e670ac5daf
* 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>
Generating the documentation
To generate the documentation, you first have to build it. Several packages are necessary to build the doc, you can install them with the following command, at the root of the code repository:
pip install -e . -r docs-requirements.txt
You will also need nodejs. Please refer to their installation page
NOTE
You only need to generate the documentation to inspect it locally (if you're planning changes and want to
check how they look before committing for instance). You don't have to git commit the built documentation.
Building the documentation
Once you have setup the doc-builder and additional packages, you can generate the documentation by
typing the following command:
doc-builder build lerobot docs/source/ --build_dir ~/tmp/test-build
You can adapt the --build_dir to set any temporary folder that you prefer. This command will create it and generate
the MDX files that will be rendered as the documentation on the main website. You can inspect them in your favorite
Markdown editor.
Previewing the documentation
To preview the docs, first install the watchdog module with:
pip install watchdog
Then run the following command:
doc-builder preview lerobot docs/source/
The docs will be viewable at http://localhost:3000. You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives.
NOTE
The preview command only works with existing doc files. When you add a completely new file, you need to update _toctree.yml & restart preview command (ctrl-c to stop it & call doc-builder preview ... again).
Adding a new element to the navigation bar
Accepted files are Markdown (.md).
Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting
the filename without the extension in the _toctree.yml file.
Renaming section headers and moving sections
It helps to keep the old links working when renaming the section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums, and Social media and it'd make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information.
Therefore, we simply keep a little map of moved sections at the end of the document where the original section was. The key is to preserve the original anchor.
So if you renamed a section from: "Section A" to "Section B", then you can add at the end of the file:
Sections that were moved:
[ <a href="#section-b">Section A</a><a id="section-a"></a> ]
and of course, if you moved it to another file, then:
Sections that were moved:
[ <a href="../new-file#section-b">Section A</a><a id="section-a"></a> ]
Use the relative style to link to the new file so that the versioned docs continue to work.
For an example of a rich moved sections set please see the very end of the transformers Trainer doc.
Adding a new tutorial
Adding a new tutorial or section is done in two steps:
- Add a new file under
./source. This file can either be ReStructuredText (.rst) or Markdown (.md). - Link that file in
./source/_toctree.ymlon the correct toc-tree.
Make sure to put your new file under the proper section. If you have a doubt, feel free to ask in a Github Issue or PR.
Writing source documentation
Values that should be put in code should either be surrounded by backticks: `like so`. Note that argument names
and objects like True, None or any strings should usually be put in code.
Writing a multi-line code block
Multi-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown:
```
# first line of code
# second line
# etc
```
Adding an image
Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted dataset like
the ones hosted on hf-internal-testing in which to place these files and reference
them by URL. We recommend putting them in the following dataset: huggingface/documentation-images.
If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images
to this dataset.