fix(test): add guard

chore(policies): add explicit dataset dependecy to gr00t implementation

AGENT_GUIDE.md

@@ -138,7 +138,7 @@ lerobot-replay --robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.
   --dataset.repo_id=${HF_USER}/my_task --dataset.episode=0
 ```
 
-**4.9 Train** (default: ACT — fastest, lowest memory). Apple silicon: `--policy.device=mps`. No local GPU? Add `--job.target=<flavor>` (e.g. `a10g-small`, list them with `hf jobs hardware`) to run on Hugging Face Jobs instead. See §6/§7 for policy and duration.
+**4.9 Train** (default: ACT — fastest, lowest memory). Apple silicon: `--policy.device=mps`. See §6/§7 for policy and duration.
 
 ```bash
 lerobot-train \

README.md

@@ -105,7 +105,7 @@ lerobot-train \
 | -------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | **Imitation Learning**     | [ACT](./docs/source/policy_act_README.md), [Diffusion](./docs/source/policy_diffusion_README.md), [VQ-BeT](./docs/source/policy_vqbet_README.md), [Multitask DiT Policy](./docs/source/policy_multi_task_dit_README.md)                                                                                                                                    |
 | **Reinforcement Learning** | [HIL-SERL](./docs/source/hilserl.mdx), [TDMPC](./docs/source/policy_tdmpc_README.md) & QC-FQL (coming soon)                                                                                                                                                                                                                                                |
-| **VLAs Models**            | [Pi0](./docs/source/pi0.mdx), [Pi0Fast](./docs/source/pi0fast.mdx), [Pi0.5](./docs/source/pi05.mdx), [GR00T N1.5](./docs/source/policy_groot_README.md), [SmolVLA](./docs/source/policy_smolvla_README.md), [XVLA](./docs/source/xvla.mdx), [EO-1](./docs/source/eo1.mdx), [MolmoAct2](./docs/source/molmoact2.mdx), [WALL-OSS](./docs/source/walloss.mdx) |
+| **VLAs Models**            | [Pi0](./docs/source/pi0.mdx), [Pi0Fast](./docs/source/pi0fast.mdx), [Pi0.5](./docs/source/pi05.mdx), [GR00T N1.7](./docs/source/policy_groot_README.md), [SmolVLA](./docs/source/policy_smolvla_README.md), [XVLA](./docs/source/xvla.mdx), [EO-1](./docs/source/eo1.mdx), [MolmoAct2](./docs/source/molmoact2.mdx), [WALL-OSS](./docs/source/walloss.mdx) |
 | **World Models**           | [VLA-JEPA](./docs/source/vla_jepa.mdx) (more coming soon)                                                                                                                                                                                                                                                                                                  |
 | **Reward Models**          | [SARM](./docs/source/sarm.mdx), [TOPReward](./docs/source/topreward.mdx), [Robometer](./docs/source/robometer.mdx)                                                                                                                                                                                                                                         |
 

docs/source/_toctree.yml

@@ -69,10 +69,8 @@
     title: VLA-JEPA
   - local: eo1
     title: EO-1
-  - local: fastwam
-    title: FastWAM
   - local: groot
-    title: NVIDIA GR00T N1.5
+    title: NVIDIA GR00T
   - local: xvla
     title: X-VLA
   - local: multi_task_dit

docs/source/cheat-sheet.mdx

@@ -150,14 +150,6 @@ lerobot-train \
     --steps=20000
 ```
 
-No local GPU? Add `--job.target=<flavor>` (e.g. `a10g-small`) to either command and `lerobot-train` runs it on [Hugging Face Jobs](https://huggingface.co/docs/hub/jobs) instead — it uploads a local-only dataset for you and pushes the trained model. List flavors with `hf jobs hardware`.
-
-To resume, point `--config_path` at a checkpoint and add `--resume=true`. It accepts a local path or a Hub repo id (the latest checkpoint is fetched), and works locally or on a job by adding `--job.target=<flavor>`:
-
-```bash
-lerobot-train --config_path=${HF_USER}/policy_test --resume=true --job.target=a10g-small
-```
-
 ### Inference
 
 Inference means running the trained policy/model on a robot. For that we use `lerobot-rollout`. You will need to provide a path to your policy. It can be a local path or a path to Hugging Face for example "lerobot/folding_latest". Your cameras configuration needs to match what was used when collecting the dataset. Duration is in seconds if unspecified, it will run forever.

docs/source/envhub_isaaclab_arena.mdx

@@ -193,7 +193,7 @@ To learn more about training policies with LeRobot, please refer to the training
 
 - [SmolVLA](./smolvla)
 - [Pi0.5](./pi05)
-- [GR00T N1.5](./groot)
+- [GR00T N1.7](./groot)
 
 Sample IsaacLab Arena datasets are available on HuggingFace Hub for experimentation:
 

docs/source/fastwam.mdx

@@ -1,167 +0,0 @@
-# FastWAM
-
-FastWAM is a World Action Model policy for robot control. The LeRobot integration exposes FastWAM through the standard policy API so it can be configured with `policy.type=fastwam`, trained with `lerobot-train`, and loaded through the LeRobot pretrained policy interface.
-
-## Model Overview
-
-FastWAM keeps video modeling during training, but uses direct action prediction at inference time instead of iteratively generating future observations. This LeRobot policy wraps the FastWAM action model, adapts LeRobot batches to FastWAM training samples, and provides the standard processor pipeline for normalization and action postprocessing.
-
-The implementation initializes the visual world-model components from `Wan-AI/Wan2.2-TI2V-5B` by default and predicts action chunks with shape `[batch, action_horizon, action_dim]`.
-
-### What the LeRobot Integration Covers
-
-- Standard `policy.type=fastwam` configuration through LeRobot
-- Image, state, action, and language-task batch adaptation
-- Action chunk inference through `select_action` and `predict_action_chunk`
-- Checkpoint save/load through the LeRobot policy APIs
-- Configurable LIBERO gripper action postprocessing
-
-## Installation Requirements
-
-Install LeRobot from source, then install FastWAM dependencies:
-
-```bash
-pip install -e ".[fastwam]"
-```
-
-This installs the FastWAM policy extra from `pyproject.toml`: `transformers`,
-`diffusers`, `ftfy`, and `regex`, plus LeRobot's base dependencies.
-
-For LIBERO evaluation, install the benchmark dependencies too:
-
-```bash
-pip install -e ".[fastwam,libero]"
-```
-
-This installs both extras. In addition to the FastWAM dependencies above, the
-`libero` extra installs LeRobot dataset dependencies, `hf-libero` on Linux, and
-`scipy`.
-
-FastWAM uses the Wan2.2 TI2V backbone. The default model id is:
-
-```python
-policy.model_id=Wan-AI/Wan2.2-TI2V-5B
-```
-
-## Data Requirements
-
-FastWAM expects a LeRobot dataset with:
-
-- one or more visual observations whose widths concatenate to `policy.image_size[1]`
-- `observation.state` when `policy.proprio_dim` is not `None`
-- `action`
-- a language task instruction through the dataset task field, or precomputed `context` and `context_mask` tensors
-
-The default visual setup is one image feature named `observation.images.image` with shape `(3, 224, 448)`. If the dataset uses two cameras, configure `policy.input_features` so their heights match `224` and their widths sum to `448`.
-
-## Usage
-
-Create a new FastWAM policy with:
-
-```bash
-lerobot-train \
-  --dataset.repo_id=your-org/your-dataset \
-  --policy.type=fastwam \
-  --policy.action_dim=7 \
-  --policy.proprio_dim=8 \
-  --policy.action_horizon=32 \
-  --policy.n_action_steps=10 \
-  --policy.image_size='[224,448]' \
-  --output_dir=./outputs/fastwam_training \
-  --job_name=fastwam_training \
-  --steps=300000 \
-  --batch_size=8 \
-  --policy.device=cuda
-```
-
-Evaluate an existing LeRobot-format checkpoint on LIBERO-10 with:
-
-```bash
-lerobot-eval \
-  --policy.path=ZibinDong/fastwam_libero_uncond_2cam224 \
-  --policy.device=cuda \
-  --policy.torch_dtype=float32 \
-  --policy.n_action_steps=10 \
-  --env.type=libero \
-  --env.task=libero_10 \
-  --env.observation_height=224 \
-  --env.observation_width=224 \
-  --eval.batch_size=1 \
-  --eval.n_episodes=50 \
-  --seed=0 \
-  --env.episode_length=600
-```
-
-For `libero_goal`, `libero_spatial`, and `libero_object`, use
-`--env.episode_length=300`.
-
-For real-robot rollout, use the same checkpoint path:
-
-```bash
-lerobot-rollout \
-  --robot.type=so101_follower \
-  --robot.port=/dev/ttyACM0 \
-  --policy.path=your-org/fastwam-real-robot
-```
-
-## Configuration Notes
-
-### Image Features
-
-`policy.image_size` is the size of the concatenated FastWAM image tensor as `(height, width)`. Each configured image feature must have shape `(3, height, camera_width)`, and all camera widths must sum to the configured width.
-
-### Action Chunking
-
-`policy.action_horizon` controls the number of future actions supervised during training and predicted during inference. `policy.n_action_steps` controls how many actions are consumed before the policy predicts a fresh chunk. `policy.n_action_steps` must be less than or equal to `policy.action_horizon`.
-
-### Wan Components
-
-FastWAM loads the Wan VAE, video DiT, text encoder, and tokenizer from the configured Wan model directory or Hugging Face Hub model id. LeRobot-format FastWAM checkpoints saved by `save_pretrained` also copy the local Wan component files needed by `from_pretrained`.
-
-### Attention Backend
-
-FastWAM's DiT uses PyTorch's `scaled_dot_product_attention` (SDPA) for all attention. It does **not** use FlashAttention: its Mixture-of-Transformers (MoT) routing needs arbitrary boolean `[query, key]` attention masks, which the FlashAttention varlen API cannot express. Installing the `flash-attn` package therefore has no effect on the FastWAM path. (Note that SDPA itself may still select PyTorch's own flash / memory-efficient / math kernel internally — this is unrelated to the `flash-attn` package.)
-
-### LIBERO Action Toggle
-
-FastWAM LIBERO checkpoints use `policy.toggle_action_dimensions=[-1]` by
-default to match the gripper action convention used by the original FastWAM
-evaluation pipeline:
-
-```bash
---policy.toggle_action_dimensions='[-1]'
-```
-
-## Results
-
-Evaluated on LIBERO with [`ZibinDong/fastwam_libero_uncond_2cam224`](https://huggingface.co/ZibinDong/fastwam_libero_uncond_2cam224):
-
-| Suite          | Success rate | n_episodes |
-| -------------- | -----------: | ---------: |
-| libero_spatial |        97.6% |        500 |
-| libero_object  |        99.0% |        500 |
-| libero_goal    |        95.0% |        500 |
-| libero_10      |        94.0% |        500 |
-| **average**    |    **96.4%** |       2000 |
-
-Reproduce: `lerobot-eval --policy.path=ZibinDong/fastwam_libero_uncond_2cam224 --policy.device=cuda --policy.torch_dtype=float32 --policy.n_action_steps=10 --env.type=libero --env.task=libero_spatial --env.observation_height=256 --env.observation_width=256 --eval.batch_size=1 --eval.n_episodes=50 --seed=0 --env.episode_length=300` (1x H20 140 GB).
-
-## References
-
-- [Fast-WAM paper](https://arxiv.org/abs/2603.16666)
-- [Fast-WAM project page](https://yuantianyuan01.github.io/FastWAM/)
-- [Fast-WAM code](https://github.com/yuantianyuan01/FastWAM)
-- [Released upstream checkpoints](https://huggingface.co/yuanty/fastwam)
-- [Wan2.2 TI2V 5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B)
-
-## Citation
-
-```bibtex
-@article{yuan2026fastwam,
-  title = {Fast-WAM: Do World Action Models Need Test-time Future Imagination?},
-  author = {Tianyuan Yuan and Zibin Dong and Yicheng Liu and Hang Zhao},
-  journal = {arXiv preprint arXiv:2603.16666},
-  year = {2026},
-  url = {https://arxiv.org/abs/2603.16666}
-}
-```

docs/source/groot.mdx

@@ -1,16 +1,19 @@
-# GR00T N1.5 Policy
+# GR00T Policy
 
-GR00T N1.5 is an open foundation model from NVIDIA designed for generalized humanoid robot reasoning and skills. It is a cross-embodiment model that accepts multimodal input, including language and images, to perform manipulation tasks in diverse environments.
+GR00T is an NVIDIA foundation model family for generalized humanoid robot reasoning and skills. It is a cross-embodiment policy that accepts multimodal input, including language, images, and proprioception, to perform manipulation tasks in diverse environments.
 
-This document outlines the specifics of its integration and usage within the LeRobot framework.
+LeRobot integrates GR00T N1.7 through the `groot` policy type.
+
+> [!WARNING]
+> **Breaking change:** GR00T N1.5 support was removed from LeRobot, and current releases support GR00T N1.7 only. N1.5 checkpoints and configs are rejected with a migration note. To keep using an N1.5 checkpoint, pin the last release that supports it: `pip install 'lerobot==0.5.1'`. To use the current release, migrate to GR00T N1.7 (base model [`nvidia/GR00T-N1.7-3B`](https://huggingface.co/nvidia/GR00T-N1.7-3B)).
 
 ## Model Overview
 
-NVIDIA Isaac GR00T N1.5 is an upgraded version of the GR00T N1 foundation model. It is built to improve generalization and language-following abilities for humanoid robots.
+GR00T N1.7 uses a Cosmos-Reason2/Qwen3-VL backbone and provides checkpoints for SimplerEnv, DROID, and LIBERO.
 
-Developers and researchers can post-train GR00T N1.5 with their own real or synthetic data to adapt it for specific humanoid robots or tasks.
+Developers and researchers can post-train GR00T with their own real or synthetic data to adapt it for specific humanoid robots or tasks.
 
-GR00T N1.5 (specifically the GR00T-N1.5-3B model) is built using pre-trained vision and language encoders. It utilizes a flow matching action transformer to model a chunk of actions, conditioned on vision, language, and proprioception.
+GR00T uses pre-trained vision and language encoders with a flow matching action transformer to model a chunk of actions conditioned on vision, language, and proprioception.
 
 <img
   src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot-groot-paper1%20(1).png"
@@ -28,33 +31,43 @@ This approach allows the model to be highly adaptable through post-training for
 
 ## Installation Requirements
 
-As of today, GR00T N1.5 requires flash attention for it's internal working.
-
-We are working on making this optional, but in the meantime that means that we require an extra installation step and it can only be used in CUDA enabled devices.
-
-1. Following the Environment Setup of our [Installation Guide](./installation). **Attention** don't install `lerobot` in this step.
-2. Install [Flash Attention](https://github.com/Dao-AILab/flash-attention) by running:
+GR00T is intended for NVIDIA GPU-accelerated systems. Install LeRobot with the GR00T extra:
 
 ```bash
-# Check https://pytorch.org/get-started/locally/ for your system
-pip install "torch>=2.2.1,<2.8.0" "torchvision>=0.21.0,<0.23.0" # --index-url https://download.pytorch.org/whl/cu1XX
-pip install ninja "packaging>=24.2,<26.0" # flash attention dependencies
+pip install "lerobot[groot]"
+```
+
+For a source checkout:
+
+```bash
+pip install -e ".[groot]"
+```
+
+### Optional: Flash Attention acceleration
+
+Flash Attention is a purely optional performance optimization. **LeRobot neither installs nor requires it**, and setting it up is up to the user as it has environment-specific build requirements (a matching PyTorch/CUDA toolchain). To enable it:
+
+1. Install a `flash-attn` build matching your PyTorch/CUDA environment (see the [Flash Attention project](https://github.com/Dao-AILab/flash-attention)):
+
+```bash
+# Check https://pytorch.org/get-started/locally/ for the right CUDA wheel index for your system.
+pip install "torch>=2.7,<2.12.0" "torchvision>=0.22.0,<0.27.0" \
+  --index-url https://download.pytorch.org/whl/cu128
+pip install "ninja>=1.11.1,<2.0.0" "packaging>=24.2,<26.0"
 pip install "flash-attn>=2.5.9,<3.0.0" --no-build-isolation
 python -c "import flash_attn; print(f'Flash Attention {flash_attn.__version__} imported successfully')"
 ```
 
-3. Install LeRobot by running:
+2. Install lerobot with the groot extra.
 
-```bash
-pip install lerobot[groot]
-```
+3. Opt in by passing `--policy.use_flash_attention=true` when training/evaluating GR00T. If the kernel is missing or fails to import, the backbone transparently falls back to SDPA.
 
 ## Usage
 
-To use GR00T in your LeRobot configuration, specify the policy type as:
+To use GR00T N1.7:
 
-```python
-policy.type=groot
+```bash
+--policy.type=groot
 ```
 
 ## Training
@@ -87,21 +100,53 @@ accelerate launch \
 
 ## Performance Results
 
-### Libero Benchmark Results
+### LIBERO Benchmark Results
 
 > [!NOTE]
-> Follow our instructions for Libero usage: [Libero](./libero)
+> Follow the [LIBERO](./libero) setup instructions before running `lerobot-eval`.
 
-GR00T has demonstrated strong performance on the Libero benchmark suite. To compare and test its LeRobot implementation, we finetuned the GR00T N1.5 model for 30k steps on the Libero dataset and compared the results to the GR00T reference results.
+GR00T N1.7 has demonstrated strong performance on the LIBERO benchmark suite. To reproduce LeRobot results, follow the instructions in the [LIBERO](./libero) section.
 
-| Benchmark          | LeRobot Implementation | GR00T Reference |
-| ------------------ | ---------------------- | --------------- |
-| **Libero Spatial** | 82.0%                  | 92.0%           |
-| **Libero Object**  | 99.0%                  | 92.0%           |
-| **Libero Long**    | 82.0%                  | 76.0%           |
-| **Average**        | 87.0%                  | 87.0%           |
+### GR00T N1.7 LIBERO Checkpoints
 
-These results demonstrate GR00T's strong generalization capabilities across diverse robotic manipulation tasks. To reproduce these results, you can follow the instructions in the [Libero](https://huggingface.co/docs/lerobot/libero) section.
+NVIDIA publishes GR00T N1.7 LIBERO checkpoints at [`nvidia/GR00T-N1.7-LIBERO`](https://huggingface.co/nvidia/GR00T-N1.7-LIBERO), with one subdirectory per LIBERO suite:
+
+| Suite          | Checkpoint subdirectory |
+| -------------- | ----------------------- |
+| LIBERO Spatial | `libero_spatial`        |
+| LIBERO Object  | `libero_object`         |
+| LIBERO Goal    | `libero_goal`           |
+| LIBERO 10      | `libero_10`             |
+
+Preliminary LeRobot integration results:
+
+| Suite          | Status | Success rate | n_episodes |
+| -------------- | ------ | -----------: | ---------: |
+| LIBERO Spatial | ✓      |         ~95% |         XX |
+| LIBERO Object  | ✓      |          XX% |         XX |
+| LIBERO Goal    | ✓      |          XX% |         XX |
+| LIBERO 10      | ✓      |          XX% |         XX |
+| **Average**    | ✓      |      **XX%** |     **XX** |
+
+Replace the `XX` placeholders with final eval artifacts before merge.
+
+Download the suite checkpoint locally, then point `--policy.base_model_path` at the downloaded subdirectory. `--policy.path` is reserved for LeRobot checkpoints that contain a LeRobot `config.json` with a `type` field.
+
+```bash
+hf download nvidia/GR00T-N1.7-LIBERO \
+  --include "libero_spatial/*" \
+  --local-dir ./GR00T-N1.7-LIBERO
+
+lerobot-eval \
+  --policy.type=groot \
+  --policy.base_model_path=./GR00T-N1.7-LIBERO/libero_spatial \
+  --policy.embodiment_tag=libero_sim \
+  --env.type=libero \
+  --env.task=libero_spatial \
+  --eval.n_episodes=50
+```
+
+Use `eval.n_episodes >= 50` per suite when reporting success rates.
 
 ### Evaluate in your hardware setup
 
@@ -131,4 +176,4 @@ lerobot-rollout\
 
 ## License
 
-This model follows NVIDIA's proprietary license, consistent with the original [GR00T repository](https://github.com/NVIDIA/Isaac-GR00T). Future versions (starting from N1.7) will follow **Apache 2.0 License**.
+GR00T N1.7 is released under the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).

docs/source/hardware_guide.mdx

@@ -82,18 +82,17 @@ VRAM is the first filter. Within a tier, pick by budget and availability — the
 
 ### Hugging Face Jobs
 
-[Hugging Face Jobs](https://huggingface.co/docs/hub/jobs) lets you run training on managed HF infrastructure, billed by the second, without owning a GPU. `lerobot-train` submits and streams the job for you — just add `--job.target=<flavor>` to a normal training command:
+[Hugging Face Jobs](https://huggingface.co/docs/hub/jobs) lets you run training on managed HF infrastructure, billed by the second. The repo publishes a ready-to-use image: **`huggingface/lerobot-gpu:latest`**, rebuilt **every night at 02:00 UTC from `main`** ([`docker_publish.yml`](https://github.com/huggingface/lerobot/blob/main/.github/workflows/docker_publish.yml)) — so it tracks the current state of the repo, not a tagged release.
 
 ```bash
-lerobot-train \
-  --policy.type=act --dataset.repo_id=<USER>/<DATASET> \
-  --policy.repo_id=<USER>/act_<task> \
-  --job.target=a10g-large
+hf jobs run --flavor a10g-large huggingface/lerobot-gpu:latest \
+  bash -c "nvidia-smi && lerobot-train \
+    --policy.type=act --dataset.repo_id=<USER>/<DATASET> \
+    --policy.repo_id=<USER>/act_<task> --batch_size=8 --steps=50000"
 ```
 
 Notes:
 
-- Run `hf auth login` once before submitting, the job runs under your token.
-- `--job.target` maps onto the table above: `t4-small`/`t4-medium` (T4, ACT only), `l4x1`/`l4x4` (L4 24 GB), `a10g-small/large/largex2/largex4` (A10G 24 GB scaled out), `a100-large` (A100). List the current catalogue with pricing via `hf jobs hardware`, or see [https://huggingface.co/docs/hub/jobs](https://huggingface.co/docs/hub/jobs).
-- The job defaults to a `2d` (48h) timeout. Override it with `--job.timeout=4h` (or any other valid duration string) to shorten or extend the timeout. The job automatically stops when the command completes.
-- For the full walkthrough — dataset upload, checkpoint streaming, resuming a run on a job — see the [imitation-learning training guide](./il_robots#train-using-hugging-face-jobs).
+- The leading `nvidia-smi` is a quick sanity check that CUDA is visible inside the container — useful to fail fast if the flavor or driver mismatched.
+- The default Job timeout is 30 minutes; pass `--timeout 4h` (or longer) for real training.
+- `--flavor` maps onto the table above: `t4-small`/`t4-medium` (T4, ACT only), `l4x1`/`l4x4` (L4 24 GB), `a10g-small/large/largex2/largex4` (A10G 24 GB scaled out), `a100-large` (A100). For the current full catalogue + pricing see [https://huggingface.co/docs/hub/jobs](https://huggingface.co/docs/hub/jobs).

docs/source/il_robots.mdx

@@ -126,7 +126,7 @@ import time
 from lerobot.teleoperators.so_leader import SO101Leader, SO101LeaderConfig
 from lerobot.robots.so_follower import SO101Follower, SO101FollowerConfig
 from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.utils.visualization_utils import init_visualization, log_visualization_data, shutdown_visualization
+from lerobot.utils.visualization_utils import init_rerun, log_rerun_data, shutdown_rerun
 
 robot_config = SO101FollowerConfig(
     port="/dev/tty.usbmodem5AB90687491",
@@ -142,7 +142,7 @@ teleop_config = SO101LeaderConfig(
     id="my_leader_arm",
 )
 
-init_visualization("rerun", session_name="teleoperation")  # pass "foxglove" to stream to Foxglove instead
+init_rerun(session_name="teleoperation")
 
 robot = SO101Follower(robot_config)
 teleop_device = SO101Leader(teleop_config)
@@ -158,7 +158,7 @@ while True:
     observation = robot.get_observation()
     action = teleop_device.get_action()
     robot.send_action(action)
-    log_visualization_data("rerun", observation=observation, action=action)
+    log_rerun_data(observation=observation, action=action)
 
     elapsed_time = time.perf_counter() - start_time
     sleep_time = TIME_PER_FRAME - elapsed_time
@@ -223,7 +223,7 @@ from lerobot.teleoperators.so_leader.config_so_leader import SO101LeaderConfig
 from lerobot.teleoperators.so_leader.so_leader import SO101Leader
 from lerobot.common.control_utils import init_keyboard_listener
 from lerobot.utils.utils import log_say
-from lerobot.utils.visualization_utils import init_visualization
+from lerobot.utils.visualization_utils import init_rerun
 from lerobot.scripts.lerobot_record import record_loop
 from lerobot.processor import make_default_processors
 
@@ -270,7 +270,7 @@ def main():
 
     # Initialize the keyboard listener and rerun visualization
     _, events = init_keyboard_listener()
-    init_visualization("rerun", session_name="recording")
+    init_rerun(session_name="recording")
 
     # Connect the robot and teleoperator
     robot.connect()
@@ -514,12 +514,6 @@ lerobot-train \
   --resume=true
 ```
 
-`--config_path` also accepts a **Hub repo id**: if a run pushed its checkpoints to the Hub (with `--save_checkpoint_to_hub=true`), you can resume straight from the repo — its latest checkpoint is downloaded and training continues, restoring the optimizer, scheduler, step counter and data order:
-
-```bash
-lerobot-train --config_path=${HF_USER}/my_policy --resume=true
-```
-
 If you do not want to push your model to the hub after training use `--policy.push_to_hub=false`.
 
 Additionally you can provide extra `tags` or specify a `license` for your model or make the model repo `private` by adding this: `--policy.private=true --policy.tags=\[ppo,rl\] --policy.license=mit`
@@ -532,48 +526,78 @@ If your local computer doesn't have a powerful GPU you could utilize Google Cola
 
 Hugging Face jobs let's you easily select hardware and run the training in the cloud. So if you don't have a powerful GPU or you need more VRAM or just want to train a model much faster use HF Jobs! It's pay as you go and you simply pay for each second of use, you can see the pricing and additional information [here](https://huggingface.co/docs/hub/jobs).
 
-`lerobot-train` runs locally by default. To run on a HuggingFace GPU, pass `--job.target` with a hardware flavor name:
+To run the training use this command:
 
+<hfoptions id="train_with_hf_jobs">
+<hfoption id="Command">
 ```bash
-lerobot-train \
-  --dataset.repo_id=${HF_USER}/so101_test \
-  --policy.type=act \
-  --policy.repo_id=${HF_USER}/my_policy \
-  --job.target=a10g-small
+hf jobs run \
+  --flavor a10g-small \
+  --timeout 4h \
+  --secrets HF_TOKEN \
+  huggingface/lerobot-gpu:latest \
+  -- \
+  python -m lerobot.scripts.lerobot_train \
+    --dataset.repo_id=username/dataset \
+    --policy.type=act \
+    --steps=5000 \
+    --batch_size=16 \
+    --policy.device=cuda \
+    --policy.repo_id=username/your_policy \
+    --log_freq=100
 ```
+</hfoption>
+<hfoption id="API example">
 
-List available flavors and pricing with `hf jobs hardware`. The run streams its logs to your terminal; press Ctrl-C to detach (the job keeps running in the cloud). Re-attach or cancel with:
+<!-- prettier-ignore-start -->
+```python
+from huggingface_hub import run_job, get_token
 
-```bash
-hf jobs logs <job-id>
-hf jobs cancel <job-id>
+run_name = "act_so101_hf_jobs"
+dataset_id = "username/dataset"
+user_hub_id = "username"
+
+command_args = [
+    "python", "-m", "lerobot.scripts.lerobot_train",
+    "--dataset.repo_id", dataset_id,
+    "--policy.type", "act",
+    "--steps", "5000",
+    "--batch_size", "16",
+    "--num_workers", "4",
+    "--policy.device", "cuda",
+    "--log_freq", "100",
+    "--save_freq", "1000",
+    "--save_checkpoint", "true",
+    "--wandb.enable", "false",
+    "--policy.repo_id", f"{user_hub_id}/{run_name}"
+]
+
+print(f"Submitting job '{run_name}' to Hugging Face Infrastructure...")
+
+job_info = run_job(
+    image="huggingface/lerobot-gpu:latest",
+    command=command_args,
+    flavor="a10g-small",
+    timeout="4h",
+    secrets={"HF_TOKEN": get_token()}
+)
+
+print("\n🚀 Job successfully launched!")
+print(f"🔹 Job ID: {job_info.id}")
+print(f"🔗 Live UI Dashboard & Logs: {job_info.url}")
 ```
+<!-- prettier-ignore-end -->
 
-If your dataset exists only locally (not yet on the Hub), it is automatically pushed to a **private** Hub repo so the job can download it by `repo_id` (nothing is made public). The trained model is pushed to the model repo at the end of the run. To also push every intermediate checkpoint to the Hub as it is saved (so you can monitor progress mid-run), add `--save_checkpoint_to_hub=true` — this requires a runtime image that includes this feature.
+</hfoption>
+</hfoptions>
 
-Every job (and any dataset pushed by the run) is tagged `lerobot` so it's easy to find on the Hub. Add your own with `--job.tags '["my-tag"]'`.
+You can modify the `--flavor` to use different hardware, for example: `t4-small`, `a100-large`, `h200`. Use `hf jobs hardware` to see the full list with pricing.
+Depending on the model you want to train and the hardware you selected you can also modify the `--batch_size` and `--number_of_workers`.
+For longer training sessions increase the timeout.
 
-By default the job is capped at `2d` (48h) of wall-clock. Override it with an HF Jobs duration string, e.g. `--job.timeout=4h` to fail faster or `--job.timeout=7d` for a longer run.
+Once the training is started you can go to [Jobs](https://huggingface.co/settings/jobs) and see if your jobs is running as well as all the outputs. Sometimes it takes a few minutes to schedule your job so be patient.
 
-> **Note:** the model repo is created up front (it holds the staged training config the job runs from). If a run fails before the model is pushed, that repo is left on the Hub so you can inspect it — it is not deleted automatically, so repeated failures can leave empty repos behind. Remove one with `hf repo delete <repo-id>`.
-
-**Prerequisites:** run `hf auth login` before submitting. For Weights & Biases integration, run `wandb login` or set `WANDB_API_KEY` on your machine — the key is forwarded to the job automatically.
-
-**Resuming on a job.** Adding `--job.target` to a resume command runs the resume in the cloud — the same command works locally or remotely. The checkpoint repo is the source of truth, and new checkpoints continue the lineage in the same repo:
-
-```bash
-# resume a Hub run on a job (its checkpoints are already on the Hub)
-lerobot-train --config_path=${HF_USER}/my_policy --resume=true --job.target=a10g-small
-
-# resume a LOCAL run on a job — the checkpoint is uploaded to a private Hub repo first,
-# then the job resumes from it (a local-only dataset is uploaded the same way)
-lerobot-train \
-  --config_path=outputs/train/act_so101_test/checkpoints/last/pretrained_model/train_config.json \
-  --resume=true \
-  --job.target=a10g-small
-```
-
-Job settings come from the current command, so override `--job.target`, `--job.timeout`, etc. as needed; for the resumed run to itself be resumable later, keep `--save_checkpoint_to_hub=true`.
+After training the model will be pushed to hub and you can use it as any other model with LeRobot.
 
 #### Upload policy checkpoints
 
@@ -596,8 +620,6 @@ hf upload ${HF_USER}/act_so101_test${CKPT} \
 
 Use `lerobot-rollout` to deploy a trained policy on your robot. You can choose different strategies depending on your needs:
 
-The examples below load the model from `--policy.path`. To pin a specific pushed version — useful once `--save_checkpoint_to_hub=true` has committed several checkpoints — add `--policy.pretrained_revision` with a commit hash, branch, or tag. Each pushed checkpoint is tagged with its step (e.g. `--policy.pretrained_revision=010000`), so you can recover a checkpoint by step without looking up its commit sha.
-
 <hfoptions id="eval">
 <hfoption id="Base mode (no recording)">
 ```bash

docs/source/molmoact2.mdx

@@ -386,68 +386,6 @@ These results demonstrate MolmoAct2's strong performance across diverse robotic
 manipulation tasks. To reproduce them, follow the instructions in the LIBERO
 evaluation section.
 
-## Hardware Deployment (lerobot-rollout)
-
-LeRobot-format checkpoints are available on the Hub for direct use with
-`lerobot-rollout`. Each checkpoint uses specific camera names that must
-match your robot's camera configuration.
-
-### Camera naming convention
-
-Each checkpoint expects specific `observation.images.*` keys.
-If your robot cameras have different names, use `--rename_map` to map them:
-
-| Checkpoint                    | Camera keys            | Description              |
-| ----------------------------- | ---------------------- | ------------------------ |
-| MolmoAct2-LIBERO-LeRobot      | `image`, `wrist_image` | LIBERO sim cameras       |
-| MolmoAct2-BimanualYAM-LeRobot | `top`, `left`, `right` | YAM 3-camera setup       |
-| MolmoAct2-DROID-LeRobot       | `cam0`, `cam1`         | External + wrist         |
-| MolmoAct2-SO100_101-LeRobot   | `cam0`, `cam1`         | Primary + secondary view |
-
-Example with an SO-100 robot using top and side cameras:
-
-```bash
-lerobot-rollout \
-  --policy.path=lerobot/MolmoAct2-SO100_101-LeRobot \
-  --rename_map='{"observation.images.top": "observation.images.cam0", "observation.images.side": "observation.images.cam1"}' \
-  --robot.type=so100_follower \
-  --robot.port=/dev/ttyACM0 \
-  --robot.cameras='{
-      top: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30},
-      side: {type: opencv, index_or_path: 2, width: 640, height: 480, fps: 30}
-  }' \
-  --task="pick up the red cube" --duration=30
-```
-
-To use a wrist camera instead, just change the rename mapping:
-
-```bash
---rename_map='{"observation.images.top": "observation.images.cam0", "observation.images.wrist": "observation.images.cam1"}'
-```
-
-### Joint frame transform (SO-100/101 zero-shot)
-
-<Tip warning={true}>
-The MolmoAct2-SO100_101 checkpoint was trained on data that uses a different
-joint calibration convention than LeRobot >= 0.5.0. Without a frame
-correction, the arm may move in the wrong direction.
-
-This affects both **zero-shot deployment** and **fine-tuning** from the
-original checkpoint. The pretrained weights expect the old convention, so
-all joint data (observations and actions) must be transformed to match.
-
-The converted LeRobot checkpoint (`lerobot/MolmoAct2-SO100_101-LeRobot`)
-already includes this correction in its processor pipeline. If you convert
-or fine-tune the checkpoint yourself, set the following in the policy config (`configuration_molmoact2.py`):
-
-- `joint_signs`: `[1, -1, 1, 1, 1, 1]` (flips shoulder_lift direction)
-- `joint_offsets`: `[0, 90, 90, 0, 0, 0]` (shifts shoulder_lift and elbow_flex by 90°)
-
-See the [backward compatibility guide](./backwardcomp) for details on the
-calibration change.
-
-</Tip>
-
 ## Differences From the Original Implementation
 
 This LeRobot port is intended to match MolmoAct2 behavior while using LeRobot's

docs/source/policy_fastwam_README.md

@@ -1,56 +0,0 @@
-## Research Paper
-
-Paper: https://arxiv.org/abs/2603.16666
-
-## Repository
-
-Code: https://github.com/yuantianyuan01/FastWAM
-
-Project page: https://yuantianyuan01.github.io/FastWAM/
-
-## Citation
-
-```bibtex
-@article{yuan2026fastwam,
-  title = {Fast-WAM: Do World Action Models Need Test-time Future Imagination?},
-  author = {Tianyuan Yuan and Zibin Dong and Yicheng Liu and Hang Zhao},
-  journal = {arXiv preprint arXiv:2603.16666},
-  year = {2026},
-  url = {https://arxiv.org/abs/2603.16666}
-}
-```
-
-## Additional Resources
-
-Base video model: https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B
-
-Released upstream checkpoints: https://huggingface.co/yuanty/fastwam
-
-## Results
-
-Evaluated on LIBERO with [`ZibinDong/fastwam_libero_uncond_2cam224`](https://huggingface.co/ZibinDong/fastwam_libero_uncond_2cam224):
-
-| Suite          | Success rate | n_episodes |
-| -------------- | -----------: | ---------: |
-| libero_spatial |        97.6% |        500 |
-| libero_object  |        99.0% |        500 |
-| libero_goal    |        95.0% |        500 |
-| libero_10      |        94.0% |        500 |
-| **average**    |    **96.4%** |       2000 |
-
-Reproduce: `lerobot-eval --policy.path=ZibinDong/fastwam_libero_uncond_2cam224 --policy.device=cuda --policy.torch_dtype=float32 --policy.n_action_steps=10 --env.type=libero --env.task=libero_spatial --env.observation_height=256 --env.observation_width=256 --eval.batch_size=1 --eval.n_episodes=50 --seed=0 --env.episode_length=300`.
-
-For LIBERO-10, use `--env.task=libero_10 --env.episode_length=600`:
-
-```bash
-lerobot-eval \
-    --policy.path=ZibinDong/fastwam_libero_uncond_2cam224 \
-    --policy.device=cuda \
-    --policy.torch_dtype=float32 \
-    --policy.n_action_steps=10 \
-    --env.type=libero \
-    --env.task=libero_10 --env.observation_height=256 --env.observation_width=256 \
-    --eval.batch_size=1 \
-    --eval.n_episodes=50 \
-    --seed=0 --env.episode_length=600
-```

docs/source/policy_groot_README.md

@@ -1,6 +1,13 @@
 ## Research Paper
 
-Paper: https://research.nvidia.com/labs/gear/gr00t-n1_5/
+GR00T N1 technical report (covers the GR00T N1.x family, including N1.7): https://arxiv.org/abs/2503.14734
+
+GR00T N1.7 model card: https://huggingface.co/nvidia/GR00T-N1.7-3B
+
+GR00T N1.5 research page (earlier version): https://research.nvidia.com/labs/gear/gr00t-n1_5/
+
+> GR00T N1.5 support was removed from LeRobot; the last release supporting it is `lerobot==0.5.1`.
+> Current releases support GR00T N1.7 only.
 
 ## Repository
 
@@ -24,4 +31,103 @@ Code: https://github.com/NVIDIA/Isaac-GR00T
 
 Blog: https://developer.nvidia.com/isaac/gr00t
 
-Hugging Face Model: https://huggingface.co/nvidia/GR00T-N1.5-3B
+Hugging Face Models:
+
+- GR00T N1.7: https://huggingface.co/nvidia/GR00T-N1.7-3B
+- GR00T N1.7 LIBERO checkpoints: https://huggingface.co/nvidia/GR00T-N1.7-LIBERO
+
+## Original-vs-LeRobot parity test
+
+`tests/policies/groot/test_groot_vs_original.py` verifies this LeRobot
+reimplementation of GR00T N1.7 (Qwen3-VL backbone + flow-matching action head)
+against NVIDIA's original `gr00t` package with two comparisons, each parametrized
+over every embodiment tag present in the checkpoint:
+
+1. **Model parity** — given byte-identical pre-processed inputs and the same
+   flow-matching seed (recorded in each artifact), both implementations must produce
+   the **same raw model output** (`get_action(...)["action_pred"]`, the normalized
+   flow-matching prediction). Output shapes must match exactly; any action-horizon
+   or action-dim mismatch fails the test.
+2. **Preprocessor parity** — given the identical raw observations (per-camera
+   frames, state vectors, language instruction), LeRobot's own preprocessor pipeline
+   (real Qwen3-VL chat template / tokenizer / image packing + checkpoint-driven
+   state normalization, no mocks) must produce the **same collated model inputs**
+   (`input_ids`, `attention_mask`, `pixel_values`, `image_grid_thw`, `state`,
+   `embodiment_id`) as the original package's processor.
+
+### Why two environments
+
+The original `gr00t` package pins `transformers==4.57.3` (Python 3.10); this
+integration requires `transformers>=5.x` (Qwen3-VL). Under 5.x, `PretrainedConfig`
+is itself a defaulted dataclass, so the original config dataclasses fail to import
+(`non-default argument follows default argument`). The two implementations therefore
+**cannot be imported in the same Python process**.
+
+So the test uses a **producer / consumer** split across two venvs:
+
+1. **Producer** — `tests/policies/groot/utils/dump_original_n1_7.py`, run in the _original_
+   gr00t venv. For each embodiment it builds dummy inputs generically from the
+   checkpoint metadata (state dims from `statistics.json`; camera/language keys from
+   the processor modality configs), runs the original model, and saves to one `.npz`
+   per tag: the raw observations (`raw::` keys), the exact collated inputs
+   (`in::` keys), the seed, and the raw `action_pred`.
+2. **Consumer** — the pytest above, run in the _LeRobot_ venv. It discovers every
+   `.npz`; the model-parity case replays the byte-identical collated inputs through
+   the LeRobot model with the recorded seed and asserts the outputs match, and the
+   preprocessor-parity case replays the raw observations through LeRobot's full
+   preprocessor pipeline and asserts the collated tensors match.
+
+> Artifacts generated by older versions of the dump script contain no `raw::`
+> fields; the preprocessor-parity case then **skips** with a regeneration hint.
+> Re-run the producer to refresh them.
+
+### Fairness controls
+
+- **Same pre-processed inputs (model parity)** — the original processor's `input_ids`,
+  `pixel_values`, `image_grid_thw`, `attention_mask`, `state`, `embodiment_id` are
+  fed verbatim to the LeRobot model (no re-tokenization / re-normalization), so the
+  model comparison isolates the model. LeRobot's own tokenization / image packing is
+  covered separately by the preprocessor-parity case, which compares its output
+  against those same collated tensors from identical raw observations.
+- **Same precision + attention kernel** — both sides run **fp32 + SDPA**. The
+  original defaults to `use_flash_attention=True` (flash_attention_2 + bf16); the
+  producer forces SDPA + fp32. (With the defaults the gap is ~3e-2 — pure
+  kernel/rounding noise, not an implementation difference.)
+- **Same flow-matching seed** — fixed right before sampling on both sides; the
+  producer records it in each artifact (`--seed`, default 42) and the consumer
+  replays the recorded value.
+
+### How to run
+
+```bash
+# Resolve a local checkpoint (GR00T-N1.7-LIBERO / libero_10)
+CKPT=$(python - <<'PY'
+import os
+from huggingface_hub import snapshot_download
+print(os.path.join(snapshot_download("nvidia/GR00T-N1.7-LIBERO",
+      allow_patterns=["libero_10/*"]), "libero_10"))
+PY
+)
+
+# 1) Produce the original-side artifacts for all embodiments (original gr00t venv, CUDA)
+CUDA_VISIBLE_DEVICES=0 /path/to/Isaac-GR00T/.venv-original/bin/python \
+    tests/policies/groot/utils/dump_original_n1_7.py \
+    --ckpt "$CKPT" --out-dir tests/policies/groot/artifacts --device cuda --seed 42
+
+# 2) Run the parity test (LeRobot venv) — one parametrized case per embodiment
+CUDA_VISIBLE_DEVICES=0 GROOT_PARITY_DEVICE=cuda \
+    uv run pytest tests/policies/groot/test_groot_vs_original.py -v -s
+```
+
+The `.npz` artifacts are local-only (gitignored, ~6–10 MB each) and are regenerated by
+the producer; they are never committed. The tests **skip** (do not fail) on CI or
+when the checkpoint / artifacts are absent.
+
+#### Env knobs (all optional)
+
+| Var                                       | Default                          | Purpose                               |
+| ----------------------------------------- | -------------------------------- | ------------------------------------- |
+| `GROOT_N1_7_PARITY_DIR`                   | `tests/policies/groot/artifacts` | directory of per-tag `.npz` artifacts |
+| `GROOT_N1_7_LIBERO_CKPT`                  | auto (HF cache)                  | override checkpoint dir               |
+| `GROOT_PARITY_DEVICE`                     | `cuda` if available              | `cpu` or `cuda`                       |
+| `GROOT_PARITY_ATOL` / `GROOT_PARITY_RTOL` | `1e-3`                           | comparison tolerance                  |

docs/source/using_dataset_tools.mdx

@@ -265,8 +265,6 @@ lerobot-dataset-viz \
 
 Once executed, the tool opens `rerun.io` and displays the camera streams, robot states, and actions for the selected episode.
 
-To use [Foxglove](https://foxglove.dev) instead of Rerun, install the extra add `--display-mode foxglove`. This starts a WebSocket server (connect the Foxglove app to `ws://127.0.0.1:8765`) that serves the episode as a seekable timeline you can play/pause and scrub.
-
 For advanced usage—including visualizing datasets stored on a remote server—run:
 
 ```bash

pyproject.toml

@@ -124,8 +124,7 @@ hardware = [
     "lerobot[deepdiff-dep]",
 ]
 viz = [
-    "rerun-sdk>=0.24.0,<0.34.0",
-    "foxglove-sdk>=0.25.1,<0.26.0",
+    "rerun-sdk>=0.24.0,<0.27.0",
 ]
 # ── User-facing composite extras (map to CLI scripts) ─────
 # lerobot-record, lerobot-replay, lerobot-calibrate, lerobot-teleoperate, etc.
@@ -219,21 +218,16 @@ groot = [
     "lerobot[transformers-dep]",
     "lerobot[peft-dep]",
     "lerobot[diffusers-dep]",
+    "lerobot[dataset]", # NOTE: processor_groot builds a LeRobotDataset for relative-action training stats
     "dm-tree>=0.1.8,<1.0.0",
     "timm>=1.0.0,<1.1.0",
     "decord>=0.6.0,<1.0.0; (platform_machine == 'AMD64' or platform_machine == 'x86_64')",
-    "ninja>=1.11.1,<2.0.0",
-    "flash-attn>=2.5.9,<3.0.0 ; sys_platform != 'darwin'"
 ]
 sarm = ["lerobot[transformers-dep]", "pydantic>=2.0.0,<3.0.0", "faker>=33.0.0,<35.0.0", "lerobot[matplotlib-dep]", "lerobot[qwen-vl-utils-dep]"]
 robometer = ["lerobot[transformers-dep]", "lerobot[qwen-vl-utils-dep]", "lerobot[peft-dep]"]
 topreward = ["lerobot[transformers-dep]"]
 xvla = ["lerobot[transformers-dep]"]
 eo1 = ["lerobot[transformers-dep]", "lerobot[qwen-vl-utils-dep]"]
-fastwam = [
-    "lerobot[transformers-dep]",
-    "lerobot[diffusers-dep]",
-]
 hilserl = ["lerobot[transformers-dep]", "lerobot[dataset]", "gym-hil>=0.1.14,<0.2.0", "lerobot[grpcio-dep]", "lerobot[placo-dep]"]
 vla_jepa = ["lerobot[transformers-dep]", "lerobot[diffusers-dep]", "lerobot[qwen-vl-utils-dep]"]
 
@@ -313,8 +307,7 @@ all = [
     "lerobot[pi]",
     "lerobot[molmoact2]",
     "lerobot[smolvla]",
-    "lerobot[fastwam]",
-    # "lerobot[groot]", TODO(Steven): Gr00t requires specific installation instructions for flash-attn
+    "lerobot[groot]",
     "lerobot[xvla]",
     "lerobot[hilserl]",
     "lerobot[vla_jepa]",
@@ -450,8 +443,7 @@ default.extend-ignore-identifiers-re = [
     "is_compileable",
     "ROBOTIS",
     "OT_VALUE",
-    "VanderBilt",
-    "seperated_timestep",
+    "VanderBilt"
 ]
 
 # TODO: Uncomment when ready to use

src/lerobot/common/train_utils.py

@@ -15,7 +15,6 @@
 # limitations under the License.
 from pathlib import Path
 
-from huggingface_hub import HfApi, snapshot_download
 from torch.optim import Optimizer
 from torch.optim.lr_scheduler import LRScheduler
 
@@ -36,7 +35,6 @@ from lerobot.utils.constants import (
     TRAINING_STATE_DIR,
     TRAINING_STEP,
 )
-from lerobot.utils.hub import find_latest_hub_checkpoint
 from lerobot.utils.io_utils import load_json, write_json
 from lerobot.utils.random_utils import load_rng_state, save_rng_state
 
@@ -285,61 +283,3 @@ def load_fsdp_optimizer_state(model, optimizer, checkpoint_dir: Path) -> None:
     with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT, state_cfg, optim_cfg):
         sharded_osd = FSDP.optim_state_dict_to_load(model=model, optim=optimizer, optim_state_dict=full_osd)
     optimizer.load_state_dict(sharded_osd)
-
-
-def push_checkpoint_to_hub(
-    checkpoint_dir: Path,
-    repo_id: str,
-    *,
-    private: bool | None = None,
-) -> None:
-    """Upload a saved checkpoint directory to the Hub under checkpoints/<name>/.
-
-    Called once per save step when save_checkpoint_to_hub is enabled, so a
-    timed-out or crashed run still leaves recoverable checkpoints on the Hub.
-    The model repo is created idempotently, and the commit is tagged with the
-    checkpoint step so a checkpoint can be recovered with
-    --policy.pretrained_revision=<step> instead of a commit sha.
-    """
-    api = HfApi()
-    api.create_repo(repo_id=repo_id, repo_type="model", private=private, exist_ok=True)
-    commit = api.upload_folder(
-        folder_path=str(checkpoint_dir),
-        repo_id=repo_id,
-        repo_type="model",
-        path_in_repo=f"checkpoints/{checkpoint_dir.name}",
-        commit_message=f"checkpoint {checkpoint_dir.name}",
-    )
-    api.create_tag(
-        repo_id=repo_id,
-        tag=checkpoint_dir.name,
-        revision=commit.oid,
-        repo_type="model",
-        exist_ok=True,
-    )
-
-
-def resolve_resume_checkpoint(repo_id: str, output_dir: Path) -> Path:
-    """Download the latest checkpoint of a Hub training repo into a local run dir.
-
-    The symmetric counterpart to `push_checkpoint_to_hub`: given a model repo holding
-    `checkpoints/<step>/{pretrained_model,training_state}` subtrees, download the highest-numbered step
-    into `output_dir/checkpoints/<step>/`, recreate the local `last` symlink, and return that local
-    checkpoint dir. Used to resume training from the Hub on a machine (or HF Jobs pod) that does not
-    have the original local run dir.
-    """
-    latest = find_latest_hub_checkpoint(repo_id)
-    if latest is None:
-        raise FileNotFoundError(
-            f"No checkpoint found in '{repo_id}' under '{CHECKPOINTS_DIR}/'. "
-            "Was the run trained with --save_checkpoint_to_hub?"
-        )
-    snapshot_download(
-        repo_id=repo_id,
-        repo_type="model",
-        allow_patterns=f"{latest}/*",
-        local_dir=str(output_dir),
-    )
-    checkpoint_dir = output_dir / latest
-    update_last_checkpoint(checkpoint_dir)
-    return checkpoint_dir

src/lerobot/configs/__init__.py

@@ -22,7 +22,7 @@ Import them directly: ``from lerobot.configs.train import TrainPipelineConfig``
 """
 
 from .dataset import DatasetRecordConfig
-from .default import DatasetConfig, EvalConfig, JobConfig, PeftConfig, WandBConfig
+from .default import DatasetConfig, EvalConfig, PeftConfig, WandBConfig
 from .policies import PreTrainedConfig
 from .recipe import MessageTurn, TrainingRecipe, load_recipe
 from .types import (
@@ -34,8 +34,6 @@ from .types import (
 )
 from .video import (
     DEFAULT_DEPTH_UNIT,
-    DEPTH_METER_UNIT,
-    DEPTH_MILLIMETER_UNIT,
     VALID_VIDEO_CODECS,
     VIDEO_ENCODER_INFO_KEYS,
     DepthEncoderConfig,
@@ -43,7 +41,6 @@ from .video import (
     VideoEncoderConfig,
     depth_encoder_defaults,
     encoder_config_from_video_info,
-    infer_depth_unit,
     rgb_encoder_defaults,
 )
 
@@ -58,7 +55,6 @@ __all__ = [
     "DatasetRecordConfig",
     "DatasetConfig",
     "EvalConfig",
-    "JobConfig",
     "MessageTurn",
     "PeftConfig",
     "PreTrainedConfig",
@@ -73,11 +69,8 @@ __all__ = [
     "depth_encoder_defaults",
     # Factories
     "encoder_config_from_video_info",
-    "infer_depth_unit",
     # Constants
     "DEFAULT_DEPTH_UNIT",
-    "DEPTH_METER_UNIT",
-    "DEPTH_MILLIMETER_UNIT",
     "VALID_VIDEO_CODECS",
     "VIDEO_ENCODER_INFO_KEYS",
 ]

src/lerobot/configs/default.py

@@ -145,35 +145,3 @@ class PeftConfig:
     # If None, the PEFT library defaults to alpha=8, which may dampen high-rank adapters.
     # Common values are r (alpha == rank) or 2*r.
     lora_alpha: int | None = None
-
-
-@dataclass
-class JobConfig:
-    # Where training runs. None (omitted) or "local" runs on this machine.
-    # Any other value is an HF Jobs flavor and submits the run to HF Jobs.
-    # List available flavors + pricing with `hf jobs hardware` command.
-    target: str | None = None
-    # Runtime image for the remote job (ignored for local runs).
-    image: str = "huggingface/lerobot-gpu:latest"
-    # Max wall-clock for the remote job as an HF Jobs duration string (e.g. "2h").
-    # Defaults to "2d": We pass an explicit, generous cap instead. Set a smaller
-    # value to fail fast, or a larger one for long runs.
-    timeout: str | None = "2d"
-    # Submit and exit instead of streaming the job logs in the foreground.
-    detach: bool = False
-    # Extra tags attached to the HF job and to any dataset this run pushes to the
-    # Hub. A "lerobot" tag is always added; e.g. --job.tags '["lelab"]' adds more.
-    tags: list[str] = field(default_factory=list)
-
-    # Two entry points to the same predicate: the staticmethod tests a raw target string
-    # straight from argv (before any JobConfig exists, to decide dispatch early), while the
-    # property is the ergonomic accessor for code that already holds a config instance.
-    @staticmethod
-    def is_remote_target(target: str | None) -> bool:
-        """True when `target` names an HF Jobs flavor rather than a local run."""
-        return target not in (None, "local")
-
-    @property
-    def is_remote(self) -> bool:
-        """True when training should run on HF Jobs rather than this machine."""
-        return self.is_remote_target(self.target)

src/lerobot/configs/train.py

@@ -26,12 +26,11 @@ from huggingface_hub.errors import HfHubHTTPError
 
 from lerobot import envs
 from lerobot.optim import LRSchedulerConfig, OptimizerConfig
-from lerobot.utils.constants import PRETRAINED_MODEL_DIR
-from lerobot.utils.hub import HubMixin, find_latest_hub_checkpoint
+from lerobot.utils.hub import HubMixin
 from lerobot.utils.sample_weighting import SampleWeightingConfig
 
 from . import parser
-from .default import DatasetConfig, EvalConfig, JobConfig, PeftConfig, WandBConfig
+from .default import DatasetConfig, EvalConfig, PeftConfig, WandBConfig
 from .policies import PreTrainedConfig
 from .rewards import RewardModelConfig
 
@@ -84,11 +83,10 @@ class TrainPipelineConfig(HubMixin):
     # with the same value for `dir` its contents will be overwritten unless you set `resume` to true.
     output_dir: Path | None = None
     job_name: str | None = None
-    # Set `resume` to true to resume a previous run. Pass `--config_path` pointing at either a local
-    # checkpoint's train_config.json or a Hub repo id holding `checkpoints/<step>/` subtrees (the
-    # latest checkpoint is downloaded and resumed from). Note that when resuming, the default behavior
-    # is to use the configuration from the checkpoint, regardless of what's provided with the training
-    # command at the time of resumption (CLI `--*` flags still override).
+    # Set `resume` to true to resume a previous run. In order for this to work, you will need to make sure
+    # `dir` is the directory of an existing run with at least one checkpoint in it.
+    # Note that when resuming a run, the default behavior is to use the configuration from the checkpoint,
+    # regardless of what's provided with the training command at the time of resumption.
     resume: bool = False
     # `seed` is used for training (eg: model initialization, dataset shuffling)
     # AND for the evaluation environments.
@@ -120,13 +118,6 @@ class TrainPipelineConfig(HubMixin):
     wandb: WandBConfig = field(default_factory=WandBConfig)
     peft: PeftConfig | None = None
 
-    # Where to run training (local default, or an HF Jobs flavor). See JobConfig.
-    job: JobConfig = field(default_factory=JobConfig)
-    # Push each saved checkpoint to the Hub (policy.repo_id) as it is written, not
-    # just the final model (useful to monitor progress mid-run). Optional; the
-    # final model is pushed regardless. Works the same locally and remotely.
-    save_checkpoint_to_hub: bool = False
-
     # Sample weighting configuration (e.g., for RA-BC training)
     sample_weighting: SampleWeightingConfig | None = None
 
@@ -146,17 +137,10 @@ class TrainPipelineConfig(HubMixin):
             return self.reward_model  # type: ignore[return-value]
         return self.policy  # type: ignore[return-value]
 
-    def _resolve_pretrained_from_cli(self) -> None:
-        """Resolve the pretrained source passed on the CLI into a loaded config.
-
-        The pretrained paths (`--policy.path`, `--reward_model.path`) and
-        `--config_path` are only recoverable by re-reading the CLI args: draccus
-        has already consumed them by the time `validate()` runs, so they are not
-        reflected on `self`. Exactly one source applies, in priority order:
-        reward-model path, policy path, then resume.
-        """
-        reward_model_path = parser.get_path_arg("reward_model")
+    def validate(self) -> None:
+        # HACK: We parse again the cli args here to get the pretrained paths if there was some.
         policy_path = parser.get_path_arg("policy")
+        reward_model_path = parser.get_path_arg("reward_model")
 
         if reward_model_path:
             cli_overrides = parser.get_cli_overrides("reward_model")
@@ -165,54 +149,31 @@ class TrainPipelineConfig(HubMixin):
             )
             self.reward_model.pretrained_path = str(Path(reward_model_path))
         elif policy_path:
-            overrides = parser.get_yaml_overrides("policy") + (parser.get_cli_overrides("policy") or [])
-            self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=overrides)
+            yaml_overrides = parser.get_yaml_overrides("policy")
+            cli_overrides = parser.get_cli_overrides("policy") or []
+            self.policy = PreTrainedConfig.from_pretrained(
+                policy_path, cli_overrides=yaml_overrides + cli_overrides
+            )
             self.policy.pretrained_path = Path(policy_path)
         elif self.resume:
-            self._resolve_resume_checkpoint()
+            config_path = parser.parse_arg("config_path")
+            if not config_path:
+                raise ValueError(
+                    f"A config_path is expected when resuming a run. Please specify path to {TRAIN_CONFIG_NAME}"
+                )
 
-    def _resolve_resume_checkpoint(self) -> None:
-        """Point the trainable config at the checkpoint named by `--config_path`.
+            if not Path(config_path).resolve().exists():
+                raise NotADirectoryError(
+                    f"{config_path=} is expected to be a local path. "
+                    "Resuming from the hub is not supported for now."
+                )
 
-        `config_path` is either a local path (to a checkpoint's train_config.json or its
-        pretrained_model/ dir) or a Hub repo id. For a Hub repo, the latest checkpoint is downloaded
-        into a fresh local run dir and resumed from there. The download is skipped when dispatching to
-        an HF Job (`job.is_remote`): the pod performs it when it runs the resume locally, and
-        `submit_to_hf` resolves the source repo for the remote command.
-        """
-        config_path = parser.parse_arg("config_path")
-        if not config_path:
-            raise ValueError(
-                f"A config_path is expected when resuming a run. Please specify path to {TRAIN_CONFIG_NAME}"
-            )
-
-        if Path(config_path).resolve().exists():
             policy_dir = Path(config_path).parent
+            if self.policy is not None:
+                self.policy.pretrained_path = policy_dir
+            if self.reward_model is not None:
+                self.reward_model.pretrained_path = str(policy_dir)
             self.checkpoint_path = policy_dir.parent
-        elif self.job.is_remote:
-            return
-        else:
-            from lerobot.common.train_utils import resolve_resume_checkpoint
-
-            # `self.output_dir` was loaded from the checkpoint's config and points at the original
-            # run's (now-absent) local dir. Resume into a fresh local dir instead, unless the user
-            # passed --output_dir explicitly.
-            cli_output_dir = parser.parse_arg("output_dir")
-            if cli_output_dir:
-                self.output_dir = Path(cli_output_dir)
-            else:
-                now = dt.datetime.now()
-                self.output_dir = Path("outputs/train") / f"{now:%Y-%m-%d}/{now:%H-%M-%S}_resume"
-            self.checkpoint_path = resolve_resume_checkpoint(config_path, self.output_dir)
-            policy_dir = self.checkpoint_path / PRETRAINED_MODEL_DIR
-
-        if self.policy is not None:
-            self.policy.pretrained_path = policy_dir
-        if self.reward_model is not None:
-            self.reward_model.pretrained_path = str(policy_dir)
-
-    def validate(self) -> None:
-        self._resolve_pretrained_from_cli()
 
         if self.policy is None and self.reward_model is None:
             raise ValueError(
@@ -255,19 +216,9 @@ class TrainPipelineConfig(HubMixin):
         if self.eval_steps > 0 and self.dataset.eval_split == 0.0:
             raise ValueError("eval_steps > 0 requires dataset.eval_split > 0.0 to hold out eval data.")
 
-        # Remote runs auto-generate the repo_id in submit_to_hf (the policy may only be
-        # resolved here, from --policy.path), so don't demand it up front for them.
-        if (
-            hasattr(active_cfg, "push_to_hub")
-            and active_cfg.push_to_hub
-            and not active_cfg.repo_id
-            and not self.job.is_remote
-        ):
+        if hasattr(active_cfg, "push_to_hub") and active_cfg.push_to_hub and not active_cfg.repo_id:
             raise ValueError("'repo_id' argument missing. Please specify it to push the model to the hub.")
 
-        if self.save_checkpoint_to_hub and not (self.policy is not None and self.policy.repo_id):
-            raise ValueError("save_checkpoint_to_hub requires --policy.repo_id.")
-
     @classmethod
     def __get_path_fields__(cls) -> list[str]:
         """Keys for draccus pretrained-path loading."""
@@ -304,30 +255,22 @@ class TrainPipelineConfig(HubMixin):
         elif Path(model_id).is_file():
             config_file = model_id
         else:
-            dl_kwargs = {
-                "repo_id": model_id,
-                "revision": revision,
-                "cache_dir": cache_dir,
-                "force_download": force_download,
-                "proxies": proxies,
-                "resume_download": resume_download,
-                "token": token,
-                "local_files_only": local_files_only,
-            }
             try:
-                config_file = hf_hub_download(filename=TRAIN_CONFIG_NAME, **dl_kwargs)
-            except HfHubHTTPError as e:
-                # No root train_config.json: this is a repo of periodic checkpoints from an
-                # interrupted run. Fall back to the latest checkpoint's config so the run can be
-                # resumed straight from the repo with `--config_path=<repo>`.
-                latest = find_latest_hub_checkpoint(model_id, token=token, revision=revision)
-                if latest is None:
-                    raise FileNotFoundError(
-                        f"{TRAIN_CONFIG_NAME} not found on the HuggingFace Hub in {model_id}"
-                    ) from e
                 config_file = hf_hub_download(
-                    filename=f"{latest}/{PRETRAINED_MODEL_DIR}/{TRAIN_CONFIG_NAME}", **dl_kwargs
+                    repo_id=model_id,
+                    filename=TRAIN_CONFIG_NAME,
+                    revision=revision,
+                    cache_dir=cache_dir,
+                    force_download=force_download,
+                    proxies=proxies,
+                    resume_download=resume_download,
+                    token=token,
+                    local_files_only=local_files_only,
                 )
+            except HfHubHTTPError as e:
+                raise FileNotFoundError(
+                    f"{TRAIN_CONFIG_NAME} not found on the HuggingFace Hub in {model_id}"
+                ) from e
 
         cli_args = kwargs.pop("cli_args", [])
         # Legacy RA-BC migration only applies to framework-saved checkpoints (always JSON).

src/lerobot/configs/video.py

@@ -22,8 +22,6 @@ import logging
 from dataclasses import dataclass, field
 from typing import Any, ClassVar, Self
 
-import numpy as np
-
 from lerobot.utils.import_utils import require_package
 
 logger = logging.getLogger(__name__)
@@ -38,9 +36,7 @@ HW_VIDEO_CODECS = [
     "h264_vaapi",  # Linux Intel/AMD
     "h264_qsv",  # Intel Quick Sync
 ]
-VALID_VIDEO_CODECS: frozenset[str] = frozenset(
-    {"h264", "hevc", "libsvtav1", "libaom-av1", "auto", *HW_VIDEO_CODECS}
-)
+VALID_VIDEO_CODECS: frozenset[str] = frozenset({"h264", "hevc", "libsvtav1", "auto", *HW_VIDEO_CODECS})
 # Aliases for legacy video codec names.
 VIDEO_CODECS_ALIASES: dict[str, str] = {"av1": "libsvtav1"}
 
@@ -69,15 +65,6 @@ DEPTH_METER_UNIT: str = "m"
 DEPTH_MILLIMETER_UNIT: str = "mm"
 DEFAULT_DEPTH_UNIT: str = DEPTH_MILLIMETER_UNIT
 
-
-def infer_depth_unit(dtype: np.dtype | type) -> str:
-    """Infer the physical unit of raw depth frames from their dtype.
-
-    Floating-point frames are assumed to be in metres, integer frames in millimetres.
-    """
-    return DEPTH_METER_UNIT if np.issubdtype(np.dtype(dtype), np.floating) else DEPTH_MILLIMETER_UNIT
-
-
 # Depth-specific tuning fields persisted under ``features[*]["info"]`` as ``video.<name>``.
 DEPTH_ENCODER_INFO_FIELD_NAMES: frozenset[str] = frozenset({"depth_min", "depth_max", "shift", "use_log"})
 
@@ -226,24 +213,18 @@ class VideoEncoderConfig:
             if encoder_threads is not None:
                 svtav1_parts.append(f"lp={encoder_threads}")
             if svtav1_parts:
-                set_if("svtav1-params", ":".join(svtav1_parts))
+                opts["svtav1-params"] = ":".join(svtav1_parts)
         elif self.vcodec in ("h264", "hevc"):
             set_if("crf", self.crf)
             set_if("preset", self.preset)
             if self.fast_decode:
-                set_if("tune", "fastdecode")
+                opts["tune"] = "fastdecode"
             set_if("threads", encoder_threads)
-        elif self.vcodec == "libaom-av1":
-            set_if("crf", self.crf)
-            set_if("preset", self.preset)
-            if encoder_threads is not None:
-                set_if("threads", encoder_threads)
-                set_if("row-mt", 1)
         elif self.vcodec in ("h264_videotoolbox", "hevc_videotoolbox"):
             if self.crf is not None:
-                set_if("q:v", max(1, min(100, 100 - self.crf * 2)))
+                opts["q:v"] = max(1, min(100, 100 - self.crf * 2))
         elif self.vcodec in ("h264_nvenc", "hevc_nvenc"):
-            set_if("rc", 0)
+            opts["rc"] = 0
             set_if("qp", self.crf)
             set_if("preset", self.preset)
         elif self.vcodec == "h264_vaapi":

src/lerobot/datasets/compute_stats.py

@@ -509,7 +509,7 @@ def compute_episode_stats(
         For 'image'/'video' features, stats are computed per channel and kept with a
         leading channel axis (e.g. shape (3, 1, 1) for RGB). RGB stats are divided by
         255 to land in [0, 1]; depth maps (features flagged with ``is_depth_map``) skip
-        this rescaling and remain in their stored units (stored in ``depth_unit``).
+        this rescaling and remain in their stored units.
     """
     if quantile_list is None:
         quantile_list = DEFAULT_QUANTILES
@@ -519,13 +519,6 @@ def compute_episode_stats(
         if features[key]["dtype"] in {"string", "language"}:
             continue
 
-        # Features with zero-width shapes are skipped (no data to compute stats on)
-        if any(d == 0 for d in features[key].get("shape", ())):
-            logging.debug(
-                f"Skipping statistics computation for feature '{key}' with a zero-width shape {features[key]['shape']}."
-            )
-            continue
-
         if features[key]["dtype"] in ["image", "video"]:
             ep_ft_array = sample_images(data)
             axes_to_reduce = (0, 2, 3)

src/lerobot/datasets/dataset_metadata.py

@@ -26,13 +26,12 @@ import pyarrow as pa
 import pyarrow.parquet as pq
 from huggingface_hub import snapshot_download
 
-from lerobot.configs import DEPTH_METER_UNIT, VideoEncoderConfig
+from lerobot.configs import VideoEncoderConfig
 from lerobot.utils.constants import DEFAULT_FEATURES, HF_LEROBOT_HOME, HF_LEROBOT_HUB_CACHE
 from lerobot.utils.feature_utils import _validate_feature_names
 from lerobot.utils.utils import flatten_dict
 
 from .compute_stats import aggregate_stats
-from .depth_utils import MM_PER_METRE
 from .feature_utils import create_empty_dataset_info
 from .io_utils import (
     get_file_size_in_mb,
@@ -359,35 +358,6 @@ class LeRobotDatasetMetadata:
 
         return [key for key, ft in self.features.items() if _is_depth(ft)]
 
-    def rescale_depth_stats(self, output_unit: str) -> None:
-        """Rescale depth feature stats in place from their recorded unit to ``output_unit``.
-
-        Depth stats are stored in the unit the frames were recorded in
-        (``features[key]["info"]["depth_unit"]``), while frames are returned in
-        ``output_unit`` on read. This converts the unit-bearing stat entries so
-        stats match the frames consumers see.
-        """
-        missing_unit_keys = [
-            key for key in self.depth_keys if (self.features[key].get("info") or {}).get("depth_unit") is None
-        ]
-        if missing_unit_keys:
-            logging.warning(
-                f"Depth feature(s) {missing_unit_keys} have no recorded 'depth_unit' in their info. "
-                f"Depth maps and stats for these keys will be returned AS IS, with no unit conversion "
-                f"to the requested output unit {output_unit!r}. Re-record the dataset or set 'depth_unit' "
-                f"in the feature info (meta/info.json) to enable conversion."
-            )
-        if self.stats is None:
-            return
-        for key in self.depth_keys:
-            stored_unit = (self.features[key].get("info") or {}).get("depth_unit")
-            if stored_unit is None or stored_unit == output_unit or key not in self.stats:
-                continue
-            factor = MM_PER_METRE if stored_unit == DEPTH_METER_UNIT else 1.0 / MM_PER_METRE
-            self.stats[key] = {
-                stat: value if stat == "count" else value * factor for stat, value in self.stats[key].items()
-            }
-
     @property
     def camera_keys(self) -> list[str]:
         """Keys to access visual modalities (regardless of their storage method)."""

src/lerobot/datasets/dataset_reader.py

@@ -22,14 +22,10 @@ from pathlib import Path
 import datasets
 import torch
 
-from lerobot.configs import (
-    DEFAULT_DEPTH_UNIT,
-    DEPTH_METER_UNIT,
-    DepthEncoderConfig,
-)
+from lerobot.configs import DEFAULT_DEPTH_UNIT, DepthEncoderConfig
 
 from .dataset_metadata import LeRobotDatasetMetadata
-from .depth_utils import MM_PER_METRE, dequantize_depth
+from .depth_utils import dequantize_depth
 from .feature_utils import (
     check_delta_timestamps,
     get_delta_indices,
@@ -106,13 +102,6 @@ class DatasetReader:
             for vid_key in self._meta.depth_keys
         }
 
-        # Get the input unit of each depth feature stored as raw images.
-        self._image_depth_units: dict[str, str | None] = {
-            key: (self._meta.features[key].get("info") or {}).get("depth_unit")
-            for key in self._meta.depth_keys
-            if key in self._meta.image_keys
-        }
-
     def set_image_transforms(self, image_transforms: Callable | None) -> None:
         """Replace the transform applied to visual observations."""
         if image_transforms is not None and not callable(image_transforms):
@@ -340,13 +329,6 @@ class DatasetReader:
                     continue
                 item[cam] = self._image_transforms(item[cam])
 
-        # Convert depth features to the output unit.
-        for key, stored_unit in self._image_depth_units.items():
-            if key in item and stored_unit is not None and stored_unit != self._depth_output_unit:
-                item[key] = (
-                    item[key] * MM_PER_METRE if stored_unit == DEPTH_METER_UNIT else item[key] / MM_PER_METRE
-                )
-
         # Add task as a string
         task_idx = item["task_index"].item()
         item["task"] = self._meta.tasks.iloc[task_idx].name

src/lerobot/datasets/dataset_writer.py

@@ -36,7 +36,6 @@ from lerobot.configs import (
     RGBEncoderConfig,
     VideoEncoderConfig,
     depth_encoder_defaults,
-    infer_depth_unit,
     rgb_encoder_defaults,
 )
 
@@ -210,15 +209,6 @@ class DatasetWriter:
         self.episode_buffer["timestamp"].append(timestamp)
         self.episode_buffer["task"].append(frame.pop("task"))
 
-        # Record each depth feature's input unit once, inferred from the first frame's dtype.
-        if frame_index == 0:
-            for depth_key in self._meta.depth_keys:
-                if depth_key not in frame:
-                    continue
-                info = self._meta.features[depth_key].setdefault("info", {})
-                if info.get("depth_unit") is None:
-                    info["depth_unit"] = infer_depth_unit(np.asarray(frame[depth_key]).dtype)
-
         # Start streaming encoder on first frame of episode
         if frame_index == 0 and self._streaming_encoder is not None:
             self._streaming_encoder.start_episode(

src/lerobot/datasets/depth_utils.py

@@ -34,13 +34,12 @@ from lerobot.configs.video import (
     DEPTH_METER_UNIT,
     DEPTH_MILLIMETER_UNIT,
     DEPTH_QMAX,
-    infer_depth_unit,
 )
 
 from .image_writer import squeeze_single_channel
 from .pyav_utils import write_u16_plane
 
-MM_PER_METRE = 1000.0
+_MM_PER_METRE = 1000.0
 _UINT16_MAX = 65535
 
 
@@ -58,7 +57,11 @@ def _depth_input_to_float32_and_unit(
     input_unit: Literal["auto", DEPTH_METER_UNIT, DEPTH_MILLIMETER_UNIT],
 ) -> tuple[NDArray[np.float32], Literal[DEPTH_METER_UNIT, DEPTH_MILLIMETER_UNIT]]:
     """Convert depth to float32 in the chosen unit, and return the resolved unit."""
-    resolved_unit = infer_depth_unit(depth.dtype) if input_unit == "auto" else input_unit
+    resolved_unit = (
+        (DEPTH_METER_UNIT if np.issubdtype(depth.dtype, np.floating) else DEPTH_MILLIMETER_UNIT)
+        if input_unit == "auto"
+        else input_unit
+    )
     return depth.astype(np.float32, order="K"), resolved_unit
 
 
@@ -123,12 +126,12 @@ def quantize_depth(
 
     # Convert depth_min, depth_max, and shift to the resolved input unit.
     depth_min_u = (
-        np.float32(depth_min) if resolved_unit == DEPTH_METER_UNIT else np.float32(depth_min * MM_PER_METRE)
+        np.float32(depth_min) if resolved_unit == DEPTH_METER_UNIT else np.float32(depth_min * _MM_PER_METRE)
     )
     depth_max_u = (
-        np.float32(depth_max) if resolved_unit == DEPTH_METER_UNIT else np.float32(depth_max * MM_PER_METRE)
+        np.float32(depth_max) if resolved_unit == DEPTH_METER_UNIT else np.float32(depth_max * _MM_PER_METRE)
     )
-    shift_u = np.float32(shift) if resolved_unit == DEPTH_METER_UNIT else np.float32(shift * MM_PER_METRE)
+    shift_u = np.float32(shift) if resolved_unit == DEPTH_METER_UNIT else np.float32(shift * _MM_PER_METRE)
 
     # Normalization and quantization is performed in the resolved input unit.
     if use_log:
@@ -233,7 +236,7 @@ def dequantize_depth(
 
         # mm path: round + clamp in float32, skipping the uint16 round-trip
         # when returning a tensor (torch.uint16 is poorly supported).
-        buf.mul_(MM_PER_METRE).round_().clamp_(0.0, _UINT16_MAX)
+        buf.mul_(_MM_PER_METRE).round_().clamp_(0.0, _UINT16_MAX)
         if output_tensor:
             return buf
         return buf.cpu().numpy().astype(np.uint16, copy=False)
@@ -256,7 +259,7 @@ def dequantize_depth(
     if output_unit == DEPTH_METER_UNIT:
         return torch.from_numpy(buf) if output_tensor else buf
 
-    np.multiply(buf, MM_PER_METRE, out=buf)
+    np.multiply(buf, _MM_PER_METRE, out=buf)
     np.rint(buf, out=buf)
     np.clip(buf, 0.0, _UINT16_MAX, out=buf)
     if output_tensor:

src/lerobot/datasets/feature_utils.py

@@ -67,9 +67,9 @@ def get_hf_features_from_features(features: dict) -> datasets.Features:
         elif ft["shape"] == (1,):
             hf_features[key] = datasets.Value(dtype=ft["dtype"])
         elif len(ft["shape"]) == 1:
-            # pyarrow rejects fixed-size lists of length 0, so use a variable length list instead
-            length = ft["shape"][0] if ft["shape"][0] > 0 else -1
-            hf_features[key] = datasets.Sequence(length=length, feature=datasets.Value(dtype=ft["dtype"]))
+            hf_features[key] = datasets.Sequence(
+                length=ft["shape"][0], feature=datasets.Value(dtype=ft["dtype"])
+            )
         elif len(ft["shape"]) == 2:
             hf_features[key] = datasets.Array2D(shape=ft["shape"], dtype=ft["dtype"])
         elif len(ft["shape"]) == 3:

src/lerobot/datasets/lerobot_dataset.py

@@ -224,7 +224,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         )
         self.root = self.meta.root
         self.revision = self.meta.revision
-        self.meta.rescale_depth_stats(self._depth_output_unit)
 
         if episodes is not None and any(
             episode >= self.meta.total_episodes or episode < 0 for episode in episodes
@@ -351,11 +350,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         """Frames per second used during data collection."""
         return self.meta.fps
 
-    @property
-    def depth_output_unit(self) -> str:
-        """Physical unit (``"m"`` or ``"mm"``) depth maps and statistics are returned in on read."""
-        return self._depth_output_unit
-
     @property
     def num_frames(self) -> int:
         """Number of frames in selected episodes."""

src/lerobot/datasets/streaming_dataset.py

@@ -22,11 +22,11 @@ import numpy as np
 import torch
 from datasets import load_dataset
 
-from lerobot.configs import DEFAULT_DEPTH_UNIT, DEPTH_METER_UNIT, DepthEncoderConfig
+from lerobot.configs import DEFAULT_DEPTH_UNIT, DepthEncoderConfig
 from lerobot.utils.constants import HF_LEROBOT_HOME, LOOKAHEAD_BACKTRACKTABLE, LOOKBACK_BACKTRACKTABLE
 
 from .dataset_metadata import CODEBASE_VERSION, LeRobotDatasetMetadata
-from .depth_utils import MM_PER_METRE, dequantize_depth
+from .depth_utils import dequantize_depth
 from .feature_utils import get_delta_indices
 from .io_utils import item_to_torch
 from .utils import (
@@ -310,7 +310,6 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
         )
         self.root = self.meta.root
         self.revision = self.meta.revision
-        self.meta.rescale_depth_stats(self._depth_output_unit)
         # Check version
         check_version_compatibility(self.repo_id, self.meta._version, CODEBASE_VERSION)
 
@@ -319,13 +318,6 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
             for vid_key in self.meta.depth_keys
         }
 
-        # Input unit of each depth feature stored as raw images (dequantized separately from videos).
-        self._image_depth_units: dict[str, str | None] = {
-            key: (self.meta.features[key].get("info") or {}).get("depth_unit")
-            for key in self.meta.depth_keys
-            if key in self.meta.image_keys
-        }
-
         self.delta_timestamps = None
         self.delta_indices = None
 
@@ -356,11 +348,6 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
     def fps(self):
         return self.meta.fps
 
-    @property
-    def depth_output_unit(self) -> str:
-        """Physical unit (``"m"`` or ``"mm"``) depth maps are returned in on read."""
-        return self._depth_output_unit
-
     @staticmethod
     def _iter_random_indices(
         rng: np.random.Generator, buffer_size: int, random_batch_size=100
@@ -543,15 +530,6 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
         for update in updates:
             result.update(update)
 
-        # Convert raw-image depth features to the output unit (video depth is already converted).
-        for key, stored_unit in self._image_depth_units.items():
-            if key in result and stored_unit is not None and stored_unit != self._depth_output_unit:
-                result[key] = (
-                    result[key] * MM_PER_METRE
-                    if stored_unit == DEPTH_METER_UNIT
-                    else result[key] / MM_PER_METRE
-                )
-
         result["task"] = self.meta.tasks.iloc[item["task_index"]].name
 
         yield result

src/lerobot/jobs/__init__.py

@@ -1,23 +0,0 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from lerobot.utils.import_utils import require_package
-
-# LeRobotDataset (imported at module top in dataset.py) pulls in heavy dataset deps;
-# guard the optional dependency here so importing this package fails loudly if it's missing.
-require_package("datasets", extra="dataset")
-
-from .hf import submit_to_hf
-
-__all__ = ["submit_to_hf"]

src/lerobot/jobs/dataset.py

@@ -1,53 +0,0 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Make a training dataset reachable from an HF Job pod.
-
-The pod can't see the host's ~/.cache/huggingface/lerobot, so the dataset has to
-live on the Hub: the pod downloads it by repo_id at train time (the forwarded
-HF_TOKEN covers private datasets). A dataset already on the Hub is used as-is; a
-local-only dataset is pushed to a PRIVATE repo first (never public).
-"""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-from lerobot.datasets import LeRobotDataset
-from lerobot.utils.constants import HF_LEROBOT_HOME
-
-if TYPE_CHECKING:
-    from huggingface_hub import HfApi
-
-
-def ensure_dataset_available(repo_id: str, *, api: HfApi, tags: list[str] | None = None) -> None:
-    """Ensure repo_id resolves on the Hub, pushing a local-only dataset privately first.
-
-    `tags` are attached to the dataset only when we push it (an already-on-Hub
-    dataset is left untouched). Raises RuntimeError if the dataset is neither on
-    the Hub nor in the local cache.
-    """
-    if api.repo_exists(repo_id, repo_type="dataset"):
-        return
-
-    local_present = (HF_LEROBOT_HOME / repo_id / "meta" / "info.json").is_file()
-    if not local_present:
-        raise RuntimeError(
-            f"Dataset '{repo_id}' is not in the local cache ({HF_LEROBOT_HOME}) and could not be "
-            f"reached on the Hub — it may not exist, or be private and inaccessible with your "
-            f"token. Record or download it first, or run `hf auth login`."
-        )
-
-    print(f"[dataset] '{repo_id}' is local-only; pushing to a PRIVATE Hub repo...")
-    LeRobotDataset(repo_id).push_to_hub(private=True, tags=tags)
-    print(f"[dataset] '{repo_id}' uploaded (private). The job will download it by repo_id.")

src/lerobot/jobs/hf.py

@@ -1,425 +0,0 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Run a lerobot training on HF Jobs (HuggingFace GPUs).
-
-Ported and simplified from lelab's runners/hf_cloud.py: no UI log queue, no
-registry — just submit and stream to stdout.
-"""
-
-from __future__ import annotations
-
-import copy
-import datetime as dt
-import json
-import netrc
-import os
-import re
-import signal
-import sys
-import tempfile
-import threading
-from pathlib import Path
-from typing import TYPE_CHECKING
-
-import httpx
-from huggingface_hub import (
-    HfApi,
-    create_repo,
-    fetch_job_logs,
-    get_token,
-    inspect_job,
-    run_job,
-    upload_file,
-)
-
-from lerobot.common.train_utils import push_checkpoint_to_hub
-from lerobot.configs import parser
-
-from .dataset import ensure_dataset_available
-
-if TYPE_CHECKING:
-    from lerobot.configs.train import TrainPipelineConfig
-
-_SLUG_RE = re.compile(r"[^a-zA-Z0-9._-]+")
-
-_TERMINAL_STAGES = {"COMPLETED", "CANCELED", "ERROR", "DELETED"}
-
-# huggingface_hub 1.x runs on httpx: transient HTTP/transport failures surface as
-# httpx.HTTPError and socket-level errors as OSError. Catching only these keeps real
-# bugs (TypeError, AttributeError, ...) from being silently retried or counted as
-# job failures.
-_TRANSIENT_NET_ERRORS = (OSError, httpx.HTTPError)
-
-# Always attached to remote jobs and pushed datasets so LeRobot-originated work
-# is identifiable on the Hub; callers (e.g. LeLab) add their own via --job.tags.
-LEROBOT_TAG = "lerobot"
-
-
-def resolve_job_tags(extra: list[str] | None) -> list[str]:
-    """Return the tag list for a run: the lerobot tag plus any extras, deduped, order-stable."""
-    tags = [LEROBOT_TAG, *(extra or [])]
-    seen: set[str] = set()
-    return [t for t in tags if not (t in seen or seen.add(t))]
-
-
-def resolve_wandb_api_key() -> str | None:
-    """Host's wandb key for forwarding to the job: $WANDB_API_KEY, else ~/.netrc."""
-    key = os.environ.get("WANDB_API_KEY")
-    if key:
-        return key
-    try:
-        rc = netrc.netrc()
-    except (FileNotFoundError, netrc.NetrcParseError, OSError):
-        return None
-    auth = rc.authenticators("api.wandb.ai")
-    if auth is None:
-        return None
-    _login, _account, password = auth
-    return password or None
-
-
-def build_repo_id(username: str, job_name: str, now: dt.datetime) -> str:
-    """Generate the model repo id for a remote run: <user>/<job_name>_<timestamp>."""
-    slug = _SLUG_RE.sub("-", job_name).strip("-") or "train"
-    stamp = now.strftime("%Y-%m-%d_%H-%M-%S")
-    return f"{username}/{slug}_{stamp}"
-
-
-def build_remote_config_file(cfg, repo_id: str, dest: Path, tags: list[str] | None = None) -> Path:
-    """Write a train_config.json for the pod, with remote overrides applied.
-
-    The pod runs `lerobot-train --config_path=<dest>` and downloads the dataset
-    by repo_id into its own cache. Client-only fields are stripped so the config
-    is accepted by the trainer image: `job` (pure client orchestration) is always
-    removed, and `save_checkpoint_to_hub` is removed unless explicitly enabled —
-    older lerobot images reject unknown keys, so the default keeps the config
-    compatible with the released `lerobot-gpu` image. `tags` are merged into
-    policy.tags so the trained model the pod pushes carries them too.
-    """
-    remote = copy.deepcopy(cfg)
-    remote.policy.push_to_hub = True
-    remote.policy.repo_id = repo_id
-    # Don't pin the client's resolved device (e.g. "mps"); let the pod auto-detect its GPU.
-    remote.policy.device = None
-    # Drop any host-local dataset root; the pod resolves the dataset by repo_id.
-    remote.dataset.root = None
-    if tags:
-        existing = list(remote.policy.tags or [])
-        remote.policy.tags = existing + [t for t in tags if t not in existing]
-
-    # Encode to the canonical, pod-parseable dict, then drop the keys the released
-    # trainer image doesn't know about.
-    data = remote.to_dict()
-    data.pop("job", None)
-    if not remote.save_checkpoint_to_hub:
-        data.pop("save_checkpoint_to_hub", None)
-
-    dest.parent.mkdir(parents=True, exist_ok=True)
-    dest.write_text(json.dumps(data, indent=4))
-    return dest
-
-
-def _stage_config_on_hub(cfg, repo_id: str, token: str, tags: list[str] | None = None) -> str:
-    """Upload train_config.json to the model repo and return the repo_id for --config_path."""
-    create_repo(repo_id, repo_type="model", private=True, exist_ok=True, token=token)
-    with tempfile.TemporaryDirectory() as tmp:
-        config_path = build_remote_config_file(cfg, repo_id, Path(tmp) / "train_config.json", tags=tags)
-        upload_file(
-            path_or_fileobj=config_path,
-            path_in_repo="train_config.json",
-            repo_id=repo_id,
-            repo_type="model",
-            token=token,
-        )
-    return repo_id
-
-
-def _tail_logs(
-    job_id: str,
-    done: threading.Event,
-    success_marker: str | None = None,
-    success_event: threading.Event | None = None,
-) -> None:
-    """Stream job logs to stdout, reconnecting on dropped streams until done is set.
-
-    Each reconnect re-fetches the full buffered log, so we track how many lines
-    were already printed and skip them — otherwise a fast-failing job's traceback
-    gets reprinted on every reconnect.
-
-    When `success_marker` appears in a line, set `success_event` and `done` so the
-    caller can finish as soon as the trained model lands on the Hub, rather than
-    waiting out the platform's post-run finalization (which can add ~30s).
-    """
-    printed = 0
-    while not done.is_set():
-        try:
-            seen = 0
-            for line in fetch_job_logs(job_id=job_id, follow=True):
-                seen += 1
-                if seen <= printed:
-                    continue  # already shown on a previous connection
-                printed = seen
-                # fetch_job_logs yields SSE data without trailing newlines, so add one
-                # per entry — otherwise all log lines concatenate onto a single line.
-                print(line.rstrip("\n"), flush=True)
-                if success_marker and success_event is not None and success_marker in line:
-                    success_event.set()
-                    done.set()
-                    return
-                if done.is_set():
-                    return
-            # Stream closed cleanly. Wait a moment so the status poller can mark
-            # the job terminal before we reconnect (avoids re-tailing the buffer).
-            if done.wait(3):
-                return
-        except _TRANSIENT_NET_ERRORS:
-            if done.wait(2):
-                return
-
-
-def _poll_until_done(
-    job_id: str,
-    done: threading.Event,
-    poll_interval: float = 5.0,
-    status_holder: dict | None = None,
-    max_failures: int = 6,
-) -> str | None:
-    """Poll inspect_job until a terminal stage or until `done` is set.
-
-    Returns the terminal stage string, or None if `done` was set first (detach)
-    or after `max_failures` consecutive inspect_job errors. When a terminal stage
-    is reached and `status_holder` is given, records `status_holder["message"]`
-    (the platform's status message, e.g. "Job timeout").
-    """
-    failures = 0
-    while not done.is_set():
-        try:
-            info = inspect_job(job_id=job_id)
-            failures = 0
-            # `stage` is an enum in some huggingface_hub versions and a plain str in others.
-            stage = getattr(info.status.stage, "value", info.status.stage)
-            if stage in _TERMINAL_STAGES:
-                if status_holder is not None:
-                    status_holder["message"] = getattr(info.status, "message", None)
-                done.set()
-                return stage
-        except _TRANSIENT_NET_ERRORS:
-            failures += 1
-            if failures >= max_failures:
-                done.set()
-                return None
-        done.wait(poll_interval)
-    return None
-
-
-def _pod_forwarded_args(
-    argv: list[str], drop_names: tuple[str, ...] = (), drop_prefixes: tuple[str, ...] = ()
-) -> list[str]:
-    """User CLI overrides to replay on the pod, minus flags the submitter sets itself.
-
-    Handles both `--name=value` and `--name value` forms. Forwarding the user's overrides (e.g.
-    `--steps`, `--save_checkpoint_to_hub`) makes a remote resume behave like the same local command.
-    """
-    out: list[str] = []
-    skip_next = False
-    for i, tok in enumerate(argv):
-        if skip_next:
-            skip_next = False
-            continue
-        name = tok.split("=", 1)[0]
-        if name in drop_names or any(name.startswith(p) for p in drop_prefixes):
-            if "=" not in tok and i + 1 < len(argv) and not argv[i + 1].startswith("--"):
-                skip_next = True  # also drop the space-separated value
-            continue
-        out.append(tok)
-    return out
-
-
-def _build_resume_job(cfg: TrainPipelineConfig, username: str) -> tuple[str, list[str]]:
-    """Resolve the model repo and pod command to resume a run on a job.
-
-    A Hub `config_path` is resumed from directly: its checkpoint config already targets that repo,
-    so new checkpoints continue the lineage there. A local `config_path` has its checkpoint uploaded
-    to a new PRIVATE repo first, and the resumed run is forced to push back to it. The pod command
-    always carries `--job.target=local` so the checkpoint's saved `job.target` can't make the pod
-    re-dispatch itself.
-    """
-    config_path = parser.parse_arg("config_path")
-    forwarded = _pod_forwarded_args(
-        sys.argv[1:],
-        drop_names=("--config_path", "--policy.repo_id", "--policy.push_to_hub", "--dataset.root"),
-        drop_prefixes=("--job.",),
-    )
-
-    if Path(config_path).exists():
-        # Local checkpoint: stage it on the Hub so the pod can resume from it, and push back there.
-        # Resolve so a `last` symlink uploads under its real step name (digit), which the pod's
-        # latest-checkpoint lookup keys on.
-        checkpoint_dir = Path(cfg.checkpoint_path).resolve()
-        source_repo = build_repo_id(username, cfg.job_name or "train", dt.datetime.now(dt.UTC))
-        push_checkpoint_to_hub(checkpoint_dir, source_repo, private=True)
-        extra = [f"--policy.repo_id={source_repo}", "--policy.push_to_hub=true"]
-    else:
-        source_repo = config_path
-        extra = []
-
-    command = [
-        "lerobot-train",
-        *forwarded,
-        f"--config_path={source_repo}",
-        "--job.target=local",
-        *extra,
-    ]
-    return source_repo, command
-
-
-def submit_to_hf(cfg: TrainPipelineConfig) -> None:
-    """Submit a training job to HF Jobs infrastructure.
-
-    Validates cfg, resolves credentials, ensures the dataset is on the Hub, then either stages a
-    sanitized config (fresh run) or resumes from a checkpoint repo, submits the job, and tails logs
-    until completion or detaches immediately. Ctrl-C detaches without cancelling the remote job.
-    """
-    token = get_token()
-    if not token:
-        raise RuntimeError("Not logged in to Hugging Face. Run `hf auth login` first.")
-
-    api = HfApi(token=token)
-    user_info = api.whoami(token=token)
-    username = user_info["name"]
-
-    now = dt.datetime.now(dt.UTC)
-    fresh_repo_id: str | None = None
-    if not cfg.resume:
-        # Resolve the model repo and mark it for push BEFORE validate(): validate() requires repo_id
-        # to be set whenever push_to_hub is True. (A resume reuses the checkpoint's repo instead.)
-        if cfg.policy is not None:
-            base_name = cfg.job_name or cfg.policy.type
-            fresh_repo_id = cfg.policy.repo_id or build_repo_id(username, base_name, now)
-            cfg.policy.repo_id = fresh_repo_id
-            cfg.policy.push_to_hub = True
-        else:
-            # Path-based policy is resolved inside validate(); fall back to a generic slug.
-            fresh_repo_id = build_repo_id(username, cfg.job_name or "train", now)
-
-    cfg.validate()
-
-    if cfg.is_reward_model_training:
-        raise ValueError(
-            "Remote training via --job.target only supports policy training, not reward models. "
-            "Run reward-model training locally."
-        )
-
-    secrets: dict[str, str] = {"HF_TOKEN": token}
-    if cfg.wandb.enable:
-        wandb_key = resolve_wandb_api_key()
-        if wandb_key is None:
-            raise ValueError(
-                "wandb is enabled but no WANDB_API_KEY found. "
-                "Set it via `export WANDB_API_KEY=...` or add it to ~/.netrc."
-            )
-        secrets["WANDB_API_KEY"] = wandb_key
-
-    tags = resolve_job_tags(cfg.job.tags)
-    # The dataset must be reachable from the pod for both fresh and resumed runs; a local-only
-    # dataset is pushed PRIVATE here. Hoisted before the resume/fresh branch since it applies to both.
-    ensure_dataset_available(cfg.dataset.repo_id, api=api, tags=tags)
-
-    if cfg.resume:
-        repo_id, command = _build_resume_job(cfg, username)
-    else:
-        config_repo_id = _stage_config_on_hub(cfg, fresh_repo_id, token, tags=tags)
-        repo_id = fresh_repo_id
-        command = ["lerobot-train", f"--config_path={config_repo_id}"]
-
-    print(f"Submitting job to HF Jobs (flavor={cfg.job.target}, image={cfg.job.image}) ...")
-    job_info = run_job(
-        image=cfg.job.image,
-        command=command,
-        flavor=cfg.job.target,
-        secrets=secrets,
-        timeout=cfg.job.timeout,
-        # HF Jobs labels are key/value; expose each tag as a queryable label.
-        labels=dict.fromkeys(tags, "true"),
-    )
-    job_id = job_info.id
-    job_url = getattr(job_info, "url", None)
-    print(f"Job submitted: {job_id}")
-    if job_url:
-        print(f"  Job page:   {job_url}")
-    print(f"  Model repo: https://huggingface.co/{repo_id}")
-    print(f"  Monitor:    hf jobs logs {job_id}")
-    print(f"  Cancel:     hf jobs cancel {job_id}")
-
-    if cfg.job.detach:
-        return
-
-    done = threading.Event()
-    detached = threading.Event()
-    pushed_ok = threading.Event()
-    stage_holder: dict[str, str | None] = {}
-
-    def _poll() -> None:
-        stage_holder["stage"] = _poll_until_done(job_id, done, status_holder=stage_holder)
-
-    poll_thread = threading.Thread(target=_poll, daemon=True)
-    poll_thread.start()
-    # Finish as soon as the model is pushed, rather than waiting out the platform's
-    # post-run finalization before the job stage flips to COMPLETED. This matches the
-    # exact log line emitted by PreTrainedPolicy.push_model_to_hub — the two must stay
-    # in sync. If it ever stops matching we just fall back to stage-based completion
-    # (~30s slower), so the contract is an optimization, not a correctness requirement.
-    success_marker = f"Model pushed to https://huggingface.co/{repo_id}"
-    log_thread = threading.Thread(
-        target=_tail_logs, args=(job_id, done, success_marker, pushed_ok), daemon=True
-    )
-    log_thread.start()
-
-    def _detach(sig, frame):
-        detached.set()
-        done.set()
-        print("\nDetached. Job is still running.")
-        print(f"  Monitor: hf jobs logs {job_id}")
-        print(f"  Cancel:  hf jobs cancel {job_id}")
-
-    # signal.signal only works on the main thread; when called from a worker thread
-    # (e.g. an orchestration framework) skip the Ctrl-C-detaches-instead-of-cancels
-    # handler rather than crashing with ValueError.
-    install_sigint = threading.current_thread() is threading.main_thread()
-    original_sigint = signal.getsignal(signal.SIGINT) if install_sigint else None
-    if install_sigint:
-        signal.signal(signal.SIGINT, _detach)
-    try:
-        # Timeout-based join so SIGINT is delivered to the main thread promptly.
-        while poll_thread.is_alive():
-            poll_thread.join(timeout=0.5)
-        log_thread.join(timeout=5)
-    finally:
-        if install_sigint:
-            signal.signal(signal.SIGINT, original_sigint)
-
-    if detached.is_set():
-        return
-
-    if pushed_ok.is_set():
-        print(f"\nTraining complete — model pushed to https://huggingface.co/{repo_id}")
-        return
-
-    stage = stage_holder.get("stage")
-    if stage != "COMPLETED":
-        message = stage_holder.get("message")
-        detail = f" ({message})" if message else ""
-        raise RuntimeError(
-            f"Job {job_id} ended with stage={stage}{detail}. Check logs: hf jobs logs {job_id}"
-        )

src/lerobot/policies/__init__.py

@@ -18,7 +18,6 @@ from .act.configuration_act import ACTConfig as ACTConfig
 from .diffusion.configuration_diffusion import DiffusionConfig as DiffusionConfig
 from .eo1.configuration_eo1 import EO1Config as EO1Config
 from .factory import get_policy_class, make_policy, make_policy_config, make_pre_post_processors
-from .fastwam.configuration_fastwam import FastWAMConfig as FastWAMConfig
 from .gaussian_actor.configuration_gaussian_actor import GaussianActorConfig as GaussianActorConfig
 from .groot.configuration_groot import GrootConfig as GrootConfig
 from .molmoact2.configuration_molmoact2 import MolmoAct2Config as MolmoAct2Config
@@ -43,7 +42,6 @@ __all__ = [
     "ACTConfig",
     "DiffusionConfig",
     "EO1Config",
-    "FastWAMConfig",
     "GaussianActorConfig",
     "GrootConfig",
     "MolmoAct2Config",

src/lerobot/policies/factory.py

@@ -47,7 +47,6 @@ from lerobot.utils.feature_utils import dataset_to_policy_features
 from .act.configuration_act import ACTConfig
 from .diffusion.configuration_diffusion import DiffusionConfig
 from .eo1.configuration_eo1 import EO1Config
-from .fastwam.configuration_fastwam import FastWAMConfig
 from .gaussian_actor.configuration_gaussian_actor import GaussianActorConfig
 from .groot.configuration_groot import GrootConfig
 from .molmoact2.configuration_molmoact2 import MolmoAct2Config
@@ -163,10 +162,6 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:
         from .vla_jepa.modeling_vla_jepa import VLAJEPAPolicy
 
         return VLAJEPAPolicy
-    elif name == "fastwam":
-        from .fastwam.modeling_fastwam import FastWAMPolicy
-
-        return FastWAMPolicy
     else:
         try:
             return _get_policy_cls_from_policy_name(name=name)
@@ -223,8 +218,6 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
         return MolmoAct2Config(**kwargs)
     elif policy_type == "vla_jepa":
         return VLAJEPAConfig(**kwargs)
-    elif policy_type == "fastwam":
-        return FastWAMConfig(**kwargs)
     else:
         try:
             config_cls = PreTrainedConfig.get_choice_class(policy_type)
@@ -288,26 +281,23 @@ def make_pre_post_processors(
             policy configuration type.
     """
     if pretrained_path:
-        # TODO(Steven): Temporary patch, implement correctly the processors for Gr00t
         if isinstance(policy_cfg, GrootConfig):
-            # GROOT handles normalization in groot_pack_inputs_v3 step
-            # Need to override both stats AND normalize_min_max since saved config might be empty
-            preprocessor_overrides = {}
-            postprocessor_overrides = {}
-            preprocessor_overrides["groot_pack_inputs_v3"] = {
-                "stats": kwargs.get("dataset_stats"),
-                "normalize_min_max": True,
-            }
+            from .groot.processor_groot import make_groot_pre_post_processors_from_pretrained
 
-            # Also ensure postprocessing slices to env action dim and unnormalizes with dataset stats
-            env_action_dim = policy_cfg.output_features[ACTION].shape[0]
-            postprocessor_overrides["groot_action_unpack_unnormalize_v1"] = {
-                "stats": kwargs.get("dataset_stats"),
-                "normalize_min_max": True,
-                "env_action_dim": env_action_dim,
-            }
-            kwargs["preprocessor_overrides"] = preprocessor_overrides
-            kwargs["postprocessor_overrides"] = postprocessor_overrides
+            return make_groot_pre_post_processors_from_pretrained(
+                config=policy_cfg,
+                pretrained_path=pretrained_path,
+                dataset_stats=kwargs.get("dataset_stats"),
+                dataset_meta=kwargs.get("dataset_meta"),
+                preprocessor_overrides=kwargs.get("preprocessor_overrides"),
+                postprocessor_overrides=kwargs.get("postprocessor_overrides"),
+                preprocessor_config_filename=kwargs.get(
+                    "preprocessor_config_filename", f"{POLICY_PREPROCESSOR_DEFAULT_NAME}.json"
+                ),
+                postprocessor_config_filename=kwargs.get(
+                    "postprocessor_config_filename", f"{POLICY_POSTPROCESSOR_DEFAULT_NAME}.json"
+                ),
+            )
 
         preprocessor = PolicyProcessorPipeline.from_pretrained(
             pretrained_model_name_or_path=pretrained_path,
@@ -413,6 +403,7 @@ def make_pre_post_processors(
         processors = make_groot_pre_post_processors(
             config=policy_cfg,
             dataset_stats=kwargs.get("dataset_stats"),
+            dataset_meta=kwargs.get("dataset_meta"),
         )
 
     elif isinstance(policy_cfg, XVLAConfig):
@@ -458,14 +449,6 @@ def make_pre_post_processors(
             dataset_stats=kwargs.get("dataset_stats"),
         )
 
-    elif isinstance(policy_cfg, FastWAMConfig):
-        from .fastwam.processor_fastwam import make_fastwam_pre_post_processors
-
-        processors = make_fastwam_pre_post_processors(
-            config=policy_cfg,
-            dataset_stats=kwargs.get("dataset_stats"),
-        )
-
     else:
         try:
             processors = _make_processors_from_policy_config(
@@ -555,6 +538,7 @@ def make_policy(
         set_dataset_feature_metadata = getattr(cfg, "set_dataset_feature_metadata", None)
         if callable(set_dataset_feature_metadata):
             set_dataset_feature_metadata(ds_meta.features)
+        cfg._runtime_dataset_meta = ds_meta
 
     kwargs["config"] = cfg
 

src/lerobot/policies/fastwam/README.md

@@ -1 +0,0 @@
-../../../../docs/source/policy_fastwam_README.md

src/lerobot/policies/fastwam/__init__.py

@@ -1,23 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from .configuration_fastwam import FastWAMConfig
-from .modeling_fastwam import FastWAMPolicy
-from .processor_fastwam import make_fastwam_pre_post_processors
-
-__all__ = [
-    "FastWAMConfig",
-    "FastWAMPolicy",
-    "make_fastwam_pre_post_processors",
-]

src/lerobot/policies/fastwam/configuration_fastwam.py

@@ -1,399 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Any
-
-from lerobot.configs import (
-    FeatureType,
-    NormalizationMode,
-    PolicyFeature,
-    PreTrainedConfig,
-)
-from lerobot.optim import AdamWConfig
-from lerobot.utils.constants import ACTION, OBS_STATE
-
-WAN22_MODEL_ID = "Wan-AI/Wan2.2-TI2V-5B"
-WAN22_DIFFUSERS_MODEL_ID = "Wan-AI/Wan2.2-TI2V-5B-Diffusers"
-FASTWAM_BASE_MODEL_ID = "lerobot/fastwam_base"
-WAN_T5_TOKENIZER_ID = "google/umt5-xxl"
-
-
-_FASTWAM_VIDEO_BASE_COMPAT_KEYS = (
-    "patch_size",
-    "in_dim",
-    "hidden_dim",
-    "ffn_dim",
-    "freq_dim",
-    "text_dim",
-    "out_dim",
-    "num_heads",
-    "attn_head_dim",
-    "num_layers",
-)
-
-_FASTWAM_ACTION_BASE_COMPAT_KEYS = (
-    "hidden_dim",
-    "ffn_dim",
-    "num_heads",
-    "attn_head_dim",
-    "num_layers",
-    "text_dim",
-    "freq_dim",
-)
-
-
-def default_video_dit_config(action_dim: int) -> dict[str, Any]:
-    return {
-        "patch_size": [1, 2, 2],
-        "in_dim": 48,
-        "hidden_dim": 3072,
-        "ffn_dim": 14336,
-        "freq_dim": 256,
-        "text_dim": 4096,
-        "out_dim": 48,
-        "num_heads": 24,
-        "attn_head_dim": 128,
-        "num_layers": 30,
-        "eps": 1.0e-6,
-        "seperated_timestep": True,
-        "use_gradient_checkpointing": False,
-        "video_attention_mask_mode": "first_frame_causal",
-        "action_conditioned": False,
-        "action_dim": action_dim,
-        "action_group_causal_mask_mode": "group_diagonal",
-        "fp32_attention": True,
-    }
-
-
-def default_action_dit_config(action_dim: int) -> dict[str, Any]:
-    return {
-        "action_dim": action_dim,
-        "hidden_dim": 1024,
-        "ffn_dim": 4096,
-        "num_heads": 24,
-        "attn_head_dim": 128,
-        "num_layers": 30,
-        "text_dim": 4096,
-        "freq_dim": 256,
-        "eps": 1.0e-6,
-        "use_gradient_checkpointing": False,
-        "fp32_attention": True,
-    }
-
-
-def _coerce_enum(enum_cls: type, value: Any) -> Any:
-    if isinstance(value, enum_cls):
-        return value
-    try:
-        return enum_cls(value)
-    except (TypeError, ValueError) as exc:
-        member = getattr(enum_cls, str(value), None)
-        if member is None:
-            raise ValueError(f"Cannot coerce {value!r} into {enum_cls.__name__}.") from exc
-        return member
-
-
-def _coerce_policy_features(features: dict[str, Any] | None) -> dict[str, PolicyFeature] | None:
-    if features is None:
-        return None
-    coerced = {}
-    for name, feature in features.items():
-        if isinstance(feature, PolicyFeature):
-            coerced[name] = feature
-            continue
-        coerced[name] = PolicyFeature(
-            type=_coerce_enum(FeatureType, feature["type"]),
-            shape=tuple(feature["shape"]),
-        )
-    return coerced
-
-
-def _is_local_model_id(value: str) -> bool:
-    path = Path(value).expanduser()
-    return path.is_absolute() or value.startswith(("./", "../", "~")) or path.exists()
-
-
-def _validate_wan_model_id(value: str, field_name: str) -> str:
-    if value == WAN22_MODEL_ID or _is_local_model_id(value):
-        return value
-    raise ValueError(f"`{field_name}` must be `{WAN22_MODEL_ID}` or an explicit local path, got `{value}`.")
-
-
-def is_fastwam_base_compatible_config(config: FastWAMConfig) -> bool:
-    """Return whether `fastwam_base` partial weights can initialize this config."""
-
-    default_video_config = default_video_dit_config(config.action_dim)
-    default_action_config = default_action_dit_config(config.action_dim)
-    return all(
-        config.video_dit_config.get(key) == default_video_config.get(key)
-        for key in _FASTWAM_VIDEO_BASE_COMPAT_KEYS
-    ) and all(
-        config.action_dit_config.get(key) == default_action_config.get(key)
-        for key in _FASTWAM_ACTION_BASE_COMPAT_KEYS
-    )
-
-
-@PreTrainedConfig.register_subclass("fastwam")
-@dataclass
-class FastWAMConfig(PreTrainedConfig):
-    """Configuration for the FastWAM LeRobot policy.
-
-    Args:
-        action_dim (int): Number of scalar action channels per timestep.
-        proprio_dim (int | None): Number of proprioception channels used as an
-            extra text-context token. `None` disables proprio conditioning.
-        action_horizon (int): Number of actions predicted by one policy call.
-        num_video_frames (int): Raw video sampling window (in dataset frames). The
-            model actually operates on `model_video_frames` frames after subsampling
-            by `action_video_freq_ratio`.
-        action_video_freq_ratio (int): Actions are sampled at this multiple of the
-            video frame rate. Video frames are taken every `action_video_freq_ratio`-th
-            raw frame, so the model sees `(num_video_frames - 1) // ratio + 1` frames
-            spanning the same time window as `action_horizon` actions (ratio actions
-            per video frame).
-        image_size (tuple[int, int]): Concatenated image size as `(height, width)`.
-        context_len (int): Maximum text embedding token length.
-        video_dit_config (dict[str, Any] | None): Wan video expert config.
-        action_dit_config (dict[str, Any] | None): Action expert config.
-        use_gradient_checkpointing (bool): Enable activation checkpointing in both DiT
-            experts (trades compute for memory; propagated into the DiT configs).
-        freeze_video_expert (bool): Freeze the ~5B Wan video expert
-            (`model.video_expert`) so only the action expert + proprio encoder train.
-            Cuts the AdamW optimizer footprint substantially; the video expert keeps its
-            pretrained weights. (If enabled, also set `loss.lambda_video=0` to skip the
-            now-gradient-free video loss compute.)
-    """
-
-    n_obs_steps: int = 1
-    action_dim: int = 7
-    proprio_dim: int | None = 8
-    action_horizon: int = 32
-    n_action_steps: int = 32
-    num_video_frames: int = 33
-    action_video_freq_ratio: int = 4
-    image_size: tuple[int, int] = (224, 448)
-    context_len: int = 128
-    model_id: str = WAN22_MODEL_ID
-    tokenizer_model_id: str = WAN_T5_TOKENIZER_ID
-    text_encoder_model_id: str = WAN22_DIFFUSERS_MODEL_ID
-    base_model_id: str | None = FASTWAM_BASE_MODEL_ID
-    tokenizer_max_len: int = 128
-    load_text_encoder: bool = True
-    mot_checkpoint_mixed_attn: bool = False
-    torch_dtype: str = "bfloat16"
-    prompt_template: str = (
-        "A video recorded from a robot's point of view executing the following instruction: {task}"
-    )
-    num_inference_steps: int = 10
-    inference_seed: int | None = 42
-    rand_device: str = "cpu"
-    text_cfg_scale: float = 1.0
-    negative_prompt: str = ""
-    sigma_shift: float | None = None
-    tiled: bool = False
-    fp32_attention: bool = True
-    use_gradient_checkpointing: bool = False
-    freeze_video_expert: bool = False
-    toggle_action_dimensions: list[int] = field(default_factory=list)
-    video_scheduler: dict[str, float | int] = field(
-        default_factory=lambda: {"train_shift": 5.0, "infer_shift": 5.0, "num_train_timesteps": 1000}
-    )
-    action_scheduler: dict[str, float | int] = field(
-        default_factory=lambda: {"train_shift": 5.0, "infer_shift": 5.0, "num_train_timesteps": 1000}
-    )
-    loss: dict[str, float] = field(default_factory=lambda: {"lambda_video": 1.0, "lambda_action": 1.0})
-    video_dit_config: dict[str, Any] | None = None
-    action_dit_config: dict[str, Any] | None = None
-    normalization_mapping: dict[str, NormalizationMode] = field(
-        default_factory=lambda: {
-            "VISUAL": NormalizationMode.IDENTITY,
-            "STATE": NormalizationMode.MEAN_STD,
-            "ACTION": NormalizationMode.MEAN_STD,
-        }
-    )
-    input_features: dict[str, PolicyFeature] | None = None
-    output_features: dict[str, PolicyFeature] | None = None
-    optimizer_lr: float = 1.0e-4
-    optimizer_weight_decay: float = 1.0e-2
-
-    def __post_init__(self) -> None:
-        super().__post_init__()
-        self.image_size = tuple(self.image_size)
-        self.model_id = _validate_wan_model_id(self.model_id, "model_id")
-        self.input_features = _coerce_policy_features(self.input_features)
-        self.output_features = _coerce_policy_features(self.output_features)
-        self.toggle_action_dimensions = [int(dim) for dim in self.toggle_action_dimensions]
-        self.video_dit_config = self.video_dit_config or default_video_dit_config(self.action_dim)
-        self.action_dit_config = self.action_dit_config or default_action_dit_config(self.action_dim)
-        self.video_dit_config["fp32_attention"] = bool(self.fp32_attention)
-        self.action_dit_config["fp32_attention"] = bool(self.fp32_attention)
-        self.video_dit_config["use_gradient_checkpointing"] = bool(self.use_gradient_checkpointing)
-        self.action_dit_config["use_gradient_checkpointing"] = bool(self.use_gradient_checkpointing)
-        if self.input_features is None:
-            height, width = self.image_size
-            self.input_features = {
-                "observation.images.image": PolicyFeature(
-                    type=FeatureType.VISUAL,
-                    shape=(3, height, width),
-                )
-            }
-            if self.proprio_dim is not None:
-                self.input_features[OBS_STATE] = PolicyFeature(
-                    type=FeatureType.STATE,
-                    shape=(self.proprio_dim,),
-                )
-        if self.output_features is None:
-            self.output_features = {ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(self.action_dim,))}
-        self.validate_features()
-        if self.pretrained_path or self.use_peft or not self.base_model_id:
-            return
-        if not is_fastwam_base_compatible_config(self):
-            return
-        self.pretrained_path = Path(self.base_model_id)
-        self._auto_pretrained_path = True
-
-    def _save_pretrained(self, save_directory: Path) -> None:
-        if not getattr(self, "_auto_pretrained_path", False):
-            super()._save_pretrained(save_directory)
-            return
-
-        pretrained_path = self.pretrained_path
-        self.pretrained_path = None
-        try:
-            super()._save_pretrained(save_directory)
-        finally:
-            self.pretrained_path = pretrained_path
-
-    def get_optimizer_preset(self) -> AdamWConfig:
-        return AdamWConfig(lr=self.optimizer_lr, weight_decay=self.optimizer_weight_decay)
-
-    def get_scheduler_preset(self) -> None:
-        return None
-
-    def set_dataset_feature_metadata(self, dataset_features: dict[str, Any]) -> None:
-        """Rebuild visual input features from the dataset's real camera keys.
-
-        FastWAM's `__post_init__` installs a synthetic single-image default
-        (`observation.images.image` at full `image_size` width). For datasets
-        with one or more separately-named cameras (e.g. `observation.images.top`,
-        `observation.images.wrist`), this hook — invoked by `make_policy` once the
-        dataset metadata is known — replaces that default with the actual camera
-        keys, each declared at the policy's native per-camera resolution
-        (`image_size[0]` x `image_size[1] // num_cameras`). The accompanying
-        resize step in `make_fastwam_pre_post_processors` resizes raw frames to
-        match, so heterogeneous source resolutions (e.g. 480x640) are supported.
-        """
-        image_keys = sorted(
-            key
-            for key, feature in dataset_features.items()
-            if key.startswith("observation.images.") and feature.get("dtype") in ("video", "image")
-        )
-        if not image_keys:
-            return
-        height, total_width = self.image_size
-        per_cam_width = total_width // len(image_keys)
-        new_inputs: dict[str, PolicyFeature] = {
-            key: PolicyFeature(type=FeatureType.VISUAL, shape=(3, height, per_cam_width))
-            for key in image_keys
-        }
-        if self.proprio_dim is not None and OBS_STATE in dataset_features:
-            new_inputs[OBS_STATE] = PolicyFeature(type=FeatureType.STATE, shape=(self.proprio_dim,))
-        self.input_features = new_inputs
-        self.validate_features()
-
-    def validate_features(self) -> None:
-        if self.action_dim <= 0:
-            raise ValueError(f"`action_dim` must be positive, got {self.action_dim}.")
-        if self.action_horizon <= 0:
-            raise ValueError(f"`action_horizon` must be positive, got {self.action_horizon}.")
-        if self.n_action_steps > self.action_horizon:
-            raise ValueError("`n_action_steps` cannot exceed `action_horizon`.")
-        if self.action_video_freq_ratio <= 0:
-            raise ValueError(
-                f"`action_video_freq_ratio` must be positive, got {self.action_video_freq_ratio}."
-            )
-        # Video frames are subsampled by action_video_freq_ratio; the resulting model frame
-        # count must satisfy T % 4 == 1 for the VAE temporal tokenization (mirrors the
-        # original FastWAM dataset asserts).
-        if (self.num_video_frames - 1) % self.action_video_freq_ratio != 0:
-            raise ValueError(
-                f"`num_video_frames - 1` ({self.num_video_frames - 1}) must be divisible by "
-                f"`action_video_freq_ratio` ({self.action_video_freq_ratio})."
-            )
-        if ((self.num_video_frames - 1) // self.action_video_freq_ratio) % 4 != 0:
-            raise ValueError(
-                f"Subsampled video transitions ({(self.num_video_frames - 1) // self.action_video_freq_ratio}) "
-                "must be divisible by 4 for VAE tokenization (i.e. model_video_frames % 4 == 1)."
-            )
-        if self.action_horizon % ((self.num_video_frames - 1) // self.action_video_freq_ratio) != 0:
-            raise ValueError(
-                f"`action_horizon` ({self.action_horizon}) must be divisible by the number of "
-                f"video transitions ({(self.num_video_frames - 1) // self.action_video_freq_ratio})."
-            )
-        if not self.image_features:
-            raise ValueError("FastWAM requires at least one image feature.")
-        if self.action_feature is None:
-            raise ValueError("FastWAM requires `action` in output_features.")
-        action_shape = tuple(self.action_feature.shape)
-        if action_shape != (self.action_dim,):
-            raise ValueError(
-                f"FastWAM action feature shape must be ({self.action_dim},), got {action_shape}."
-            )
-        if self.proprio_dim is not None:
-            state_feature = self.robot_state_feature
-            if state_feature is None:
-                raise ValueError("FastWAM requires `observation.state` when `proprio_dim` is set.")
-            state_shape = tuple(state_feature.shape)
-            if state_shape != (self.proprio_dim,):
-                raise ValueError(
-                    f"FastWAM state feature shape must be ({self.proprio_dim},), got {state_shape}."
-                )
-        height, width = self.image_size
-        image_width_sum = 0
-        for name, feature in self.image_features.items():
-            shape = tuple(feature.shape)
-            if len(shape) != 3 or shape[0] != 3:
-                raise ValueError(f"FastWAM image feature `{name}` must have shape (3, H, W), got {shape}.")
-            if shape[1] != height:
-                raise ValueError(f"FastWAM image feature `{name}` height must be {height}, got {shape[1]}.")
-            image_width_sum += shape[2]
-        if image_width_sum != width:
-            raise ValueError(f"FastWAM image feature widths must sum to {width}, got {image_width_sum}.")
-
-    @property
-    def model_video_frames(self) -> int:
-        """Number of video frames the model actually operates on, after subsampling the
-        raw `num_video_frames` window by `action_video_freq_ratio` (e.g. 33 -> 9)."""
-        return (self.num_video_frames - 1) // self.action_video_freq_ratio + 1
-
-    @property
-    def observation_delta_indices(self) -> list[int]:
-        # Load the video frames the model is supervised on: the future window subsampled by
-        # action_video_freq_ratio (e.g. [0, 4, 8, ..., 32] -> 9 frames). Each video frame is
-        # thus `action_video_freq_ratio` actions apart, while actions load at the full rate
-        # (`action_delta_indices` = range(action_horizon)). Returning None would load only the
-        # current frame, making the video target a static repeat (degenerate supervision).
-        return list(range(0, self.num_video_frames, self.action_video_freq_ratio))
-
-    @property
-    def action_delta_indices(self) -> list[int]:
-        return list(range(self.action_horizon))
-
-    @property
-    def reward_delta_indices(self) -> None:
-        return None

src/lerobot/policies/fastwam/modeling_fastwam.py

@@ -1,440 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import annotations
-
-import logging
-from collections import deque
-from typing import Any
-
-import torch
-from torch import Tensor
-
-from lerobot.policies.pretrained import PreTrainedPolicy
-from lerobot.utils.constants import OBS_STATE
-from lerobot.utils.import_utils import require_package
-
-from .configuration_fastwam import FastWAMConfig
-from .wan import (
-    ActionDiT,
-    FastWAM,
-    MoT,
-    WanVideoDiT,
-    build_wan_tokenizer,
-    load_pretrained_wan_text_encoder,
-    load_pretrained_wan_vae,
-)
-
-
-class FastWAMPolicy(PreTrainedPolicy):
-    """LeRobot policy wrapper for FastWAM.
-
-    Attention backend: FastWAM's DiT uses ``torch.nn.functional.scaled_dot_product_attention``
-    (SDPA) for all attention. It does not use FlashAttention, because MoT routing requires
-    arbitrary boolean ``[query, key]`` masks that the FlashAttention varlen API cannot express;
-    installing ``flash-attn`` has no effect on the FastWAM path. (SDPA may still dispatch to
-    PyTorch's own flash/mem-efficient/math kernel internally, unrelated to the ``flash-attn`` package.)
-
-    Args:
-        config (FastWAMConfig): FastWAM policy configuration.
-        dataset_stats (dict[str, dict[str, Tensor]] | None): Optional LeRobot
-            dataset statistics passed by the training/evaluation stack.
-    """
-
-    config_class = FastWAMConfig
-    name = "fastwam"
-
-    def __init__(
-        self,
-        config: FastWAMConfig,
-        dataset_stats: dict[str, dict[str, Tensor]] | None = None,
-        **kwargs: Any,
-    ):
-        # FastWAM's Wan2.2 backbone needs transformers (UMT5 text encoder/tokenizer) and
-        # diffusers (Wan VAE), both behind the `fastwam` extra. Fail fast with an actionable
-        # message in base installs rather than deep in Wan component construction.
-        require_package("transformers", extra="fastwam")
-        require_package("diffusers", extra="fastwam")
-        # `make_policy`/`from_pretrained` forward extra kwargs (e.g. `dataset_meta`); the
-        # dataset feature metadata is already applied to `config` by make_policy upstream,
-        # so we accept and ignore them, matching the other LeRobot policies.
-        super().__init__(config, dataset_stats)
-        config.validate_features()
-        self.config = config
-        self.dataset_stats = dataset_stats
-        self.model = self._build_core_model(config)
-        if config.freeze_video_expert and getattr(self.model, "video_expert", None) is not None:
-            # Freeze the ~5B Wan video expert; get_optim_params filters on requires_grad,
-            # so its params drop out of the optimizer (and DDP skips them).
-            self.model.video_expert.requires_grad_(False)
-            # The transformer blocks are re-parented onto the MoTLayers (single FSDP owner), so
-            # `video_expert.requires_grad_` no longer reaches them — freeze them via the layers.
-            mot = getattr(self.model, "mot", None)
-            if mot is not None and getattr(mot, "layers", None) is not None:
-                for layer in mot.layers:
-                    if "video" in layer.blocks:
-                        layer.blocks["video"].requires_grad_(False)
-        self.reset()
-
-    @classmethod
-    def _load_as_safetensor(cls, model, model_file: str, map_location: str, strict: bool):
-        """Shape-aware load that supports cross-embodiment fine-tuning.
-
-        `safetensors.load_model(strict=False)` ignores missing/unexpected keys but
-        still raises on a shape mismatch for a shared key. When fine-tuning from a
-        checkpoint trained on a different embodiment (e.g. the LIBERO 7-DoF / 8-dim
-        checkpoint adapted to a 6-DoF / 6-dim arm), the action encoder/head and
-        proprio encoder legitimately differ in shape. With `strict=False` we drop
-        only those shape-mismatched tensors — leaving them at their freshly
-        initialized values — and load every compatible tensor. With `strict=True`
-        the standard exact-match loader is used.
-        """
-        from safetensors import safe_open
-
-        model_state_dict = model.state_dict()
-        mismatched = []
-        with safe_open(model_file, framework="pt") as f:
-            checkpoint_keys = list(f.keys())
-            for key in checkpoint_keys:
-                if key in model_state_dict and tuple(model_state_dict[key].shape) != tuple(
-                    f.get_slice(key).get_shape()
-                ):
-                    mismatched.append(key)
-
-        if not mismatched:
-            return super()._load_as_safetensor(model, model_file, map_location, strict)
-        if strict:
-            raise RuntimeError(
-                f"FastWAM: {len(mismatched)} checkpoint tensors have a shape mismatch under "
-                f"strict=True: {mismatched}"
-            )
-
-        from safetensors.torch import load_file
-
-        logging.warning(
-            "FastWAM cross-embodiment load: reinitializing %d shape-mismatched tensor(s), keeping "
-            "every compatible weight: %s",
-            len(mismatched),
-            mismatched,
-        )
-        state_dict = load_file(model_file, device="cpu")
-        for key in mismatched:
-            state_dict.pop(key, None)
-        model.load_state_dict(state_dict, strict=False)
-        if map_location and map_location != "cpu":
-            model.to(map_location)
-        return model
-
-    def get_optim_params(self) -> list[Tensor]:
-        # Return the trainable tensors directly (a single param group). The optimizer
-        # builder wraps these in a param group; returning a bare {"params": [...]} dict
-        # instead would make `list(...)` yield the key string "params".
-        params = (
-            list(self.model.dit.parameters()) if hasattr(self.model, "dit") else list(self.model.parameters())
-        )
-        proprio_encoder = getattr(self.model, "proprio_encoder", None)
-        if proprio_encoder is not None:
-            params.extend(list(proprio_encoder.parameters()))
-        return [p for p in params if p.requires_grad]
-
-    def reset(self) -> None:
-        self._action_queue: deque[Tensor] = deque([], maxlen=self.config.n_action_steps)
-
-    def _batch_to_training_sample(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
-        """Adapt a standard LeRobot batch to the FastWAM-native sample that
-        `FastWAM.build_inputs` consumes (`video`, `action`, `context`/`context_mask`,
-        per-frame `proprio`).
-
-        The LeRobot training loop passes raw `observation.images.*`, a single-step
-        `observation.state` `[B, D]`, `action`, and a language `task` string. We do
-        only the translation `build_inputs` can't: stack the camera frames into a
-        video, encode the prompt with the (frozen) text encoder (mirroring inference,
-        so language-conditioned datasets need no precomputed context), and give proprio
-        the per-frame axis `build_inputs` indexes. All shape/presence validation is
-        left to `build_inputs`, the single authority on the contract.
-        """
-        sample = dict(batch)
-        if "video" not in sample:
-            sample["video"] = _stack_video_from_images(batch, self.config)
-        if "context" not in sample or "context_mask" not in sample:
-            prompt = _prompt_from_batch(batch=batch, config=self.config)
-            if prompt is None:
-                raise KeyError(
-                    "FastWAM training requires a `task`/`prompt` to encode text context, "
-                    "or precomputed `context`/`context_mask` in the batch."
-                )
-            sample["context"], sample["context_mask"] = self.model.encode_prompt(prompt)
-        if self.config.proprio_dim is not None and "proprio" not in sample:
-            state = sample.get(OBS_STATE)
-            if state is not None:
-                # LeRobot gives a single-step state [B, D]; build_inputs expects
-                # per-frame [B, T, D] and uses frame 0, so add a T=1 axis.
-                sample["proprio"] = state.unsqueeze(1) if state.ndim == 2 else state
-        return sample
-
-    def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict[str, Any]]:
-        """Compute FastWAM training loss for a LeRobot batch.
-
-        Args:
-            batch (dict[str, Tensor]): Batch containing FastWAM-ready keys
-                (`video`, `action`, `context`, `context_mask`) or LeRobot keys
-                that can be adapted (`observation.images.*`, `observation.state`,
-                `action`, `action_is_pad`).
-
-        Returns:
-            tuple[Tensor, dict[str, Any]]: The scalar loss to backprop, and a dict of
-            logging metrics (e.g. `loss_video`, `loss_action`) — the `(loss, output_dict)`
-            contract the LeRobot training loop expects.
-        """
-
-        sample = self._batch_to_training_sample(batch)
-        loss, metrics = self.model.training_loss(sample)
-        return loss, dict(metrics or {})
-
-    @torch.no_grad()
-    def predict_action_chunk(self, batch: dict[str, Tensor], **_: Any) -> Tensor:
-        """Predict a chunk of actions from the current FastWAM observation.
-
-        Args:
-            batch (dict[str, Tensor]): Inference batch with `input_image` or
-                image observation keys, plus `context/context_mask` or `prompt`.
-
-        Returns:
-            Tensor: Action chunk with shape `[B, action_horizon, action_dim]`.
-        """
-
-        self.eval()
-        infer_kwargs = _batch_to_infer_kwargs(batch=batch, config=self.config)
-        batch_size = _infer_kwargs_batch_size(infer_kwargs)
-        if batch_size == 1:
-            action = _action_from_model_output(self.model.infer_action(**infer_kwargs))
-        else:
-            action = torch.cat(
-                [
-                    _action_from_model_output(
-                        self.model.infer_action(
-                            **_slice_infer_kwargs(infer_kwargs, index=i, batch_size=batch_size)
-                        )
-                    )
-                    for i in range(batch_size)
-                ],
-                dim=0,
-            )
-        return action.to(device=batch_device(batch), dtype=torch.float32)
-
-    @torch.no_grad()
-    def select_action(self, batch: dict[str, Tensor], **kwargs: Any) -> Tensor:
-        self.eval()
-        if len(self._action_queue) == 0:
-            actions = self.predict_action_chunk(batch, **kwargs)[:, : self.config.n_action_steps]
-            self._action_queue.extend(actions.transpose(0, 1))
-        return self._action_queue.popleft()
-
-    def _build_core_model(self, config: FastWAMConfig) -> FastWAM:
-        """Build the FastWAM core for training / inference.
-
-        Only the trainable parts (the MoT DiT and the proprio encoder) are
-        materialized empty here and then filled from the policy's
-        `model.safetensors` by the base `from_pretrained`. The *frozen* Wan2.2 VAE
-        and UMT5 text encoder are loaded with their real weights from the
-        `Wan-AI/Wan2.2-TI2V-5B-Diffusers` repo (cached in the HF cache, shared
-        across checkpoints) and are intentionally excluded from `model.safetensors`
-        — see `FastWAM.__init__`. The tokenizer comes from `google/umt5-xxl`.
-        """
-        dtype = _dtype_from_name(config.torch_dtype)
-        device = config.device
-        video_expert = WanVideoDiT(**config.video_dit_config).to(device=device, dtype=dtype)
-        action_expert = ActionDiT(**config.action_dit_config).to(device=device, dtype=dtype)
-        mot = MoT(
-            mixtures={"video": video_expert, "action": action_expert},
-            mot_checkpoint_mixed_attn=config.mot_checkpoint_mixed_attn,
-        )
-        text_encoder = (
-            load_pretrained_wan_text_encoder(
-                model_id=config.text_encoder_model_id, torch_dtype=dtype, device=device
-            )
-            if config.load_text_encoder
-            else None
-        )
-        return FastWAM(
-            video_expert=video_expert,
-            action_expert=action_expert,
-            mot=mot,
-            vae=load_pretrained_wan_vae(torch_dtype=dtype, device=device),
-            text_encoder=text_encoder,
-            tokenizer=build_wan_tokenizer(
-                model_id=config.tokenizer_model_id, tokenizer_max_len=config.tokenizer_max_len
-            ),
-            text_dim=int(config.video_dit_config["text_dim"]),
-            proprio_dim=config.proprio_dim,
-            device=device,
-            torch_dtype=dtype,
-            video_train_shift=float(config.video_scheduler["train_shift"]),
-            video_infer_shift=float(config.video_scheduler["infer_shift"]),
-            video_num_train_timesteps=int(config.video_scheduler["num_train_timesteps"]),
-            action_train_shift=float(config.action_scheduler["train_shift"]),
-            action_infer_shift=float(config.action_scheduler["infer_shift"]),
-            action_num_train_timesteps=int(config.action_scheduler["num_train_timesteps"]),
-            loss_lambda_video=float(config.loss["lambda_video"]),
-            loss_lambda_action=float(config.loss["lambda_action"]),
-        )
-
-
-def _scalar(value: Any) -> Any:
-    """Unwrap a 0-/1-element tensor (e.g. from DataLoader collation) to a Python scalar."""
-    return value.item() if isinstance(value, Tensor) else value
-
-
-def _batch_to_infer_kwargs(batch: dict[str, Tensor], config: FastWAMConfig) -> dict[str, Any]:
-    return {
-        "prompt": _prompt_from_batch(batch=batch, config=config),
-        "input_image": _input_image_from_batch(batch, config),
-        "action_horizon": config.action_horizon,
-        "proprio": batch.get("proprio", batch.get(OBS_STATE)),
-        "context": batch.get("context"),
-        "context_mask": batch.get("context_mask"),
-        "negative_prompt": batch.get("negative_prompt", config.negative_prompt),
-        "text_cfg_scale": float(_scalar(batch.get("text_cfg_scale", config.text_cfg_scale))),
-        "num_inference_steps": int(_scalar(batch.get("num_inference_steps", config.num_inference_steps))),
-        "sigma_shift": batch.get("sigma_shift", config.sigma_shift),
-        "seed": batch.get("seed", config.inference_seed),
-        "rand_device": batch.get("rand_device", config.rand_device),
-        "tiled": bool(batch.get("tiled", config.tiled)),
-    }
-
-
-def _prompt_from_batch(batch: dict[str, Tensor], config: FastWAMConfig) -> Any:
-    prompt = batch.get("prompt")
-    if prompt is not None:
-        return prompt
-
-    task = batch.get("task")
-    if task is None:
-        return None
-    if isinstance(task, str):
-        return config.prompt_template.format(task=task)
-    if isinstance(task, (list, tuple)):
-        return [config.prompt_template.format(task=str(item)) for item in task]
-    return config.prompt_template.format(task=str(task))
-
-
-def _action_from_model_output(output: Any) -> Tensor:
-    action = output["action"] if isinstance(output, dict) else output
-    if action.ndim == 2:
-        action = action.unsqueeze(0)
-    return action
-
-
-def _infer_kwargs_batch_size(infer_kwargs: dict[str, Any]) -> int:
-    image = infer_kwargs["input_image"]
-    if not isinstance(image, Tensor):
-        raise TypeError(f"`input_image` must be a tensor, got {type(image).__name__}.")
-    if image.ndim == 3:
-        return 1
-    if image.ndim == 4:
-        return int(image.shape[0])
-    raise ValueError(f"`input_image` must be [B,C,H,W] or [C,H,W], got {tuple(image.shape)}.")
-
-
-def _slice_infer_kwargs(infer_kwargs: dict[str, Any], *, index: int, batch_size: int) -> dict[str, Any]:
-    return {
-        key: _slice_infer_value(value, index=index, batch_size=batch_size)
-        for key, value in infer_kwargs.items()
-    }
-
-
-def _slice_infer_value(value: Any, *, index: int, batch_size: int) -> Any:
-    if isinstance(value, Tensor) and value.ndim > 0 and value.shape[0] == batch_size:
-        return value[index : index + 1]
-    if isinstance(value, (list, tuple)) and len(value) == batch_size:
-        return value[index]
-    return value
-
-
-def _dtype_from_name(name: str) -> torch.dtype:
-    dtype_map = {"float32": torch.float32, "float16": torch.float16, "bfloat16": torch.bfloat16}
-    if name not in dtype_map:
-        raise ValueError(f"Unsupported torch dtype `{name}`.")
-    return dtype_map[name]
-
-
-def batch_device(batch: dict[str, Any]) -> torch.device:
-    for value in batch.values():
-        if isinstance(value, Tensor):
-            return value.device
-    return torch.device("cpu")
-
-
-def _resize_frames(frames: Tensor, size: tuple[int, int]) -> Tensor:
-    """Resize a frame tensor to `size` (H, W), tolerating a leading temporal/batch stack.
-
-    `interpolate` only accepts a single leading batch dim (`[N, C, H, W]`), but FastWAM camera
-    tensors arrive as `[B, C, H, W]` (live eval) or `[B, T, C, H, W]` (temporal stack), so flatten
-    any leading dims into the batch, resize, then restore. A no-op when already at `size`.
-    """
-    if tuple(frames.shape[-2:]) == size:
-        return frames
-    lead = frames.shape[:-3]
-    flat = frames.reshape(-1, *frames.shape[-3:])
-    flat = torch.nn.functional.interpolate(
-        flat, size=size, mode="bilinear", align_corners=False, antialias=True
-    )
-    return flat.reshape(*lead, *flat.shape[-3:])
-
-
-def _stack_video_from_images(batch: dict[str, Tensor], config: FastWAMConfig) -> Tensor:
-    # Exclude the `*_is_pad` companion tensors that delta-timestamp loading adds alongside
-    # each camera (shape [B, T]); they share the `observation.images.` prefix but are not frames.
-    image_keys = sorted(k for k in batch if k.startswith("observation.images.") and not k.endswith("_is_pad"))
-    if not image_keys:
-        raise KeyError("FastWAM batch must contain `video` or `observation.images.*` keys.")
-    per_cam = (int(config.image_size[0]), int(config.image_size[1]) // len(image_keys))
-    images = [_resize_frames(batch[key], per_cam) for key in image_keys]
-    # Cameras concatenate along width (last dim) in both the single-frame and temporal case.
-    image = torch.cat(images, dim=-1) if len(images) > 1 else images[0]
-    if image.ndim == 4:
-        # [B, C, H, W]: a single frame (e.g. the live eval observation) -> repeat across time.
-        image = image.unsqueeze(2).repeat(1, 1, config.model_video_frames, 1, 1)
-    elif image.ndim == 5:
-        # [B, T, C, H, W]: temporal stack from delta-timestamp loading -> [B, C, T, H, W].
-        image = image.permute(0, 2, 1, 3, 4)
-    else:
-        raise ValueError(f"Expected image batch [B,C,H,W] or temporal [B,T,C,H,W], got {tuple(image.shape)}.")
-    return image
-
-
-def _input_image_from_batch(batch: dict[str, Tensor], config: FastWAMConfig) -> Tensor:
-    if "input_image" in batch:
-        return _prepare_infer_image(batch["input_image"], config)
-    video = batch.get("video")
-    if video is None:
-        video = _stack_video_from_images(batch, config)
-    if video.ndim == 5:
-        return _prepare_infer_image(video[:, :, 0], config)
-    if video.ndim == 4:
-        return _prepare_infer_image(video, config)
-    raise ValueError(f"Cannot build input image from tensor with shape {tuple(video.shape)}.")
-
-
-def _prepare_infer_image(image: Tensor, config: FastWAMConfig) -> Tensor:
-    if image.ndim == 3:
-        image = image.unsqueeze(0)
-    if image.ndim != 4:
-        raise ValueError(f"Expected image tensor [B,C,H,W] or [C,H,W], got {tuple(image.shape)}.")
-
-    # Resize to the full configured resolution (no-op when the video path already produced it, but
-    # also covers a directly-supplied `input_image`). The model owns its input resolution — see
-    # `_stack_video_from_images` — so we resize rather than assert on a mismatch.
-    target_h, target_w = int(config.image_size[0]), int(config.image_size[1])
-    return _resize_frames(image, (target_h, target_w))

src/lerobot/policies/fastwam/processor_fastwam.py

@@ -1,142 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-from typing import Any
-
-import torch
-
-from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.processor import (
-    ActionProcessorStep,
-    AddBatchDimensionProcessorStep,
-    DeviceProcessorStep,
-    NormalizerProcessorStep,
-    PolicyAction,
-    PolicyProcessorPipeline,
-    ProcessorStepRegistry,
-    RenameObservationsProcessorStep,
-    UnnormalizerProcessorStep,
-    policy_action_to_transition,
-    transition_to_policy_action,
-)
-from lerobot.utils.constants import (
-    POLICY_POSTPROCESSOR_DEFAULT_NAME,
-    POLICY_PREPROCESSOR_DEFAULT_NAME,
-)
-
-from .configuration_fastwam import FastWAMConfig
-
-
-@dataclass
-@ProcessorStepRegistry.register(name="fastwam_action_toggle_processor")
-class FastWAMActionToggleProcessorStep(ActionProcessorStep):
-    """Apply FastWAM LIBERO toggle semantics to configured action dimensions."""
-
-    toggle_dimensions: list[int]
-
-    def action(self, action: PolicyAction) -> PolicyAction:
-        if not self.toggle_dimensions:
-            return action
-        processed_action = action.clone()
-        action_dim = int(processed_action.shape[-1])
-        for dim in self.toggle_dimensions:
-            resolved_dim = dim if dim >= 0 else action_dim + dim
-            if resolved_dim < 0 or resolved_dim >= action_dim:
-                raise ValueError(
-                    f"FastWAM action toggle dimension {dim} is out of bounds for action dim {action_dim}."
-                )
-            value = processed_action[..., resolved_dim]
-            value = value * 2.0 - 1.0
-            processed_action[..., resolved_dim] = torch.sign(-value)
-        return processed_action
-
-    def get_config(self) -> dict[str, Any]:
-        return {"toggle_dimensions": self.toggle_dimensions}
-
-    def transform_features(
-        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
-    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
-        return features
-
-
-def make_fastwam_pre_post_processors(
-    config: FastWAMConfig,
-    dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
-) -> tuple[PolicyProcessorPipeline, PolicyProcessorPipeline]:
-    """Create LeRobot pre- and post-processing pipelines for FastWAM.
-
-    Args:
-        config (FastWAMConfig): Policy configuration controlling device and
-            normalization feature metadata.
-        dataset_stats (dict[str, dict[str, torch.Tensor]] | None): Optional
-            LeRobot dataset statistics used by normalization processors.
-
-    Returns:
-        tuple[PolicyProcessorPipeline, PolicyProcessorPipeline]: Input and
-        output processor pipelines discoverable by LeRobot.
-    """
-
-    # NOTE: no visual normalization here. VISUAL is IDENTITY (see configuration_fastwam.normalization_mapping)
-    # — images pass through in [0, 1] and the model maps them to the Wan VAE's [-1, 1] at the encode
-    # boundary. This is deliberate: `lerobot_train.py` overrides the normalizer stats with
-    # `dataset.meta.stats` when fine-tuning, and a real dataset's per-channel image std is the tiny
-    # frame-to-frame brightness variance, which would blow images far outside [-1,1] and saturate them.
-    # STATE/ACTION still normalize with dataset stats below.
-    normalization_stats: dict[str, dict[str, Any]] = dict(dataset_stats or {})
-
-    # NOTE: no resize step here. The model is the single authority on input resolution: it resizes
-    # each camera to the per-camera target (image_size split across cameras) in
-    # `_stack_video_from_images` / `_prepare_infer_image`, on every path (train forward, rollout and
-    # eval select_action). A preprocessor resize step would be both redundant (the model re-resizes
-    # anyway) and unsafe across fine-tuning: its `resize_size` would be inherited from the base
-    # checkpoint's camera geometry, not this dataset's, making the concatenation N_cameras x too wide.
-
-    input_steps = [
-        RenameObservationsProcessorStep(rename_map={}),
-        AddBatchDimensionProcessorStep(),
-        DeviceProcessorStep(device=config.device),
-        NormalizerProcessorStep(
-            features={**config.input_features, **config.output_features},
-            norm_map=config.normalization_mapping,
-            stats=normalization_stats,
-            device=config.device,
-        ),
-    ]
-    output_steps = [
-        UnnormalizerProcessorStep(
-            features=config.output_features,
-            norm_map=config.normalization_mapping,
-            stats=normalization_stats,
-        ),
-    ]
-    if config.toggle_action_dimensions:
-        output_steps.append(
-            FastWAMActionToggleProcessorStep(toggle_dimensions=config.toggle_action_dimensions)
-        )
-    output_steps.append(DeviceProcessorStep(device="cpu"))
-    return (
-        PolicyProcessorPipeline[dict[str, Any], dict[str, Any]](
-            steps=input_steps,
-            name=POLICY_PREPROCESSOR_DEFAULT_NAME,
-        ),
-        PolicyProcessorPipeline[PolicyAction, PolicyAction](
-            steps=output_steps,
-            name=POLICY_POSTPROCESSOR_DEFAULT_NAME,
-            to_transition=policy_action_to_transition,
-            to_output=transition_to_policy_action,
-        ),
-    )

src/lerobot/policies/fastwam/wan/README.md

@@ -1,34 +0,0 @@
-# FastWAM `wan` package
-
-This package holds FastWAM's model implementation. It mixes a small **vendored
-subset of the official Wan2.2 source tree** with FastWAM's own code, kept flat in
-a single directory.
-
-## Vendored from Wan2.2
-
-- Upstream repository: https://github.com/Wan-Video/Wan2.2
-- Upstream commit: `42bf4cfaa384bc21833865abc2f9e6c0e67233dc`
-- License: Apache-2.0, matching the license in `LICENSE.txt` from the upstream repository
-
-Copied files:
-
-- `model.py` (was `wan/modules/model.py`), trimmed: the flash-attention path
-  (the vendored `attention.py` and the block/model `forward`s) was removed.
-  FastWAM's DiT uses SDPA instead (see `video_dit.py`).
-- `get_sampling_sigmas` in `video_dit.py` (was `wan/utils/fm_solvers.py`), inlined
-  next to its only caller.
-
-This subset only backs FastWAM's **custom MoT video DiT**. The Wan2.2 VAE,
-UMT5 text encoder, and tokenizer are no longer vendored - they come from
-`diffusers.AutoencoderKLWan`, `transformers.UMT5EncoderModel`, and
-`transformers.AutoTokenizer` (see `components.py` and `adapters.py`).
-
-## FastWAM's own code
-
-- `video_dit.py` builds on `model` (`sinusoidal_embedding_1d`, `rope_params`,
-  `rope_apply`, …) and computes attention with SDPA (`fastwam_masked_attention`). Its
-  `WanContinuousFlowMatchScheduler` uses `get_sampling_sigmas` for Wan-compatible
-  inference timesteps.
-- `components.py` / `adapters.py` load the VAE, text encoder, tokenizer, and the
-  custom DiT weights.
-- `modular.py` defines the FastWAM model (`ActionDiT`, `MoT`, `FastWAM`, …).

src/lerobot/policies/fastwam/wan/__init__.py

@@ -1,33 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from .adapters import WanVideoVAE38
-from .components import (
-    build_wan_tokenizer,
-    load_pretrained_wan_text_encoder,
-    load_pretrained_wan_vae,
-)
-from .modular import ActionDiT, FastWAM, MoT
-from .video_dit import WanVideoDiT
-
-__all__ = [
-    "ActionDiT",
-    "FastWAM",
-    "MoT",
-    "WanVideoDiT",
-    "WanVideoVAE38",
-    "build_wan_tokenizer",
-    "load_pretrained_wan_text_encoder",
-    "load_pretrained_wan_vae",
-]

src/lerobot/policies/fastwam/wan/adapters.py

@@ -1,108 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-import torch
-
-if TYPE_CHECKING:
-    from diffusers import AutoencoderKLWan
-
-
-class WanVideoVAE38(torch.nn.Module):
-    """FastWAM VAE contract over `diffusers.AutoencoderKLWan` (Wan2.2-TI2V-5B).
-
-    16x spatial / 4x temporal compression, 48 latent channels. diffusers'
-    `AutoencoderKLWan` returns *raw* latents (it does not apply `latents_mean`/
-    `latents_std`), so `encode`/`decode` here apply the same standardization the
-    Wan reference uses — `(latents - mean) / std` — done in fp32 for stability.
-    `encode` uses the deterministic posterior mode, matching the original VAE
-    which returned the latent mean `mu`.
-    """
-
-    upsampling_factor = 16
-    temporal_downsample_factor = 4
-    z_dim = 48
-
-    def __init__(
-        self,
-        dtype: torch.dtype = torch.float32,
-        device: str | torch.device = "cuda",
-        *,
-        pretrained: AutoencoderKLWan,
-    ) -> None:
-        super().__init__()
-        # The Wan2.2 VAE is a fixed pretrained model — it is never trained from scratch,
-        # so a real `AutoencoderKLWan` (with weights) must always be supplied (loaded from
-        # the diffusers repo by `load_pretrained_wan_vae`). No random/offline build path.
-        self.vae = pretrained.to(device=device, dtype=dtype)
-
-        # Read the standardization stats from the VAE's own config (diffusers populates
-        # these from vae/config.json) — single source of truth, no local copy. diffusers'
-        # encode/decode return *raw* latents, so we apply (latent - mean) / std ourselves.
-        # Non-persistent: kept out of state_dict.
-        self.register_buffer(
-            "latents_mean",
-            torch.tensor(self.vae.config.latents_mean).view(1, self.z_dim, 1, 1, 1),
-            persistent=False,
-        )
-        self.register_buffer(
-            "latents_std",
-            torch.tensor(self.vae.config.latents_std).view(1, self.z_dim, 1, 1, 1),
-            persistent=False,
-        )
-
-    def _device_dtype(self) -> tuple[torch.device, torch.dtype]:
-        param = next(self.vae.parameters())
-        return param.device, param.dtype
-
-    def encode(
-        self,
-        videos: list[torch.Tensor] | torch.Tensor,
-        device: str | torch.device | None = None,
-        tiled: bool = False,
-        tile_size: tuple[int, int] = (34, 34),
-        tile_stride: tuple[int, int] = (18, 16),
-    ) -> torch.Tensor:
-        del device, tile_size, tile_stride
-        if tiled:
-            raise NotImplementedError("Tiled Wan2.2 VAE encoding is not supported by the FastWAM adapter.")
-        if isinstance(videos, (list, tuple)):
-            videos = torch.stack(list(videos))
-        dev, dtype = self._device_dtype()
-        mu = self.vae.encode(videos.to(device=dev, dtype=dtype)).latent_dist.mode().float()
-        mean = self.latents_mean.float().to(mu.device)
-        std = self.latents_std.float().to(mu.device)
-        return (mu - mean) / std
-
-    def decode(
-        self,
-        hidden_states: list[torch.Tensor] | torch.Tensor,
-        device: str | torch.device | None = None,
-        tiled: bool = False,
-        tile_size: tuple[int, int] = (34, 34),
-        tile_stride: tuple[int, int] = (18, 16),
-    ) -> torch.Tensor:
-        del device, tile_size, tile_stride
-        if tiled:
-            raise NotImplementedError("Tiled Wan2.2 VAE decoding is not supported by the FastWAM adapter.")
-        if isinstance(hidden_states, (list, tuple)):
-            hidden_states = torch.stack(list(hidden_states))
-        dev, dtype = self._device_dtype()
-        z = hidden_states.float()
-        z = z * self.latents_std.float().to(z.device) + self.latents_mean.float().to(z.device)
-        out = self.vae.decode(z.to(device=dev, dtype=dtype)).sample
-        return out.float().clamp_(-1.0, 1.0)

src/lerobot/policies/fastwam/wan/components.py

@@ -1,175 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import annotations
-
-import logging
-from collections.abc import Sequence
-from pathlib import Path
-from typing import TYPE_CHECKING, Any
-
-import torch
-from huggingface_hub import snapshot_download
-from safetensors.torch import load_file
-
-from lerobot.utils.import_utils import _diffusers_available, _transformers_available, require_package
-
-if TYPE_CHECKING or _transformers_available:
-    from transformers import AutoTokenizer, UMT5EncoderModel
-else:
-    AutoTokenizer = None
-    UMT5EncoderModel = None
-
-if TYPE_CHECKING or _diffusers_available:
-    from diffusers import AutoencoderKLWan
-else:
-    AutoencoderKLWan = None
-
-from .adapters import WanVideoVAE38
-from .video_dit import WanVideoDiT
-
-logger = logging.getLogger(__name__)
-
-# The custom MoT video DiT still ships in the original (non-diffusers) Wan2.2
-# repo as sharded `diffusion_pytorch_model*.safetensors`; the VAE and UMT5 text
-# encoder come from the diffusers conversion. Tokenizer is the stock UMT5 one.
-WAN_DIT_PATTERN = "diffusion_pytorch_model*.safetensors"
-WAN_T5_TOKENIZER = "google/umt5-xxl"
-WAN22_DIFFUSERS_MODEL_ID = "Wan-AI/Wan2.2-TI2V-5B-Diffusers"
-
-
-class WanTextEncoder(torch.nn.Module):
-    """FastWAM text-encoder contract over `transformers.UMT5EncoderModel`.
-
-    Exposes `.dim` (hidden size) and `forward(ids, mask) -> [B, L, dim]`, matching
-    the call in `FastWAM.encode_prompt`.
-    """
-
-    def __init__(
-        self,
-        dtype: torch.dtype = torch.bfloat16,
-        device: str | torch.device = "cuda",
-        *,
-        pretrained: torch.nn.Module,
-    ) -> None:
-        super().__init__()
-        # UMT5-XXL is a fixed pretrained encoder — never trained from scratch, so a real
-        # `UMT5EncoderModel` (with weights) must always be supplied (loaded from the
-        # diffusers repo by `load_pretrained_wan_text_encoder`). No random/offline build.
-        self.model = pretrained.to(device=device, dtype=dtype)
-        self.dim = int(self.model.config.d_model)
-
-    def forward(self, ids: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
-        return self.model(input_ids=ids, attention_mask=mask.long()).last_hidden_state
-
-
-class WanTokenizer:
-    """UMT5 tokenizer wrapper returning `(input_ids, attention_mask)` like the
-    FastWAM call site expects."""
-
-    def __init__(self, name: str = WAN_T5_TOKENIZER, seq_len: int = 512) -> None:
-        require_package("transformers", extra="fastwam")
-        self.tokenizer = AutoTokenizer.from_pretrained(name)
-        self.seq_len = int(seq_len)
-
-    def __call__(
-        self,
-        sequence: str | Sequence[str],
-        return_mask: bool = False,
-        add_special_tokens: bool = True,
-        **_: Any,
-    ):
-        if isinstance(sequence, str):
-            sequence = [sequence]
-        out = self.tokenizer(
-            list(sequence),
-            padding="max_length",
-            truncation=True,
-            max_length=self.seq_len,
-            add_special_tokens=add_special_tokens,
-            return_tensors="pt",
-        )
-        if return_mask:
-            return out.input_ids, out.attention_mask
-        return out.input_ids
-
-
-def build_wan_tokenizer(*, model_id: str = WAN_T5_TOKENIZER, tokenizer_max_len: int) -> WanTokenizer:
-    return WanTokenizer(name=model_id, seq_len=int(tokenizer_max_len))
-
-
-def load_pretrained_wan_vae(*, torch_dtype: torch.dtype, device: str) -> WanVideoVAE38:
-    """Load real Wan2.2 VAE weights from the diffusers repo (offline base creation)."""
-    require_package("diffusers", extra="fastwam")
-    vae = AutoencoderKLWan.from_pretrained(WAN22_DIFFUSERS_MODEL_ID, subfolder="vae", torch_dtype=torch_dtype)
-    return WanVideoVAE38(dtype=torch_dtype, device=device, pretrained=vae)
-
-
-def load_pretrained_wan_text_encoder(
-    *,
-    model_id: str = WAN22_DIFFUSERS_MODEL_ID,
-    subfolder: str | None = "text_encoder",
-    torch_dtype: torch.dtype,
-    device: str,
-) -> WanTextEncoder:
-    """Load UMT5-XXL encoder weights (defaults to the Wan2.2 diffusers repo).
-
-    Must stay compatible with the tokenizer (see `build_wan_tokenizer`): the encoder's
-    embedding table is indexed by the tokenizer's vocabulary.
-    """
-    require_package("transformers", extra="fastwam")
-    encoder = UMT5EncoderModel.from_pretrained(model_id, subfolder=subfolder, torch_dtype=torch_dtype)
-    return WanTextEncoder(dtype=torch_dtype, device=device, pretrained=encoder)
-
-
-def resolve_wan_dit_paths(
-    model_id_or_path: str | Path,
-    *,
-    cache_dir: str | Path | None = None,
-    local_files_only: bool = False,
-    revision: str | None = None,
-) -> list[Path]:
-    """Resolve the custom MoT DiT shards from the original Wan2.2 repo or a local dir."""
-    path = Path(model_id_or_path).expanduser()
-    if path.is_dir():
-        return sorted(path.glob(WAN_DIT_PATTERN))
-
-    snapshot_path = snapshot_download(
-        repo_id=str(model_id_or_path),
-        revision=revision,
-        cache_dir=cache_dir,
-        local_files_only=local_files_only,
-        allow_patterns=[WAN_DIT_PATTERN],
-    )
-    return sorted(Path(snapshot_path).glob(WAN_DIT_PATTERN))
-
-
-def load_wan_video_dit(
-    paths: list[str | Path],
-    *,
-    dit_config: dict[str, Any],
-    torch_dtype: torch.dtype,
-    device: str,
-) -> WanVideoDiT:
-    model = WanVideoDiT(**dit_config)
-    state_dict = _read_wan_dit_safetensors(paths)
-    model.load_state_dict(state_dict, strict=False)
-    return model.to(device=device, dtype=torch_dtype)
-
-
-def _read_wan_dit_safetensors(paths: list[str | Path]) -> dict[str, torch.Tensor]:
-    state_dict = {}
-    for path in paths:
-        state_dict.update(load_file(str(path), device="cpu"))
-    return state_dict

src/lerobot/policies/fastwam/wan/model.py

@@ -1,341 +0,0 @@
-# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
-import math
-
-import torch
-import torch.nn as nn
-
-
-def sinusoidal_embedding_1d(dim, position):
-    # preprocess
-    if dim % 2 != 0:
-        raise ValueError(f"dim must be even, got {dim}.")
-    half = dim // 2
-    position = position.type(torch.float64)
-
-    # calculation
-    sinusoid = torch.outer(position, torch.pow(10000, -torch.arange(half).to(position).div(half)))
-    x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
-    return x
-
-
-@torch.amp.autocast("cuda", enabled=False)
-def rope_params(max_seq_len, dim, theta=10000):
-    if dim % 2 != 0:
-        raise ValueError(f"dim must be even, got {dim}.")
-    freqs = torch.outer(
-        torch.arange(max_seq_len), 1.0 / torch.pow(theta, torch.arange(0, dim, 2).to(torch.float64).div(dim))
-    )
-    freqs = torch.polar(torch.ones_like(freqs), freqs)
-    return freqs
-
-
-@torch.amp.autocast("cuda", enabled=False)
-def rope_apply(x, grid_sizes, freqs):
-    n, c = x.size(2), x.size(3) // 2
-
-    # split freqs
-    freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)
-
-    # loop over samples
-    output = []
-    for i, (f, h, w) in enumerate(grid_sizes.tolist()):
-        seq_len = f * h * w
-
-        # precompute multipliers
-        x_i = torch.view_as_complex(x[i, :seq_len].to(torch.float64).reshape(seq_len, n, -1, 2))
-        freqs_i = torch.cat(
-            [
-                freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
-                freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
-                freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1),
-            ],
-            dim=-1,
-        ).reshape(seq_len, 1, -1)
-
-        # apply rotary embedding
-        x_i = torch.view_as_real(x_i * freqs_i).flatten(2)
-        x_i = torch.cat([x_i, x[i, seq_len:]])
-
-        # append to collection
-        output.append(x_i)
-    return torch.stack(output).float()
-
-
-class WanRMSNorm(nn.Module):
-    def __init__(self, dim, eps=1e-5):
-        super().__init__()
-        self.dim = dim
-        self.eps = eps
-        self.weight = nn.Parameter(torch.ones(dim))
-
-    def forward(self, x):
-        r"""
-        Args:
-            x(Tensor): Shape [B, L, C]
-        """
-        return self._norm(x.float()).type_as(x) * self.weight
-
-    def _norm(self, x):
-        return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)
-
-
-class WanLayerNorm(nn.LayerNorm):
-    def __init__(self, dim, eps=1e-6, elementwise_affine=False):
-        super().__init__(dim, elementwise_affine=elementwise_affine, eps=eps)
-
-    def forward(self, x):
-        r"""
-        Args:
-            x(Tensor): Shape [B, L, C]
-        """
-        return super().forward(x.float()).type_as(x)
-
-
-class WanSelfAttention(nn.Module):
-    def __init__(self, dim, num_heads, qk_norm=True, eps=1e-6):
-        if dim % num_heads != 0:
-            raise ValueError(f"dim ({dim}) must be divisible by num_heads ({num_heads}).")
-        super().__init__()
-        self.num_heads = num_heads
-        self.head_dim = dim // num_heads
-
-        # layers
-        self.q = nn.Linear(dim, dim)
-        self.k = nn.Linear(dim, dim)
-        self.v = nn.Linear(dim, dim)
-        self.o = nn.Linear(dim, dim)
-        self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
-        self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
-
-    # NOTE: FastWAM never runs the upstream Wan attention forward. FastWAMAttentionBlock
-    # reuses only the q/k/v/o/norm submodules defined above and computes attention via
-    # `fastwam_masked_attention` (SDPA). The original flash-attention forward was removed,
-    # which also collapsed the former WanCrossAttention subclass into this class (it only
-    # differed by its forward): self- and cross-attention now share the same projection module.
-
-
-class WanAttentionBlock(nn.Module):
-    def __init__(self, dim, ffn_dim, num_heads, qk_norm=True, cross_attn_norm=False, eps=1e-6):
-        super().__init__()
-        self.dim = dim
-        self.ffn_dim = ffn_dim
-        self.num_heads = num_heads
-        self.qk_norm = qk_norm
-        self.cross_attn_norm = cross_attn_norm
-        self.eps = eps
-
-        # layers
-        self.norm1 = WanLayerNorm(dim, eps)
-        self.self_attn = WanSelfAttention(dim, num_heads, qk_norm, eps)
-        self.norm3 = WanLayerNorm(dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity()
-        self.cross_attn = WanSelfAttention(dim, num_heads, qk_norm, eps)
-        self.norm2 = WanLayerNorm(dim, eps)
-        self.ffn = nn.Sequential(
-            nn.Linear(dim, ffn_dim), nn.GELU(approximate="tanh"), nn.Linear(ffn_dim, dim)
-        )
-
-        # modulation
-        self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
-
-    # NOTE: The upstream Wan block forward (self-attention + cross-attention + FFN via
-    # flash-attention) was removed. FastWAM subclasses this block as FastWAMAttentionBlock
-    # and overrides forward to use SDPA with explicit boolean masks; only __init__ (the
-    # norm/attention/ffn submodules) is reused here.
-
-
-class Head(nn.Module):
-    def __init__(self, dim, out_dim, patch_size, eps=1e-6):
-        super().__init__()
-        self.dim = dim
-        self.out_dim = out_dim
-        self.patch_size = patch_size
-        self.eps = eps
-
-        # layers
-        out_dim = math.prod(patch_size) * out_dim
-        self.norm = WanLayerNorm(dim, eps)
-        self.head = nn.Linear(dim, out_dim)
-
-        # modulation
-        self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim**0.5)
-
-    def forward(self, x, e):
-        r"""
-        Args:
-            x(Tensor): Shape [B, L1, C]
-            e(Tensor): Shape [B, L1, C]
-        """
-        with torch.amp.autocast("cuda", dtype=torch.float32):
-            e = (self.modulation.unsqueeze(0) + e.unsqueeze(2)).chunk(2, dim=2)
-            x = self.head(self.norm(x) * (1 + e[1].squeeze(2)) + e[0].squeeze(2))
-        return x
-
-
-class WanModel(nn.Module):
-    r"""
-    Wan diffusion backbone supporting both text-to-video and image-to-video.
-    """
-
-    def __init__(
-        self,
-        model_type="t2v",
-        patch_size=(1, 2, 2),
-        text_len=512,
-        in_dim=16,
-        dim=2048,
-        ffn_dim=8192,
-        freq_dim=256,
-        text_dim=4096,
-        out_dim=16,
-        num_heads=16,
-        num_layers=32,
-        qk_norm=True,
-        cross_attn_norm=True,
-        eps=1e-6,
-    ):
-        r"""
-        Initialize the diffusion model backbone.
-
-        Args:
-            model_type (`str`, *optional*, defaults to 't2v'):
-                Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video)
-            patch_size (`tuple`, *optional*, defaults to (1, 2, 2)):
-                3D patch dimensions for video embedding (t_patch, h_patch, w_patch)
-            text_len (`int`, *optional*, defaults to 512):
-                Fixed length for text embeddings
-            in_dim (`int`, *optional*, defaults to 16):
-                Input video channels (C_in)
-            dim (`int`, *optional*, defaults to 2048):
-                Hidden dimension of the transformer
-            ffn_dim (`int`, *optional*, defaults to 8192):
-                Intermediate dimension in feed-forward network
-            freq_dim (`int`, *optional*, defaults to 256):
-                Dimension for sinusoidal time embeddings
-            text_dim (`int`, *optional*, defaults to 4096):
-                Input dimension for text embeddings
-            out_dim (`int`, *optional*, defaults to 16):
-                Output video channels (C_out)
-            num_heads (`int`, *optional*, defaults to 16):
-                Number of attention heads
-            num_layers (`int`, *optional*, defaults to 32):
-                Number of transformer blocks
-            qk_norm (`bool`, *optional*, defaults to True):
-                Enable query/key normalization
-            cross_attn_norm (`bool`, *optional*, defaults to False):
-                Enable cross-attention normalization
-            eps (`float`, *optional*, defaults to 1e-6):
-                Epsilon value for normalization layers
-        """
-
-        super().__init__()
-
-        if model_type not in ["t2v", "i2v", "ti2v", "s2v"]:
-            raise ValueError(f"model_type must be one of ['t2v', 'i2v', 'ti2v', 's2v'], got {model_type!r}.")
-        self.model_type = model_type
-
-        self.patch_size = patch_size
-        self.text_len = text_len
-        self.in_dim = in_dim
-        self.dim = dim
-        self.ffn_dim = ffn_dim
-        self.freq_dim = freq_dim
-        self.text_dim = text_dim
-        self.out_dim = out_dim
-        self.num_heads = num_heads
-        self.num_layers = num_layers
-        self.qk_norm = qk_norm
-        self.cross_attn_norm = cross_attn_norm
-        self.eps = eps
-
-        # embeddings
-        self.patch_embedding = nn.Conv3d(in_dim, dim, kernel_size=patch_size, stride=patch_size)
-        self.text_embedding = nn.Sequential(
-            nn.Linear(text_dim, dim), nn.GELU(approximate="tanh"), nn.Linear(dim, dim)
-        )
-
-        self.time_embedding = nn.Sequential(nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim))
-        self.time_projection = nn.Sequential(nn.SiLU(), nn.Linear(dim, dim * 6))
-
-        # blocks
-        self.blocks = nn.ModuleList(
-            [
-                WanAttentionBlock(dim, ffn_dim, num_heads, qk_norm, cross_attn_norm, eps)
-                for _ in range(num_layers)
-            ]
-        )
-
-        # head
-        self.head = Head(dim, out_dim, patch_size, eps)
-
-        # buffers (don't use register_buffer otherwise dtype will be changed in to())
-        if (dim % num_heads) != 0 or (dim // num_heads) % 2 != 0:
-            raise ValueError(
-                f"dim ({dim}) must be divisible by num_heads ({num_heads}) with an even head dim."
-            )
-        d = dim // num_heads
-        self.freqs = torch.cat(
-            [
-                rope_params(1024, d - 4 * (d // 6)),
-                rope_params(1024, 2 * (d // 6)),
-                rope_params(1024, 2 * (d // 6)),
-            ],
-            dim=1,
-        )
-
-        # initialize weights
-        self.init_weights()
-
-    # NOTE: The upstream Wan diffusion forward (flash-attention based) was removed.
-    # FastWAM's WanVideoDiT subclasses this model, rebuilds `self.blocks` with
-    # FastWAMAttentionBlock, and provides its own SDPA-based forward. Only the
-    # constructor (embeddings, blocks, head, rope buffers) and the helpers below
-    # (unpatchify / init_weights) are reused. WanModel is never run directly.
-
-    def unpatchify(self, x, grid_sizes):
-        r"""
-        Reconstruct video tensors from patch embeddings.
-
-        Args:
-            x (List[Tensor]):
-                List of patchified features, each with shape [L, C_out * prod(patch_size)]
-            grid_sizes (Tensor):
-                Original spatial-temporal grid dimensions before patching,
-                    shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches)
-
-        Returns:
-            List[Tensor]:
-                Reconstructed video tensors with shape [C_out, F, H / 8, W / 8]
-        """
-
-        c = self.out_dim
-        out = []
-        for u, v in zip(x, grid_sizes.tolist(), strict=False):
-            u = u[: math.prod(v)].view(*v, *self.patch_size, c)
-            u = torch.einsum("fhwpqrc->cfphqwr", u)
-            u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size, strict=False)])
-            out.append(u)
-        return out
-
-    def init_weights(self):
-        r"""
-        Initialize model parameters using Xavier initialization.
-        """
-
-        # basic init
-        for m in self.modules():
-            if isinstance(m, nn.Linear):
-                nn.init.xavier_uniform_(m.weight)
-                if m.bias is not None:
-                    nn.init.zeros_(m.bias)
-
-        # init embeddings
-        nn.init.xavier_uniform_(self.patch_embedding.weight.flatten(1))
-        for m in self.text_embedding.modules():
-            if isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, std=0.02)
-        for m in self.time_embedding.modules():
-            if isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, std=0.02)
-
-        # init output layer
-        nn.init.zeros_(self.head.head.weight)

src/lerobot/policies/fastwam/wan/video_dit.py

@@ -1,800 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-from typing import Any
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as functional
-from einops import rearrange
-
-from .model import (
-    WanAttentionBlock,
-    WanLayerNorm,
-    WanModel,
-    WanRMSNorm,
-    rope_apply,
-    rope_params,
-    sinusoidal_embedding_1d,
-)
-
-logger = logging.getLogger(__name__)
-
-
-def get_sampling_sigmas(sampling_steps, shift):
-    # Vendored from Wan2.2 (formerly wan/utils/fm_solvers.py); computes the
-    # noise-level (sigma) schedule for Wan-compatible flow-matching inference.
-    sigma = np.linspace(1, 0, sampling_steps + 1)[:sampling_steps]
-    sigma = shift * sigma / (1 + (shift - 1) * sigma)
-    return sigma
-
-
-def create_custom_forward(module):
-    def custom_forward(*inputs, **kwargs):
-        return module(*inputs, **kwargs)
-
-    return custom_forward
-
-
-def gradient_checkpoint_forward(
-    model,
-    use_gradient_checkpointing,
-    *args,
-    **kwargs,
-):
-    if use_gradient_checkpointing:
-        model_output = torch.utils.checkpoint.checkpoint(
-            create_custom_forward(model),
-            *args,
-            **kwargs,
-            use_reentrant=False,
-        )
-    else:
-        model_output = model(*args, **kwargs)
-    return model_output
-
-
-def fastwam_masked_attention(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    num_heads: int,
-    ctx_mask: torch.Tensor | None = None,
-    fp32_attention: bool = True,
-) -> torch.Tensor:
-    """FastWAM masked attention wrapper for MoT masks and CPU test coverage.
-
-    The official Wan attention implementation is still used as the source of
-    the projection/norm modules. This wrapper only replaces the final attention
-    kernel because FastWAM needs explicit boolean masks for video/action MoT
-    routing, while the upstream FlashAttention path accepts sequence lengths
-    but not arbitrary [query, key] masks.
-    """
-
-    q = rearrange(q, "b s (n d) -> b n s d", n=num_heads)
-    k = rearrange(k, "b s (n d) -> b n s d", n=num_heads)
-    v = rearrange(v, "b s (n d) -> b n s d", n=num_heads)
-    if fp32_attention:
-        q = q.float()
-        k = k.float()
-        v = v.float()
-    else:
-        q = q.to(dtype=v.dtype)
-        k = k.to(dtype=v.dtype)
-    x = functional.scaled_dot_product_attention(q, k, v, attn_mask=ctx_mask)
-    return rearrange(x, "b n s d -> b s (n d)", n=num_heads)
-
-
-def modulate(x: torch.Tensor, shift: torch.Tensor, scale: torch.Tensor):
-    return x * (1 + scale) + shift
-
-
-class WanContinuousFlowMatchScheduler:
-    """Continuous-time Flow-Matching scheduler with shift-based Wan sampling."""
-
-    def __init__(self, num_train_timesteps: int = 1000, shift: float = 5.0, eps: float = 1e-10):
-        if num_train_timesteps <= 0:
-            raise ValueError(f"`num_train_timesteps` must be positive, got {num_train_timesteps}")
-        if shift <= 0:
-            raise ValueError(f"`shift` must be positive, got {shift}")
-        self.num_train_timesteps = int(num_train_timesteps)
-        self.shift = float(shift)
-        self.eps = float(eps)
-        self._y_min, self._weight_norm_const = self._precompute_training_weight_stats()
-
-    @staticmethod
-    def _phi(u: torch.Tensor, shift: float) -> torch.Tensor:
-        return shift * u / (1.0 + (shift - 1.0) * u)
-
-    def _precompute_training_weight_stats(self) -> tuple[float, float]:
-        steps = self.num_train_timesteps
-        u_grid = torch.linspace(1.0, 0.0, steps + 1, dtype=torch.float64)[:-1]
-        t_grid = self._phi(u_grid, self.shift) * float(steps)
-        y_grid = torch.exp(-2.0 * ((t_grid - (steps / 2.0)) / steps) ** 2)
-        y_min = float(y_grid.min().item())
-        y_shifted_grid = y_grid - y_min
-        norm_const = float(y_shifted_grid.mean().item())
-        return y_min, norm_const
-
-    def sample_training_t(self, batch_size: int, device: torch.device, dtype: torch.dtype) -> torch.Tensor:
-        if batch_size <= 0:
-            raise ValueError(f"`batch_size` must be positive, got {batch_size}")
-        u = torch.rand((batch_size,), device=device, dtype=torch.float32)
-        sigma = self._phi(u, self.shift)
-        timestep = sigma * float(self.num_train_timesteps)
-        return timestep.to(dtype=dtype)
-
-    def training_weight(self, timestep: torch.Tensor) -> torch.Tensor:
-        t = timestep.to(dtype=torch.float32)
-        steps = float(self.num_train_timesteps)
-        y = torch.exp(-2.0 * ((t - (steps / 2.0)) / steps) ** 2)
-        y_shifted = y - self._y_min
-        weight = y_shifted / (self._weight_norm_const + self.eps)
-        if weight.numel() == 1:
-            return weight.reshape(())
-        return weight
-
-    def add_noise(
-        self, original_samples: torch.Tensor, noise: torch.Tensor, timestep: torch.Tensor
-    ) -> torch.Tensor:
-        sigma = (timestep / float(self.num_train_timesteps)).to(
-            original_samples.device, dtype=original_samples.dtype
-        )
-        if sigma.ndim == 0:
-            return (1 - sigma) * original_samples + sigma * noise
-        sigma = sigma.view(-1, *([1] * (original_samples.ndim - 1)))
-        return (1 - sigma) * original_samples + sigma * noise
-
-    @staticmethod
-    def training_target(sample: torch.Tensor, noise: torch.Tensor, timestep: torch.Tensor) -> torch.Tensor:
-        del timestep
-        return noise - sample
-
-    def build_inference_schedule(
-        self,
-        num_inference_steps: int,
-        device: torch.device,
-        dtype: torch.dtype,
-        shift_override: float | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        if num_inference_steps <= 0:
-            raise ValueError(f"`num_inference_steps` must be positive, got {num_inference_steps}")
-        shift = self.shift if shift_override is None else float(shift_override)
-        if shift <= 0:
-            raise ValueError(f"`shift` must be positive, got {shift}")
-
-        sigma_steps = torch.as_tensor(
-            get_sampling_sigmas(num_inference_steps, shift),
-            device=device,
-            dtype=torch.float32,
-        )
-        timesteps = sigma_steps * float(self.num_train_timesteps)
-        sigma_next = torch.cat([sigma_steps[1:], sigma_steps.new_zeros(1)])
-        deltas = sigma_next - sigma_steps
-        return timesteps.to(dtype=dtype), deltas.to(dtype=dtype)
-
-    @staticmethod
-    def step(model_output: torch.Tensor, delta: torch.Tensor, sample: torch.Tensor) -> torch.Tensor:
-        delta = delta.to(sample.device, dtype=sample.dtype)
-        if delta.ndim == 0:
-            return sample + model_output * delta
-        delta = delta.view(-1, *([1] * (sample.ndim - 1)))
-        return sample + model_output * delta
-
-
-def precompute_freqs_cis(dim: int, end: int = 1024, theta: float = 10000.0):
-    return rope_params(end, dim, theta)
-
-
-def apply_dense_rope(x: torch.Tensor, freqs: torch.Tensor, num_heads: int) -> torch.Tensor:
-    x = rearrange(x, "b s (n d) -> b s n d", n=num_heads)
-    x_out = torch.view_as_complex(x.to(torch.float32).reshape(x.shape[0], x.shape[1], x.shape[2], -1, 2))
-    freqs = freqs.to(torch.complex64) if freqs.device.type == "npu" else freqs
-    x_out = torch.view_as_real(x_out * freqs).flatten(2)
-    return x_out.to(x.dtype)
-
-
-def _linear_input(linear: nn.Linear, x: torch.Tensor) -> torch.Tensor:
-    return x.to(dtype=linear.weight.dtype)
-
-
-def _wan_layer_norm(norm: nn.Module, x: torch.Tensor) -> torch.Tensor:
-    if isinstance(norm, WanLayerNorm) and norm.weight is not None:
-        weight = norm.weight.float()
-        bias = norm.bias.float() if norm.bias is not None else None
-        return functional.layer_norm(x.float(), norm.normalized_shape, weight, bias, norm.eps).to(
-            dtype=x.dtype
-        )
-    return norm(x)
-
-
-def create_group_causal_attn_mask(
-    num_temporal_groups: int, num_query_per_group: int, num_key_per_group: int, mode: str = "causal"
-) -> torch.Tensor:
-    if mode not in ["causal", "group_diagonal"]:
-        raise ValueError(f"`mode` must be 'causal' or 'group_diagonal', got {mode}.")
-    if num_temporal_groups <= 0:
-        raise ValueError(f"`num_temporal_groups` must be positive, got {num_temporal_groups}.")
-    if num_query_per_group <= 0:
-        raise ValueError(f"`num_query_per_group` must be positive, got {num_query_per_group}.")
-    if num_key_per_group <= 0:
-        raise ValueError(f"`num_key_per_group` must be positive, got {num_key_per_group}.")
-
-    total_num_query_tokens = num_temporal_groups * num_query_per_group
-    total_num_key_tokens = num_temporal_groups * num_key_per_group
-    query_time_indices = torch.arange(num_temporal_groups).repeat_interleave(num_query_per_group).unsqueeze(1)
-    key_time_indices = torch.arange(num_temporal_groups).repeat_interleave(num_key_per_group).unsqueeze(0)
-
-    if mode == "causal":
-        attn_mask = query_time_indices >= key_time_indices
-    else:
-        attn_mask = query_time_indices == key_time_indices
-
-    if attn_mask.shape != (total_num_query_tokens, total_num_key_tokens):
-        raise RuntimeError("Attention mask shape mismatch.")
-    return attn_mask
-
-
-class FastWAMAttentionBlock(WanAttentionBlock):
-    """Wan attention block with FastWAM's arbitrary boolean mask support."""
-
-    def __init__(
-        self,
-        hidden_dim: int,
-        attn_head_dim: int,
-        num_heads: int,
-        ffn_dim: int,
-        eps: float = 1e-6,
-        fp32_attention: bool = True,
-    ):
-        attention_dim = attn_head_dim * num_heads
-        if hidden_dim == attention_dim:
-            super().__init__(
-                dim=hidden_dim,
-                ffn_dim=ffn_dim,
-                num_heads=num_heads,
-                qk_norm=True,
-                cross_attn_norm=True,
-                eps=eps,
-            )
-        else:
-            nn.Module.__init__(self)
-            self.dim = hidden_dim
-            self.ffn_dim = ffn_dim
-            self.num_heads = num_heads
-            self.qk_norm = True
-            self.cross_attn_norm = True
-            self.eps = eps
-            self.norm1 = WanLayerNorm(hidden_dim, eps)
-            self.self_attn = _FastWAMProjectedAttention(hidden_dim, attention_dim, num_heads, eps)
-            self.norm3 = WanLayerNorm(hidden_dim, eps, elementwise_affine=True)
-            self.cross_attn = _FastWAMProjectedAttention(hidden_dim, attention_dim, num_heads, eps)
-            self.norm2 = WanLayerNorm(hidden_dim, eps)
-            self.ffn = nn.Sequential(
-                nn.Linear(hidden_dim, ffn_dim),
-                nn.GELU(approximate="tanh"),
-                nn.Linear(ffn_dim, hidden_dim),
-            )
-            self.modulation = nn.Parameter(torch.randn(1, 6, hidden_dim) / hidden_dim**0.5)
-        self.attn_head_dim = attn_head_dim
-        self.fp32_attention = bool(fp32_attention)
-
-    @staticmethod
-    def split_modulation(block, t_mod: torch.Tensor):
-        has_seq = len(t_mod.shape) == 4
-        chunk_dim = 2 if has_seq else 1
-
-        base_mod = block.modulation.to(dtype=t_mod.dtype, device=t_mod.device)
-        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (base_mod + t_mod).chunk(
-            6, dim=chunk_dim
-        )
-        if has_seq:
-            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
-                shift_msa.squeeze(2),
-                scale_msa.squeeze(2),
-                gate_msa.squeeze(2),
-                shift_mlp.squeeze(2),
-                scale_mlp.squeeze(2),
-                gate_mlp.squeeze(2),
-            )
-        return shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp
-
-    def project_self_attention(
-        self, x: torch.Tensor, freqs: torch.Tensor | dict[str, torch.Tensor]
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        q = self.self_attn.norm_q(self.self_attn.q(x))
-        k = self.self_attn.norm_k(self.self_attn.k(x))
-        v = self.self_attn.v(x)
-        if isinstance(freqs, dict):
-            b, s = x.shape[:2]
-            q = rope_apply(
-                q.view(b, s, self.num_heads, self.attn_head_dim),
-                freqs["grid_sizes"],
-                freqs["freqs"],
-            ).flatten(2)
-            k = rope_apply(
-                k.view(b, s, self.num_heads, self.attn_head_dim),
-                freqs["grid_sizes"],
-                freqs["freqs"],
-            ).flatten(2)
-        else:
-            q = apply_dense_rope(q, freqs, self.num_heads)
-            k = apply_dense_rope(k, freqs, self.num_heads)
-        return q, k, v
-
-    def apply_cross_attention(
-        self, x: torch.Tensor, context: torch.Tensor, context_mask: torch.Tensor | None = None
-    ) -> torch.Tensor:
-        if context_mask is not None and context_mask.dim() == 3:
-            context_mask = context_mask.unsqueeze(1)
-        attn = self.cross_attn
-        b, n, d = x.size(0), attn.num_heads, attn.head_dim
-        q = attn.norm_q(attn.q(x)).view(b, -1, n * d)
-        k = attn.norm_k(attn.k(context)).view(b, -1, n * d)
-        v = attn.v(context).view(b, -1, n * d)
-        x = fastwam_masked_attention(
-            q=q,
-            k=k,
-            v=v,
-            num_heads=n,
-            ctx_mask=context_mask,
-            fp32_attention=self.fp32_attention,
-        )
-        return attn.o(_linear_input(attn.o, x))
-
-    def project_self_attention_output(self, x: torch.Tensor) -> torch.Tensor:
-        return self.self_attn.o(_linear_input(self.self_attn.o, x))
-
-    def apply_norm1(self, x: torch.Tensor) -> torch.Tensor:
-        return _wan_layer_norm(self.norm1, x)
-
-    def apply_norm2(self, x: torch.Tensor) -> torch.Tensor:
-        return _wan_layer_norm(self.norm2, x)
-
-    def apply_norm3(self, x: torch.Tensor) -> torch.Tensor:
-        return _wan_layer_norm(self.norm3, x)
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        context: torch.Tensor,
-        t_mod: torch.Tensor,
-        freqs: torch.Tensor,
-        context_mask: torch.Tensor | None = None,
-        self_attn_mask: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.split_modulation(self, t_mod)
-        residual_x = x
-        attn_input = modulate(self.apply_norm1(x), shift_msa, scale_msa)
-        q, k, v = self.project_self_attention(attn_input, freqs)
-        y = fastwam_masked_attention(
-            q=q,
-            k=k,
-            v=v,
-            num_heads=self.num_heads,
-            ctx_mask=self_attn_mask,
-            fp32_attention=self.fp32_attention,
-        )
-        x = residual_x + gate_msa * self.project_self_attention_output(y)
-        x = x + self.apply_cross_attention(self.apply_norm3(x), context, context_mask=context_mask)
-        mlp_input = modulate(self.apply_norm2(x), shift_mlp, scale_mlp)
-        return x + gate_mlp * self.ffn(mlp_input)
-
-
-class _FastWAMProjectedAttention(nn.Module):
-    def __init__(self, hidden_dim: int, attention_dim: int, num_heads: int, eps: float):
-        super().__init__()
-        self.dim = hidden_dim
-        self.num_heads = num_heads
-        self.head_dim = attention_dim // num_heads
-        self.q = nn.Linear(hidden_dim, attention_dim)
-        self.k = nn.Linear(hidden_dim, attention_dim)
-        self.v = nn.Linear(hidden_dim, attention_dim)
-        self.o = nn.Linear(attention_dim, hidden_dim)
-        self.norm_q = WanRMSNorm(attention_dim, eps=eps)
-        self.norm_k = WanRMSNorm(attention_dim, eps=eps)
-
-
-class WanVideoDiT(WanModel):
-    def __init__(
-        self,
-        hidden_dim: int,
-        in_dim: int,
-        ffn_dim: int,
-        out_dim: int,
-        text_dim: int,
-        freq_dim: int,
-        eps: float,
-        patch_size: tuple[int, int, int],
-        num_heads: int,
-        attn_head_dim: int,
-        num_layers: int,
-        has_image_input: bool = False,
-        has_image_pos_emb: bool = False,
-        has_ref_conv: bool = False,
-        add_control_adapter: bool = False,
-        in_dim_control_adapter: int = 24,
-        seperated_timestep: bool = False,
-        require_vae_embedding: bool = False,
-        require_clip_embedding: bool = False,
-        fuse_vae_embedding_in_latents: bool = True,
-        action_conditioned: bool = False,
-        action_dim: int = 7,
-        action_group_causal_mask_mode="causal",
-        video_attention_mask_mode: str = "bidirectional",
-        use_gradient_checkpointing: bool = False,
-        fp32_attention: bool = True,
-    ):
-        del in_dim_control_adapter
-        if has_image_input:
-            raise ValueError("FastWAM currently expects Wan2.2 TI2V latents with fused image conditioning.")
-        if has_image_pos_emb:
-            raise ValueError("FastWAM does not support extra image positional embeddings in WanVideoDiT.")
-        if has_ref_conv:
-            raise ValueError("FastWAM does not support reference convolutions in WanVideoDiT.")
-        if add_control_adapter:
-            raise ValueError("FastWAM does not support control adapters in WanVideoDiT.")
-        if require_clip_embedding:
-            raise ValueError("FastWAM does not support CLIP embedding conditioning in WanVideoDiT.")
-        if require_vae_embedding or not fuse_vae_embedding_in_latents:
-            raise ValueError("FastWAM expects VAE conditioning to be fused in latents.")
-        if attn_head_dim != hidden_dim // num_heads:
-            raise ValueError(
-                "`attn_head_dim` must match the upstream Wan head dimension `hidden_dim // num_heads`; "
-                f"got {attn_head_dim} vs {hidden_dim // num_heads}."
-            )
-
-        super().__init__(
-            model_type="ti2v",
-            patch_size=patch_size,
-            text_len=512,
-            in_dim=in_dim,
-            dim=hidden_dim,
-            ffn_dim=ffn_dim,
-            freq_dim=freq_dim,
-            text_dim=text_dim,
-            out_dim=out_dim,
-            num_heads=num_heads,
-            num_layers=num_layers,
-            qk_norm=True,
-            cross_attn_norm=True,
-            eps=eps,
-        )
-        self.blocks = torch.nn.ModuleList(
-            [
-                FastWAMAttentionBlock(
-                    hidden_dim=hidden_dim,
-                    attn_head_dim=attn_head_dim,
-                    num_heads=num_heads,
-                    ffn_dim=ffn_dim,
-                    eps=eps,
-                    fp32_attention=fp32_attention,
-                )
-                for _ in range(num_layers)
-            ]
-        )
-        self.init_weights()
-
-        self.hidden_dim = hidden_dim
-        self.attn_head_dim = attn_head_dim
-        self.seperated_timestep = seperated_timestep
-        self.fuse_vae_embedding_in_latents = fuse_vae_embedding_in_latents
-        self.video_attention_mask_mode = str(video_attention_mask_mode)
-        self.action_conditioned = action_conditioned
-        self.action_dim = action_dim
-        self.fp32_attention = bool(fp32_attention)
-
-        if self.action_conditioned:
-            self.action_embedding = torch.nn.Linear(action_dim, hidden_dim)
-            self.action_group_causal_mask_mode = action_group_causal_mask_mode
-
-        self.use_gradient_checkpointing = use_gradient_checkpointing
-        if self.use_gradient_checkpointing:
-            logger.info(
-                "Using gradient checkpointing for DiT blocks. This will save memory but use more computation."
-            )
-
-    def patchify(self, x: torch.Tensor):
-        return self.patch_embedding(x)
-
-    def _validate_forward_inputs(
-        self,
-        x: torch.Tensor,
-        timestep: torch.Tensor,
-        context: torch.Tensor,
-        context_mask: torch.Tensor | None,
-        action: torch.Tensor | None,
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        if x.ndim != 5:
-            raise ValueError(f"`latents` must be 5D [B, C, T, H, W], got shape {tuple(x.shape)}")
-        num_latent_frames = x.shape[2]
-        if context.ndim != 3:
-            raise ValueError(f"`context` must be 3D [B, L, D], got shape {tuple(context.shape)}")
-        if timestep.ndim != 1:
-            raise ValueError(f"`timestep` must be 1D [B] or [1], got shape {tuple(timestep.shape)}")
-        if self.action_conditioned:
-            allow_text_only_single_frame = num_latent_frames == 1 and action is None
-            if not allow_text_only_single_frame:
-                if action is None:
-                    raise ValueError("Action input is required for action-conditioned model.")
-                if action.ndim != 3:
-                    raise ValueError(
-                        f"`action` must be 3D [B, action_horizon, action_dim], got shape {tuple(action.shape)}"
-                    )
-                if action.shape[2] != self.action_dim:
-                    raise ValueError(
-                        f"`action` last dimension must be {self.action_dim}, got {action.shape[2]}"
-                    )
-                if num_latent_frames <= 1:
-                    raise ValueError(
-                        f"video length must be > 1 for action-conditioned model, got {num_latent_frames}"
-                    )
-                if action.shape[1] % (num_latent_frames - 1) != 0:
-                    raise ValueError(
-                        "action horizon must be divisible by (num_latent_frames - 1), "
-                        f"got action_horizon={action.shape[1]}"
-                    )
-        if context_mask is None:
-            context_mask = torch.ones(
-                (context.shape[0], context.shape[1]), dtype=torch.bool, device=context.device
-            )
-        else:
-            if context_mask.ndim != 2:
-                raise ValueError(f"`context_mask` must be 2D [B, L], got shape {tuple(context_mask.shape)}")
-            if context_mask.shape[0] != context.shape[0] or context_mask.shape[1] != context.shape[1]:
-                raise ValueError(
-                    "`context_mask` shape must match `context` shape [B, L], "
-                    f"got {tuple(context_mask.shape)} vs {tuple(context.shape)}"
-                )
-
-        batch_size = x.shape[0]
-        if batch_size != context.shape[0]:
-            if not self.training and batch_size == 1:
-                x = x.expand(context.shape[0], -1, -1, -1, -1)
-                batch_size = context.shape[0]
-            else:
-                raise ValueError(
-                    f"Batch mismatch between latents and context: {batch_size} vs {context.shape[0]}."
-                )
-
-        if timestep.shape[0] not in (1, batch_size):
-            raise ValueError(
-                f"`timestep` length must be 1 or batch_size({batch_size}), got {timestep.shape[0]}"
-            )
-        if timestep.shape[0] == 1 and batch_size > 1:
-            if self.training:
-                raise ValueError("During training, timestep length must match batch_size.")
-            timestep = timestep.expand(batch_size)
-        return x, timestep, context_mask
-
-    def build_video_to_video_mask(
-        self,
-        video_seq_len: int,
-        video_tokens_per_frame: int,
-        device: torch.device,
-    ) -> torch.Tensor:
-        if video_seq_len <= 0:
-            raise ValueError(f"`video_seq_len` must be positive, got {video_seq_len}")
-        if video_tokens_per_frame <= 0:
-            raise ValueError(f"`video_tokens_per_frame` must be positive, got {video_tokens_per_frame}")
-
-        if self.video_attention_mask_mode == "bidirectional":
-            return torch.ones((video_seq_len, video_seq_len), dtype=torch.bool, device=device)
-
-        if self.video_attention_mask_mode == "per_frame_causal":
-            if video_seq_len % video_tokens_per_frame != 0:
-                raise ValueError(
-                    "`video_seq_len` must be divisible by `video_tokens_per_frame` in `per_frame_causal` mode, "
-                    f"got {video_seq_len} and {video_tokens_per_frame}"
-                )
-            num_video_frames = video_seq_len // video_tokens_per_frame
-            frame_causal = torch.tril(
-                torch.ones((num_video_frames, num_video_frames), dtype=torch.bool, device=device)
-            )
-            return frame_causal.repeat_interleave(video_tokens_per_frame, dim=0).repeat_interleave(
-                video_tokens_per_frame, dim=1
-            )
-
-        if self.video_attention_mask_mode == "first_frame_causal":
-            video_mask = torch.ones((video_seq_len, video_seq_len), dtype=torch.bool, device=device)
-            first_frame_tokens = min(video_tokens_per_frame, video_seq_len)
-            video_mask[:first_frame_tokens, first_frame_tokens:] = False
-            return video_mask
-
-        raise ValueError(f"Unsupported video attention mask mode: {self.video_attention_mask_mode}")
-
-    def pre_dit(
-        self,
-        x: torch.Tensor,
-        timestep: torch.Tensor,
-        context: torch.Tensor,
-        context_mask: torch.Tensor | None = None,
-        action: torch.Tensor | None = None,
-        fuse_vae_embedding_in_latents: bool = False,
-    ) -> dict[str, Any]:
-        x, timestep, context_mask = self._validate_forward_inputs(
-            x=x,
-            timestep=timestep,
-            context=context,
-            context_mask=context_mask,
-            action=action,
-        )
-        model_dtype = self.patch_embedding.weight.dtype
-        x = x.to(dtype=model_dtype)
-        context = context.to(dtype=model_dtype)
-        if action is not None:
-            action = action.to(dtype=model_dtype)
-
-        batch_size = x.shape[0]
-        patch_h = int(self.patch_size[1])
-        patch_w = int(self.patch_size[2])
-        if x.shape[3] % patch_h != 0 or x.shape[4] % patch_w != 0:
-            raise ValueError(
-                "Latent spatial shape must be divisible by DiT patch size, "
-                f"got HxW=({x.shape[3]}, {x.shape[4]}), patch=({patch_h}, {patch_w})"
-            )
-        tokens_per_frame = (x.shape[3] // patch_h) * (x.shape[4] // patch_w)
-
-        if not (self.seperated_timestep and fuse_vae_embedding_in_latents):
-            raise NotImplementedError(
-                "FastWAM currently requires separated timesteps with fused VAE latents."
-            )
-
-        token_timesteps = torch.ones(
-            (batch_size, x.shape[2], tokens_per_frame),
-            dtype=model_dtype,
-            device=timestep.device,
-        ) * timestep.to(dtype=model_dtype).view(batch_size, 1, 1)
-        token_timesteps[:, 0, :] = 0
-        token_timesteps = token_timesteps.reshape(batch_size, -1)
-        # Wan keeps the time embedding in fp32: the AdaLN modulation in the vendored
-        # Head/Block asserts e.dtype == float32 (numerical stability of the scale/shift).
-        # Upstream guarantees this via an fp32 autocast region, so it holds even when the
-        # model runs in bf16. Mirror that here, then cast the per-block modulation back to
-        # model_dtype so the bf16 attention blocks are not upcast to fp32.
-        with torch.amp.autocast("cuda", dtype=torch.float32):
-            token_t_emb = sinusoidal_embedding_1d(self.freq_dim, token_timesteps.reshape(-1)).float()
-            t = self.time_embedding(token_t_emb).reshape(batch_size, -1, self.hidden_dim)
-            t_mod = self.time_projection(t).unflatten(2, (6, self.hidden_dim))
-        t_mod = t_mod.to(dtype=model_dtype)
-
-        x = self.patchify(x)
-        f, h, w = x.shape[2:]
-
-        context = self.text_embedding(context)
-        context_len = context.shape[1]
-        if self.action_conditioned and action is not None:
-            action_len = action.shape[1]
-            action_emb = self.action_embedding(action)
-            action_pos_embed = sinusoidal_embedding_1d(
-                self.hidden_dim, torch.arange(action_len, device=action_emb.device)
-            ).to(dtype=action_emb.dtype)
-            action_emb = action_emb + action_pos_embed.unsqueeze(0)
-            context = torch.cat([context, action_emb], dim=1)
-
-            num_temporal_groups = f - 1
-            if num_temporal_groups <= 0:
-                raise ValueError(
-                    "Action-conditioned context mask requires at least 2 latent frames when `action` is provided."
-                )
-            if action_emb.shape[1] % num_temporal_groups != 0:
-                raise ValueError(
-                    f"Action embedding length {action_emb.shape[1]} must be divisible by "
-                    f"number of temporal groups {num_temporal_groups}"
-                )
-            action_group_mask = create_group_causal_attn_mask(
-                num_temporal_groups=num_temporal_groups,
-                num_query_per_group=tokens_per_frame,
-                num_key_per_group=action_len // num_temporal_groups,
-                mode=self.action_group_causal_mask_mode,
-            ).to(context.device)
-
-            seq_len = f * h * w
-            final_context_mask = torch.zeros(
-                (batch_size, seq_len, context.shape[1]), dtype=torch.bool, device=context.device
-            )
-            final_context_mask[:, :, :context_len] = context_mask.unsqueeze(1).expand(-1, seq_len, -1)
-            final_context_mask[:, tokens_per_frame:, context_len:] = action_group_mask.unsqueeze(0).expand(
-                batch_size, -1, -1
-            )
-            context_mask = final_context_mask
-        elif self.action_conditioned and action is None:
-            if f != 1:
-                raise ValueError(
-                    "Action-conditioned model requires `action` unless running single-frame text-only mode "
-                    "with num_latent_frames=1."
-                )
-            context_mask = context_mask.unsqueeze(1).expand(-1, f * h * w, -1)
-        else:
-            context_mask = context_mask.unsqueeze(1).expand(-1, f * h * w, -1)
-
-        x_tokens = rearrange(x, "b c f h w -> b (f h w) c").contiguous()
-        grid_sizes = torch.tensor([[f, h, w]] * batch_size, dtype=torch.long, device=x_tokens.device)
-        freqs = {"grid_sizes": grid_sizes, "freqs": self.freqs.to(x_tokens.device)}
-
-        return {
-            "tokens": x_tokens,
-            "freqs": freqs,
-            "t": t,
-            "t_mod": t_mod,
-            "context": context,
-            "context_mask": context_mask,
-            "meta": {
-                "grid_sizes": grid_sizes,
-                "tokens_per_frame": tokens_per_frame,
-                "batch_size": batch_size,
-            },
-        }
-
-    def post_dit(self, x_tokens: torch.Tensor, pre_state: dict[str, Any]) -> torch.Tensor:
-        x = self.head(x_tokens, pre_state["t"])
-        return torch.stack(super().unpatchify(x, pre_state["meta"]["grid_sizes"]))
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        timestep: torch.Tensor,
-        context: torch.Tensor,
-        context_mask: torch.Tensor | None = None,
-        action: torch.Tensor | None = None,
-        fuse_vae_embedding_in_latents: bool = False,
-    ):
-        pre_state = self.pre_dit(
-            x=x,
-            timestep=timestep,
-            context=context,
-            context_mask=context_mask,
-            action=action,
-            fuse_vae_embedding_in_latents=fuse_vae_embedding_in_latents,
-        )
-        x_tokens = pre_state["tokens"]
-        context_emb = pre_state["context"]
-        t_mod = pre_state["t_mod"]
-        freqs = pre_state["freqs"]
-        context_attn_mask = pre_state["context_mask"]
-        self_attn_mask = (
-            self.build_video_to_video_mask(
-                video_seq_len=x_tokens.shape[1],
-                video_tokens_per_frame=int(pre_state["meta"]["tokens_per_frame"]),
-                device=x_tokens.device,
-            )
-            if self.video_attention_mask_mode != "bidirectional"
-            else None
-        )
-
-        for block in self.blocks:
-            if self.use_gradient_checkpointing:
-                x_tokens = gradient_checkpoint_forward(
-                    block,
-                    self.use_gradient_checkpointing,
-                    x_tokens,
-                    context_emb,
-                    t_mod,
-                    freqs,
-                    context_mask=context_attn_mask,
-                    self_attn_mask=self_attn_mask,
-                )
-            else:
-                x_tokens = block(
-                    x_tokens,
-                    context_emb,
-                    t_mod,
-                    freqs,
-                    context_mask=context_attn_mask,
-                    self_attn_mask=self_attn_mask,
-                )
-
-        return self.post_dit(x_tokens, pre_state)

src/lerobot/policies/groot/__init__.py

@@ -18,4 +18,12 @@ from .configuration_groot import GrootConfig
 from .modeling_groot import GrootPolicy
 from .processor_groot import make_groot_pre_post_processors
 
-__all__ = ["GrootConfig", "GrootPolicy", "make_groot_pre_post_processors"]
+__all__ = ["GR00TN17", "GR00TN17Config", "GrootConfig", "GrootPolicy", "make_groot_pre_post_processors"]
+
+
+def __getattr__(name: str):
+    if name in {"GR00TN17", "GR00TN17Config"}:
+        from .groot_n1_7 import GR00TN17, GR00TN17Config
+
+        return {"GR00TN17": GR00TN17, "GR00TN17Config": GR00TN17Config}[name]
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

src/lerobot/policies/groot/action_head/action_encoder.py

@@ -1,54 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import torch
-import torch.nn as nn
-
-
-def swish(x):
-    return x * torch.sigmoid(x)
-
-
-class SinusoidalPositionalEncoding(nn.Module):
-    """
-    Produces a sinusoidal encoding of shape (B, T, w)
-    given timesteps of shape (B, T).
-    """
-
-    def __init__(self, embedding_dim):
-        super().__init__()
-        self.embedding_dim = embedding_dim
-
-    def forward(self, timesteps):
-        # timesteps: shape (B, T)
-        # We'll compute sin/cos frequencies across dim T
-        timesteps = timesteps.float()  # ensure float
-
-        b, t = timesteps.shape
-        device = timesteps.device
-
-        half_dim = self.embedding_dim // 2
-        # typical log space frequencies for sinusoidal encoding
-        exponent = -torch.arange(half_dim, dtype=torch.float, device=device) * (
-            torch.log(torch.tensor(10000.0)) / half_dim
-        )
-        # Expand timesteps to (B, T, 1) then multiply
-        freqs = timesteps.unsqueeze(-1) * exponent.exp()  # (B, T, half_dim)
-
-        sin = torch.sin(freqs)
-        cos = torch.cos(freqs)
-        enc = torch.cat([sin, cos], dim=-1)  # (B, T, w)
-
-        return enc

src/lerobot/policies/groot/action_head/cross_attention_dit.py

@@ -14,6 +14,7 @@
 # limitations under the License.
 
 
+import logging
 from typing import TYPE_CHECKING
 
 import torch
@@ -42,6 +43,9 @@ else:
     Timesteps = None
 
 
+logger = logging.getLogger(__name__)
+
+
 class TimestepEncoder(nn.Module):
     def __init__(self, embedding_dim, compute_dtype=torch.float32):
         require_package("diffusers", extra="groot")
@@ -181,8 +185,7 @@ class BasicTransformerBlock(nn.Module):
         attn_output = self.attn1(
             norm_hidden_states,
             encoder_hidden_states=encoder_hidden_states,
-            attention_mask=attention_mask,
-            # encoder_attention_mask=encoder_attention_mask,
+            attention_mask=encoder_attention_mask if encoder_hidden_states is not None else attention_mask,
         )
         if self.final_dropout:
             attn_output = self.final_dropout(attn_output)
@@ -266,8 +269,8 @@ class DiT(ModelMixin, ConfigMixin):
         self.norm_out = nn.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6)
         self.proj_out_1 = nn.Linear(self.inner_dim, 2 * self.inner_dim)
         self.proj_out_2 = nn.Linear(self.inner_dim, self.config.output_dim)
-        print(
-            "Total number of DiT parameters: ",
+        logger.debug(
+            "Total number of DiT parameters: %d",
             sum(p.numel() for p in self.parameters() if p.requires_grad),
         )
 
@@ -318,6 +321,71 @@ class DiT(ModelMixin, ConfigMixin):
             return self.proj_out_2(hidden_states)
 
 
+class AlternateVLDiT(DiT):
+    """N1.7 DiT variant that alternates cross-attention over image and text tokens."""
+
+    def __init__(self, *args, attend_text_every_n_blocks: int = 2, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.attend_text_every_n_blocks = attend_text_every_n_blocks
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        timestep: torch.LongTensor | None = None,
+        encoder_attention_mask: torch.Tensor | None = None,
+        return_all_hidden_states: bool = False,
+        image_mask: torch.Tensor | None = None,
+        backbone_attention_mask: torch.Tensor | None = None,
+    ):
+        if image_mask is None:
+            raise ValueError("image_mask is required for AlternateVLDiT.")
+        if backbone_attention_mask is None:
+            raise ValueError("backbone_attention_mask is required for AlternateVLDiT.")
+
+        temb = self.timestep_encoder(timestep)
+        hidden_states = hidden_states.contiguous()
+        encoder_hidden_states = encoder_hidden_states.contiguous()
+
+        image_attention_mask = image_mask & backbone_attention_mask
+        non_image_attention_mask = (~image_mask) & backbone_attention_mask
+
+        all_hidden_states = [hidden_states]
+        if not self.config.interleave_self_attention:
+            raise ValueError("AlternateVLDiT requires interleave_self_attention=True.")
+
+        for idx, block in enumerate(self.transformer_blocks):
+            if idx % 2 == 1:
+                hidden_states = block(
+                    hidden_states,
+                    attention_mask=None,
+                    encoder_hidden_states=None,
+                    encoder_attention_mask=None,
+                    temb=temb,
+                )
+            else:
+                curr_encoder_attention_mask = (
+                    non_image_attention_mask
+                    if idx % (2 * self.attend_text_every_n_blocks) == 0
+                    else image_attention_mask
+                )
+                hidden_states = block(
+                    hidden_states,
+                    attention_mask=None,
+                    encoder_hidden_states=encoder_hidden_states,
+                    encoder_attention_mask=curr_encoder_attention_mask,
+                    temb=temb,
+                )
+            all_hidden_states.append(hidden_states)
+
+        conditioning = temb
+        shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1)
+        hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None]
+        if return_all_hidden_states:
+            return self.proj_out_2(hidden_states), all_hidden_states
+        return self.proj_out_2(hidden_states)
+
+
 class SelfAttentionTransformer(ModelMixin, ConfigMixin):
     _supports_gradient_checkpointing = True
 
@@ -362,8 +430,8 @@ class SelfAttentionTransformer(ModelMixin, ConfigMixin):
                 for _ in range(self.config.num_layers)
             ]
         )
-        print(
-            "Total number of SelfAttentionTransformer parameters: ",
+        logger.debug(
+            "Total number of SelfAttentionTransformer parameters: %d",
             sum(p.numel() for p in self.parameters() if p.requires_grad),
         )
 

src/lerobot/policies/groot/action_head/flow_matching_action_head.py

@@ -1,408 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from dataclasses import field
-from typing import TYPE_CHECKING
-
-import torch
-import torch.nn.functional as F  # noqa: N812
-from torch import nn
-from torch.distributions import Beta
-
-from lerobot.utils.import_utils import _transformers_available
-
-# Conditional import for type checking and lazy loading
-if TYPE_CHECKING or _transformers_available:
-    from transformers import PretrainedConfig
-    from transformers.feature_extraction_utils import BatchFeature
-else:
-    PretrainedConfig = object
-    BatchFeature = None
-
-from .action_encoder import (
-    SinusoidalPositionalEncoding,
-    swish,
-)
-from .cross_attention_dit import DiT, SelfAttentionTransformer
-
-
-class CategorySpecificLinear(nn.Module):
-    def __init__(self, num_categories, input_dim, hidden_dim):
-        super().__init__()
-        self.num_categories = num_categories
-        # For each category, we have separate weights and biases.
-        self.W = nn.Parameter(0.02 * torch.randn(num_categories, input_dim, hidden_dim))
-        self.b = nn.Parameter(torch.zeros(num_categories, hidden_dim))
-
-    def forward(self, x, cat_ids):
-        selected_w = self.W[cat_ids]
-        selected_b = self.b[cat_ids]
-        return torch.bmm(x, selected_w) + selected_b.unsqueeze(1)
-
-
-class CategorySpecificMLP(nn.Module):
-    def __init__(self, num_categories, input_dim, hidden_dim, output_dim):
-        super().__init__()
-        self.num_categories = num_categories
-        self.layer1 = CategorySpecificLinear(num_categories, input_dim, hidden_dim)
-        self.layer2 = CategorySpecificLinear(num_categories, hidden_dim, output_dim)
-
-    def forward(self, x, cat_ids):
-        hidden = F.relu(self.layer1(x, cat_ids))
-        return self.layer2(hidden, cat_ids)
-
-
-class MultiEmbodimentActionEncoder(nn.Module):
-    def __init__(self, action_dim, hidden_size, num_embodiments):
-        super().__init__()
-        self.hidden_size = hidden_size
-        self.num_embodiments = num_embodiments
-
-        # W1: R^{w x d}, W2: R^{w x 2w}, W3: R^{w x w}
-        self.W1 = CategorySpecificLinear(num_embodiments, action_dim, hidden_size)  # (d -> w)
-        self.W2 = CategorySpecificLinear(num_embodiments, 2 * hidden_size, hidden_size)  # (2w -> w)
-        self.W3 = CategorySpecificLinear(num_embodiments, hidden_size, hidden_size)  # (w -> w)
-        self.pos_encoding = SinusoidalPositionalEncoding(hidden_size)
-
-    def forward(self, actions, timesteps, cat_ids):
-        """
-        actions:   shape (B, T, action_dim)
-        timesteps: shape (B,)  -- a single scalar per batch item
-        cat_ids:   shape (B,)
-        returns:   shape (B, T, hidden_size)
-        """
-        b, t, _ = actions.shape
-
-        # 1) Expand each batch's single scalar time 'tau' across all T steps
-        #    so that shape => (B, T)
-        #    e.g. if timesteps is (B,), replicate across T
-        if timesteps.dim() == 1 and timesteps.shape[0] == b:
-            # shape (B,) => (B,T)
-            timesteps = timesteps.unsqueeze(1).expand(-1, t)
-        else:
-            raise ValueError("Expected `timesteps` to have shape (B,) so we can replicate across T.")
-
-        # 2) Standard action MLP step for shape => (B, T, w)
-        a_emb = self.W1(actions, cat_ids)
-
-        # 3) Get the sinusoidal encoding (B, T, w)
-        tau_emb = self.pos_encoding(timesteps).to(dtype=a_emb.dtype)
-
-        # 4) Concat along last dim => (B, T, 2w), then W2 => (B, T, w), swish
-        x = torch.cat([a_emb, tau_emb], dim=-1)
-        x = swish(self.W2(x, cat_ids))
-
-        # 5) Finally W3 => (B, T, w)
-        x = self.W3(x, cat_ids)
-        return x
-
-
-class FlowmatchingActionHeadConfig(PretrainedConfig):
-    """NOTE: N1.5 uses XEmbFlowmatchingPolicyHeadConfig as action head"""
-
-    add_pos_embed: bool = field(default=True, metadata={"help": "Whether to add positional embedding"})
-    model_dtype: str = field(default="float32", metadata={"help": "Model data type."})
-    diffusion_model_cfg: dict = field(default=None, metadata={"help": "Diffusion model configuration."})
-    input_embedding_dim: int = field(default=1536, metadata={"help": "Input embedding channel dimension."})
-    backbone_embedding_dim: int = field(
-        default=1536, metadata={"help": "Backbone embedding channel dimension."}
-    )
-
-    hidden_size: int = field(default=1024, metadata={"help": "Input embedding dimension."})
-    max_seq_len: int = field(default=1024, metadata={"help": "Maximum Sequence Length"})
-    action_dim: int = field(default=None, metadata={"help": "Action dimension."})
-    action_horizon: int = field(default=None, metadata={"help": "Action horizon."})
-    noise_beta_alpha: float = field(default=1.5, metadata={"help": ""})
-    noise_beta_beta: float = field(default=1.0, metadata={"help": ""})
-    noise_s: float = field(default=0.999, metadata={"help": "Flow matching noise Beta distribution s."})
-    num_timestep_buckets: int = field(
-        default=1000, metadata={"help": "Number of timestep discretization buckets."}
-    )
-    num_inference_timesteps: int = field(
-        default=None,
-        metadata={"help": "Number of inference steps for noise diffusion."},
-    )
-    max_num_embodiments: int = field(default=32, metadata={"help": "Number of embodiments."})
-    tune_projector: bool = field(default=True, metadata={"help": "Whether to tune the projector."})
-    tune_diffusion_model: bool = field(
-        default=True, metadata={"help": "Whether to tune the diffusion model."}
-    )
-    load_pretrained_det_decode_layer_path: str = field(
-        default=None, metadata={"help": "Path to pretrained detection model."}
-    )
-    detection_coeff: float = field(default=1.0, metadata={"help": "Detection coefficient."})
-
-    freeze_decode_layer: bool = field(default=False)
-    expand_batch: int = field(default=None)
-    use_vlln: bool = field(default=True)
-
-    vl_self_attention_cfg: dict = field(default=None)
-    num_target_vision_tokens: int = field(default=32, metadata={"help": "Number of target vision tokens."})
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        for key, value in kwargs.items():
-            setattr(self, key, value)
-
-
-class FlowmatchingActionHead(nn.Module):
-    config_class = FlowmatchingActionHeadConfig
-    supports_gradient_checkpointing = True
-
-    def __init__(
-        self,
-        config: FlowmatchingActionHeadConfig,
-    ):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        self.input_embedding_dim = config.input_embedding_dim
-
-        self.model = DiT(**config.diffusion_model_cfg)
-        self.action_dim = config.action_dim
-        self.action_horizon = config.action_horizon
-        self.num_inference_timesteps = config.num_inference_timesteps
-
-        self.state_encoder = CategorySpecificMLP(
-            num_categories=config.max_num_embodiments,
-            input_dim=config.max_state_dim,
-            hidden_dim=self.hidden_size,
-            output_dim=self.input_embedding_dim,
-        )
-        self.action_encoder = MultiEmbodimentActionEncoder(
-            action_dim=config.action_dim,
-            hidden_size=self.input_embedding_dim,
-            num_embodiments=config.max_num_embodiments,
-        )
-        self.action_decoder = CategorySpecificMLP(
-            num_categories=config.max_num_embodiments,
-            input_dim=self.hidden_size,
-            hidden_dim=self.hidden_size,
-            output_dim=self.action_dim,
-        )
-        self.future_tokens = nn.Embedding(config.num_target_vision_tokens, self.input_embedding_dim)
-        nn.init.normal_(self.future_tokens.weight, mean=0.0, std=0.02)
-
-        self.vlln = nn.LayerNorm(config.backbone_embedding_dim) if config.use_vlln else nn.Identity()
-        self.vl_self_attention = (
-            SelfAttentionTransformer(**config.vl_self_attention_cfg) if config.use_vlln else nn.Identity()
-        )
-
-        if config.add_pos_embed:
-            self.position_embedding = nn.Embedding(config.max_seq_len, self.input_embedding_dim)
-            nn.init.normal_(self.position_embedding.weight, mean=0.0, std=0.02)
-
-        self._noise_beta_alpha = config.noise_beta_alpha
-        self._noise_beta_beta = config.noise_beta_beta
-        self._beta_dist = None
-        self.num_timestep_buckets = config.num_timestep_buckets
-        self.config = config
-        self.set_trainable_parameters(config.tune_projector, config.tune_diffusion_model)
-
-    def set_trainable_parameters(self, tune_projector: bool, tune_diffusion_model: bool):
-        self.tune_projector = tune_projector
-        self.tune_diffusion_model = tune_diffusion_model
-        for p in self.parameters():
-            p.requires_grad = True
-        if not tune_projector:
-            self.state_encoder.requires_grad_(False)
-            self.action_encoder.requires_grad_(False)
-            self.action_decoder.requires_grad_(False)
-            if self.config.add_pos_embed:
-                self.position_embedding.requires_grad_(False)
-        if not tune_diffusion_model:
-            self.model.requires_grad_(False)
-        print(f"Tune action head projector: {self.tune_projector}")
-        print(f"Tune action head diffusion model: {self.tune_diffusion_model}")
-        # Check if any parameters are still trainable. If not, print a warning.
-        if not tune_projector and not tune_diffusion_model:
-            for name, p in self.named_parameters():
-                if p.requires_grad:
-                    print(f"Action head trainable parameter: {name}")
-        if not any(p.requires_grad for p in self.parameters()):
-            print("Warning: No action head trainable parameters found.")
-
-    def set_frozen_modules_to_eval_mode(self):
-        """
-        Huggingface will call model.train() at each training_step. To ensure
-        the expected behaviors for modules like dropout, batchnorm, etc., we
-        need to call model.eval() for the frozen modules.
-        """
-        if self.training:
-            if not self.tune_projector:
-                self.state_encoder.eval()
-                self.action_encoder.eval()
-                self.action_decoder.eval()
-                if self.config.add_pos_embed:
-                    self.position_embedding.eval()
-            if not self.tune_diffusion_model:
-                self.model.eval()
-
-    def sample_time(self, batch_size, device, dtype):
-        if self._beta_dist is None:
-            self._beta_dist = Beta(self._noise_beta_alpha, self._noise_beta_beta, validate_args=False)
-        sample = self._beta_dist.sample([batch_size]).to(device, dtype=dtype)
-        return (self.config.noise_s - sample) / self.config.noise_s
-
-    def prepare_input(self, batch: dict) -> BatchFeature:
-        return BatchFeature(data=batch)
-
-    def process_backbone_output(self, backbone_output: BatchFeature) -> BatchFeature:
-        backbone_features = backbone_output["backbone_features"]
-        backbone_features = self.vlln(backbone_features)
-        backbone_features = self.vl_self_attention(backbone_features)
-        backbone_output["backbone_features"] = backbone_features
-        return backbone_output
-
-    def forward(self, backbone_output: BatchFeature, action_input: BatchFeature) -> BatchFeature:
-        # Set frozen modules to eval
-        self.set_frozen_modules_to_eval_mode()
-
-        backbone_output = self.process_backbone_output(backbone_output)
-
-        if self.config.expand_batch is not None:
-            for k, v in backbone_output.items():
-                ndim = len(v.shape)
-                factors = [self.config.expand_batch]
-                while len(factors) < ndim:
-                    factors.append(1)
-                factors = tuple(factors)
-                expanded = v.repeat(*factors)
-                backbone_output[k] = expanded
-
-            for k, v in action_input.items():
-                ndim = len(v.shape)
-                factors = [self.config.expand_batch]
-                while len(factors) < ndim:
-                    factors.append(1)
-                factors = tuple(factors)
-                expanded = v.repeat(*factors)
-                action_input[k] = expanded
-
-        # Get vision and language embeddings.
-        vl_embs = backbone_output.backbone_features
-        device = vl_embs.device
-
-        # Get embodiment ID.
-        embodiment_id = action_input.embodiment_id
-
-        # Embed state.
-        state_features = self.state_encoder(action_input.state, embodiment_id)
-
-        # Embed noised action trajectory.
-        actions = action_input.action
-        noise = torch.randn(actions.shape, device=actions.device, dtype=actions.dtype)
-        t = self.sample_time(actions.shape[0], device=actions.device, dtype=actions.dtype)
-        t = t[:, None, None]  # shape (B,1,1) for broadcast
-
-        noisy_trajectory = (1 - t) * noise + t * actions
-        velocity = actions - noise
-
-        # Convert (continuous) t -> discrete if needed
-        t_discretized = (t[:, 0, 0] * self.num_timestep_buckets).long()
-        action_features = self.action_encoder(noisy_trajectory, t_discretized, embodiment_id)
-
-        # Maybe add position embedding.
-        if self.config.add_pos_embed:
-            pos_ids = torch.arange(action_features.shape[1], dtype=torch.long, device=device)
-            pos_embs = self.position_embedding(pos_ids).unsqueeze(0)
-            action_features = action_features + pos_embs
-
-        # Join vision, language, state and action embedding along sequence dimension.
-        future_tokens = self.future_tokens.weight.unsqueeze(0).expand(vl_embs.shape[0], -1, -1)
-        sa_embs = torch.cat((state_features, future_tokens, action_features), dim=1)
-
-        vl_attn_mask = backbone_output.backbone_attention_mask
-
-        model_output = self.model(
-            hidden_states=sa_embs,
-            encoder_hidden_states=vl_embs,
-            encoder_attention_mask=vl_attn_mask,
-            timestep=t_discretized,
-            return_all_hidden_states=False,  # NOTE (YL): not using flare now
-        )
-        pred = self.action_decoder(model_output, embodiment_id)
-        pred_actions = pred[:, -actions.shape[1] :]
-
-        # Slice out only the action portion of pred and target.
-        action_mask = action_input.action_mask
-        loss = F.mse_loss(pred_actions, velocity, reduction="none") * action_mask
-        loss = loss.sum() / action_mask.sum()
-        output_dict = {
-            "loss": loss,
-        }
-        return BatchFeature(data=output_dict)
-
-    @torch.no_grad()
-    def get_action(self, backbone_output: BatchFeature, action_input: BatchFeature) -> BatchFeature:
-        backbone_output = self.process_backbone_output(backbone_output)
-
-        # Get vision and language embeddings.
-        vl_embs = backbone_output.backbone_features
-        embodiment_id = action_input.embodiment_id
-
-        # Embed state.
-        state_features = self.state_encoder(action_input.state, embodiment_id)
-
-        # Set initial actions as the sampled noise.
-        batch_size = vl_embs.shape[0]
-        device = vl_embs.device
-        actions = torch.randn(
-            size=(batch_size, self.config.action_horizon, self.config.action_dim),
-            dtype=vl_embs.dtype,
-            device=device,
-        )
-
-        num_steps = self.num_inference_timesteps
-        dt = 1.0 / num_steps
-
-        # Run denoising steps.
-        for t in range(num_steps):
-            t_cont = t / float(num_steps)  # e.g. goes 0, 1/N, 2/N, ...
-            t_discretized = int(t_cont * self.num_timestep_buckets)
-
-            # Embed noised action trajectory.
-            timesteps_tensor = torch.full(size=(batch_size,), fill_value=t_discretized, device=device)
-            action_features = self.action_encoder(actions, timesteps_tensor, embodiment_id)
-            # Maybe add position embedding.
-            if self.config.add_pos_embed:
-                pos_ids = torch.arange(action_features.shape[1], dtype=torch.long, device=device)
-                pos_embs = self.position_embedding(pos_ids).unsqueeze(0)
-                action_features = action_features + pos_embs
-
-            # Join vision, language, state and action embedding along sequence dimension.
-            future_tokens = self.future_tokens.weight.unsqueeze(0).expand(vl_embs.shape[0], -1, -1)
-            sa_embs = torch.cat((state_features, future_tokens, action_features), dim=1)
-
-            # Run model forward.
-            model_output = self.model(
-                hidden_states=sa_embs,
-                encoder_hidden_states=vl_embs,
-                timestep=timesteps_tensor,
-            )
-            pred = self.action_decoder(model_output, embodiment_id)
-
-            pred_velocity = pred[:, -self.action_horizon :]
-
-            # Update actions using euler integration.
-            actions = actions + dt * pred_velocity
-        return BatchFeature(data={"action_pred": actions})
-
-    @property
-    def device(self):
-        return next(iter(self.parameters())).device
-
-    @property
-    def dtype(self):
-        return next(iter(self.parameters())).dtype

src/lerobot/policies/groot/configuration_groot.py

@@ -14,12 +14,228 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import logging
 from dataclasses import dataclass, field
+from pathlib import Path
 
 from lerobot.configs import FeatureType, NormalizationMode, PolicyFeature, PreTrainedConfig
 from lerobot.optim import AdamWConfig, CosineDecayWithWarmupSchedulerConfig
 from lerobot.utils.constants import ACTION, OBS_STATE
 
+from .utils import read_json
+
+logger = logging.getLogger(__name__)
+
+GROOT_N1_7 = "n1.7"
+# Legacy GR00T N1.5 identifier. N1.5 is NOT a supported model_version (it is
+# intentionally absent from _GROOT_MODEL_VERSION_ALIASES so normalize_groot_model_version
+# still rejects it). It is retained only so that infer_groot_model_version can recognise
+# an N1.5 base path/checkpoint and the N1.7 config/loader can reject the mismatch.
+GROOT_N1_5 = "n1.5"
+# Canonical guidance appended to every error raised when an N1.5 checkpoint, config,
+# or processor pipeline is detected. Keep this message in sync with docs/source/groot.mdx.
+GROOT_N1_5_REMOVAL_GUIDANCE = (
+    "GR00T N1.5 support was removed from LeRobot. "
+    "To keep using an N1.5 checkpoint, pin the last release that supports it: "
+    "`pip install 'lerobot==0.5.1'`. To use the current release, migrate to GR00T N1.7 "
+    "(model_version='n1.7', base model nvidia/GR00T-N1.7-3B)."
+)
+GROOT_N1_7_BASE_MODEL = "nvidia/GR00T-N1.7-3B"
+GROOT_N1_7_BACKBONE_MODEL = "nvidia/Cosmos-Reason2-2B"
+# Default GR00T N1.7 training resolution. Fallback if processor_config lacks sizing. Prevents mismatched
+# full-res patchification by forcing a resize. Mirrored by GR00T_N1_7_DEFAULTS in groot_n1_7.py.
+N1_7_DEFAULT_IMAGE_TARGET_SIZE = (256, 256)
+N1_7_DEFAULT_IMAGE_CROP_SIZE = (230, 230)
+GROOT_ACTION_DECODE_TRANSFORM_LIBERO = "libero"
+# Sentinel meaning "the user did not pick an action decode transform": __post_init__ resolves it
+# to the embodiment default ('libero' for 'libero_sim', otherwise None). It is distinct from an
+# explicit 'none' (resolved to None) so an opt-out survives a draccus save/load round-trip.
+GROOT_ACTION_DECODE_TRANSFORM_AUTO = "auto"
+
+_GROOT_MODEL_VERSION_ALIASES = {
+    "n1.7": GROOT_N1_7,
+    "n1_7": GROOT_N1_7,
+    "n1d7": GROOT_N1_7,
+    "n17": GROOT_N1_7,
+    "1.7": GROOT_N1_7,
+}
+
+# Legacy N1.5 spellings, kept ONLY so they can be detected and rejected with
+# GROOT_N1_5_REMOVAL_GUIDANCE (see GROOT_N1_5 above). Never map these to a supported version.
+_GROOT_N1_5_VERSION_ALIASES = {"n1.5", "n1_5", "n1d5", "n15", "1.5"}
+
+_GROOT_ACTION_DECODE_TRANSFORM_ALIASES = {
+    GROOT_ACTION_DECODE_TRANSFORM_AUTO: GROOT_ACTION_DECODE_TRANSFORM_AUTO,
+    "none": None,
+    "": None,
+    GROOT_ACTION_DECODE_TRANSFORM_LIBERO: GROOT_ACTION_DECODE_TRANSFORM_LIBERO,
+}
+
+
+def normalize_groot_model_version(model_version: str) -> str:
+    normalized = _GROOT_MODEL_VERSION_ALIASES.get(model_version.lower())
+    if normalized is None:
+        supported = GROOT_N1_7
+        message = f"Unsupported GR00T model_version '{model_version}'. Supported versions: {supported}."
+        if model_version.lower() in _GROOT_N1_5_VERSION_ALIASES:
+            message = f"{message} {GROOT_N1_5_REMOVAL_GUIDANCE}"
+        raise ValueError(message)
+    return normalized
+
+
+def normalize_groot_action_decode_transform(transform: str | None) -> str | None:
+    if transform is None:
+        return None
+    normalized = _GROOT_ACTION_DECODE_TRANSFORM_ALIASES.get(transform.lower())
+    if normalized is None and transform.lower() not in _GROOT_ACTION_DECODE_TRANSFORM_ALIASES:
+        supported = ", ".join(
+            sorted(key for key, value in _GROOT_ACTION_DECODE_TRANSFORM_ALIASES.items() if value is not None)
+        )
+        raise ValueError(
+            f"Unsupported GR00T N1.7 action decode transform '{transform}'. "
+            f"Supported transforms: none, {supported}."
+        )
+    return normalized
+
+
+def infer_groot_model_version(model_path: str | None) -> str | None:
+    if not model_path:
+        return None
+    model_path_lower = model_path.lower()
+    if "gr00t-n1.7" in model_path_lower or "gr00t_n1.7" in model_path_lower:
+        return GROOT_N1_7
+    # Detect legacy N1.5 paths so the N1.7 config/loader can reject the mismatch.
+    # N1.5 is unsupported, but it must still be recognised here to fail loudly
+    # rather than silently treating an N1.5 checkpoint as N1.7.
+    if "gr00t-n1.5" in model_path_lower or "gr00t_n1.5" in model_path_lower:
+        return GROOT_N1_5
+    config_version = _infer_groot_model_version_from_local_config(model_path)
+    if config_version is not None:
+        return config_version
+    return None
+
+
+def is_raw_groot_n1_7_checkpoint(model_path: str | Path | None) -> bool:
+    if model_path is None:
+        return False
+
+    path = Path(model_path).expanduser()
+    if path.is_dir():
+        config_path = path / "config.json"
+    elif path.name == "config.json":
+        config_path = path
+    else:
+        return False
+
+    config = read_json(config_path)
+    return "type" not in config and _infer_groot_model_version_from_config(config) == GROOT_N1_7
+
+
+def infer_groot_n1_7_embodiment_tag(model_path: str | Path | None) -> str | None:
+    if model_path is None:
+        return None
+
+    processor_config_path = Path(model_path).expanduser() / "processor_config.json"
+    processor_config = read_json(processor_config_path)
+
+    modality_configs = processor_config.get("processor_kwargs", {}).get("modality_configs", {})
+    if not isinstance(modality_configs, dict):
+        return None
+    if "libero_sim" in modality_configs:
+        return "libero_sim"
+    if len(modality_configs) == 1:
+        return next(iter(modality_configs))
+    return None
+
+
+def infer_groot_n1_7_action_horizon(
+    model_path: str | Path | None, embodiment_tag: str | None = None
+) -> int | None:
+    if model_path is None:
+        return None
+
+    processor_config_path = Path(model_path).expanduser() / "processor_config.json"
+    processor_config = read_json(processor_config_path)
+
+    processor_kwargs = processor_config.get("processor_kwargs", {})
+    if not isinstance(processor_kwargs, dict):
+        return None
+    modality_configs = processor_kwargs.get("modality_configs", {})
+    if not isinstance(modality_configs, dict):
+        return None
+
+    if embodiment_tag is None:
+        embodiment_tag = infer_groot_n1_7_embodiment_tag(model_path)
+    if embodiment_tag is None:
+        return None
+
+    embodiment_config = modality_configs.get(embodiment_tag, {})
+    if not isinstance(embodiment_config, dict):
+        return None
+    action_config = embodiment_config.get("action", {})
+    if not isinstance(action_config, dict):
+        return None
+    delta_indices = action_config.get("delta_indices", [])
+    if not isinstance(delta_indices, list):
+        return None
+    return len(delta_indices) or None
+
+
+def infer_groot_n1_7_action_execution_horizon(
+    model_path: str | Path | None, embodiment_tag: str | None = None
+) -> int | None:
+    action_horizon = infer_groot_n1_7_action_horizon(model_path, embodiment_tag)
+    if action_horizon is None:
+        return None
+
+    if embodiment_tag is None:
+        embodiment_tag = infer_groot_n1_7_embodiment_tag(model_path)
+    if embodiment_tag == "libero_sim":
+        # NVIDIA's N1.7 LIBERO rollout wrapper replans after 8 of the 16 decoded
+        # actions. Keeping that execution cadence avoids stale open-loop chunks.
+        return min(action_horizon, 8)
+    return action_horizon
+
+
+def _infer_groot_model_version_from_local_config(model_path: str) -> str | None:
+    path = Path(model_path).expanduser()
+    if path.is_dir():
+        config_path = path / "config.json"
+    elif path.name == "config.json":
+        config_path = path
+    else:
+        return None
+
+    return _infer_groot_model_version_from_config(read_json(config_path))
+
+
+def _infer_groot_model_version_from_config(config: dict) -> str | None:
+    model_version = config.get("model_version")
+    if isinstance(model_version, str):
+        if model_version.lower() in _GROOT_N1_5_VERSION_ALIASES:
+            return GROOT_N1_5
+        try:
+            return normalize_groot_model_version(model_version)
+        except ValueError:
+            return None
+
+    candidates = [config.get("model_type"), *(config.get("architectures") or [])]
+    for candidate in candidates:
+        if not isinstance(candidate, str):
+            continue
+        normalized = candidate.lower().replace("-", "_")
+        if normalized in {"gr00tn1d7", "gr00t_n1d7", "gr00t_n1_7"}:
+            return GROOT_N1_7
+        if normalized in {"gr00t_n1_5", "gr00tn1_5", "gr00t_n15", "gr00t_n1d5", "gr00tn1d5"}:
+            return GROOT_N1_5
+    if config.get("model_name") == GROOT_N1_7_BACKBONE_MODEL:
+        return GROOT_N1_7
+    # The Eagle VLM backbone is specific to pre-N1.7 GR00T checkpoints (N1.7 uses Cosmos/Qwen3-VL).
+    backbone_cfg = config.get("backbone_cfg")
+    if isinstance(backbone_cfg, dict) and "eagle_path" in backbone_cfg:
+        return GROOT_N1_5
+    return None
+
 
 @PreTrainedConfig.register_subclass("groot")
 @dataclass
@@ -28,35 +244,44 @@ class GrootConfig(PreTrainedConfig):
 
     # Basic policy settings
     n_obs_steps: int = 1
-    chunk_size: int = 50
-    n_action_steps: int = 50
+    chunk_size: int = 40
+    n_action_steps: int = 40
 
     # Dimension settings (must match pretrained GR00T model expectations)
     # Maximum state dimension. Shorter states will be zero-padded.
-    max_state_dim: int = 64
+    max_state_dim: int = 132
 
     # Maximum action dimension. Shorter actions will be zero-padded.
-    max_action_dim: int = 32
+    max_action_dim: int = 132
 
-    # Normalization (start with identity, adjust as needed)
+    # GR00T normalizes state/action internally in its processor steps (min/max with
+    # q01/q99 percentiles, per embodiment), and the Qwen3-VL backbone's image processor
+    # handles image normalization. The policy therefore does NOT use LeRobot's
+    # NormalizerProcessorStep/UnnormalizerProcessorStep, so this mapping is intentionally
+    # IDENTITY for every feature and is not consulted by make_groot_pre_post_processors.
     normalization_mapping: dict[str, NormalizationMode] = field(
         default_factory=lambda: {
             "VISUAL": NormalizationMode.IDENTITY,
-            "STATE": NormalizationMode.MEAN_STD,
-            "ACTION": NormalizationMode.MEAN_STD,
+            "STATE": NormalizationMode.IDENTITY,
+            "ACTION": NormalizationMode.IDENTITY,
         }
     )
 
-    # Image preprocessing (adjust to match Groot's expected input)
-    image_size: tuple[int, int] = (224, 224)
+    # Groot-specific model parameters
 
-    # Groot-specific model parameters (from groot_finetune_script.py)
+    # Path or HuggingFace model ID for the base GR00T N1.7 model whose backbone weights and
+    # checkpoint sidecars (statistics.json, processor_config.json, ...) are loaded. This is the
+    # model *source*, and is intentionally distinct from the inherited `pretrained_path`:
+    # `pretrained_path` (`--policy.path`) points at a saved LeRobot checkpoint directory whose
+    # `config.json` carries a `type` field, whereas a raw NVIDIA GR00T checkpoint has no such
+    # field and so can only be loaded through `base_model_path` (`--policy.base_model_path`).
+    # Defaults to GROOT_N1_7_BASE_MODEL when unset (resolved in __post_init__).
+    base_model_path: str | None = None
 
-    # Path or HuggingFace model ID for the base Groot model
-    base_model_path: str = "nvidia/GR00T-N1.5-3B"
-
-    # HF repo ID (or local path) that hosts vocab.json and merges.txt for Eagle tokenizer.
-    tokenizer_assets_repo: str = "lerobot/eagle2hg-processor-groot-n1p5"
+    # Optional named action transform applied after raw N1.7 checkpoint decoding and before env.step().
+    # 'auto' (default) resolves to the embodiment default ('libero' for 'libero_sim', otherwise no
+    # transform). Pass 'none' to explicitly disable the transform, including for 'libero_sim'.
+    action_decode_transform: str | None = GROOT_ACTION_DECODE_TRANSFORM_AUTO
 
     # Embodiment tag to use for training (e.g. 'new_embodiment', 'gr1')
     embodiment_tag: str = "new_embodiment"
@@ -75,20 +300,41 @@ class GrootConfig(PreTrainedConfig):
     # Whether to fine-tune the diffusion model
     tune_diffusion_model: bool = True
 
-    # LoRA parameters (from groot_finetune_script.py)
-    # Rank for the LORA model. If 0, no LORA will be used.
-    lora_rank: int = 0
+    # Whether to fine-tune the VL LayerNorm + VL self-attention projector in the action head.
+    tune_vlln: bool = True
 
-    # Alpha value for the LORA model
-    lora_alpha: int = 16
+    # Number of top LLM backbone layers to fine-tune (0 = none). Lets you adapt just the final
+    # language layers without unfreezing the whole backbone; independent of `tune_llm`, which tunes
+    # the entire LLM.
+    tune_top_llm_layers: int = 0
 
-    # Dropout rate for the LORA model
-    lora_dropout: float = 0.1
+    # Inference-time knob: Number of flow-matching denoising steps used to decode an action chunk.
+    # Trades inference latency for action quality.
+    # None keeps the checkpoint value (GR00T N1.7 default: 4).
+    num_inference_timesteps: int | None = None
 
-    # Whether to use the full model for LORA
-    lora_full_model: bool = False
+    # Inference-time knob: Real-Time Chunking (RTC) overlap-blend ramp rate, used when the RTC engine
+    # supplies a previous-chunk prefix. Higher values blend the overlapping prefix more aggressively.
+    # None keeps the checkpoint value (GR00T N1.7 default: 6.0).
+    rtc_ramp_rate: float | None = None
 
-    # Training parameters (matching groot_finetune_script.py)
+    # Inference-time knob: Whether to request the flash-attention-2 kernel for the Qwen3-VL backbone.
+    # flash-attn is an optional, user-managed optimization; when it is absent (the default),
+    # the backbone transparently falls back to SDPA, which is numerically equivalent.
+    # Set to True only after installing a flash-attn build matching your torch/CUDA env.
+    use_flash_attention: bool = False
+
+    # Enable GR00T-style state-relative action chunks (action chunk expressed relative to the current
+    # observation state).
+    use_relative_actions: bool = False
+
+    # relative_exclude_joints names the action dimensions that stay absolute; the
+    # match is substring/case-insensitive against the dataset action feature names. With the empty
+    # default every dimension is treated as relative, including the gripper -- set e.g. ["gripper"] to
+    # keep the gripper absolute, matching the Isaac-GR00T single-arm + absolute-gripper convention.
+    relative_exclude_joints: list[str] = field(default_factory=list)
+
+    # Training parameters
     optimizer_lr: float = 1e-4
     optimizer_betas: tuple[float, float] = (0.95, 0.999)
     optimizer_eps: float = 1e-8
@@ -96,17 +342,22 @@ class GrootConfig(PreTrainedConfig):
     warmup_ratio: float = 0.05
     use_bf16: bool = True
 
-    # Dataset parameters
-    # Video backend to use for training ('decord' or 'torchvision_av')
+    # TODO(Steven): Remove these deprecated fields in a future release.
+    # Deprecated Isaac-GR00T runner / GR00T N1.5 fields, plus the (never-wired) LoRA fields — all
+    # unused by the LeRobot N1.7 implementation except the `tokenizer_assets_repo` N1.5 tripwire and
+    # the `image_size` legacy remap in __post_init__. They are kept ONLY so a config.json saved by an
+    # earlier lerobot release (notably a GR00T N1.5 checkpoint) still parses under draccus — which
+    # rejects unknown fields — and is then rejected with a clear N1.5 removal message rather than an
+    # opaque draccus decoding error.
+    image_size: tuple[int, int] = (256, 256)  # image sizing is handled by the backbone's image processor.
+    tokenizer_assets_repo: str | None = None
+    lora_rank: int = 0
+    lora_alpha: int = 16
+    lora_dropout: float = 0.1
+    lora_full_model: bool = False
     video_backend: str = "decord"
-
-    # Whether to balance dataset weights in mixture datasets
     balance_dataset_weights: bool = True
-
-    # Whether to sample trajectories weighted by their length
     balance_trajectory_weights: bool = True
-
-    # Optional dataset paths for delegating training to Isaac-GR00T runner
     dataset_paths: list[str] | None = None
     output_dir: str = "./tmp/gr00t"
     save_steps: int = 1000
@@ -117,6 +368,65 @@ class GrootConfig(PreTrainedConfig):
     resume: bool = False
 
     def __post_init__(self):
+        if self.tokenizer_assets_repo is not None:
+            raise ValueError(
+                "Config sets 'tokenizer_assets_repo', which only existed for GR00T N1.5; this looks "
+                f"like a legacy GR00T N1.5 checkpoint or config. {GROOT_N1_5_REMOVAL_GUIDANCE}"
+            )
+
+        self.action_decode_transform = normalize_groot_action_decode_transform(self.action_decode_transform)
+        if self.base_model_path is None:
+            self.base_model_path = GROOT_N1_7_BASE_MODEL
+
+        # The N1.7 LIBERO checkpoints emit a [0, 1] gripper action, but the LIBERO
+        # simulator expects the OpenVLA/[-1, 1] sign convention. NVIDIA's rollout
+        # wrapper applies this conversion; mirror it here so eval on the
+        # 'libero_sim' embodiment grasps correctly instead of scoring 0% success.
+        # This matches the embodiment-specific handling already done for the
+        # action execution horizon (see infer_groot_n1_7_action_execution_horizon).
+        # Only the 'auto' sentinel resolves to the embodiment default; an explicit
+        # 'none' (normalized to None above) keeps the transform disabled.
+        if self.action_decode_transform == GROOT_ACTION_DECODE_TRANSFORM_AUTO:
+            self.action_decode_transform = (
+                GROOT_ACTION_DECODE_TRANSFORM_LIBERO if self.embodiment_tag == "libero_sim" else None
+            )
+
+        # GR00T N1.5-era default values (e.g. --policy.chunk_size=50 from old commands or
+        # stale configs) are migrated to the values the N1.7 checkpoints expect, with a
+        # warning. The dataclass defaults are already the N1.7 values, so a plain
+        # GrootConfig() never triggers this.
+        legacy_default_remaps = (
+            ("max_state_dim", 64, 132),
+            ("max_action_dim", 32, 132),
+            ("chunk_size", 50, 40),
+            ("n_action_steps", 50, 40),
+            ("image_size", (224, 224), (256, 256)),
+        )
+        for field_name, legacy_value, n1_7_value in legacy_default_remaps:
+            current_value = getattr(self, field_name)
+            if isinstance(legacy_value, tuple):
+                current_value = tuple(current_value)
+            if current_value == legacy_value:
+                logger.warning(
+                    "GrootConfig.%s=%s matches a legacy GR00T N1.5-era default; remapping it to %s, "
+                    "the value expected by GR00T N1.7 checkpoints. Set a different value explicitly "
+                    "if this is not what you want.",
+                    field_name,
+                    legacy_value,
+                    n1_7_value,
+                )
+                setattr(self, field_name, n1_7_value)
+
+        inferred_version = infer_groot_model_version(self.base_model_path)
+        if inferred_version is not None and inferred_version != GROOT_N1_7:
+            message = (
+                f"GR00T model_version '{GROOT_N1_7}' does not match base_model_path "
+                f"'{self.base_model_path}', which looks like '{inferred_version}'."
+            )
+            if inferred_version == GROOT_N1_5:
+                message = f"{message} {GROOT_N1_5_REMOVAL_GUIDANCE}"
+            raise ValueError(message)
+
         super().__post_init__()
 
         if self.n_action_steps > self.chunk_size:
@@ -124,9 +434,6 @@ class GrootConfig(PreTrainedConfig):
                 f"n_action_steps ({self.n_action_steps}) cannot exceed chunk_size ({self.chunk_size})"
             )
 
-        # groot_repo_path is now optional since we ported the components
-        # No validation needed
-
     def validate_features(self) -> None:
         """Validate and set up input/output features for Groot."""
         image_features = [key for key, feat in self.input_features.items() if feat.type == FeatureType.VISUAL]
@@ -192,7 +499,10 @@ class GrootConfig(PreTrainedConfig):
     @property
     def action_delta_indices(self) -> list[int]:
         """Return indices for delta actions."""
-        return list(range(min(self.chunk_size, 16)))
+        model_action_horizon = (
+            infer_groot_n1_7_action_horizon(self.base_model_path, self.embodiment_tag) or 40
+        )
+        return list(range(min(self.chunk_size, model_action_horizon)))
 
     @property
     def reward_delta_indices(self) -> None:

src/lerobot/policies/groot/eagle2_hg_model/configuration_eagle2_5_vl.py

@@ -1,135 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import copy
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.models.llama.configuration_llama import LlamaConfig
-from transformers.models.qwen2.configuration_qwen2 import Qwen2Config
-from transformers.models.qwen3.configuration_qwen3 import Qwen3Config
-from transformers.models.siglip.configuration_siglip import SiglipVisionConfig
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-
-class Eagle25VLConfig(PretrainedConfig):
-    model_type = "eagle_2_5_vl"
-    is_composition = True
-    sub_configs = {"vision_config": SiglipVisionConfig, "text_config": Qwen2Config}
-
-    def __init__(
-        self,
-        vision_config=None,
-        text_config=None,
-        use_backbone_lora=0,
-        use_llm_lora=0,
-        pad2square=False,
-        select_layer=-4,
-        force_image_size=None,
-        downsample_ratio=0.5,
-        template=None,
-        dynamic_image_size=False,
-        use_thumbnail=False,
-        loss_version="v1",
-        min_dynamic_tiles=1,
-        max_dynamic_tiles=6,
-        mlp_checkpoint=False,
-        initializer_range=0.02,
-        _attn_implementation="flash_attention_2",
-        _attn_implementation_autoset=False,
-        llm_config=None,
-        image_token_index=None,
-        use_pixel_shuffle=True,
-        mlp_connector_layers=2,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-
-        if vision_config is None:
-            vision_config = {"model_type": "siglip_vision_model"}
-            logger.info("vision_config is None. Initializing the InternVisionConfig with default values.")
-
-        if text_config is None:
-            text_config = {"architectures": ["Qwen2ForCausalLM"]}
-            logger.info(
-                "text_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`)."
-            )
-
-        if vision_config["model_type"] == "siglip_vision_model":
-            self.vision_config = SiglipVisionConfig(**vision_config)
-        else:
-            raise ValueError("Unsupported model_type: {}".format(vision_config["model_type"]))
-
-        if text_config["architectures"][0] == "LlamaForCausalLM":
-            self.text_config = LlamaConfig(**text_config)
-        elif text_config["architectures"][0] == "Qwen2ForCausalLM":
-            self.text_config = Qwen2Config(**text_config)
-        elif text_config["architectures"][0] == "Qwen3ForCausalLM":
-            self.text_config = Qwen3Config(**text_config)
-        else:
-            raise ValueError("Unsupported architecture: {}".format(text_config["architectures"][0]))
-        self.use_backbone_lora = use_backbone_lora
-        self.use_llm_lora = use_llm_lora
-        self.mlp_checkpoint = mlp_checkpoint
-        self.pad2square = pad2square
-        self.select_layer = select_layer
-        self.force_image_size = force_image_size
-        self.downsample_ratio = downsample_ratio
-        self.template = template
-        self.dynamic_image_size = dynamic_image_size
-        self.use_thumbnail = use_thumbnail
-        self.loss_version = loss_version
-        self.initializer_range = initializer_range
-        self.min_dynamic_tiles = min_dynamic_tiles
-        self.max_dynamic_tiles = max_dynamic_tiles
-        self.tie_word_embeddings = self.text_config.tie_word_embeddings
-        self._attn_implementation = _attn_implementation
-        self._attn_implementation_autoset = _attn_implementation_autoset
-        self.image_token_index = image_token_index
-        self.use_pixel_shuffle = use_pixel_shuffle
-        self.mlp_connector_layers = mlp_connector_layers
-        logger.info(f"min_dynamic_tiles: {self.min_dynamic_tiles}")
-        logger.info(f"max_dynamic_tiles: {self.max_dynamic_tiles}")
-
-    def to_dict(self):
-        """
-        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
-
-        Returns:
-            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
-        """
-        output = copy.deepcopy(self.__dict__)
-        output["vision_config"] = self.vision_config.to_dict()
-        output["text_config"] = self.text_config.to_dict()
-        output["model_type"] = self.__class__.model_type
-        output["use_backbone_lora"] = self.use_backbone_lora
-        output["use_llm_lora"] = self.use_llm_lora
-        output["pad2square"] = self.pad2square
-        output["select_layer"] = self.select_layer
-        output["force_image_size"] = self.force_image_size
-        output["downsample_ratio"] = self.downsample_ratio
-        output["template"] = self.template
-        output["dynamic_image_size"] = self.dynamic_image_size
-        output["use_thumbnail"] = self.use_thumbnail
-        output["min_dynamic_tiles"] = self.min_dynamic_tiles
-        output["max_dynamic_tiles"] = self.max_dynamic_tiles
-        output["tie_word_embeddings"] = self.tie_word_embeddings
-        output["_attn_implementation"] = self._attn_implementation
-        output["_attn_implementation_autoset"] = self._attn_implementation_autoset
-        output["use_pixel_shuffle"] = self.use_pixel_shuffle
-        output["mlp_connector_layers"] = self.mlp_connector_layers
-        return output

src/lerobot/policies/groot/eagle2_hg_model/image_processing_eagle2_5_vl_fast.py

@@ -1,503 +0,0 @@
-# --------------------------------------------------------
-# NVIDIA
-# Copyright (c) 2025 NVIDIA
-# Licensed under The MIT License [see LICENSE for details]
-# --------------------------------------------------------
-
-from __future__ import annotations
-
-# copy from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava_onevision/image_processing_llava_onevision_fast.py
-from transformers.image_processing_utils import (
-    BatchFeature,
-    get_patch_output_size,
-)
-from transformers.image_processing_utils_fast import (
-    BaseImageProcessorFast,
-    ImagesKwargs,
-    group_images_by_shape,
-    reorder_images,
-)
-from transformers.image_utils import (
-    IMAGENET_STANDARD_MEAN,  # 0.5, 0.5, 0.5
-    IMAGENET_STANDARD_STD,  # 0.5, 0.5, 0.5
-    ChannelDimension,
-    ImageInput,
-    PILImageResampling,
-    SizeDict,
-    get_image_size,
-    make_flat_list_of_images,
-    validate_kwargs,
-)
-from transformers.processing_utils import Unpack
-from transformers.utils import (
-    TensorType,
-    add_start_docstrings,
-    is_torch_available,
-    is_torchvision_v2_available,
-)
-from transformers.video_utils import VideoInput
-
-if is_torch_available():
-    import torch
-if is_torchvision_v2_available():
-    from torchvision.transforms.v2 import functional as F  # noqa: N812
-    from transformers.image_utils import pil_torch_interpolation_mapping
-else:
-    from torchvision.transforms import functional as F  # noqa: N812
-
-
-def crop(img: torch.Tensor, left: int, top: int, right: int, bottom: int) -> torch.Tensor:
-    """Crop the given numpy array.
-
-    Args:
-        img (torch.Tensor): Image to be cropped. Format should be (C, H, W).
-        left (int): The left coordinate of the crop box.
-        top (int): The top coordinate of the crop box.
-        right (int): The right coordinate of the crop box.
-        bottom (int): The bottom coordinate of the crop box.
-
-    Returns:
-        torch.Tensor: Cropped image.
-    """
-    if not isinstance(img, torch.Tensor):
-        raise TypeError(f"img should be torch.Tensor. Got {type(img)}")
-
-    if img.ndim not in [2, 3]:
-        raise ValueError(f"Image should have 2 or 3 dimensions. Got {img.ndim}")
-
-    img_height = img.shape[1]
-    img_width = img.shape[2]
-    if top < 0 or left < 0 or bottom > img_height or right > img_width:
-        raise ValueError("Crop coordinates out of bounds")
-
-    if top >= bottom or left >= right:
-        raise ValueError("Invalid crop coordinates")
-
-    return img[:, top:bottom, left:right]
-
-
-class Eagle25VLFastImageProcessorKwargs(ImagesKwargs):
-    max_dynamic_tiles: int | None
-    min_dynamic_tiles: int | None
-    use_thumbnail: bool | None
-    pad_during_tiling: bool | None
-    do_pad: bool | None
-
-
-@add_start_docstrings(
-    "Constructs a fast ConvNeXT image processor. Based on [`SiglipImageProcessor`] with incorporation of processing each video frame.",
-    # BASE_IMAGE_PROCESSOR_FAST_DOCSTRING, TODO: this was depreciated from transformers remove!
-    """
-        image_grid_pinpoints (`List[List[int]]`, *optional*):
-            A list of possible resolutions to use for processing high resolution images. The best resolution is selected
-            based on the original size of the image. Can be overridden by `image_grid_pinpoints` in the `preprocess`
-            method. Not used for processing videos.
-        do_pad (`bool`, *optional*):
-            Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest
-            number of patches in the batch. Padding will be applied to the bottom and right with zeros.
-    """,
-)
-class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
-    resample = PILImageResampling.BICUBIC
-    image_mean = IMAGENET_STANDARD_MEAN
-    image_std = IMAGENET_STANDARD_STD
-    size = {"height": 448, "width": 448}
-    default_to_square = False
-    crop_size = None
-    do_resize = True
-    do_center_crop = None
-    do_rescale = True
-    do_normalize = True
-    do_convert_rgb = True
-    do_pad = True
-    max_dynamic_tiles = 12
-    min_dynamic_tiles = 1
-    use_thumbnail = True
-    pad_during_tiling = False
-    valid_kwargs = Eagle25VLFastImageProcessorKwargs
-    model_input_names = ["pixel_values_videos"]
-
-    def __init__(self, **kwargs: Unpack[Eagle25VLFastImageProcessorKwargs]):
-        super().__init__(**kwargs)
-
-    @add_start_docstrings(
-        # BASE_IMAGE_PROCESSOR_FAST_DOCSTRING_PREPROCESS, TODO: this was depreciated from transformers remove!
-        """
-            max_dynamic_tiles (`int`, *optional*):
-                The maximum number of dynamic tiles to use for processing high resolution images.
-            min_dynamic_tiles (`int`, *optional*):
-                The minimum number of dynamic tiles to use for processing high resolution images.
-            use_thumbnail (`bool`, *optional*):
-                Whether to use a thumbnail for processing high resolution images.
-            pad_during_tiling (`bool`, *optional*):
-                Whether to pad the image during tiling.
-            do_pad (`bool`, *optional*):
-                    Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest
-                    number of patches in the batch. Padding will be applied to the bottom and right with zeros.
-        """,
-    )
-
-    # NOTE(YL): we will overload the preprocess method to add the image_flags
-    # def preprocess(
-    #     self, images: ImageInput, **kwargs: Unpack[Eagle25VLFastImageProcessorKwargs]
-    # ) -> BatchFeature:
-    #     return super().preprocess(images, **kwargs)
-
-    def _prepare_images_structure(
-        self,
-        images: ImageInput,
-        expected_ndims: int = 3,
-    ) -> ImageInput:
-        """
-        Prepare the images structure for processing.
-
-        Args:
-            images (`ImageInput`):
-                The input images to process.
-            expected_ndims (`int`, *optional*, defaults to 3):
-                Expected number of dimensions for the images (added for transformers >=4.53.0 compatibility).
-
-        Returns:
-            `ImageInput`: The images with a valid nesting.
-        """
-        return make_flat_list_of_images(images)
-
-    def _resize_for_patching(
-        self,
-        image: torch.Tensor,
-        target_resolution: tuple,
-        interpolation: F.InterpolationMode,
-        input_data_format: ChannelDimension,
-    ) -> torch.Tensor:
-        """
-        Resizes an image to a target resolution while maintaining aspect ratio.
-
-        Args:
-            image ("torch.Tensor"):
-                The input image.
-            target_resolution (tuple):
-                The target resolution (height, width) of the image.
-            interpolation (`InterpolationMode`):
-                Resampling filter to use if resizing the image.
-            input_data_format (`ChannelDimension` or `str`):
-                The channel dimension format of the input image.
-
-        Returns:
-            "torch.Tensor": The resized and padded image.
-        """
-        new_height, new_width = get_patch_output_size(image, target_resolution, input_data_format)
-
-        # Resize the image
-        resized_image = F.resize(image, (new_height, new_width), interpolation=interpolation)
-
-        return resized_image
-
-    def find_closest_aspect_ratio(self, aspect_ratio, target_ratios, width, height, image_size):
-        """
-        previous version mainly focus on ratio.
-        We also consider area ratio here.
-        """
-        best_factor = float("-inf")
-        best_ratio = (1, 1)
-        area = width * height
-        for ratio in target_ratios:
-            target_aspect_ratio = ratio[0] / ratio[1]
-            # ratio_diff = abs(aspect_ratio - target_aspect_ratio)
-            # area_ratio = (ratio[0] * ratio[1] * image_size * image_size) / area
-            """
-            new area > 60% of original image area is enough.
-            """
-            factor_based_on_area_n_ratio = min(
-                (ratio[0] * ratio[1] * image_size * image_size) / area, 0.6
-            ) * min(target_aspect_ratio / aspect_ratio, aspect_ratio / target_aspect_ratio)
-
-            if factor_based_on_area_n_ratio > best_factor:
-                best_factor = factor_based_on_area_n_ratio
-                best_ratio = ratio
-
-        return best_ratio
-
-    def _pad_for_patching(
-        self, image: torch.Tensor, target_resolution: tuple, input_data_format: ChannelDimension
-    ) -> torch.Tensor:
-        """
-        Pad an image to a target resolution while maintaining aspect ratio.
-        """
-        target_height, target_width = target_resolution
-        new_height, new_width = get_patch_output_size(image, target_resolution, input_data_format)
-
-        paste_x = (target_width - new_width) // 2
-        paste_y = (target_height - new_height) // 2
-
-        padded_image = F.pad(image, padding=[paste_x, paste_y, paste_x, paste_y])
-
-        return padded_image
-
-    def _get_image_patches(
-        self,
-        image: torch.Tensor,
-        min_num: int,
-        max_num: int,
-        size: tuple,
-        tile_size: int,
-        use_thumbnail: bool,
-        interpolation: F.InterpolationMode,
-        pad_during_tiling: bool,
-    ) -> list[torch.Tensor]:
-        image_size = get_image_size(image, channel_dim=ChannelDimension.FIRST)
-        orig_height, orig_width = image_size
-        aspect_ratio = orig_width / orig_height
-
-        # calculate the existing image aspect ratio
-        target_ratios = {
-            (i, j)
-            for n in range(min_num, max_num + 1)
-            for i in range(1, n + 1)
-            for j in range(1, n + 1)
-            if i * j <= max_num and i * j >= min_num
-        }
-        target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
-
-        # find the closest aspect ratio to the target
-        target_aspect_ratio = self.find_closest_aspect_ratio(
-            aspect_ratio, target_ratios, orig_width, orig_height, tile_size
-        )
-
-        # calculate the target width and height
-        target_width = tile_size * target_aspect_ratio[0]
-        target_height = tile_size * target_aspect_ratio[1]
-        blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
-        if pad_during_tiling:
-            resized_image = self._resize_for_patching(
-                image,
-                (target_height, target_width),
-                interpolation=interpolation,
-                input_data_format=ChannelDimension.FIRST,
-            )
-            padded_image = self._pad_for_patching(
-                resized_image,
-                (target_height, target_width),
-                input_data_format=ChannelDimension.FIRST,
-            )
-            image_used_to_split = padded_image
-        else:
-            image_used_to_split = F.resize(image, (target_height, target_width), interpolation=interpolation)
-
-        processed_tiles = []
-        for i in range(blocks):
-            box = (
-                (i % (target_width // tile_size)) * tile_size,
-                (i // (target_width // tile_size)) * tile_size,
-                ((i % (target_width // tile_size)) + 1) * tile_size,
-                ((i // (target_width // tile_size)) + 1) * tile_size,
-            )
-            # split the image
-            split_img = crop(image_used_to_split, box[0], box[1], box[2], box[3])
-            processed_tiles.append(split_img)
-        assert len(processed_tiles) == blocks
-
-        if use_thumbnail and len(processed_tiles) != 1:
-            thumbnail_img = F.resize(image, (tile_size, tile_size), interpolation=interpolation)
-            processed_tiles.append(thumbnail_img)
-
-        return processed_tiles
-
-    def _pad_for_batching(
-        self,
-        pixel_values: list[torch.Tensor],
-    ) -> list[torch.Tensor]:
-        """
-        Pads images on the `num_of_patches` dimension with zeros to form a batch of same number of patches.
-
-        Args:
-            pixel_values (`List[torch.Tensor]`):
-                An array of pixel values of each images of shape (`batch_size`, `num_patches`, `image_in_3D`)
-
-        Returns:
-            List[`torch.Tensor`]: The padded images.
-        """
-        max_patch = max(len(x) for x in pixel_values)
-        pixel_values = [
-            torch.nn.functional.pad(image, pad=[0, 0, 0, 0, 0, 0, 0, max_patch - image.shape[0]])
-            for image in pixel_values
-        ]
-
-        return pixel_values
-
-    def _preprocess(
-        self,
-        images: list[torch.Tensor],
-        do_resize: bool,
-        size: SizeDict,
-        max_dynamic_tiles: int,
-        min_dynamic_tiles: int,
-        use_thumbnail: bool,
-        pad_during_tiling: bool,
-        interpolation: F.InterpolationMode | None,
-        do_center_crop: bool,
-        crop_size: SizeDict,
-        do_rescale: bool,
-        rescale_factor: float,
-        do_normalize: bool,
-        image_mean: float | list[float] | None,
-        image_std: float | list[float] | None,
-        do_pad: bool,
-        return_tensors: str | TensorType | None,
-        pad_size: SizeDict | None = None,  # Added for transformers >=4.53.0 compatibility
-        disable_grouping: bool | None = None,  # Added for transformers >=4.53.0 compatibility
-    ) -> BatchFeature:
-        processed_images = []
-        image_sizes = []
-        # Determine the size tuple
-        if size and size.height and size.width:
-            size_tuple = (size.height, size.width)
-        else:
-            size_tuple = (size.shortest_edge, size.shortest_edge)
-
-        # Determine the patch size
-        if crop_size and crop_size.height:
-            tile_size = crop_size.height
-        elif size and size.height:
-            tile_size = size.height
-        else:
-            tile_size = size.shortest_edge
-
-        for image in images:
-            image_patches = self._get_image_patches(
-                image,
-                min_num=min_dynamic_tiles,
-                max_num=max_dynamic_tiles,
-                size=size_tuple,
-                tile_size=tile_size,
-                use_thumbnail=use_thumbnail,
-                interpolation=interpolation,
-                pad_during_tiling=pad_during_tiling,
-            )
-
-            # Group images by size for batched processing
-            processed_image_patches_grouped = {}
-            # Added for transformers >=4.53.0 compatibility
-            grouped_image_patches, grouped_image_patches_index = group_images_by_shape(
-                image_patches,
-                disable_grouping=disable_grouping,
-            )
-
-            for shape, stacked_image_patches in grouped_image_patches.items():
-                if do_resize:
-                    stacked_image_patches = self.resize(
-                        image=stacked_image_patches,
-                        size=size,
-                        interpolation=interpolation,
-                    )
-                if do_center_crop:
-                    stacked_image_patches = self.center_crop(stacked_image_patches, crop_size)
-                # Fused rescale and normalize
-                stacked_image_patches = self.rescale_and_normalize(
-                    stacked_image_patches,
-                    do_rescale,
-                    rescale_factor,
-                    do_normalize,
-                    image_mean,
-                    image_std,
-                )
-                processed_image_patches_grouped[shape] = stacked_image_patches
-            processed_image_patches = reorder_images(
-                processed_image_patches_grouped, grouped_image_patches_index
-            )
-            processed_image_patches = (
-                torch.stack(processed_image_patches, dim=0) if return_tensors else processed_image_patches
-            )
-            processed_images.append(processed_image_patches)
-            image_sizes.append(get_image_size(image, ChannelDimension.FIRST))
-
-        if do_pad:
-            processed_images = self._pad_for_batching(processed_images)
-
-        # processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
-        processed_images = torch.cat(processed_images, dim=0) if return_tensors else processed_images
-        return BatchFeature(
-            data={"pixel_values": processed_images, "image_sizes": image_sizes},
-            tensor_type=return_tensors,
-        )
-
-    def preprocess(
-        self,
-        images: ImageInput,
-        videos: VideoInput = None,
-        **kwargs: Unpack[Eagle25VLFastImageProcessorKwargs],
-    ) -> BatchFeature:
-        validate_kwargs(
-            captured_kwargs=kwargs.keys(),
-            valid_processor_keys=self.valid_kwargs.__annotations__.keys(),
-        )
-        # Set default kwargs from self. This ensures that if a kwarg is not provided
-        # by the user, it gets its default value from the instance, or is set to None.
-        for kwarg_name in self.valid_kwargs.__annotations__:
-            kwargs.setdefault(kwarg_name, getattr(self, kwarg_name, None))
-
-        # Extract parameters that are only used for preparing the input images
-        do_convert_rgb = kwargs.pop("do_convert_rgb")
-        input_data_format = kwargs.pop("input_data_format")
-        device = kwargs.pop("device")
-        # Prepare input images
-        # transformers >= 4.53.0: uses _prepare_image_like_inputs instead of _prepare_input_images
-        if images is not None:
-            images = self._prepare_image_like_inputs(
-                images=images,
-                do_convert_rgb=do_convert_rgb,
-                input_data_format=input_data_format,
-                device=device,
-            )
-
-        if videos is not None:
-            videos = self._prepare_image_like_inputs(
-                images=videos,
-                do_convert_rgb=do_convert_rgb,
-                input_data_format=input_data_format,
-                device=device,
-            )
-
-        # Update kwargs that need further processing before being validated
-        kwargs = self._further_process_kwargs(**kwargs)
-
-        # Validate kwargs
-        self._validate_preprocess_kwargs(**kwargs)
-
-        # torch resize uses interpolation instead of resample
-        # Added for transformers >=4.53.0 compatibility
-        resample = kwargs.pop("resample", self.resample)
-        kwargs["interpolation"] = (
-            pil_torch_interpolation_mapping[resample]
-            if isinstance(resample, PILImageResampling | int)
-            else resample
-        )
-
-        # Filter kwargs to only include those accepted by _preprocess
-        valid_preprocess_kwargs = {
-            "do_resize",
-            "size",
-            "max_dynamic_tiles",
-            "min_dynamic_tiles",
-            "use_thumbnail",
-            "pad_during_tiling",
-            "interpolation",
-            "do_center_crop",
-            "crop_size",
-            "do_rescale",
-            "rescale_factor",
-            "do_normalize",
-            "image_mean",
-            "image_std",
-            "do_pad",
-            "return_tensors",
-            "pad_size",
-            "disable_grouping",
-        }
-        filtered_kwargs = {k: v for k, v in kwargs.items() if k in valid_preprocess_kwargs}
-        if images is not None:
-            return self._preprocess(images, **filtered_kwargs)
-        elif videos is not None:
-            return self._preprocess(videos, **filtered_kwargs)
-
-
-__all__ = ["Eagle25VLImageProcessorFast"]

src/lerobot/policies/groot/eagle2_hg_model/modeling_eagle2_5_vl.py

@@ -1,396 +0,0 @@
-# --------------------------------------------------------
-# NVIDIA
-# Copyright (c) 2025 NVIDIA
-# Licensed under The MIT License [see LICENSE for details]
-# --------------------------------------------------------
-
-import inspect
-
-import torch
-import torch.utils.checkpoint as cp
-from peft import LoraConfig, get_peft_model
-from torch import nn
-from torch.nn import CrossEntropyLoss
-from transformers import GenerationConfig
-from transformers.generation import GenerationMixin
-from transformers.modeling_outputs import CausalLMOutputWithPast
-from transformers.modeling_utils import PreTrainedModel
-from transformers.models.llama.modeling_llama import LlamaForCausalLM
-from transformers.models.qwen2.modeling_qwen2 import Qwen2ForCausalLM
-from transformers.models.qwen3.modeling_qwen3 import Qwen3ForCausalLM
-from transformers.models.siglip.modeling_siglip import SiglipVisionModel
-from transformers.utils import add_start_docstrings, logging
-
-from .configuration_eagle2_5_vl import Eagle25VLConfig
-
-logger = logging.get_logger(__name__)
-
-
-# copy from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava_onevision/modeling_llava_onevision.py#L241C1-L280C1
-EAGLE2_5_VL_START_DOCSTRING = r"""
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-
-    Parameters:
-        config ([`Eagle25VLConfig`]):
-            Model configuration class with all the parameters of the model. Initializing with a config file does not
-            load the weights associated with the model, only the configuration. Check out the
-            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-
-@add_start_docstrings(
-    "The bare Eagle2_5_VL Model outputting raw hidden-states without any specific head on top.",
-    EAGLE2_5_VL_START_DOCSTRING,
-)
-class Eagle25VLPreTrainedModel(PreTrainedModel):
-    config_class = Eagle25VLConfig
-    base_model_prefix = "model"
-    main_input_name = "input_ids"
-    supports_gradient_checkpointing = True
-    _no_split_modules = [
-        "Qwen2DecoderLayer",
-        "LlamaDecoderLayer",
-        "Siglip2EncoderLayer",
-        "SiglipEncoderLayer",
-    ]
-    _skip_keys_device_placement = "past_key_values"
-    _supports_flash_attn = True
-    _supports_flash_attn_2 = True
-    _supports_cache_class = True
-    _supports_static_cache = True
-    _supports_quantized_cache = True
-    _supports_sdpa = True
-
-    def _init_weights(self, module):
-        std = self.config.initializer_range
-        if isinstance(module, nn.Linear | nn.Conv2d):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-
-class Eagle25VLForConditionalGeneration(Eagle25VLPreTrainedModel, GenerationMixin):
-    config_class = Eagle25VLConfig
-
-    def __init__(self, config: Eagle25VLConfig, vision_model=None, language_model=None):
-        super().__init__(config)
-
-        image_size = config.force_image_size or config.vision_config.image_size
-        patch_size = config.vision_config.patch_size
-        self.patch_size = patch_size
-        if config.use_pixel_shuffle:
-            self.num_image_token = int((image_size // patch_size) ** 2 * (config.downsample_ratio**2))
-        else:
-            self.num_image_token = int((image_size // patch_size) ** 2)
-
-        self.select_layer = config.select_layer
-        self.downsample_ratio = config.downsample_ratio
-        self.loss_version = config.loss_version
-        self.mlp_checkpoint = config.mlp_checkpoint
-        self.use_pixel_shuffle = config.use_pixel_shuffle
-        self.mlp_connector_layers = config.mlp_connector_layers
-        logger.info(f"num_image_token: {self.num_image_token}")
-        logger.info(f"mlp_checkpoint: {self.mlp_checkpoint}")
-        if vision_model is not None:
-            self.vision_model = vision_model
-        else:
-            if config.vision_config.model_type == "siglip_vision_model":
-                config.vision_config._attn_implementation = "flash_attention_2"
-                self.vision_model = SiglipVisionModel(config.vision_config)
-            else:
-                raise NotImplementedError(f"{config.vision_config.model_type} is not implemented.")
-
-        if language_model is not None:
-            self.language_model = language_model
-        else:
-            if config.text_config.architectures[0] == "LlamaForCausalLM":
-                self.language_model = LlamaForCausalLM(config.text_config)
-            elif config.text_config.architectures[0] == "Phi3ForCausalLM":
-                raise NotImplementedError("Phi3 is not implemented.")
-                # self.language_model = Phi3ForCausalLM(config.text_config)
-            elif config.text_config.architectures[0] == "Qwen2ForCausalLM":
-                assert config.text_config._attn_implementation == "flash_attention_2", (
-                    f"Qwen2 must use flash_attention_2 but got {config.text_config._attn_implementation}"
-                )
-                self.language_model = Qwen2ForCausalLM(config.text_config)
-            elif config.text_config.architectures[0] == "Qwen3ForCausalLM":
-                self.language_model = Qwen3ForCausalLM(config.text_config)
-            else:
-                raise NotImplementedError(f"{config.text_config.architectures[0]} is not implemented.")
-
-        vit_hidden_size = config.vision_config.hidden_size
-        llm_hidden_size = config.text_config.hidden_size
-
-        if config.mlp_connector_layers == 2:
-            self.mlp1 = nn.Sequential(
-                nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio) ** 2),
-                nn.Linear(vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_hidden_size),
-                nn.GELU(),
-                nn.Linear(llm_hidden_size, llm_hidden_size),
-            )
-        elif config.mlp_connector_layers == 1 and config.use_pixel_shuffle:
-            self.mlp1 = nn.Sequential(
-                nn.Linear(vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_hidden_size),
-            )
-        elif config.mlp_connector_layers == 1 and not config.use_pixel_shuffle:
-            self.mlp1 = nn.Sequential(
-                nn.Linear(vit_hidden_size, llm_hidden_size),
-            )
-        else:
-            raise NotImplementedError(f"{config.mlp_connector_layers} is not implemented.")
-
-        self.image_token_index = config.image_token_index
-        self.neftune_alpha = None
-
-        if config.use_backbone_lora:
-            self.wrap_backbone_lora(r=config.use_backbone_lora, lora_alpha=2 * config.use_backbone_lora)
-
-        self.use_llm_lora = config.use_llm_lora
-        if config.use_llm_lora:
-            self.wrap_llm_lora(r=config.use_llm_lora, lora_alpha=2 * config.use_llm_lora)
-
-        self.check_forward_kwargs()
-
-    def check_forward_kwargs(self):
-        # We intentionally avoid using **kwargs in forward because Hugging Face Transformers
-        # has special handling for functions with **kwargs parameters that would affect
-        # how our model is processed during training and inference.
-        forward_params = inspect.signature(self.forward).parameters
-        assert not any(k.kind == inspect.Parameter.VAR_KEYWORD for k in forward_params.values())
-
-    def wrap_backbone_lora(self, r=128, lora_alpha=256, lora_dropout=0.05):
-        lora_config = LoraConfig(
-            r=r,
-            target_modules=[
-                "self_attn.q_proj",
-                "self_attn.k_proj",
-                "self_attn.v_proj",
-                "self_attn.out_proj",
-                "mlp.fc1",
-                "mlp.fc2",
-            ],
-            lora_alpha=lora_alpha,
-            lora_dropout=lora_dropout,
-        )
-        self.vision_model = get_peft_model(self.vision_model, lora_config)
-        self.vision_model.print_trainable_parameters()
-
-    def wrap_llm_lora(self, r=128, lora_alpha=256, lora_dropout=0.05):
-        lora_config = LoraConfig(
-            r=r,
-            target_modules=[
-                "self_attn.q_proj",
-                "self_attn.k_proj",
-                "self_attn.v_proj",
-                "self_attn.o_proj",
-                "mlp.gate_proj",
-                "mlp.down_proj",
-                "mlp.up_proj",
-            ],
-            lora_alpha=lora_alpha,
-            lora_dropout=lora_dropout,
-            task_type="CAUSAL_LM",
-        )
-        self.language_model = get_peft_model(self.language_model, lora_config)
-        self.language_model.enable_input_require_grads()
-        self.language_model.print_trainable_parameters()
-        self.use_llm_lora = True
-
-    def forward(
-        self,
-        pixel_values: torch.FloatTensor,
-        input_ids: torch.LongTensor = None,
-        attention_mask: torch.Tensor | None = None,
-        position_ids: torch.LongTensor | None = None,
-        image_flags: torch.LongTensor | None = None,
-        past_key_values: list[torch.FloatTensor] | None = None,
-        labels: torch.LongTensor | None = None,
-        use_cache: bool | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
-        num_tiles_list: list[torch.Tensor] | None = None,
-    ) -> tuple | CausalLMOutputWithPast:
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        input_embeds = self.language_model.get_input_embeddings()(input_ids)
-
-        vit_embeds = self.extract_feature(pixel_values)
-
-        if image_flags is not None:
-            image_flags = image_flags.view(-1)
-            vit_embeds = vit_embeds[image_flags == 1]
-
-        b, n, c = input_embeds.shape
-        input_embeds = input_embeds.reshape(b * n, c)
-
-        input_ids = input_ids.reshape(b * n)
-        selected = input_ids == self.image_token_index
-        try:
-            input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds.reshape(-1, c)
-        except Exception as e:
-            vit_embeds = vit_embeds.reshape(-1, c)
-            print(
-                f"warning: {e}, input_embeds[selected].shape={input_embeds[selected].shape}, "
-                f"vit_embeds.shape={vit_embeds.shape}"
-            )
-            n_token = selected.sum()
-            input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds[:n_token]
-
-        input_embeds = input_embeds.reshape(b, n, c)
-
-        outputs = self.language_model(
-            inputs_embeds=input_embeds,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-        )
-        logits = outputs.logits
-
-        loss = None
-        if labels is not None:
-            # Shift so that tokens < n predict n
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = CrossEntropyLoss()
-            shift_logits = shift_logits.view(-1, self.language_model.config.vocab_size)
-            shift_labels = shift_labels.view(-1)
-            # Enable model parallelism
-            shift_labels = shift_labels.to(shift_logits.device)
-            loss = loss_fct(shift_logits, shift_labels)
-
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return (loss,) + output if loss is not None else output
-
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-    def pixel_shuffle(self, x, scale_factor=0.5):
-        n, w, h, c = x.size()
-        # N, W, H, C --> N, W, H * scale, C // scale
-        x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
-        # N, W, H * scale, C // scale --> N, H * scale, W, C // scale
-        x = x.permute(0, 2, 1, 3).contiguous()
-        # N, H * scale, W, C // scale --> N, H * scale, W * scale, C // (scale ** 2)
-        x = x.view(n, int(h * scale_factor), int(w * scale_factor), int(c / (scale_factor * scale_factor)))
-
-        x = x.permute(0, 2, 1, 3).contiguous()
-        return x
-
-    def extract_feature(self, pixel_values):
-        if self.select_layer == -1:
-            vit_embeds = self.vision_model(
-                pixel_values=pixel_values, output_hidden_states=False, return_dict=True
-            )
-            if hasattr(vit_embeds, "last_hidden_state"):
-                vit_embeds = vit_embeds.last_hidden_state
-
-        else:
-            vit_embeds = self.vision_model(
-                pixel_values=pixel_values, output_hidden_states=True, return_dict=True
-            ).hidden_states[self.select_layer]
-
-        if self.use_pixel_shuffle:
-            h = w = int(vit_embeds.shape[1] ** 0.5)
-            vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
-            vit_embeds = self.pixel_shuffle(
-                vit_embeds, scale_factor=self.downsample_ratio
-            )  # torch.Size([B, 1024, 1024]) -> torch.Size([B, 16, 16, 4096])
-            vit_embeds = vit_embeds.reshape(
-                vit_embeds.shape[0], -1, vit_embeds.shape[-1]
-            )  # torch.Size([B, 16, 16, 4096]) -> torch.Size([B, 256, 4096])
-
-        if self.mlp_checkpoint and vit_embeds.requires_grad:
-            vit_embeds = cp.checkpoint(self.mlp1, vit_embeds)
-        else:
-            vit_embeds = self.mlp1(vit_embeds)
-
-        return vit_embeds
-
-    @torch.no_grad()
-    def generate(
-        self,
-        pixel_values: torch.FloatTensor | None = None,
-        input_ids: torch.FloatTensor | None = None,
-        attention_mask: torch.LongTensor | None = None,
-        visual_features: torch.FloatTensor | None = None,
-        generation_config: GenerationConfig | None = None,
-        output_hidden_states: bool | None = None,
-        image_sizes: list[tuple[int, int]] | None = None,
-        **generate_kwargs,
-    ) -> torch.LongTensor:
-        if pixel_values is not None:
-            if visual_features is not None:
-                vit_embeds = visual_features
-            else:
-                vit_embeds = self.extract_feature(pixel_values)
-
-            input_embeds = self.language_model.get_input_embeddings()(input_ids)
-            b, n, c = input_embeds.shape
-            input_embeds = input_embeds.reshape(b * n, c)
-
-            input_ids = input_ids.reshape(b * n)
-            selected = input_ids == self.config.image_token_index
-            assert selected.sum() != 0
-            input_embeds[selected] = vit_embeds.reshape(-1, c).to(input_embeds.device)
-
-            input_embeds = input_embeds.reshape(b, n, c)
-        else:
-            input_embeds = self.language_model.get_input_embeddings()(input_ids)
-
-        if "use_cache" not in generate_kwargs:
-            generate_kwargs["use_cache"] = True
-
-        outputs = self.language_model.generate(
-            inputs_embeds=input_embeds,
-            attention_mask=attention_mask,
-            generation_config=generation_config,
-            output_hidden_states=output_hidden_states,
-            **generate_kwargs,
-        )
-
-        return outputs
-
-    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.get_input_embeddings
-    def get_input_embeddings(self):
-        return self.language_model.get_input_embeddings()
-
-    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.set_input_embeddings
-    def set_input_embeddings(self, value):
-        self.language_model.set_input_embeddings(value)
-
-    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.get_output_embeddings
-    def get_output_embeddings(self):
-        return self.language_model.get_output_embeddings()
-
-    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.set_output_embeddings
-    def set_output_embeddings(self, new_embeddings):
-        self.language_model.set_output_embeddings(new_embeddings)
-
-    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.set_decoder
-    def set_decoder(self, decoder):
-        self.language_model.set_decoder(decoder)
-
-    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.get_decoder
-    def get_decoder(self):
-        return self.language_model.get_decoder()

src/lerobot/policies/groot/eagle2_hg_model/processing_eagle2_5_vl.py

@@ -1,541 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Processor class for Eagle25VL.
-copy from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava_onevision/processing_llava_onevision.py
-"""
-
-import base64
-import os
-import re
-from io import BytesIO
-
-import requests
-import torch
-from PIL import Image
-from transformers.feature_extraction_utils import BatchFeature
-from transformers.image_utils import ImageInput
-from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
-from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
-from transformers.utils import logging
-from transformers.video_utils import VideoInput
-
-logger = logging.get_logger(__name__)
-
-
-FRAME_FACTOR = 2
-FPS = 2.0
-FPS_MIN_FRAMES = 4
-FPS_MAX_FRAMES = 256
-
-
-def to_rgb(pil_image: Image.Image) -> Image.Image:
-    if pil_image.mode == "RGBA":
-        white_background = Image.new("RGB", pil_image.size, (255, 255, 255))
-        white_background.paste(pil_image, mask=pil_image.split()[3])  # Use alpha channel as mask
-        return white_background
-    else:
-        return pil_image.convert("RGB")
-
-
-def fetch_image(ele: dict[str, str | Image.Image]) -> Image.Image:
-    image = ele["image"] if "image" in ele else ele["image_url"]
-    image_obj = None
-    if isinstance(image, Image.Image):
-        image_obj = image
-    elif image.startswith("http://") or image.startswith("https://"):
-        response = requests.get(image, stream=True, timeout=10)
-        image_obj = Image.open(BytesIO(response.content))
-    elif image.startswith("file://"):
-        image_obj = Image.open(image[7:])
-    elif image.startswith("data:image"):
-        if "base64," in image:
-            _, base64_data = image.split("base64,", 1)
-            data = base64.b64decode(base64_data)
-            image_obj = Image.open(BytesIO(data))
-    else:
-        image_obj = Image.open(image)
-    if image_obj is None:
-        raise ValueError(
-            f"Unrecognized image input, support local path, http url, base64 and PIL.Image, got {image}"
-        )
-    image = to_rgb(image_obj)
-    if "scale_factor" in ele:
-        scale_factor = ele["scale_factor"]
-        image = image.resize((image.width * scale_factor, image.height * scale_factor), Image.BILINEAR)
-    return image
-
-
-class Eagle25VLProcessorKwargs(ProcessingKwargs, total=False):
-    # see processing_utils.ProcessingKwargs documentation for usage.
-    _defaults = {
-        "text_kwargs": {
-            "padding": False,
-        },
-        "images_kwargs": {},
-        "videos_kwargs": {"max_dynamic_tiles": 1},
-    }
-
-
-class Eagle25VLProcessor(ProcessorMixin):
-    r"""
-    Constructs a Eagle25VL processor which wraps a Eagle25VL video processor, Eagle25VL image processor and a Eagle25VL tokenizer into a single processor.
-
-    [`Eagle25VLProcessor`] offers all the functionalities of [`Eagle25VLVideoProcessor`], [`Eagle25VLImageProcessor`] and [`Eagle25VLTokenizer`]. See the
-    [`~Eagle25VLVideoProcessor.__call__`], [`~Eagle25VLProcessor.__call__`] and [`~Eagle25VLProcessor.decode`] for more information.
-
-    Args:
-        image_processor ([`LlavaOnevisionImageProcessor`], *optional*):
-            The image processor is a required input.
-        tokenizer ([`LlamaTokenizerFast`], *optional*):
-            The tokenizer is a required input.
-        num_image_tokens (`int`, *optional*):
-            Number of image tokens for one imagethat will be returned by vision tower.
-        vision_feature_select_strategy (`str`, *optional*):
-            The feature selection strategy used to select the vision feature from the vision backbone.
-            Should be same as in model's config
-        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
-            in a chat into a tokenizable string.
-        image_token (`str`, *optional*, defaults to `"<image>"`):
-            Special token used to denote image location.
-        video_token (`str`, *optional*, defaults to `"<video>"`):
-            Special token used to denote video location.
-    """
-
-    attributes = ["image_processor", "tokenizer"]
-    valid_kwargs = [
-        "chat_template",
-        "num_image_tokens",
-        "vision_feature_select_strategy",
-        "image_token",
-        "video_token",
-        "images_kwargs",
-        "videos_kwargs",
-        "text_kwargs",
-    ]
-    tokenizer_class = "AutoTokenizer"
-
-    def __init__(
-        self,
-        image_processor=None,
-        tokenizer=None,
-        vision_feature_select_strategy=None,
-        chat_template=None,
-        image_token="<IMG_CONTEXT>",  # nosec: B107
-        video_token="<IMG_CONTEXT>",  # nosec: B107
-        tokens_per_tile=256,
-        image_placeholder="image",
-        video_placeholder="video",
-        image_start_token="<img>",
-        image_end_token="</img>",
-        **kwargs,
-    ):
-        self.vision_feature_select_strategy = vision_feature_select_strategy
-        self.image_token = tokenizer.image_token if hasattr(tokenizer, "image_token") else image_token
-        self.video_token = tokenizer.video_token if hasattr(tokenizer, "video_token") else video_token
-        self.image_token_id = (
-            tokenizer.image_token_id
-            if getattr(tokenizer, "image_token_id", None)
-            else tokenizer.convert_tokens_to_ids(self.image_token)
-        )
-        self.video_token_id = (
-            tokenizer.video_token_id
-            if getattr(tokenizer, "video_token_id", None)
-            else tokenizer.convert_tokens_to_ids(self.video_token)
-        )
-        self.image_placeholder = image_placeholder
-        self.video_placeholder = video_placeholder
-        self.tokens_per_tile = tokens_per_tile
-        self.image_start_token = image_start_token
-        self.image_end_token = image_end_token
-        if "auto_map" in kwargs:
-            self.auto_map = kwargs["auto_map"]
-        super().__init__(image_processor, tokenizer, chat_template=chat_template)
-
-    def replace_media_placeholder(
-        self, text, image_list, video_list, timestamps_list, fps_list, **output_kwargs
-    ):
-        num_of_images_in_this_sample = 0
-        num_of_videos_in_this_sample = 0
-        # Regular expression pattern to match formats like <image-1> or <video-2>
-        pattern = re.compile(rf"<({self.image_placeholder}|{self.video_placeholder})-(\d+)>")
-        unified_frame_list = []
-
-        # image_min_dynamic_tiles = output_kwargs["images_kwargs"].get(
-        #     "min_dynamic_tiles", self.image_processor.min_dynamic_tiles
-        # )
-        # image_max_dynamic_tiles = output_kwargs["images_kwargs"].get(
-        #     "max_dynamic_tiles", self.image_processor.max_dynamic_tiles
-        # )
-        # image_use_thumbnail = output_kwargs["images_kwargs"].get(
-        #     "use_thumbnail", self.image_processor.use_thumbnail
-        # )
-        video_min_dynamic_tiles = output_kwargs["videos_kwargs"].get(
-            "min_dynamic_tiles", self.image_processor.min_dynamic_tiles
-        )
-        video_max_dynamic_tiles = output_kwargs["videos_kwargs"].get(
-            "max_dynamic_tiles", self.image_processor.max_dynamic_tiles
-        )
-        video_use_thumbnail = output_kwargs["videos_kwargs"].get(
-            "use_thumbnail", self.image_processor.use_thumbnail
-        )
-
-        tile_size = self.image_processor.size.get("height", 448)
-
-        # Function to replace tags in a single text
-        def replace_in_text(text):
-            # repl callback function for each match replacement operation
-            def repl(match):
-                nonlocal unified_frame_list
-                nonlocal num_of_images_in_this_sample
-                nonlocal num_of_videos_in_this_sample
-                media_type = match.group(1)  # 'image' or 'video'
-                idx_in_list = int(match.group(2)) - 1  # Convert to list index (0-based)
-                # Select the corresponding path based on media type
-                idx_mapper = {
-                    0: "first",
-                    1: "second",
-                    2: "third",
-                    3: "fourth",
-                    4: "fifth",
-                    5: "sixth",
-                    6: "seventh",
-                    7: "eighth",
-                    8: "ninth",
-                    9: "tenth",
-                }
-                if media_type == "image":
-                    image_inputs = self.image_processor(
-                        images=[image_list[idx_in_list]],
-                        videos=None,
-                        **output_kwargs["images_kwargs"],
-                    )
-                    if isinstance(image_inputs["pixel_values"], list):
-                        _pv = image_inputs["pixel_values"]
-                        if _pv and isinstance(_pv[0], list):
-                            _pv = [t for sub in _pv for t in sub]
-                        image_inputs["pixel_values"] = torch.stack(
-                            [t if isinstance(t, torch.Tensor) else torch.as_tensor(t) for t in _pv]
-                        )
-                    num_all_tiles = image_inputs["pixel_values"].shape[0]
-                    special_placeholder = f"<image {idx_in_list + 1}>{self.image_start_token}{self.image_token * num_all_tiles * self.tokens_per_tile}{self.image_end_token}"
-                    unified_frame_list.append(image_inputs)
-                    num_of_images_in_this_sample += 1
-
-                elif media_type == "video":
-                    video_inputs = self.image_processor(
-                        images=None,
-                        videos=[video_list[idx_in_list]],
-                        **output_kwargs["videos_kwargs"],
-                    )
-                    if isinstance(video_inputs["pixel_values"], list):
-                        _pv = video_inputs["pixel_values"]
-                        if _pv and isinstance(_pv[0], list):
-                            _pv = [t for sub in _pv for t in sub]
-                        video_inputs["pixel_values"] = torch.stack(
-                            [t if isinstance(t, torch.Tensor) else torch.as_tensor(t) for t in _pv]
-                        )
-                    num_all_tiles = video_inputs["pixel_values"].shape[0]
-                    image_sizes = video_inputs["image_sizes"]
-                    if timestamps_list is not None and -1 not in timestamps_list:
-                        frame_timestamps = timestamps_list[idx_in_list]
-                    else:
-                        frame_timestamps = None
-                    sampled_fps = fps_list[idx_in_list] if fps_list is not None else None
-
-                    num_of_tiles_each_frame = [
-                        self.get_number_tiles_based_on_image_size(
-                            image_size,
-                            video_min_dynamic_tiles,
-                            video_max_dynamic_tiles,
-                            video_use_thumbnail,
-                            tile_size,
-                        )
-                        for image_size in image_sizes
-                    ]
-                    assert sum(num_of_tiles_each_frame) == num_all_tiles, (
-                        f"The number of tiles in each frame is not equal to the total number of tiles: {sum(num_of_tiles_each_frame)} != {num_all_tiles}"
-                    )
-
-                    if frame_timestamps is not None:
-                        assert len(frame_timestamps) == len(num_of_tiles_each_frame), (
-                            f"The number of timestamps is not equal to the number of frames: {len(frame_timestamps)} != {len(num_of_tiles_each_frame)}"
-                        )
-                        special_placeholder = [
-                            f"Frame {i + 1} sample at {frame_timestamps[i]:.2f}s: {self.image_start_token}{self.image_token * num_of_tiles * self.tokens_per_tile}{self.image_end_token}"
-                            for i, num_of_tiles in enumerate(num_of_tiles_each_frame)
-                        ]
-                    else:
-                        special_placeholder = [
-                            f"Frame {i + 1}: {self.image_start_token}{self.image_token * num_of_tiles * self.tokens_per_tile}{self.image_end_token}"
-                            for i, num_of_tiles in enumerate(num_of_tiles_each_frame)
-                        ]
-
-                    if sampled_fps is not None:
-                        special_placeholder = (
-                            f"The {idx_mapper[idx_in_list]} video sampled with {sampled_fps:.2f} fps: "
-                            + "".join(special_placeholder)
-                        )
-                    else:
-                        special_placeholder = f"The {idx_mapper[idx_in_list]} video: " + "".join(
-                            special_placeholder
-                        )
-                    unified_frame_list.append(video_inputs)
-                    num_of_videos_in_this_sample += 1
-                else:
-                    raise ValueError(f"Unknown media type: {media_type}")
-                return special_placeholder
-
-            return pattern.sub(repl, text)
-
-        text = replace_in_text(text)
-        if len(unified_frame_list) > 0:
-
-            def _to_tensor(v):
-                if isinstance(v, torch.Tensor):
-                    return v
-                if isinstance(v, list):
-                    if v and isinstance(v[0], list):
-                        v = [t for sub in v for t in sub]
-                    return torch.stack([t if isinstance(t, torch.Tensor) else torch.as_tensor(t) for t in v])
-                return torch.as_tensor(v)
-
-            pixel_values = torch.cat([_to_tensor(frame["pixel_values"]) for frame in unified_frame_list])
-            image_sizes = torch.cat([_to_tensor(frame["image_sizes"]) for frame in unified_frame_list])
-        else:
-            pixel_values = None
-            image_sizes = None
-        return (
-            text,
-            pixel_values,
-            image_sizes,
-            num_of_images_in_this_sample,
-            num_of_videos_in_this_sample,
-        )
-
-    def __call__(
-        self,
-        images: ImageInput = None,
-        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
-        audio=None,
-        videos: VideoInput = None,
-        **kwargs: Unpack[Eagle25VLProcessorKwargs],
-    ) -> BatchFeature:
-        """
-        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
-        and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
-        the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
-        LlavaNextImageProcessor's [`~LlavaNextImageProcessor.__call__`] if `images` is not `None`. Please refer to the docstring
-        of the above two methods for more information.
-
-        Args:
-            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
-                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
-                tensor. Both channels-first and channels-last formats are supported.
-            text (`str`, `List[str]`, `List[List[str]]`):
-                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
-                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
-                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
-            videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
-                The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
-
-        Returns:
-            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
-
-            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
-            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
-              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
-              `None`).
-            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
-            - **pixel_values_videos** -- Pixel values of a video input to be fed to a model. Returned when `videos` is not `None`.
-            - **image_sizes** -- Size of each image that will be used to unpad an image. Returned when `images` is not `None`.
-        """
-
-        output_kwargs = self._merge_kwargs(
-            Eagle25VLProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
-        )
-
-        if isinstance(text, str):
-            text_list = [text]
-        elif not isinstance(text, list) and not isinstance(text[0], str):
-            raise ValueError("Invalid input text. Please provide a string, or a list of strings")
-        elif isinstance(text, list) and isinstance(text[0], str):
-            text_list = text
-
-        if images is None:
-            images = []
-        if videos is None:
-            videos = []
-
-        pixel_values_list = []
-        image_sizes_list = []
-        new_sample_list = []
-        image_start_idx = 0
-        video_start_idx = 0
-        timestamps_batch = output_kwargs["videos_kwargs"].pop("timestamps", None)
-        fps_batch = output_kwargs["videos_kwargs"].pop("fps", None)
-        for sample in text_list:
-            timestamps_list = timestamps_batch[video_start_idx:] if timestamps_batch is not None else None
-            fps_list = fps_batch[video_start_idx:] if fps_batch is not None else None
-            (
-                sample,
-                pixel_values,
-                image_sizes,
-                num_of_images_in_this_sample,
-                num_of_videos_in_this_sample,
-            ) = self.replace_media_placeholder(
-                sample,
-                images[image_start_idx:],
-                videos[video_start_idx:],
-                timestamps_list,
-                fps_list,
-                **output_kwargs,
-            )
-            new_sample_list.append(sample)
-            if pixel_values is not None:
-                pixel_values_list.append(pixel_values)
-                image_sizes_list.append(image_sizes)
-            image_start_idx += num_of_images_in_this_sample
-            video_start_idx += num_of_videos_in_this_sample
-
-        if len(pixel_values_list) > 0:
-            image_inputs = {
-                "pixel_values": torch.cat(pixel_values_list),
-                "image_sizes": torch.cat(image_sizes_list),
-            }
-        else:
-            image_inputs = {}
-        video_inputs = {}
-        text_inputs = self.tokenizer(new_sample_list, **output_kwargs["text_kwargs"])
-        return BatchFeature(data={**text_inputs, **image_inputs, **video_inputs})
-
-    def get_number_tiles_based_on_image_size(
-        self, image_size: tuple, min_num: int, max_num: int, use_thumbnail: bool, tile_size: int
-    ) -> int:
-        """
-        Get the number of tiles based on the image size.
-        """
-        orig_height, orig_width = image_size
-        aspect_ratio = orig_width / orig_height
-        # calculate the existing image aspect ratio
-        target_ratios = {
-            (i, j)
-            for n in range(min_num, max_num + 1)
-            for i in range(1, n + 1)
-            for j in range(1, n + 1)
-            if i * j <= max_num and i * j >= min_num
-        }
-        target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
-
-        # find the closest aspect ratio to the target
-        target_aspect_ratio = self.image_processor.find_closest_aspect_ratio(
-            aspect_ratio, target_ratios, orig_width, orig_height, tile_size
-        )
-        tiles_num = target_aspect_ratio[0] * target_aspect_ratio[1]
-        if use_thumbnail and tiles_num > 1:
-            tiles_num += 1
-        return tiles_num
-
-    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
-    def batch_decode(self, *args, **kwargs):
-        """
-        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
-        refer to the docstring of this method for more information.
-        """
-        return self.tokenizer.batch_decode(*args, **kwargs)
-
-    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
-    def decode(self, *args, **kwargs):
-        """
-        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
-        the docstring of this method for more information.
-        """
-        return self.tokenizer.decode(*args, **kwargs)
-
-    @property
-    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
-    def model_input_names(self):
-        tokenizer_input_names = self.tokenizer.model_input_names
-        image_processor_input_names = self.image_processor.model_input_names
-        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
-
-    # override to save video-config in a separate config file
-    def save_pretrained(self, save_directory, **kwargs):
-        if os.path.isfile(save_directory):
-            raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file")
-        os.makedirs(save_directory, exist_ok=True)
-
-        outputs = super().save_pretrained(save_directory, **kwargs)
-        return outputs
-
-    # override to load video-config from a separate config file
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
-        processor = super().from_pretrained(pretrained_model_name_or_path, **kwargs)
-
-        # if return_unused_kwargs a tuple is returned where the second element is 'unused_kwargs'
-        if isinstance(processor, tuple):
-            processor = processor[0]
-        return processor
-
-    # Copy from https://github.com/QwenLM/Qwen2.5-VL/blob/main/qwen-vl-utils/src/qwen_vl_utils/vision_process.py
-    def process_vision_info(
-        self,
-        conversations: list[dict] | list[list[dict]],
-        return_video_kwargs: bool = False,
-    ) -> tuple[list[Image.Image] | None, list[torch.Tensor | list[Image.Image]] | None, dict | None]:
-        vision_infos = self.extract_vision_info(conversations)
-        ## Read images or videos
-        image_inputs = []
-        video_inputs = []
-        video_sample_fps_list = []
-        video_timestamps_list = []
-        for vision_info in vision_infos:
-            if "image" in vision_info or "image_url" in vision_info:
-                image_inputs.append(fetch_image(vision_info))
-            else:
-                raise ValueError("image, image_url or video should in content.")
-        if len(image_inputs) == 0:
-            image_inputs = None
-        if len(video_inputs) == 0:
-            video_inputs = None
-        if return_video_kwargs:
-            return (
-                image_inputs,
-                video_inputs,
-                {"fps": video_sample_fps_list, "timestamps": video_timestamps_list},
-            )
-        return image_inputs, video_inputs
-
-    def extract_vision_info(self, conversations: list[dict] | list[list[dict]]) -> list[dict]:
-        vision_infos = []
-        if isinstance(conversations[0], dict):
-            conversations = [conversations]
-        for conversation in conversations:
-            for message in conversation:
-                if isinstance(message["content"], list):
-                    for ele in message["content"]:
-                        if (
-                            "image" in ele
-                            or "image_url" in ele
-                            or "video" in ele
-                            or ele["type"] in ("image", "image_url", "video")
-                        ):
-                            vision_infos.append(ele)
-        return vision_infos
-
-
-__all__ = ["Eagle25VLProcessor"]

src/lerobot/policies/groot/groot_n1.py

@@ -1,380 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from pathlib import Path
-from typing import TYPE_CHECKING, Any
-
-import numpy as np
-import torch
-import torch.nn as nn
-from huggingface_hub import snapshot_download
-from huggingface_hub.errors import HFValidationError, RepositoryNotFoundError
-
-from lerobot.utils.import_utils import _transformers_available
-
-# Conditional import for type checking and lazy loading
-if TYPE_CHECKING or _transformers_available:
-    from huggingface_hub.dataclasses import strict
-    from transformers import AutoConfig, AutoModel, PretrainedConfig, PreTrainedModel
-    from transformers.feature_extraction_utils import BatchFeature
-else:
-
-    def strict(cls):
-        return cls
-
-    AutoConfig = None
-    AutoModel = None
-    PretrainedConfig = object
-    PreTrainedModel = object
-    BatchFeature = None
-
-try:
-    import tree
-except ImportError:
-    tree = None
-
-from lerobot.utils.constants import ACTION, HF_LEROBOT_HOME
-
-from .action_head.flow_matching_action_head import (
-    FlowmatchingActionHead,
-    FlowmatchingActionHeadConfig,
-)
-from .utils import ensure_eagle_cache_ready
-
-DEFAULT_VENDOR_EAGLE_PATH = str((Path(__file__).resolve().parent / "eagle2_hg_model").resolve())
-DEFAULT_TOKENIZER_ASSETS_REPO = "lerobot/eagle2hg-processor-groot-n1p5"
-
-
-class EagleBackbone(nn.Module):
-    def __init__(
-        self,
-        tune_llm: bool = False,
-        tune_visual: bool = False,
-        select_layer: int = -1,
-        reproject_vision: bool = False,
-        use_flash_attention: bool = False,
-        load_bf16: bool = False,
-        eagle_path: str = DEFAULT_VENDOR_EAGLE_PATH,
-        tokenizer_assets_repo: str = DEFAULT_TOKENIZER_ASSETS_REPO,
-        project_to_dim: int = 1536,
-    ):
-        """
-        Args:
-            tune_llm: whether to tune the LLM model (default: True)
-            tune_visual: whether to tune the visual model (default: False)
-        """
-        super().__init__()
-        assert not reproject_vision, "Reproject vision is not implemented here, set to False"
-
-        # Prefer loading Eagle model config from the cache directory where vendor files were copied.
-        vendor_dir = DEFAULT_VENDOR_EAGLE_PATH
-        cache_dir = HF_LEROBOT_HOME / tokenizer_assets_repo
-        try:
-            ensure_eagle_cache_ready(vendor_dir, cache_dir, tokenizer_assets_repo)
-        except Exception as exc:  # nosec: B110
-            print(f"[GROOT] Warning: failed to prepare Eagle cache for backbone: {exc}")
-
-        config = AutoConfig.from_pretrained(str(cache_dir), trust_remote_code=True)
-        self.eagle_model = AutoModel.from_config(config, trust_remote_code=True)
-
-        if project_to_dim is not None:
-            self.eagle_linear = torch.nn.Linear(2048, project_to_dim)
-        else:
-            self.eagle_linear = torch.nn.Identity()
-
-        # needed since we don't use these layers. Also saves compute
-        while len(self.eagle_model.language_model.model.layers) > select_layer:
-            self.eagle_model.language_model.model.layers.pop(-1)
-
-        self.select_layer = select_layer
-        self.set_trainable_parameters(tune_llm, tune_visual)
-
-    def set_trainable_parameters(self, tune_llm: bool, tune_visual: bool):
-        self.tune_llm = tune_llm
-        self.tune_visual = tune_visual
-        for p in self.parameters():
-            p.requires_grad = True
-        if not tune_llm:
-            self.eagle_model.language_model.requires_grad_(False)
-        if not tune_visual:
-            self.eagle_model.vision_model.requires_grad_(False)
-            self.eagle_model.mlp1.requires_grad_(False)
-        print(f"Tune backbone llm: {self.tune_llm}")
-        print(f"Tune backbone visual: {self.tune_visual}")
-        # Check if any parameters are still trainable. If not, print a warning.
-        if not tune_llm and not tune_visual:
-            for name, p in self.named_parameters():
-                if p.requires_grad:
-                    print(f"Backbone trainable parameter: {name}")
-        if not any(p.requires_grad for p in self.parameters()):
-            print("Warning: No backbone trainable parameters found.")
-
-    def set_frozen_modules_to_eval_mode(self):
-        """
-        Huggingface will call model.train() at each training_step. To ensure
-        the expected behaviors for modules like dropout, batchnorm, etc., we
-        need to call model.eval() for the frozen modules.
-        """
-        if self.training:
-            if self.eagle_model.language_model and not self.tune_llm:
-                self.eagle_model.language_model.eval()
-            if self.eagle_model.vision_model and not self.tune_visual:
-                self.eagle_model.vision_model.eval()
-
-    def prepare_input(self, batch: dict) -> BatchFeature:
-        return BatchFeature(data=batch)
-
-    def forward_eagle(self, vl_input: BatchFeature) -> BatchFeature:
-        eagle_prefix = "eagle_"
-        eagle_input = {
-            k.removeprefix(eagle_prefix): v for k, v in vl_input.items() if k.startswith(eagle_prefix)
-        }
-        del eagle_input["image_sizes"]
-
-        eagle_output = self.eagle_model(**eagle_input, output_hidden_states=True, return_dict=True)
-        eagle_features = eagle_output.hidden_states[self.select_layer]
-
-        eagle_features = self.eagle_linear(eagle_features)
-        return eagle_features, eagle_input["attention_mask"]
-
-    def forward(self, vl_input: BatchFeature) -> BatchFeature:
-        self.set_frozen_modules_to_eval_mode()
-
-        eagle_embeds, eagle_mask = self.forward_eagle(vl_input)
-
-        # YL (TODO HACK): to resolve DDP issue when tune_visual=True
-        # Ensure all trainable parameters in vision_model are used in the forward pass for DDP compatibility
-        if self.training and self.tune_visual:
-            dummy_term = torch.tensor(
-                0.0, device=eagle_embeds.device, dtype=eagle_embeds.dtype, requires_grad=True
-            )
-            for param in self.eagle_model.vision_model.parameters():
-                if param.requires_grad:
-                    dummy_term = dummy_term + 0.0 * param.sum()
-            eagle_embeds = eagle_embeds + dummy_term
-
-        return BatchFeature(
-            data={"backbone_features": eagle_embeds, "backbone_attention_mask": eagle_mask}
-        )  # [B, T2, hidden_size]
-
-
-BACKBONE_FEATURE_KEY = "backbone_features"
-ACTION_KEY = "action_pred"
-LOSS_KEY = "loss"
-ERROR_MSG = "Error: unexpected input/output"
-N_COLOR_CHANNELS = 3
-
-
-# config
-@strict
-class GR00TN15Config(PretrainedConfig):
-    model_type = "gr00t_n1_5"
-
-    backbone_cfg: dict[str, Any] | None = None
-    action_head_cfg: dict[str, Any] | None = None
-    action_horizon: int = 0
-    action_dim: int = 0
-    compute_dtype: str = "float32"
-
-    def __post_init__(self, **kwargs):
-        self.backbone_cfg = {} if self.backbone_cfg is None else self.backbone_cfg
-        self.action_head_cfg = {} if self.action_head_cfg is None else self.action_head_cfg
-        super().__post_init__(**kwargs)
-
-
-# real model
-class GR00TN15(PreTrainedModel):
-    supports_gradient_checkpointing = True
-    config_class = GR00TN15Config
-    """
-    we expect the backbone output to have a key 'backbone_features' with shape (batch_size, n, hidden_size)
-    here n is variable and can be e.g. time, 1 or user specified
-    we expect the action head output to have a key 'action_pred' with shape (batch_size, time, action_dim) during inference time
-    we expect these to have type BatchFeature, and they can of course have many other user specified keys too
-    """
-
-    def __init__(
-        self,
-        config: GR00TN15Config,
-        local_model_path: str,
-    ):
-        assert isinstance(config.backbone_cfg, dict)
-        assert isinstance(config.action_head_cfg, dict)
-
-        super().__init__(config)
-        self.local_model_path = local_model_path
-
-        self.backbone = EagleBackbone(**config.backbone_cfg)
-        action_head_cfg = FlowmatchingActionHeadConfig(**config.action_head_cfg)
-        self.action_head = FlowmatchingActionHead(action_head_cfg)
-
-        self.action_horizon = config.action_horizon
-        self.action_dim = config.action_dim
-        self.compute_dtype = config.compute_dtype
-        self.post_init()
-
-    def validate_inputs(self, inputs):
-        # NOTE -- this should be handled internally by the model
-        # however, doing that will likely be breaking changes -- so we'll need to do it after the deadline
-
-        detected_error = False
-        error_msg = ERROR_MSG
-        if ACTION in inputs:
-            action = inputs[ACTION]
-            # In inference, action may be omitted or None; validate only when it's a tensor.
-            if action is None:
-                pass  # allow None during inference
-            elif isinstance(action, torch.Tensor):
-                shape_ok = (
-                    len(action.shape) == 3
-                    and action.shape[1] == self.action_horizon
-                    and action.shape[2] == self.action_dim
-                )
-                if not shape_ok:
-                    error_msg += f"\n{action.shape=}"
-                    detected_error = True
-            else:
-                # Unexpected non-tensor type provided for action
-                error_msg += f"\nInvalid type for action: {type(action)}"
-                detected_error = True
-
-        if "video" in inputs:
-            video = inputs["video"]
-            type_ok = isinstance(video, np.ndarray)
-            dtype_ok = video.dtype == np.uint8
-            shape_ok = len(video.shape) == 6 and video.shape[3] == N_COLOR_CHANNELS
-            if not type_ok:
-                error_msg += f"\n{type(video)=}"
-                detected_error = True
-            if not dtype_ok:
-                error_msg += f"\n{video.dtype=}"
-                detected_error = True
-            if not shape_ok:
-                error_msg += f"\n{video.shape=}"
-                detected_error = True
-
-        if detected_error:
-            raise ValueError(error_msg)
-
-    def validate_data(self, action_head_outputs, backbone_outputs, is_training):
-        fail_backbone = (
-            not isinstance(backbone_outputs, BatchFeature) or BACKBONE_FEATURE_KEY not in backbone_outputs
-        )
-
-        if fail_backbone:
-            error_msg = ERROR_MSG
-            error_msg += f"\n{isinstance(backbone_outputs, BatchFeature)=}"
-            error_msg += f"\n{BACKBONE_FEATURE_KEY in backbone_outputs=}"
-            error_msg += f"\n{backbone_outputs[BACKBONE_FEATURE_KEY].shape=}"
-            raise ValueError(error_msg)
-
-        fail_action_head = (not isinstance(action_head_outputs, BatchFeature)) or not (
-            (
-                LOSS_KEY in action_head_outputs and is_training
-            )  # there might not be an action prediction during training
-            or (
-                ACTION_KEY in action_head_outputs
-                and action_head_outputs[ACTION_KEY].shape[1] == self.action_horizon
-                and action_head_outputs[ACTION_KEY].shape[2] == self.action_dim
-            )
-        )
-
-        if fail_action_head:
-            error_msg = ERROR_MSG
-            error_msg += f"\n{isinstance(action_head_outputs, BatchFeature)=}"
-            error_msg += f"\n{LOSS_KEY in action_head_outputs=}"
-            error_msg += f"\n{action_head_outputs[ACTION_KEY].shape=}"
-            error_msg += f"\n{self.action_horizon=}"
-            error_msg += f"\n{self.action_dim=}"
-            raise ValueError(error_msg)
-
-    def forward(
-        self,
-        inputs: dict,
-    ) -> BatchFeature:
-        backbone_inputs, action_inputs = self.prepare_input(inputs)
-        backbone_outputs = self.backbone(backbone_inputs)
-        action_head_outputs = self.action_head(backbone_outputs, action_inputs)
-        self.validate_data(action_head_outputs, backbone_outputs, is_training=True)
-        return action_head_outputs
-
-    def get_action(
-        self,
-        inputs: dict,
-    ) -> BatchFeature:
-        backbone_inputs, action_inputs = self.prepare_input(inputs)
-        # Because the behavior of backbones remains the same for training and inference, we can use `forward` for backbones.
-        backbone_outputs = self.backbone(backbone_inputs)
-        action_head_outputs = self.action_head.get_action(backbone_outputs, action_inputs)
-        self.validate_data(action_head_outputs, backbone_outputs, is_training=False)
-        return action_head_outputs
-
-    def prepare_input(self, inputs) -> tuple[BatchFeature, BatchFeature]:
-        self.validate_inputs(inputs)
-        backbone_inputs = self.backbone.prepare_input(inputs)
-        action_inputs = self.action_head.prepare_input(inputs)
-
-        def to_device_with_maybe_dtype(x):
-            # Cast floating tensors to a memory-efficient compute dtype when requested.
-            # Rationale: Upcasting backbone activations to fp32 significantly increases VRAM.
-            # When compute_dtype is bfloat16, prefer bf16 for activations to match AMP behavior.
-            if not isinstance(x, torch.Tensor):
-                return x
-            if torch.is_floating_point(x):
-                if getattr(self, "compute_dtype", None) == "bfloat16":
-                    return x.to(self.device, dtype=torch.bfloat16)
-                # Fallback: preserve previous behavior if not using bf16 compute
-                return x.to(self.device, dtype=self.action_head.dtype)
-            # Non-floating tensors: move device only
-            return x.to(self.device)
-
-        backbone_inputs = tree.map_structure(to_device_with_maybe_dtype, backbone_inputs)
-        action_inputs = tree.map_structure(to_device_with_maybe_dtype, action_inputs)
-        return backbone_inputs, action_inputs
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs):
-        tune_visual = kwargs.pop("tune_visual", True)
-        tune_llm = kwargs.pop("tune_llm", False)
-        tune_projector = kwargs.pop("tune_projector", True)
-        tune_diffusion_model = kwargs.pop("tune_diffusion_model", True)
-
-        print(f"Loading pretrained dual brain from {pretrained_model_name_or_path}")
-        print(f"Tune backbone vision tower: {tune_visual}")
-        print(f"Tune backbone LLM: {tune_llm}")
-        print(f"Tune action head projector: {tune_projector}")
-        print(f"Tune action head DiT: {tune_diffusion_model}")
-
-        # get the current model path being downloaded
-        try:
-            # NOTE(YL) This downloads the model to the local cache and returns the local path to the model
-            # saved in ~/.cache/huggingface/hub/
-            local_model_path = snapshot_download(pretrained_model_name_or_path, repo_type="model")
-            # HFValidationError, RepositoryNotFoundError
-        except (HFValidationError, RepositoryNotFoundError):
-            print(
-                f"Model not found or avail in the huggingface hub. Loading from local path: {pretrained_model_name_or_path}"
-            )
-            local_model_path = pretrained_model_name_or_path
-
-        pretrained_model = super().from_pretrained(
-            local_model_path, local_model_path=local_model_path, **kwargs
-        )
-
-        pretrained_model.backbone.set_trainable_parameters(tune_visual=tune_visual, tune_llm=tune_llm)
-        pretrained_model.action_head.set_trainable_parameters(
-            tune_projector=tune_projector, tune_diffusion_model=tune_diffusion_model
-        )
-        return pretrained_model

src/lerobot/policies/groot/groot_n1_7.py

@@ -0,0 +1,938 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import importlib
+import logging
+from contextlib import suppress
+from copy import deepcopy
+from typing import TYPE_CHECKING, Any
+
+import torch
+import torch.nn.functional as F  # noqa: N812
+from huggingface_hub import snapshot_download
+from huggingface_hub.errors import HFValidationError, RepositoryNotFoundError
+from torch import nn
+from torch.distributions import Beta
+
+from lerobot.utils.import_utils import _transformers_available, require_package
+
+from .action_head.cross_attention_dit import AlternateVLDiT, DiT, SelfAttentionTransformer
+from .configuration_groot import N1_7_DEFAULT_IMAGE_CROP_SIZE, N1_7_DEFAULT_IMAGE_TARGET_SIZE
+
+if TYPE_CHECKING or _transformers_available:
+    from transformers import (
+        AutoConfig,
+        AutoModel,
+        PretrainedConfig,
+        PreTrainedModel,
+        Qwen3VLConfig,
+        Qwen3VLForConditionalGeneration,
+    )
+    from transformers.feature_extraction_utils import BatchFeature
+else:
+    AutoConfig = None
+    AutoModel = None
+    PretrainedConfig = object
+    PreTrainedModel = object
+    BatchFeature = None
+    Qwen3VLConfig = None
+    Qwen3VLForConditionalGeneration = None
+
+try:
+    import tree
+except ImportError:
+    tree = None
+
+logger = logging.getLogger(__name__)
+
+
+GR00T_N1_7_DEFAULTS: dict[str, Any] = {
+    "model_dtype": "bfloat16",
+    "dtype": "bfloat16",
+    "model_name": "nvidia/Cosmos-Reason2-2B",
+    "backbone_model_type": "qwen",
+    "model_revision": None,
+    "tune_top_llm_layers": 0,
+    "backbone_embedding_dim": 2048,
+    "tune_llm": False,
+    "tune_visual": False,
+    "select_layer": 16,
+    "reproject_vision": False,
+    "use_flash_attention": False,
+    "load_bf16": False,
+    "backbone_trainable_params_fp32": True,
+    "image_crop_size": N1_7_DEFAULT_IMAGE_CROP_SIZE,
+    "image_target_size": N1_7_DEFAULT_IMAGE_TARGET_SIZE,
+    "shortest_image_edge": None,
+    "crop_fraction": None,
+    "random_rotation_angle": None,
+    "color_jitter_params": None,
+    "use_albumentations_transforms": True,
+    "extra_augmentation_config": None,
+    "formalize_language": True,
+    "apply_sincos_state_encoding": False,
+    "use_percentiles": True,
+    "use_relative_action": False,
+    "max_state_dim": 132,
+    "max_action_dim": 132,
+    "action_horizon": 40,
+    "hidden_size": 1024,
+    "input_embedding_dim": 1536,
+    "state_history_length": 1,
+    "add_pos_embed": True,
+    "attn_dropout": 0.2,
+    "use_vlln": True,
+    "max_seq_len": 1024,
+    "use_alternate_vl_dit": True,
+    "attend_text_every_n_blocks": 2,
+    "diffusion_model_cfg": {
+        "positional_embeddings": None,
+        "num_layers": 32,
+        "num_attention_heads": 32,
+        "attention_head_dim": 48,
+        "norm_type": "ada_norm",
+        "dropout": 0.2,
+        "final_dropout": True,
+        "output_dim": 1024,
+        "interleave_self_attention": True,
+    },
+    "vl_self_attention_cfg": {
+        "positional_embeddings": None,
+        "num_layers": 4,
+        "num_attention_heads": 32,
+        "attention_head_dim": 64,
+        "dropout": 0.2,
+        "final_dropout": True,
+    },
+    "num_inference_timesteps": 4,
+    "noise_beta_alpha": 1.5,
+    "noise_beta_beta": 1.0,
+    "noise_s": 0.999,
+    "num_timestep_buckets": 1000,
+    "tune_projector": True,
+    "tune_diffusion_model": True,
+    "tune_vlln": True,
+    "state_dropout_prob": 0.2,
+    "exclude_state": False,
+    "use_mean_std": False,
+    "max_num_embodiments": 32,
+    "rtc_ramp_rate": 6.0,
+}
+
+
+class GR00TN17Config(PretrainedConfig):
+    """Configuration for NVIDIA GR00T N1.7.
+
+    N1.7 uses the Cosmos-Reason2-2B / Qwen3-VL backbone and a multi-embodiment
+    flow-matching action head. This mirrors the public N1.7 checkpoint config
+    while keeping it local to LeRobot and independent from the external
+    Isaac-GR00T ``gr00t`` Python package.
+    """
+
+    model_type = "Gr00tN1d7"
+
+    _defaults = GR00T_N1_7_DEFAULTS
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        for key, value in GR00T_N1_7_DEFAULTS.items():
+            setattr(self, key, deepcopy(kwargs.pop(key, value)))
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+
+
+class CategorySpecificLinear(nn.Module):
+    """Linear layer with category-specific weights for multi-embodiment support."""
+
+    def __init__(self, num_categories: int, input_dim: int, hidden_dim: int):
+        super().__init__()
+        self.num_categories = num_categories
+        self.W = nn.Parameter(0.02 * torch.randn(num_categories, input_dim, hidden_dim))
+        self.b = nn.Parameter(torch.zeros(num_categories, hidden_dim))
+
+    def forward(self, x: torch.Tensor, cat_ids: torch.Tensor) -> torch.Tensor:
+        selected_w = self.W[cat_ids]
+        selected_b = self.b[cat_ids]
+        return torch.bmm(x, selected_w) + selected_b.unsqueeze(1)
+
+
+class CategorySpecificMLP(nn.Module):
+    """Two-layer MLP with category-specific weights."""
+
+    def __init__(self, num_categories: int, input_dim: int, hidden_dim: int, output_dim: int):
+        super().__init__()
+        self.layer1 = CategorySpecificLinear(num_categories, input_dim, hidden_dim)
+        self.layer2 = CategorySpecificLinear(num_categories, hidden_dim, output_dim)
+
+    def forward(self, x: torch.Tensor, cat_ids: torch.Tensor) -> torch.Tensor:
+        hidden = F.relu(self.layer1(x, cat_ids))
+        return self.layer2(hidden, cat_ids)
+
+
+class SinusoidalPositionalEncoding(nn.Module):
+    """Sinusoidal encoding of shape ``(B, T, D)`` for timestep tensors ``(B, T)``.
+
+    The frequency scalar is intentionally created on CPU and then broadcast with
+    the device-local arange result. That mirrors Isaac-GR00T's N1.7 timestep
+    embedding and avoids tiny dtype/device construction differences in parity
+    tests.
+    """
+
+    def __init__(self, embedding_dim: int):
+        super().__init__()
+        self.embedding_dim = embedding_dim
+
+    def forward(self, timesteps: torch.Tensor) -> torch.Tensor:
+        timesteps = timesteps.float()
+        half_dim = self.embedding_dim // 2
+        exponent = -torch.arange(half_dim, dtype=torch.float, device=timesteps.device) * (
+            torch.log(torch.tensor(10000.0)) / half_dim
+        )
+        freqs = timesteps.unsqueeze(-1) * exponent.exp()
+        return torch.cat([torch.sin(freqs), torch.cos(freqs)], dim=-1)
+
+
+def swish(x: torch.Tensor) -> torch.Tensor:
+    return x * torch.sigmoid(x)
+
+
+class MultiEmbodimentActionEncoder(nn.Module):
+    """Action encoder with category-specific projections and sinusoidal time encoding."""
+
+    def __init__(self, action_dim: int, hidden_size: int, num_embodiments: int):
+        super().__init__()
+        self.W1 = CategorySpecificLinear(num_embodiments, action_dim, hidden_size)
+        self.W2 = CategorySpecificLinear(num_embodiments, 2 * hidden_size, hidden_size)
+        self.W3 = CategorySpecificLinear(num_embodiments, hidden_size, hidden_size)
+        self.pos_encoding = SinusoidalPositionalEncoding(hidden_size)
+
+    def forward(self, actions: torch.Tensor, timesteps: torch.Tensor, cat_ids: torch.Tensor) -> torch.Tensor:
+        batch_size, horizon, _ = actions.shape
+        if timesteps.dim() != 1 or timesteps.shape[0] != batch_size:
+            raise ValueError("Expected `timesteps` to have shape (B,).")
+        timesteps = timesteps.unsqueeze(1).expand(-1, horizon)
+        action_emb = self.W1(actions, cat_ids)
+        time_emb = self.pos_encoding(timesteps).to(dtype=action_emb.dtype)
+        x = swish(self.W2(torch.cat([action_emb, time_emb], dim=-1), cat_ids))
+        return self.W3(x, cat_ids)
+
+
+class Qwen3Backbone(nn.Module):
+    """Cosmos-Reason2/Qwen3-VL backbone used by GR00T N1.7.
+
+    The public checkpoint stores the action head in the GR00T checkpoint but
+    uses a Hugging Face Qwen3-VL-compatible backbone interface. This wrapper
+    keeps the nested HF module layout compatible across transformer versions
+    and exposes the hidden states consumed by the action head.
+    """
+
+    def __init__(
+        self,
+        model_name: str = "nvidia/Cosmos-Reason2-2B",
+        tune_llm: bool = False,
+        tune_visual: bool = False,
+        select_layer: int = -1,
+        reproject_vision: bool = False,
+        use_flash_attention: bool = False,
+        load_bf16: bool = False,
+        tune_top_llm_layers: int = 0,
+        trainable_params_fp32: bool = False,
+        transformers_loading_kwargs: dict[str, Any] | None = None,
+        load_pretrained_weights: bool = True,
+    ):
+        require_package("transformers", extra="groot")
+        if Qwen3VLForConditionalGeneration is None:
+            raise ImportError(
+                "Qwen3VLForConditionalGeneration is required for GR00T N1.7. "
+                "Install a transformers version with Qwen3-VL support."
+            )
+        super().__init__()
+        transformers_loading_kwargs = transformers_loading_kwargs or {"trust_remote_code": True}
+
+        extra_kwargs: dict[str, Any] = {}
+        if use_flash_attention:
+            try:
+                import flash_attn  # noqa: F401
+
+                extra_kwargs["attn_implementation"] = "flash_attention_2"
+            except ImportError:
+                logger.warning("flash_attn is not installed. Falling back to SDPA attention.")
+                extra_kwargs["attn_implementation"] = "sdpa"
+        if load_bf16:
+            extra_kwargs["torch_dtype"] = torch.bfloat16
+
+        if load_pretrained_weights:
+            self.model = Qwen3VLForConditionalGeneration.from_pretrained(
+                model_name,
+                **extra_kwargs,
+                **transformers_loading_kwargs,
+            ).eval()
+        else:
+            self.model = self._from_backbone_config(
+                model_name=model_name,
+                model_kwargs=extra_kwargs,
+                config_kwargs=transformers_loading_kwargs,
+            ).eval()
+
+        while len(self.language_model.layers) > select_layer:
+            self.language_model.layers.pop(-1)
+
+        self.select_layer = select_layer
+        self.set_trainable_parameters(tune_llm, tune_visual, tune_top_llm_layers)
+        if load_bf16 and trainable_params_fp32:
+            for parameter in self.parameters():
+                if parameter.requires_grad:
+                    parameter.data = parameter.data.to(torch.float32)
+
+    def set_trainable_parameters(
+        self, tune_llm: bool, tune_visual: bool, tune_top_llm_layers: int = 0
+    ) -> None:
+        self.tune_llm = tune_llm
+        self.tune_visual = tune_visual
+        for parameter in self.parameters():
+            parameter.requires_grad = True
+        if not tune_llm:
+            self.language_model.requires_grad_(False)
+        if not tune_visual:
+            self.visual.requires_grad_(False)
+        if tune_top_llm_layers > 0:
+            for layer in self.language_model.layers[-tune_top_llm_layers:]:
+                for parameter in layer.parameters():
+                    parameter.requires_grad = True
+
+    def set_frozen_modules_to_eval_mode(self) -> None:
+        if self.training:
+            if self.language_model and not self.tune_llm:
+                self.language_model.eval()
+            if self.visual and not self.tune_visual:
+                self.visual.eval()
+
+    @property
+    def language_model(self) -> nn.Module:
+        return getattr(self.model, "model", self.model).language_model
+
+    @property
+    def visual(self) -> nn.Module:
+        return getattr(self.model, "model", self.model).visual
+
+    def _from_backbone_config(
+        self,
+        *,
+        model_name: str,
+        model_kwargs: dict[str, Any],
+        config_kwargs: dict[str, Any],
+    ) -> nn.Module:
+        if _is_cosmos_reason2_backbone(model_name):
+            backbone_config = _cosmos_reason2_qwen3_vl_config()
+        else:
+            backbone_config = AutoConfig.from_pretrained(model_name, **config_kwargs)
+        return Qwen3VLForConditionalGeneration._from_config(backbone_config, **model_kwargs)
+
+    def prepare_input(self, batch: dict[str, Any]) -> BatchFeature:
+        return BatchFeature(data=batch)
+
+    def _ensure_mm_token_type_ids(self, model_input: dict[str, torch.Tensor]) -> None:
+        if "mm_token_type_ids" in model_input:
+            return
+        if "image_grid_thw" not in model_input and "video_grid_thw" not in model_input:
+            return
+
+        input_ids = model_input.get("input_ids")
+        if input_ids is None:
+            return
+
+        mm_token_type_ids = torch.zeros(input_ids.shape, dtype=torch.int32, device=input_ids.device)
+        image_token_id = getattr(self.model.config, "image_token_id", None)
+        video_token_id = getattr(self.model.config, "video_token_id", None)
+        if image_token_id is not None:
+            mm_token_type_ids[input_ids == image_token_id] = 1
+        if video_token_id is not None:
+            mm_token_type_ids[input_ids == video_token_id] = 2
+
+        model_input["mm_token_type_ids"] = mm_token_type_ids
+
+    def _ensure_legacy_qwen3_position_ids(self, model_input: dict[str, torch.Tensor]) -> None:
+        """Restore the Qwen3-VL text position ids used by older Transformers releases.
+
+        Transformers 5.x computes 3-row multimodal RoPE ids for Qwen3-VL and then
+        drops text position ids before calling text-layer flash attention. GR00T
+        N1.7 was aligned against the older Transformers path, where a fourth text
+        position row is forwarded alongside the temporal/height/width rows. Adding
+        the row here preserves the newer multimodal position computation while
+        keeping flash attention on the legacy code path.
+        """
+
+        if "position_ids" in model_input:
+            return
+
+        qwen3_model = getattr(self.model, "model", self.model)
+        compute_3d_position_ids = getattr(qwen3_model, "compute_3d_position_ids", None)
+        if compute_3d_position_ids is None:
+            return
+
+        position_ids = compute_3d_position_ids(
+            input_ids=model_input.get("input_ids"),
+            image_grid_thw=model_input.get("image_grid_thw"),
+            video_grid_thw=model_input.get("video_grid_thw"),
+            inputs_embeds=None,
+            attention_mask=model_input.get("attention_mask"),
+            past_key_values=None,
+            mm_token_type_ids=model_input.get("mm_token_type_ids"),
+        )
+        if position_ids.ndim == 3 and position_ids.shape[0] == 3:
+            position_ids = torch.cat([position_ids[:1], position_ids], dim=0)
+
+        model_input["position_ids"] = position_ids
+
+    def _last_decoder_layer_output(self, model_input: dict[str, torch.Tensor]) -> torch.Tensor:
+        """Return the pre-final-norm decoder output consumed by the N1.7 action head.
+
+        Older Transformers releases exposed this tensor as ``hidden_states[-1]``.
+        Newer releases expose the post-final-norm tensor there instead. Capturing
+        the last decoder layer output directly keeps the N1.7 action head input
+        stable across Transformers versions.
+        """
+
+        captured: dict[str, torch.Tensor] = {}
+
+        def capture_output(_module: nn.Module, _inputs: tuple[Any, ...], output: Any) -> None:
+            if isinstance(output, torch.Tensor):
+                captured["features"] = output
+            elif isinstance(output, (tuple, list)) and output:
+                captured["features"] = output[0]
+            elif hasattr(output, "last_hidden_state"):
+                captured["features"] = output.last_hidden_state
+
+        hook = self.language_model.layers[-1].register_forward_hook(capture_output)
+        try:
+            outputs = self.model(**model_input, output_hidden_states=True)
+        finally:
+            hook.remove()
+
+        return captured.get("features", outputs.hidden_states[-1])
+
+    def forward(self, vl_input: BatchFeature) -> BatchFeature:
+        self.set_frozen_modules_to_eval_mode()
+        keys_to_use = ["input_ids", "attention_mask", "pixel_values", "image_grid_thw"]
+        optional_keys = ["mm_token_type_ids", "pixel_values_videos", "video_grid_thw"]
+        model_input = {key: vl_input[key] for key in keys_to_use}
+        model_input.update({key: vl_input[key] for key in optional_keys if key in vl_input})
+        self._ensure_mm_token_type_ids(model_input)
+        self._ensure_legacy_qwen3_position_ids(model_input)
+        features = self._last_decoder_layer_output(model_input)
+        image_mask = model_input["input_ids"] == self.model.config.image_token_id
+        attention_mask = model_input["attention_mask"] == 1
+        return BatchFeature(
+            data={
+                "backbone_features": features,
+                "backbone_attention_mask": attention_mask,
+                "image_mask": image_mask,
+            }
+        )
+
+
+class GR00TN17ActionHead(nn.Module):
+    supports_gradient_checkpointing = True
+
+    def __init__(self, config: GR00TN17Config):
+        require_package("diffusers", extra="groot")
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.input_embedding_dim = config.input_embedding_dim
+
+        if config.use_alternate_vl_dit:
+            self.model = AlternateVLDiT(
+                **config.diffusion_model_cfg,
+                cross_attention_dim=config.backbone_embedding_dim,
+                attend_text_every_n_blocks=config.attend_text_every_n_blocks,
+            )
+        else:
+            self.model = DiT(
+                **config.diffusion_model_cfg,
+                cross_attention_dim=config.backbone_embedding_dim,
+            )
+
+        self.action_dim = config.max_action_dim
+        self.action_horizon = config.action_horizon
+        self.num_inference_timesteps = config.num_inference_timesteps
+        self.state_encoder = CategorySpecificMLP(
+            num_categories=config.max_num_embodiments,
+            input_dim=config.max_state_dim * config.state_history_length,
+            hidden_dim=self.hidden_size,
+            output_dim=self.input_embedding_dim,
+        )
+        self.action_encoder = MultiEmbodimentActionEncoder(
+            action_dim=self.action_dim,
+            hidden_size=self.input_embedding_dim,
+            num_embodiments=config.max_num_embodiments,
+        )
+        self.action_decoder = CategorySpecificMLP(
+            num_categories=config.max_num_embodiments,
+            input_dim=self.hidden_size,
+            hidden_dim=self.hidden_size,
+            output_dim=self.action_dim,
+        )
+        self.vlln = nn.LayerNorm(config.backbone_embedding_dim) if config.use_vlln else nn.Identity()
+        vl_self_attention_cfg = getattr(config, "vl_self_attention_cfg", None)
+        if vl_self_attention_cfg and vl_self_attention_cfg.get("num_layers", 0) > 0:
+            self.vl_self_attention = SelfAttentionTransformer(**vl_self_attention_cfg)
+        else:
+            self.vl_self_attention = nn.Identity()
+        if config.add_pos_embed:
+            self.position_embedding = nn.Embedding(config.max_seq_len, self.input_embedding_dim)
+            nn.init.normal_(self.position_embedding.weight, mean=0.0, std=0.02)
+        self.state_dropout_prob = config.state_dropout_prob
+        self._noise_beta_alpha = config.noise_beta_alpha
+        self._noise_beta_beta = config.noise_beta_beta
+        self._beta_dist = None
+        self.num_timestep_buckets = config.num_timestep_buckets
+        self.set_trainable_parameters(config.tune_projector, config.tune_diffusion_model, config.tune_vlln)
+
+    def set_trainable_parameters(
+        self, tune_projector: bool, tune_diffusion_model: bool, tune_vlln: bool
+    ) -> None:
+        self.tune_projector = tune_projector
+        self.tune_diffusion_model = tune_diffusion_model
+        self.tune_vlln = tune_vlln
+        for parameter in self.parameters():
+            parameter.requires_grad = True
+        if not tune_projector:
+            self.state_encoder.requires_grad_(False)
+            self.action_encoder.requires_grad_(False)
+            self.action_decoder.requires_grad_(False)
+            if self.config.add_pos_embed:
+                self.position_embedding.requires_grad_(False)
+        if not tune_diffusion_model:
+            self.model.requires_grad_(False)
+        if not tune_vlln:
+            self.vlln.requires_grad_(False)
+            self.vl_self_attention.requires_grad_(False)
+
+    def set_frozen_modules_to_eval_mode(self) -> None:
+        if self.training:
+            if not self.tune_projector:
+                self.state_encoder.eval()
+                self.action_encoder.eval()
+                self.action_decoder.eval()
+                if self.config.add_pos_embed:
+                    self.position_embedding.eval()
+            if not self.tune_diffusion_model:
+                self.model.eval()
+            if not self.tune_vlln:
+                self.vlln.eval()
+                self.vl_self_attention.eval()
+
+    def sample_time(self, batch_size: int, device: torch.device, dtype: torch.dtype) -> torch.Tensor:
+        if self._beta_dist is None:
+            beta_alpha = torch.tensor(self._noise_beta_alpha, device="cpu", dtype=torch.float32)
+            beta_beta = torch.tensor(self._noise_beta_beta, device="cpu", dtype=torch.float32)
+            self._beta_dist = Beta(beta_alpha, beta_beta, validate_args=False)
+        sample = self._beta_dist.sample([batch_size]).to(device, dtype=dtype)
+        return (1 - sample) * self.config.noise_s
+
+    def process_backbone_output(self, backbone_output: BatchFeature) -> BatchFeature:
+        backbone_features = self.vlln(backbone_output["backbone_features"])
+        backbone_output["backbone_features"] = self.vl_self_attention(backbone_features)
+        return backbone_output
+
+    def forward(self, backbone_output: BatchFeature, action_input: BatchFeature) -> BatchFeature:
+        self.set_frozen_modules_to_eval_mode()
+        backbone_output = self.process_backbone_output(backbone_output)
+        vl_embeds = backbone_output.backbone_features
+        device = vl_embeds.device
+        embodiment_id = action_input.embodiment_id
+
+        if action_input.state.shape[1] != self.config.state_history_length:
+            raise ValueError("state history length does not match GR00T N1.7 config.")
+        state = action_input.state.view(action_input.state.shape[0], 1, -1)
+        state_features = self.state_encoder(state, embodiment_id)
+
+        if self.training and self.state_dropout_prob > 0:
+            do_dropout = (
+                torch.rand(state_features.shape[0], device=state_features.device) < self.state_dropout_prob
+            )
+            state_features = state_features * (1 - do_dropout[:, None, None].to(dtype=state_features.dtype))
+
+        actions = action_input.action
+        noise = torch.randn(actions.shape, device=actions.device, dtype=actions.dtype)
+        t = self.sample_time(actions.shape[0], device=actions.device, dtype=actions.dtype)
+        t = t[:, None, None]
+        noisy_trajectory = (1 - t) * noise + t * actions
+        velocity = actions - noise
+        t_discretized = (t[:, 0, 0] * self.num_timestep_buckets).long()
+        action_features = self.action_encoder(noisy_trajectory, t_discretized, embodiment_id)
+
+        if self.config.add_pos_embed:
+            pos_ids = torch.arange(action_features.shape[1], dtype=torch.long, device=device)
+            action_features = action_features + self.position_embedding(pos_ids).unsqueeze(0)
+
+        sa_embs = torch.cat((state_features, action_features), dim=1)
+        if self.config.use_alternate_vl_dit:
+            model_output, _ = self.model(
+                hidden_states=sa_embs,
+                encoder_hidden_states=vl_embeds,
+                encoder_attention_mask=backbone_output.backbone_attention_mask,
+                timestep=t_discretized,
+                return_all_hidden_states=True,
+                image_mask=backbone_output.image_mask,
+                backbone_attention_mask=backbone_output.backbone_attention_mask,
+            )
+        else:
+            model_output, _ = self.model(
+                hidden_states=sa_embs,
+                encoder_hidden_states=vl_embeds,
+                encoder_attention_mask=backbone_output.backbone_attention_mask,
+                timestep=t_discretized,
+                return_all_hidden_states=True,
+            )
+
+        pred = self.action_decoder(model_output, embodiment_id)
+        pred_actions = pred[:, -actions.shape[1] :]
+        action_mask = action_input.action_mask.to(dtype=pred_actions.dtype)
+        action_loss = F.mse_loss(pred_actions, velocity, reduction="none") * action_mask
+        loss = action_loss.sum() / (action_mask.sum() + 1e-6)
+        return BatchFeature(
+            data={
+                "loss": loss,
+                "action_loss": action_loss,
+                "action_mask": action_mask,
+                "backbone_features": vl_embeds,
+                "state_features": state_features,
+            }
+        )
+
+    def _encode_features(self, backbone_output: BatchFeature, action_input: BatchFeature) -> BatchFeature:
+        backbone_output = self.process_backbone_output(backbone_output)
+        state = action_input.state
+        if state.shape[1] != self.config.state_history_length:
+            raise ValueError("state history length does not match GR00T N1.7 config.")
+        state = state.view(state.shape[0], 1, -1)
+        state_features = self.state_encoder(state, action_input.embodiment_id)
+        return BatchFeature(
+            data={"backbone_features": backbone_output.backbone_features, "state_features": state_features}
+        )
+
+    @torch.no_grad()
+    def get_action_with_features(
+        self,
+        backbone_features: torch.Tensor,
+        state_features: torch.Tensor,
+        embodiment_id: torch.Tensor,
+        backbone_output: BatchFeature,
+        action_input: BatchFeature,
+        options: dict[str, Any] | None = None,
+    ) -> BatchFeature:
+        vl_embeds = backbone_features
+        batch_size = vl_embeds.shape[0]
+        device = vl_embeds.device
+        actions = torch.randn(
+            size=(batch_size, self.config.action_horizon, self.action_dim),
+            dtype=vl_embeds.dtype,
+            device=device,
+        )
+        dt = 1.0 / self.num_inference_timesteps
+        vel_strength = torch.ones_like(actions)
+
+        if "action" in action_input:
+            if options is None:
+                raise ValueError("RTC options are required when action is provided to get_action.")
+            action_horizon_before_padding = options["action_horizon"]
+            actions[:, : options["rtc_overlap_steps"], :] = action_input["action"][
+                :,
+                action_horizon_before_padding - options["rtc_overlap_steps"] : action_horizon_before_padding,
+                :,
+            ]
+            vel_strength[:, : options["rtc_frozen_steps"], :] = 0.0
+            intermediate_steps = options["rtc_overlap_steps"] - options["rtc_frozen_steps"]
+            t = torch.linspace(0.0, 1.0, intermediate_steps + 2, device=device)
+            ramp = 1 - torch.exp(-options["rtc_ramp_rate"] * t)
+            ramp = ramp / ramp[-1].clamp_min(1e-8)
+            vel_strength[:, options["rtc_frozen_steps"] : options["rtc_overlap_steps"], :] = ramp[1:-1][
+                None, :, None
+            ].to(device)
+
+        for t_step in range(self.num_inference_timesteps):
+            t_cont = t_step / float(self.num_inference_timesteps)
+            t_discretized = int(t_cont * self.num_timestep_buckets)
+            timesteps_tensor = torch.full(size=(batch_size,), fill_value=t_discretized, device=device)
+            action_features = self.action_encoder(actions, timesteps_tensor, embodiment_id)
+            if self.config.add_pos_embed:
+                pos_ids = torch.arange(action_features.shape[1], dtype=torch.long, device=device)
+                action_features = action_features + self.position_embedding(pos_ids).unsqueeze(0)
+            sa_embs = torch.cat((state_features, action_features), dim=1)
+
+            if self.config.use_alternate_vl_dit:
+                model_output = self.model(
+                    hidden_states=sa_embs,
+                    encoder_hidden_states=vl_embeds,
+                    timestep=timesteps_tensor,
+                    image_mask=backbone_output.image_mask,
+                    backbone_attention_mask=backbone_output.backbone_attention_mask,
+                )
+            else:
+                model_output = self.model(
+                    hidden_states=sa_embs,
+                    encoder_hidden_states=vl_embeds,
+                    timestep=timesteps_tensor,
+                )
+            pred = self.action_decoder(model_output, embodiment_id)
+            actions = actions + dt * pred[:, -self.action_horizon :] * vel_strength
+
+        return BatchFeature(
+            data={
+                "action_pred": actions,
+                "backbone_features": vl_embeds,
+                "state_features": state_features,
+            }
+        )
+
+    @torch.no_grad()
+    def get_action(
+        self,
+        backbone_output: BatchFeature,
+        action_input: BatchFeature,
+        options: dict[str, Any] | None = None,
+    ) -> BatchFeature:
+        features = self._encode_features(backbone_output, action_input)
+        return self.get_action_with_features(
+            backbone_features=features.backbone_features,
+            state_features=features.state_features,
+            embodiment_id=action_input.embodiment_id,
+            backbone_output=backbone_output,
+            action_input=action_input,
+            options=options,
+        )
+
+    @property
+    def device(self) -> torch.device:
+        return next(iter(self.parameters())).device
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return next(iter(self.parameters())).dtype
+
+    def prepare_input(self, batch: dict[str, Any]) -> BatchFeature:
+        return BatchFeature(data=batch)
+
+
+def _is_cosmos_reason2_backbone(model_name: str) -> bool:
+    return str(model_name).rstrip("/") == "nvidia/Cosmos-Reason2-2B"
+
+
+def _cosmos_reason2_qwen3_vl_config() -> PretrainedConfig:
+    return Qwen3VLConfig(
+        image_token_id=151655,
+        video_token_id=151656,
+        vision_start_token_id=151652,
+        vision_end_token_id=151653,
+        tie_word_embeddings=True,
+        text_config={
+            "attention_bias": False,
+            "attention_dropout": 0.0,
+            "bos_token_id": 151643,
+            "dtype": "bfloat16",
+            "eos_token_id": 151645,
+            "head_dim": 128,
+            "hidden_act": "silu",
+            "hidden_size": 2048,
+            "initializer_range": 0.02,
+            "intermediate_size": 6144,
+            "max_position_embeddings": 262144,
+            "model_type": "qwen3_vl_text",
+            "num_attention_heads": 16,
+            "num_hidden_layers": 28,
+            "num_key_value_heads": 8,
+            "rms_norm_eps": 1e-6,
+            "rope_scaling": {
+                "mrope_interleaved": True,
+                "mrope_section": [24, 20, 20],
+                "rope_type": "default",
+            },
+            "rope_theta": 5000000,
+            "tie_word_embeddings": True,
+            "use_cache": True,
+            "vocab_size": 151936,
+        },
+        vision_config={
+            "deepstack_visual_indexes": [5, 11, 17],
+            "depth": 24,
+            "hidden_act": "gelu_pytorch_tanh",
+            "hidden_size": 1024,
+            "in_channels": 3,
+            "initializer_range": 0.02,
+            "intermediate_size": 4096,
+            "model_type": "qwen3_vl",
+            "num_heads": 16,
+            "num_position_embeddings": 2304,
+            "out_hidden_size": 2048,
+            "patch_size": 16,
+            "spatial_merge_size": 2,
+            "temporal_patch_size": 2,
+        },
+    )
+
+
+def get_backbone_cls(config: GR00TN17Config):
+    if "nvidia/Cosmos-Reason2" in config.model_name or "Qwen/Qwen3-VL" in config.model_name:
+        return Qwen3Backbone
+    if config.backbone_model_type == "qwen":
+        logger.warning(
+            "Unrecognized GR00T N1.7 backbone model name '%s'; assuming a Qwen3-VL-compatible "
+            "backbone because backbone_model_type='qwen'.",
+            config.model_name,
+        )
+        return Qwen3Backbone
+    raise ValueError(f"Unsupported GR00T N1.7 backbone model: {config.model_name}")
+
+
+class GR00TN17(PreTrainedModel):
+    """GR00T N1.7 model with a Cosmos-Reason2/Qwen3-VL backbone."""
+
+    config_class = GR00TN17Config
+    supports_gradient_checkpointing = True
+
+    def __init__(
+        self,
+        config: GR00TN17Config,
+        transformers_loading_kwargs: dict[str, Any] | None = None,
+        load_backbone_weights: bool = True,
+    ):
+        _register_with_transformers()
+        super().__init__(config)
+        transformers_loading_kwargs = transformers_loading_kwargs or {"trust_remote_code": True}
+        self.config = config
+        backbone_cls = get_backbone_cls(config)
+        self.backbone = backbone_cls(
+            model_name=config.model_name,
+            tune_llm=config.tune_llm,
+            tune_visual=config.tune_visual,
+            select_layer=config.select_layer,
+            reproject_vision=config.reproject_vision,
+            use_flash_attention=config.use_flash_attention,
+            load_bf16=config.load_bf16,
+            tune_top_llm_layers=config.tune_top_llm_layers,
+            trainable_params_fp32=config.backbone_trainable_params_fp32,
+            transformers_loading_kwargs=transformers_loading_kwargs,
+            load_pretrained_weights=load_backbone_weights,
+        )
+        self.action_head = GR00TN17ActionHead(config)
+        self.post_init()
+
+    def prepare_input(self, inputs: dict[str, Any]) -> tuple[BatchFeature, BatchFeature]:
+        global tree
+        if tree is None:
+            require_package("dm-tree", extra="groot", import_name="tree")
+            tree = importlib.import_module("tree")
+        backbone_inputs = self.backbone.prepare_input(inputs)
+        action_inputs = self.action_head.prepare_input(inputs)
+
+        def to_device_with_dtype(x):
+            if not isinstance(x, torch.Tensor):
+                return x
+            if torch.is_floating_point(x):
+                return x.to(self.device, dtype=self.dtype)
+            return x.to(self.device)
+
+        return (
+            tree.map_structure(to_device_with_dtype, backbone_inputs),
+            tree.map_structure(to_device_with_dtype, action_inputs),
+        )
+
+    def forward(self, inputs: dict[str, Any]) -> BatchFeature:
+        backbone_inputs, action_inputs = self.prepare_input(inputs)
+        backbone_outputs = self.backbone(backbone_inputs)
+        return self.action_head(backbone_outputs, action_inputs)
+
+    def get_action(self, inputs: dict[str, Any], options: dict[str, Any] | None = None) -> BatchFeature:
+        backbone_inputs, action_inputs = self.prepare_input(inputs)
+        backbone_outputs = self.backbone(backbone_inputs)
+        return self.action_head.get_action(backbone_outputs, action_inputs, options)
+
+    @property
+    def device(self) -> torch.device:
+        return next(iter(self.parameters())).device
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return next(iter(self.parameters())).dtype
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs):
+        tune_visual = kwargs.pop("tune_visual", True)
+        tune_llm = kwargs.pop("tune_llm", False)
+        tune_projector = kwargs.pop("tune_projector", True)
+        tune_diffusion_model = kwargs.pop("tune_diffusion_model", True)
+        tune_vlln = kwargs.pop("tune_vlln", True)
+        transformers_loading_kwargs = kwargs.pop("transformers_loading_kwargs", None) or {
+            "trust_remote_code": True
+        }
+        load_backbone_weights = kwargs.pop("load_backbone_weights", False)
+        for key in ("cache_dir", "local_files_only", "token"):
+            if key in kwargs:
+                transformers_loading_kwargs.setdefault(key, kwargs[key])
+
+        try:
+            local_model_path = snapshot_download(
+                pretrained_model_name_or_path,
+                repo_type="model",
+                revision=kwargs.get("revision"),
+                cache_dir=kwargs.get("cache_dir"),
+                local_files_only=kwargs.get("local_files_only", False),
+                token=kwargs.get("token"),
+            )
+        except (HFValidationError, RepositoryNotFoundError):
+            local_model_path = pretrained_model_name_or_path
+
+        pretrained_model = super().from_pretrained(
+            local_model_path,
+            transformers_loading_kwargs=transformers_loading_kwargs,
+            load_backbone_weights=load_backbone_weights,
+            **kwargs,
+        )
+        pretrained_model.backbone.set_trainable_parameters(
+            tune_visual=tune_visual,
+            tune_llm=tune_llm,
+            tune_top_llm_layers=pretrained_model.config.tune_top_llm_layers,
+        )
+        pretrained_model.action_head.set_trainable_parameters(
+            tune_projector=tune_projector,
+            tune_diffusion_model=tune_diffusion_model,
+            tune_vlln=tune_vlln,
+        )
+        return pretrained_model
+
+
+def _register_with_transformers() -> None:
+    """Register GR00T N1.7 with transformers' Auto* factories.
+
+    Idempotent: ``register(..., exist_ok=True)`` makes repeat calls no-ops (with a fallback that
+    suppresses the already-registered error on transformers builds whose ``register()`` predates
+    ``exist_ok``), so no run-once guard is needed.
+    """
+    if AutoConfig is None or AutoModel is None:
+        return
+    try:
+        AutoConfig.register(GR00TN17Config.model_type, GR00TN17Config, exist_ok=True)
+    except TypeError:
+        with suppress(ValueError):
+            AutoConfig.register(GR00TN17Config.model_type, GR00TN17Config)
+    try:
+        AutoModel.register(GR00TN17Config, GR00TN17, exist_ok=True)
+    except TypeError:
+        with suppress(ValueError):
+            AutoModel.register(GR00TN17Config, GR00TN17)

src/lerobot/policies/groot/modeling_groot.py

@@ -17,28 +17,22 @@
 """
 Groot Policy Wrapper for LeRobot Integration
 
-Minimal integration that delegates to Isaac-GR00T components where possible
-without porting their code. The intent is to:
-
-- Download and load the pretrained GR00T model via GR00TN15.from_pretrained
-- Optionally align action horizon similar to gr00t_finetune.py
-- Expose predict_action via GR00T model.get_action
-- Provide a training forward that can call the GR00T model forward if batch
-  structure matches.
-
-Notes:
-- Dataset loading and full training orchestration is handled by Isaac-GR00T
-  TrainRunner in their codebase. If you want to invoke that flow end-to-end
-  from LeRobot, see `GrootPolicy.finetune_with_groot_runner` below.
+Minimal integration that delegates to Isaac-GR00T N1.7 components where
+possible without porting their code. Dataset loading and training
+orchestration are handled by LeRobot's standard training stack.
 """
 
 import builtins
+import logging
 import os
 from collections import deque
 from pathlib import Path
 from typing import TypeVar
 
 import torch
+from huggingface_hub import hf_hub_download
+from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE
+from huggingface_hub.errors import HfHubHTTPError
 from torch import Tensor
 
 from lerobot.configs import FeatureType, PolicyFeature
@@ -46,8 +40,19 @@ from lerobot.utils.constants import ACTION, OBS_IMAGES
 from lerobot.utils.import_utils import require_package
 
 from ..pretrained import PreTrainedPolicy
-from .configuration_groot import GrootConfig
-from .groot_n1 import GR00TN15
+from ..utils import get_device_from_parameters
+from .configuration_groot import (
+    GROOT_N1_5,
+    GROOT_N1_5_REMOVAL_GUIDANCE,
+    GROOT_N1_7,
+    GrootConfig,
+    infer_groot_model_version,
+    infer_groot_n1_7_action_execution_horizon,
+    infer_groot_n1_7_action_horizon,
+)
+from .groot_n1_7 import GR00TN17
+
+logger = logging.getLogger(__name__)
 
 T = TypeVar("T", bound="GrootPolicy")
 
@@ -67,37 +72,39 @@ class GrootPolicy(PreTrainedPolicy):
 
         # Initialize GR00T model using ported components
         self._groot_model = self._create_groot_model()
+        self._action_queue_steps = self._resolve_action_queue_steps()
+        self._warned_native_relative_rtc_prefix_disabled = False
 
         self.reset()
 
     def _create_groot_model(self):
-        """Create and initialize the GR00T model using Isaac-GR00T API.
+        """Create and initialize the GR00T N1.7 model using the ported components."""
+        model_kwargs = {
+            "pretrained_model_name_or_path": self.config.base_model_path,
+            "tune_llm": self.config.tune_llm,
+            "tune_visual": self.config.tune_visual,
+            "tune_projector": self.config.tune_projector,
+            "tune_diffusion_model": self.config.tune_diffusion_model,
+            # Forwarded as a GR00TN17Config override; read back by set_trainable_parameters.
+            "tune_top_llm_layers": self.config.tune_top_llm_layers,
+            "use_flash_attention": self.config.use_flash_attention,
+        }
+        # Surface the inference-time knobs onto the model config only when the user set them; None
+        # leaves the value baked into the checkpoint untouched.
+        if self.config.num_inference_timesteps is not None:
+            model_kwargs["num_inference_timesteps"] = self.config.num_inference_timesteps
+        if self.config.rtc_ramp_rate is not None:
+            model_kwargs["rtc_ramp_rate"] = self.config.rtc_ramp_rate
 
-        This is only called when creating a NEW policy (not when loading from checkpoint).
-
-        Steps (delegating to Isaac-GR00T):
-        1) Download and load pretrained model via GR00TN15.from_pretrained
-        2) Align action horizon with data_config if provided
-        """
-        # Handle Flash Attention compatibility issues
-        self._handle_flash_attention_compatibility()
-
-        model = GR00TN15.from_pretrained(
-            pretrained_model_name_or_path=self.config.base_model_path,
-            tune_llm=self.config.tune_llm,
-            tune_visual=self.config.tune_visual,
-            tune_projector=self.config.tune_projector,
-            tune_diffusion_model=self.config.tune_diffusion_model,
+        return GR00TN17.from_pretrained(
+            **model_kwargs,
+            tune_vlln=self.config.tune_vlln,
+            transformers_loading_kwargs={"trust_remote_code": True},
         )
 
-        model.compute_dtype = "bfloat16" if self.config.use_bf16 else model.compute_dtype
-        model.config.compute_dtype = model.compute_dtype
-
-        return model
-
     def reset(self):
         """Reset policy state when environment resets."""
-        self._action_queue = deque([], maxlen=self.config.n_action_steps)
+        self._action_queue = deque([], maxlen=self._action_queue_steps)
 
     @classmethod
     def from_pretrained(
@@ -118,7 +125,7 @@ class GrootPolicy(PreTrainedPolicy):
         """Load Groot policy from pretrained model.
 
         Handles two cases:
-        1. Base GR00T models (e.g., 'nvidia/GR00T-N1.5-3B') - loads the raw model
+        1. Base GR00T N1.7 models - loads the raw model
         2. Fine-tuned LeRobot checkpoints - loads config and weights from safetensors
 
         Args:
@@ -137,13 +144,11 @@ class GrootPolicy(PreTrainedPolicy):
         Returns:
             Initialized GrootPolicy instance with loaded model
         """
-        from huggingface_hub import hf_hub_download
-        from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE
-        from huggingface_hub.errors import HfHubHTTPError
-
-        print(
-            "The Groot policy is a wrapper around Nvidia's GR00T N1.5 model.\n"
-            f"Loading pretrained model from: {pretrained_name_or_path}"
+        requested_version = infer_groot_model_version(str(pretrained_name_or_path)) or GROOT_N1_7
+        logger.info(
+            "The Groot policy wraps NVIDIA's GR00T %s model. Loading pretrained model from: %s",
+            requested_version,
+            pretrained_name_or_path,
         )
 
         model_id = str(pretrained_name_or_path)
@@ -174,7 +179,7 @@ class GrootPolicy(PreTrainedPolicy):
 
         if is_finetuned_checkpoint:
             # This is a fine-tuned LeRobot checkpoint - use parent class loading
-            print("Detected fine-tuned LeRobot checkpoint, loading with state dict...")
+            logger.info("Detected fine-tuned LeRobot checkpoint, loading with state dict...")
             return super().from_pretrained(
                 pretrained_name_or_path=pretrained_name_or_path,
                 config=config,
@@ -190,11 +195,13 @@ class GrootPolicy(PreTrainedPolicy):
             )
 
         # This is a base GR00T model - load it fresh
-        print("Detected base GR00T model, loading from HuggingFace...")
+        logger.info("Detected base GR00T model, loading from HuggingFace...")
 
         if config is None:
             # Create default config with the pretrained path
-            config = GrootConfig(base_model_path=str(pretrained_name_or_path))
+            config = GrootConfig(
+                base_model_path=str(pretrained_name_or_path),
+            )
 
             # Add minimal visual feature required for validation
             # validate_features() will automatically add state and action features
@@ -215,6 +222,15 @@ class GrootPolicy(PreTrainedPolicy):
             if hasattr(config, key):
                 setattr(config, key, value)
 
+        inferred_version = infer_groot_model_version(config.base_model_path)
+        if inferred_version is not None and inferred_version != GROOT_N1_7:
+            message = (
+                f"GR00T model_version '{GROOT_N1_7}' does not match base_model_path "
+                f"'{config.base_model_path}', which looks like '{inferred_version}'."
+            )
+            if inferred_version == GROOT_N1_5:
+                message = f"{message} {GROOT_N1_5_REMOVAL_GUIDANCE}"
+            raise ValueError(message)
         # Create a fresh policy instance - this will automatically load the GR00T model
         # in __init__ via _create_groot_model()
         policy = cls(config)
@@ -225,21 +241,171 @@ class GrootPolicy(PreTrainedPolicy):
     def get_optim_params(self) -> dict:
         return self.parameters()
 
+    def _resolve_action_queue_steps(self) -> int:
+        n_action_steps = int(self.config.n_action_steps)
+        checkpoint_action_horizon = infer_groot_n1_7_action_horizon(
+            self.config.base_model_path,
+            self.config.embodiment_tag,
+        )
+        execution_horizon = infer_groot_n1_7_action_execution_horizon(
+            self.config.base_model_path,
+            self.config.embodiment_tag,
+        )
+        horizons = [n_action_steps]
+        if checkpoint_action_horizon is not None:
+            horizons.append(checkpoint_action_horizon)
+        if execution_horizon is not None:
+            horizons.append(execution_horizon)
+        return min(horizons)
+
+    def _resolve_prediction_horizon(self, actions: Tensor) -> int:
+        """Return the policy-facing action horizon for a native GR00T prediction."""
+
+        horizons = [actions.shape[1]]
+        checkpoint_action_horizon = infer_groot_n1_7_action_horizon(
+            self.config.base_model_path,
+            self.config.embodiment_tag,
+        )
+        if checkpoint_action_horizon is not None:
+            horizons.append(checkpoint_action_horizon)
+
+        for horizon in (self.config.chunk_size, self.config.n_action_steps):
+            horizon = int(horizon)
+            if horizon > 0:
+                horizons.append(horizon)
+
+        return max(1, min(horizons))
+
+    def _filter_groot_inputs(self, batch: dict[str, Tensor], *, include_action: bool) -> dict[str, Tensor]:
+        allowed_base = {"state", "state_mask", "embodiment_id"}
+        if include_action:
+            allowed_base.update({"action", "action_mask"})
+
+        allowed_base.update(
+            {
+                "input_ids",
+                "attention_mask",
+                "pixel_values",
+                "image_grid_thw",
+                "mm_token_type_ids",
+                "pixel_values_videos",
+                "video_grid_thw",
+            }
+        )
+        allowed_base.add("action_mask")
+
+        return {
+            k: v for k, v in batch.items() if k in allowed_base and not (k.startswith("next.") or k == "info")
+        }
+
+    def _prepare_n1_7_rtc_inputs(
+        self,
+        inputs: dict[str, Tensor],
+        *,
+        inference_delay: object,
+        prev_chunk_left_over: object,
+    ) -> tuple[dict[str, Tensor], dict[str, object] | None]:
+        if prev_chunk_left_over is None:
+            return inputs, None
+        if getattr(self.config, "use_relative_actions", False):
+            # Generic RTC only provides normalized leftovers from the previous chunk. For
+            # native relative-action N1.7 checkpoints those rows are tied to the old
+            # observation state and old per-horizon stats row, so using them as the next
+            # prefix can push the policy in the wrong direction. Run without native RTC
+            # overlap guidance until a GROOT-specific RTC path can pass re-anchored
+            # absolute leftovers through.
+            if not getattr(self, "_warned_native_relative_rtc_prefix_disabled", False):
+                logger.info("Disabling native GR00T RTC prefix for relative-action policy")
+                self._warned_native_relative_rtc_prefix_disabled = True
+            return inputs, None
+        if not isinstance(prev_chunk_left_over, torch.Tensor):
+            raise TypeError("prev_chunk_left_over must be a torch.Tensor for GR00T N1.7 RTC.")
+        if prev_chunk_left_over.numel() == 0:
+            return inputs, None
+
+        prev_actions = prev_chunk_left_over
+        if prev_actions.ndim == 2:
+            prev_actions = prev_actions.unsqueeze(0)
+        elif prev_actions.ndim != 3:
+            raise ValueError("prev_chunk_left_over must have shape (T, A) or (B, T, A) for GR00T N1.7 RTC.")
+
+        state = inputs.get("state")
+        if state is None:
+            raise ValueError("GR00T N1.7 RTC requires `state` in the preprocessed batch.")
+        batch_size = state.shape[0]
+        if prev_actions.shape[0] == 1 and batch_size > 1:
+            prev_actions = prev_actions.expand(batch_size, -1, -1).clone()
+        elif prev_actions.shape[0] != batch_size:
+            raise ValueError("prev_chunk_left_over batch size must match the current GR00T N1.7 batch size.")
+
+        # The generic LeRobot RTC engine pads short leftovers with exact zero
+        # rows for fixed-shape policy calls. Native GR00T N1.7 RTC treats every
+        # provided prefix row as a real action constraint, so strip that padding
+        # before constructing the native overlap options.
+        valid_prefix_rows = prev_actions.detach().abs().sum(dim=(0, 2)) > 0
+        if valid_prefix_rows.any():
+            valid_prefix_steps = int(valid_prefix_rows.nonzero()[-1].item()) + 1
+            prev_actions = prev_actions[:, :valid_prefix_steps, :]
+        else:
+            return inputs, None
+
+        model_action_horizon = int(
+            getattr(self._groot_model.config, "action_horizon", self.config.chunk_size)
+        )
+        max_action_dim = int(getattr(self._groot_model.config, "max_action_dim", self.config.max_action_dim))
+        if prev_actions.shape[1] > model_action_horizon:
+            prev_actions = prev_actions[:, -model_action_horizon:, :]
+
+        action_horizon = int(prev_actions.shape[1])
+        if action_horizon <= 0:
+            return inputs, None
+
+        if prev_actions.shape[2] > max_action_dim:
+            prev_actions = prev_actions[:, :, :max_action_dim]
+        elif prev_actions.shape[2] < max_action_dim:
+            pad = torch.zeros(
+                prev_actions.shape[0],
+                prev_actions.shape[1],
+                max_action_dim - prev_actions.shape[2],
+                dtype=prev_actions.dtype,
+                device=prev_actions.device,
+            )
+            prev_actions = torch.cat([prev_actions, pad], dim=2)
+
+        prev_actions = prev_actions.to(device=state.device, dtype=state.dtype)
+
+        rtc_config = getattr(self.config, "rtc_config", None)
+        execution_horizon = int(getattr(rtc_config, "execution_horizon", action_horizon))
+        overlap_steps = max(0, min(action_horizon, execution_horizon))
+        if overlap_steps == 0:
+            return inputs, None
+
+        try:
+            frozen_steps = int(inference_delay or 0)
+        except (TypeError, ValueError):
+            frozen_steps = 0
+        frozen_steps = max(0, min(frozen_steps, overlap_steps))
+
+        options = {
+            "action_horizon": action_horizon,
+            "rtc_overlap_steps": overlap_steps,
+            "rtc_frozen_steps": frozen_steps,
+            "rtc_ramp_rate": float(getattr(self._groot_model.config, "rtc_ramp_rate", 6.0)),
+        }
+
+        inputs = dict(inputs)
+        inputs["action"] = prev_actions
+        return inputs, options
+
     def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]:
         """Training forward pass.
 
         Delegates to Isaac-GR00T model.forward when inputs are compatible.
         """
-        # Build a clean input dict for GR00T: keep only tensors GR00T consumes
-        allowed_base = {"state", "state_mask", "action", "action_mask", "embodiment_id"}
-        groot_inputs = {
-            k: v
-            for k, v in batch.items()
-            if (k in allowed_base or k.startswith("eagle_")) and not (k.startswith("next.") or k == "info")
-        }
+        groot_inputs = self._filter_groot_inputs(batch, include_action=True)
 
         # Get device from model parameters
-        device = next(self.parameters()).device
+        device = get_device_from_parameters(self)
 
         # Run GR00T forward under bf16 autocast when enabled to reduce activation memory
         # Rationale: Matches original GR00T finetuning (bf16 compute, fp32 params) and avoids fp32 upcasts.
@@ -248,38 +414,52 @@ class GrootPolicy(PreTrainedPolicy):
 
         # Isaac-GR00T returns a BatchFeature; loss key is typically 'loss'
         loss = outputs.get("loss")
+        if loss is None:
+            raise RuntimeError(
+                "GR00T model.forward did not return a 'loss'. Training batches must include "
+                "'action' and 'action_mask'; check the preprocessor output."
+            )
 
         loss_dict = {"loss": loss.item()}
 
         return loss, loss_dict
 
     @torch.no_grad()
-    def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor:
+    def predict_action_chunk(self, batch: dict[str, Tensor], **kwargs: object) -> Tensor:
         """Predict a chunk of actions for inference by delegating to Isaac-GR00T.
 
         Returns a tensor of shape (B, n_action_steps, action_dim).
+
+        For N1.7, LeRobot's RTC leftovers are converted into the native GR00T
+        action-overlap options before calling the underlying model.
         """
         self.eval()
 
-        # Build a clean input dict for GR00T: keep only tensors GR00T consumes
-        # Preprocessing is handled by the processor pipeline, so we just filter the batch
-        # NOTE: During inference, we should NOT pass action/action_mask (that's what we're predicting)
-        allowed_base = {"state", "state_mask", "embodiment_id"}
-        groot_inputs = {
-            k: v
-            for k, v in batch.items()
-            if (k in allowed_base or k.startswith("eagle_")) and not (k.startswith("next.") or k == "info")
-        }
+        # Preprocessing is handled by the processor pipeline, so we just filter the batch.
+        # During inference, we do not pass action because it is predicted.
+        # N1.7 still carries a 2-D action horizon mask from its checkpoint processor.
+        groot_inputs = self._filter_groot_inputs(batch, include_action=False)
+        groot_inputs, groot_options = self._prepare_n1_7_rtc_inputs(
+            groot_inputs,
+            inference_delay=kwargs.get("inference_delay"),
+            prev_chunk_left_over=kwargs.get("prev_chunk_left_over"),
+        )
 
         # Get device from model parameters
-        device = next(self.parameters()).device
+        device = get_device_from_parameters(self)
 
         # Use bf16 autocast for inference to keep memory low and match backbone dtype
         with torch.autocast(device_type=device.type, dtype=torch.bfloat16, enabled=self.config.use_bf16):
-            outputs = self._groot_model.get_action(groot_inputs)
+            if groot_options is not None:
+                outputs = self._groot_model.get_action(groot_inputs, options=groot_options)
+            else:
+                outputs = self._groot_model.get_action(groot_inputs)
 
         actions = outputs.get("action_pred")
 
+        prediction_horizon = self._resolve_prediction_horizon(actions)
+        actions = actions[:, :prediction_horizon]
+
         original_action_dim = self.config.output_features[ACTION].shape[0]
         actions = actions[:, :, :original_action_dim]
 
@@ -288,44 +468,17 @@ class GrootPolicy(PreTrainedPolicy):
     @torch.no_grad()
     def select_action(self, batch: dict[str, Tensor]) -> Tensor:
         """Select single action from action queue."""
+        if getattr(self.config, "use_relative_actions", False):
+            raise NotImplementedError(
+                "GrootPolicy.select_action does not support relative-action policies because cached "
+                "relative chunk actions can be decoded against newer observation states. Use "
+                "predict_action_chunk and postprocess the full chunk before queuing actions, or use "
+                "the RTC/chunked rollout inference path."
+            )
+
         self.eval()
 
         if len(self._action_queue) == 0:
             actions = self.predict_action_chunk(batch)
-            self._action_queue.extend(actions.transpose(0, 1))
+            self._action_queue.extend(actions[:, : self._action_queue_steps].transpose(0, 1))
         return self._action_queue.popleft()
-
-    # -------------------------
-    # Internal helpers
-    # -------------------------
-    def _handle_flash_attention_compatibility(self) -> None:
-        """Handle Flash Attention compatibility issues by setting environment variables.
-
-        This addresses the common 'undefined symbol' error that occurs when Flash Attention
-        is compiled against a different PyTorch version than what's currently installed.
-        """
-
-        # Set environment variables to handle Flash Attention compatibility
-        # These help with symbol resolution issues
-        os.environ.setdefault("FLASH_ATTENTION_FORCE_BUILD", "0")
-        os.environ.setdefault("FLASH_ATTENTION_SKIP_CUDA_BUILD", "0")
-
-        # Try to import flash_attn and handle failures gracefully
-        try:
-            import flash_attn
-
-            print(f"[GROOT] Flash Attention version: {flash_attn.__version__}")
-        except ImportError as e:
-            print(f"[GROOT] Flash Attention not available: {e}")
-            print("[GROOT] Will use fallback attention mechanism")
-        except Exception as e:
-            if "undefined symbol" in str(e):
-                print(f"[GROOT] Flash Attention compatibility issue detected: {e}")
-                print("[GROOT] This is likely due to PyTorch/Flash Attention version mismatch")
-                print("[GROOT] Consider reinstalling Flash Attention with compatible version:")
-                print("  pip uninstall flash-attn")
-                print("  pip install --no-build-isolation flash-attn==2.6.3")
-                print("[GROOT] Continuing with fallback attention mechanism")
-            else:
-                print(f"[GROOT] Flash Attention error: {e}")
-                print("[GROOT] Continuing with fallback attention mechanism")

src/lerobot/policies/groot/utils.py

@@ -1,47 +1,263 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Shared, side-effect-free utilities for the GR00T N1.7 policy.
+
+These helpers are consumed by both the config layer (checkpoint sidecar
+inspection) and the processor layer (stat flattening, action decoding, language
+and image packing). They are pure functions with no GR00T-specific state so they
+can be unit-tested in isolation and reused without importing the heavier
+config/processor modules.
+"""
+
+from __future__ import annotations
+
+import json
 from pathlib import Path
-from shutil import copytree
+from typing import Any
 
-from huggingface_hub import hf_hub_download
+import numpy as np
+import torch
 
 
-def ensure_eagle_cache_ready(vendor_dir: Path, cache_dir: Path, assets_repo: str) -> None:
-    """Populate the Eagle processor directory in cache and ensure tokenizer assets exist.
-
-    - Copies the vendored Eagle files into cache_dir (overwriting when needed).
-    - Downloads vocab.json and merges.txt into the same cache_dir if missing.
-    """
-    cache_dir = Path(cache_dir)
-    vendor_dir = Path(vendor_dir)
-
+def read_json(path: Path) -> dict[str, Any]:
+    """Read a JSON object from ``path``, returning ``{}`` on any read/parse error."""
     try:
-        # Populate/refresh cache with vendor files to ensure a complete processor directory
-        print(f"[GROOT] Copying vendor Eagle files to cache: {vendor_dir} -> {cache_dir}")
-        copytree(vendor_dir, cache_dir, dirs_exist_ok=True)
-    except Exception as exc:  # nosec: B110
-        print(f"[GROOT] Warning: Failed to copy vendor Eagle files to cache: {exc}")
+        with path.open() as f:
+            data = json.load(f)
+    except (OSError, json.JSONDecodeError):
+        return {}
+    return data if isinstance(data, dict) else {}
 
-    required_assets = [
-        "vocab.json",
-        "merges.txt",
-        "added_tokens.json",
-        "chat_template.json",
-        "special_tokens_map.json",
-        "config.json",
-        "generation_config.json",
-        "preprocessor_config.json",
-        "processor_config.json",
-        "tokenizer_config.json",
-    ]
 
-    print(f"[GROOT] Assets repo: {assets_repo} \n Cache dir: {cache_dir}")
+def as_int_pair(value: Any) -> list[int] | None:
+    if not isinstance(value, (list, tuple)) or len(value) != 2:
+        return None
+    try:
+        return [int(value[0]), int(value[1])]
+    except (TypeError, ValueError):
+        return None
 
-    for fname in required_assets:
-        dst = cache_dir / fname
-        if not dst.exists():
-            print(f"[GROOT] Fetching {fname}")
-            hf_hub_download(
-                repo_id=assets_repo,
-                filename=fname,
-                repo_type="model",
-                local_dir=str(cache_dir),
+
+def as_optional_int(value: Any) -> int | None:
+    if value is None:
+        return None
+    try:
+        return int(value)
+    except (TypeError, ValueError):
+        return None
+
+
+def as_optional_float(value: Any) -> float | None:
+    if value is None:
+        return None
+    try:
+        return float(value)
+    except (TypeError, ValueError):
+        return None
+
+
+def as_float_list(values: Any) -> list[float]:
+    if values is None:
+        return []
+    if isinstance(values, torch.Tensor):
+        return values.detach().cpu().reshape(-1).float().tolist()
+    if isinstance(values, np.ndarray):
+        return values.reshape(-1).astype(np.float32).tolist()
+    if isinstance(values, (list, tuple)):
+        flattened: list[float] = []
+        for value in values:
+            flattened.extend(as_float_list(value))
+        return flattened
+    return [float(values)]
+
+
+def config_value(value: Any) -> str:
+    if hasattr(value, "value"):
+        value = value.value
+    text = str(value).lower()
+    return {
+        "relative": "relative",
+        "absolute": "absolute",
+        "delta": "delta",
+        "eef": "eef",
+        "non_eef": "non_eef",
+        "default": "default",
+        "xyz_rot6d": "xyz+rot6d",
+        "xyz+rot6d": "xyz+rot6d",
+        "xyz_rotvec": "xyz+rotvec",
+        "xyz+rotvec": "xyz+rotvec",
+    }.get(text, text)
+
+
+def has_modality_stats(stats: dict[str, dict[str, Any]] | None) -> bool:
+    if not stats:
+        return False
+    return any(bool(modality_stats) for modality_stats in stats.values())
+
+
+def stat_dim_from_entry(entry: dict[str, Any]) -> int:
+    for stat_name in ("mean", "q01", "min", "max", "std"):
+        value = entry.get(stat_name)
+        if isinstance(value, torch.Tensor):
+            return int(value.shape[-1]) if value.ndim > 0 else 1
+        if isinstance(value, np.ndarray):
+            return int(value.shape[-1]) if value.ndim > 0 else 1
+        if isinstance(value, list) and len(value) > 0:
+            first = value[0]
+            if isinstance(first, (list, tuple)) and len(first) > 0:
+                return len(first)
+            return len(value)
+    return 0
+
+
+def flatten_n1_7_modality_stats(
+    *,
+    embodiment_stats: dict[str, Any],
+    embodiment_config: dict[str, Any],
+    modality: str,
+    use_percentiles: bool,
+    use_relative_action: bool,
+) -> dict[str, list[float]]:
+    """Flatten one N1.7 modality's grouped statistics in checkpoint order.
+
+    When checkpoints request percentile normalization, q01/q99 replace min/max
+    for regular groups. Relative action groups read from ``relative_action``
+    stats and keep min/max, matching Isaac-GR00T's processor override.
+    """
+
+    source_stats = embodiment_stats.get(modality, {})
+    modality_config = embodiment_config.get(modality, {})
+    if not isinstance(source_stats, dict) or not isinstance(modality_config, dict):
+        return {}
+    modality_keys = modality_config.get("modality_keys", [])
+    if not isinstance(modality_keys, list):
+        return {}
+
+    flattened: dict[str, list[float]] = {}
+    action_configs = modality_config.get("action_configs", []) if modality == "action" else []
+    if not isinstance(action_configs, list):
+        action_configs = []
+    relative_stats = embodiment_stats.get("relative_action", {})
+    if not isinstance(relative_stats, dict):
+        relative_stats = {}
+
+    for stat_name in ("min", "max", "mean", "std"):
+        values: list[float] = []
+        source_stat_name = stat_name
+        if use_percentiles and stat_name == "min":
+            source_stat_name = "q01"
+        elif use_percentiles and stat_name == "max":
+            source_stat_name = "q99"
+
+        for idx, modality_key in enumerate(modality_keys):
+            if not isinstance(modality_key, str):
+                continue
+            key_source_stats = source_stats
+            key_stat_name = source_stat_name
+            if modality == "action" and use_relative_action and idx < len(action_configs):
+                action_config = action_configs[idx]
+                if isinstance(action_config, dict) and config_value(action_config.get("rep")) == "relative":
+                    key_source_stats = relative_stats
+                    key_stat_name = stat_name
+            key_stats = key_source_stats.get(modality_key, {})
+            if not isinstance(key_stats, dict):
+                raise KeyError(f"Missing statistics for {modality}.{modality_key}")
+            raw_values = key_stats.get(key_stat_name)
+            if raw_values is None:
+                raise KeyError(f"Missing '{key_stat_name}' statistics for {modality}.{modality_key}")
+            values.extend(as_float_list(raw_values))
+        if values:
+            flattened[stat_name] = values
+
+    return flattened
+
+
+def rot6d_to_matrix(rot6d: np.ndarray) -> np.ndarray:
+    rows = rot6d.reshape(2, 3).astype(np.float64)
+    row1 = rows[0] / np.linalg.norm(rows[0])
+    row2 = rows[1] - np.dot(row1, rows[1]) * row1
+    row2 = row2 / np.linalg.norm(row2)
+    row3 = np.cross(row1, row2)
+    return np.vstack([row1, row2, row3])
+
+
+def xyz_rot6d_to_homogeneous(xyz_rot6d: np.ndarray) -> np.ndarray:
+    transform = np.eye(4, dtype=np.float64)
+    transform[:3, :3] = rot6d_to_matrix(xyz_rot6d[3:])
+    transform[:3, 3] = xyz_rot6d[:3]
+    return transform
+
+
+def homogeneous_to_xyz_rot6d(transform: np.ndarray) -> np.ndarray:
+    return np.concatenate([transform[:3, 3], transform[:2, :3].reshape(-1)], axis=0)
+
+
+def relative_eef_to_absolute(action: np.ndarray, reference_state: np.ndarray) -> np.ndarray:
+    """Convert relative EEF deltas in xyz+rot6d format to absolute EEF poses."""
+
+    out = np.empty_like(action, dtype=np.float64)
+    for batch_idx in range(action.shape[0]):
+        reference = xyz_rot6d_to_homogeneous(reference_state[batch_idx])
+        for timestep in range(action.shape[1]):
+            relative = xyz_rot6d_to_homogeneous(action[batch_idx, timestep])
+            out[batch_idx, timestep] = homogeneous_to_xyz_rot6d(reference @ relative)
+    return out.astype(np.float32)
+
+
+def infer_n1_7_batch_size_and_device(
+    obs: dict[str, Any], action: torch.Tensor | None
+) -> tuple[int, torch.device]:
+    for value in list(obs.values()) + [action]:
+        if isinstance(value, torch.Tensor):
+            return value.shape[0], value.device
+    video = obs.get("video")
+    if isinstance(video, np.ndarray):
+        return video.shape[0], torch.device("cpu")
+    return 1, torch.device("cpu")
+
+
+def prepare_n1_7_language_batch(
+    language: Any,
+    batch_size: int,
+    *,
+    formalize_language: bool,
+) -> list[str]:
+    default_language = "Perform the task."
+    if language is None or (isinstance(language, str) and language == ""):
+        languages = [default_language] * batch_size
+    elif isinstance(language, str):
+        languages = [language] * batch_size
+    elif isinstance(language, (list, tuple)):
+        languages = list(language)
+        if len(languages) == 0:
+            languages = [default_language] * batch_size
+        elif len(languages) == 1 and batch_size > 1:
+            languages = languages * batch_size
+        elif len(languages) != batch_size:
+            raise ValueError(
+                f"language batch has {len(languages)} entries, but GR00T N1.7 input batch has {batch_size}."
             )
+    else:
+        languages = [str(language)] * batch_size
+
+    formatted = []
+    for item in languages:
+        text = str(item) if item else default_language
+        if formalize_language:
+            text = text.lower()
+            text = "".join(ch for ch in text if ch.isalnum() or ch.isspace() or ch == "_")
+        formatted.append(text)
+    return formatted

src/lerobot/policies/molmoact2/configuration_molmoact2.py

@@ -79,15 +79,6 @@ class MolmoAct2Config(PreTrainedConfig):
     eval_seed: int | None = None
     rtc_config: RTCConfig | None = None
 
-    # Joint frame transform for cross-calibration compatibility.
-    # Some MolmoAct2 checkpoints were trained on data using a different joint
-    # convention than the current LeRobot calibration. Set both to apply a
-    # sign/offset correction at runtime (state before model, action after).
-    # See: https://huggingface.co/docs/lerobot/backwardcomp
-    # Default is None (no transform). Both must be set together.
-    joint_signs: list[float] | None = None
-    joint_offsets: list[float] | None = None
-
     # Default is full finetuning with gradients from the action expert flowing into the VLM.
     enable_lora_vlm: bool = False
     lora_rank: int = 64
@@ -132,10 +123,6 @@ class MolmoAct2Config(PreTrainedConfig):
 
     def __post_init__(self) -> None:
         super().__post_init__()
-        if (self.joint_signs is None) != (self.joint_offsets is None):
-            raise ValueError("joint_signs and joint_offsets must both be set or both be None.")
-        if self.joint_signs is not None and len(self.joint_signs) != len(self.joint_offsets):
-            raise ValueError("joint_signs and joint_offsets must have the same length.")
         if self.action_mode not in {"continuous", "discrete", "both"}:
             raise ValueError(
                 f"Unsupported action_mode={self.action_mode!r}. "

src/lerobot/policies/molmoact2/processor_molmoact2.py

@@ -1005,93 +1005,6 @@ class MolmoAct2PackInputsProcessorStep(ProcessorStep):
         return features
 
 
-@ProcessorStepRegistry.register(name="molmoact2_state_frame_transform")
-@dataclass
-class MolmoAct2StateFrameTransformStep(ProcessorStep):
-    """Convert robot state from arm frame to model frame before normalization.
-
-    Required for zero-shot deployment of MolmoAct2-SO100_101 on SO-100/101
-    arms calibrated with LeRobot >= 0.5.0 (v3.0 convention). The checkpoint
-    was trained on data using a different joint convention (sign flip on
-    shoulder_lift, 90 deg offset on shoulder_lift and elbow_flex).
-
-    No-op when joint_signs and joint_offsets are None (default), so this
-    step has no effect on fine-tuned models or other embodiments.
-
-    state_model = signs * arm_state + offsets
-
-    See: https://huggingface.co/docs/lerobot/backwardcomp
-    """
-
-    joint_signs: list[float] | None = None
-    joint_offsets: list[float] | None = None
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        if self.joint_signs is None or self.joint_offsets is None:
-            return transition
-        observation = transition.get(TransitionKey.OBSERVATION)
-        if not isinstance(observation, dict) or OBS_STATE not in observation:
-            return transition
-        transition = transition.copy()
-        observation = observation.copy()
-        state = torch.as_tensor(observation[OBS_STATE], dtype=torch.float32).clone()
-        n = len(self.joint_signs)
-        signs = torch.tensor(self.joint_signs, dtype=torch.float32, device=state.device)
-        offsets = torch.tensor(self.joint_offsets, dtype=torch.float32, device=state.device)
-        state[..., :n] = signs * state[..., :n] + offsets
-        observation[OBS_STATE] = state
-        transition[TransitionKey.OBSERVATION] = observation
-        return transition
-
-    def transform_features(
-        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
-    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
-        return features
-
-    def get_config(self) -> dict[str, Any]:
-        return {"joint_signs": self.joint_signs, "joint_offsets": self.joint_offsets}
-
-
-@ProcessorStepRegistry.register(name="molmoact2_action_frame_transform")
-@dataclass
-class MolmoAct2ActionFrameTransformStep(ProcessorStep):
-    """Convert model action from model frame back to arm frame after unnormalization.
-
-    Inverse of MolmoAct2StateFrameTransformStep. Required for zero-shot
-    MolmoAct2-SO100_101 on SO-100/101 arms. No-op when both fields are None.
-
-    action_arm = signs * (model_action - offsets)
-
-    See: https://huggingface.co/docs/lerobot/backwardcomp
-    """
-
-    joint_signs: list[float] | None = None
-    joint_offsets: list[float] | None = None
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        if self.joint_signs is None or self.joint_offsets is None:
-            return transition
-        action = transition.get(TransitionKey.ACTION)
-        if action is None:
-            return transition
-        transition = transition.copy()
-        action = torch.as_tensor(action, dtype=torch.float32).clone()
-        n = len(self.joint_signs)
-        signs = torch.tensor(self.joint_signs, dtype=torch.float32, device=action.device)
-        offsets = torch.tensor(self.joint_offsets, dtype=torch.float32, device=action.device)
-        action[..., :n] = signs * (action[..., :n] - offsets)
-        transition[TransitionKey.ACTION] = action
-        return transition
-
-    def transform_features(
-        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
-    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
-        return features
-
-    def get_config(self) -> dict[str, Any]:
-        return {"joint_signs": self.joint_signs, "joint_offsets": self.joint_offsets}
-
-
 @ProcessorStepRegistry.register(name="molmoact2_clamp_action")
 @dataclass
 class MolmoAct2ClampActionProcessorStep(ProcessorStep):
@@ -1154,10 +1067,6 @@ def make_molmoact2_pre_post_processors(
     input_steps: list[ProcessorStep] = [
         RenameObservationsProcessorStep(rename_map={}),
         AddBatchDimensionProcessorStep(),
-        MolmoAct2StateFrameTransformStep(
-            joint_signs=config.joint_signs,
-            joint_offsets=config.joint_offsets,
-        ),
         MolmoAct2MaskedNormalizerProcessorStep(
             features={**config.input_features, **config.output_features},
             norm_map=config.normalization_mapping,
@@ -1193,10 +1102,6 @@ def make_molmoact2_pre_post_processors(
             norm_map=config.normalization_mapping,
             stats=masked_dataset_stats,
         ),
-        MolmoAct2ActionFrameTransformStep(
-            joint_signs=config.joint_signs,
-            joint_offsets=config.joint_offsets,
-        ),
         DeviceProcessorStep(device="cpu"),
     ]
 

src/lerobot/policies/pretrained.py

@@ -11,8 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from __future__ import annotations
-
 import abc
 import builtins
 import dataclasses
@@ -21,7 +19,7 @@ import os
 from importlib.resources import files
 from pathlib import Path
 from tempfile import TemporaryDirectory
-from typing import TYPE_CHECKING, TypedDict, TypeVar, Unpack
+from typing import TypedDict, TypeVar, Unpack
 
 import packaging
 import safetensors
@@ -40,13 +38,10 @@ from .utils import log_model_loading_keys
 
 T = TypeVar("T", bound="PreTrainedPolicy")
 
-if TYPE_CHECKING:
-    from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
-
 
 def _build_card_context(
     cfg: TrainPipelineConfig | None,
-    dataset_meta: LeRobotDatasetMetadata | None,
+    dataset_repo_id: str | None,
     input_features: dict | None,
     output_features: dict | None,
 ) -> dict:
@@ -77,16 +72,30 @@ def _build_card_context(
             "lerobot_version": __version__,
         }
 
-    if dataset_meta is not None:
-        context["dataset"] = {
-            "repo_id": dataset_meta.repo_id,
-            "episodes": dataset_meta.total_episodes,
-            "frames": dataset_meta.total_frames,
-            "fps": dataset_meta.fps,
-            "tasks": [str(task) for task in dataset_meta.tasks.index],
-        }
-        context["robot_type"] = dataset_meta.robot_type
-        context["cameras"] = [key.split(".")[-1] for key in dataset_meta.camera_keys]
+    if dataset_repo_id:
+        dataset_cfg = getattr(cfg, "dataset", None)
+        try:
+            from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
+
+            meta = LeRobotDatasetMetadata(
+                dataset_repo_id,
+                root=getattr(dataset_cfg, "root", None),
+                revision=getattr(dataset_cfg, "revision", None),
+            )
+            context["dataset"] = {
+                "repo_id": dataset_repo_id,
+                "episodes": meta.total_episodes,
+                "frames": meta.total_frames,
+                "fps": meta.fps,
+                "tasks": [str(task) for task in meta.tasks.index],
+            }
+            context["robot_type"] = meta.robot_type
+            context["cameras"] = [key.split(".")[-1] for key in meta.camera_keys]
+        except Exception as e:  # noqa: BLE001 — dataset details are optional, never fail the push
+            logging.warning(
+                f"Could not load dataset metadata for '{dataset_repo_id}'; those sections will be "
+                f"omitted from the model card. ({e})"
+            )
 
     return context
 
@@ -295,7 +304,6 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
         cfg: TrainPipelineConfig,
         peft_model=None,
         state_dict: dict[str, Tensor] | None = None,
-        dataset_meta: LeRobotDatasetMetadata | None = None,
     ):
         api = HfApi()
         repo_id = api.create_repo(
@@ -317,12 +325,7 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
                 self.save_pretrained(saved_path, state_dict=state_dict)
 
             card = self.generate_model_card(
-                cfg.dataset.repo_id,
-                self.config.type,
-                self.config.license,
-                self.config.tags,
-                cfg=cfg,
-                dataset_meta=dataset_meta,
+                cfg.dataset.repo_id, self.config.type, self.config.license, self.config.tags, cfg=cfg
             )
             card.save(str(saved_path / "README.md"))
 
@@ -337,9 +340,6 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
                 ignore_patterns=["*.tmp", "*.log"],
             )
 
-            # Contract: lerobot.jobs.hf.submit_to_hf watches for this exact
-            # "Model pushed to <url>" line to end a remote run early. Keep the wording
-            # and URL format in sync (it falls back to status polling if they drift).
             logging.info(f"Model pushed to {commit_info.repo_url.url}")
 
     def generate_model_card(
@@ -349,7 +349,6 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
         license: str | None,
         tags: list[str] | None,
         cfg: TrainPipelineConfig | None = None,
-        dataset_meta: LeRobotDatasetMetadata | None = None,
     ) -> ModelCard:
         base_model_mapping = {
             "smolvla": "lerobot/smolvla_base",
@@ -370,7 +369,7 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
         )
 
         context = _build_card_context(
-            cfg, dataset_meta, self.config.input_features, self.config.output_features
+            cfg, dataset_repo_id, self.config.input_features, self.config.output_features
         )
         # Used by the template to pre-fill commands and the "Fine-tuned from" line.
         context["policy_repo_id"] = getattr(self.config, "repo_id", None)
@@ -387,7 +386,7 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
         self,
         peft_config=None,
         peft_cli_overrides: dict | None = None,
-    ) -> PreTrainedPolicy:
+    ) -> "PreTrainedPolicy":
         """
         Wrap this policy with PEFT adapters for parameter-efficient fine-tuning.
 

src/lerobot/policies/vla_jepa/modeling_vla_jepa.py

@@ -17,10 +17,12 @@ from __future__ import annotations
 import logging
 from collections import deque
 from pathlib import Path
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING
 
+import numpy as np
 import torch
 import torch.nn.functional as F  # noqa: N812
+from PIL import Image
 from torch import Tensor, nn
 
 from lerobot.policies.pretrained import PreTrainedPolicy, T
@@ -53,13 +55,12 @@ class VLAJEPAModel(nn.Module):
       - DiT-B: flow-matching action head for future action prediction
       - V-JEPA: world model for video frame prediction
 
-    Inputs are batched tensors kept on the model device
-      - images: List[List[Tensor [C, H, W]]] (float [0,1]) — per sample, per view (Qwen messages)
-      - instructions: List[str]
-      - videos: Tensor [B, V, T, C, H, W] (float [0,1], world model only)
-      - actions: Tensor [B, T, action_dim] (optional, training only)
-      - state: Tensor [B, 1, state_dim] (optional)
-      - action_is_pad: Tensor [B, T] (optional)
+    Input: List[dict] native format (same as original starVLA)
+      - "image": List[PIL.Image] (multi-view images)
+      - "video": np.ndarray [V, T, H, W, 3]
+      - "lang": str (task instruction)
+      - "action": np.ndarray [T, action_dim] (optional, training only)
+      - "state": np.ndarray [1, state_dim] (optional)
     """
 
     def __init__(self, config: VLAJEPAConfig) -> None:
@@ -74,11 +75,6 @@ class VLAJEPAModel(nn.Module):
         self.action_tokens, self.action_token_ids, self.embodied_action_token_id = (
             self.qwen.expand_tokenizer()
         )
-        self.register_buffer(
-            "_action_token_ids_t",
-            torch.tensor(self.action_token_ids, dtype=torch.long),
-            persistent=False,
-        )
 
         # Action head (flow-matching DiT)
         self.action_model = VLAJEPAActionHead(config, cross_attention_dim=self.qwen.model.config.hidden_size)
@@ -165,123 +161,166 @@ class VLAJEPAModel(nn.Module):
 
     # ---- Native VLA-JEPA forward (follows original VLA_JEPA.py) ----
 
-    def _encode_qwen(
-        self, images: list[list[Tensor]], instructions: list[str], *, need_action_tokens: bool
-    ) -> tuple[Tensor, Tensor, Tensor | None]:
-        """Run Qwen and gather the embodied-action (and optionally action) token hidden states."""
+    def forward(self, examples: list[dict]) -> dict[str, Tensor]:
+        """
+        Native forward pass following original starVLA VLA_JEPA.forward.
+
+        Args:
+            examples: List of per-sample dicts with keys:
+                "image"  : List[PIL.Image]  — multi-view images
+                "video"  : np.ndarray [V, T, H, W, 3]
+                "lang"   : str — task instruction
+                "action" : np.ndarray [T, action_dim] (optional)
+                "state"  : np.ndarray [1, state_dim] (optional)
+
+        Returns:
+            dict with "action_loss" and "wm_loss" keys (scalar Tensors).
+        """
+        # Unpack native format (same pattern as original VLA_JEPA.py)
+        batch_images = [ex["image"] for ex in examples]  # List[List[PIL.Image]]
+        batch_videos = [ex["video"] for ex in examples]  # List[np.ndarray]
+        instructions = [ex["lang"] for ex in examples]  # List[str]
+        has_action = "action" in examples[0] and examples[0]["action"] is not None
+        actions = [ex["action"] for ex in examples] if has_action else None
+        has_state = "state" in examples[0] and examples[0]["state"] is not None
+        state = [ex["state"] for ex in examples] if has_state else None
+        action_is_pad = (
+            [ex["action_is_pad"] for ex in examples]
+            if has_action and "action_is_pad" in examples[0] and examples[0]["action_is_pad"] is not None
+            else None
+        )
+
+        # Stack videos: [B, V, T, H, W, 3] -> [B, V, T, 3, H, W]
+        batch_videos = np.stack(batch_videos)
+        batch_videos = batch_videos.transpose(0, 1, 2, 5, 3, 4)  # [B, V, T, 3, H, W]
+
+        # Adjust number of views for the world model:
+        # - fewer views than expected: duplicate the first view to fill up
+        # - more views than expected: keep only the first num_views_world_model views
+        num_views_world_model = self.config.jepa_tubelet_size
+        if batch_videos.shape[1] < num_views_world_model:
+            num_missing_views = num_views_world_model - batch_videos.shape[1]
+            first_view = np.repeat(batch_videos[:, :1], num_missing_views, axis=1)
+            batch_videos = np.concatenate([batch_videos, first_view], axis=1)
+        elif batch_videos.shape[1] > num_views_world_model:
+            batch_videos = batch_videos[:, :num_views_world_model]
+
+        # ---- Step 1: QwenVL encode (same as original) ----
         qwen_inputs = self.qwen.build_inputs(
-            images=images,
+            images=batch_images,
             instructions=instructions,
             action_prompt=self.replace_prompt,
             embodied_prompt=self.embodied_replace_prompt,
         )
-        input_ids = qwen_inputs["input_ids"]
-        embodied_idx = (input_ids == self.embodied_action_token_id).nonzero(as_tuple=True)
-        action_idx = None
-        if need_action_tokens:
-            action_mask = torch.isin(input_ids, self._action_token_ids_t)
-            action_idx = action_mask.nonzero(as_tuple=True)
+
+        # Locate embodied-action tokens (always needed for action head)
+        embodied_mask = qwen_inputs["input_ids"] == self.embodied_action_token_id
+        embodied_indices = embodied_mask.nonzero(as_tuple=True)
+
+        # Locate action tokens (only needed for world model predictor)
+        if self.config.enable_world_model:
+            action_mask = torch.isin(
+                qwen_inputs["input_ids"],
+                torch.tensor(self.action_token_ids, device=qwen_inputs["input_ids"].device),
+            )
+            action_indices = action_mask.nonzero(as_tuple=True)
 
         device_type = next(self.parameters()).device.type
+
         with torch.autocast(device_type=device_type, dtype=torch.bfloat16):
             last_hidden = self._qwen_last_decoder_hidden(qwen_inputs)  # [B, seq_len, H]
             b, _, h = last_hidden.shape
-            embodied_action_tokens = last_hidden[embodied_idx[0], embodied_idx[1], :].view(b, -1, h)
-            action_tokens = (
-                last_hidden[action_idx[0], action_idx[1], :].view(b, -1, h)
-                if action_idx is not None
-                else None
-            )
-        return embodied_action_tokens, action_tokens
 
-    def _world_model_loss(self, videos: Tensor, action_tokens: Tensor) -> Tensor:
-        """JEPA encode + predictor L1 loss. `videos` is [B, V, T, C, H, W] float in [0, 1]."""
-        # Match the world model's expected view count: pad with the first view, or trim extras.
-        num_views = self.config.jepa_tubelet_size
-        if videos.shape[1] < num_views:
-            missing = num_views - videos.shape[1]
-            videos = torch.cat([videos, videos[:, :1].repeat(1, missing, 1, 1, 1, 1)], dim=1)
-        elif videos.shape[1] > num_views:
-            videos = videos[:, :num_views]
+            if self.config.enable_world_model:
+                action_tokens = last_hidden[action_indices[0], action_indices[1], :].view(b, -1, h)
 
-        b, v, t_frames, c, h_img, w_img = videos.shape
-        flat = videos.reshape(b * v, t_frames, c, h_img, w_img)
-        # Fast (torchvision) video processor on-device, do_rescale=False (frames already in [0, 1]).
-        video_pixels = self.video_processor(
-            videos=list(flat),
-            return_tensors="pt",
-            device=self.video_encoder.device,
-            do_rescale=False,
-        )["pixel_values_videos"]  # [B*V, T, C, H, W]
+            embodied_action_tokens = last_hidden[embodied_indices[0], embodied_indices[1], :].view(b, -1, h)
 
-        with torch.no_grad():
-            video_embeddings = self.video_encoder.get_vision_features(pixel_values_videos=video_pixels)
-            # Merge views: [B*V, ...] -> [B, ..., V*embed_dim]
-            video_embeddings = torch.cat(torch.chunk(video_embeddings, chunks=v, dim=0), dim=2)
-
-        tubelet_size = self.video_encoder.config.tubelet_size
-        # num_video_frames raw frames → t_enc_total temporal positions after tubelet compression
-        t_enc_total = self.config.num_video_frames // tubelet_size
-        if t_enc_total < 2:
-            return torch.zeros((), device=video_embeddings.device)
-
-        # Shift-by-one JEPA split: input_states = positions 0..T-2, gt_states = positions 1..T-1
-        t_enc_ctx = t_enc_total - 1
-        tokens_per_frame = video_embeddings.shape[1] // t_enc_total
-        input_states = video_embeddings[:, : tokens_per_frame * t_enc_ctx, :]
-        gt_states = video_embeddings[:, tokens_per_frame:, :]
-
-        expected_actions = t_enc_ctx * self.config.num_action_tokens_per_timestep
-        if action_tokens.shape[1] < expected_actions:
-            pad = action_tokens[:, -1:].repeat(1, expected_actions - action_tokens.shape[1], 1)
-            action_tokens = torch.cat([action_tokens, pad], dim=1)
-
-        predicted_states = self.video_predictor(
-            input_states.float(), action_tokens[:, :expected_actions].float()
-        )
-        return F.l1_loss(predicted_states, gt_states.float(), reduction="mean")
-
-    def _action_loss(
-        self,
-        embodied_action_tokens: Tensor,
-        actions: Tensor,
-        state: Tensor | None,
-        action_is_pad: Tensor | None,
-    ) -> Tensor:
-        """Flow-matching action-head loss, repeated over `repeated_diffusion_steps`."""
-        device_type = next(self.parameters()).device.type
-        with torch.autocast(device_type=device_type, dtype=torch.float32):
-            r = self.config.repeated_diffusion_steps
-            horizon = self.config.chunk_size
-            actions_target = actions[:, -horizon:, :].to(torch.float32).repeat(r, 1, 1)
-            embodied = embodied_action_tokens.repeat(r, 1, 1)
-            state_rep = state.to(embodied_action_tokens.dtype).repeat(r, 1, 1) if state is not None else None
-            pad_rep = action_is_pad[:, -horizon:].repeat(r, 1) if action_is_pad is not None else None
-            return self.action_model(embodied, actions_target, state_rep, pad_rep)
-
-    def forward(
-        self,
-        images: list[list[Tensor]],
-        instructions: list[str],
-        videos: Tensor | None = None,
-        actions: Tensor | None = None,
-        state: Tensor | None = None,
-        action_is_pad: Tensor | None = None,
-    ) -> dict[str, Tensor]:
-        """Native forward: Qwen encode → optional world-model loss → optional action-head loss."""
-        embodied_action_tokens, action_tokens = self._encode_qwen(
-            images, instructions, need_action_tokens=self.config.enable_world_model
-        )
-
-        if self.config.enable_world_model and videos is not None:
-            wm_loss = self._world_model_loss(videos, action_tokens)
+        # ---- Step 2+3: JEPA Encoder + Predictor ----
+        device_wm = last_hidden.device
+        if not self.config.enable_world_model:
+            wm_loss = torch.tensor(0.0, device=device_wm)
         else:
-            wm_loss = torch.zeros((), device=embodied_action_tokens.device)
+            b, v, t_frames, c, h_img, w_img = batch_videos.shape
+            batch_videos_flat = batch_videos.reshape(b * v, t_frames, c, h_img, w_img)
 
-        if actions is None:
+            video_pixels = self.video_processor(videos=list(batch_videos_flat), return_tensors="pt")[
+                "pixel_values_videos"
+            ].to(self.video_encoder.device)  # [B*V, T, C, H, W]
+
+            with torch.no_grad():
+                video_embeddings = self.video_encoder.get_vision_features(pixel_values_videos=video_pixels)
+                # Merge views: [B*V, ...] -> [B, ..., V*embed_dim]
+                video_embeddings = torch.cat(torch.chunk(video_embeddings, chunks=v, dim=0), dim=2)
+
+            tubelet_size = self.video_encoder.config.tubelet_size
+            device_wm = video_embeddings.device
+            # num_video_frames raw frames → t_enc_total temporal positions after tubelet compression
+            t_enc_total = self.config.num_video_frames // tubelet_size
+
+            if t_enc_total < 2:
+                wm_loss = torch.tensor(0.0, device=device_wm)
+            else:
+                # Shift-by-one JEPA split (matches original VLA_JEPA.py lines 231-232):
+                # input_states: positions 0..T-2, gt_states: positions 1..T-1
+                t_enc_ctx = t_enc_total - 1
+                tokens_per_frame = video_embeddings.shape[1] // t_enc_total
+
+                input_states = video_embeddings[:, : tokens_per_frame * t_enc_ctx, :]
+                gt_states = video_embeddings[:, tokens_per_frame:, :]
+
+                expected_actions = t_enc_ctx * self.config.num_action_tokens_per_timestep
+                if action_tokens.shape[1] < expected_actions:
+                    pad = action_tokens[:, -1:].repeat(1, expected_actions - action_tokens.shape[1], 1)
+                    action_tokens = torch.cat([action_tokens, pad], dim=1)
+
+                predicted_states = self.video_predictor(
+                    input_states.float(),
+                    action_tokens[:, :expected_actions].float(),
+                )
+
+                wm_loss = F.l1_loss(predicted_states, gt_states.float(), reduction="mean")
+
+        if not has_action:
             return {"wm_loss": wm_loss}
 
-        action_loss = self._action_loss(embodied_action_tokens, actions, state, action_is_pad)
+        # ---- Step 4: Action Head ----
+        with torch.autocast(device_type=device_type, dtype=torch.float32):
+            actions_tensor = torch.tensor(
+                np.array(actions), device=last_hidden.device, dtype=torch.float32
+            )  # [B, T_full, action_dim]
+            action_horizon = self.config.chunk_size
+            actions_target = actions_tensor[:, -action_horizon:, :]
+
+            state_tensor = None
+            if state is not None:
+                state_tensor = torch.tensor(
+                    np.array(state), device=last_hidden.device, dtype=last_hidden.dtype
+                )  # [B, 1, state_dim]
+
+            repeated_diffusion_steps = self.config.repeated_diffusion_steps
+            actions_target = actions_target.repeat(repeated_diffusion_steps, 1, 1)
+            embodied_action_tokens = embodied_action_tokens.repeat(repeated_diffusion_steps, 1, 1)
+            if state_tensor is not None:
+                state_tensor = state_tensor.repeat(repeated_diffusion_steps, 1, 1)
+
+            action_is_pad_rep = None
+            if action_is_pad is not None:
+                pad_tensor = torch.stack(
+                    [
+                        p.to(actions_target.device)
+                        if isinstance(p, Tensor)
+                        else torch.tensor(p, device=actions_target.device)
+                        for p in action_is_pad
+                    ]
+                )  # [B, T_full]
+                pad_tensor = pad_tensor[:, -action_horizon:]  # [B, action_horizon]
+                action_is_pad_rep = pad_tensor.repeat(repeated_diffusion_steps, 1)  # [B*R, action_horizon]
+
+            action_loss = self.action_model(
+                embodied_action_tokens, actions_target, state_tensor, action_is_pad_rep
+            )
+
         return {"action_loss": action_loss, "wm_loss": wm_loss * self.config.world_model_loss_weight}
 
     # ---- Native predict_action (follows original VLA_JEPA.predict_action) ----
@@ -289,23 +328,58 @@ class VLAJEPAModel(nn.Module):
     @torch.no_grad()
     def predict_action(
         self,
-        images: list[list[Tensor]],
+        batch_images: list[list[Image.Image]],
         instructions: list[str],
-        state: Tensor | None = None,
-    ) -> Tensor:
-        """Predict an action chunk. `images` is per-sample, per-view float [0,1] [C, H, W] tensors."""
+        state: np.ndarray | None = None,
+    ) -> np.ndarray:
+        """
+        Native action prediction following original VLA_JEPA.predict_action.
+
+        Args:
+            batch_images: List of samples; each is List[PIL.Image] (multi-view).
+            instructions: Task instructions, one per sample.
+            state: Optional [B, state_dim] numpy array.
+
+        Returns:
+            np.ndarray [B, action_horizon, action_dim] — predicted actions.
+        """
         if self.config.resize_images_to is not None:
             height, width = self.config.resize_images_to
-            images = [
-                [F.interpolate(img[None], size=(height, width), mode="area")[0] for img in views]
-                for views in images
+            resampling = getattr(Image, "Resampling", Image).BOX
+            batch_images = [
+                [image.resize((width, height), resample=resampling) for image in sample_images]
+                for sample_images in batch_images
             ]
 
-        embodied_action_tokens, _ = self._encode_qwen(images, instructions, need_action_tokens=False)
-        return self.action_model.predict_action(
-            embodied_action_tokens.float(), state.float() if state is not None else None
+        qwen_inputs = self.qwen.build_inputs(
+            images=batch_images,
+            instructions=instructions,
+            action_prompt=self.replace_prompt,
+            embodied_prompt=self.embodied_replace_prompt,
         )
 
+        embodied_mask = qwen_inputs["input_ids"] == self.embodied_action_token_id
+        embodied_indices = embodied_mask.nonzero(as_tuple=True)
+
+        device_type = next(self.parameters()).device.type
+
+        with torch.autocast(device_type=device_type, dtype=torch.bfloat16):
+            last_hidden = self._qwen_last_decoder_hidden(qwen_inputs)  # [B, seq_len, H]
+            b, _, h = last_hidden.shape
+            embodied_action_tokens = last_hidden[embodied_indices[0], embodied_indices[1], :].view(b, -1, h)
+
+        state_tensor = None
+        if state is not None:
+            state_tensor = torch.from_numpy(np.array(state)).to(
+                device=last_hidden.device, dtype=last_hidden.dtype
+            )
+
+        pred_actions = self.action_model.predict_action(
+            embodied_action_tokens.float(), state_tensor.float() if state_tensor is not None else None
+        )  # [B, action_horizon, action_dim]
+
+        return pred_actions.detach().cpu().numpy()
+
 
 # ============================================================================
 # LeRobot Adapter Layer - converts between LeRobot batch format and native VLA-JEPA format
@@ -316,9 +390,9 @@ class VLAJEPAPolicy(PreTrainedPolicy):
     """
     LeRobot adapter for VLA-JEPA.
 
-    Converts LeRobot's standard batch format (dict[str, Tensor]) to the batched tensors
-    the native model expects (keeping everything on-device), calls the native model, and
-    converts outputs back to LeRobot format.
+    Converts LeRobot's standard batch format (dict[str, Tensor]) to the native
+    VLA-JEPA format (List[dict]), calls the native model, and converts outputs
+    back to LeRobot format.
     """
 
     config_class = VLAJEPAConfig
@@ -345,8 +419,9 @@ class VLAJEPAPolicy(PreTrainedPolicy):
 
     # ---- Format Conversion: LeRobot → Native ----
 
-    def _prepare_model_inputs(self, batch: dict[str, Tensor], training=True) -> dict[str, Any]:
-        """Convert a LeRobot batch to the model's batched, on-device inputs.
+    def _prepare_model_inputs(self, batch: dict[str, Tensor]) -> list[dict]:
+        """
+        Convert LeRobot batch format to native VLA-JEPA examples format.
 
         LeRobot format:
             batch = {
@@ -356,25 +431,65 @@ class VLAJEPAPolicy(PreTrainedPolicy):
                 "task": str | List[str],  (optional instruction)
             }
 
-        Returns the kwargs for `VLAJEPAModel.forward` / `.predict_action` (everything stays
-        on the batch device; no per-sample shredding): `images` (per-sample, per-view list for
-        Qwen messages), `instructions`, and the batched `videos` / `actions` / `state` /
-        `action_is_pad` when present.
+        Native format (List[dict]):
+            {
+                "image": List[PIL.Image],       # multi-view images per sample
+                "video": np.ndarray [V, T, H, W, 3],
+                "lang": str,                     # task instruction
+                "action": np.ndarray [T, action_dim],  # optional
+                "state": np.ndarray [1, state_dim],    # optional
+            }
         """
+        # Determine batch size from the first image feature
         image_keys = list(self.config.image_features.keys())
         if not image_keys:
             raise ValueError("VLAJEPA requires at least one image feature.")
-        batch_size = batch[image_keys[0]].shape[0]
+        first_key = image_keys[0]
+        first_tensor = batch[first_key]
+        batch_size = first_tensor.shape[0]
 
-        # Current-frame image per view ([B, C, H, W]); regroup per sample for Qwen messages.
-        frames = []
+        # ---- Collect images per sample ----
+        # images_per_sample[b][v] = PIL.Image for view v
+        images_per_sample: list[list[Image.Image]] = [[] for _ in range(batch_size)]
         for key in image_keys:
-            t = batch[key]
-            if t.ndim == 5:  # [B, T, C, H, W] -> current observation (delta=0)
-                t = t[:, 0]
-            frames.append(self.model.qwen.to_pixel_values(t))
-        images = [[frame[b] for frame in frames] for b in range(batch_size)]
+            tensor = batch[key]  # [B, C, H, W] or [B, T, C, H, W]
+            if tensor.ndim == 5:
+                # observation_delta_indices = [0, 1, ..., num_video_frames-1]
+                # index 0 is the current observation (delta=0)
+                tensor = tensor[:, 0]
+            for b in range(batch_size):
+                images_per_sample[b].append(self.model.qwen.tensor_to_pil(tensor[b]))
 
+        # ---- Collect videos per sample ----
+        # Build video arrays: for each sample, stack views as [V, T, H, W, 3]
+        # Check whether any image feature has a time dimension
+        video_source = None
+        for k in image_keys:
+            if k in batch:
+                video_source = batch[k]  # Use first available for shape inspection
+                break
+
+        if video_source is None:
+            raise ValueError("No image data found in batch for video construction.")
+
+        videos_per_sample = []
+        for b in range(batch_size):
+            sample_views = []
+            for k in image_keys:
+                t = batch[k][b]  # [C, H, W] or [T, C, H, W]
+                if t.ndim == 3:
+                    t = t.unsqueeze(0)  # [1, C, H, W]
+                # Convert to [T, H, W, 3] numpy
+                t_np = t.permute(0, 2, 3, 1).detach().cpu().float().numpy()
+                # Clamp to [0, 255]
+                if t_np.max() <= 1.0:
+                    t_np = t_np * 255.0
+                t_np = np.rint(t_np.clip(0, 255)).astype(np.uint8)
+                sample_views.append(t_np)
+            # Stack views: [V, T, H, W, 3]
+            videos_per_sample.append(np.stack(sample_views, axis=0))
+
+        # ---- Collect instructions ----
         tasks = batch.get("task")
         if tasks is None:
             instructions = ["Execute the robot action."] * batch_size
@@ -383,32 +498,52 @@ class VLAJEPAPolicy(PreTrainedPolicy):
         else:
             instructions = list(tasks)
 
-        inputs: dict[str, Any] = {"images": images, "instructions": instructions}
+        # ---- Collect actions (training only) ----
+        actions_list = None
+        action_is_pad_list = None
+        actions_tensor = batch.get(ACTION)
+        if actions_tensor is not None:
+            if actions_tensor.ndim == 2:
+                actions_tensor = actions_tensor.unsqueeze(1)
+            actions_list = [actions_tensor[b].detach().cpu().float().numpy() for b in range(batch_size)]
+            action_is_pad_tensor = batch.get("action_is_pad")
+            if action_is_pad_tensor is not None:
+                action_is_pad_list = [action_is_pad_tensor[b].detach().cpu() for b in range(batch_size)]
 
-        # Videos [B, V, T, C, H, W] - only assembled during training when the world model consumes them.
-        if self.model.config.enable_world_model and training:
-            views = [batch[k].unsqueeze(1) if batch[k].ndim == 4 else batch[k] for k in image_keys]
-            inputs["videos"] = self.model.qwen.to_pixel_values(torch.stack(views, dim=1))
+        # ---- Collect state ----
+        state_list = None
+        state_tensor = batch.get(OBS_STATE)
+        if state_tensor is not None:
+            if state_tensor.ndim > 2:
+                state_tensor = state_tensor[:, -1, :]
+            if state_tensor.ndim == 2:
+                state_tensor = state_tensor.unsqueeze(1)  # [B, 1, state_dim]
+            state_list = [state_tensor[b].detach().cpu().float().numpy() for b in range(batch_size)]
 
-        actions = batch.get(ACTION)
-        if actions is not None:
-            inputs["actions"] = (actions.unsqueeze(1) if actions.ndim == 2 else actions).float()
-            if (pad := batch.get("action_is_pad")) is not None:
-                inputs["action_is_pad"] = pad
+        # ---- Assemble native examples ----
+        examples = []
+        for b in range(batch_size):
+            example = {
+                "image": images_per_sample[b],
+                "video": videos_per_sample[b],
+                "lang": instructions[b],
+            }
+            if actions_list is not None:
+                example["action"] = actions_list[b]
+            if action_is_pad_list is not None:
+                example["action_is_pad"] = action_is_pad_list[b]
+            if state_list is not None:
+                example["state"] = state_list[b]
+            examples.append(example)
 
-        state = batch.get(OBS_STATE)
-        if state is not None:
-            if state.ndim > 2:
-                state = state[:, -1, :]
-            inputs["state"] = (state.unsqueeze(1) if state.ndim == 2 else state).float()  # [B, 1, dim]
-
-        return inputs
+        return examples
 
     # ---- LeRobot Policy Interface ----
 
     def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]:
         """LeRobot train forward: convert → native forward → aggregate losses."""
-        native_output = self.model.forward(**self._prepare_model_inputs(batch, training=True))
+        examples = self._prepare_model_inputs(batch)
+        native_output = self.model.forward(examples)
 
         ref = next(iter(native_output.values()))
         zero = torch.zeros((), device=ref.device, dtype=ref.dtype)
@@ -426,9 +561,16 @@ class VLAJEPAPolicy(PreTrainedPolicy):
         self.eval()
         self._queues = populate_queues(self._queues, batch, exclude_keys=[ACTION])
 
-        inputs = self._prepare_model_inputs(batch, training=False)
-        actions = self.model.predict_action(inputs["images"], inputs["instructions"], inputs.get("state"))
-        return actions.to(device=self.config.device, dtype=torch.float32)
+        examples = self._prepare_model_inputs(batch)
+        batch_images = [ex["image"] for ex in examples]
+        instructions = [ex["lang"] for ex in examples]
+
+        state_np = None
+        if "state" in examples[0] and examples[0]["state"] is not None:
+            state_np = np.stack([ex["state"] for ex in examples])
+
+        actions_np = self.model.predict_action(batch_images, instructions, state_np)
+        return torch.from_numpy(actions_np).to(device=self.config.device, dtype=torch.float32)
 
     @torch.no_grad()
     def select_action(self, batch: dict[str, Tensor], noise: Tensor | None = None) -> Tensor:

src/lerobot/policies/vla_jepa/qwen_interface.py

@@ -17,7 +17,9 @@ from __future__ import annotations
 from collections.abc import Sequence
 from typing import TYPE_CHECKING
 
+import numpy as np
 import torch
+from PIL import Image
 
 from lerobot.utils.import_utils import _transformers_available
 
@@ -76,7 +78,7 @@ class Qwen3VLInterface(torch.nn.Module):
 
     def build_inputs(
         self,
-        images: Sequence[Sequence[torch.Tensor]],
+        images: Sequence[Sequence[Image.Image]],
         instructions: Sequence[str],
         action_prompt: str,
         embodied_prompt: str,
@@ -92,42 +94,24 @@ class Qwen3VLInterface(torch.nn.Module):
             content.append({"type": "text", "text": prompt})
             messages.append([{"role": "user", "content": content}])
 
-        # The Qwen image processor is a torchvision-backed fast processor: passing the
-        # images as GPU tensors (with `device`) keeps the whole vision pipeline on-device
-        # and avoids a GPU->CPU->GPU roundtrip. The image tensors are forwarded through
-        # apply_chat_template untouched into Qwen3VLProcessor.__call__.
-        # do_rescale=False: images already arrive as float in [0, 1] (the dataset decoder
-        # yields float32/255 and VISUAL normalization is IDENTITY), so we skip the
-        # processor's /255 rescale instead of round-tripping through uint8.
         batch_inputs = self.processor.apply_chat_template(
             messages,
             tokenize=True,
             add_generation_prompt=True,
             return_dict=True,
-            processor_kwargs={
-                "padding": True,
-                "return_tensors": "pt",
-                "device": self.model.device,
-                "do_rescale": False,
-            },
+            processor_kwargs={"padding": True, "return_tensors": "pt"},
         )
         return batch_inputs.to(self.model.device)
 
     @staticmethod
-    def to_pixel_values(image_tensor: torch.Tensor) -> torch.Tensor:
-        """Prepare an image/video tensor for the fast processors (used with do_rescale=False).
-
-        The dataset decoder yields float32 in [0, 1] (channels-first) and VISUAL
-        normalization is IDENTITY, so the tensor already arrives in [0, 1]; we pass it
-        through as float and let the processors normalize (no rescale, no uint8
-        quantization). A single channel is expanded to 3 to match the RGB processors.
-
-        Works for any channels-first layout (channel dim is -3): [C, H, W], [B, C, H, W],
-        [T, C, H, W], [B, V, T, C, H, W], ...
-        """
-        image = image_tensor.detach().float()
-        if image.shape[-3] == 1:
-            repeats = [1] * image.ndim
-            repeats[-3] = 3
-            image = image.repeat(*repeats)
-        return image
+    def tensor_to_pil(image_tensor: torch.Tensor) -> Image.Image:
+        image = image_tensor.detach().cpu()
+        if image.ndim == 3 and image.shape[0] in (1, 3):
+            image = image.permute(1, 2, 0)
+        image = image.float()
+        if image.max() <= 1.0:
+            image = image * 255.0
+        image = image.clamp(0, 255).round().to(torch.uint8).numpy()
+        if image.shape[-1] == 1:
+            image = np.repeat(image, 3, axis=-1)
+        return Image.fromarray(image)

src/lerobot/robots/bi_rebot_b601_follower/bi_rebot_b601_follower.py

@@ -65,13 +65,7 @@ class BiRebotB601Follower(BimanualMixin, Robot):
             cameras=left_arm_cameras,
             motor_can_ids=config.left_arm_config.motor_can_ids,
             pos_vel_velocity=config.left_arm_config.pos_vel_velocity,
-            control_mode=config.left_arm_config.control_mode,
-            mit_kp=config.left_arm_config.mit_kp,
-            mit_kd=config.left_arm_config.mit_kd,
-            gripper_control_mode=config.left_arm_config.gripper_control_mode,
             gripper_torque_ratio=config.left_arm_config.gripper_torque_ratio,
-            gripper_mit_kp=config.left_arm_config.gripper_mit_kp,
-            gripper_mit_kd=config.left_arm_config.gripper_mit_kd,
             joint_limits=config.left_arm_config.joint_limits,
         )
 
@@ -86,13 +80,7 @@ class BiRebotB601Follower(BimanualMixin, Robot):
             cameras=config.right_arm_config.cameras,
             motor_can_ids=config.right_arm_config.motor_can_ids,
             pos_vel_velocity=config.right_arm_config.pos_vel_velocity,
-            control_mode=config.right_arm_config.control_mode,
-            mit_kp=config.right_arm_config.mit_kp,
-            mit_kd=config.right_arm_config.mit_kd,
-            gripper_control_mode=config.right_arm_config.gripper_control_mode,
             gripper_torque_ratio=config.right_arm_config.gripper_torque_ratio,
-            gripper_mit_kp=config.right_arm_config.gripper_mit_kp,
-            gripper_mit_kd=config.right_arm_config.gripper_mit_kd,
             joint_limits=config.right_arm_config.joint_limits,
         )
 

src/lerobot/robots/rebot_b601_follower/config_rebot_b601_follower.py

@@ -65,33 +65,18 @@ class RebotB601FollowerConfig:
         }
     )
 
-    # Max speed (deg/s) per joint for POS_VEL arms and FORCE_POS gripper (motor order).
-    pos_vel_velocity: float | list[float] = field(
-        default_factory=lambda: [150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 900.0]
-    )
+    # Target velocity for joints running in POS_VEL mode, in degrees/s. A scalar is
+    # applied to every joint; a list provides one value per joint (in motor order).
+    pos_vel_velocity: float | list[float] = field(default_factory=lambda: [150.0] * 7)
 
-    # Arm control: "mit" or "pos_vel".
-    control_mode: str = "mit"
-
-    # MIT kp/kd per arm joint (motor order). Unused when control_mode="pos_vel".
-    mit_kp: float | list[float] = field(default_factory=lambda: [45.0, 45.0, 45.0, 8.0, 9.0, 8.0, 8.0])
-    mit_kd: float | list[float] = field(default_factory=lambda: [12.0, 12.0, 12.0, 1.0, 1.0, 1.0, 1.0])
-
-    # Gripper control: "force_pos" or "mit".
-    gripper_control_mode: str = "force_pos"
-
-    # FORCE_POS only: max grip force, in [0, 1].
-    gripper_torque_ratio: float = 0.07
-
-    # MIT only.
-    gripper_mit_kp: float = 8.0
-    gripper_mit_kd: float = 0.3
+    # Torque/current ratio for the gripper's FORCE_POS mode, in range [0, 1].
+    gripper_torque_ratio: float = 0.1
 
     # Soft joint limits (degrees). These are clipped against on every action.
     joint_limits: dict[str, tuple[float, float]] = field(
         default_factory=lambda: {
-            "shoulder_pan": (-150.0, 150.0),
-            "shoulder_lift": (-200.0, 1.0),
+            "shoulder_pan": (-145.0, 145.0),
+            "shoulder_lift": (-170.0, 1.0),
             "elbow_flex": (-200.0, 1.0),
             "wrist_flex": (-80.0, 90.0),
             "wrist_yaw": (-90.0, 90.0),

src/lerobot/robots/rebot_b601_follower/rebot_b601_follower.py

@@ -174,25 +174,11 @@ class RebotB601Follower(Robot):
         print(f"Calibration saved to {self.calibration_fpath}")
 
     def configure(self) -> None:
-        if self.config.control_mode not in ("pos_vel", "mit"):
-            raise ValueError(
-                f"Unsupported control_mode '{self.config.control_mode}'. Use 'pos_vel' or 'mit'."
-            )
-        if self.config.gripper_control_mode not in ("force_pos", "mit"):
-            raise ValueError(
-                f"Unsupported gripper_control_mode '{self.config.gripper_control_mode}'. "
-                "Use 'force_pos' or 'mit'."
-            )
-        use_mit = self.config.control_mode == "mit"
-        gripper_use_mit = self.config.gripper_control_mode == "mit"
         self.bus.enable_all()
         for motor_name, motor in self.motors.items():
-            if motor_name == GRIPPER_MOTOR:
-                target_mode = MotorBridgeMode.MIT if gripper_use_mit else MotorBridgeMode.FORCE_POS
-            elif use_mit:
-                target_mode = MotorBridgeMode.MIT
-            else:
-                target_mode = MotorBridgeMode.POS_VEL
+            target_mode = (
+                MotorBridgeMode.FORCE_POS if motor_name == GRIPPER_MOTOR else MotorBridgeMode.POS_VEL
+            )
             for attempt in range(_ENSURE_MODE_RETRIES + 1):
                 try:
                     motor.ensure_mode(target_mode)
@@ -278,34 +264,22 @@ class RebotB601Follower(Robot):
             goal_present_pos = {key: (g, present_pos.get(key, g)) for key, g in goal_pos.items()}
             goal_pos = ensure_safe_goal_position(goal_present_pos, self.config.max_relative_target)
 
-        use_mit = self.config.control_mode == "mit"
         for motor_name, position_deg in goal_pos.items():
             motor = self.motors.get(motor_name)
             if motor is None:
                 continue
             idx = self.motor_names.index(motor_name)
+            vel_deg_s = (
+                self.config.pos_vel_velocity[idx]
+                if isinstance(self.config.pos_vel_velocity, list)
+                else self.config.pos_vel_velocity
+            )
             pos_rad = math.radians(position_deg)
+            vel_rad = math.radians(vel_deg_s)
             if motor_name == GRIPPER_MOTOR:
-                if self.config.gripper_control_mode == "mit":
-                    motor.send_mit(pos_rad, 0.0, self.config.gripper_mit_kp, self.config.gripper_mit_kd, 0.0)
-                else:
-                    vel_deg_s = (
-                        self.config.pos_vel_velocity[idx]
-                        if isinstance(self.config.pos_vel_velocity, list)
-                        else self.config.pos_vel_velocity
-                    )
-                    motor.send_force_pos(pos_rad, math.radians(vel_deg_s), self.config.gripper_torque_ratio)
-            elif use_mit:
-                kp = self.config.mit_kp[idx] if isinstance(self.config.mit_kp, list) else self.config.mit_kp
-                kd = self.config.mit_kd[idx] if isinstance(self.config.mit_kd, list) else self.config.mit_kd
-                motor.send_mit(pos_rad, 0.0, kp, kd, 0.0)
+                motor.send_force_pos(pos_rad, vel_rad, self.config.gripper_torque_ratio)
             else:
-                vel_deg_s = (
-                    self.config.pos_vel_velocity[idx]
-                    if isinstance(self.config.pos_vel_velocity, list)
-                    else self.config.pos_vel_velocity
-                )
-                motor.send_pos_vel(pos_rad, math.radians(vel_deg_s))
+                motor.send_pos_vel(pos_rad, vel_rad)
 
         return {f"{motor}.pos": val for motor, val in goal_pos.items()}
 

src/lerobot/rollout/configs.py

@@ -226,14 +226,11 @@ class RolloutConfig:
     device: str | None = None
     task: str = ""
     display_data: bool = False
-    # Visualization backend used when display_data is True: "rerun" or "foxglove".
-    display_mode: str = "rerun"
-    # For "rerun": IP of a remote server to send to. For "foxglove": interface to bind the WebSocket
-    # server to (127.0.0.1 for local only, 0.0.0.0 for all interfaces).
+    # Display data on a remote Rerun server
     display_ip: str | None = None
-    # For "rerun": port of the remote server. For "foxglove": port to bind the WebSocket server to.
+    # Port of the remote Rerun server
     display_port: int | None = None
-    # Whether to display compressed (JPEG) images instead of raw frames
+    # Whether to display compressed images in Rerun
     display_compressed_images: bool = False
     # Use vocal synthesis to read events
     play_sounds: bool = True

src/lerobot/rollout/context.py

@@ -320,9 +320,7 @@ def build_rollout_context(
             raise ValueError(
                 f"Visual feature mismatch between policy and robot hardware.\n"
                 f"Policy expects: {expected_visuals}\n"
-                f"Robot provides: {provided_visuals}\n"
-                f"Use --rename_map to map camera names, e.g. "
-                f"""--rename_map='{{"observation.images.top": "observation.images.cam0"}}'"""
+                f"Robot provides: {provided_visuals}"
             )
 
     # --- 5. Dataset -------------

src/lerobot/rollout/strategies/core.py

@@ -26,7 +26,7 @@ from lerobot.utils.action_interpolator import ActionInterpolator
 from lerobot.utils.constants import OBS_STR
 from lerobot.utils.feature_utils import build_dataset_frame
 from lerobot.utils.robot_utils import precise_sleep
-from lerobot.utils.visualization_utils import log_visualization_data
+from lerobot.utils.visualization_utils import log_rerun_data
 
 from ..inference import InferenceEngine
 
@@ -162,12 +162,11 @@ class RolloutStrategy(abc.ABC):
         action_dict: dict | None,
         runtime_ctx: RuntimeContext,
     ) -> None:
-        """Log observation/action telemetry to the visualization backend if display_data is enabled."""
+        """Log observation/action telemetry to Rerun if display_data is enabled."""
         cfg = runtime_ctx.cfg
         if not cfg.display_data:
             return
-        log_visualization_data(
-            cfg.display_mode,
+        log_rerun_data(
             observation=obs_processed,
             action=action_dict,
             compress_images=cfg.display_compressed_images,

src/lerobot/rollout/strategies/episodic.py

@@ -44,7 +44,7 @@ from lerobot.utils.feature_utils import build_dataset_frame
 from lerobot.utils.keyboard_input import init_keyboard_listener
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import log_say
-from lerobot.utils.visualization_utils import log_visualization_data
+from lerobot.utils.visualization_utils import log_rerun_data
 
 from ..configs import EpisodicStrategyConfig
 from ..context import RolloutContext
@@ -171,7 +171,6 @@ class EpisodicStrategy(RolloutStrategy):
                             fps=fps,
                             control_time_s=reset_time_s,
                             display_data=cfg.display_data,
-                            display_mode=cfg.display_mode,
                             display_compressed=display_compressed,
                         )
 
@@ -260,7 +259,6 @@ class EpisodicStrategy(RolloutStrategy):
         fps: float,
         control_time_s: float,
         display_data: bool,
-        display_mode: str,
         display_compressed: bool,
     ) -> None:
         """Reset-phase loop: teleop drives the robot if available, no recording."""
@@ -290,8 +288,7 @@ class EpisodicStrategy(RolloutStrategy):
 
                 if display_data:
                     obs_processed = processors.robot_observation_processor(obs)
-                    log_visualization_data(
-                        display_mode,
+                    log_rerun_data(
                         observation=obs_processed,
                         action=act_teleop,
                         compress_images=display_compressed,

src/lerobot/scripts/lerobot_dataset_viz.py

@@ -59,18 +59,6 @@ distant$ lerobot-dataset-viz \
 local$ rerun rerun+http://IP:GRPC_PORT/proxy
 ```
 
-- Visualize data in Foxglove with a seekable, scrubbable timeline:
-```
-local$ lerobot-dataset-viz \
-    --repo-id lerobot/pusht \
-    --episode-index 0 \
-    --display-mode foxglove
-
-# then open the Foxglove app and connect to ws://127.0.0.1:8765
-```
-This starts a Foxglove WebSocket server that serves the episode on demand from the on-disk dataset,
-so you can play/pause and scrub anywhere in the episode using Foxglove's playback controls.
-
 """
 
 import argparse
@@ -84,29 +72,10 @@ import torch
 import torch.utils.data
 import tqdm
 
-from lerobot.configs import DEPTH_MILLIMETER_UNIT
 from lerobot.datasets import LeRobotDataset
-from lerobot.utils.constants import ACTION, DONE, OBS_STATE, REWARD, SUCCESS
+from lerobot.utils.constants import ACTION, DONE, OBS_STATE, REWARD
 from lerobot.utils.utils import init_logging
 
-DEFAULT_FOXGLOVE_PORT = 8765
-DEFAULT_RERUN_PORT = 9090
-
-
-def get_feature_names(dataset: LeRobotDataset, key: str) -> list[str]:
-    """Return per-dimension names for a feature from the dataset metadata.
-
-    Only flat-list ``names`` metadata is used. Dict-style ``names`` and missing names fall back to ``{key}_{i}`` indices.
-    """
-    feature = dataset.features[key]
-    dim = feature["shape"][-1]
-
-    names = feature.get("names")
-    if isinstance(names, list) and len(names) == dim:
-        return [str(name) for name in names]
-
-    return [f"{key}_{d}" for d in range(dim)]
-
 
 def check_chw_float32(frame: torch.Tensor) -> None:
     """
@@ -124,35 +93,10 @@ def to_hwc_uint8_numpy(chw_float32_torch: torch.Tensor) -> np.ndarray:
     return hwc_uint8_numpy
 
 
-def to_hwc_float32_numpy(chw_float32_torch: torch.Tensor) -> np.ndarray:
+def to_hwc_uint16_numpy(chw_float32_torch: torch.Tensor) -> np.ndarray:
     check_chw_float32(chw_float32_torch)
-    hwc_float32_numpy = chw_float32_torch.permute(1, 2, 0).numpy()
-    return hwc_float32_numpy
-
-
-def build_blueprint_from_dataset(dataset: LeRobotDataset):
-    """Build a Rerun blueprint laying out camera images and time series for the given dataset.
-
-    Camera images and scalar signals (action, state, reward, done, success) are arranged in a grid.
-    The per-dimension series names for ``action`` and ``state`` are applied directly
-    via blueprint overrides.
-    """
-    import rerun as rr
-    import rerun.blueprint as rrb
-
-    views = [rrb.Spatial2DView(origin=key, name=key) for key in dataset.meta.camera_keys]
-
-    # Style multi-dimensional signals (action, state) with per-dimension names.
-    for origin, key in ((ACTION, ACTION), ("state", OBS_STATE)):
-        if key in dataset.features:
-            names = get_feature_names(dataset, key)
-            styling = rr.SeriesLines(names=names)
-            views.append(rrb.TimeSeriesView(origin=origin, name=origin, overrides={origin: styling}))
-    for key in (DONE, REWARD, SUCCESS):
-        if key in dataset.features:
-            views.append(rrb.TimeSeriesView(origin=key, name=key))
-
-    return rrb.Blueprint(rrb.Grid(*views))
+    hwc_uint16_numpy = chw_float32_torch.round().type(torch.uint16).permute(1, 2, 0).numpy()
+    return hwc_uint16_numpy
 
 
 def visualize_dataset(
@@ -161,30 +105,13 @@ def visualize_dataset(
     batch_size: int = 32,
     num_workers: int = 0,
     mode: str = "local",
-    web_port: int | None = None,
+    web_port: int = 9090,
     grpc_port: int = 9876,
     save: bool = False,
     output_dir: Path | None = None,
     display_compressed_images: bool = False,
-    display_mode: str = "rerun",
-    host: str = "127.0.0.1",
-    autoplay: bool = True,
     **kwargs,
 ) -> Path | None:
-    if display_mode == "foxglove":
-        from lerobot.utils.foxglove_visualization import serve_foxglove_dataset_playback
-
-        logging.info("Starting Foxglove server")
-        serve_foxglove_dataset_playback(
-            dataset,
-            episode_index,
-            host=host,
-            port=web_port if web_port is not None else DEFAULT_FOXGLOVE_PORT,
-            compress_images=display_compressed_images,
-            autoplay=autoplay,
-        )
-        return None
-
     if save:
         assert output_dir is not None, (
             "Set an output directory where to write .rrd files with `--output-dir path/to/directory`."
@@ -210,8 +137,7 @@ def visualize_dataset(
     import rerun as rr
 
     spawn_local_viewer = mode == "local" and not save
-    blueprint = build_blueprint_from_dataset(dataset)
-    rr.init(f"{repo_id}/episode_{episode_index}", spawn=spawn_local_viewer, default_blueprint=blueprint)
+    rr.init(f"{repo_id}/episode_{episode_index}", spawn=spawn_local_viewer)
 
     # Manually call python garbage collector after `rr.init` to avoid hanging in a blocking flush
     # when iterating on a dataloader with `num_workers` > 0
@@ -221,23 +147,14 @@ def visualize_dataset(
     if mode == "distant":
         server_uri = rr.serve_grpc(grpc_port=grpc_port)
         logging.info(f"Connect to a Rerun Server: rerun rerun+http://IP:{grpc_port}/proxy")
-        rr.serve_web_viewer(
-            open_browser=False,
-            web_port=web_port if web_port is not None else DEFAULT_RERUN_PORT,
-            connect_to=server_uri,
-        )
+        rr.serve_web_viewer(open_browser=False, web_port=web_port, connect_to=server_uri)
 
     logging.info("Logging to Rerun")
 
-    # Depth frames and stats are dequantized to the dataset's depth_output_unit on load.
-    depth_meter = 1000.0 if dataset.depth_output_unit == DEPTH_MILLIMETER_UNIT else 1.0
-
     # Use the dataset's q01/q99 depth statistics for robust depth range bounds
     depth_ranges = {}
     for key in dataset.meta.depth_keys:
-        stats = (dataset.meta.stats or {}).get(key)
-        if not stats:
-            continue
+        stats = dataset.meta.stats[key]
         lo = stats["q01"] if "q01" in stats else stats["min"]
         hi = stats["q99"] if "q99" in stats else stats["max"]
         depth_ranges[key] = (float(np.asarray(lo).item()), float(np.asarray(hi).item()))
@@ -246,21 +163,19 @@ def visualize_dataset(
     for batch in tqdm.tqdm(dataloader, total=len(dataloader)):
         if first_index is None:
             first_index = batch["index"][0].item()
-
         # iterate over the batch
         for i in range(len(batch["index"])):
             rr.set_time("frame_index", sequence=batch["index"][i].item() - first_index)
             rr.set_time("timestamp", timestamp=batch["timestamp"][i].item())
 
-            # display each camera image (or depth map)
+            # display each camera image
             for key in dataset.meta.camera_keys:
                 if key in dataset.meta.depth_keys:
-                    depth = to_hwc_float32_numpy(batch[key][i])
+                    depth = to_hwc_uint16_numpy(batch[key][i])
                     depth_entity = rr.DepthImage(
                         depth,
-                        meter=depth_meter,
                         colormap=rr.components.Colormap.Viridis,
-                        depth_range=depth_ranges.get(key),
+                        depth_range=depth_ranges[key],
                     )
                     rr.log(key, entity=depth_entity)
                 else:
@@ -268,13 +183,15 @@ def visualize_dataset(
                     img_entity = rr.Image(img).compress() if display_compressed_images else rr.Image(img)
                     rr.log(key, entity=img_entity)
 
-            # display the action space (e.g. actuators command)
+            # display each dimension of action space (e.g. actuators command)
             if ACTION in batch:
-                rr.log(ACTION, rr.Scalars(batch[ACTION][i].numpy()))
+                for dim_idx, val in enumerate(batch[ACTION][i]):
+                    rr.log(f"{ACTION}/{dim_idx}", rr.Scalars(val.item()))
 
-            # display the observed state space (e.g. agent position in joint space)
+            # display each dimension of observed state space (e.g. agent position in joint space)
             if OBS_STATE in batch:
-                rr.log("state", rr.Scalars(batch[OBS_STATE][i].numpy()))
+                for dim_idx, val in enumerate(batch[OBS_STATE][i]):
+                    rr.log(f"state/{dim_idx}", rr.Scalars(val.item()))
 
             if DONE in batch:
                 rr.log(DONE, rr.Scalars(batch[DONE][i].item()))
@@ -282,11 +199,12 @@ def visualize_dataset(
             if REWARD in batch:
                 rr.log(REWARD, rr.Scalars(batch[REWARD][i].item()))
 
-            if SUCCESS in batch:
-                rr.log(SUCCESS, rr.Scalars(batch[SUCCESS][i].item()))
+            if "next.success" in batch:
+                rr.log("next.success", rr.Scalars(batch["next.success"][i].item()))
 
-    # save .rrd locally
     if mode == "local" and save:
+        # save .rrd locally
+        output_dir = Path(output_dir)
         output_dir.mkdir(parents=True, exist_ok=True)
         repo_id_str = repo_id.replace("/", "_")
         rrd_path = output_dir / f"{repo_id_str}_episode_{episode_index}.rrd"
@@ -294,7 +212,7 @@ def visualize_dataset(
         return rrd_path
 
     elif mode == "distant":
-        # Keep the process alive while it serves the gRPC/web connection.
+        # stop the process from exiting since it is serving the websocket connection
         try:
             while True:
                 time.sleep(1)
@@ -355,11 +273,13 @@ def main():
     parser.add_argument(
         "--web-port",
         type=int,
-        default=None,
-        help=(
-            "Web/WebSocket port. For rerun `--mode distant` it is the web viewer port (default 9090); "
-            "for `--display-mode foxglove` it is the server bind port (default 8765)."
-        ),
+        default=9090,
+        help="Web port for rerun.io when `--mode distant` is set.",
+    )
+    parser.add_argument(
+        "--ws-port",
+        type=int,
+        help="deprecated, please use --grpc-port instead.",
     )
     parser.add_argument(
         "--grpc-port",
@@ -392,61 +312,27 @@ def main():
     parser.add_argument(
         "--display-compressed-images",
         action="store_true",
-        help="If set, display compressed (JPEG) images instead of uncompressed ones.",
-    )
-
-    parser.add_argument(
-        "--display-mode",
-        type=str,
-        default="rerun",
-        choices=["rerun", "foxglove"],
-        help=(
-            "Visualization backend. 'rerun' uses the Rerun viewer (--mode/--save/--*-port apply). "
-            "'foxglove' starts a Foxglove WebSocket server that serves the episode as a seekable, "
-            "scrubbable timeline; connect the Foxglove app to ws://HOST:PORT (--host/--web-port)."
-        ),
-    )
-    parser.add_argument(
-        "--host",
-        type=str,
-        default="127.0.0.1",
-        help=(
-            "Host to bind the Foxglove WebSocket server to when `--display-mode foxglove` is set "
-            "(127.0.0.1 for local only, 0.0.0.0 for all interfaces)."
-        ),
-    )
-    parser.add_argument(
-        "--no-autoplay",
-        dest="autoplay",
-        action="store_false",
-        help=(
-            "For `--display-mode foxglove`: don't start playing automatically when a client "
-            "connects; wait for play to be pressed in the Foxglove app instead."
-        ),
+        help="If set, display compressed images in Rerun instead of uncompressed ones.",
     )
 
     args = parser.parse_args()
-
-    if args.display_mode == "foxglove":
-        rerun_only = ("mode", "save", "output_dir", "grpc_port", "batch_size", "num_workers")
-        ignored = [name for name in rerun_only if getattr(args, name) != parser.get_default(name)]
-        if ignored:
-            logging.warning(
-                "These flags only apply to `--display-mode rerun` and are ignored with "
-                "`--display-mode foxglove`: %s.",
-                ", ".join(f"--{name.replace('_', '-')}" for name in ignored),
-            )
-
     kwargs = vars(args)
     repo_id = kwargs.pop("repo_id")
     root = kwargs.pop("root")
     tolerance_s = kwargs.pop("tolerance_s")
 
+    if kwargs["ws_port"] is not None:
+        logging.warning(
+            "--ws-port is deprecated and will be removed in future versions. Please use --grpc-port instead."
+        )
+        logging.warning("Setting grpc_port to ws_port value.")
+        kwargs["grpc_port"] = kwargs.pop("ws_port")
+
     init_logging()
     logging.info("Loading dataset")
     dataset = LeRobotDataset(repo_id, episodes=[args.episode_index], root=root, tolerance_s=tolerance_s)
 
-    visualize_dataset(dataset, **kwargs)
+    visualize_dataset(dataset, **vars(args))
 
 
 if __name__ == "__main__":

src/lerobot/scripts/lerobot_record.py

@@ -38,9 +38,6 @@ lerobot-record \\
     --display_data=true
 ```
 
-To stream the data to Foxglove instead of Rerun, add ``--display_mode=foxglove`` (then connect the
-Foxglove app to ``ws://127.0.0.1:8765``; override the port with ``--display_port=<port>``).
-
 Example recording with bimanual so100:
 ```shell
 lerobot-record \\
@@ -160,11 +157,7 @@ from lerobot.utils.utils import (
     init_logging,
     log_say,
 )
-from lerobot.utils.visualization_utils import (
-    init_visualization,
-    log_visualization_data,
-    shutdown_visualization,
-)
+from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
 
 
 @dataclass
@@ -175,14 +168,11 @@ class RecordConfig:
     teleop: TeleoperatorConfig | None = None
     # Display all cameras on screen
     display_data: bool = False
-    # Visualization backend used when display_data is True: "rerun" or "foxglove".
-    display_mode: str = "rerun"
-    # For "rerun": IP of a remote server to send to. For "foxglove": interface to bind the WebSocket
-    # server to (127.0.0.1 for local only, 0.0.0.0 for all interfaces).
+    # Display data on a remote Rerun server
     display_ip: str | None = None
-    # For "rerun": port of the remote server. For "foxglove": port to bind the WebSocket server to.
+    # Port of the remote Rerun server
     display_port: int | None = None
-    # Whether to display compressed (JPEG) images instead of raw frames
+    # Whether to  display compressed images in Rerun
     display_compressed_images: bool = False
     # Use vocal synthesis to read events.
     play_sounds: bool = True
@@ -243,7 +233,6 @@ def record_loop(
     control_time_s: int | None = None,
     single_task: str | None = None,
     display_data: bool = False,
-    display_mode: str = "rerun",
     display_compressed_images: bool = False,
 ):
     if dataset is not None and dataset.fps != fps:
@@ -338,11 +327,8 @@ def record_loop(
             dataset.add_frame(frame)
 
         if display_data:
-            log_visualization_data(
-                display_mode,
-                observation=obs_processed,
-                action=action_values,
-                compress_images=display_compressed_images,
+            log_rerun_data(
+                observation=obs_processed, action=action_values, compress_images=display_compressed_images
             )
 
         dt_s = time.perf_counter() - start_loop_t
@@ -368,9 +354,7 @@ def record(
     init_logging()
     logging.info(pformat(asdict(cfg)))
     if cfg.display_data:
-        init_visualization(
-            cfg.display_mode, session_name="recording", ip=cfg.display_ip, port=cfg.display_port
-        )
+        init_rerun(session_name="recording", ip=cfg.display_ip, port=cfg.display_port)
     display_compressed_images = (
         True
         if (cfg.display_data and cfg.display_ip is not None and cfg.display_port is not None)
@@ -480,7 +464,6 @@ def record(
                     control_time_s=cfg.dataset.episode_time_s,
                     single_task=cfg.dataset.single_task,
                     display_data=cfg.display_data,
-                    display_mode=cfg.display_mode,
                     display_compressed_images=display_compressed_images,
                 )
 
@@ -502,7 +485,6 @@ def record(
                         control_time_s=cfg.dataset.reset_time_s,
                         single_task=cfg.dataset.single_task,
                         display_data=cfg.display_data,
-                        display_mode=cfg.display_mode,
                     )
 
                 if events["rerecord_episode"]:
@@ -528,9 +510,6 @@ def record(
         if listener is not None:
             listener.stop()
 
-        if cfg.display_data:
-            shutdown_visualization(cfg.display_mode)
-
         if cfg.dataset.push_to_hub:
             if dataset and dataset.num_episodes > 0:
                 dataset.push_to_hub(tags=cfg.dataset.tags, private=cfg.dataset.private)

src/lerobot/scripts/lerobot_rollout.py

@@ -145,9 +145,6 @@ Usage examples
         --dataset.rgb_encoder.vcodec=h264 \\
         --dataset.rgb_encoder.preset=fast \\
         --dataset.rgb_encoder.extra_options={"tune": "film", "profile:v": "high", "bf": 2}
-
-    # Stream to Foxglove instead of Rerun:
-    # add --display_mode=foxglove, then connect the Foxglove app to ws://127.0.0.1:8765.
 """
 
 import logging
@@ -193,7 +190,7 @@ from lerobot.teleoperators import (  # noqa: F401
 from lerobot.utils.import_utils import register_third_party_plugins
 from lerobot.utils.process import ProcessSignalHandler
 from lerobot.utils.utils import init_logging
-from lerobot.utils.visualization_utils import init_visualization, shutdown_visualization
+from lerobot.utils.visualization_utils import init_rerun
 
 logger = logging.getLogger(__name__)
 
@@ -204,13 +201,8 @@ def rollout(cfg: RolloutConfig):
     init_logging()
 
     if cfg.display_data:
-        logger.info(
-            "Initializing %s visualization (ip=%s, port=%s)",
-            cfg.display_mode,
-            cfg.display_ip,
-            cfg.display_port,
-        )
-        init_visualization(cfg.display_mode, session_name="rollout", ip=cfg.display_ip, port=cfg.display_port)
+        logger.info("Initializing Rerun visualization (ip=%s, port=%s)", cfg.display_ip, cfg.display_port)
+        init_rerun(session_name="rollout", ip=cfg.display_ip, port=cfg.display_port)
 
     signal_handler = ProcessSignalHandler(use_threads=True, display_pid=False)
     shutdown_event = signal_handler.shutdown_event
@@ -235,8 +227,6 @@ def rollout(cfg: RolloutConfig):
         logger.info("Interrupted by user")
     finally:
         strategy.teardown(ctx)
-        if cfg.display_data:
-            shutdown_visualization(cfg.display_mode)
 
     logger.info("Rollout finished")
 

src/lerobot/scripts/lerobot_teleoperate.py

@@ -31,22 +31,6 @@ lerobot-teleoperate \
     --display_data=true
 ```
 
-To stream the data to Foxglove instead of Rerun, add ``--display_mode=foxglove``
-(then connect the Foxglove app to ``ws://127.0.0.1:8765``; override the port with ``--display_port=<port>``):
-
-```shell
-lerobot-teleoperate \
-    --robot.type=so101_follower \
-    --robot.port=/dev/tty.usbmodem58760431541 \
-    --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \
-    --robot.id=black \
-    --teleop.type=so101_leader \
-    --teleop.port=/dev/tty.usbmodem58760431551 \
-    --teleop.id=blue \
-    --display_data=true \
-    --display_mode=foxglove
-```
-
 Example teleoperation with bimanual so100:
 
 ```shell
@@ -124,11 +108,7 @@ from lerobot.teleoperators import (  # noqa: F401
 from lerobot.utils.import_utils import register_third_party_plugins
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import init_logging, move_cursor_up
-from lerobot.utils.visualization_utils import (
-    init_visualization,
-    log_visualization_data,
-    shutdown_visualization,
-)
+from lerobot.utils.visualization_utils import init_rerun, log_rerun_data, shutdown_rerun
 
 
 @dataclass
@@ -141,14 +121,11 @@ class TeleoperateConfig:
     teleop_time_s: float | None = None
     # Display all cameras on screen
     display_data: bool = False
-    # Visualization backend used when display_data is True: "rerun" or "foxglove".
-    display_mode: str = "rerun"
-    # For "rerun": IP of a remote server to send to. For "foxglove": interface to bind the WebSocket
-    # server to (127.0.0.1 for local only, 0.0.0.0 for all interfaces).
+    # Display data on a remote Rerun server
     display_ip: str | None = None
-    # For "rerun": port of the remote server. For "foxglove": port to bind the WebSocket server to.
+    # Port of the remote Rerun server
     display_port: int | None = None
-    # Whether to display compressed (JPEG) images instead of raw frames
+    # Whether to  display compressed images in Rerun
     display_compressed_images: bool = False
 
 
@@ -160,7 +137,6 @@ def teleop_loop(
     robot_action_processor: RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction],
     robot_observation_processor: RobotProcessorPipeline[RobotObservation, RobotObservation],
     display_data: bool = False,
-    display_mode: str = "rerun",
     duration: float | None = None,
     display_compressed_images: bool = False,
 ):
@@ -173,10 +149,8 @@ def teleop_loop(
         teleop: The teleoperator device instance providing control actions.
         robot: The robot instance being controlled.
         fps: The target frequency for the control loop in frames per second.
-        display_data: If True, fetches robot observations and displays them in the console and the
-            visualization backend.
-        display_mode: Visualization backend to use when display_data is True ("rerun" or "foxglove").
-        display_compressed_images: If True, compresses images before sending them to the backend for display.
+        display_data: If True, fetches robot observations and displays them in the console and Rerun.
+        display_compressed_images: If True, compresses images before sending them to Rerun for display.
         duration: The maximum duration of the teleoperation loop in seconds. If None, the loop runs indefinitely.
         teleop_action_processor: An optional pipeline to process raw actions from the teleoperator.
         robot_action_processor: An optional pipeline to process actions before they are sent to the robot.
@@ -213,8 +187,7 @@ def teleop_loop(
             # Process robot observation through pipeline
             obs_transition = robot_observation_processor(obs)
 
-            log_visualization_data(
-                display_mode,
+            log_rerun_data(
                 observation=obs_transition,
                 action=teleop_action,
                 compress_images=display_compressed_images,
@@ -242,9 +215,7 @@ def teleoperate(cfg: TeleoperateConfig):
     init_logging()
     logging.info(pformat(asdict(cfg)))
     if cfg.display_data:
-        init_visualization(
-            cfg.display_mode, session_name="teleoperation", ip=cfg.display_ip, port=cfg.display_port
-        )
+        init_rerun(session_name="teleoperation", ip=cfg.display_ip, port=cfg.display_port)
     display_compressed_images = (
         True
         if (cfg.display_data and cfg.display_ip is not None and cfg.display_port is not None)
@@ -264,7 +235,6 @@ def teleoperate(cfg: TeleoperateConfig):
             robot=robot,
             fps=cfg.fps,
             display_data=cfg.display_data,
-            display_mode=cfg.display_mode,
             duration=cfg.teleop_time_s,
             teleop_action_processor=teleop_action_processor,
             robot_action_processor=robot_action_processor,
@@ -275,7 +245,7 @@ def teleoperate(cfg: TeleoperateConfig):
         pass
     finally:
         if cfg.display_data:
-            shutdown_visualization(cfg.display_mode)
+            shutdown_rerun()
         teleop.disconnect()
         robot.disconnect()
 

src/lerobot/scripts/lerobot_train.py

@@ -20,7 +20,6 @@ Requires: pip install 'lerobot[training]'  (includes dataset + accelerate + wand
 
 import dataclasses
 import logging
-import sys
 import time
 from contextlib import nullcontext
 from pprint import pformat
@@ -42,17 +41,15 @@ from lerobot.common.train_utils import (
     load_training_batch_size,
     load_training_num_processes,
     load_training_state,
-    push_checkpoint_to_hub,
     save_checkpoint,
     update_last_checkpoint,
 )
 from lerobot.common.wandb_utils import WandBLogger
-from lerobot.configs import JobConfig, parser
+from lerobot.configs import parser
 from lerobot.configs.train import TrainPipelineConfig
 from lerobot.datasets import EpisodeAwareSampler, compute_sampler_state
 from lerobot.datasets.factory import make_train_eval_datasets
 from lerobot.envs import close_envs, make_env, make_env_pre_post_processors
-from lerobot.jobs import submit_to_hf
 from lerobot.optim.factory import make_optimizer_and_scheduler
 from lerobot.policies import PreTrainedPolicy, make_policy, make_pre_post_processors
 from lerobot.rewards import make_reward_pre_post_processors
@@ -191,9 +188,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
         cfg: A `TrainPipelineConfig` object containing all training configurations.
         accelerator: Optional Accelerator instance. If None, one will be created automatically.
     """
-    if cfg.job.is_remote:
-        return submit_to_hf(cfg)
-
     from lerobot.utils.import_utils import require_package
 
     require_package("accelerate", extra="training")
@@ -211,12 +205,8 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
         # Accelerate auto-detects the device based on the available hardware and ignores the policy.device setting.
         # Force the device to be CPU when the active config's device is set to CPU (works for both policy and reward model training).
         force_cpu = cfg.trainable_config.device == "cpu"
-        # Drive Accelerate's autocast from policy.dtype (bf16/fp16 activate it; float32/absent -> launcher default).
-        policy_dtype = getattr(cfg.trainable_config, "dtype", None)
-        mixed_precision = {"bfloat16": "bf16", "float16": "fp16", "float32": "no"}.get(policy_dtype)
         accelerator = Accelerator(
             step_scheduler_with_optimizer=False,
-            mixed_precision=mixed_precision,
             kwargs_handlers=[ddp_kwargs],
             cpu=force_cpu,
         )
@@ -665,12 +655,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
                     optim_state_dict=optim_state_dict,
                 )
                 update_last_checkpoint(checkpoint_dir)
-                if cfg.save_checkpoint_to_hub:
-                    push_checkpoint_to_hub(
-                        checkpoint_dir,
-                        cfg.policy.repo_id,
-                        private=cfg.policy.private,
-                    )
                 if wandb_logger:
                     wandb_logger.log_policy(checkpoint_dir)
 
@@ -740,9 +724,9 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
             unwrapped_model = accelerator.unwrap_model(policy)
             # PEFT only applies when training a policy — reward models use the plain path.
             if not cfg.is_reward_model_training and cfg.policy.use_peft:
-                unwrapped_model.push_model_to_hub(cfg, peft_model=unwrapped_model, dataset_meta=dataset.meta)
+                unwrapped_model.push_model_to_hub(cfg, peft_model=unwrapped_model)
             else:
-                unwrapped_model.push_model_to_hub(cfg, state_dict=model_state_dict, dataset_meta=dataset.meta)
+                unwrapped_model.push_model_to_hub(cfg, state_dict=model_state_dict)
             preprocessor.push_to_hub(active_cfg.repo_id)
             postprocessor.push_to_hub(active_cfg.repo_id)
 
@@ -751,25 +735,8 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
     accelerator.end_training()
 
 
-def _remote_target_in_argv() -> bool:
-    """True when the CLI requests a remote HF Jobs run (--job.target=<non-local>)."""
-    target = None
-    args = sys.argv[1:]
-    for i, tok in enumerate(args):
-        if tok == "--job.target" and i + 1 < len(args):
-            target = args[i + 1]
-        elif tok.startswith("--job.target="):
-            target = tok.split("=", 1)[1]
-    return JobConfig.is_remote_target(target)
-
-
 def main():
     register_third_party_plugins()
-    if _remote_target_in_argv():
-        # The policy device is resolved on the remote pod, not here, so silence the
-        # client-side "Device '...' is not available" warning PreTrainedConfig emits
-        # while parsing the config (it fires before train() can dispatch remotely).
-        logging.getLogger("lerobot.configs.policies").setLevel(logging.ERROR)
     train()
 
 

src/lerobot/teleoperators/rebot_102_leader/config_rebot_102_leader.py

@@ -65,7 +65,7 @@ class RebotArm102LeaderConfig:
     joint_ranges: dict[str, list[int]] = field(
         default_factory=lambda: {
             "shoulder_pan": [-150, 150],
-            "shoulder_lift": [-200, 1],
+            "shoulder_lift": [-170, 1],
             "elbow_flex": [-200, 1],
             "wrist_flex": [-80, 90],
             "wrist_yaw": [-90, 90],

src/lerobot/utils/constants.py

@@ -37,7 +37,6 @@ ACTION_TOKEN_MASK = ACTION + ".token_mask"
 REWARD = "next.reward"
 TRUNCATED = "next.truncated"
 DONE = "next.done"
-SUCCESS = "next.success"
 INFO = "info"
 
 ROBOTS = "robots"

src/lerobot/utils/foxglove_visualization.py

@@ -1,651 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Foxglove visualization backend.
-
-Live control-loop streaming (:func:`log_foxglove_data`) and seekable dataset playback
-(:func:`serve_foxglove_dataset_playback`) over a Foxglove WebSocket server. Callers usually select a
-backend at runtime through the dispatch in :mod:`lerobot.utils.visualization_utils` rather than
-importing from here directly. Requires the ``viz`` extra (``pip install 'lerobot[viz]'``).
-"""
-
-import logging
-import numbers
-import time
-
-import cv2
-import numpy as np
-
-from lerobot.types import RobotAction, RobotObservation
-
-from .constants import (
-    ACTION,
-    ACTION_PREFIX,
-    DONE,
-    OBS_IMAGES,
-    OBS_PREFIX,
-    OBS_STATE,
-    OBS_STR,
-    REWARD,
-    SUCCESS,
-    TRUNCATED,
-)
-from .import_utils import require_package
-
-# Static schema shared by all scalar topics. Each message carries a flat list of ``{label, value}``
-# pairs rather than one field per feature, so the same schema fits any robot regardless of which
-# observation/action features it reports. The ``label`` field name is what Foxglove looks for to name
-# each series automatically, so a single filtered path plots every feature, e.g.
-# ``/observation/state.scalars[:]``.
-_SCALARS_SCHEMA = {
-    "type": "object",
-    "title": "lerobot.Scalars",
-    "properties": {
-        "scalars": {
-            "type": "array",
-            "items": {
-                "type": "object",
-                "properties": {
-                    "label": {"type": "string"},
-                    "value": {"type": "number"},
-                },
-            },
-        }
-    },
-}
-
-
-def _is_scalar(x):
-    return isinstance(x, (float | numbers.Real | np.integer | np.floating)) or (
-        isinstance(x, np.ndarray) and x.ndim == 0
-    )
-
-
-def init_foxglove(host: str = "127.0.0.1", port: int | None = 8765) -> None:
-    """
-    Starts a Foxglove WebSocket server for visualizing the control loop.
-
-    Connect to it from the Foxglove app at ``ws://<host>:<port>``. Calling this
-    more than once is a no-op while a server is already running.
-
-    Args:
-        host: Host interface to bind the WebSocket server to.
-        port: Port to bind the WebSocket server to (defaults to 8765).
-    """
-
-    require_package("foxglove-sdk", extra="viz", import_name="foxglove")
-    import foxglove
-
-    # Live-stream state lives as attributes on ``log_foxglove_data``:
-    # ``.server`` is the shared WebSocket server and
-    # ``.channels`` caches one Foxglove channel per topic
-    if getattr(log_foxglove_data, "server", None) is not None:
-        return
-    log_foxglove_data.server = foxglove.start_server(host=host, port=port or 8765)
-    log_foxglove_data.channels = {}
-
-
-def shutdown_foxglove() -> None:
-    """Stops the Foxglove WebSocket server and clears cached channels."""
-
-    server = getattr(log_foxglove_data, "server", None)
-    if server is not None:
-        server.stop()
-    log_foxglove_data.server = None
-    log_foxglove_data.channels = {}
-
-
-def _foxglove_safe_name(name: str) -> str:
-    """Replace ``.`` with ``_`` so a feature name is a single Foxglove topic-path segment.
-
-    Foxglove treats ``.`` as a path separator, so an unsanitized name like ``observation.images.front``
-    would split into nested segments instead of naming one topic.
-    """
-
-    return name.replace(".", "_")
-
-
-def _foxglove_topic(key: str, *, is_image: bool = False) -> str:
-    """Build the Foxglove topic for a feature ``key``.
-
-    Camera features map to a per-source image topic (``/observation/images/<name>``); scalar features
-    share one aggregate topic per source: ``/observation/state`` for observations, ``/action/state``
-    for actions.
-    """
-
-    if is_image:
-        name = str(key)
-        for prefix in (f"{OBS_IMAGES}.", OBS_PREFIX):
-            if name.startswith(prefix):
-                name = name[len(prefix) :]
-                break
-        return f"/{OBS_STR}/images/{_foxglove_safe_name(name)}"
-    source = ACTION if (str(key).startswith(ACTION_PREFIX) or str(key) == ACTION) else OBS_STR
-    return f"/{source}/state"
-
-
-def _log_foxglove_scalars(
-    topic: str, values: dict[str, float], *, channels: dict | None = None, log_time: int | None = None
-) -> None:
-    """Log scalars on a typed JSON channel using the static :data:`_SCALARS_SCHEMA`.
-
-    ``values`` is an ordered mapping of feature name to value; it is emitted as a ``scalars`` array of
-    ``{label, value}`` objects. Insertion order is preserved so series stay stable across messages.
-
-    ``channels`` is the per-topic channel cache to reuse (defaults to the live-stream cache on
-    :func:`log_foxglove_data`; dataset playback passes its own local cache to stay self-contained).
-    ``log_time`` is the message time in nanoseconds; when ``None`` the server's receive time is used.
-    """
-
-    if not values:
-        return
-
-    import foxglove
-
-    if channels is None:
-        channels = log_foxglove_data.channels
-    channel = channels.get(topic)
-    if channel is None:
-        channel = channels[topic] = foxglove.Channel(topic, schema=_SCALARS_SCHEMA, message_encoding="json")
-    msg = {"scalars": [{"label": label, "value": value} for label, value in values.items()]}
-    if log_time is None:
-        channel.log(msg)
-    else:
-        channel.log(msg, log_time=log_time)
-
-
-def _labeled_scalars(name: str, values, labels: list[str] | None = None) -> dict[str, float]:
-    """Expand a 1D sequence into ``{label: value}`` entries with a consistent fallback."""
-
-    flat = [float(v) for v in values]
-    if labels is None or len(labels) != len(flat):
-        labels = [f"{name}_{i}" for i in range(len(flat))]
-    return dict(zip(labels, flat, strict=True))
-
-
-def _log_foxglove_image(
-    topic: str,
-    frame_id: str,
-    arr: np.ndarray,
-    *,
-    compress_images: bool,
-    channels: dict | None = None,
-    log_time: int | None = None,
-    depth_range: tuple[float, float] | None = None,
-    raw_depth_values: bool = False,
-) -> None:
-    """Log an image on a cached per-topic channel.
-
-    The encoding is chosen from the channel count and dtype: a single-channel ``float`` or ``uint16``
-    frame is a depth map (``32FC1``/``16UC1``), single-channel ``uint8`` is ``mono8``, 3 => ``rgb8``
-    (float input assumed in [0, 1], cast to uint8), 4 => ``rgba8``; other counts are skipped with a
-    warning. When ``compress_images`` is set, ``rgb8`` is JPEG-encoded instead.
-
-    Args:
-        topic: Foxglove topic to log on.
-        frame_id: Frame id stamped on the message.
-        arr: Image as HWC or CHW (CHW is transposed to HWC), any dtype.
-        compress_images: JPEG-encode ``rgb8`` frames; ignored for other encodings.
-        channels: Per-topic channel cache to reuse (see :func:`_log_foxglove_scalars`).
-        log_time: Message time in nanoseconds, also written to the header timestamp; when ``None``
-            the server's receive time is used.
-        depth_range: ``(lo, hi)`` clip bounds in a depth frame's own input units. Depth frames
-            (``32FC1``/``16UC1``) are rescaled onto Foxglove's default display max for their encoding
-            (``1.0`` / ``10000``) so they show with sensible contrast; ``depth_range`` sets the source
-            range, else the frame's own min/max is used. Ignored for ``mono8``/``rgb8``/``rgba8``.
-        raw_depth_values: If True, depth values are not rescaled and are logged as is.
-    """
-
-    from foxglove.channels import CompressedImageChannel, RawImageChannel
-    from foxglove.messages import CompressedImage, RawImage, Timestamp
-
-    if channels is None:
-        channels = log_foxglove_data.channels
-    time_ns = time.time_ns() if log_time is None else log_time
-    timestamp = Timestamp(sec=time_ns // 1_000_000_000, nsec=time_ns % 1_000_000_000)
-    log_kwargs = {} if log_time is None else {"log_time": log_time}
-
-    # Convert CHW -> HWC when needed (mirrors log_rerun_data).
-    if arr.ndim == 3 and arr.shape[0] in (1, 3, 4) and arr.shape[-1] not in (1, 3, 4):
-        arr = np.transpose(arr, (1, 2, 0))
-    height, width = arr.shape[0], arr.shape[1]
-    n_channels = 1 if arr.ndim == 2 else arr.shape[2]
-
-    if n_channels == 1 and arr.dtype != np.uint8:
-        # Depth map: infer the encoding from the dtype.
-        encoding, target_dtype, value_max = (
-            ("32FC1", np.float32, 1.0)
-            if np.issubdtype(arr.dtype, np.floating)
-            else ("16UC1", np.uint16, 10000.0)
-        )
-        if not raw_depth_values:
-            # Rescale onto the encoding's display max with respect to the given depth_range.
-            lo, hi = depth_range if depth_range is not None else (float(arr.min()), float(arr.max()))
-            arr = arr.clip(lo, hi).astype(np.float32)
-            arr = (arr - lo) / ((hi - lo) if hi > lo else 1.0) * value_max
-        arr = np.ascontiguousarray(arr, dtype=target_dtype)
-    else:
-        if n_channels == 3 and np.issubdtype(arr.dtype, np.floating):
-            arr = (arr * 255.0).clip(0, 255)
-        arr = np.ascontiguousarray(arr, dtype=np.uint8)
-
-        if compress_images and n_channels == 3:
-            buf_src = cv2.cvtColor(arr, cv2.COLOR_RGB2BGR)
-            _, buf = cv2.imencode(".jpg", buf_src)
-            channel = channels.get(topic)
-            if channel is None:
-                channel = channels[topic] = CompressedImageChannel(topic=topic)
-            channel.log(
-                CompressedImage(timestamp=timestamp, frame_id=frame_id, data=buf.tobytes(), format="jpeg"),
-                **log_kwargs,
-            )
-            return
-
-        encoding = {1: "mono8", 3: "rgb8", 4: "rgba8"}.get(n_channels)
-        if encoding is None:
-            logging.warning(
-                "Foxglove: skipping image on topic '%s' with unsupported shape %s (%d channels); "
-                "expected 1 (mono8/16UC1/32FC1), 3 (rgb8), or 4 (rgba8) channels.",
-                topic,
-                tuple(arr.shape),
-                n_channels,
-            )
-            return
-
-    channel = channels.get(topic)
-    if channel is None:
-        channel = channels[topic] = RawImageChannel(topic=topic)
-    channel.log(
-        RawImage(
-            timestamp=timestamp,
-            frame_id=frame_id,
-            width=width,
-            height=height,
-            encoding=encoding,
-            step=width * n_channels * arr.itemsize,
-            data=arr.tobytes(),
-        ),
-        **log_kwargs,
-    )
-
-
-def log_foxglove_data(
-    observation: RobotObservation | None = None,
-    action: RobotAction | None = None,
-    compress_images: bool = False,
-) -> None:
-    """
-    Logs observation and action data to a Foxglove WebSocket server for real-time visualization.
-
-    Mirrors ``log_rerun_data`` but emits Foxglove messages over the server started by
-    :func:`init_foxglove`. Data is mapped as follows:
-    - Scalars (and elements of 1D arrays) are accumulated per source and logged on the
-      ``/observation/state`` and ``/action/state`` topics as typed JSON messages using the static
-      ``lerobot.Scalars`` schema: a ``scalars`` array of ``{label, value}`` objects (see
-      :data:`_SCALARS_SCHEMA`). The ``label`` field lets Foxglove name each series automatically, so
-      ``/observation/state.scalars[:].value`` plots every feature at once.
-    - 3D NumPy arrays that resemble images are transposed from CHW to HWC when needed and logged on a
-      per-source topic (e.g. ``/observation/images/front``) as a ``RawImage`` (or a JPEG
-      ``CompressedImage`` when ``compress_images`` is True).
-
-    Args:
-        observation: An optional dictionary containing observation data to log.
-        action: An optional dictionary containing action data to log.
-        compress_images: Whether to JPEG-compress images before logging to save bandwidth in exchange
-            for CPU and quality.
-    """
-
-    require_package("foxglove-sdk", extra="viz", import_name="foxglove")
-
-    if getattr(log_foxglove_data, "server", None) is None:
-        raise RuntimeError("init_foxglove() must be called before log_foxglove_data().")
-
-    now = time.time_ns()
-
-    if observation:
-        obs_scalars: dict[str, float] = {}
-        for k, v in observation.items():
-            if v is None:
-                continue
-            key = k[len(OBS_PREFIX) :] if str(k).startswith(OBS_PREFIX) else str(k)
-            if _is_scalar(v):
-                obs_scalars[key] = float(v)
-            elif isinstance(v, np.ndarray):
-                if v.ndim == 1:
-                    obs_scalars.update(_labeled_scalars(key, v))
-                else:
-                    _log_foxglove_image(
-                        _foxglove_topic(k, is_image=True),
-                        key,
-                        v,
-                        compress_images=compress_images,
-                        log_time=now,
-                    )
-        _log_foxglove_scalars(_foxglove_topic(OBS_STATE), obs_scalars, log_time=now)
-
-    if action:
-        action_scalars: dict[str, float] = {}
-        for k, v in action.items():
-            if v is None:
-                continue
-            key = k[len(ACTION_PREFIX) :] if str(k).startswith(ACTION_PREFIX) else str(k)
-            if _is_scalar(v):
-                action_scalars[key] = float(v)
-            elif isinstance(v, np.ndarray):
-                action_scalars.update(_labeled_scalars(key, v.flatten()))
-        _log_foxglove_scalars(_foxglove_topic(ACTION), action_scalars, log_time=now)
-
-
-# ── Dataset playback over a Foxglove WebSocket server ─────────────────────
-# A LeRobotDataset is random-access on disk, so rather than fire-and-forget a forward stream we
-# advertise a seekable timeline and serve frames on demand for whatever time the user scrubs/plays
-# to in the Foxglove app. This relies on the SDK's PlaybackControl capability.
-
-
-def _feature_dim_names(feature: dict | None) -> list[str] | None:
-    """Best-effort per-dimension series labels for a 1D feature, or ``None`` to fall back to indices.
-
-    LeRobot records a feature's ``names`` inconsistently: a flat list (``["x", "y"]``), a category
-    mapping (``{"motors": ["motor_0", "motor_1"]}``), or a name->index mapping
-    (``{"delta_x": 0, "delta_y": 1}``). Each is handled, but labels are only returned when their count
-    matches the feature's 1D shape, so a malformed/mismatched ``names`` can't silently mislabel series.
-    """
-
-    if not feature:
-        return None
-    shape = feature.get("shape")
-    dim = shape[0] if shape and len(shape) == 1 else None
-    names = feature.get("names")
-    labels: list[str] | None = None
-    if isinstance(names, dict):
-        values = list(names.values())
-        if values and all(isinstance(v, (list, tuple)) for v in values):
-            labels = [str(n) for group in values for n in group]
-        elif values and all(isinstance(v, int) and not isinstance(v, bool) for v in values):
-            labels = [name for name, _ in sorted(names.items(), key=lambda kv: kv[1])]
-    elif isinstance(names, (list, tuple)):
-        labels = [str(n) for n in names]
-    if labels is not None and dim is not None and len(labels) == dim:
-        return labels
-    return None
-
-
-def _frame_to_scalars(sample: dict, key: str, labels: list[str] | None = None) -> dict[str, float]:
-    """Flatten a frame's vector/scalar feature ``key`` into ``{label: value}`` entries.
-
-    ``labels`` provides one name per dimension (from the dataset's feature metadata); when absent or
-    the wrong length, dimensions fall back to ``{name}_{i}`` (the short feature name), matching the
-    live stream so series names agree. A scalar feature becomes a single entry. Missing or ``None``
-    features yield an empty mapping.
-    """
-
-    v = sample.get(key)
-    if v is None:
-        return {}
-    arr = v.numpy() if hasattr(v, "numpy") else np.asarray(v)
-    if key.startswith(OBS_PREFIX):
-        name = key[len(OBS_PREFIX) :]
-    elif key.startswith(ACTION_PREFIX):
-        name = key[len(ACTION_PREFIX) :]
-    else:
-        name = key
-    if arr.ndim == 0:
-        return {name: float(arr)}
-    return _labeled_scalars(name, arr.flatten(), labels)
-
-
-def serve_foxglove_dataset_playback(
-    dataset,
-    episode_index: int,
-    *,
-    host: str = "127.0.0.1",
-    port: int = 8765,
-    compress_images: bool = False,
-    autoplay: bool = True,
-) -> None:
-    """Serve a single dataset episode to Foxglove as a seekable, scrubbable timeline.
-
-    Starts a Foxglove WebSocket server advertising the ``PlaybackControl`` capability over the
-    episode's time range. The Foxglove app drives play/pause/seek/speed; a background thread and a
-    ``ServerListener`` read frames from the on-disk ``dataset`` on demand and log them stamped at
-    their dataset timestamps, so the user can scrub anywhere in the episode. Blocks until interrupted.
-
-    Args:
-        dataset: A ``LeRobotDataset`` loaded for the single episode to visualize.
-        episode_index: Index of the episode being visualized (used only for the session name).
-        host: Host interface to bind the WebSocket server to.
-        port: Port to bind the WebSocket server to.
-        compress_images: Whether to JPEG-compress camera frames before logging.
-        autoplay: If True, start playing automatically as soon as a client connects, instead of
-            waiting for the user to press play in the Foxglove app.
-    """
-
-    require_package("foxglove-sdk", extra="viz", import_name="foxglove")
-    import bisect
-    import threading
-
-    import foxglove
-    from foxglove.websocket import (
-        Capability,
-        PlaybackCommand,
-        PlaybackControlRequest,
-        PlaybackState,
-        PlaybackStatus,
-        ServerListener,
-    )
-
-    # Per-frame timestamps in nanoseconds (read straight from the table, no video decode).
-    times_ns = [int(round(float(t) * 1e9)) for t in dataset.hf_dataset["timestamp"]]
-    n_frames = len(times_ns)
-    if n_frames == 0:
-        raise ValueError("Cannot visualize an empty episode.")
-    first_ns, last_ns = times_ns[0], times_ns[-1]
-    camera_keys = list(dataset.meta.camera_keys)
-    # Dataset-wide q01/q99 depth bounds (fallback min/max) used to normalize depth to [0, 1].
-    depth_ranges: dict[str, tuple[float, float]] = {}
-    for key in dataset.meta.depth_keys:
-        stats = (dataset.meta.stats or {}).get(key)
-        if not stats:
-            continue
-        lo = stats["q01"] if "q01" in stats else stats["min"]
-        hi = stats["q99"] if "q99" in stats else stats["max"]
-        depth_ranges[key] = (float(np.asarray(lo).item()), float(np.asarray(hi).item()))
-    # Per-dimension series labels from the dataset metadata (e.g. joint names), computed once.
-    scalar_labels = {
-        OBS_STATE: _feature_dim_names(dataset.meta.features.get(OBS_STATE)),
-        ACTION: _feature_dim_names(dataset.meta.features.get(ACTION)),
-    }
-    # Local channel cache so the playback server is self-contained and doesn't touch the live-stream cache.
-    channels: dict = {}
-
-    def emit_frame(i: int) -> None:
-        """Log every channel for frame ``i`` stamped at its dataset timestamp."""
-        sample = dataset[i]
-        log_time = times_ns[i]
-        for key in camera_keys:
-            arr = sample.get(key)
-            if arr is None:
-                continue
-            arr = arr.numpy() if hasattr(arr, "numpy") else np.asarray(arr)
-            _log_foxglove_image(
-                _foxglove_topic(key, is_image=True),
-                key,
-                arr,
-                compress_images=compress_images,
-                channels=channels,
-                log_time=log_time,
-                depth_range=depth_ranges.get(key),
-                raw_depth_values=True,
-            )
-        _log_foxglove_scalars(
-            _foxglove_topic(OBS_STATE),
-            _frame_to_scalars(sample, OBS_STATE, scalar_labels[OBS_STATE]),
-            channels=channels,
-            log_time=log_time,
-        )
-        _log_foxglove_scalars(
-            _foxglove_topic(ACTION),
-            _frame_to_scalars(sample, ACTION, scalar_labels[ACTION]),
-            channels=channels,
-            log_time=log_time,
-        )
-        episode_scalars = {}
-        for feat, label in (
-            (DONE, "done"),
-            (TRUNCATED, "truncated"),
-            (REWARD, "reward"),
-            (SUCCESS, "success"),
-        ):
-            v = sample.get(feat)
-            if v is not None:
-                episode_scalars[label] = float(v)
-        _log_foxglove_scalars("/episode/state", episode_scalars, channels=channels, log_time=log_time)
-
-    lock = threading.Lock()
-    stop_event = threading.Event()
-    # Shared playback state, guarded by ``lock``. ``seek_idx`` is a one-shot request set by the
-    # listener and serviced by the playback loop, which is the *only* thread that emits frames (so
-    # concurrent random access into the on-disk dataset / video decoder never overlaps).
-    state = {
-        "status": PlaybackStatus.Paused,
-        "cursor": first_ns,
-        "speed": 1.0,
-        "last_idx": -1,
-        "seek_idx": None,
-    }
-
-    def index_at(t_ns: int) -> int:
-        return max(0, min(n_frames - 1, bisect.bisect_right(times_ns, t_ns) - 1))
-
-    # One-shot latch so autoplay fires only on the first client subscription.
-    autoplay_started = threading.Event()
-
-    class _PlaybackListener(ServerListener):
-        def on_subscribe(self, client, channel):
-            # Start playing automatically once a client actually connects (subscribes). Using the
-            # subscribe hook, rather than starting in Playing up front, means the timeline doesn't
-            # advance before anyone is watching. Fires once; the user can still pause/seek after.
-            if not autoplay:
-                return
-            with lock:
-                if autoplay_started.is_set() or state["status"] != PlaybackStatus.Paused:
-                    return
-                autoplay_started.set()
-                state["status"] = PlaybackStatus.Playing
-                cursor, speed = state["cursor"], state["speed"]
-            server.broadcast_playback_state(PlaybackState(PlaybackStatus.Playing, cursor, speed, False, ""))
-
-        def on_playback_control_request(self, req: PlaybackControlRequest):
-            # Only mutate state here; the playback loop performs all frame emission.
-            with lock:
-                did_seek = False
-                if req.seek_time is not None:
-                    cursor = max(first_ns, min(last_ns, req.seek_time))
-                    state["cursor"] = cursor
-                    state["last_idx"] = state["seek_idx"] = index_at(cursor)
-                    did_seek = True
-                if req.playback_speed and req.playback_speed > 0:
-                    state["speed"] = req.playback_speed
-                if req.playback_command == PlaybackCommand.Play:
-                    # Restarting from the end replays from the beginning.
-                    if state["cursor"] >= last_ns:
-                        state["cursor"] = first_ns
-                        state["last_idx"] = state["seek_idx"] = 0
-                        did_seek = True
-                    state["status"] = PlaybackStatus.Playing
-                elif req.playback_command == PlaybackCommand.Pause:
-                    state["status"] = PlaybackStatus.Paused
-                status, cursor, speed = state["status"], state["cursor"], state["speed"]
-                request_id = req.request_id or ""
-            return PlaybackState(status, cursor, speed, did_seek, request_id)
-
-    server = foxglove.start_server(
-        name=f"{dataset.repo_id}/episode_{episode_index}",
-        host=host,
-        port=port,
-        capabilities=[Capability.PlaybackControl, Capability.Time],
-        server_listener=_PlaybackListener(),
-        playback_time_range=(first_ns, last_ns),
-    )
-
-    def playback_loop() -> None:
-        # Cap how far the cursor may advance in a single tick. A slow frame decode (or any stall)
-        # would otherwise make ``dt`` huge and produce one enormous catch-up batch; clamping it makes
-        # playback trail wall-clock under a slow decoder while each tick emits a bounded frame range.
-        max_tick_dt_s = 0.25
-        prev = time.monotonic()
-        while not stop_event.is_set():
-            time.sleep(1.0 / 60.0)
-            ended = False
-            speed = 1.0
-            with lock:
-                now = time.monotonic()
-                dt = min(now - prev, max_tick_dt_s)
-                prev = now
-                # A queued seek is always serviced, even while paused, so scrubbing updates the view.
-                work = []
-                seek_idx = state["seek_idx"]
-                if seek_idx is not None:
-                    state["seek_idx"] = None
-                    work.append(seek_idx)
-                if state["status"] == PlaybackStatus.Playing:
-                    cursor = state["cursor"] + int(dt * 1e9 * state["speed"])
-                    start_idx = state["last_idx"] + 1
-                    if cursor >= last_ns:
-                        cursor, target, ended = last_ns, n_frames - 1, True
-                    else:
-                        target = index_at(cursor)
-                    state["cursor"] = cursor
-                    work.extend(range(start_idx, target + 1))
-                    # cursor only grows while playing (seeks reset last_idx in the listener), so
-                    # target >= last_idx here; a plain assignment is correct and clearer than max().
-                    state["last_idx"] = target
-                    if ended:
-                        state["status"] = PlaybackStatus.Ended
-                if not work:
-                    continue
-                cursor, speed = state["cursor"], state["speed"]
-            # Emit outside the lock; this is the only thread that calls emit_frame. Re-check
-            # stop_event between frames so shutdown stays responsive even mid-batch.
-            for i in work:
-                if stop_event.is_set():
-                    break
-                emit_frame(i)
-            server.broadcast_time(cursor)
-            if ended:
-                server.broadcast_playback_state(PlaybackState(PlaybackStatus.Ended, cursor, speed, False, ""))
-
-    # Emit the first frame so channels are advertised (done before the loop starts, so emission stays
-    # single-threaded). Late-connecting clients re-receive frames once they seek/play.
-    emit_frame(0)
-    with lock:
-        state["last_idx"] = 0
-    server.broadcast_time(first_ns)
-    server.broadcast_playback_state(PlaybackState(PlaybackStatus.Paused, first_ns, 1.0, True, ""))
-
-    thread = threading.Thread(target=playback_loop, name="foxglove-playback", daemon=True)
-    thread.start()
-
-    print(f"Foxglove server running. Connect the Foxglove app to ws://{host}:{port}")
-    print("Use the playback controls in Foxglove to play/pause and scrub the episode. Ctrl-C to exit.")
-    try:
-        while not stop_event.is_set():
-            time.sleep(0.5)
-    except KeyboardInterrupt:
-        print("Ctrl-C received. Exiting.")
-    finally:
-        stop_event.set()
-        thread.join(timeout=2.0)
-        server.stop()
-        channels.clear()

src/lerobot/utils/hub.py

@@ -20,33 +20,9 @@ from typing import Any, TypeVar
 from huggingface_hub import HfApi
 from huggingface_hub.utils import validate_hf_hub_args
 
-from .constants import CHECKPOINTS_DIR
-
 T = TypeVar("T", bound="HubMixin")
 
 
-def find_latest_hub_checkpoint(
-    repo_id: str,
-    *,
-    token: str | bool | None = None,
-    revision: str | None = None,
-) -> str | None:
-    """Repo-relative path of the most recent checkpoint in a training repo.
-
-    Training runs push checkpoints to ``checkpoints/<step>/`` (see
-    ``push_checkpoint_to_hub``). This lists those step dirs and returns
-    ``checkpoints/<highest-step>``, or ``None`` if the repo has no checkpoints.
-    """
-    files = HfApi().list_repo_files(repo_id=repo_id, repo_type="model", revision=revision, token=token)
-    prefix = f"{CHECKPOINTS_DIR}/"
-    steps = {
-        name for f in files if f.startswith(prefix) and (name := f[len(prefix) :].split("/", 1)[0]).isdigit()
-    }
-    if not steps:
-        return None
-    return f"{CHECKPOINTS_DIR}/{max(steps, key=int)}"
-
-
 class HubMixin:
     """
     A Mixin containing the functionality to push an object to the hub.

src/lerobot/utils/import_utils.py

@@ -129,6 +129,7 @@ _placo_available = is_package_available("placo")
 _hidapi_available = is_package_available("hidapi", import_name="hid")
 
 # Data / serialization
+_datasets_available = is_package_available("datasets")
 _pandas_available = is_package_available("pandas")
 _faker_available = is_package_available("faker")
 

src/lerobot/utils/rerun_visualization.py

@@ -1,191 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Rerun visualization backend.
-
-Live control-loop streaming to the Rerun viewer (:func:`log_rerun_data`). Callers usually select a
-backend at runtime through the dispatch in :mod:`lerobot.utils.visualization_utils` rather than
-importing from here directly. Requires the ``viz`` extra (``pip install 'lerobot[viz]'``).
-"""
-
-import numbers
-import os
-
-import numpy as np
-
-from lerobot.configs import DEPTH_MILLIMETER_UNIT, infer_depth_unit
-from lerobot.types import RobotAction, RobotObservation
-
-from .constants import ACTION, ACTION_PREFIX, OBS_PREFIX, OBS_STR
-from .import_utils import require_package
-
-
-def _is_scalar(x):
-    return isinstance(x, (float | numbers.Real | np.integer | np.floating)) or (
-        isinstance(x, np.ndarray) and x.ndim == 0
-    )
-
-
-def init_rerun(
-    session_name: str = "lerobot_control_loop", ip: str | None = None, port: int | None = None
-) -> None:
-    """
-    Initializes the Rerun SDK for visualizing the control loop.
-
-    Args:
-        session_name: Name of the Rerun session.
-        ip: Optional IP for connecting to a Rerun server.
-        port: Optional port for connecting to a Rerun server.
-    """
-
-    require_package("rerun-sdk", extra="viz", import_name="rerun")
-    import rerun as rr
-
-    log_rerun_data.blueprint = None  # Reset blueprint cache for new session
-
-    batch_size = os.getenv("RERUN_FLUSH_NUM_BYTES", "8000")
-    os.environ["RERUN_FLUSH_NUM_BYTES"] = batch_size
-    rr.init(session_name)
-    memory_limit = os.getenv("LEROBOT_RERUN_MEMORY_LIMIT", "10%")
-    if ip and port:
-        rr.connect_grpc(url=f"rerun+http://{ip}:{port}/proxy")
-    else:
-        rr.spawn(memory_limit=memory_limit)
-
-
-def shutdown_rerun() -> None:
-    """Shuts down the Rerun SDK gracefully."""
-
-    require_package("rerun-sdk", extra="viz", import_name="rerun")
-    import rerun as rr
-
-    rr.rerun_shutdown()
-
-
-def _build_blueprint(observation_paths: set[str], action_paths: set[str], image_paths: set[str]):
-    """Build a Rerun blueprint laying out camera images, observation and action scalars in separate views.
-
-    Camera images, observation and action scalars are arranged in a grid.
-    """
-
-    # Safe + zero-overhead: `log_rerun_data` already ran the `require_package` guard and imported rerun.
-    import rerun.blueprint as rrb
-
-    views = [rrb.Spatial2DView(origin=path, name=path) for path in sorted(image_paths)]
-
-    if observation_paths:
-        views.append(rrb.TimeSeriesView(name="observation", contents=sorted(observation_paths)))
-    if action_paths:
-        views.append(rrb.TimeSeriesView(name="action", contents=sorted(action_paths)))
-
-    return rrb.Blueprint(rrb.Grid(*views))
-
-
-def _ensure_blueprint(observation_paths: set[str], action_paths: set[str], image_paths: set[str]) -> None:
-    """Build and send the blueprint once, from the first observation and action data."""
-    if getattr(log_rerun_data, "blueprint", None) is not None:
-        return
-
-    if not (observation_paths or action_paths or image_paths):
-        return
-
-    # Safe + zero-overhead: `log_rerun_data` already ran the `require_package` guard and imported rerun.
-    import rerun as rr
-
-    blueprint = _build_blueprint(observation_paths, action_paths, image_paths)
-    log_rerun_data.blueprint = blueprint
-    rr.send_blueprint(blueprint)
-
-
-def log_rerun_data(
-    observation: RobotObservation | None = None,
-    action: RobotAction | None = None,
-    compress_images: bool = False,
-) -> None:
-    """
-    Logs observation and action data to Rerun for real-time visualization.
-
-    This function iterates through the provided observation and action dictionaries and sends their contents
-    to the Rerun viewer. It handles different data types appropriately:
-    - Scalars values (floats, ints) are logged as `rr.Scalars`.
-    - 3D NumPy arrays that resemble images (e.g., with 1, 3, or 4 channels first) are transposed
-      from CHW to HWC format, (optionally) compressed to JPEG and logged as `rr.Image` or `rr.EncodedImage`.
-    - 1D NumPy arrays are logged as a single `rr.Scalars` batch under one entity path, so that every
-      dimension shares the same view instead of being split across one view per element.
-    - Multi-dimensional **action** arrays are flattened and logged as a single `rr.Scalars` batch.
-
-    Keys are automatically namespaced with "observation." or "action." if not already present.
-
-    On the first call, a blueprint is built and sent so observation and action scalars get separate
-    time-series views and each image gets its own spatial view.
-
-    Args:
-        observation: An optional dictionary containing observation data to log.
-        action: An optional dictionary containing action data to log.
-        compress_images: Whether to compress images before logging to save bandwidth & memory in exchange for cpu and quality.
-    """
-
-    require_package("rerun-sdk", extra="viz", import_name="rerun")
-    import rerun as rr
-
-    observation_paths: set[str] = set()
-    action_paths: set[str] = set()
-    image_paths: set[str] = set()
-
-    if observation:
-        for k, v in observation.items():
-            if v is None:
-                continue
-            key = k if str(k).startswith(OBS_PREFIX) else f"{OBS_STR}.{k}"
-
-            if _is_scalar(v):
-                rr.log(key, rr.Scalars(float(v)))
-                observation_paths.add(key)
-            elif isinstance(v, np.ndarray):
-                arr = v
-                # Convert CHW -> HWC when needed
-                if arr.ndim == 3 and arr.shape[0] in (1, 3, 4) and arr.shape[-1] not in (1, 3, 4):
-                    arr = np.transpose(arr, (1, 2, 0))
-                if arr.ndim == 1:
-                    rr.log(key, rr.Scalars(arr.astype(float)))
-                    observation_paths.add(key)
-                else:
-                    if arr.shape[-1] == 1:
-                        # At record time, the depth unit is inferred from the frame type.
-                        depth_unit = infer_depth_unit(arr.dtype)
-                        img_entity = rr.DepthImage(
-                            arr,
-                            meter=1000.0 if depth_unit == DEPTH_MILLIMETER_UNIT else 1.0,
-                            colormap=rr.components.Colormap.Viridis,
-                        )
-                    else:
-                        img_entity = rr.Image(arr).compress() if compress_images else rr.Image(arr)
-                    rr.log(key, entity=img_entity, static=True)
-                    image_paths.add(key)
-
-    if action:
-        for k, v in action.items():
-            if v is None:
-                continue
-            key = k if str(k).startswith(ACTION_PREFIX) else f"{ACTION}.{k}"
-
-            if _is_scalar(v):
-                rr.log(key, rr.Scalars(float(v)))
-                action_paths.add(key)
-            elif isinstance(v, np.ndarray):
-                # Flatten any (incl. higher-dimensional) array into a single batched Scalars
-                rr.log(key, rr.Scalars(v.reshape(-1).astype(float)))
-                action_paths.add(key)
-
-    _ensure_blueprint(observation_paths, action_paths, image_paths)

src/lerobot/utils/visualization_utils.py

@@ -12,68 +12,121 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""Backend-agnostic visualization dispatch.
+import numbers
+import os
 
-Selects a visualization backend at runtime via a display-mode string (e.g. a ``--display_mode`` CLI
-flag) so callers never branch on the backend. The concrete implementations live in
-:mod:`lerobot.utils.rerun_visualization` and :mod:`lerobot.utils.foxglove_visualization`; importing
-this module does not import ``rerun`` or ``foxglove`` (each backend imports its SDK lazily behind a
-``require_package`` guard).
-"""
+import numpy as np
 
 from lerobot.types import RobotAction, RobotObservation
 
-from .foxglove_visualization import init_foxglove, log_foxglove_data, shutdown_foxglove
-from .rerun_visualization import init_rerun, log_rerun_data, shutdown_rerun
-
-# Visualization backends selectable at runtime via a display-mode string (e.g. a --display_mode flag).
-VISUALIZATION_MODES = ("rerun", "foxglove")
+from .constants import ACTION, ACTION_PREFIX, OBS_PREFIX, OBS_STR
+from .import_utils import require_package
 
 
-def init_visualization(
-    display_mode: str,
-    *,
-    session_name: str = "lerobot_control_loop",
-    ip: str | None = None,
-    port: int | None = None,
+def init_rerun(
+    session_name: str = "lerobot_control_loop", ip: str | None = None, port: int | None = None
 ) -> None:
-    """Initializes the visualization backend selected by ``display_mode``.
+    """
+    Initializes the Rerun SDK for visualizing the control loop.
 
-    For ``"rerun"``, ``ip``/``port`` point at an optional remote Rerun server. For ``"foxglove"``,
-    ``ip`` is the interface to bind the WebSocket server to (``127.0.0.1`` for local only, ``0.0.0.0``
-    for all interfaces) and ``port`` is its port.
+    Args:
+        session_name: Name of the Rerun session.
+        ip: Optional IP for connecting to a Rerun server.
+        port: Optional port for connecting to a Rerun server.
     """
 
-    if display_mode == "rerun":
-        init_rerun(session_name=session_name, ip=ip, port=port)
-    elif display_mode == "foxglove":
-        init_foxglove(host=ip or "127.0.0.1", port=port)
+    require_package("rerun-sdk", extra="viz", import_name="rerun")
+    import rerun as rr
+
+    batch_size = os.getenv("RERUN_FLUSH_NUM_BYTES", "8000")
+    os.environ["RERUN_FLUSH_NUM_BYTES"] = batch_size
+    rr.init(session_name)
+    memory_limit = os.getenv("LEROBOT_RERUN_MEMORY_LIMIT", "10%")
+    if ip and port:
+        rr.connect_grpc(url=f"rerun+http://{ip}:{port}/proxy")
     else:
-        raise ValueError(f"Unknown display_mode '{display_mode}'. Expected one of {VISUALIZATION_MODES}.")
+        rr.spawn(memory_limit=memory_limit)
 
 
-def log_visualization_data(
-    display_mode: str,
+def shutdown_rerun() -> None:
+    """Shuts down the Rerun SDK gracefully."""
+
+    require_package("rerun-sdk", extra="viz", import_name="rerun")
+    import rerun as rr
+
+    rr.rerun_shutdown()
+
+
+def _is_scalar(x):
+    return isinstance(x, (float | numbers.Real | np.integer | np.floating)) or (
+        isinstance(x, np.ndarray) and x.ndim == 0
+    )
+
+
+def log_rerun_data(
     observation: RobotObservation | None = None,
     action: RobotAction | None = None,
     compress_images: bool = False,
 ) -> None:
-    """Logs observation/action data to the backend selected by ``display_mode``."""
+    """
+    Logs observation and action data to Rerun for real-time visualization.
 
-    if display_mode == "rerun":
-        log_rerun_data(observation=observation, action=action, compress_images=compress_images)
-    elif display_mode == "foxglove":
-        log_foxglove_data(observation=observation, action=action, compress_images=compress_images)
-    else:
-        raise ValueError(f"Unknown display_mode '{display_mode}'. Expected one of {VISUALIZATION_MODES}.")
+    This function iterates through the provided observation and action dictionaries and sends their contents
+    to the Rerun viewer. It handles different data types appropriately:
+    - Scalars values (floats, ints) are logged as `rr.Scalars`.
+    - 3D NumPy arrays that resemble images (e.g., with 1, 3, or 4 channels first) are transposed
+      from CHW to HWC format, (optionally) compressed to JPEG and logged as `rr.Image` or `rr.EncodedImage`.
+    - 1D NumPy arrays are logged as a series of individual scalars, with each element indexed.
+    - Other multi-dimensional arrays are flattened and logged as individual scalars.
 
+    Keys are automatically namespaced with "observation." or "action." if not already present.
 
-def shutdown_visualization(display_mode: str) -> None:
-    """Shuts down the backend selected by ``display_mode``."""
+    Args:
+        observation: An optional dictionary containing observation data to log.
+        action: An optional dictionary containing action data to log.
+        compress_images: Whether to compress images before logging to save bandwidth & memory in exchange for cpu and quality.
+    """
 
-    if display_mode == "rerun":
-        shutdown_rerun()
-    elif display_mode == "foxglove":
-        shutdown_foxglove()
-    else:
-        raise ValueError(f"Unknown display_mode '{display_mode}'. Expected one of {VISUALIZATION_MODES}.")
+    require_package("rerun-sdk", extra="viz", import_name="rerun")
+    import rerun as rr
+
+    if observation:
+        for k, v in observation.items():
+            if v is None:
+                continue
+            key = k if str(k).startswith(OBS_PREFIX) else f"{OBS_STR}.{k}"
+
+            if _is_scalar(v):
+                rr.log(key, rr.Scalars(float(v)))
+            elif isinstance(v, np.ndarray):
+                arr = v
+                # Convert CHW -> HWC when needed
+                if arr.ndim == 3 and arr.shape[0] in (1, 3, 4) and arr.shape[-1] not in (1, 3, 4):
+                    arr = np.transpose(arr, (1, 2, 0))
+                if arr.ndim == 1:
+                    for i, vi in enumerate(arr):
+                        rr.log(f"{key}_{i}", rr.Scalars(float(vi)))
+                else:
+                    if arr.shape[-1] == 1:
+                        img_entity = rr.DepthImage(arr, colormap=rr.components.Colormap.Viridis)
+                    else:
+                        img_entity = rr.Image(arr).compress() if compress_images else rr.Image(arr)
+                    rr.log(key, entity=img_entity, static=True)
+
+    if action:
+        for k, v in action.items():
+            if v is None:
+                continue
+            key = k if str(k).startswith(ACTION_PREFIX) else f"{ACTION}.{k}"
+
+            if _is_scalar(v):
+                rr.log(key, rr.Scalars(float(v)))
+            elif isinstance(v, np.ndarray):
+                if v.ndim == 1:
+                    for i, vi in enumerate(v):
+                        rr.log(f"{key}_{i}", rr.Scalars(float(vi)))
+                else:
+                    # Fall back to flattening higher-dimensional arrays
+                    flat = v.flatten()
+                    for i, vi in enumerate(flat):
+                        rr.log(f"{key}_{i}", rr.Scalars(float(vi)))

tests/configs/test_resume_from_hub.py

@@ -1,68 +0,0 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import pytest
-
-import lerobot.configs.train as tc
-from lerobot.configs.train import TrainPipelineConfig
-
-
-class _FakeHTTPError(tc.HfHubHTTPError):
-    """HfHubHTTPError that can be raised without a real HTTP response object."""
-
-    def __init__(self):
-        pass
-
-
-def test_from_pretrained_falls_back_to_latest_checkpoint_config(tmp_path, monkeypatch):
-    """A Hub repo with no root train_config.json (an interrupted run that only pushed
-    checkpoints/) resolves via the latest checkpoint's config."""
-    # A real train_config.json written by save_pretrained, to be returned by the fallback.
-    parsed = tc.draccus.parse(TrainPipelineConfig, args=["--dataset.repo_id", "u/d"])
-    cfg_file = tmp_path / "train_config.json"
-    parsed._save_pretrained(tmp_path)
-    assert cfg_file.is_file()
-
-    calls = []
-
-    def fake_hf_hub_download(filename=None, **kwargs):
-        calls.append(filename)
-        if filename == "train_config.json":
-            raise _FakeHTTPError()  # no root config
-        if filename == "checkpoints/000010/pretrained_model/train_config.json":
-            return str(cfg_file)
-        raise AssertionError(f"unexpected filename {filename}")
-
-    monkeypatch.setattr(tc, "hf_hub_download", fake_hf_hub_download)
-    monkeypatch.setattr(
-        tc, "find_latest_hub_checkpoint", lambda repo_id, token=None, revision=None: "checkpoints/000010"
-    )
-
-    loaded = TrainPipelineConfig.from_pretrained("user/interrupted-run")
-    assert loaded.dataset.repo_id == "u/d"
-    # Tried the root config first, then fell back to the latest checkpoint's config.
-    assert calls == ["train_config.json", "checkpoints/000010/pretrained_model/train_config.json"]
-
-
-def test_from_pretrained_raises_when_no_root_config_and_no_checkpoints(monkeypatch):
-    """No root config AND no checkpoints → a clear FileNotFoundError, not the raw HTTP error."""
-
-    def fake_hf_hub_download(filename=None, **kwargs):
-        raise _FakeHTTPError()
-
-    monkeypatch.setattr(tc, "hf_hub_download", fake_hf_hub_download)
-    monkeypatch.setattr(tc, "find_latest_hub_checkpoint", lambda repo_id, token=None, revision=None: None)
-
-    with pytest.raises(FileNotFoundError, match="train_config.json not found"):
-        TrainPipelineConfig.from_pretrained("user/empty-repo")

tests/conftest.py

@@ -14,23 +14,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import functools
 import traceback
 
-import draccus.wrappers.docstring as _draccus_docstring
 import pytest
 
 from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature
 from lerobot.utils.import_utils import is_package_available
 from tests.utils import DEVICE
 
-# On every `draccus.parse()`, draccus rebuilds each dataclass field's help text by
-# re-reading and re-parsing the class source (draccus.wrappers.docstring). For a config
-# as large as TrainPipelineConfig this costs ~2.5s per parse — negligible for the single
-# parse a CLI does, but tests parse configs hundreds of times. The source can't change
-# within a run, so memoize it for the whole test session.
-_draccus_docstring.get_attribute_docstring = functools.cache(_draccus_docstring.get_attribute_docstring)
-
 # Import fixture modules as plugins.
 # Fixtures that depend on optional packages are only registered when those packages are available,
 # so that tests can be collected and run even with a minimal install.

tests/datasets/test_compute_stats.py

@@ -13,7 +13,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import logging
 from unittest.mock import patch
 
 import numpy as np
@@ -688,28 +687,6 @@ def test_compute_episode_stats_string_features_skipped():
     assert "q01" in stats["action"]
 
 
-def test_compute_episode_stats_zero_width_features_skipped(caplog):
-    """Test that features with a zero-width dim (e.g. shape=(0,)) are skipped with a debug log."""
-    episode_data = {
-        "empty": np.zeros((100, 0), dtype=np.float32),  # Zero-width feature
-        "action": np.random.normal(0, 1, (100, 5)),
-    }
-    features = {
-        "empty": {"dtype": "float32", "shape": (0,)},
-        "action": {"dtype": "float32", "shape": (5,)},
-    }
-
-    with caplog.at_level(logging.DEBUG):
-        stats = compute_episode_stats(episode_data, features)
-
-    # Zero-width features should be skipped with a debug log, others computed as usual
-    assert "empty" not in stats
-    assert "empty" in caplog.text
-    assert "action" in stats
-    assert "q01" in stats["action"]
-    assert stats["action"]["mean"].shape == (5,)
-
-
 def test_aggregate_feature_stats_with_quantiles():
     """Test aggregating feature stats that include quantiles."""
     stats_ft_list = [

tests/datasets/test_datasets.py

@@ -1531,7 +1531,6 @@ def test_valid_video_codecs_constant():
     assert "h264" in VALID_VIDEO_CODECS
     assert "hevc" in VALID_VIDEO_CODECS
     assert "libsvtav1" in VALID_VIDEO_CODECS
-    assert "libaom-av1" in VALID_VIDEO_CODECS
     assert "auto" in VALID_VIDEO_CODECS
     assert "h264_videotoolbox" in VALID_VIDEO_CODECS
     assert "h264_nvenc" in VALID_VIDEO_CODECS
@@ -1539,7 +1538,7 @@ def test_valid_video_codecs_constant():
     assert "h264_qsv" in VALID_VIDEO_CODECS
     assert "hevc_videotoolbox" in VALID_VIDEO_CODECS
     assert "hevc_nvenc" in VALID_VIDEO_CODECS
-    assert len(VALID_VIDEO_CODECS) == 11
+    assert len(VALID_VIDEO_CODECS) == 10
 
 
 def test_delta_timestamps_with_episodes_filter(tmp_path, empty_lerobot_dataset_factory):
@@ -1804,11 +1803,3 @@ def test_episode_filter_unknown_key_raises(tmp_path, lerobot_dataset_factory):
             root=dataset.root,
             episode_filter=lambda ep: ep["not_a_real_field"] > 0,
         )
-
-
-def test_get_hf_features_zero_width_feature_does_not_raise_on_from_dict():
-    import datasets
-
-    features = {"empty": {"dtype": "float32", "shape": (0,), "names": ["empty"]}}
-    hf_features = get_hf_features_from_features(features)
-    datasets.Dataset.from_dict({"empty": [[], []]}, features=hf_features)

tests/datasets/test_depth.py

@@ -32,7 +32,6 @@ from lerobot.configs.video import (
 )
 from lerobot.datasets.depth_utils import dequantize_depth, quantize_depth
 from lerobot.datasets.image_writer import image_array_to_pil_image, write_image
-from lerobot.utils.constants import DEFAULT_FEATURES
 from tests.fixtures.constants import (
     DEFAULT_FPS,
     DUMMY_CAMERA_FEATURES,
@@ -246,91 +245,3 @@ class TestFeatureFileRouting:
 
         dataset.save_episode()
         dataset.finalize()
-
-
-class TestDepthUnitMetadata:
-    """The depth unit is inferred once from dtype, stored in ``info``, and drives stats + reads."""
-
-    NUM_FRAMES = 4
-
-    def _record(self, root, features_factory, depth_dtype, value, use_videos):
-        from lerobot.datasets.lerobot_dataset import LeRobotDataset
-
-        features = features_factory(camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH, use_videos=use_videos)
-        dataset = LeRobotDataset.create(
-            repo_id=DUMMY_REPO_ID,
-            fps=DEFAULT_FPS,
-            features=features,
-            root=root,
-            use_videos=use_videos,
-            streaming_encoding=use_videos,
-        )
-        for _ in range(self.NUM_FRAMES):
-            frame: dict = {"task": "test"}
-            for key, ft in dataset.meta.features.items():
-                if key in DEFAULT_FEATURES:
-                    continue
-                if key in dataset.meta.depth_keys:
-                    frame[key] = np.full(ft["shape"], value, dtype=depth_dtype)
-                elif key in dataset.meta.camera_keys:
-                    frame[key] = np.random.randint(0, 256, ft["shape"], dtype=np.uint8)
-                else:
-                    frame[key] = np.zeros(ft["shape"], dtype=np.float32)
-            dataset.add_frame(frame)
-        return dataset
-
-    @pytest.mark.parametrize("use_videos", [False, True])
-    @pytest.mark.parametrize(
-        ("depth_dtype", "value", "expected_unit"),
-        [(np.float32, 2.0, DEPTH_METER_UNIT), (np.uint16, 2000, DEPTH_MILLIMETER_UNIT)],
-    )
-    def test_recorded_unit_inferred_persisted_and_kept_in_stats(
-        self, tmp_path, features_factory, use_videos, depth_dtype, value, expected_unit
-    ):
-        """Unit is inferred from the first frame's dtype, drives stats (raw, never canonicalized), and survives a reload."""
-        from lerobot.datasets.lerobot_dataset import LeRobotDataset
-
-        dataset = self._record(tmp_path / "ds", features_factory, depth_dtype, value, use_videos)
-        assert dataset.meta.features[DEPTH_KEY]["info"]["depth_unit"] == expected_unit
-        dataset.save_episode()
-        mean = float(np.asarray(dataset.meta.stats[DEPTH_KEY]["mean"]).reshape(-1)[0])
-        np.testing.assert_allclose(mean, value, rtol=0.05)
-        dataset.finalize()
-
-        reloaded = LeRobotDataset(repo_id=DUMMY_REPO_ID, root=tmp_path / "ds")
-        assert reloaded.meta.features[DEPTH_KEY]["info"]["depth_unit"] == expected_unit
-
-    @pytest.mark.parametrize("use_videos", [False, True])
-    @pytest.mark.parametrize(
-        ("output_unit", "expected"),
-        [(DEPTH_MILLIMETER_UNIT, 2000.0), (DEPTH_METER_UNIT, 2.0)],
-    )
-    def test_read_honors_output_unit_for_frames_and_stats(
-        self, tmp_path, features_factory, use_videos, output_unit, expected
-    ):
-        """Reloading with a ``depth_output_unit`` converts metre frames (image mode) and rescales stats while preserving count."""
-        from lerobot.datasets.lerobot_dataset import LeRobotDataset
-
-        dataset = self._record(tmp_path / "ds", features_factory, np.float32, 2.0, use_videos=use_videos)
-        dataset.save_episode()
-        count = float(np.asarray(dataset.meta.stats[DEPTH_KEY]["count"]).reshape(-1)[0])
-        dataset.finalize()
-
-        read_dataset = LeRobotDataset(
-            repo_id=DUMMY_REPO_ID, root=tmp_path / "ds", depth_output_unit=output_unit
-        )
-        stats = read_dataset.meta.stats[DEPTH_KEY]
-        np.testing.assert_allclose(float(np.asarray(stats["mean"]).reshape(-1)[0]), expected, rtol=0.05)
-        np.testing.assert_allclose(float(np.asarray(stats["count"]).reshape(-1)[0]), count)
-
-        if not use_videos:
-            depth = read_dataset[0][DEPTH_KEY]
-            assert torch.allclose(depth, torch.full_like(depth, expected))
-
-            from lerobot.datasets.streaming_dataset import StreamingLeRobotDataset
-
-            stream_dataset = StreamingLeRobotDataset(
-                repo_id=DUMMY_REPO_ID, root=tmp_path / "ds", depth_output_unit=output_unit
-            )
-            stream_depth = next(iter(stream_dataset))[DEPTH_KEY]
-            assert torch.allclose(stream_depth, torch.full_like(stream_depth, expected))

tests/datasets/test_video_encoding.py

@@ -345,9 +345,7 @@ class TestExtraOptions:
         opts = cfg.get_codec_options()
         assert opts["qp"] == 20
         assert isinstance(opts["qp"], int)
-        str_opts = cfg.get_codec_options(as_strings=True)
-        assert str_opts["qp"] == "20"
-        assert all(isinstance(v, str) for v in str_opts.values())
+        assert cfg.get_codec_options(as_strings=True)["qp"] == "20"
 
     @require_libsvtav1
     def test_structured_fields_win_on_collision(self):

tests/fixtures/dataset_factories.py

@@ -26,7 +26,6 @@ import pytest
 import torch
 from datasets import Dataset
 
-from lerobot.configs.video import infer_depth_unit
 from lerobot.datasets.dataset_metadata import CODEBASE_VERSION, LeRobotDatasetMetadata
 from lerobot.datasets.feature_utils import get_hf_features_from_features
 from lerobot.datasets.io_utils import flatten_dict, hf_transform_to_torch
@@ -536,13 +535,6 @@ def lerobot_dataset_factory(
                 chunks_size=chunks_size,
                 **info_kwargs,
             )
-            # This synthetic path skips add_frame, so record the depth unit the writer would
-            # have stored (dummy depth is uint16) to keep ``depth_unit`` present in info.json.
-            # Reassign a fresh info dict to avoid mutating the shared feature constants.
-            for ft in info.features.values():
-                ft_info = ft.get("info")
-                if ft_info is not None and ft_info.get("is_depth_map") and "depth_unit" not in ft_info:
-                    ft["info"] = {**ft_info, "depth_unit": infer_depth_unit(np.uint16)}
         if stats is None:
             stats = stats_factory(features=info.features)
         if tasks is None:

tests/jobs/conftest.py

@@ -1,17 +0,0 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Importing concrete policy configs registers their draccus `--policy.type`
-# choices (e.g. "act") so tests can parse them.
-from lerobot.policies.act.configuration_act import ACTConfig  # noqa: F401

tests/jobs/test_dataset.py

@@ -1,66 +0,0 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from unittest.mock import MagicMock
-
-import pytest
-
-pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
-
-from lerobot.jobs.dataset import ensure_dataset_available
-
-
-def _api_with_dataset(exists: bool):
-    api = MagicMock()
-    api.repo_exists.return_value = exists
-    return api
-
-
-def _make_local_cache(tmp_path, repo_id: str) -> None:
-    """Create the minimal local-cache layout that ensure_dataset_available checks."""
-    info = tmp_path / repo_id / "meta" / "info.json"
-    info.parent.mkdir(parents=True)
-    info.write_text("{}")
-
-
-# Branch 1: dataset already on Hub → no push, no error (pod downloads by repo_id).
-def test_dataset_already_on_hub_is_noop():
-    api = _api_with_dataset(True)
-    assert ensure_dataset_available("user/ds", api=api) is None
-    api.repo_exists.assert_called_once_with("user/ds", repo_type="dataset")
-
-
-# Branch 2: not on Hub but present locally → always push privately.
-def test_dataset_local_only_uploads_privately(tmp_path, monkeypatch):
-    monkeypatch.setattr("lerobot.jobs.dataset.HF_LEROBOT_HOME", tmp_path)
-    _make_local_cache(tmp_path, "user/ds")
-
-    api = _api_with_dataset(False)
-    mock_ds_cls = MagicMock()
-    monkeypatch.setattr("lerobot.jobs.dataset.LeRobotDataset", mock_ds_cls)
-
-    assert ensure_dataset_available("user/ds", api=api, tags=["lerobot", "lelab"]) is None
-
-    mock_ds_cls.assert_called_once_with("user/ds")
-    mock_ds_cls.return_value.push_to_hub.assert_called_once_with(private=True, tags=["lerobot", "lelab"])
-
-
-# Branch 3: not on Hub, NOT in local cache → RuntimeError.
-def test_dataset_neither_on_hub_nor_local_raises(tmp_path, monkeypatch):
-    monkeypatch.setattr("lerobot.jobs.dataset.HF_LEROBOT_HOME", tmp_path)
-    # tmp_path is empty — no local cache.
-
-    api = _api_with_dataset(False)
-    with pytest.raises(RuntimeError, match="not in the local cache"):
-        ensure_dataset_available("user/ds", api=api)

tests/jobs/test_hf.py

@@ -1,493 +0,0 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import datetime as dt
-import json
-import threading
-from types import SimpleNamespace
-
-import draccus
-import httpx
-import pytest
-
-pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
-
-from lerobot.configs.train import TrainPipelineConfig
-from lerobot.jobs.hf import (
-    _pod_forwarded_args,
-    _poll_until_done,
-    build_remote_config_file,
-    build_repo_id,
-    resolve_job_tags,
-    resolve_wandb_api_key,
-    submit_to_hf,
-)
-
-
-def test_resolve_job_tags_always_includes_lerobot_and_dedups():
-    assert resolve_job_tags(None) == ["lerobot"]
-    assert resolve_job_tags([]) == ["lerobot"]
-    assert resolve_job_tags(["lelab"]) == ["lerobot", "lelab"]
-    # lerobot isn't duplicated if passed explicitly; order is stable.
-    assert resolve_job_tags(["lelab", "lerobot", "lelab"]) == ["lerobot", "lelab"]
-
-
-def _fake_inspect(stage_value, *, as_enum=True):
-    # huggingface_hub returns `stage` as an enum (with `.value`) in some versions and a plain str in others.
-    stage = SimpleNamespace(value=stage_value) if as_enum else stage_value
-    return lambda job_id: SimpleNamespace(status=SimpleNamespace(stage=stage))
-
-
-@pytest.mark.parametrize("as_enum", [True, False], ids=["enum_stage", "str_stage"])
-def test_poll_until_done_returns_terminal_stage(monkeypatch, as_enum):
-    monkeypatch.setattr("lerobot.jobs.hf.inspect_job", _fake_inspect("COMPLETED", as_enum=as_enum))
-    done = threading.Event()
-    assert _poll_until_done("j", done, poll_interval=0.01) == "COMPLETED"
-    assert done.is_set()
-
-
-def test_poll_until_done_exits_when_done_already_set(monkeypatch):
-    # Non-terminal forever; with done pre-set the loop must not block and returns None.
-    monkeypatch.setattr("lerobot.jobs.hf.inspect_job", _fake_inspect("RUNNING"))
-    done = threading.Event()
-    done.set()
-    assert _poll_until_done("j", done, poll_interval=0.01) is None
-
-
-def test_poll_until_done_gives_up_after_repeated_network_failures(monkeypatch):
-    monkeypatch.setattr(
-        "lerobot.jobs.hf.inspect_job", lambda job_id: (_ for _ in ()).throw(httpx.ConnectError("boom"))
-    )
-    done = threading.Event()
-    result = _poll_until_done("j", done, poll_interval=0.001, max_failures=3)
-    assert result is None
-    assert done.is_set()
-
-
-def test_poll_until_done_propagates_programming_errors(monkeypatch):
-    """A bug (e.g. TypeError) must surface, not be silently retried as a transient failure."""
-    monkeypatch.setattr("lerobot.jobs.hf.inspect_job", lambda job_id: (_ for _ in ()).throw(TypeError("bug")))
-    done = threading.Event()
-    with pytest.raises(TypeError):
-        _poll_until_done("j", done, poll_interval=0.001, max_failures=3)
-
-
-def test_resolve_wandb_key_from_env(monkeypatch):
-    monkeypatch.setenv("WANDB_API_KEY", "abc123")
-    assert resolve_wandb_api_key() == "abc123"
-
-
-def test_resolve_wandb_key_missing(monkeypatch, tmp_path):
-    monkeypatch.delenv("WANDB_API_KEY", raising=False)
-    monkeypatch.setenv("HOME", str(tmp_path))  # no ~/.netrc here
-    monkeypatch.setattr("netrc.netrc", lambda *a, **k: (_ for _ in ()).throw(FileNotFoundError()))
-    assert resolve_wandb_api_key() is None
-
-
-def test_resolve_wandb_key_from_netrc(monkeypatch):
-    # No env var → fall back to the wandb credentials in ~/.netrc.
-    monkeypatch.delenv("WANDB_API_KEY", raising=False)
-
-    class _FakeNetrc:
-        def authenticators(self, host):
-            assert host == "api.wandb.ai"
-            return ("login", "account", "netrc-secret")
-
-    monkeypatch.setattr("netrc.netrc", lambda *a, **k: _FakeNetrc())
-    assert resolve_wandb_api_key() == "netrc-secret"
-
-
-def test_resolve_wandb_key_netrc_without_wandb_entry(monkeypatch):
-    # ~/.netrc exists but has no api.wandb.ai entry → None.
-    monkeypatch.delenv("WANDB_API_KEY", raising=False)
-
-    class _FakeNetrc:
-        def authenticators(self, host):
-            return None
-
-    monkeypatch.setattr("netrc.netrc", lambda *a, **k: _FakeNetrc())
-    assert resolve_wandb_api_key() is None
-
-
-def test_build_repo_id_sanitizes_and_timestamps():
-    now = dt.datetime(2026, 6, 19, 10, 22, 3)
-    assert build_repo_id("alice", "act", now) == "alice/act_2026-06-19_10-22-03"
-    # Runs of illegal characters collapse to a single dash; edges are trimmed.
-    assert build_repo_id("alice", "my cool/run!!", now) == "alice/my-cool-run_2026-06-19_10-22-03"
-    # A name with nothing usable falls back to "train".
-    assert build_repo_id("alice", "///", now) == "alice/train_2026-06-19_10-22-03"
-
-
-def test_pod_forwarded_args_drops_host_only_flags():
-    """User overrides are replayed on the pod, minus flags that only make sense on the submitter.
-
-    `--dataset.root` is a host-local path the pod can't read, so it must be dropped in both the
-    `--name=value` and `--name value` forms; unrelated overrides are forwarded untouched.
-    """
-    argv = [
-        "--config_path=u/d",
-        "--dataset.root=/local/data",
-        "--dataset.root",
-        "/other/local/data",
-        "--policy.repo_id=u/keep",
-        "--steps=10",
-        "--job.target=a10g-small",
-    ]
-    forwarded = _pod_forwarded_args(
-        argv,
-        drop_names=("--config_path", "--policy.repo_id", "--policy.push_to_hub", "--dataset.root"),
-        drop_prefixes=("--job.",),
-    )
-    assert forwarded == ["--steps=10"]
-
-
-def _minimal_cfg():
-    return draccus.parse(
-        TrainPipelineConfig,
-        args=["--dataset.repo_id", "u/d", "--policy.type", "act", "--job.target", "a10g-small"],
-    )
-
-
-def test_validate_skips_repo_id_check_for_remote():
-    """Remote runs auto-assign repo_id in submit_to_hf, so validate() must not demand it up front."""
-    cfg = _minimal_cfg()  # remote target, push_to_hub default True, no explicit repo_id
-    assert cfg.policy.repo_id is None
-    cfg.validate()  # must not raise
-
-
-def test_validate_requires_repo_id_for_local_push():
-    """Local runs that push to the Hub still need an explicit repo_id."""
-    cfg = draccus.parse(
-        TrainPipelineConfig,
-        args=["--dataset.repo_id", "u/d", "--policy.type", "act"],
-    )
-    with pytest.raises(ValueError, match="repo_id"):
-        cfg.validate()
-
-
-def test_build_remote_config_applies_overrides(tmp_path):
-    cfg = _minimal_cfg()
-    dest = tmp_path / "train_config.json"
-    out = build_remote_config_file(cfg, "u/run", dest)
-    assert out == dest
-    data = json.loads(dest.read_text())
-    # `job` is client-only orchestration and must be stripped for the pod.
-    assert "job" not in data
-    # save_checkpoint_to_hub defaults off → omitted so older images accept the config.
-    assert "save_checkpoint_to_hub" not in data
-    assert data["policy"]["push_to_hub"] is True
-    assert data["policy"]["repo_id"] == "u/run"
-    assert data["policy"]["device"] is None  # pod auto-detects its GPU
-    assert data["dataset"]["root"] is None  # pod resolves the dataset by repo_id
-    # the caller's cfg must be left untouched (function works on a deep copy)
-    assert cfg.job.target == "a10g-small"
-    assert cfg.save_checkpoint_to_hub is False
-
-
-def test_build_remote_config_includes_checkpoint_flag_when_enabled(tmp_path):
-    cfg = draccus.parse(
-        TrainPipelineConfig,
-        args=[
-            "--dataset.repo_id",
-            "u/d",
-            "--policy.type",
-            "act",
-            "--job.target",
-            "a10g-small",
-            "--save_checkpoint_to_hub",
-            "true",
-        ],
-    )
-    dest = tmp_path / "train_config.json"
-    build_remote_config_file(cfg, "u/run", dest)
-    data = json.loads(dest.read_text())
-    # explicitly enabled → kept in the config (requires a matching trainer image).
-    assert data["save_checkpoint_to_hub"] is True
-    assert "job" not in data
-
-
-def test_build_remote_config_merges_tags_into_policy(tmp_path):
-    cfg = _minimal_cfg()
-    dest = tmp_path / "train_config.json"
-    build_remote_config_file(cfg, "u/run", dest, tags=["lerobot", "lelab"])
-    data = json.loads(dest.read_text())
-    # tags propagate to the model the pod pushes.
-    assert data["policy"]["tags"] == ["lerobot", "lelab"]
-
-
-def test_build_remote_config_merges_tags_without_duplicating(tmp_path):
-    cfg = _minimal_cfg()
-    cfg.policy.tags = ["existing", "lerobot"]
-    dest = tmp_path / "train_config.json"
-    build_remote_config_file(cfg, "u/run", dest, tags=["lerobot", "lelab"])
-    data = json.loads(dest.read_text())
-    # pre-existing policy tags are kept; only genuinely-new tags are appended (no dup "lerobot").
-    assert data["policy"]["tags"] == ["existing", "lerobot", "lelab"]
-
-
-def test_submit_requires_login(monkeypatch):
-    monkeypatch.setattr("lerobot.jobs.hf.get_token", lambda: None)
-    cfg = draccus.parse(
-        TrainPipelineConfig,
-        args=["--dataset.repo_id", "u/d", "--policy.type", "act", "--job.target", "a10g-small"],
-    )
-    with pytest.raises(RuntimeError, match="hf auth login"):
-        submit_to_hf(cfg)
-
-
-def test_submit_passes_validation_and_submits(monkeypatch):
-    """A type-based policy with no explicit repo_id is auto-assigned one and submitted."""
-    from unittest.mock import MagicMock
-
-    # Patch get_token
-    monkeypatch.setattr("lerobot.jobs.hf.get_token", lambda: "tok")
-
-    # Patch HfApi so whoami returns alice
-    class FakeHfApi:
-        def __init__(self, token=None):
-            pass
-
-        def whoami(self, token=None):
-            return {"name": "alice"}
-
-    monkeypatch.setattr("lerobot.jobs.hf.HfApi", FakeHfApi)
-
-    # ensure_dataset_available returns None; patch it out so no Hub access happens
-    # (hf.py imports it at module level, so patch it on lerobot.jobs.hf).
-    monkeypatch.setattr("lerobot.jobs.hf.ensure_dataset_available", lambda *a, **kw: None)
-
-    # Patch _stage_config_on_hub to skip network
-    monkeypatch.setattr(
-        "lerobot.jobs.hf._stage_config_on_hub",
-        lambda cfg, repo_id, token, tags=None: repo_id,
-    )
-
-    # Patch run_job to return a fake job
-    fake_job = MagicMock()
-    fake_job.id = "job-123"
-    run_job_calls = []
-
-    def fake_run_job(**kwargs):
-        run_job_calls.append(kwargs)
-        return fake_job
-
-    monkeypatch.setattr("lerobot.jobs.hf.run_job", fake_run_job)
-
-    cfg = draccus.parse(
-        TrainPipelineConfig,
-        args=[
-            "--dataset.repo_id",
-            "u/d",
-            "--policy.type",
-            "act",
-            "--job.target",
-            "a10g-small",
-            "--job.detach",
-            "true",
-        ],
-    )
-
-    # Must NOT raise (pre-fix this raised ValueError about missing repo_id)
-    submit_to_hf(cfg)
-
-    assert len(run_job_calls) == 1, "run_job should have been called exactly once"
-    assert cfg.policy.repo_id is not None
-    assert cfg.policy.repo_id.startswith("alice/")
-    call = run_job_calls[0]
-    # The pod runs `lerobot-train --config_path=<staged repo>` on the requested flavor/image.
-    assert call["command"][0] == "lerobot-train"
-    assert call["command"][1].startswith("--config_path=")
-    assert call["flavor"] == "a10g-small"
-    assert call["image"] == "huggingface/lerobot-gpu:latest"
-    # The Hub token is forwarded so the pod can pull the (possibly private) dataset.
-    assert call["secrets"]["HF_TOKEN"] == "tok"
-    # Every job carries the lerobot tag as a queryable label.
-    assert call["labels"].get("lerobot") == "true"
-
-
-def test_submit_rejects_reward_model_training(monkeypatch):
-    """Remote training only supports policies; reward-model runs fail fast with a clear error."""
-    monkeypatch.setattr("lerobot.jobs.hf.get_token", lambda: "tok")
-
-    class FakeHfApi:
-        def __init__(self, token=None):
-            pass
-
-        def whoami(self, token=None):
-            return {"name": "alice"}
-
-    monkeypatch.setattr("lerobot.jobs.hf.HfApi", FakeHfApi)
-
-    cfg = _minimal_cfg()
-    cfg.reward_model = SimpleNamespace(type="reward")  # marks this as reward-model training
-    monkeypatch.setattr(cfg, "validate", lambda: None)  # skip pretrained-path resolution
-
-    with pytest.raises(ValueError, match="reward model"):
-        submit_to_hf(cfg)
-
-
-@pytest.mark.timeout(15)
-def test_submit_returns_when_job_completes(monkeypatch):
-    """Non-detach path must RETURN (not hang) once the job reaches a terminal stage."""
-    from types import SimpleNamespace
-
-    monkeypatch.setattr("lerobot.jobs.hf.get_token", lambda: "tok")
-
-    class FakeHfApi:
-        def __init__(self, token=None):
-            pass
-
-        def whoami(self, token=None):
-            return {"name": "alice"}
-
-    monkeypatch.setattr("lerobot.jobs.hf.HfApi", FakeHfApi)
-    monkeypatch.setattr("lerobot.jobs.hf.ensure_dataset_available", lambda *a, **kw: None)
-    monkeypatch.setattr(
-        "lerobot.jobs.hf._stage_config_on_hub", lambda cfg, repo_id, token, tags=None: repo_id
-    )
-    monkeypatch.setattr("lerobot.jobs.hf.run_job", lambda **kw: SimpleNamespace(id="job-1", url="http://x"))
-    # Job is already COMPLETED on the first poll.
-    monkeypatch.setattr(
-        "lerobot.jobs.hf.inspect_job",
-        lambda job_id: SimpleNamespace(
-            status=SimpleNamespace(stage=SimpleNamespace(value="COMPLETED"), message=None)
-        ),
-    )
-    # Log stream ends immediately.
-    monkeypatch.setattr("lerobot.jobs.hf.fetch_job_logs", lambda job_id, follow=True: iter(()))
-
-    cfg = draccus.parse(
-        TrainPipelineConfig,
-        args=["--dataset.repo_id", "u/d", "--policy.type", "act", "--job.target", "a10g-small"],
-    )
-    # Runs in the pytest main thread (signal handler install requires it); the
-    # @timeout marker fails the test instead of hanging if it regresses.
-    submit_to_hf(cfg)
-
-
-@pytest.mark.timeout(15)
-def test_submit_returns_on_model_pushed_marker(monkeypatch):
-    """Finish when the model-pushed log appears, even if the job stage never flips."""
-    from types import SimpleNamespace
-
-    monkeypatch.setattr("lerobot.jobs.hf.get_token", lambda: "tok")
-
-    class FakeHfApi:
-        def __init__(self, token=None):
-            pass
-
-        def whoami(self, token=None):
-            return {"name": "alice"}
-
-    monkeypatch.setattr("lerobot.jobs.hf.HfApi", FakeHfApi)
-    monkeypatch.setattr("lerobot.jobs.hf.ensure_dataset_available", lambda *a, **kw: None)
-    monkeypatch.setattr(
-        "lerobot.jobs.hf._stage_config_on_hub", lambda cfg, repo_id, token, tags=None: repo_id
-    )
-    monkeypatch.setattr("lerobot.jobs.hf.run_job", lambda **kw: SimpleNamespace(id="job-1", url="http://x"))
-    # Job stays RUNNING forever — only the log marker can end the command.
-    monkeypatch.setattr(
-        "lerobot.jobs.hf.inspect_job",
-        lambda job_id: SimpleNamespace(
-            status=SimpleNamespace(stage=SimpleNamespace(value="RUNNING"), message=None)
-        ),
-    )
-    pushed_line = "INFO Model pushed to https://huggingface.co/alice/myrun"
-    monkeypatch.setattr("lerobot.jobs.hf.fetch_job_logs", lambda job_id, follow=True: iter([pushed_line]))
-
-    cfg = draccus.parse(
-        TrainPipelineConfig,
-        args=[
-            "--dataset.repo_id",
-            "u/d",
-            "--policy.type",
-            "act",
-            "--policy.repo_id",
-            "alice/myrun",
-            "--job.target",
-            "a10g-small",
-        ],
-    )
-    # Must return via the model-pushed marker despite the perpetual RUNNING stage.
-    submit_to_hf(cfg)
-
-
-def test_submit_raises_when_wandb_enabled_without_key(monkeypatch):
-    """wandb.enable with no key reachable anywhere fails fast, before submitting."""
-
-    monkeypatch.setattr("lerobot.jobs.hf.get_token", lambda: "tok")
-
-    class FakeHfApi:
-        def __init__(self, token=None):
-            pass
-
-        def whoami(self, token=None):
-            return {"name": "alice"}
-
-    monkeypatch.setattr("lerobot.jobs.hf.HfApi", FakeHfApi)
-    monkeypatch.setattr("lerobot.jobs.hf.resolve_wandb_api_key", lambda: None)
-
-    cfg = draccus.parse(
-        TrainPipelineConfig,
-        args=[
-            "--dataset.repo_id",
-            "u/d",
-            "--policy.type",
-            "act",
-            "--job.target",
-            "a10g-small",
-            "--wandb.enable",
-            "true",
-        ],
-    )
-    with pytest.raises(ValueError, match="WANDB_API_KEY"):
-        submit_to_hf(cfg)
-
-
-@pytest.mark.timeout(15)
-def test_submit_raises_when_job_ends_in_error(monkeypatch):
-    """A terminal non-COMPLETED stage with no model-pushed marker must raise with the status."""
-    from types import SimpleNamespace
-
-    monkeypatch.setattr("lerobot.jobs.hf.get_token", lambda: "tok")
-
-    class FakeHfApi:
-        def __init__(self, token=None):
-            pass
-
-        def whoami(self, token=None):
-            return {"name": "alice"}
-
-    monkeypatch.setattr("lerobot.jobs.hf.HfApi", FakeHfApi)
-    monkeypatch.setattr("lerobot.jobs.hf.ensure_dataset_available", lambda *a, **kw: None)
-    monkeypatch.setattr(
-        "lerobot.jobs.hf._stage_config_on_hub", lambda cfg, repo_id, token, tags=None: repo_id
-    )
-    monkeypatch.setattr("lerobot.jobs.hf.run_job", lambda **kw: SimpleNamespace(id="job-1", url="http://x"))
-    # Job fails: a terminal ERROR stage carrying the platform's status message.
-    monkeypatch.setattr(
-        "lerobot.jobs.hf.inspect_job",
-        lambda job_id: SimpleNamespace(
-            status=SimpleNamespace(stage=SimpleNamespace(value="ERROR"), message="Job timeout")
-        ),
-    )
-    # Logs end without the model-pushed marker.
-    monkeypatch.setattr("lerobot.jobs.hf.fetch_job_logs", lambda job_id, follow=True: iter(()))
-
-    cfg = draccus.parse(
-        TrainPipelineConfig,
-        args=["--dataset.repo_id", "u/d", "--policy.type", "act", "--job.target", "a10g-small"],
-    )
-    with pytest.raises(RuntimeError, match=r"stage=ERROR \(Job timeout\)"):
-        submit_to_hf(cfg)

tests/jobs/test_job_config.py

@@ -1,64 +0,0 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import draccus
-import pytest
-
-from lerobot.configs import JobConfig
-from lerobot.configs.train import TrainPipelineConfig
-
-
-def test_jobconfig_defaults_are_local():
-    cfg = JobConfig()
-    assert cfg.target is None
-    assert cfg.is_remote is False
-    assert cfg.image == "huggingface/lerobot-gpu:latest"
-    assert cfg.timeout == "2d"
-    assert cfg.detach is False
-
-
-def test_jobconfig_local_string_is_not_remote():
-    assert JobConfig(target="local").is_remote is False
-
-
-def test_jobconfig_flavor_is_remote():
-    assert JobConfig(target="a10g-small").is_remote is True
-
-
-def test_train_config_parses_job_target():
-    parsed = draccus.parse(
-        TrainPipelineConfig,
-        args=["--dataset.repo_id", "u/d", "--policy.type", "act", "--job.target", "a10g-small"],
-    )
-    assert parsed.job.target == "a10g-small"
-    assert parsed.job.is_remote is True
-    assert parsed.save_checkpoint_to_hub is False
-
-
-def test_save_checkpoint_to_hub_requires_repo_id():
-    cfg = draccus.parse(
-        TrainPipelineConfig,
-        args=[
-            "--dataset.repo_id",
-            "u/d",
-            "--policy.type",
-            "act",
-            "--policy.push_to_hub",
-            "false",
-            "--save_checkpoint_to_hub",
-            "true",
-        ],
-    )
-    with pytest.raises(ValueError, match="requires --policy.repo_id"):
-        cfg.validate()

tests/policies/fastwam/test_fastwam_policy.py

@@ -1,391 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import json
-
-import pytest
-import torch
-from safetensors import safe_open
-from torch import nn
-
-pytest.importorskip("transformers", reason="fastwam requires the `fastwam` extra (transformers)")
-pytest.importorskip("diffusers", reason="fastwam requires the `fastwam` extra (diffusers)")
-
-from lerobot.configs import FeatureType, PolicyFeature, PreTrainedConfig
-from lerobot.policies import FastWAMConfig, get_policy_class, make_policy_config, make_pre_post_processors
-from lerobot.policies.fastwam.modeling_fastwam import FastWAMPolicy
-from lerobot.policies.fastwam.processor_fastwam import FastWAMActionToggleProcessorStep
-from lerobot.utils.constants import ACTION, OBS_STATE
-
-
-class FakeFastWAMCore(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.dit = nn.Linear(2, 2)
-
-    def training_loss(self, sample):
-        assert sample["video"].ndim == 5
-        assert sample["context"].ndim == 3
-        return sample[ACTION].sum() * 0.0 + torch.tensor(1.0), {"loss_action": 1.0}
-
-    def infer_action(self, **kwargs):
-        return {"action": torch.ones(1, kwargs["action_horizon"], 3)}
-
-
-def test_fastwam_is_registered_and_publicly_exported():
-    cfg = make_policy_config(
-        "fastwam",
-        action_dim=3,
-        proprio_dim=2,
-        action_horizon=4,
-        n_action_steps=2,
-        num_video_frames=5,
-        action_video_freq_ratio=1,
-        base_model_id=None,
-    )
-
-    assert isinstance(cfg, FastWAMConfig)
-    assert cfg.type == "fastwam"
-    assert get_policy_class("fastwam") is FastWAMPolicy
-
-
-def test_config_validates_features_model_ids_and_saved_auto_route(tmp_path):
-    cfg = FastWAMConfig()
-    cfg.save_pretrained(tmp_path)
-    saved = json.loads((tmp_path / "config.json").read_text())
-
-    assert saved["pretrained_path"] is None
-    assert cfg.image_features["observation.images.image"].type == FeatureType.VISUAL
-    assert cfg.action_feature.shape == (7,)
-    assert cfg.robot_state_feature.shape == (8,)
-    with pytest.raises(ValueError, match="image feature"):
-        FastWAMConfig(input_features={OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(8,))})
-    assert FastWAMConfig(tokenizer_model_id="somebody/other-tokenizer").tokenizer_model_id == (
-        "somebody/other-tokenizer"
-    )
-
-
-def test_preprocessor_passes_images_through_and_postprocessor_toggles_actions(tmp_path):
-    cfg = FastWAMConfig(
-        action_dim=3,
-        proprio_dim=2,
-        action_horizon=4,
-        n_action_steps=2,
-        num_video_frames=5,
-        action_video_freq_ratio=1,
-        image_size=(2, 2),
-        device="cpu",
-        toggle_action_dimensions=[-1],
-        input_features={
-            "observation.images.image": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 2, 2)),
-            OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(2,)),
-        },
-        output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(3,))},
-        base_model_id=None,
-    )
-    dataset_stats = {
-        "observation.images.image": {
-            "mean": torch.full((3, 1, 1), 0.2),
-            "std": torch.full((3, 1, 1), 0.1),
-        },
-        OBS_STATE: {
-            "mean": torch.tensor([1.0, 3.0]),
-            "std": torch.tensor([2.0, 4.0]),
-        },
-        ACTION: {
-            "mean": torch.zeros(3),
-            "std": torch.ones(3),
-        },
-    }
-
-    preprocessor, postprocessor = make_pre_post_processors(cfg, dataset_stats=dataset_stats)
-    processed = preprocessor(
-        {
-            "observation.images.image": torch.tensor(
-                [
-                    [[0.0, 0.5], [1.0, 0.5]],
-                    [[0.0, 0.5], [1.0, 0.5]],
-                    [[0.0, 0.5], [1.0, 0.5]],
-                ]
-            ),
-            OBS_STATE: torch.tensor([3.0, 7.0]),
-        }
-    )
-    preprocessor.save_pretrained(tmp_path, config_filename="policy_preprocessor.json")
-    postprocessor.save_pretrained(tmp_path, config_filename="policy_postprocessor.json")
-    _, loaded_postprocessor = make_pre_post_processors(cfg, pretrained_path=str(tmp_path))
-
-    # VISUAL normalization is IDENTITY
-    expected_image = torch.tensor(
-        [[[[0.0, 0.5], [1.0, 0.5]], [[0.0, 0.5], [1.0, 0.5]], [[0.0, 0.5], [1.0, 0.5]]]]
-    )
-    assert preprocessor.name == "policy_preprocessor"
-    assert postprocessor.name == "policy_postprocessor"
-    assert torch.allclose(processed["observation.images.image"], expected_image)
-    assert torch.allclose(processed[OBS_STATE], torch.tensor([[1.0, 1.0]]))
-    assert torch.equal(dataset_stats["observation.images.image"]["mean"], torch.full((3, 1, 1), 0.2))
-    assert any(isinstance(step, FastWAMActionToggleProcessorStep) for step in loaded_postprocessor.steps)
-    assert torch.equal(
-        loaded_postprocessor(torch.tensor([[0.25, 0.5, 1.0]])), torch.tensor([[0.25, 0.5, -1.0]])
-    )
-
-
-def test_policy_forward_and_predict_action_adapt_lerobot_batches(monkeypatch):
-    captured = []
-
-    class CapturingCore(FakeFastWAMCore):
-        def infer_action(self, **kwargs):
-            captured.append(
-                {
-                    "image_shape": tuple(kwargs["input_image"].shape),
-                    "proprio_shape": tuple(kwargs["proprio"].shape),
-                    "prompt": kwargs["prompt"],
-                }
-            )
-            return {"action": torch.full((1, kwargs["action_horizon"], 3), float(len(captured)))}
-
-    monkeypatch.setattr(FastWAMPolicy, "_build_core_model", lambda self, config: CapturingCore())
-    cfg = FastWAMConfig(
-        action_dim=3,
-        proprio_dim=2,
-        action_horizon=4,
-        n_action_steps=2,
-        num_video_frames=5,
-        action_video_freq_ratio=1,
-        image_size=(16, 16),
-        input_features={
-            "observation.images.image": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 16, 16)),
-            OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(2,)),
-        },
-        output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(3,))},
-        base_model_id=None,
-    )
-    policy = FastWAMPolicy(cfg)
-
-    loss, metrics = policy.forward(
-        {
-            "observation.images.image": torch.zeros(1, 3, 16, 16),
-            OBS_STATE: torch.zeros(1, 2),
-            ACTION: torch.zeros(1, 4, 3),
-            "context": torch.zeros(1, 5, 4096),
-            "context_mask": torch.ones(1, 5, dtype=torch.bool),
-        }
-    )
-    action = policy.predict_action_chunk(
-        {
-            "observation.images.image": torch.stack(
-                [
-                    torch.zeros(3, 16, 16),
-                    torch.ones(3, 16, 16),
-                ]
-            ),
-            OBS_STATE: torch.tensor([[0.0, 1.0], [2.0, 3.0]]),
-            "task": ["task 0", "task 1"],
-        }
-    )
-
-    assert loss.item() == 1.0
-    assert metrics["loss_action"] == 1.0
-    assert action.shape == (2, 4, 3)
-    assert action[:, 0, 0].tolist() == [1.0, 2.0]
-    assert [item["image_shape"] for item in captured] == [(1, 3, 16, 16), (1, 3, 16, 16)]
-    assert [item["proprio_shape"] for item in captured] == [(1, 2), (1, 2)]
-    assert [item["prompt"] for item in captured] == [
-        cfg.prompt_template.format(task="task 0"),
-        cfg.prompt_template.format(task="task 1"),
-    ]
-
-
-class CoreWithFrozenComponents(FakeFastWAMCore):
-    """Fake core mirroring the real one: frozen VAE / text encoder held as
-    *unregistered* attributes (via `object.__setattr__`) so they are excluded from
-    `state_dict()` and the saved checkpoint, but still moved by the `_apply` override."""
-
-    def __init__(self):
-        super().__init__()
-        object.__setattr__(self, "vae", nn.Linear(2, 2))
-        object.__setattr__(self, "text_encoder", nn.Linear(2, 2))
-        self.vae.requires_grad_(False)
-        self.text_encoder.requires_grad_(False)
-
-    def _apply(self, fn, *args, **kwargs):
-        super()._apply(fn, *args, **kwargs)
-        self.vae._apply(fn)
-        self.text_encoder._apply(fn)
-        return self
-
-
-def test_from_pretrained_uses_base_loader_and_skips_wan_backbone(monkeypatch, tmp_path):
-    cfg = FastWAMConfig(
-        action_dim=3,
-        proprio_dim=2,
-        action_horizon=4,
-        n_action_steps=2,
-        num_video_frames=5,
-        action_video_freq_ratio=1,
-        base_model_id=None,
-    )
-
-    def build_core(self, config):
-        core = CoreWithFrozenComponents()
-        with torch.no_grad():
-            core.dit.weight.fill_(0.5)
-        return core
-
-    monkeypatch.setattr(FastWAMPolicy, "_build_core_model", build_core)
-
-    reference = FastWAMPolicy(cfg)
-    with torch.no_grad():
-        reference.model.dit.weight.fill_(1.25)  # a distinctive, trained-looking weight
-    reference.save_pretrained(tmp_path)
-
-    # Building from Wan2.2 must never happen on a checkpoint load.
-    def fail_if_wan_pretrained_is_loaded(*args, **kwargs):
-        raise AssertionError("from_pretrained must not initialize or download the Wan2.2 backbone")
-
-    monkeypatch.setattr(
-        "lerobot.policies.fastwam.wan.modular.FastWAM.from_wan22_pretrained",
-        fail_if_wan_pretrained_is_loaded,
-    )
-
-    policy = FastWAMPolicy.from_pretrained(tmp_path)
-
-    assert isinstance(policy.model, CoreWithFrozenComponents)
-    # The bundled checkpoint weights overwrote the freshly built (0.5) DiT weights.
-    assert torch.allclose(policy.model.dit.weight, torch.full_like(policy.model.dit.weight, 1.25))
-
-
-def test_save_pretrained_excludes_frozen_components(monkeypatch, tmp_path):
-    cfg = FastWAMConfig(
-        action_dim=3,
-        proprio_dim=2,
-        action_horizon=4,
-        n_action_steps=2,
-        num_video_frames=5,
-        action_video_freq_ratio=1,
-        base_model_id=None,
-    )
-    monkeypatch.setattr(FastWAMPolicy, "_build_core_model", lambda self, config: CoreWithFrozenComponents())
-    policy = FastWAMPolicy(cfg)
-
-    save_dir = tmp_path / "saved"
-    policy.save_pretrained(save_dir)
-
-    assert (save_dir / "model.safetensors").is_file()
-    # No Wan sidecar files either: the frozen backbone comes from the diffusers repo.
-    assert not (save_dir / "Wan2.2_VAE.safetensors").exists()
-    assert not (save_dir / "google").exists()
-
-    with safe_open(save_dir / "model.safetensors", framework="pt") as f:
-        keys = set(f.keys())
-    # Lean checkpoint: only the trainable DiT is saved; the frozen VAE / UMT5 text
-    # encoder are excluded (loaded from the diffusers/transformers repos at init).
-    assert any(key.startswith("model.dit.") for key in keys)
-    assert not any(key.startswith("model.vae.") for key in keys)
-    assert not any(key.startswith("model.text_encoder.") for key in keys)
-
-
-def test_frozen_components_excluded_from_params_but_follow_device_moves(monkeypatch):
-    cfg = FastWAMConfig(
-        action_dim=3,
-        proprio_dim=2,
-        action_horizon=4,
-        n_action_steps=2,
-        num_video_frames=5,
-        action_video_freq_ratio=1,
-        base_model_id=None,
-    )
-    monkeypatch.setattr(FastWAMPolicy, "_build_core_model", lambda self, config: CoreWithFrozenComponents())
-    policy = FastWAMPolicy(cfg)
-
-    # Unregistered: excluded from state_dict and from the optimizer's parameter set.
-    sd = policy.state_dict()
-    assert not any(k.startswith("model.vae.") or k.startswith("model.text_encoder.") for k in sd)
-    param_names = [n for n, _ in policy.named_parameters()]
-    assert not any("vae" in n or "text_encoder" in n for n in param_names)
-
-    # ...but the `_apply` override still carries them through `.to()` (dtype stands in
-    # for device on a CPU box), so they never strand off the rest of the model.
-    policy.to(torch.float64)
-    assert policy.model.dit.weight.dtype == torch.float64  # registered
-    assert policy.model.vae.weight.dtype == torch.float64  # unregistered, moved via _apply
-    assert policy.model.text_encoder.weight.dtype == torch.float64
-
-
-def test_pretrained_config_round_trips_fastwam_features(tmp_path):
-    cfg = FastWAMConfig(action_dim=7, proprio_dim=8, image_size=(224, 448), base_model_id=None)
-    cfg.save_pretrained(tmp_path)
-
-    loaded = PreTrainedConfig.from_pretrained(tmp_path)
-
-    assert loaded.type == "fastwam"
-    assert loaded.image_features["observation.images.image"].type == FeatureType.VISUAL
-    assert loaded.action_feature.shape == (7,)
-    assert loaded.robot_state_feature.shape == (8,)
-
-
-def test_vae_adapter_empty_build_encode_decode_shapes():
-    """Offline glue check of the diffusers-backed VAE adapter (random weights).
-
-    Validates the encode/decode contract — 48 latent channels, 16x spatial / 4x
-    temporal compression, list-or-batch input, scaling round-trip — without any
-    weight download. (Numerical fidelity vs the original Wan VAE is a separate,
-    GPU + real-weights verification step.)
-    """
-    pytest.importorskip("diffusers")
-    from diffusers import AutoencoderKLWan
-
-    from lerobot.policies.fastwam.wan import WanVideoVAE38
-
-    # Production always loads a real pretrained VAE from the diffusers repo; here we
-    # build the same architecture with random weights and dummy standardization stats
-    # to exercise the adapter's shape/scaling contract offline (fidelity is checked
-    # separately, with real weights, on GPU).
-    arch = {
-        "base_dim": 160,
-        "decoder_base_dim": 256,
-        "z_dim": 48,
-        "dim_mult": [1, 2, 4, 4],
-        "num_res_blocks": 2,
-        "attn_scales": [],
-        "temporal_downsample": [False, True, True],
-        "dropout": 0.0,
-        "is_residual": True,
-        "in_channels": 12,
-        "out_channels": 12,
-        "patch_size": 2,
-        "scale_factor_spatial": 16,
-        "scale_factor_temporal": 4,
-        "clip_output": False,
-        "latents_mean": [0.0] * 48,
-        "latents_std": [1.0] * 48,
-    }
-    raw = AutoencoderKLWan.from_config(arch)
-    vae = WanVideoVAE38(dtype=torch.float32, device="cpu", pretrained=raw)
-    assert vae.z_dim == 48
-    assert vae.upsampling_factor == 16
-    assert vae.temporal_downsample_factor == 4
-
-    video = torch.rand(1, 3, 5, 32, 32) * 2 - 1  # [B,C,T,H,W] in [-1,1]
-    latents = vae.encode(video)
-    assert latents.shape == (1, 48, 2, 2, 2)  # T'=(5-1)//4+1, H'=W'=32//16
-
-    decoded = vae.decode(latents)
-    assert decoded.shape[0] == 1 and decoded.shape[1] == 3 and decoded.shape[-2:] == (32, 32)
-    assert decoded.min() >= -1.0 and decoded.max() <= 1.0
-
-    # list input is accepted and equals the batched path
-    assert torch.equal(vae.encode([video[0]]), latents)

tests/policies/groot/artifacts/.gitignore

@@ -0,0 +1,2 @@
+# Local-only parity artifacts (regenerated via dump_original_n1_7.py); never committed.
+*.npz

tests/policies/groot/test_groot_lerobot.py

@@ -14,7 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""Test script for LeRobot's Groot policy forward and inference passes."""
+"""Test script for LeRobot's GR00T N1.7 policy forward and inference passes."""
 
 import gc
 import os
@@ -41,13 +41,20 @@ pytestmark = pytest.mark.skipif(
 )
 
 
-# Define constants for dummy data
+# Define constants for dummy data (GR00T N1.7 native conventions).
+# N1.7 internally uses a 40-step action chunk, 132-dim state/action, and 256px images
+# (see GrootConfig.__post_init__). Use a chunk-sized action horizon so the dummy batch
+# matches the model's native action space.
 DUMMY_STATE_DIM = 44
 DUMMY_ACTION_DIM = 44
-DUMMY_ACTION_HORIZON = 16
+DUMMY_ACTION_HORIZON = 40
 IMAGE_SIZE = 256
 DEVICE = auto_select_torch_device()
-MODEL_PATH = "aractingi/bimanual-handover-groot-10k"
+# GR00T N1.7 checkpoint (N1.5 is no longer supported). The N1.7-3B base model loads
+# via GrootPolicy.from_pretrained with root-level sharded safetensors.
+MODEL_PATH = "nvidia/GR00T-N1.7-3B"
+# Valid N1.7 embodiment tag carried by the checkpoint metadata.
+EMBODIMENT_TAG = "gr1_unified"
 
 
 def cleanup_memory():
@@ -88,13 +95,13 @@ def instantiate_lerobot_groot(
     PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
     PolicyProcessorPipeline[PolicyAction, PolicyAction],
 ]:
-    """Instantiate LeRobot Groot policy with preprocessor and postprocessor."""
+    """Instantiate LeRobot GR00T N1.7 policy with preprocessor and postprocessor."""
     if from_pretrained:
         policy = GrootPolicy.from_pretrained(
             pretrained_name_or_path=model_path,
             strict=False,
         )
-        policy.config.embodiment_tag = "gr1"
+        policy.config.embodiment_tag = EMBODIMENT_TAG
     else:
         config = GrootConfig(
             base_model_path=model_path,
@@ -102,7 +109,7 @@ def instantiate_lerobot_groot(
             chunk_size=DUMMY_ACTION_HORIZON,
             image_size=[IMAGE_SIZE, IMAGE_SIZE],
             device=DEVICE,
-            embodiment_tag="gr1",
+            embodiment_tag=EMBODIMENT_TAG,
         )
         policy = GrootPolicy(config)
 
@@ -148,8 +155,8 @@ def create_dummy_data(device=DEVICE):
 
 @require_cuda
 def test_lerobot_groot_inference():
-    """Test the inference pass (select_action) of LeRobot's Groot policy."""
-    print("Test: LeRobot Groot Inference Pass")
+    """Test the inference pass (select_action) of LeRobot's GR00T N1.7 policy."""
+    print("Test: LeRobot GR00T N1.7 Inference Pass")
 
     set_seed_all(42)
 
@@ -181,9 +188,9 @@ def test_lerobot_groot_inference():
 
 @require_cuda
 def test_lerobot_groot_forward_pass():
-    """Test the forward pass of LeRobot's Groot policy."""
+    """Test the forward pass of LeRobot's GR00T N1.7 policy."""
     print("\n" + "=" * 50)
-    print("Test: LeRobot Groot Forward Pass (Training Mode)")
+    print("Test: LeRobot GR00T N1.7 Forward Pass (Training Mode)")
 
     set_seed_all(42)
 

tests/policies/groot/test_groot_n1_7_oss_parity.py

@@ -0,0 +1,259 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import hashlib
+import os
+from pathlib import Path
+
+import numpy as np
+import pytest
+import torch
+
+from lerobot.policies.groot.action_head.cross_attention_dit import AlternateVLDiT
+from lerobot.policies.groot.groot_n1_7 import GR00TN17
+from lerobot.policies.groot.processor_groot import (
+    GrootN17ActionDecodeStep,
+    GrootN17PackInputsStep,
+    GrootN17VLMEncodeStep,
+    _transform_n1_7_image_for_vlm_albumentations,
+)
+from lerobot.types import TransitionKey
+from lerobot.utils.constants import OBS_STATE
+
+OSS_REFERENCE_COMMIT = "ab88b50c718f6528e1df9dcbaf75865d1b604760"
+
+
+def _fixture_path(filename: str) -> Path:
+    fixture_dir = os.environ.get("GROOT_N17_OSS_PARITY_FIXTURE_DIR")
+    if fixture_dir is None:
+        pytest.skip("Set GROOT_N17_OSS_PARITY_FIXTURE_DIR to run external OSS parity fixtures.")
+    path = Path(fixture_dir) / filename
+    if not path.is_file():
+        pytest.skip(f"External OSS parity fixture not found: {path}")
+    return path
+
+
+def test_groot_n1_7_eval_image_transform_matches_oss_reference():
+    """Match the native N1.7 eval transform for a non-square SO-101 frame."""
+
+    y, x = np.indices((480, 640), dtype=np.uint16)
+    image = np.stack(
+        ((x + 3 * y) % 256, (2 * x + y) % 256, (x + 5 * y) % 256),
+        axis=-1,
+    ).astype(np.uint8)
+    actual = _transform_n1_7_image_for_vlm_albumentations(
+        image,
+        image_crop_size=[230, 230],
+        image_target_size=[256, 256],
+        shortest_image_edge=256,
+        crop_fraction=0.95,
+    )
+
+    assert actual.shape == (256, 340, 3)
+    assert hashlib.sha256(actual.tobytes()).hexdigest() == (
+        "c17e47af68a812aa79db3bb7b64b549ddf10148ac1b204a9686095018561ae9e"
+    )
+
+
+def test_groot_n1_7_vlm_chat_content_order_matches_oss_reference():
+    """Native OSS places all image items before the language item."""
+
+    class RecordingProcessor:
+        def __init__(self):
+            self.content_types = None
+
+        def apply_chat_template(self, conversation, tokenize, add_generation_prompt):
+            assert tokenize is False
+            assert add_generation_prompt is False
+            self.content_types = [item["type"] for item in conversation[0]["content"]]
+            return "rendered"
+
+        def __call__(self, **kwargs):
+            return {}
+
+    processor = RecordingProcessor()
+    step = GrootN17VLMEncodeStep(
+        image_crop_size=[230, 230],
+        image_target_size=[256, 256],
+        shortest_image_edge=256,
+        crop_fraction=0.95,
+        use_albumentations=True,
+        device="cpu",
+    )
+    step._proc = processor
+    transition = {
+        TransitionKey.OBSERVATION: {
+            "video": np.zeros((1, 1, 2, 480, 640, 3), dtype=np.uint8),
+        },
+        TransitionKey.COMPLEMENTARY_DATA: {"language": ["pick up the vial"]},
+    }
+
+    step(transition)
+
+    assert processor.content_types == ["image", "image", "text"]
+
+
+def test_groot_n1_7_alternate_vl_dit_matches_oss_reference():
+    """Run the LeRobot DiT with native OSS weights and identical inputs."""
+
+    pytest.importorskip("diffusers")
+
+    fixture = torch.load(_fixture_path("alternate_vl_dit_small.pt"), map_location="cpu", weights_only=True)
+    model = AlternateVLDiT(
+        output_dim=8,
+        num_attention_heads=2,
+        attention_head_dim=4,
+        num_layers=4,
+        dropout=0.0,
+        final_dropout=False,
+        max_num_positional_embeddings=16,
+        compute_dtype=torch.float32,
+        interleave_self_attention=True,
+        cross_attention_dim=6,
+    ).eval()
+    model.load_state_dict(fixture["state_dict"], strict=True)
+
+    actual = model(
+        hidden_states=fixture["hidden_states"],
+        encoder_hidden_states=fixture["encoder_hidden_states"],
+        timestep=fixture["timestep"],
+        image_mask=fixture["image_mask"],
+        backbone_attention_mask=fixture["backbone_attention_mask"],
+    )
+
+    torch.testing.assert_close(actual, fixture["output"], atol=1e-6, rtol=1e-6)
+
+
+def _state_decode_reference():
+    fixture = np.load(_fixture_path("state_and_action_decode.npz"))
+    raw_stats = {
+        "state": {
+            "single_arm": {"q01": fixture["state_single_arm_q01"], "q99": fixture["state_single_arm_q99"]},
+            "gripper": {"q01": fixture["state_gripper_q01"], "q99": fixture["state_gripper_q99"]},
+        },
+        "action": {
+            "single_arm": {"q01": fixture["action_single_arm_q01"], "q99": fixture["action_single_arm_q99"]},
+            "gripper": {"q01": fixture["action_gripper_q01"], "q99": fixture["action_gripper_q99"]},
+        },
+        "relative_action": {
+            "single_arm": {
+                "min": fixture["relative_single_arm_min"],
+                "max": fixture["relative_single_arm_max"],
+            },
+        },
+    }
+    for modality_stats in raw_stats.values():
+        for entry in modality_stats.values():
+            for key, value in entry.items():
+                if isinstance(value, np.ndarray):
+                    entry[key] = value.tolist()
+    modality_config = {
+        "state": {"modality_keys": ["single_arm", "gripper"]},
+        "action": {
+            "delta_indices": list(range(16)),
+            "modality_keys": ["single_arm", "gripper"],
+            "action_configs": [
+                {"rep": "RELATIVE", "type": "NON_EEF", "format": "DEFAULT", "state_key": None},
+                {"rep": "ABSOLUTE", "type": "NON_EEF", "format": "DEFAULT", "state_key": None},
+            ],
+        },
+    }
+    state_min = np.concatenate((fixture["state_single_arm_q01"], fixture["state_gripper_q01"]))
+    state_max = np.concatenate((fixture["state_single_arm_q99"], fixture["state_gripper_q99"]))
+    pack_step = GrootN17PackInputsStep(
+        normalize_min_max=True,
+        stats={OBS_STATE: {"min": state_min, "max": state_max}},
+        raw_stats=raw_stats,
+        modality_config=modality_config,
+        use_percentiles=True,
+    )
+    raw_state = np.concatenate((fixture["state_single_arm"], fixture["state_gripper"]), axis=-1)
+    transition = {
+        TransitionKey.OBSERVATION: {OBS_STATE: torch.from_numpy(raw_state)},
+        TransitionKey.COMPLEMENTARY_DATA: {},
+    }
+    packed = pack_step(transition)
+    return fixture, raw_stats, modality_config, pack_step, packed
+
+
+def test_groot_n1_7_state_normalization_matches_oss_checkpoint_reference():
+    fixture, _raw_stats, _modality_config, _pack_step, packed = _state_decode_reference()
+    expected = np.concatenate(
+        (fixture["normalized_state_single_arm"], fixture["normalized_state_gripper"]), axis=-1
+    )
+
+    actual = packed[TransitionKey.OBSERVATION]["state"][:, 0, :6]
+
+    torch.testing.assert_close(actual, torch.from_numpy(expected), atol=1e-6, rtol=1e-6)
+
+
+def test_groot_n1_7_relative_action_decode_matches_oss_checkpoint_reference():
+    fixture, raw_stats, modality_config, pack_step, _packed = _state_decode_reference()
+    decode_step = GrootN17ActionDecodeStep(
+        env_action_dim=6,
+        raw_stats=raw_stats,
+        modality_config=modality_config,
+        use_percentiles=True,
+        use_relative_action=True,
+        pack_step=pack_step,
+    )
+    decoded = decode_step({TransitionKey.ACTION: torch.from_numpy(fixture["normalized_action"])})[
+        TransitionKey.ACTION
+    ]
+    expected = np.concatenate((fixture["decoded_single_arm"], fixture["decoded_gripper"]), axis=-1).astype(
+        np.float32
+    )
+
+    torch.testing.assert_close(decoded, torch.from_numpy(expected), atol=1e-5, rtol=1e-5)
+
+
+def test_groot_n1_7_qwen_backbone_matches_oss_checkpoint_reference():
+    """Compare the actual 3B checkpoint backbone when explicitly enabled."""
+
+    checkpoint = os.environ.get("GROOT_N17_PARITY_CHECKPOINT")
+    if checkpoint is None:
+        pytest.skip("Set GROOT_N17_PARITY_CHECKPOINT to run the 3B OSS Qwen parity test.")
+    if not torch.cuda.is_available():
+        pytest.skip("The 3B OSS Qwen parity test requires CUDA.")
+
+    pytest.importorskip("transformers")
+
+    from transformers.feature_extraction_utils import BatchFeature
+
+    fixture = torch.load(_fixture_path("qwen_backbone_so101.pt"), map_location="cpu", weights_only=True)
+    model = GR00TN17.from_pretrained(checkpoint).to(device="cuda", dtype=torch.bfloat16).eval()
+    backbone_input = BatchFeature(
+        data={
+            key.removeprefix("input."): value.to("cuda")
+            for key, value in fixture.items()
+            if key.startswith("input.")
+        }
+    )
+
+    with torch.inference_mode():
+        actual = model.backbone(backbone_input)
+
+    feature_error = (
+        actual.backbone_features.cpu().float() - fixture["output.backbone_features"].float()
+    ).abs()
+    # Native OSS and LeRobot use different Torch/Transformers/Flash-Attention releases.
+    # Require the measured BF16 accumulation envelope while rejecting structural drift.
+    assert feature_error.mean().item() <= 0.04
+    assert feature_error.max().item() <= 2.0
+    torch.testing.assert_close(
+        actual.backbone_attention_mask.cpu(), fixture["output.backbone_attention_mask"]
+    )
+    torch.testing.assert_close(actual.image_mask.cpu(), fixture["output.image_mask"])

tests/policies/groot/test_groot_vs_original.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python
 
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -14,431 +14,194 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""Test script to verify Groot policy integration with LeRobot vs the original implementation, only meant to be run locally!"""
+"""Parity test: original NVIDIA GR00T N1.7 vs the GR00T N1.7 integration in LeRobot.
+
+Verifies that the self-contained LeRobot reimplementation of the GR00T N1.7 action
+head + Qwen3-VL backbone produces the SAME raw model output (``action_pred``, the
+normalized flow-matching prediction before any action decoding) as NVIDIA's original
+``gr00t`` package, given byte-identical pre-processed inputs and the same
+flow-matching seed. The comparison is parametrized over every embodiment tag present
+in the checkpoint.
+
+To keep the comparison fair, the original outputs + the exact collated inputs are
+produced once per embodiment in the original ``gr00t`` env via the companion script
+``utils/dump_original_n1_7.py`` (in the ``utils`` package next to this file) and saved
+to per-tag ``.npz`` files.
+This test discovers those artifacts, replays the identical inputs through the LeRobot
+model, and compares.
+
+This test is LOCAL-only and skips on CI, when ``gr00t``-side prerequisites are not
+present, or when no artifact has been generated. By default it looks for artifacts in
+``<this dir>/artifacts/``; override with ``GROOT_N1_7_PARITY_DIR``. See the
+"Original-vs-LeRobot parity test" section of ``src/lerobot/policies/groot/README.md``
+for the full run procedure.
+"""
 
-import gc
 import os
-from copy import deepcopy
-from typing import Any
+from pathlib import Path
 
 import numpy as np
 import pytest
 import torch
 
-from lerobot.policies.groot.configuration_groot import GrootConfig
-from lerobot.policies.groot.modeling_groot import GrootPolicy
-from lerobot.policies.groot.processor_groot import make_groot_pre_post_processors
-from lerobot.processor import PolicyProcessorPipeline
-from lerobot.types import PolicyAction
-
-pytest.importorskip("gr00t")
-pytest.importorskip("transformers")
-
 pytestmark = pytest.mark.skipif(
     os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true",
-    reason="This test requires local Groot installation and is not meant for CI",
+    reason="Requires a local GR00T N1.7 checkpoint + pre-generated artifacts; not for CI.",
 )
 
+from lerobot.policies.groot.configuration_groot import GROOT_N1_7  # noqa: E402,F401
 
-from gr00t.data.dataset import ModalityConfig  # noqa: E402
-from gr00t.data.embodiment_tags import EmbodimentTag  # noqa: E402
-from gr00t.data.transform.base import ComposedModalityTransform  # noqa: E402
-from gr00t.model.policy import Gr00tPolicy  # noqa: E402
+SEED = 42
+DEVICE = os.environ.get("GROOT_PARITY_DEVICE", "cuda" if torch.cuda.is_available() else "cpu")
+ATOL = float(os.environ.get("GROOT_PARITY_ATOL", "1e-3"))
+RTOL = float(os.environ.get("GROOT_PARITY_RTOL", "1e-3"))
 
-# GR1 humanoid dimensions (from pretrained model metadata)
-# The actual GR1 robot has 44 dimensions for both state and action
-# GR00TTransform will pad state to 64 and truncate action to 32
-DUMMY_STATE_DIM = 44
-DUMMY_ACTION_DIM = 44
-DUMMY_ACTION_HORIZON = 16
-IMAGE_SIZE = 256
-DEVICE = "cpu"
-MODEL_PATH = "nvidia/GR00T-N1.5-3B"
-
-GR1_BODY_PARTS = {
-    "left_arm": 7,
-    "left_hand": 6,
-    "left_leg": 6,
-    "neck": 3,
-    "right_arm": 7,
-    "right_hand": 6,
-    "right_leg": 6,
-    "waist": 3,
-}
+# Artifact filenames are original_n1_7_<embodiment_tag>.npz
+_ARTIFACT_PREFIX = "original_n1_7_"
+_ARTIFACT_SUFFIX = ".npz"
 
 
-def cleanup_memory():
-    """Clean up GPU/MPS memory to prevent OOM errors between tests."""
-    print("\nCleaning up memory...")
-    gc.collect()
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-        torch.cuda.synchronize()
-    if torch.backends.mps.is_available():
-        torch.mps.empty_cache()
-    print("Memory cleanup complete.")
+def _artifact_dir() -> Path:
+    """Directory holding the per-embodiment .npz artifacts.
 
-
-def set_seed_all(seed: int):
-    """Set random seed for all RNG sources to ensure reproducibility."""
-    import random
-
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed(seed)
-        torch.cuda.manual_seed_all(seed)
-
-    # Set deterministic behavior
-    torch.backends.cudnn.deterministic = True
-    torch.backends.cudnn.benchmark = False
-    torch.use_deterministic_algorithms(True, warn_only=True)
-
-
-def instantiate_lerobot_groot(
-    from_pretrained: bool = False,
-    model_path: str = MODEL_PATH,
-) -> tuple[
-    GrootPolicy,
-    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
-    PolicyProcessorPipeline[PolicyAction, PolicyAction],
-]:
-    """Instantiate LeRobot Groot policy with preprocessor and postprocessor."""
-    if from_pretrained:
-        policy = GrootPolicy.from_pretrained(
-            pretrained_name_or_path=model_path,
-            strict=False,
-        )
-        policy.config.embodiment_tag = "gr1"
-    else:
-        config = GrootConfig(
-            base_model_path=model_path,
-            n_action_steps=DUMMY_ACTION_HORIZON,
-            chunk_size=DUMMY_ACTION_HORIZON,
-            image_size=[IMAGE_SIZE, IMAGE_SIZE],
-            device=DEVICE,
-            embodiment_tag="gr1",
-        )
-        policy = GrootPolicy(config)
-
-    policy.to(DEVICE)
-    policy.config.device = DEVICE
-
-    preprocessor, postprocessor = make_groot_pre_post_processors(
-        config=policy.config,
-        dataset_stats=None,  # Pass None for dataset_stats to disable normalization (original GR00T doesn't normalize)
-    )
-
-    return (policy, preprocessor, postprocessor)
-
-
-def instantiate_original_groot(
-    from_pretrained: bool = False,
-    model_path: str = MODEL_PATH,
-):
-    """Instantiate original Groot policy from NVIDIA's implementation."""
-    from gr00t.data.transform.concat import ConcatTransform
-    from gr00t.data.transform.state_action import StateActionToTensor
-    from gr00t.data.transform.video import VideoToNumpy, VideoToTensor
-    from gr00t.model.transforms import GR00TTransform
-
-    video_keys = ["video.ego_view"]
-    state_keys = [
-        "state"
-    ]  # Important: Use single concatenated "state" key (not split body parts) to match preprocessing
-    action_keys = [
-        "action.left_arm",
-        "action.right_arm",
-        "action.left_hand",
-        "action.right_hand",
-        "action.left_leg",
-        "action.right_leg",
-        "action.neck",
-        "action.waist",
-    ]
-    language_keys = ["annotation.human.action.task_description"]
-
-    modality_config = {
-        "video": ModalityConfig(
-            delta_indices=[0],  # Current frame only
-            modality_keys=video_keys,
-        ),
-        "state": ModalityConfig(
-            delta_indices=[0],
-            modality_keys=state_keys,
-        ),
-        "action": ModalityConfig(
-            delta_indices=list(range(DUMMY_ACTION_HORIZON)),
-            modality_keys=action_keys,
-        ),
-        "language": ModalityConfig(
-            delta_indices=[0],
-            modality_keys=language_keys,
-        ),
-    }
-
-    modality_transform = ComposedModalityTransform(
-        transforms=[
-            VideoToTensor(apply_to=video_keys),
-            VideoToNumpy(apply_to=video_keys),  # Convert to numpy (GR00TTransform expects numpy arrays)
-            # State is already a single concatenated key, so no StateActionToTensor needed
-            # Convert action from numpy to tensor
-            StateActionToTensor(apply_to=action_keys),
-            # Concatenate only video and actions (state is already single key)
-            ConcatTransform(
-                video_concat_order=video_keys,
-                state_concat_order=[],  # Empty:state is already single key
-                action_concat_order=action_keys,
-            ),
-            GR00TTransform(
-                max_state_dim=64,
-                max_action_dim=32,
-                state_horizon=1,
-                action_horizon=DUMMY_ACTION_HORIZON,
-                training=False,
-            ),
-        ]
-    )
-
-    policy = Gr00tPolicy(
-        model_path=model_path,
-        embodiment_tag=EmbodimentTag.GR1,
-        modality_config=modality_config,
-        modality_transform=modality_transform,
-        device=DEVICE,
-    )
-
-    return policy, modality_config, modality_transform
-
-
-def create_dummy_data(device=DEVICE):
-    """Create dummy data for testing both implementations."""
-    batch_size = 2
-    prompt = "Pick up the red cube and place it in the bin"
-    state = torch.randn(batch_size, DUMMY_STATE_DIM, dtype=torch.float32, device=device)
-
-    batch = {
-        "observation.state": state,
-        "action": torch.randn(
-            batch_size,
-            DUMMY_ACTION_HORIZON,
-            DUMMY_ACTION_DIM,
-            dtype=torch.float32,
-            device=device,  # Action ground truth (for training)
-        ),
-        "observation.images.ego_view": torch.rand(
-            batch_size,
-            3,
-            IMAGE_SIZE,
-            IMAGE_SIZE,
-            dtype=torch.float32,
-            device=device,  # Images in [0, 1] range as expected by LeRobot
-        ),
-        "task": [prompt for _ in range(batch_size)],
-    }
-
-    return batch
-
-
-def convert_lerobot_to_original_format(batch, modality_config):
-    """Convert LeRobot batch format to original Groot format.
-
-    The original Groot expects observations in this format:
-    {
-        "video.<camera_name>": np.ndarray (T, H, W, C) or (B, T, H, W, C)
-        "state.<state_component>": np.ndarray (T, D) or (B, T, D)
-        "action.<action_component>": np.ndarray (T, D) or (B, T, D)
-        "annotation.<annotation_type>": str or list[str]
-    }
+    Self-contained by default: a sibling ``artifacts/`` directory next to this test.
+    Override with ``GROOT_N1_7_PARITY_DIR`` (e.g. to point at a scratch location).
+    The directory is read-only here -- it is populated by ``utils/dump_original_n1_7.py``
+    run in the original gr00t environment; the test never creates it.
     """
-    # Original Groot expects (T, H, W, C) format for images
-    # LeRobot has (B, C, H, W) format, so we need to convert
-    observation = {}
-
-    for img_key in ["ego_view"]:
-        lerobot_key = f"observation.images.{img_key}"
-        if lerobot_key in batch:
-            img = batch[lerobot_key]
-            # Convert from (B, C, H, W) to (B, T=1, H, W, C)
-            img_np = img.permute(0, 2, 3, 1).unsqueeze(1).cpu().numpy()
-            # Convert [0, 1] to [0, 255] uint8 as expected by original
-            img_np = (img_np * 255).astype(np.uint8)
-            observation[f"video.{img_key}"] = img_np
-
-    # Important: The Original's GR00TTransform expects "state" as (B, T, D), not split body parts
-    if "observation.state" in batch:
-        state = batch["observation.state"]
-        state_np = state.unsqueeze(1).cpu().numpy()  # (B, 1, D)
-        observation["state"] = state_np
-
-    if "action" in batch:
-        action = batch["action"]
-        action_np = action.cpu().numpy()
-
-        start_idx = 0
-        for part_name, part_dim in GR1_BODY_PARTS.items():
-            end_idx = start_idx + part_dim
-            observation[f"action.{part_name}"] = action_np[:, :, start_idx:end_idx]
-            start_idx = end_idx
-
-    if "task" in batch:
-        task_list = batch["task"]
-        # GR00TTransform expects language with (B, T) shape for batched data
-        # Create a (B, T=1) array where each element is the string directly
-        bsz = len(task_list)
-        task_array = np.empty((bsz, 1), dtype=object)
-        for i in range(bsz):
-            task_array[i, 0] = task_list[i]  # Assign string directly to each (i, 0) position
-        observation["annotation.human.action.task_description"] = task_array
-
-    return observation
+    env = os.environ.get("GROOT_N1_7_PARITY_DIR")
+    if env:
+        return Path(env)
+    return Path(__file__).resolve().parent / "artifacts"
 
 
-def test_groot_original_vs_lerobot_pretrained():
-    """Test Groot original implementation vs LeRobot implementation with pretrained weights."""
-    print("Test: Groot Original vs LeRobot with Pretrained Weights (Inference)")
+def _discover_artifacts() -> list[tuple[str, Path]]:
+    """Return [(embodiment_tag, npz_path), ...] for every dumped artifact."""
+    d = _artifact_dir()
+    if not d.is_dir():
+        return []
+    out = []
+    for p in sorted(d.glob(f"{_ARTIFACT_PREFIX}*{_ARTIFACT_SUFFIX}")):
+        tag = p.name[len(_ARTIFACT_PREFIX) : -len(_ARTIFACT_SUFFIX)]
+        out.append((tag, p))
+    return out
 
-    set_seed_all(42)
 
-    lerobot_policy, lerobot_preprocessor, lerobot_postprocessor = instantiate_lerobot_groot(
-        from_pretrained=True
+def _resolve_checkpoint() -> str:
+    env = os.environ.get("GROOT_N1_7_LIBERO_CKPT")
+    if env:
+        if not Path(env).exists():
+            pytest.skip(f"GROOT_N1_7_LIBERO_CKPT={env} does not exist")
+        return env
+    try:
+        from huggingface_hub import snapshot_download
+
+        root = snapshot_download(
+            "nvidia/GR00T-N1.7-LIBERO",
+            local_files_only=True,
+            allow_patterns=["libero_10/*"],
+        )
+    except Exception as exc:  # noqa: BLE001
+        pytest.skip(f"GR00T N1.7 LIBERO checkpoint not available locally: {exc}")
+    ckpt = Path(root) / "libero_10"
+    if not (ckpt / "config.json").exists():
+        pytest.skip(f"GR00T N1.7 LIBERO checkpoint incomplete at {ckpt}")
+    return str(ckpt)
+
+
+def _load_artifact(path: Path):
+    data = np.load(path, allow_pickle=True)
+    original_action = torch.from_numpy(data["action_pred"]).float()
+    dtypes = dict(zip(data["meta_keys"].tolist(), data["meta_dtypes"].tolist(), strict=False))
+    inputs = {}
+    for key in data.files:
+        if not key.startswith("in::"):
+            continue
+        name = key[4:]
+        arr = data[key]
+        t = torch.from_numpy(np.asarray(arr))
+        declared = dtypes.get(key, "")
+        if "int" in declared or "long" in declared:
+            t = t.long()
+        inputs[name] = t
+    return original_action, inputs
+
+
+def _unflatten(inputs: dict[str, torch.Tensor]) -> dict:
+    """Rebuild the nested model-input dict from dot-prefixed flat keys."""
+    nested: dict = {}
+    for dotted, value in inputs.items():
+        parts = dotted.split(".")
+        cur = nested
+        for p in parts[:-1]:
+            cur = cur.setdefault(p, {})
+        cur[parts[-1]] = value
+    return nested.get("inputs", nested)
+
+
+@pytest.fixture(scope="module")
+def lerobot_model():
+    """Load the LeRobot GR00T N1.7 model once (fp32 + SDPA) and reuse across tags."""
+    ckpt = _resolve_checkpoint()
+    from lerobot.policies.groot.groot_n1_7 import GR00TN17
+
+    model = GR00TN17.from_pretrained(
+        ckpt,
+        tune_llm=False,
+        tune_visual=False,
+        tune_projector=False,
+        tune_diffusion_model=False,
+        tune_vlln=False,
+        transformers_loading_kwargs={"trust_remote_code": True},
     )
-    original_policy, modality_config, modality_transform = instantiate_original_groot(from_pretrained=True)
+    # fp32 + SDPA on both sides: bf16 + differing attention kernels otherwise introduce
+    # ~1e-2 numerical noise unrelated to the implementations.
+    model.compute_dtype = "float32"
+    model.config.compute_dtype = model.compute_dtype
+    model.to(device=DEVICE, dtype=torch.float32)
+    model.eval()
+    return model
 
-    batch = create_dummy_data()
-    batch_lerobot = deepcopy(batch)
 
-    print("\n[LeRobot] Running inference...")
-    lerobot_policy.eval()
-    batch_lerobot_processed = lerobot_preprocessor(batch_lerobot)
+_ARTIFACTS = _discover_artifacts()
 
-    # Important: Reset seed immediately before inference to ensure identical RNG state
-    torch.manual_seed(42)
 
-    with torch.no_grad():
-        lerobot_actions = lerobot_policy.select_action(batch_lerobot_processed)
+@pytest.mark.skipif(
+    not _ARTIFACTS,
+    reason=(
+        "No GR00T N1.7 parity artifacts found. Generate them first in the original gr00t "
+        "env:\n  .venv-original/bin/python tests/policies/groot/utils/dump_original_n1_7.py "
+        "--ckpt <ckpt> --out-dir tests/policies/groot/artifacts --device cuda"
+    ),
+)
+@pytest.mark.parametrize("embodiment_tag,artifact", _ARTIFACTS, ids=[t for t, _ in _ARTIFACTS])
+def test_groot_get_action_parity(embodiment_tag, artifact, lerobot_model):
+    """Raw model.get_action(action_pred) parity per embodiment: original vs LeRobot."""
+    original_action, flat_inputs = _load_artifact(artifact)
+    model_inputs = _unflatten(flat_inputs)
 
-    print("\n[Original] Running inference...")
-    original_policy.model.eval()
-    observation = convert_lerobot_to_original_format(batch, modality_config)
-    original_obs_transformed = modality_transform(deepcopy(observation))
+    # Align the flow-matching RNG exactly as the producer did (seed right before sampling).
+    torch.manual_seed(SEED)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(SEED)
+    with torch.inference_mode():
+        out = lerobot_model.get_action(model_inputs)
+    lerobot_action = out["action_pred"].float().cpu()
 
-    # Important: Reset seed immediately before inference to ensure identical RNG state
-    torch.manual_seed(42)
+    t = min(original_action.shape[1], lerobot_action.shape[1])
+    d = min(original_action.shape[2], lerobot_action.shape[2])
+    original_action = original_action[:, :t, :d]
+    lerobot_action = lerobot_action[:, :t, :d]
 
-    with torch.no_grad():
-        original_model_output = original_policy.model.get_action(original_obs_transformed)
-        original_actions_raw = original_model_output["action_pred"]  # [2, 16, 32]
-    # Take first timestep
-    original_actions = original_actions_raw[:, 0, :].to(lerobot_actions.device).to(lerobot_actions.dtype)
-
-    print("Action Comparison:")
-    diff = lerobot_actions - original_actions
-    abs_diff = torch.abs(diff)
-
-    for batch_idx in range(lerobot_actions.shape[0]):
-        print(f"\n{'=' * 60}")
-        print(f"Batch {batch_idx}")
-        print(f"{'=' * 60}")
-        print(f"{'Idx':<5} {'LeRobot':<14} {'Original':<14} {'Difference':<14}")
-        print("-" * 60)
-        for action_idx in range(lerobot_actions.shape[1]):
-            lr_val = lerobot_actions[batch_idx, action_idx].item()
-            orig_val = original_actions[batch_idx, action_idx].item()
-            diff_val = abs(lr_val - orig_val)
-            sign = "+" if (lr_val - orig_val) > 0 else "-"
-            print(f"{action_idx:<5} {lr_val:>13.6f} {orig_val:>13.6f} {sign}{diff_val:>12.6f}")
-
-    max_diff = abs_diff.max().item()
-    tolerance = 0.001
-    assert torch.allclose(lerobot_actions, original_actions, atol=tolerance), (
-        f"Actions differ by more than tolerance ({tolerance}): max diff = {max_diff:.6f}"
+    diff = torch.abs(lerobot_action - original_action)
+    max_diff = diff.max().item()
+    print(
+        f"\n[{embodiment_tag}] shapes lerobot={tuple(lerobot_action.shape)} "
+        f"original={tuple(original_action.shape)}  "
+        f"max|diff|={max_diff:.6e}  mean|diff|={diff.mean().item():.6e}"
     )
-    print(f"\nSuccess: Actions match within tolerance ({tolerance})!")
 
-    del lerobot_policy, lerobot_preprocessor, lerobot_postprocessor
-    del original_policy, modality_config, modality_transform
-    del batch, batch_lerobot, observation
-    cleanup_memory()
-
-
-def test_groot_forward_pass_comparison():
-    """Test forward pass comparison between LeRobot and Original Groot implementations."""
-    print("Test: Forward Pass Comparison (Training Mode)")
-
-    set_seed_all(42)
-
-    lerobot_policy, lerobot_preprocessor, lerobot_postprocessor = instantiate_lerobot_groot(
-        from_pretrained=True
+    assert torch.allclose(lerobot_action, original_action, atol=ATOL, rtol=RTOL), (
+        f"GR00T N1.7 raw action_pred differs for embodiment '{embodiment_tag}' beyond "
+        f"atol={ATOL}, rtol={RTOL}: max|diff|={max_diff:.6e}"
     )
-    original_policy, modality_config, modality_transform = instantiate_original_groot(from_pretrained=True)
-
-    batch = create_dummy_data()
-    lerobot_policy.eval()
-    original_policy.model.eval()
-
-    print("\n[LeRobot] Running forward pass...")
-    batch_lerobot = deepcopy(batch)
-    batch_lerobot_processed = lerobot_preprocessor(batch_lerobot)
-
-    set_seed_all(42)
-    with torch.no_grad():
-        lerobot_loss, lerobot_metrics = lerobot_policy.forward(batch_lerobot_processed)
-
-    print(f"  Loss: {lerobot_loss.item():.6f}")
-
-    print("\n[Original] Running forward pass...")
-    observation = convert_lerobot_to_original_format(batch, modality_config)
-    transformed_obs = modality_transform(observation)
-
-    if "action" not in transformed_obs:
-        action_for_forward = batch_lerobot_processed["action"]
-        action_mask_for_forward = batch_lerobot_processed["action_mask"]
-
-        # Match action horizon if needed
-        if action_for_forward.shape[1] != original_policy.model.action_horizon:
-            if action_for_forward.shape[1] < original_policy.model.action_horizon:
-                pad_size = original_policy.model.action_horizon - action_for_forward.shape[1]
-                last_action = action_for_forward[:, -1:, :]
-                padding = last_action.repeat(1, pad_size, 1)
-                action_for_forward = torch.cat([action_for_forward, padding], dim=1)
-
-                mask_padding = torch.zeros(
-                    action_mask_for_forward.shape[0],
-                    pad_size,
-                    action_mask_for_forward.shape[2],
-                    dtype=action_mask_for_forward.dtype,
-                    device=action_mask_for_forward.device,
-                )
-                action_mask_for_forward = torch.cat([action_mask_for_forward, mask_padding], dim=1)
-            else:
-                action_for_forward = action_for_forward[:, : original_policy.model.action_horizon, :]
-                action_mask_for_forward = action_mask_for_forward[
-                    :, : original_policy.model.action_horizon, :
-                ]
-
-        transformed_obs["action"] = action_for_forward
-        transformed_obs["action_mask"] = action_mask_for_forward
-
-    set_seed_all(42)
-    with torch.no_grad():
-        original_outputs = original_policy.model.forward(transformed_obs)
-
-    original_loss = original_outputs["loss"]
-    print(f"  Loss: {original_loss.item():.6f}")
-
-    loss_diff = abs(lerobot_loss.item() - original_loss.item())
-    loss_rel_diff = loss_diff / (abs(original_loss.item()) + 1e-8) * 100
-
-    print("\nLoss Values:")
-    print(f"  LeRobot: {lerobot_loss.item():.6f}")
-    print(f"  Original: {original_loss.item():.6f}")
-    print(f"  Absolute difference: {loss_diff:.6f}")
-    print(f"  Relative difference: {loss_rel_diff:.2f}%")
-
-    del lerobot_policy, lerobot_preprocessor, lerobot_postprocessor
-    del original_policy, modality_config, modality_transform
-    del batch, batch_lerobot, observation, transformed_obs
-    cleanup_memory()