() warning message shorter and clearer
---------
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com>
* Fix aggeregation of datasets when subdatasets are already a result of a previous merge
* docstring
* respond to copilot review + add regression test
* Remove unnecessary int conversion for indicies
* fix(motors): cleanup imports + fix signatures
* feat(motors): add damiao canbus + multiple fixes
* fix(motors): address comments -> last_state + different gains + sleep
* refactor(motors): reduce duplicated code + adressed some comments in the PR
* chore(motors): better timeouts
* tests(motors): damiao test and imports
* chore(deps): fix space
* Apply suggestions from code review
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
* chore(motors): remove normalization tables damiao
* fix(motors): imports and signatures
* feat(motors): add motor_type_str + recv_id to motor class and _get_motor_recv_id raises if no motor_obj.recv_id
* chore(motors): remove normalize from base motor class and damaio
* tests(motors): remove bad tests (to be replaced)
* chore(motors): updated import check
* use constant for kp and kd range and check responses in mit_control_batch()
* Add docs on setting up canbus and use damiao otor bus, also add lerobot_setup_can.py and log if there is not response from a write command
* precommit format
* supress bandit as these are intentional cli commands
* fix setup-can
* add test
* skip test in ci
* nit precommit
* update doc example
* dont import can for tests
---------
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
Co-authored-by: Pepijn <pepijn@huggingface.co>
* feat(async_inference): server always sends CPU tensors, client handles device conversion
* fix:fix the type annotation of RawObservation in src/lerobot/async_inference/helpers.py
* update the import of robot_client
---------
Co-authored-by: Sato shinji <wwwsatoshinji@gmail.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: KB <kevin-brian.n-diaye@epita.fr>
* improve image2video
* add episodes video encoding
* fix mypy failing
* iterate on review
* nit
* remove max, and let it be optional
* iterate more
* update docs
* fix test
---------
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
* fix: use features when aggregating image based datasets
* add: test asserting for data type
* add: features param to writing dataset
---------
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* feat(robots): consolidates bi SO setups
* fix(robots): solve circular dependecy
* fix(robots): teleop & record working
* feat(robots): only one SO
* fix(utils): rename bi so
* fix(scripts): bi so import
* fix(rl): remove imports
* 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>
* support wallx
* fix bugs in flow
* incorporate wallx model into lerobot
* update the policy methods
* reduce to least config and params & pass lerobot basic test
* fixed dtype bugs
* add wallx dependencies
* update
* remove flash-attn requirement && fix bug in inference and fast mode
* fix bug for inference
* add some small modifications
* fix pre-commit errors
* remove lerobot[wallx]
* fix ci
* fix precommit issues
* fix: exclude wallx extra properly in CI workflows
* fix: add uv conflicts for wallx transformers version
* fix: peft test import
* pre-commit
* only export WallXConfig from wall_x package to avoid peft import in CI
* remove torch dep
* precommit
* add import
---------
Co-authored-by: vincentchen <chenlufang@x2robot.com>
Co-authored-by: Geoffrey19 <sympathischmann35@gmail.com>
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
Co-authored-by: Pepijn <pepijn@huggingface.co>
Steven Palma