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2 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 CarolinePascal
|
6d8ef7dc60 | ||
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CarolinePascal
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ca6d764107 |
@@ -22,10 +22,6 @@ outputs
|
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rl
|
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media
|
||||
|
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# Local virtualenvs (the image provides its own)
|
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.venv
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venv
|
||||
|
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|
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# Logging
|
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logs
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|
||||
@@ -105,7 +105,7 @@ lerobot-train \
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| -------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| **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) |
|
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| **Reinforcement Learning** | [HIL-SERL](./docs/source/hilserl.mdx), [TDMPC](./docs/source/policy_tdmpc_README.md) & QC-FQL (coming soon) |
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| **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) |
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| **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) |
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| **World Models** | [VLA-JEPA](./docs/source/vla_jepa.mdx) (more coming soon) |
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| **Reward Models** | [SARM](./docs/source/sarm.mdx), [TOPReward](./docs/source/topreward.mdx), [Robometer](./docs/source/robometer.mdx) |
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@@ -69,14 +69,10 @@
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title: VLA-JEPA
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- local: eo1
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title: EO-1
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- local: lingbot_va
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title: LingBot-VA
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- local: fastwam
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title: FastWAM
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- local: evo1
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title: EVO1
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- local: groot
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title: NVIDIA GR00T
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title: NVIDIA GR00T N1.5
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- local: xvla
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title: X-VLA
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- local: multi_task_dit
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|
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@@ -193,7 +193,7 @@ To learn more about training policies with LeRobot, please refer to the training
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|
||||
- [SmolVLA](./smolvla)
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- [Pi0.5](./pi05)
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- [GR00T N1.7](./groot)
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- [GR00T N1.5](./groot)
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|
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Sample IsaacLab Arena datasets are available on HuggingFace Hub for experimentation:
|
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|
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|
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@@ -1,191 +0,0 @@
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# EVO1
|
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|
||||
EVO1 is a Vision-Language-Action policy for robot control built around an InternVL3 backbone and a continuous flow-matching action head. This LeRobot integration exposes EVO1 as a standard policy type so it can be trained and evaluated with the usual LeRobot dataset, checkpoint, and processor APIs.
|
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|
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## Model Overview
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|
||||
The policy embeds one or more camera images and the language task prompt with InternVL3, pads robot state/action vectors to fixed maximum dimensions, and predicts future action chunks with a flow-matching action head. During inference, the policy samples an action chunk and returns `n_action_steps` actions from that chunk before sampling again.
|
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|
||||
### What the LeRobot Integration Covers
|
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|
||||
- Standard `policy.type=evo1` configuration through LeRobot
|
||||
- InternVL3 image/text embedding with optional FlashAttention fallback
|
||||
- Stage-based finetuning controls for action-head-only and VLM finetuning runs
|
||||
- Continuous flow-matching action prediction
|
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- Checkpoint save/load through LeRobot policy APIs
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- Training with `lerobot-train` and evaluation with standard policy inference APIs
|
||||
|
||||
The broader EVO1 project may include additional training scripts and dataset tooling. This page focuses on the LeRobot robot-control policy path.
|
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|
||||
## Installation Requirements
|
||||
|
||||
1. Install LeRobot by following the [Installation Guide](./installation).
|
||||
2. Install EVO1 dependencies:
|
||||
|
||||
```bash
|
||||
pip install -e ".[evo1]"
|
||||
```
|
||||
|
||||
For LIBERO evaluation, install the LIBERO extra as well:
|
||||
|
||||
```bash
|
||||
pip install -e ".[evo1,libero]"
|
||||
```
|
||||
|
||||
3. Install a `flash-attn` wheel only if it is compatible with your Python, PyTorch, CUDA, and GPU stack. EVO1 falls back to standard attention when `flash_attn` is not available.
|
||||
|
||||
EVO1 uses the native Hugging Face `transformers` InternVL implementation, so `policy.vlm_model_name` must point to a natively converted checkpoint such as `OpenGVLab/InternVL3-1B-hf` (note the `-hf` suffix). The first run may download the configured VLM checkpoint unless `policy.vlm_model_name` points to a local model directory.
|
||||
|
||||
## Data Requirements
|
||||
|
||||
EVO1 expects a LeRobot dataset with:
|
||||
|
||||
- One to `policy.max_views` visual observations, for example `observation.images.image`
|
||||
- `observation.state`
|
||||
- `action`
|
||||
- A language task instruction in the dataset `task` field, or another field configured with `policy.task_field`
|
||||
|
||||
State and action vectors are padded to `policy.max_state_dim` and `policy.max_action_dim`. Predictions are cropped back to the dataset action dimension before being returned.
|
||||
|
||||
## Usage
|
||||
|
||||
To use EVO1 in a LeRobot configuration, specify:
|
||||
|
||||
```python
|
||||
policy.type=evo1
|
||||
```
|
||||
|
||||
By default, a new EVO1 policy initializes its VLM from:
|
||||
|
||||
```python
|
||||
policy.vlm_model_name=OpenGVLab/InternVL3-1B-hf
|
||||
```
|
||||
|
||||
Once a LeRobot-format EVO1 checkpoint is available, load it with:
|
||||
|
||||
```python
|
||||
policy.path=your-org/your-evo1-checkpoint
|
||||
```
|
||||
|
||||
## Training
|
||||
|
||||
### Stage 1
|
||||
|
||||
Stage 1 freezes the VLM and trains the action head:
|
||||
|
||||
```bash
|
||||
lerobot-train \
|
||||
--dataset.repo_id=your_org/your_dataset \
|
||||
--policy.type=evo1 \
|
||||
--policy.training_stage=stage1 \
|
||||
--policy.vlm_model_name=OpenGVLab/InternVL3-1B-hf \
|
||||
--policy.device=cuda \
|
||||
--policy.chunk_size=50 \
|
||||
--policy.n_action_steps=50 \
|
||||
--policy.max_state_dim=24 \
|
||||
--policy.max_action_dim=24 \
|
||||
--policy.optimizer_lr=1e-5 \
|
||||
--batch_size=4 \
|
||||
--steps=5000 \
|
||||
--output_dir=./outputs/evo1_stage1
|
||||
```
|
||||
|
||||
### Stage 2
|
||||
|
||||
Stage 2 finetunes the VLM branches and action head. A common workflow starts from a Stage 1 checkpoint:
|
||||
|
||||
```bash
|
||||
lerobot-train \
|
||||
--dataset.repo_id=your_org/your_dataset \
|
||||
--policy.path=./outputs/evo1_stage1/checkpoints/005000/pretrained_model \
|
||||
--policy.training_stage=stage2 \
|
||||
--policy.vlm_model_name=OpenGVLab/InternVL3-1B-hf \
|
||||
--policy.device=cuda \
|
||||
--policy.chunk_size=50 \
|
||||
--policy.n_action_steps=50 \
|
||||
--policy.max_state_dim=24 \
|
||||
--policy.max_action_dim=24 \
|
||||
--policy.optimizer_lr=1e-5 \
|
||||
--batch_size=4 \
|
||||
--steps=80000 \
|
||||
--output_dir=./outputs/evo1_stage2
|
||||
```
|
||||
|
||||
By default, `policy.training_stage` reapplies the finetuning defaults for that stage. This is important when
|
||||
starting Stage 2 from a Stage 1 checkpoint, because the Stage 1 checkpoint config stores the VLM finetuning
|
||||
flags as disabled. These stage defaults take precedence over saved or manually supplied `policy.finetune_*`
|
||||
flags unless `policy.apply_training_stage_defaults=false`, so set that flag only when manually controlling
|
||||
every finetuning flag.
|
||||
|
||||
### Key Training Parameters
|
||||
|
||||
| Parameter | Default | Description |
|
||||
| --------------------------------------------- | --------------------------- | ----------------------------------------------------------------- |
|
||||
| `policy.vlm_model_name` | `OpenGVLab/InternVL3-1B-hf` | Natively converted InternVL3 checkpoint or local model directory |
|
||||
| `policy.training_stage` | `stage1` | `stage1` trains the action head; `stage2` finetunes VLM branches |
|
||||
| `policy.apply_training_stage_defaults` | `true` | Reapplies stage finetuning defaults after loading a checkpoint |
|
||||
| `policy.vlm_num_layers` | `14` | Number of InternVL3 language layers kept for the policy |
|
||||
| `policy.vlm_dtype` | `bfloat16` | Requested VLM dtype |
|
||||
| `policy.use_flash_attn` | `true` | Requests FlashAttention when installed; otherwise falls back |
|
||||
| `policy.enable_gradient_checkpointing` | `true` | Enables checkpointing on supported InternVL3 modules |
|
||||
| `policy.gradient_checkpointing_use_reentrant` | `false` | Reentrant setting passed to gradient checkpointing when supported |
|
||||
| `policy.chunk_size` | `50` | Number of future actions predicted per chunk |
|
||||
| `policy.n_action_steps` | `50` | Number of actions consumed from a sampled chunk |
|
||||
| `policy.max_state_dim` | `24` | State padding dimension |
|
||||
| `policy.max_action_dim` | `24` | Action padding dimension |
|
||||
| `policy.postprocess_action_dim` | `null` | Optional action dimension returned after EVO1 postprocessing |
|
||||
| `policy.binarize_gripper` | `false` | Binarizes the postprocessed gripper channel for LIBERO-style eval |
|
||||
| `policy.task_field` | `task` | Batch field used as the language prompt |
|
||||
|
||||
## Inference
|
||||
|
||||
Try it out with a trained EVO1 checkpoint:
|
||||
|
||||
```bash
|
||||
lerobot-rollout \
|
||||
--policy.path=your-org/your-evo1-checkpoint \
|
||||
--inference.type=rtc \ # optional
|
||||
...
|
||||
```
|
||||
|
||||
## Results
|
||||
|
||||
### LIBERO Evaluation
|
||||
|
||||
> [!NOTE]
|
||||
> Benchmark results for a `lerobot`-hosted LIBERO checkpoint trained with this implementation
|
||||
> will be added once training completes.
|
||||
|
||||
The official EVO1 LIBERO rollout protocol uses the raw LIBERO camera feature names
|
||||
(`observation.images.agentview_image` and `observation.images.robot0_eye_in_hand_image`), replans every
|
||||
14 actions, and binarizes the gripper command before stepping the simulator. The EVO1 policy postprocessor
|
||||
can crop the padded 24D action back to the 7D LIBERO action space and apply that gripper binarization. To
|
||||
evaluate a LIBERO checkpoint under the same one-episode-per-task setting, keep the raw camera names instead
|
||||
of the default `image`/`image2` mapping and set the LIBERO action postprocessing flags:
|
||||
|
||||
```bash
|
||||
lerobot-eval \
|
||||
--policy.path=your-org/your-evo1-libero-checkpoint \
|
||||
--policy.vlm_model_name=OpenGVLab/InternVL3-1B-hf \
|
||||
--policy.device=cuda \
|
||||
--policy.use_flash_attn=true \
|
||||
--policy.n_action_steps=14 \
|
||||
--policy.postprocess_action_dim=7 \
|
||||
--policy.binarize_gripper=true \
|
||||
--env.type=libero \
|
||||
--env.task=libero_object \
|
||||
--env.camera_name_mapping="{agentview_image: agentview_image, robot0_eye_in_hand_image: robot0_eye_in_hand_image}" \
|
||||
--env.observation_height=448 \
|
||||
--env.observation_width=448 \
|
||||
--eval.batch_size=1 \
|
||||
--eval.n_episodes=1
|
||||
```
|
||||
|
||||
## References
|
||||
|
||||
- [EVO1 repository](https://github.com/MINT-SJTU/Evo-1)
|
||||
- [InternVL3-1B-hf](https://huggingface.co/OpenGVLab/InternVL3-1B-hf)
|
||||
|
||||
## License
|
||||
|
||||
This LeRobot integration follows the Apache 2.0 License used by LeRobot. Check the upstream EVO1 and InternVL3 model pages for the licenses of released checkpoints and data.
|
||||
+68
-161
@@ -1,19 +1,16 @@
|
||||
# GR00T Policy
|
||||
# GR00T N1.5 Policy
|
||||
|
||||
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.
|
||||
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.
|
||||
|
||||
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)).
|
||||
This document outlines the specifics of its integration and usage within the LeRobot framework.
|
||||
|
||||
## Model Overview
|
||||
|
||||
GR00T N1.7 uses a Cosmos-Reason2/Qwen3-VL backbone and provides checkpoints for SimplerEnv, DROID, and LIBERO.
|
||||
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.
|
||||
|
||||
Developers and researchers can post-train GR00T with their own real or synthetic data to adapt it for specific humanoid robots or tasks.
|
||||
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.
|
||||
|
||||
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.
|
||||
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.
|
||||
|
||||
<img
|
||||
src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot-groot-paper1%20(1).png"
|
||||
@@ -31,24 +28,33 @@ This approach allows the model to be highly adaptable through post-training for
|
||||
|
||||
## Installation Requirements
|
||||
|
||||
GR00T is intended for NVIDIA GPU-accelerated systems. Install LeRobot with the GR00T extra:
|
||||
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:
|
||||
|
||||
```bash
|
||||
pip install "lerobot[groot]"
|
||||
# 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 "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')"
|
||||
```
|
||||
|
||||
For a source checkout:
|
||||
3. Install LeRobot by running:
|
||||
|
||||
```bash
|
||||
pip install -e ".[groot]"
|
||||
pip install lerobot[groot]
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To use GR00T N1.7:
|
||||
To use GR00T in your LeRobot configuration, specify the policy type as:
|
||||
|
||||
```bash
|
||||
--policy.type=groot
|
||||
```python
|
||||
policy.type=groot
|
||||
```
|
||||
|
||||
## Training
|
||||
@@ -57,171 +63,72 @@ To use GR00T N1.7:
|
||||
|
||||
Here's a complete training command for finetuning the base GR00T model on your own dataset:
|
||||
|
||||
This command is using the `new_embodiment` flag, which is used for the SO-101 robot, [read more about how GR00T handles different embodiments.](https://github.com/NVIDIA/Isaac-GR00T/blob/main/getting_started/policy.md#--embodiment-tag).
|
||||
|
||||
```bash
|
||||
# install extra deps for training
|
||||
pip install "lerobot[training]"
|
||||
|
||||
hf auth login
|
||||
wandb login
|
||||
|
||||
export DATASET_NAME=your_data_set
|
||||
export HF_USER=your_hf_username
|
||||
export DATASET=$HF_USER/$DATASET_NAME
|
||||
export REPO_ID="${DATASET}_GR00T17" #this is the model that will be uploaded to huggingface
|
||||
export OUTPUT_DIR=outputs/train/$REPO_ID
|
||||
|
||||
lerobot-train \
|
||||
--dataset.repo_id=$DATASET \
|
||||
--dataset.image_transforms.enable=true \
|
||||
--policy.type=groot \
|
||||
--policy.device=cuda \
|
||||
--policy.base_model_path=nvidia/GR00T-N1.7-3B \
|
||||
--policy.embodiment_tag=new_embodiment \
|
||||
--policy.chunk_size=16 \
|
||||
--policy.n_action_steps=16 \
|
||||
--policy.use_relative_actions=true \
|
||||
--policy.relative_exclude_joints='["gripper"]' \
|
||||
--policy.use_bf16=true \
|
||||
--policy.push_to_hub=true \
|
||||
--policy.repo_id=$REPO_ID \
|
||||
--seed=42 \
|
||||
--batch_size=64 \
|
||||
--steps=20000 \
|
||||
--save_checkpoint=true \
|
||||
--save_freq=5000 \
|
||||
--use_policy_training_preset=true \
|
||||
--env_eval_freq=0 \
|
||||
--eval_steps=0 \
|
||||
--log_freq=10 \
|
||||
# Using a multi-GPU setup
|
||||
accelerate launch \
|
||||
--multi_gpu \
|
||||
--num_processes=$NUM_GPUS \
|
||||
$(which lerobot-train) \
|
||||
--output_dir=$OUTPUT_DIR \
|
||||
--job_name=$DATASET \
|
||||
--save_checkpoint=true \
|
||||
--batch_size=$BATCH_SIZE \
|
||||
--steps=$NUM_STEPS \
|
||||
--save_freq=$SAVE_FREQ \
|
||||
--log_freq=$LOG_FREQ \
|
||||
--policy.push_to_hub=true \
|
||||
--policy.type=groot \
|
||||
--policy.repo_id=$REPO_ID \
|
||||
--policy.tune_diffusion_model=false \
|
||||
--dataset.repo_id=$DATASET_ID \
|
||||
--wandb.enable=true \
|
||||
--wandb.disable_artifact=true
|
||||
|
||||
--wandb.disable_artifact=true \
|
||||
--job_name=$JOB_NAME
|
||||
```
|
||||
|
||||
## Performance Results
|
||||
|
||||
### LIBERO Benchmark Results
|
||||
### Libero Benchmark Results
|
||||
|
||||
> [!NOTE]
|
||||
> Follow the [LIBERO](./libero) setup instructions before running `lerobot-eval`.
|
||||
> Follow our instructions for Libero usage: [Libero](./libero)
|
||||
|
||||
GR00T N1.7 has demonstrated strong performance on the LIBERO benchmark suite. To reproduce LeRobot results, follow the instructions in the [LIBERO](./libero) section.
|
||||
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.
|
||||
|
||||
### Train on LIBERO
|
||||
| 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% |
|
||||
|
||||
Example training command for a LIBERO suite (here `libero_spatial`):
|
||||
|
||||
```bash
|
||||
IMAGE_TRANSFORMS='{
|
||||
"brightness": {"weight": 1.0, "type": "ColorJitter", "kwargs": {"brightness": [0.7, 1.3]}},
|
||||
"contrast": {"weight": 1.0, "type": "ColorJitter", "kwargs": {"contrast": [0.6, 1.4]}},
|
||||
"saturation": {"weight": 1.0, "type": "ColorJitter", "kwargs": {"saturation": [0.5, 1.5]}},
|
||||
"hue": {"weight": 1.0, "type": "ColorJitter", "kwargs": {"hue": [-0.08, 0.08]}}
|
||||
}'
|
||||
|
||||
lerobot-train \
|
||||
--dataset.repo_id=IPEC-COMMUNITY/libero_spatial_no_noops_1.0.0_lerobot \
|
||||
--dataset.root=/datasets/libero_spatial \
|
||||
--dataset.revision=main \
|
||||
--dataset.video_backend=pyav \
|
||||
--dataset.image_transforms.enable=true \
|
||||
--dataset.image_transforms.max_num_transforms=4 \
|
||||
--dataset.image_transforms.tfs="$IMAGE_TRANSFORMS" \
|
||||
--policy.type=groot \
|
||||
--policy.base_model_path=nvidia/GR00T-N1.7-3B \
|
||||
--policy.embodiment_tag=libero_sim \
|
||||
--policy.push_to_hub=false \
|
||||
--policy.use_relative_actions=false \
|
||||
--policy.max_steps=20000 \
|
||||
--batch_size=320 \
|
||||
--steps=20000 \
|
||||
--save_freq=2000 \
|
||||
--env_eval_freq=0 \
|
||||
--eval_steps=0 \
|
||||
--log_freq=10 \
|
||||
--wandb.enable=true \
|
||||
--wandb.project=lerobot \
|
||||
--wandb.mode=online \
|
||||
--wandb.disable_artifact=true \
|
||||
--num_workers=4 \
|
||||
--prefetch_factor=2 \
|
||||
--persistent_workers=true \
|
||||
--output_dir=$OUTPUT_DIR \
|
||||
--job_name=$JOB_NAME
|
||||
```
|
||||
|
||||
This will follow the recipe found [here](https://github.com/NVIDIA/Isaac-GR00T/blob/main/examples/LIBERO/README.md).
|
||||
|
||||
### GR00T N1.7 LIBERO Results
|
||||
|
||||
Preliminary LeRobot integration results (GR00T-LeRobot, `eval.n_episodes >= 50` per suite):
|
||||
|
||||
| Suite | Success rate |
|
||||
| ---------------- | -----------: |
|
||||
| LIBERO Spatial | 94% |
|
||||
| LIBERO Object | 98% |
|
||||
| LIBERO Goal | 93% |
|
||||
| LIBERO 10 (Long) | 90% |
|
||||
| **Average** | **93.75%** |
|
||||
|
||||
```bash
|
||||
export MODEL_ID=your_trained_model_on_huggingface
|
||||
|
||||
lerobot-eval \
|
||||
--policy.type=groot \
|
||||
--policy.base_model_path=$MODEL_ID \
|
||||
--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.
|
||||
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.
|
||||
|
||||
### Evaluate in your hardware setup
|
||||
|
||||
Once you have trained your model using your parameters you can run inference in your downstream task. Follow the instructions in [Policy Deployment (lerobot-rollout)](./inference). For example:
|
||||
|
||||
```bash
|
||||
# install extra deps for roullout and real hardware
|
||||
pip install "lerobot[feetech,viz]"
|
||||
|
||||
export MODEL_ID=your_trained_model_on_huggingface
|
||||
|
||||
# make sure that camera index matches your setup!
|
||||
# find index using `uv run lerobot-find-cameras opencv`
|
||||
WRIST_CAM='wrist: {type: opencv, index_or_path: 2, width: 640, height: 480, fps: 30, fourcc: "MJPG"}'
|
||||
FRONT_CAM='front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30, fourcc: "MJPG"}'
|
||||
export ROBOT_CAMERAS="{ $WRIST_CAM, $FRONT_CAM }"
|
||||
export ROBOT_ID=follower_robot
|
||||
export ROBOT_PORT=/dev/ttyACM0
|
||||
|
||||
uv run lerobot-rollout \
|
||||
--strategy.type=base \
|
||||
--policy.path=$MODEL_ID \
|
||||
--policy.base_model_path=nvidia/GR00T-N1.7-3B \
|
||||
--policy.n_action_steps=8 \
|
||||
--robot.type=so101_follower \
|
||||
--robot.port=$ROBOT_PORT \
|
||||
--robot.id=$ROBOT_ID \
|
||||
--robot.cameras="$ROBOT_CAMERAS" \
|
||||
--task="place the vial in the rack" \
|
||||
--duration=60 \
|
||||
--device=cuda \
|
||||
lerobot-rollout\
|
||||
--strategy.type=sentry \
|
||||
--strategy.upload_every_n_episodes=5 \
|
||||
--robot.type=bi_so_follower \
|
||||
--robot.left_arm_port=/dev/ttyACM1 \
|
||||
--robot.right_arm_port=/dev/ttyACM0 \
|
||||
--robot.id=bimanual_follower \
|
||||
--robot.cameras='{ right: {"type": "opencv", "index_or_path": 0, "width": 640, "height": 480, "fps": 30},
|
||||
left: {"type": "opencv", "index_or_path": 2, "width": 640, "height": 480, "fps": 30},
|
||||
top: {"type": "opencv", "index_or_path": 4, "width": 640, "height": 480, "fps": 30},
|
||||
}' \
|
||||
--display_data=true \
|
||||
--inference.type=rtc \
|
||||
--inference.rtc.enabled=True \ # set to False if it causes inference instability
|
||||
--inference.rtc.execution_horizon=8 \
|
||||
--inference.queue_threshold=0
|
||||
--dataset.repo_id=<user>/eval_groot-bimanual \
|
||||
--dataset.single_task="Grab and handover the red cube to the other arm" \
|
||||
--dataset.streaming_encoding=true \
|
||||
--dataset.encoder_threads=2 \
|
||||
# --dataset.rgb_encoder.vcodec=auto \
|
||||
--policy.path=<user>/groot-bimanual \ # your trained model
|
||||
--duration=600
|
||||
```
|
||||
|
||||
> [!NOTE]
|
||||
> Value of `inference.queue_threshold` should not exceed 5 to ensure stable inference.
|
||||
|
||||
## 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/).
|
||||
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**.
|
||||
|
||||
@@ -82,18 +82,18 @@ 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).
|
||||
- Prefer not to write the `hf jobs run` wrapper yourself? `lerobot-train` can submit the job for you: just add `--job.target=<flavor>` to a normal training command and it handles dataset upload, log streaming, and the final model push. See the [imitation-learning training guide](./il_robots).
|
||||
|
||||
@@ -532,7 +532,84 @@ 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:
|
||||
> **Tip:** if you just want to launch a standard training run, you can skip building the command below and use the integrated **Train on HF Jobs via `--job.target`** flow described further down — `lerobot-train` then submits the job, uploads a local-only dataset for you, and streams the logs.
|
||||
|
||||
To run the training manually use this command:
|
||||
|
||||
<hfoptions id="train_with_hf_jobs">
|
||||
<hfoption id="Command">
|
||||
```bash
|
||||
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">
|
||||
|
||||
<!-- prettier-ignore-start -->
|
||||
```python
|
||||
from huggingface_hub import run_job, get_token
|
||||
|
||||
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 -->
|
||||
|
||||
</hfoption>
|
||||
</hfoptions>
|
||||
|
||||
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.
|
||||
|
||||
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.
|
||||
|
||||
After training the model will be pushed to hub and you can use it as any other model with LeRobot.
|
||||
|
||||
#### Train on HF Jobs via `--job.target` (integrated CLI)
|
||||
|
||||
`lerobot-train` runs locally by default. To run on a HuggingFace GPU without constructing the Docker command yourself, pass `--job.target` with a hardware flavor name:
|
||||
|
||||
```bash
|
||||
lerobot-train \
|
||||
|
||||
@@ -1,187 +0,0 @@
|
||||
# LingBot-VA
|
||||
|
||||
LingBot-VA is an **autoregressive video-action world-model policy** built on the **Wan2.2**
|
||||
video-diffusion stack. It interleaves, in one autoregressive sequence, the prediction of
|
||||
future **video latents** and **robot actions** ("VA" = Video-Action). The LeRobot
|
||||
integration wires LingBot-VA into the standard training, evaluation and processor
|
||||
interfaces.
|
||||
|
||||
## Model Overview
|
||||
|
||||
LingBot-VA is a **dual-stream "mixture-of-transformers"**: a video/latent stream
|
||||
(`patch_embedding_mlp → blocks → proj_out`) and an action stream
|
||||
(`action_embedder → blocks → action_proj_out`) share the same 30 transformer blocks and
|
||||
text conditioning.
|
||||
|
||||
| Component | Class | Role |
|
||||
| ------------------------ | ----------------------- | ----------------------------------------------------------- |
|
||||
| DiT backbone (trainable) | `WanTransformer3DModel` | ~5B-param dual-stream transformer. |
|
||||
| VAE (frozen) | `AutoencoderKLWan` | Wan2.2 VAE, `z_dim=48`. Lazy-pulled from the source repo. |
|
||||
| Text encoder (frozen) | `UMT5EncoderModel` | UMT5-XXL, `d_model=4096`. Lazy-pulled from the source repo. |
|
||||
|
||||
At inference the policy runs an autoregressive loop per chunk: it denoises the video-latent
|
||||
stream (CFG, ~20 steps) and the action stream (~50 steps) with two independent
|
||||
flow-matching schedulers, maintaining a KV cache across chunks. Real observed keyframes are
|
||||
fed back into the KV cache as the chunk is executed (closed-loop world modeling).
|
||||
|
||||
### What the LeRobot Integration Covers
|
||||
|
||||
- Standard `policy.type=lingbot_va` configuration through LeRobot.
|
||||
- Ready-to-use LeRobot-format checkpoints on the Hub (converted from the released upstream ones).
|
||||
- Autoregressive dual-stream inference behind the standard `select_action` interface
|
||||
(single-environment eval, `--eval.batch_size=1`).
|
||||
- Opt-in saving of the policy's **predicted (imagined) videos** during eval / training.
|
||||
- Evaluation with `lerobot-eval` on LIBERO and RoboTwin.
|
||||
- Training / fine-tuning via the dual-stream flow-matching loss (`policy.forward`), see below.
|
||||
|
||||
## Installation
|
||||
|
||||
1. Install LeRobot by following the [Installation Guide](./installation).
|
||||
2. Install the LingBot-VA extra:
|
||||
|
||||
```bash
|
||||
pip install -e ".[lingbot_va]"
|
||||
```
|
||||
|
||||
## Checkpoints
|
||||
|
||||
The released upstream checkpoints have been converted to LeRobot format and pushed to the Hub:
|
||||
|
||||
| Variant | LeRobot checkpoint |
|
||||
| ---------------------- | -------------------------------- |
|
||||
| LIBERO-Long post-train | `lerobot/lingbot_va_libero_long` |
|
||||
| RoboTwin post-train | `lerobot/lingbot_va_robotwin` |
|
||||
| Pretrained base | `lerobot/lingbot_va_base` |
|
||||
|
||||
Only the trainable ~5B transformer is stored in the LeRobot
|
||||
`model.safetensors`. The frozen VAE + UMT5 + tokenizer (~20 GB) are pulled from
|
||||
`config.wan_pretrained_path` at load time (defaults to the source `robbyant/*` repo). The
|
||||
UMT5-XXL text encoder runs on CPU by default (`config.text_encoder_device`) so the 5B
|
||||
transformer + VAE fit on a single 24–32 GB GPU.
|
||||
|
||||
## Evaluation (LIBERO)
|
||||
|
||||
```bash
|
||||
lerobot-eval \
|
||||
--policy.path=lerobot/lingbot_va_libero_long \
|
||||
--policy.device=cuda \
|
||||
--env.type=libero --env.task=libero_10 \
|
||||
--env.observation_height=128 --env.observation_width=128 \
|
||||
--eval.n_episodes=50 --eval.batch_size=1 \
|
||||
--output_dir=outputs/eval/lingbot_va_libero
|
||||
```
|
||||
|
||||
LingBot-VA's streaming inference (KV cache + observed-keyframe feedback) is implemented for
|
||||
single-environment eval; use `--eval.batch_size=1`.
|
||||
|
||||
## Evaluation (RoboTwin)
|
||||
|
||||
RoboTwin 2.0 needs the SAPIEN + CuRobo simulator stack. You can use the benchmark Docker image
|
||||
(`docker/Dockerfile.benchmark.robotwin`, which also needs `warp-lang==1.3.1` and CuRobo built
|
||||
with the GPU's compute capability in `TORCH_CUDA_ARCH_LIST`). RoboTwin uses **end-effector-pose
|
||||
control**, so run with `--env.action_mode=ee`: the policy predicts per-arm `xyz+quaternion+gripper`
|
||||
deltas (`robotwin_tshape` latent layout) that are composed onto the episode's initial eef pose and
|
||||
executed via CuRobo IK.
|
||||
|
||||
```bash
|
||||
lerobot-eval \
|
||||
--policy.path=lerobot/lingbot_va_robotwin \
|
||||
--policy.device=cuda \
|
||||
--env.type=robotwin --env.task=beat_block_hammer --env.action_mode=ee \
|
||||
--eval.n_episodes=10 --eval.batch_size=1 \
|
||||
--output_dir=outputs/eval/lingbot_va_robotwin
|
||||
```
|
||||
|
||||
### Saving predicted (imagined) videos
|
||||
|
||||
Set `--policy.save_predicted_video=true` to additionally VAE-decode the predicted video
|
||||
latents and write `pred_episode_*.mp4` next to the env-rendered `eval_episode_*.mp4` videos.
|
||||
The same flag works for the periodic eval during `lerobot-train`.
|
||||
|
||||
## Training / fine-tuning
|
||||
|
||||
`LingBotVAPolicy.forward(batch)` implements the dual-stream **flow-matching** loss
|
||||
(`latent_loss + action_loss`, timestep-weighted, action-masked) from the paper: it VAE-encodes
|
||||
the camera clips into video latents, UMT5-encodes the task, noises both streams, runs the
|
||||
transformer's block-causal training pass and returns `(loss, metrics)`. Optimizer preset is AdamW
|
||||
with a linear-warmup-then-constant schedule (matching upstream).
|
||||
|
||||
Requirements:
|
||||
|
||||
- The block-causal masks use PyTorch **flex-attention**, so build the policy with
|
||||
`--policy.attn_mode=flex` for training (the default `torch` SDPA is inference-only).
|
||||
- The full 5B DiT does not fit a single 24–32 GB GPU under AdamW; fine-tune with **LoRA**
|
||||
(`--policy.use_peft=true`) and/or optimizer offload. `get_optim_params` returns only the
|
||||
trainable (e.g. adapter) parameters; the VAE + UMT5 text encoder stay frozen.
|
||||
|
||||
```bash
|
||||
lerobot-train \
|
||||
--policy.path=lerobot/lingbot_va_libero_long --policy.attn_mode=flex \
|
||||
--policy.use_peft=true \
|
||||
--dataset.repo_id=<your LeRobot-format dataset> \
|
||||
--batch_size=1 --steps=... --output_dir=outputs/train/lingbot_va
|
||||
```
|
||||
|
||||
The dataset must provide camera clips (a temporal window per camera, VAE-encoded to
|
||||
`frame_chunk_size` latent frames) and `frame_chunk_size * action_per_frame` action steps per item.
|
||||
|
||||
## Data format (action channels & camera order)
|
||||
|
||||
LingBot-VA is an **end-effector (Cartesian) pose** policy, it predicts EEF poses + gripper, not
|
||||
joint positions. Actions live in a fixed multi-embodiment **30-dim** layout; map your robot's
|
||||
action dimensions into these channels and pad the rest with `0` (`used_action_channel_ids` selects
|
||||
the channels a given checkpoint actually uses):
|
||||
|
||||
| channels | meaning |
|
||||
| -------- | ----------------------------------------------------- |
|
||||
| 0–6 | Left-arm end-effector pose |
|
||||
| 7–13 | Right-arm end-effector pose |
|
||||
| 14–20 | Left-arm joints (unused by the released checkpoints) |
|
||||
| 21–27 | Right-arm joints (unused by the released checkpoints) |
|
||||
| 28 | Left gripper |
|
||||
| 29 | Right gripper |
|
||||
|
||||
- **LIBERO** uses channels `0–6`: a 6-DoF EEF delta (xyz + rotation) + gripper (single arm).
|
||||
- **RoboTwin** uses channels `[0–6, 28, 7–13, 29]`: left EEF (xyz + quaternion) + left gripper +
|
||||
right EEF + right gripper (16 dims). The env converts these poses to joint trajectories via
|
||||
CuRobo IK — joints are never predicted.
|
||||
|
||||
Joint-space datasets (or a different EEF convention) must be remapped into this schema before
|
||||
fine-tuning these checkpoints.
|
||||
|
||||
**Camera order is fixed and order-sensitive**, per-camera latents are concatenated spatially in
|
||||
`obs_cam_keys` order, so the physical camera→slot mapping must match training:
|
||||
|
||||
| benchmark | `obs_cam_keys` (in order) | `camera_layout` |
|
||||
| --------- | ----------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------- |
|
||||
| LIBERO | `observation.images.image` (agentview / 3rd-person), `observation.images.image2` (eye-in-hand wrist) | `width_concat` (latents concatenated on width) |
|
||||
| RoboTwin | `observation.images.head_camera`, `observation.images.left_camera`, `observation.images.right_camera` | `robotwin_tshape` (full-res head below, two half-res wrists on top) |
|
||||
|
||||
The first camera is the exterior/head view and the rest are wrist views.
|
||||
|
||||
## Inference Hyperparameters (LIBERO)
|
||||
|
||||
| Key | Value |
|
||||
| -------------------------------------- | --------------------------------------------------------------------------------- |
|
||||
| height × width | 128 × 128 |
|
||||
| cameras | `observation.images.image` (agentview), `observation.images.image2` (eye-in-hand) |
|
||||
| action channels used | 0–6 (7-DoF arm + gripper) |
|
||||
| action_per_frame / frame_chunk_size | 4 / 4 |
|
||||
| attn_window | 30 |
|
||||
| video / action denoising steps | 20 / 50 |
|
||||
| guidance_scale / action_guidance_scale | 5 / 1 |
|
||||
| snr_shift / action_snr_shift | 5.0 / 0.05 |
|
||||
|
||||
These are the defaults of `LingBotVAConfig`; override any of them via `--policy.<name>=...`.
|
||||
|
||||
## Notes
|
||||
|
||||
- **Attention backend:** inference uses the `torch` SDPA backend (always available). The
|
||||
`flashattn` and `flex` backends are optional; `flex` is only needed for training.
|
||||
- **Model size:** the DiT is ~5B params and the frozen VAE+UMT5 add ~20 GB; inference needs
|
||||
roughly 18–24 GB of VRAM.
|
||||
|
||||
## License
|
||||
|
||||
LingBot-VA is released under Apache-2.0. See the
|
||||
[upstream repository](https://github.com/Robbyant/lingbot-va).
|
||||
@@ -1,18 +0,0 @@
|
||||
# EVO1
|
||||
|
||||
EVO1 is a Vision-Language-Action policy for robot control. The LeRobot
|
||||
integration uses an InternVL3 vision-language backbone with a flow-matching
|
||||
action head, and supports staged training through the standard LeRobot policy
|
||||
APIs.
|
||||
|
||||
The upstream EVO1 project is available at
|
||||
[MINT-SJTU/Evo-1](https://github.com/MINT-SJTU/Evo-1).
|
||||
|
||||
```bibtex
|
||||
@misc{evo1,
|
||||
title = {EVO1},
|
||||
author = {{MINT-SJTU}},
|
||||
year = {2025},
|
||||
howpublished = {\url{https://github.com/MINT-SJTU/Evo-1}},
|
||||
}
|
||||
```
|
||||
@@ -1,13 +1,6 @@
|
||||
## Research Paper
|
||||
|
||||
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.
|
||||
Paper: https://research.nvidia.com/labs/gear/gr00t-n1_5/
|
||||
|
||||
## Repository
|
||||
|
||||
@@ -31,108 +24,4 @@ Code: https://github.com/NVIDIA/Isaac-GR00T
|
||||
|
||||
Blog: https://developer.nvidia.com/isaac/gr00t
|
||||
|
||||
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
|
||||
|
||||
<details>
|
||||
<summary><b>Original-vs-LeRobot parity test</b></summary>
|
||||
|
||||
## 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 |
|
||||
|
||||
</details>
|
||||
Hugging Face Model: https://huggingface.co/nvidia/GR00T-N1.5-3B
|
||||
|
||||
+4
-8
@@ -164,7 +164,6 @@ pynput-dep = ["pynput>=1.7.8,<1.9.0"]
|
||||
pyzmq-dep = ["pyzmq>=26.2.1,<28.0.0"]
|
||||
motorbridge-dep = ["motorbridge>=0.3.2,<0.4.0"]
|
||||
motorbridge-smart-servo-dep = ["motorbridge-smart-servo>=0.0.4,<0.1.0"]
|
||||
timm-dep = ["timm>=1.0.0,<1.1.0"]
|
||||
|
||||
# Motors
|
||||
feetech = ["feetech-servo-sdk>=1.0.0,<2.0.0", "lerobot[pyserial-dep]", "lerobot[deepdiff-dep]"]
|
||||
@@ -220,10 +219,11 @@ 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",
|
||||
"lerobot[timm-dep]",
|
||||
"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]"]
|
||||
@@ -234,10 +234,8 @@ fastwam = [
|
||||
"lerobot[transformers-dep]",
|
||||
"lerobot[diffusers-dep]",
|
||||
]
|
||||
evo1 = ["lerobot[transformers-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]"]
|
||||
lingbot_va = ["lerobot[transformers-dep]", "lerobot[diffusers-dep]", "lerobot[accelerate-dep]"]
|
||||
|
||||
# Features
|
||||
async = ["lerobot[grpcio-dep]", "lerobot[matplotlib-dep]"]
|
||||
@@ -316,12 +314,10 @@ all = [
|
||||
"lerobot[molmoact2]",
|
||||
"lerobot[smolvla]",
|
||||
"lerobot[fastwam]",
|
||||
"lerobot[groot]",
|
||||
# "lerobot[groot]", TODO(Steven): Gr00t requires specific installation instructions for flash-attn
|
||||
"lerobot[xvla]",
|
||||
"lerobot[evo1]",
|
||||
"lerobot[hilserl]",
|
||||
"lerobot[vla_jepa]",
|
||||
"lerobot[lingbot_va]",
|
||||
"lerobot[async]",
|
||||
"lerobot[dev]",
|
||||
"lerobot[test]",
|
||||
|
||||
@@ -0,0 +1,729 @@
|
||||
#
|
||||
# This file is autogenerated by pip-compile with Python 3.12
|
||||
# by the following command:
|
||||
#
|
||||
# pip-compile --output-file=requirements-macos.txt requirements.in
|
||||
#
|
||||
-e .[all]
|
||||
# via -[all]
|
||||
absl-py==2.4.0
|
||||
# via
|
||||
# dm-control
|
||||
# dm-env
|
||||
# dm-tree
|
||||
# labmaze
|
||||
# mujoco
|
||||
accelerate==1.13.0
|
||||
# via
|
||||
# lerobot
|
||||
# peft
|
||||
aiohappyeyeballs==2.6.1
|
||||
# via aiohttp
|
||||
aiohttp==3.13.3
|
||||
# via fsspec
|
||||
aiosignal==1.4.0
|
||||
# via aiohttp
|
||||
annotated-doc==0.0.4
|
||||
# via
|
||||
# fastapi
|
||||
# typer
|
||||
annotated-types==0.7.0
|
||||
# via pydantic
|
||||
anyio==4.12.1
|
||||
# via
|
||||
# httpx
|
||||
# starlette
|
||||
# watchfiles
|
||||
asttokens==3.0.1
|
||||
# via stack-data
|
||||
attrs==25.4.0
|
||||
# via
|
||||
# aiohttp
|
||||
# dm-tree
|
||||
# jsonlines
|
||||
# rerun-sdk
|
||||
av==15.1.0
|
||||
# via
|
||||
# lerobot
|
||||
# qwen-vl-utils
|
||||
certifi==2026.2.25
|
||||
# via
|
||||
# httpcore
|
||||
# httpx
|
||||
# requests
|
||||
# sentry-sdk
|
||||
cffi==2.0.0
|
||||
# via pymunk
|
||||
cfgv==3.5.0
|
||||
# via pre-commit
|
||||
charset-normalizer==3.4.5
|
||||
# via requests
|
||||
click==8.3.1
|
||||
# via
|
||||
# typer
|
||||
# uvicorn
|
||||
# wandb
|
||||
cloudpickle==3.1.2
|
||||
# via gymnasium
|
||||
cmake==4.1.3
|
||||
# via lerobot
|
||||
cmeel==0.59.0
|
||||
# via
|
||||
# cmeel-assimp
|
||||
# cmeel-boost
|
||||
# cmeel-console-bridge
|
||||
# cmeel-octomap
|
||||
# cmeel-qhull
|
||||
# cmeel-tinyxml2
|
||||
# cmeel-urdfdom
|
||||
# cmeel-zlib
|
||||
# coal-library
|
||||
# eigenpy
|
||||
# eiquadprog
|
||||
# pin
|
||||
# placo
|
||||
# rhoban-cmeel-jsoncpp
|
||||
cmeel-assimp==5.4.3.1
|
||||
# via coal-library
|
||||
cmeel-boost==1.87.0.1
|
||||
# via
|
||||
# coal-library
|
||||
# eigenpy
|
||||
# eiquadprog
|
||||
# pin
|
||||
cmeel-console-bridge==1.0.2.3
|
||||
# via cmeel-urdfdom
|
||||
cmeel-octomap==1.10.0
|
||||
# via coal-library
|
||||
cmeel-qhull==8.0.2.1
|
||||
# via coal-library
|
||||
cmeel-tinyxml2==10.0.0
|
||||
# via cmeel-urdfdom
|
||||
cmeel-urdfdom==4.0.1
|
||||
# via pin
|
||||
cmeel-zlib==1.3.1
|
||||
# via cmeel-assimp
|
||||
coal-library==3.0.1
|
||||
# via pin
|
||||
contourpy==1.3.3
|
||||
# via
|
||||
# lerobot
|
||||
# matplotlib
|
||||
coverage[toml]==7.13.4
|
||||
# via pytest-cov
|
||||
cycler==0.12.1
|
||||
# via matplotlib
|
||||
datasets==4.6.1
|
||||
# via lerobot
|
||||
debugpy==1.8.20
|
||||
# via lerobot
|
||||
decorator==5.2.1
|
||||
# via ipython
|
||||
deepdiff==8.6.1
|
||||
# via lerobot
|
||||
diffusers==0.35.2
|
||||
# via lerobot
|
||||
dill==0.4.0
|
||||
# via
|
||||
# datasets
|
||||
# multiprocess
|
||||
distlib==0.4.0
|
||||
# via virtualenv
|
||||
dm-control==1.0.37
|
||||
# via gym-aloha
|
||||
dm-env==1.6
|
||||
# via dm-control
|
||||
dm-tree==0.1.9
|
||||
# via
|
||||
# dm-control
|
||||
# dm-env
|
||||
docopt==0.6.2
|
||||
# via num2words
|
||||
draccus==0.10.0
|
||||
# via lerobot
|
||||
dynamixel-sdk==3.8.4
|
||||
# via lerobot
|
||||
eigenpy==3.10.3
|
||||
# via coal-library
|
||||
einops==0.8.2
|
||||
# via lerobot
|
||||
eiquadprog==1.2.9
|
||||
# via placo
|
||||
etils[epath,epy]==1.14.0
|
||||
# via mujoco
|
||||
executing==2.2.1
|
||||
# via stack-data
|
||||
faker==34.0.2
|
||||
# via lerobot
|
||||
farama-notifications==0.0.4
|
||||
# via gymnasium
|
||||
fastapi==0.135.1
|
||||
# via
|
||||
# lerobot
|
||||
# teleop
|
||||
feetech-servo-sdk==1.0.0
|
||||
# via lerobot
|
||||
filelock==3.25.0
|
||||
# via
|
||||
# datasets
|
||||
# diffusers
|
||||
# huggingface-hub
|
||||
# python-discovery
|
||||
# torch
|
||||
# virtualenv
|
||||
fonttools==4.61.1
|
||||
# via matplotlib
|
||||
frozenlist==1.8.0
|
||||
# via
|
||||
# aiohttp
|
||||
# aiosignal
|
||||
fsspec[http]==2026.2.0
|
||||
# via
|
||||
# datasets
|
||||
# etils
|
||||
# huggingface-hub
|
||||
# torch
|
||||
gitdb==4.0.12
|
||||
# via gitpython
|
||||
gitpython==3.1.46
|
||||
# via wandb
|
||||
glfw==2.10.0
|
||||
# via
|
||||
# dm-control
|
||||
# mujoco
|
||||
grpcio==1.73.1
|
||||
# via
|
||||
# grpcio-tools
|
||||
# lerobot
|
||||
# reachy2-sdk
|
||||
# reachy2-sdk-api
|
||||
grpcio-tools==1.73.1
|
||||
# via
|
||||
# lerobot
|
||||
# reachy2-sdk-api
|
||||
gym-aloha==0.1.3
|
||||
# via lerobot
|
||||
gym-hil==0.1.13
|
||||
# via lerobot
|
||||
gym-pusht==0.1.6
|
||||
# via lerobot
|
||||
gymnasium==1.2.3
|
||||
# via
|
||||
# gym-aloha
|
||||
# gym-hil
|
||||
# gym-pusht
|
||||
# lerobot
|
||||
# metaworld
|
||||
h11==0.16.0
|
||||
# via
|
||||
# httpcore
|
||||
# uvicorn
|
||||
hebi-py==2.11.0
|
||||
# via lerobot
|
||||
hf-xet==1.3.2
|
||||
# via huggingface-hub
|
||||
hidapi==0.14.0.post4
|
||||
# via
|
||||
# gym-hil
|
||||
# lerobot
|
||||
httpcore==1.0.9
|
||||
# via httpx
|
||||
httptools==0.7.1
|
||||
# via uvicorn
|
||||
httpx==0.28.1
|
||||
# via
|
||||
# datasets
|
||||
# huggingface-hub
|
||||
huggingface-hub==1.6.0
|
||||
# via
|
||||
# accelerate
|
||||
# datasets
|
||||
# diffusers
|
||||
# lerobot
|
||||
# peft
|
||||
# tokenizers
|
||||
# transformers
|
||||
identify==2.6.17
|
||||
# via pre-commit
|
||||
idna==3.11
|
||||
# via
|
||||
# anyio
|
||||
# httpx
|
||||
# requests
|
||||
# yarl
|
||||
imageio[ffmpeg]==2.37.2
|
||||
# via
|
||||
# gym-aloha
|
||||
# gym-hil
|
||||
# lerobot
|
||||
# metaworld
|
||||
# scikit-image
|
||||
imageio-ffmpeg==0.6.0
|
||||
# via imageio
|
||||
importlib-metadata==8.7.1
|
||||
# via diffusers
|
||||
iniconfig==2.3.0
|
||||
# via pytest
|
||||
ipython==9.11.0
|
||||
# via meshcat
|
||||
ipython-pygments-lexers==1.1.1
|
||||
# via ipython
|
||||
ischedule==1.2.7
|
||||
# via placo
|
||||
jedi==0.19.2
|
||||
# via ipython
|
||||
jinja2==3.1.6
|
||||
# via torch
|
||||
jsonlines==4.0.0
|
||||
# via lerobot
|
||||
kiwisolver==1.4.9
|
||||
# via matplotlib
|
||||
labmaze==1.0.6
|
||||
# via dm-control
|
||||
lazy-loader==0.5
|
||||
# via scikit-image
|
||||
librt==0.8.1
|
||||
# via mypy
|
||||
lxml==6.0.2
|
||||
# via dm-control
|
||||
markdown-it-py==4.0.0
|
||||
# via rich
|
||||
markupsafe==3.0.3
|
||||
# via jinja2
|
||||
matplotlib==3.10.8
|
||||
# via lerobot
|
||||
matplotlib-inline==0.2.1
|
||||
# via ipython
|
||||
mdurl==0.1.2
|
||||
# via markdown-it-py
|
||||
mergedeep==1.3.4
|
||||
# via draccus
|
||||
meshcat==0.3.2
|
||||
# via placo
|
||||
metaworld==3.0.0
|
||||
# via lerobot
|
||||
mock-serial==0.0.1
|
||||
# via lerobot
|
||||
mpmath==1.3.0
|
||||
# via sympy
|
||||
mujoco==3.5.0
|
||||
# via
|
||||
# dm-control
|
||||
# gym-aloha
|
||||
# gym-hil
|
||||
# metaworld
|
||||
multidict==6.7.1
|
||||
# via
|
||||
# aiohttp
|
||||
# yarl
|
||||
multiprocess==0.70.18
|
||||
# via datasets
|
||||
mypy==1.19.1
|
||||
# via lerobot
|
||||
mypy-extensions==1.1.0
|
||||
# via
|
||||
# mypy
|
||||
# typing-inspect
|
||||
networkx==3.6.1
|
||||
# via
|
||||
# scikit-image
|
||||
# torch
|
||||
nodeenv==1.10.0
|
||||
# via pre-commit
|
||||
num2words==0.5.14
|
||||
# via lerobot
|
||||
numpy==2.2.6
|
||||
# via
|
||||
# accelerate
|
||||
# cmeel-boost
|
||||
# contourpy
|
||||
# datasets
|
||||
# diffusers
|
||||
# dm-control
|
||||
# dm-env
|
||||
# dm-tree
|
||||
# gymnasium
|
||||
# hebi-py
|
||||
# imageio
|
||||
# labmaze
|
||||
# lerobot
|
||||
# matplotlib
|
||||
# meshcat
|
||||
# metaworld
|
||||
# mujoco
|
||||
# opencv-python
|
||||
# opencv-python-headless
|
||||
# pandas
|
||||
# peft
|
||||
# pyquaternion
|
||||
# reachy2-sdk
|
||||
# rerun-sdk
|
||||
# scikit-image
|
||||
# scipy
|
||||
# shapely
|
||||
# teleop
|
||||
# tifffile
|
||||
# torchvision
|
||||
# transformers
|
||||
# transforms3d
|
||||
opencv-python==4.13.0.92
|
||||
# via
|
||||
# gym-pusht
|
||||
# reachy2-sdk
|
||||
opencv-python-headless==4.12.0.88
|
||||
# via lerobot
|
||||
orderly-set==5.5.0
|
||||
# via deepdiff
|
||||
packaging==25.0
|
||||
# via
|
||||
# accelerate
|
||||
# datasets
|
||||
# huggingface-hub
|
||||
# lazy-loader
|
||||
# lerobot
|
||||
# matplotlib
|
||||
# peft
|
||||
# pytest
|
||||
# qwen-vl-utils
|
||||
# reachy2-sdk
|
||||
# scikit-image
|
||||
# transformers
|
||||
# wandb
|
||||
pandas==2.3.3
|
||||
# via
|
||||
# datasets
|
||||
# lerobot
|
||||
parso==0.8.6
|
||||
# via jedi
|
||||
pathspec==1.0.4
|
||||
# via mypy
|
||||
peft==0.18.1
|
||||
# via lerobot
|
||||
pexpect==4.9.0
|
||||
# via ipython
|
||||
pillow==12.1.1
|
||||
# via
|
||||
# diffusers
|
||||
# imageio
|
||||
# matplotlib
|
||||
# meshcat
|
||||
# qwen-vl-utils
|
||||
# rerun-sdk
|
||||
# scikit-image
|
||||
# torchvision
|
||||
pin==3.4.0
|
||||
# via placo
|
||||
placo==0.9.16
|
||||
# via lerobot
|
||||
platformdirs==4.9.4
|
||||
# via
|
||||
# python-discovery
|
||||
# virtualenv
|
||||
# wandb
|
||||
pluggy==1.6.0
|
||||
# via
|
||||
# pytest
|
||||
# pytest-cov
|
||||
pre-commit==4.5.1
|
||||
# via lerobot
|
||||
prompt-toolkit==3.0.52
|
||||
# via ipython
|
||||
propcache==0.4.1
|
||||
# via
|
||||
# aiohttp
|
||||
# yarl
|
||||
protobuf==6.31.1
|
||||
# via
|
||||
# dm-control
|
||||
# grpcio-tools
|
||||
# lerobot
|
||||
# reachy2-sdk
|
||||
# reachy2-sdk-api
|
||||
# wandb
|
||||
psutil==7.2.2
|
||||
# via
|
||||
# accelerate
|
||||
# imageio
|
||||
# peft
|
||||
ptyprocess==0.7.0
|
||||
# via pexpect
|
||||
pure-eval==0.2.3
|
||||
# via stack-data
|
||||
pyarrow==23.0.1
|
||||
# via
|
||||
# datasets
|
||||
# rerun-sdk
|
||||
pycparser==3.0
|
||||
# via cffi
|
||||
pydantic==2.12.5
|
||||
# via
|
||||
# fastapi
|
||||
# wandb
|
||||
pydantic-core==2.41.5
|
||||
# via pydantic
|
||||
pygame==2.6.1
|
||||
# via
|
||||
# gym-hil
|
||||
# gym-pusht
|
||||
# lerobot
|
||||
pygments==2.19.2
|
||||
# via
|
||||
# ipython
|
||||
# ipython-pygments-lexers
|
||||
# pytest
|
||||
# rich
|
||||
pymunk==6.11.1
|
||||
# via
|
||||
# gym-pusht
|
||||
# lerobot
|
||||
pyngrok==7.5.1
|
||||
# via meshcat
|
||||
pynput==1.8.1
|
||||
# via
|
||||
# gym-hil
|
||||
# lerobot
|
||||
pyobjc-core==12.1
|
||||
# via
|
||||
# pyobjc-framework-applicationservices
|
||||
# pyobjc-framework-cocoa
|
||||
# pyobjc-framework-coretext
|
||||
# pyobjc-framework-quartz
|
||||
pyobjc-framework-applicationservices==12.1
|
||||
# via pynput
|
||||
pyobjc-framework-cocoa==12.1
|
||||
# via
|
||||
# pyobjc-framework-applicationservices
|
||||
# pyobjc-framework-coretext
|
||||
# pyobjc-framework-quartz
|
||||
pyobjc-framework-coretext==12.1
|
||||
# via pyobjc-framework-applicationservices
|
||||
pyobjc-framework-quartz==12.1
|
||||
# via
|
||||
# pynput
|
||||
# pyobjc-framework-applicationservices
|
||||
# pyobjc-framework-coretext
|
||||
pyopengl==3.1.10
|
||||
# via
|
||||
# dm-control
|
||||
# mujoco
|
||||
pyparsing==3.3.2
|
||||
# via
|
||||
# dm-control
|
||||
# matplotlib
|
||||
pyquaternion==0.9.9
|
||||
# via reachy2-sdk
|
||||
pyrealsense2-macosx==2.56.5
|
||||
# via lerobot
|
||||
pyserial==3.5
|
||||
# via
|
||||
# dynamixel-sdk
|
||||
# feetech-servo-sdk
|
||||
# lerobot
|
||||
pytest==8.4.2
|
||||
# via
|
||||
# lerobot
|
||||
# pytest-cov
|
||||
# pytest-timeout
|
||||
# teleop
|
||||
pytest-cov==7.0.0
|
||||
# via lerobot
|
||||
pytest-timeout==2.4.0
|
||||
# via lerobot
|
||||
python-dateutil==2.9.0.post0
|
||||
# via
|
||||
# faker
|
||||
# matplotlib
|
||||
# pandas
|
||||
python-discovery==1.1.1
|
||||
# via virtualenv
|
||||
python-dotenv==1.2.2
|
||||
# via uvicorn
|
||||
pytz==2026.1.post1
|
||||
# via pandas
|
||||
pyyaml==6.0.3
|
||||
# via
|
||||
# accelerate
|
||||
# datasets
|
||||
# draccus
|
||||
# hebi-py
|
||||
# huggingface-hub
|
||||
# peft
|
||||
# pre-commit
|
||||
# pyngrok
|
||||
# pyyaml-include
|
||||
# transformers
|
||||
# uvicorn
|
||||
# wandb
|
||||
pyyaml-include==1.4.1
|
||||
# via draccus
|
||||
pyzmq==27.1.0
|
||||
# via
|
||||
# lerobot
|
||||
# meshcat
|
||||
qwen-vl-utils==0.0.14
|
||||
# via lerobot
|
||||
reachy2-sdk==1.0.15
|
||||
# via lerobot
|
||||
reachy2-sdk-api==1.0.21
|
||||
# via reachy2-sdk
|
||||
regex==2026.2.28
|
||||
# via
|
||||
# diffusers
|
||||
# transformers
|
||||
requests==2.32.5
|
||||
# via
|
||||
# datasets
|
||||
# diffusers
|
||||
# dm-control
|
||||
# qwen-vl-utils
|
||||
# teleop
|
||||
# wandb
|
||||
rerun-sdk==0.26.2
|
||||
# via lerobot
|
||||
rhoban-cmeel-jsoncpp==1.9.4.9
|
||||
# via placo
|
||||
rich==14.3.3
|
||||
# via typer
|
||||
safetensors==0.7.0
|
||||
# via
|
||||
# accelerate
|
||||
# diffusers
|
||||
# lerobot
|
||||
# peft
|
||||
# transformers
|
||||
scikit-image==0.25.2
|
||||
# via
|
||||
# gym-pusht
|
||||
# lerobot
|
||||
scipy==1.17.1
|
||||
# via
|
||||
# dm-control
|
||||
# lerobot
|
||||
# metaworld
|
||||
# scikit-image
|
||||
# torchdiffeq
|
||||
sentry-sdk==2.54.0
|
||||
# via wandb
|
||||
shapely==2.1.2
|
||||
# via gym-pusht
|
||||
shellingham==1.5.4
|
||||
# via typer
|
||||
six==1.17.0
|
||||
# via
|
||||
# pynput
|
||||
# python-dateutil
|
||||
smmap==5.0.3
|
||||
# via gitdb
|
||||
stack-data==0.6.3
|
||||
# via ipython
|
||||
starlette==0.52.1
|
||||
# via fastapi
|
||||
sympy==1.14.0
|
||||
# via torch
|
||||
teleop==0.1.4
|
||||
# via lerobot
|
||||
termcolor==3.3.0
|
||||
# via lerobot
|
||||
tifffile==2026.3.3
|
||||
# via scikit-image
|
||||
tokenizers==0.22.2
|
||||
# via transformers
|
||||
toml==0.10.2
|
||||
# via draccus
|
||||
torch==2.10.0
|
||||
# via
|
||||
# accelerate
|
||||
# lerobot
|
||||
# peft
|
||||
# torchdiffeq
|
||||
# torchvision
|
||||
torchcodec==0.10.0
|
||||
# via lerobot
|
||||
torchdiffeq==0.2.5
|
||||
# via lerobot
|
||||
torchvision==0.25.0
|
||||
# via lerobot
|
||||
tornado==6.5.4
|
||||
# via meshcat
|
||||
tqdm==4.67.3
|
||||
# via
|
||||
# datasets
|
||||
# dm-control
|
||||
# huggingface-hub
|
||||
# peft
|
||||
# transformers
|
||||
traitlets==5.14.3
|
||||
# via
|
||||
# ipython
|
||||
# matplotlib-inline
|
||||
transformers==5.3.0
|
||||
# via
|
||||
# lerobot
|
||||
# peft
|
||||
transforms3d==0.4.2
|
||||
# via teleop
|
||||
typer==0.24.1
|
||||
# via
|
||||
# huggingface-hub
|
||||
# transformers
|
||||
typing-extensions==4.15.0
|
||||
# via
|
||||
# aiosignal
|
||||
# anyio
|
||||
# etils
|
||||
# faker
|
||||
# fastapi
|
||||
# gymnasium
|
||||
# huggingface-hub
|
||||
# mypy
|
||||
# pydantic
|
||||
# pydantic-core
|
||||
# rerun-sdk
|
||||
# starlette
|
||||
# torch
|
||||
# typing-inspect
|
||||
# typing-inspection
|
||||
# wandb
|
||||
typing-inspect==0.9.0
|
||||
# via draccus
|
||||
typing-inspection==0.4.2
|
||||
# via
|
||||
# fastapi
|
||||
# pydantic
|
||||
tzdata==2025.3
|
||||
# via pandas
|
||||
u-msgpack-python==2.8.0
|
||||
# via meshcat
|
||||
urllib3==2.6.3
|
||||
# via
|
||||
# requests
|
||||
# sentry-sdk
|
||||
uvicorn[standard]==0.41.0
|
||||
# via teleop
|
||||
uvloop==0.22.1
|
||||
# via uvicorn
|
||||
virtualenv==21.1.0
|
||||
# via pre-commit
|
||||
wandb==0.24.2
|
||||
# via lerobot
|
||||
watchfiles==1.1.1
|
||||
# via uvicorn
|
||||
wcwidth==0.6.0
|
||||
# via prompt-toolkit
|
||||
websocket-client==1.9.0
|
||||
# via teleop
|
||||
websockets==16.0
|
||||
# via uvicorn
|
||||
wrapt==2.1.2
|
||||
# via dm-tree
|
||||
xxhash==3.6.0
|
||||
# via datasets
|
||||
yarl==1.23.0
|
||||
# via aiohttp
|
||||
zipp==3.23.0
|
||||
# via
|
||||
# etils
|
||||
# importlib-metadata
|
||||
|
||||
# The following packages are considered to be unsafe in a requirements file:
|
||||
# setuptools
|
||||
@@ -0,0 +1,882 @@
|
||||
#
|
||||
# This file is autogenerated by pip-compile with Python 3.12
|
||||
# by the following command:
|
||||
#
|
||||
# pip-compile --output-file=requirements-ubuntu.txt requirements.in
|
||||
#
|
||||
-e .[all]
|
||||
# via -[all]
|
||||
absl-py==2.4.0
|
||||
# via
|
||||
# dm-control
|
||||
# dm-env
|
||||
# dm-tree
|
||||
# labmaze
|
||||
# mujoco
|
||||
# tensorboard
|
||||
accelerate==1.13.0
|
||||
# via
|
||||
# lerobot
|
||||
# peft
|
||||
aiohappyeyeballs==2.6.1
|
||||
# via aiohttp
|
||||
aiohttp==3.13.3
|
||||
# via fsspec
|
||||
aiosignal==1.4.0
|
||||
# via aiohttp
|
||||
annotated-doc==0.0.4
|
||||
# via
|
||||
# fastapi
|
||||
# typer
|
||||
annotated-types==0.7.0
|
||||
# via pydantic
|
||||
antlr4-python3-runtime==4.9.3
|
||||
# via
|
||||
# hydra-core
|
||||
# omegaconf
|
||||
anyio==4.12.1
|
||||
# via
|
||||
# httpx
|
||||
# starlette
|
||||
# watchfiles
|
||||
asttokens==3.0.1
|
||||
# via stack-data
|
||||
attrs==25.4.0
|
||||
# via
|
||||
# aiohttp
|
||||
# dm-tree
|
||||
# jsonlines
|
||||
# jsonschema
|
||||
# referencing
|
||||
# rerun-sdk
|
||||
av==15.1.0
|
||||
# via
|
||||
# lerobot
|
||||
# qwen-vl-utils
|
||||
bddl==1.0.1
|
||||
# via hf-libero
|
||||
certifi==2026.2.25
|
||||
# via
|
||||
# httpcore
|
||||
# httpx
|
||||
# requests
|
||||
# sentry-sdk
|
||||
cffi==2.0.0
|
||||
# via pymunk
|
||||
cfgv==3.5.0
|
||||
# via pre-commit
|
||||
charset-normalizer==3.4.5
|
||||
# via requests
|
||||
click==8.3.1
|
||||
# via
|
||||
# typer
|
||||
# uvicorn
|
||||
# wandb
|
||||
cloudpickle==3.1.2
|
||||
# via
|
||||
# gymnasium
|
||||
# hf-libero
|
||||
cmake==4.1.3
|
||||
# via lerobot
|
||||
cmeel==0.59.0
|
||||
# via
|
||||
# cmeel-assimp
|
||||
# cmeel-boost
|
||||
# cmeel-console-bridge
|
||||
# cmeel-octomap
|
||||
# cmeel-qhull
|
||||
# cmeel-tinyxml2
|
||||
# cmeel-urdfdom
|
||||
# cmeel-zlib
|
||||
# coal-library
|
||||
# eigenpy
|
||||
# eiquadprog
|
||||
# pin
|
||||
# placo
|
||||
# rhoban-cmeel-jsoncpp
|
||||
cmeel-assimp==5.4.3.1
|
||||
# via coal-library
|
||||
cmeel-boost==1.87.0.1
|
||||
# via
|
||||
# coal-library
|
||||
# eigenpy
|
||||
# eiquadprog
|
||||
# pin
|
||||
cmeel-console-bridge==1.0.2.3
|
||||
# via cmeel-urdfdom
|
||||
cmeel-octomap==1.10.0
|
||||
# via coal-library
|
||||
cmeel-qhull==8.0.2.1
|
||||
# via coal-library
|
||||
cmeel-tinyxml2==10.0.0
|
||||
# via cmeel-urdfdom
|
||||
cmeel-urdfdom==4.0.1
|
||||
# via pin
|
||||
cmeel-zlib==1.3.1
|
||||
# via cmeel-assimp
|
||||
coal-library==3.0.1
|
||||
# via pin
|
||||
contourpy==1.3.3
|
||||
# via
|
||||
# lerobot
|
||||
# matplotlib
|
||||
coverage[toml]==7.13.4
|
||||
# via pytest-cov
|
||||
cuda-bindings==12.9.4
|
||||
# via torch
|
||||
cuda-pathfinder==1.4.1
|
||||
# via cuda-bindings
|
||||
cycler==0.12.1
|
||||
# via matplotlib
|
||||
datasets==4.6.1
|
||||
# via lerobot
|
||||
debugpy==1.8.20
|
||||
# via lerobot
|
||||
decorator==5.2.1
|
||||
# via ipython
|
||||
deepdiff==8.6.1
|
||||
# via lerobot
|
||||
diffusers==0.35.2
|
||||
# via lerobot
|
||||
dill==0.4.0
|
||||
# via
|
||||
# datasets
|
||||
# multiprocess
|
||||
distlib==0.4.0
|
||||
# via virtualenv
|
||||
dm-control==1.0.37
|
||||
# via gym-aloha
|
||||
dm-env==1.6
|
||||
# via dm-control
|
||||
dm-tree==0.1.9
|
||||
# via
|
||||
# dm-control
|
||||
# dm-env
|
||||
docopt==0.6.2
|
||||
# via num2words
|
||||
draccus==0.10.0
|
||||
# via lerobot
|
||||
dynamixel-sdk==3.8.4
|
||||
# via lerobot
|
||||
easydict==1.13
|
||||
# via hf-libero
|
||||
egl-probe==1.0.2
|
||||
# via robomimic
|
||||
eigenpy==3.10.3
|
||||
# via coal-library
|
||||
einops==0.8.2
|
||||
# via
|
||||
# hf-libero
|
||||
# lerobot
|
||||
eiquadprog==1.2.9
|
||||
# via placo
|
||||
etils[epath,epy]==1.14.0
|
||||
# via mujoco
|
||||
evdev==1.9.3
|
||||
# via pynput
|
||||
executing==2.2.1
|
||||
# via stack-data
|
||||
faker==34.0.2
|
||||
# via lerobot
|
||||
farama-notifications==0.0.4
|
||||
# via gymnasium
|
||||
fastapi==0.135.1
|
||||
# via
|
||||
# lerobot
|
||||
# teleop
|
||||
fastjsonschema==2.21.2
|
||||
# via nbformat
|
||||
feetech-servo-sdk==1.0.0
|
||||
# via lerobot
|
||||
filelock==3.25.0
|
||||
# via
|
||||
# datasets
|
||||
# diffusers
|
||||
# huggingface-hub
|
||||
# python-discovery
|
||||
# torch
|
||||
# virtualenv
|
||||
fonttools==4.61.1
|
||||
# via matplotlib
|
||||
frozenlist==1.8.0
|
||||
# via
|
||||
# aiohttp
|
||||
# aiosignal
|
||||
fsspec[http]==2026.2.0
|
||||
# via
|
||||
# datasets
|
||||
# etils
|
||||
# huggingface-hub
|
||||
# torch
|
||||
future==1.0.0
|
||||
# via hf-libero
|
||||
gitdb==4.0.12
|
||||
# via gitpython
|
||||
gitpython==3.1.46
|
||||
# via wandb
|
||||
glfw==2.10.0
|
||||
# via
|
||||
# dm-control
|
||||
# mujoco
|
||||
grpcio==1.73.1
|
||||
# via
|
||||
# grpcio-tools
|
||||
# lerobot
|
||||
# reachy2-sdk
|
||||
# reachy2-sdk-api
|
||||
# tensorboard
|
||||
grpcio-tools==1.73.1
|
||||
# via
|
||||
# lerobot
|
||||
# reachy2-sdk-api
|
||||
gym-aloha==0.1.3
|
||||
# via lerobot
|
||||
gym-hil==0.1.13
|
||||
# via lerobot
|
||||
gym-pusht==0.1.6
|
||||
# via lerobot
|
||||
gymnasium==1.2.3
|
||||
# via
|
||||
# gym-aloha
|
||||
# gym-hil
|
||||
# gym-pusht
|
||||
# hf-libero
|
||||
# lerobot
|
||||
# metaworld
|
||||
h11==0.16.0
|
||||
# via
|
||||
# httpcore
|
||||
# uvicorn
|
||||
h5py==3.16.0
|
||||
# via robomimic
|
||||
hebi-py==2.11.0
|
||||
# via lerobot
|
||||
hf-egl-probe==1.0.2
|
||||
# via hf-libero
|
||||
hf-libero==0.1.3
|
||||
# via lerobot
|
||||
hf-xet==1.3.2
|
||||
# via huggingface-hub
|
||||
hidapi==0.14.0.post4
|
||||
# via
|
||||
# gym-hil
|
||||
# lerobot
|
||||
httpcore==1.0.9
|
||||
# via httpx
|
||||
httptools==0.7.1
|
||||
# via uvicorn
|
||||
httpx==0.28.1
|
||||
# via
|
||||
# datasets
|
||||
# huggingface-hub
|
||||
huggingface-hub==1.6.0
|
||||
# via
|
||||
# accelerate
|
||||
# datasets
|
||||
# diffusers
|
||||
# lerobot
|
||||
# peft
|
||||
# tokenizers
|
||||
# transformers
|
||||
hydra-core==1.3.2
|
||||
# via hf-libero
|
||||
identify==2.6.17
|
||||
# via pre-commit
|
||||
idna==3.11
|
||||
# via
|
||||
# anyio
|
||||
# httpx
|
||||
# requests
|
||||
# yarl
|
||||
imageio[ffmpeg]==2.37.2
|
||||
# via
|
||||
# gym-aloha
|
||||
# gym-hil
|
||||
# lerobot
|
||||
# metaworld
|
||||
# robomimic
|
||||
# scikit-image
|
||||
imageio-ffmpeg==0.6.0
|
||||
# via
|
||||
# imageio
|
||||
# robomimic
|
||||
importlib-metadata==8.7.1
|
||||
# via diffusers
|
||||
iniconfig==2.3.0
|
||||
# via pytest
|
||||
ipython==9.11.0
|
||||
# via meshcat
|
||||
ipython-pygments-lexers==1.1.1
|
||||
# via ipython
|
||||
ischedule==1.2.7
|
||||
# via placo
|
||||
jedi==0.19.2
|
||||
# via ipython
|
||||
jinja2==3.1.6
|
||||
# via torch
|
||||
jsonlines==4.0.0
|
||||
# via lerobot
|
||||
jsonschema==4.26.0
|
||||
# via nbformat
|
||||
jsonschema-specifications==2025.9.1
|
||||
# via jsonschema
|
||||
jupyter-core==5.9.1
|
||||
# via nbformat
|
||||
jupytext==1.19.1
|
||||
# via bddl
|
||||
kiwisolver==1.4.9
|
||||
# via matplotlib
|
||||
labmaze==1.0.6
|
||||
# via dm-control
|
||||
lazy-loader==0.5
|
||||
# via scikit-image
|
||||
librt==0.8.1
|
||||
# via mypy
|
||||
llvmlite==0.46.0
|
||||
# via numba
|
||||
lxml==6.0.2
|
||||
# via dm-control
|
||||
markdown==3.10.2
|
||||
# via tensorboard
|
||||
markdown-it-py==4.0.0
|
||||
# via
|
||||
# jupytext
|
||||
# mdit-py-plugins
|
||||
# rich
|
||||
markupsafe==3.0.3
|
||||
# via
|
||||
# jinja2
|
||||
# werkzeug
|
||||
matplotlib==3.10.8
|
||||
# via
|
||||
# hf-libero
|
||||
# lerobot
|
||||
matplotlib-inline==0.2.1
|
||||
# via ipython
|
||||
mdit-py-plugins==0.5.0
|
||||
# via jupytext
|
||||
mdurl==0.1.2
|
||||
# via markdown-it-py
|
||||
mergedeep==1.3.4
|
||||
# via draccus
|
||||
meshcat==0.3.2
|
||||
# via placo
|
||||
metaworld==3.0.0
|
||||
# via lerobot
|
||||
mock-serial==0.0.1
|
||||
# via lerobot
|
||||
mpmath==1.3.0
|
||||
# via sympy
|
||||
mujoco==3.5.0
|
||||
# via
|
||||
# dm-control
|
||||
# gym-aloha
|
||||
# gym-hil
|
||||
# hf-libero
|
||||
# metaworld
|
||||
# robosuite
|
||||
multidict==6.7.1
|
||||
# via
|
||||
# aiohttp
|
||||
# yarl
|
||||
multiprocess==0.70.18
|
||||
# via datasets
|
||||
mypy==1.19.1
|
||||
# via lerobot
|
||||
mypy-extensions==1.1.0
|
||||
# via
|
||||
# mypy
|
||||
# typing-inspect
|
||||
nbformat==5.10.4
|
||||
# via jupytext
|
||||
networkx==3.6.1
|
||||
# via
|
||||
# bddl
|
||||
# scikit-image
|
||||
# torch
|
||||
nodeenv==1.10.0
|
||||
# via pre-commit
|
||||
num2words==0.5.14
|
||||
# via lerobot
|
||||
numba==0.64.0
|
||||
# via robosuite
|
||||
numpy==2.2.6
|
||||
# via
|
||||
# accelerate
|
||||
# bddl
|
||||
# cmeel-boost
|
||||
# contourpy
|
||||
# datasets
|
||||
# diffusers
|
||||
# dm-control
|
||||
# dm-env
|
||||
# dm-tree
|
||||
# gymnasium
|
||||
# h5py
|
||||
# hebi-py
|
||||
# hf-libero
|
||||
# imageio
|
||||
# labmaze
|
||||
# lerobot
|
||||
# matplotlib
|
||||
# meshcat
|
||||
# metaworld
|
||||
# mujoco
|
||||
# numba
|
||||
# opencv-python
|
||||
# opencv-python-headless
|
||||
# pandas
|
||||
# peft
|
||||
# pyquaternion
|
||||
# reachy2-sdk
|
||||
# rerun-sdk
|
||||
# robomimic
|
||||
# robosuite
|
||||
# scikit-image
|
||||
# scipy
|
||||
# shapely
|
||||
# teleop
|
||||
# tensorboard
|
||||
# tensorboardx
|
||||
# tifffile
|
||||
# torchvision
|
||||
# transformers
|
||||
# transforms3d
|
||||
nvidia-cublas-cu12==12.8.4.1
|
||||
# via
|
||||
# nvidia-cudnn-cu12
|
||||
# nvidia-cusolver-cu12
|
||||
# torch
|
||||
nvidia-cuda-cupti-cu12==12.8.90
|
||||
# via torch
|
||||
nvidia-cuda-nvrtc-cu12==12.8.93
|
||||
# via torch
|
||||
nvidia-cuda-runtime-cu12==12.8.90
|
||||
# via torch
|
||||
nvidia-cudnn-cu12==9.10.2.21
|
||||
# via torch
|
||||
nvidia-cufft-cu12==11.3.3.83
|
||||
# via torch
|
||||
nvidia-cufile-cu12==1.13.1.3
|
||||
# via torch
|
||||
nvidia-curand-cu12==10.3.9.90
|
||||
# via torch
|
||||
nvidia-cusolver-cu12==11.7.3.90
|
||||
# via torch
|
||||
nvidia-cusparse-cu12==12.5.8.93
|
||||
# via
|
||||
# nvidia-cusolver-cu12
|
||||
# torch
|
||||
nvidia-cusparselt-cu12==0.7.1
|
||||
# via torch
|
||||
nvidia-nccl-cu12==2.27.5
|
||||
# via torch
|
||||
nvidia-nvjitlink-cu12==12.8.93
|
||||
# via
|
||||
# nvidia-cufft-cu12
|
||||
# nvidia-cusolver-cu12
|
||||
# nvidia-cusparse-cu12
|
||||
# torch
|
||||
nvidia-nvshmem-cu12==3.4.5
|
||||
# via torch
|
||||
nvidia-nvtx-cu12==12.8.90
|
||||
# via torch
|
||||
omegaconf==2.3.0
|
||||
# via hydra-core
|
||||
opencv-python==4.13.0.92
|
||||
# via
|
||||
# gym-pusht
|
||||
# hf-libero
|
||||
# reachy2-sdk
|
||||
# robosuite
|
||||
opencv-python-headless==4.12.0.88
|
||||
# via lerobot
|
||||
orderly-set==5.5.0
|
||||
# via deepdiff
|
||||
packaging==25.0
|
||||
# via
|
||||
# accelerate
|
||||
# datasets
|
||||
# huggingface-hub
|
||||
# hydra-core
|
||||
# jupytext
|
||||
# lazy-loader
|
||||
# lerobot
|
||||
# matplotlib
|
||||
# peft
|
||||
# pytest
|
||||
# qwen-vl-utils
|
||||
# reachy2-sdk
|
||||
# scikit-image
|
||||
# tensorboard
|
||||
# tensorboardx
|
||||
# transformers
|
||||
# wandb
|
||||
pandas==2.3.3
|
||||
# via
|
||||
# datasets
|
||||
# lerobot
|
||||
parso==0.8.6
|
||||
# via jedi
|
||||
pathspec==1.0.4
|
||||
# via mypy
|
||||
peft==0.18.1
|
||||
# via lerobot
|
||||
pexpect==4.9.0
|
||||
# via ipython
|
||||
pillow==12.1.1
|
||||
# via
|
||||
# diffusers
|
||||
# imageio
|
||||
# matplotlib
|
||||
# meshcat
|
||||
# qwen-vl-utils
|
||||
# rerun-sdk
|
||||
# robosuite
|
||||
# scikit-image
|
||||
# tensorboard
|
||||
# torchvision
|
||||
pin==3.4.0
|
||||
# via placo
|
||||
placo==0.9.16
|
||||
# via lerobot
|
||||
platformdirs==4.9.4
|
||||
# via
|
||||
# jupyter-core
|
||||
# python-discovery
|
||||
# virtualenv
|
||||
# wandb
|
||||
pluggy==1.6.0
|
||||
# via
|
||||
# pytest
|
||||
# pytest-cov
|
||||
pre-commit==4.5.1
|
||||
# via lerobot
|
||||
prompt-toolkit==3.0.52
|
||||
# via ipython
|
||||
propcache==0.4.1
|
||||
# via
|
||||
# aiohttp
|
||||
# yarl
|
||||
protobuf==6.31.1
|
||||
# via
|
||||
# dm-control
|
||||
# grpcio-tools
|
||||
# lerobot
|
||||
# reachy2-sdk
|
||||
# reachy2-sdk-api
|
||||
# tensorboard
|
||||
# tensorboardx
|
||||
# wandb
|
||||
psutil==7.2.2
|
||||
# via
|
||||
# accelerate
|
||||
# imageio
|
||||
# peft
|
||||
# robomimic
|
||||
ptyprocess==0.7.0
|
||||
# via pexpect
|
||||
pure-eval==0.2.3
|
||||
# via stack-data
|
||||
pyarrow==23.0.1
|
||||
# via
|
||||
# datasets
|
||||
# rerun-sdk
|
||||
pycparser==3.0
|
||||
# via cffi
|
||||
pydantic==2.12.5
|
||||
# via
|
||||
# fastapi
|
||||
# wandb
|
||||
pydantic-core==2.41.5
|
||||
# via pydantic
|
||||
pygame==2.6.1
|
||||
# via
|
||||
# gym-hil
|
||||
# gym-pusht
|
||||
# lerobot
|
||||
pygments==2.19.2
|
||||
# via
|
||||
# ipython
|
||||
# ipython-pygments-lexers
|
||||
# pytest
|
||||
# rich
|
||||
pymunk==6.11.1
|
||||
# via
|
||||
# gym-pusht
|
||||
# lerobot
|
||||
pyngrok==7.5.1
|
||||
# via meshcat
|
||||
pynput==1.8.1
|
||||
# via
|
||||
# gym-hil
|
||||
# lerobot
|
||||
pyopengl==3.1.10
|
||||
# via
|
||||
# dm-control
|
||||
# mujoco
|
||||
pyparsing==3.3.2
|
||||
# via
|
||||
# dm-control
|
||||
# matplotlib
|
||||
pyquaternion==0.9.9
|
||||
# via reachy2-sdk
|
||||
pyrealsense2==2.56.5.9235
|
||||
# via lerobot
|
||||
pyserial==3.5
|
||||
# via
|
||||
# dynamixel-sdk
|
||||
# feetech-servo-sdk
|
||||
# lerobot
|
||||
pytest==8.4.2
|
||||
# via
|
||||
# bddl
|
||||
# lerobot
|
||||
# pytest-cov
|
||||
# pytest-timeout
|
||||
# teleop
|
||||
pytest-cov==7.0.0
|
||||
# via lerobot
|
||||
pytest-timeout==2.4.0
|
||||
# via lerobot
|
||||
python-dateutil==2.9.0.post0
|
||||
# via
|
||||
# faker
|
||||
# matplotlib
|
||||
# pandas
|
||||
python-discovery==1.1.1
|
||||
# via virtualenv
|
||||
python-dotenv==1.2.2
|
||||
# via uvicorn
|
||||
python-xlib==0.33
|
||||
# via pynput
|
||||
pytz==2026.1.post1
|
||||
# via pandas
|
||||
pyyaml==6.0.3
|
||||
# via
|
||||
# accelerate
|
||||
# datasets
|
||||
# draccus
|
||||
# hebi-py
|
||||
# huggingface-hub
|
||||
# jupytext
|
||||
# omegaconf
|
||||
# peft
|
||||
# pre-commit
|
||||
# pyngrok
|
||||
# pyyaml-include
|
||||
# transformers
|
||||
# uvicorn
|
||||
# wandb
|
||||
pyyaml-include==1.4.1
|
||||
# via draccus
|
||||
pyzmq==27.1.0
|
||||
# via
|
||||
# lerobot
|
||||
# meshcat
|
||||
qwen-vl-utils==0.0.14
|
||||
# via lerobot
|
||||
reachy2-sdk==1.0.15
|
||||
# via lerobot
|
||||
reachy2-sdk-api==1.0.21
|
||||
# via reachy2-sdk
|
||||
referencing==0.37.0
|
||||
# via
|
||||
# jsonschema
|
||||
# jsonschema-specifications
|
||||
regex==2026.2.28
|
||||
# via
|
||||
# diffusers
|
||||
# transformers
|
||||
requests==2.32.5
|
||||
# via
|
||||
# datasets
|
||||
# diffusers
|
||||
# dm-control
|
||||
# qwen-vl-utils
|
||||
# teleop
|
||||
# wandb
|
||||
rerun-sdk==0.26.2
|
||||
# via lerobot
|
||||
rhoban-cmeel-jsoncpp==1.9.4.9
|
||||
# via placo
|
||||
rich==14.3.3
|
||||
# via typer
|
||||
robomimic==0.2.0
|
||||
# via hf-libero
|
||||
robosuite==1.4.0
|
||||
# via hf-libero
|
||||
rpds-py==0.30.0
|
||||
# via
|
||||
# jsonschema
|
||||
# referencing
|
||||
safetensors==0.7.0
|
||||
# via
|
||||
# accelerate
|
||||
# diffusers
|
||||
# lerobot
|
||||
# peft
|
||||
# transformers
|
||||
scikit-image==0.25.2
|
||||
# via
|
||||
# gym-pusht
|
||||
# lerobot
|
||||
scipy==1.17.1
|
||||
# via
|
||||
# dm-control
|
||||
# lerobot
|
||||
# metaworld
|
||||
# robosuite
|
||||
# scikit-image
|
||||
# torchdiffeq
|
||||
sentry-sdk==2.54.0
|
||||
# via wandb
|
||||
shapely==2.1.2
|
||||
# via gym-pusht
|
||||
shellingham==1.5.4
|
||||
# via typer
|
||||
six==1.17.0
|
||||
# via
|
||||
# pynput
|
||||
# python-dateutil
|
||||
# python-xlib
|
||||
smmap==5.0.3
|
||||
# via gitdb
|
||||
stack-data==0.6.3
|
||||
# via ipython
|
||||
starlette==0.52.1
|
||||
# via fastapi
|
||||
sympy==1.14.0
|
||||
# via torch
|
||||
teleop==0.1.4
|
||||
# via lerobot
|
||||
tensorboard==2.20.0
|
||||
# via robomimic
|
||||
tensorboard-data-server==0.7.2
|
||||
# via tensorboard
|
||||
tensorboardx==2.6.4
|
||||
# via robomimic
|
||||
termcolor==3.3.0
|
||||
# via
|
||||
# lerobot
|
||||
# robomimic
|
||||
thop==0.1.1.post2209072238
|
||||
# via hf-libero
|
||||
tifffile==2026.3.3
|
||||
# via scikit-image
|
||||
tokenizers==0.22.2
|
||||
# via transformers
|
||||
toml==0.10.2
|
||||
# via draccus
|
||||
torch==2.10.0
|
||||
# via
|
||||
# accelerate
|
||||
# lerobot
|
||||
# peft
|
||||
# robomimic
|
||||
# thop
|
||||
# torchdiffeq
|
||||
# torchvision
|
||||
torchcodec==0.10.0
|
||||
# via lerobot
|
||||
torchdiffeq==0.2.5
|
||||
# via lerobot
|
||||
torchvision==0.25.0
|
||||
# via
|
||||
# lerobot
|
||||
# robomimic
|
||||
tornado==6.5.4
|
||||
# via meshcat
|
||||
tqdm==4.67.3
|
||||
# via
|
||||
# datasets
|
||||
# dm-control
|
||||
# huggingface-hub
|
||||
# peft
|
||||
# robomimic
|
||||
# transformers
|
||||
traitlets==5.14.3
|
||||
# via
|
||||
# ipython
|
||||
# jupyter-core
|
||||
# matplotlib-inline
|
||||
# nbformat
|
||||
transformers==5.3.0
|
||||
# via
|
||||
# hf-libero
|
||||
# lerobot
|
||||
# peft
|
||||
transforms3d==0.4.2
|
||||
# via teleop
|
||||
triton==3.6.0
|
||||
# via torch
|
||||
typer==0.24.1
|
||||
# via
|
||||
# huggingface-hub
|
||||
# transformers
|
||||
typing-extensions==4.15.0
|
||||
# via
|
||||
# aiosignal
|
||||
# anyio
|
||||
# etils
|
||||
# faker
|
||||
# fastapi
|
||||
# gymnasium
|
||||
# huggingface-hub
|
||||
# mypy
|
||||
# pydantic
|
||||
# pydantic-core
|
||||
# referencing
|
||||
# rerun-sdk
|
||||
# starlette
|
||||
# torch
|
||||
# typing-inspect
|
||||
# typing-inspection
|
||||
# wandb
|
||||
typing-inspect==0.9.0
|
||||
# via draccus
|
||||
typing-inspection==0.4.2
|
||||
# via
|
||||
# fastapi
|
||||
# pydantic
|
||||
tzdata==2025.3
|
||||
# via pandas
|
||||
u-msgpack-python==2.8.0
|
||||
# via meshcat
|
||||
urllib3==2.6.3
|
||||
# via
|
||||
# requests
|
||||
# sentry-sdk
|
||||
uvicorn[standard]==0.41.0
|
||||
# via teleop
|
||||
uvloop==0.22.1
|
||||
# via uvicorn
|
||||
virtualenv==21.1.0
|
||||
# via pre-commit
|
||||
wandb==0.24.2
|
||||
# via
|
||||
# hf-libero
|
||||
# lerobot
|
||||
watchfiles==1.1.1
|
||||
# via uvicorn
|
||||
wcwidth==0.6.0
|
||||
# via prompt-toolkit
|
||||
websocket-client==1.9.0
|
||||
# via teleop
|
||||
websockets==16.0
|
||||
# via uvicorn
|
||||
werkzeug==3.1.6
|
||||
# via tensorboard
|
||||
wrapt==2.1.2
|
||||
# via dm-tree
|
||||
xxhash==3.6.0
|
||||
# via datasets
|
||||
yarl==1.23.0
|
||||
# via aiohttp
|
||||
zipp==3.23.0
|
||||
# via
|
||||
# etils
|
||||
# importlib-metadata
|
||||
|
||||
# The following packages are considered to be unsafe in a requirements file:
|
||||
# setuptools
|
||||
@@ -0,0 +1,9 @@
|
||||
# requirements.in
|
||||
|
||||
# requirements-macos.txt was generated on macOS and is platform-specific (macOS 26.3.1 25D2128 arm64).
|
||||
# Darwin MacBook-Pro.local 25.3.0 Darwin Kernel Version 25.3.0: Wed Jan 28 20:54:55 PST 2026; root:xnu-12377.91.3~2/RELEASE_ARM64_T8132 arm64
|
||||
|
||||
# requirements-ubuntu.txt was generated on Linux and is platform-specific (Ubuntu 24.04.4 LTS x86_64).
|
||||
# Linux lerobot-linux 6.17.0-14-generic #14~24.04.1-Ubuntu SMP PREEMPT_DYNAMIC Thu Jan 15 15:52:10 UTC 2 x86_64 x86_64 x86_64 GNU/Linux
|
||||
|
||||
-e .[all]
|
||||
@@ -18,7 +18,6 @@ from __future__ import annotations
|
||||
# Utilities
|
||||
########################################################################################
|
||||
import time
|
||||
from contextlib import nullcontext
|
||||
from copy import copy
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
@@ -26,6 +25,7 @@ import numpy as np
|
||||
import torch
|
||||
|
||||
from lerobot.policies import PreTrainedPolicy, prepare_observation_for_inference
|
||||
from lerobot.utils.device_utils import get_safe_autocast_context
|
||||
from lerobot.utils.import_utils import _deepdiff_available, require_package
|
||||
|
||||
if TYPE_CHECKING or _deepdiff_available:
|
||||
@@ -76,7 +76,7 @@ def predict_action(
|
||||
observation = copy(observation)
|
||||
with (
|
||||
torch.inference_mode(),
|
||||
torch.autocast(device_type=device.type) if device.type == "cuda" and use_amp else nullcontext(),
|
||||
get_safe_autocast_context(device, enabled=use_amp),
|
||||
):
|
||||
# Convert to pytorch format: channel first and float32 in [0,1] with batch dimension
|
||||
observation = prepare_observation_for_inference(observation, device, task, robot_type)
|
||||
|
||||
@@ -757,7 +757,7 @@ class RoboTwinEnvConfig(EnvConfig):
|
||||
|
||||
task: str = "beat_block_hammer" # single task or comma-separated list
|
||||
fps: int = 25
|
||||
episode_length: int = 1200
|
||||
episode_length: int = 300
|
||||
obs_type: str = "pixels_agent_pos"
|
||||
render_mode: str = "rgb_array"
|
||||
# Available cameras from RoboTwin's aloha-agilex embodiment: head_camera
|
||||
@@ -768,9 +768,6 @@ class RoboTwinEnvConfig(EnvConfig):
|
||||
# must equal what SAPIEN actually renders.
|
||||
observation_height: int = 240
|
||||
observation_width: int = 320
|
||||
# "joint": 14-d joint-space control. "ee": 16-d end-effector-pose deltas executed via CuRobo IK
|
||||
# (for world-model policies like LingBot-VA that predict per-arm xyz+quaternion+gripper poses).
|
||||
action_mode: str = "joint"
|
||||
features: dict[str, PolicyFeature] = field(
|
||||
default_factory=lambda: {
|
||||
ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(14,)),
|
||||
@@ -787,8 +784,6 @@ class RoboTwinEnvConfig(EnvConfig):
|
||||
)
|
||||
|
||||
def __post_init__(self):
|
||||
if self.action_mode == "ee":
|
||||
self.features[ACTION] = PolicyFeature(type=FeatureType.ACTION, shape=(16,))
|
||||
cam_list = [c.strip() for c in self.camera_names.split(",") if c.strip()]
|
||||
for cam in cam_list:
|
||||
self.features[f"pixels/{cam}"] = PolicyFeature(
|
||||
@@ -831,7 +826,6 @@ class RoboTwinEnvConfig(EnvConfig):
|
||||
observation_height=self.observation_height,
|
||||
observation_width=self.observation_width,
|
||||
episode_length=self.episode_length,
|
||||
action_mode=self.action_mode,
|
||||
)
|
||||
|
||||
|
||||
|
||||
@@ -17,7 +17,6 @@ from __future__ import annotations
|
||||
|
||||
import importlib
|
||||
import logging
|
||||
import os
|
||||
from collections import defaultdict
|
||||
from collections.abc import Callable, Sequence
|
||||
from functools import partial
|
||||
@@ -29,17 +28,9 @@ import torch
|
||||
from gymnasium import spaces
|
||||
|
||||
from lerobot.types import RobotObservation
|
||||
from lerobot.utils.import_utils import _scipy_available
|
||||
|
||||
from .utils import _LazyAsyncVectorEnv
|
||||
|
||||
# scipy is only used for end-effector-pose composition (``--env.action_mode=ee``); guard it so this
|
||||
# module (and its base-env unit tests, which mock the RoboTwin runtime) imports without scipy installed.
|
||||
if _scipy_available:
|
||||
from scipy.spatial.transform import Rotation
|
||||
else:
|
||||
Rotation = None
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Camera names as used by RoboTwin 2.0. The wrapper appends "_rgb" when looking
|
||||
@@ -50,124 +41,10 @@ ROBOTWIN_CAMERA_NAMES: tuple[str, ...] = (
|
||||
"right_camera",
|
||||
)
|
||||
|
||||
ACTION_DIM = 14 # 7 DOF × 2 arms (joint-space control mode)
|
||||
# End-effector-pose control mode: per arm [x, y, z, qx, qy, qz, qw, gripper] = 8, dual-arm = 16.
|
||||
# Used by world-model policies (e.g. LingBot-VA) that predict eef-pose deltas executed via CuRobo IK.
|
||||
EEF_ACTION_DIM = 16
|
||||
ACTION_DIM = 14 # 7 DOF × 2 arms
|
||||
ACTION_LOW = -1.0
|
||||
ACTION_HIGH = 1.0
|
||||
DEFAULT_EPISODE_LENGTH = 1200
|
||||
OFFICIAL_INSTRUCTION_ENV = "LEROBOT_ROBOTWIN_OFFICIAL_INSTRUCTION"
|
||||
OFFICIAL_INSTRUCTION_TYPE_ENV = "LEROBOT_ROBOTWIN_INSTRUCTION_TYPE"
|
||||
OFFICIAL_INSTRUCTION_MAX_ENV = "LEROBOT_ROBOTWIN_INSTRUCTION_MAX"
|
||||
|
||||
|
||||
def _compose_eef_pose(new_pose: np.ndarray, init_pose: np.ndarray) -> np.ndarray:
|
||||
"""Compose a single-arm predicted delta pose onto the initial pose.
|
||||
|
||||
``new_pose`` / ``init_pose`` are 8-vectors ``[x, y, z, qx, qy, qz, qw, gripper]``. Translation
|
||||
is added, rotation is composed (``init_R * new_R``), and the gripper is taken from the
|
||||
prediction. Mirrors ``add_eef_pose`` in the upstream LingBot-VA RoboTwin client.
|
||||
"""
|
||||
new_r = Rotation.from_quat(new_pose[3:7])
|
||||
init_r = Rotation.from_quat(init_pose[3:7])
|
||||
out_rot = (init_r * new_r).as_quat()
|
||||
out_trans = new_pose[:3] + init_pose[:3]
|
||||
return np.concatenate([out_trans, out_rot, new_pose[7:8]])
|
||||
|
||||
|
||||
def _add_init_eef_pose(delta_pose: np.ndarray, init_pose: np.ndarray) -> np.ndarray:
|
||||
"""Compose a dual-arm (16-d) predicted delta pose onto the initial eef pose, normalizing quats."""
|
||||
left = _compose_eef_pose(delta_pose[:8], init_pose[:8])
|
||||
right = _compose_eef_pose(delta_pose[8:], init_pose[8:])
|
||||
out = np.concatenate([left, right])
|
||||
# Normalize the two quaternions (indices 3:7 and 11:15) as the upstream client does.
|
||||
out[3:7] = out[3:7] / (np.linalg.norm(out[3:7]) + 1e-8)
|
||||
out[11:15] = out[11:15] / (np.linalg.norm(out[11:15]) + 1e-8)
|
||||
return out
|
||||
|
||||
|
||||
def _env_flag(name: str, default: bool = False) -> bool:
|
||||
raw = os.environ.get(name)
|
||||
if raw is None:
|
||||
return default
|
||||
return raw.strip().lower() in {"1", "true", "yes", "on"}
|
||||
|
||||
|
||||
def _arm_for_block(block: Any) -> str:
|
||||
return "left" if float(block.get_pose().p[0]) < 0 else "right"
|
||||
|
||||
|
||||
def _robotwin_blocks_episode_info(task_name: str, env: Any) -> dict[str, str] | None:
|
||||
"""Infer the episode-info dict used by RoboTwin's official instruction generator for block ranking."""
|
||||
if task_name == "blocks_ranking_rgb":
|
||||
return {
|
||||
"{A}": "red block",
|
||||
"{B}": "green block",
|
||||
"{C}": "blue block",
|
||||
"{a}": _arm_for_block(env.block1),
|
||||
"{b}": _arm_for_block(env.block2),
|
||||
"{c}": _arm_for_block(env.block3),
|
||||
}
|
||||
if task_name == "blocks_ranking_size":
|
||||
return {
|
||||
"{A}": "large block",
|
||||
"{B}": "medium block",
|
||||
"{C}": "small block",
|
||||
"{a}": _arm_for_block(env.block1),
|
||||
"{b}": _arm_for_block(env.block2),
|
||||
"{c}": _arm_for_block(env.block3),
|
||||
}
|
||||
return None
|
||||
|
||||
|
||||
def _generate_robotwin_official_instruction(task_name: str, env: Any) -> str:
|
||||
"""Generate language with RoboTwin's official task templates, matching its eval client."""
|
||||
fallback = task_name.replace("_", " ")
|
||||
episode_info = _robotwin_blocks_episode_info(task_name, env)
|
||||
if episode_info is None:
|
||||
logger.warning(
|
||||
"Official RoboTwin instruction is not implemented for task=%s; using %r.", task_name, fallback
|
||||
)
|
||||
return fallback
|
||||
|
||||
try:
|
||||
# Part of the robotwin simulator repo, this is being pulled by the docker image running robotwin
|
||||
# see https://github.com/RoboTwin-Platform/RoboTwin/tree/main/description
|
||||
# Used to generate the official instructions
|
||||
from description.utils.generate_episode_instructions import generate_episode_descriptions
|
||||
except Exception:
|
||||
logger.warning(
|
||||
"Failed to import RoboTwin official instruction generator; using %r.", fallback, exc_info=True
|
||||
)
|
||||
return fallback
|
||||
|
||||
instruction_type = os.environ.get(OFFICIAL_INSTRUCTION_TYPE_ENV, "seen")
|
||||
try:
|
||||
max_descriptions = int(os.environ.get(OFFICIAL_INSTRUCTION_MAX_ENV, "1000000"))
|
||||
except ValueError:
|
||||
max_descriptions = 1000000
|
||||
|
||||
results = generate_episode_descriptions(task_name, [episode_info], max_descriptions=max_descriptions)
|
||||
if not results:
|
||||
logger.warning(
|
||||
"RoboTwin generated no official instructions for task=%s; using %r.", task_name, fallback
|
||||
)
|
||||
return fallback
|
||||
|
||||
options = results[0].get(instruction_type) or results[0].get("seen") or results[0].get("unseen")
|
||||
if not options:
|
||||
logger.warning(
|
||||
"RoboTwin generated no %s official instructions for task=%s; using %r.",
|
||||
instruction_type,
|
||||
task_name,
|
||||
fallback,
|
||||
)
|
||||
return fallback
|
||||
|
||||
return str(np.random.choice(options))
|
||||
|
||||
|
||||
DEFAULT_EPISODE_LENGTH = 300
|
||||
# D435 dims from task_config/_camera_config.yml (what demo_clean.yml selects).
|
||||
DEFAULT_CAMERA_H = 240
|
||||
DEFAULT_CAMERA_W = 320
|
||||
@@ -357,7 +234,6 @@ class RoboTwinEnv(gym.Env):
|
||||
observation_width: int | None = None,
|
||||
episode_length: int = DEFAULT_EPISODE_LENGTH,
|
||||
render_mode: str = "rgb_array",
|
||||
action_mode: str = "joint",
|
||||
):
|
||||
super().__init__()
|
||||
self.task_name = task_name
|
||||
@@ -365,13 +241,6 @@ class RoboTwinEnv(gym.Env):
|
||||
self.task_description = task_name.replace("_", " ")
|
||||
self.episode_index = episode_index
|
||||
self._reset_stride = n_envs
|
||||
# "joint": 14-d joint-space actions via take_action(action). "ee": 16-d end-effector-pose
|
||||
# deltas (added onto the episode's initial eef pose) executed via take_action(.., "ee") + IK.
|
||||
if action_mode not in ("joint", "ee"):
|
||||
raise ValueError(f"action_mode must be 'joint' or 'ee'; got {action_mode!r}")
|
||||
self.action_mode = action_mode
|
||||
self._action_dim = EEF_ACTION_DIM if action_mode == "ee" else ACTION_DIM
|
||||
self._init_eef_pose: np.ndarray | None = None
|
||||
self.camera_names = list(camera_names)
|
||||
# Default to D435 dims (the camera type baked into task_config/demo_clean.yml).
|
||||
# The YAML-driven lookup is deferred to reset() so construction doesn't
|
||||
@@ -402,7 +271,7 @@ class RoboTwinEnv(gym.Env):
|
||||
}
|
||||
)
|
||||
self.action_space = spaces.Box(
|
||||
low=ACTION_LOW, high=ACTION_HIGH, shape=(self._action_dim,), dtype=np.float32
|
||||
low=ACTION_LOW, high=ACTION_HIGH, shape=(ACTION_DIM,), dtype=np.float32
|
||||
)
|
||||
|
||||
def _ensure_env(self) -> None:
|
||||
@@ -448,18 +317,6 @@ class RoboTwinEnv(gym.Env):
|
||||
|
||||
return {"pixels": images, "agent_pos": joint_state}
|
||||
|
||||
def _read_eef_pose(self) -> np.ndarray:
|
||||
"""Read the current 16-d dual-arm eef pose [left(xyz+quat)+grip, right(xyz+quat)+grip]."""
|
||||
assert self._env is not None, "_read_eef_pose called before _ensure_env()"
|
||||
ep = self._env.get_obs()["endpose"]
|
||||
pose = (
|
||||
list(ep["left_endpose"])
|
||||
+ [ep["left_gripper"]]
|
||||
+ list(ep["right_endpose"])
|
||||
+ [ep["right_gripper"]]
|
||||
)
|
||||
return np.asarray(pose, dtype=np.float64)
|
||||
|
||||
def reset(self, seed: int | None = None, **kwargs) -> tuple[RobotObservation, dict]:
|
||||
self._ensure_env()
|
||||
super().reset(seed=seed)
|
||||
@@ -473,32 +330,16 @@ class RoboTwinEnv(gym.Env):
|
||||
self.episode_index += self._reset_stride
|
||||
self._step_count = 0
|
||||
|
||||
use_official_instruction = self.task_name in {"blocks_ranking_rgb", "blocks_ranking_size"}
|
||||
if _env_flag(OFFICIAL_INSTRUCTION_ENV, default=use_official_instruction):
|
||||
self.task_description = _generate_robotwin_official_instruction(self.task_name, self._env)
|
||||
if hasattr(self._env, "set_instruction"):
|
||||
self._env.set_instruction(instruction=self.task_description)
|
||||
logger.info("RoboTwin official instruction | task=%s | %s", self.task_name, self.task_description)
|
||||
else:
|
||||
self.task_description = self.task_name.replace("_", " ")
|
||||
|
||||
# In eef mode the policy predicts pose deltas relative to the initial eef pose.
|
||||
if self.action_mode == "ee":
|
||||
self._init_eef_pose = self._read_eef_pose()
|
||||
|
||||
obs = self._get_obs()
|
||||
return obs, {"is_success": False, "task": self.task_name}
|
||||
|
||||
def step(self, action: np.ndarray) -> tuple[RobotObservation, float, bool, bool, dict[str, Any]]:
|
||||
assert self._env is not None, "step() called before reset()"
|
||||
if action.ndim != 1 or action.shape[0] != self._action_dim:
|
||||
raise ValueError(f"Expected 1-D action of shape ({self._action_dim},), got {action.shape}")
|
||||
if action.ndim != 1 or action.shape[0] != ACTION_DIM:
|
||||
raise ValueError(f"Expected 1-D action of shape ({ACTION_DIM},), got {action.shape}")
|
||||
|
||||
with torch.enable_grad():
|
||||
if self.action_mode == "ee":
|
||||
ee_action = _add_init_eef_pose(np.asarray(action, dtype=np.float64), self._init_eef_pose)
|
||||
self._env.take_action(ee_action, action_type="ee")
|
||||
elif hasattr(self._env, "take_action"):
|
||||
if hasattr(self._env, "take_action"):
|
||||
self._env.take_action(action)
|
||||
else:
|
||||
self._env.step(action)
|
||||
@@ -557,7 +398,6 @@ def _make_env_fns(
|
||||
observation_height: int,
|
||||
observation_width: int,
|
||||
episode_length: int,
|
||||
action_mode: str = "joint",
|
||||
) -> list[Callable[[], RoboTwinEnv]]:
|
||||
"""Return n_envs factory callables for a single task."""
|
||||
|
||||
@@ -570,7 +410,6 @@ def _make_env_fns(
|
||||
observation_height=observation_height,
|
||||
observation_width=observation_width,
|
||||
episode_length=episode_length,
|
||||
action_mode=action_mode,
|
||||
)
|
||||
|
||||
return [partial(_make_one, i) for i in range(n_envs)]
|
||||
@@ -584,7 +423,6 @@ def create_robotwin_envs(
|
||||
observation_height: int = DEFAULT_CAMERA_H,
|
||||
observation_width: int = DEFAULT_CAMERA_W,
|
||||
episode_length: int = DEFAULT_EPISODE_LENGTH,
|
||||
action_mode: str = "joint",
|
||||
) -> dict[str, dict[int, Any]]:
|
||||
"""Create vectorized RoboTwin 2.0 environments.
|
||||
|
||||
@@ -635,7 +473,6 @@ def create_robotwin_envs(
|
||||
observation_height=observation_height,
|
||||
observation_width=observation_width,
|
||||
episode_length=episode_length,
|
||||
action_mode=action_mode,
|
||||
)
|
||||
if is_async:
|
||||
lazy = _LazyAsyncVectorEnv(fns, cached_obs_space, cached_act_space, cached_metadata)
|
||||
|
||||
@@ -113,13 +113,11 @@ class DynamixelMotorsBus(SerialMotorsBus):
|
||||
port: str,
|
||||
motors: dict[str, Motor],
|
||||
calibration: dict[str, MotorCalibration] | None = None,
|
||||
protocol_version: int = PROTOCOL_VERSION,
|
||||
):
|
||||
require_package("dynamixel-sdk", extra="dynamixel", import_name="dynamixel_sdk")
|
||||
super().__init__(port, motors, calibration)
|
||||
self.port_handler = dxl.PortHandler(self.port)
|
||||
self.packet_handler = dxl.PacketHandler(protocol_version)
|
||||
print(f"Using protocol version {protocol_version}")
|
||||
self.packet_handler = dxl.PacketHandler(PROTOCOL_VERSION)
|
||||
self.sync_reader = dxl.GroupSyncRead(self.port_handler, self.packet_handler, 0, 0)
|
||||
self.sync_writer = dxl.GroupSyncWrite(self.port_handler, self.packet_handler, 0, 0)
|
||||
self._comm_success = dxl.COMM_SUCCESS
|
||||
|
||||
@@ -33,58 +33,6 @@
|
||||
# 2. We can change the value of the MyControlTableKey enums without impacting the client code
|
||||
|
||||
|
||||
# {data_name: (address, size_byte)}
|
||||
# https://emanual.robotis.com/docs/en/dxl/ax/{MODEL}/#control-table
|
||||
AX_SERIES_CONTROL_TABLE = {
|
||||
# EEPROM Area
|
||||
"Model_Number": (0, 2),
|
||||
"Firmware_Version": (2, 1),
|
||||
"ID": (3, 1),
|
||||
"Baud_Rate": (4, 1),
|
||||
"Return_Delay_Time": (5, 1),
|
||||
"CW_Angle_Limit": (6, 2),
|
||||
"CCW_Angle_Limit": (8, 2),
|
||||
"Temperature_Limit": (11, 1),
|
||||
"Min_Voltage_Limit": (12, 1),
|
||||
"Max_Voltage_Limit": (13, 1),
|
||||
"Max_Torque": (14, 2),
|
||||
"Status_Return_Level": (16, 1),
|
||||
"Alarm_LED": (17, 1),
|
||||
"Shutdown": (18, 1),
|
||||
# RAM Area
|
||||
"Torque_Enable": (24, 1),
|
||||
"LED": (25, 1),
|
||||
"CW_Compliance_Margin": (26, 1),
|
||||
"CCW_Compliance_Margin": (27, 1),
|
||||
"CW_Compliance_Slope": (28, 1),
|
||||
"CCW_Compliance_Slope": (29, 1),
|
||||
"Goal_Position": (30, 2),
|
||||
"Moving_Speed": (32, 2),
|
||||
"Torque_Limit": (34, 2),
|
||||
"Present_Position": (36, 2),
|
||||
"Present_Speed": (38, 2),
|
||||
"Present_Load": (40, 2),
|
||||
"Present_Voltage": (42, 1),
|
||||
"Present_Temperature": (43, 1),
|
||||
"Registered": (44, 1),
|
||||
"Moving": (46, 1),
|
||||
"Lock": (47, 1),
|
||||
"Punch": (48, 2),
|
||||
}
|
||||
|
||||
# https://emanual.robotis.com/docs/en/dxl/ax/{MODEL}/#baud-rate4
|
||||
AX_SERIES_BAUDRATE_TABLE = {
|
||||
9_600: 207,
|
||||
19_200: 103,
|
||||
57_600: 34,
|
||||
115_200: 16,
|
||||
200_000: 9,
|
||||
250_000: 7,
|
||||
400_000: 4,
|
||||
500_000: 3,
|
||||
1_000_000: 1,
|
||||
}
|
||||
|
||||
# {data_name: (address, size_byte)}
|
||||
# https://emanual.robotis.com/docs/en/dxl/x/{MODEL}/#control-table
|
||||
X_SERIES_CONTROL_TABLE = {
|
||||
@@ -166,14 +114,6 @@ X_SERIES_ENCODINGS_TABLE = {
|
||||
"Present_Velocity": X_SERIES_CONTROL_TABLE["Present_Velocity"][1],
|
||||
}
|
||||
|
||||
# {data_name: size_byte}
|
||||
AX_SERIES_ENCODINGS_TABLE = {
|
||||
"Goal_Position": AX_SERIES_CONTROL_TABLE["Goal_Position"][1],
|
||||
"Moving_Speed": AX_SERIES_CONTROL_TABLE["Moving_Speed"][1],
|
||||
"Present_Position": AX_SERIES_CONTROL_TABLE["Present_Position"][1],
|
||||
"Present_Speed": AX_SERIES_CONTROL_TABLE["Present_Speed"][1],
|
||||
}
|
||||
|
||||
MODEL_ENCODING_TABLE = {
|
||||
"x_series": X_SERIES_ENCODINGS_TABLE,
|
||||
"xl330-m077": X_SERIES_ENCODINGS_TABLE,
|
||||
@@ -182,8 +122,6 @@ MODEL_ENCODING_TABLE = {
|
||||
"xm430-w350": X_SERIES_ENCODINGS_TABLE,
|
||||
"xm540-w270": X_SERIES_ENCODINGS_TABLE,
|
||||
"xc430-w150": X_SERIES_ENCODINGS_TABLE,
|
||||
"ax_series": AX_SERIES_ENCODINGS_TABLE,
|
||||
"ax-12a": AX_SERIES_ENCODINGS_TABLE,
|
||||
}
|
||||
|
||||
# {model: model_resolution}
|
||||
@@ -196,8 +134,6 @@ MODEL_RESOLUTION = {
|
||||
"xm430-w350": 4096,
|
||||
"xm540-w270": 4096,
|
||||
"xc430-w150": 4096,
|
||||
"ax_series": 1024,
|
||||
"ax-12a": 1024,
|
||||
}
|
||||
|
||||
# {model: model_number}
|
||||
@@ -209,7 +145,6 @@ MODEL_NUMBER_TABLE = {
|
||||
"xm430-w350": 1020,
|
||||
"xm540-w270": 1120,
|
||||
"xc430-w150": 1070,
|
||||
"ax-12a": 12,
|
||||
}
|
||||
|
||||
# {model: available_operating_modes}
|
||||
@@ -231,8 +166,6 @@ MODEL_CONTROL_TABLE = {
|
||||
"xm430-w350": X_SERIES_CONTROL_TABLE,
|
||||
"xm540-w270": X_SERIES_CONTROL_TABLE,
|
||||
"xc430-w150": X_SERIES_CONTROL_TABLE,
|
||||
"ax_series": AX_SERIES_CONTROL_TABLE,
|
||||
"ax-12a": AX_SERIES_CONTROL_TABLE,
|
||||
}
|
||||
|
||||
MODEL_BAUDRATE_TABLE = {
|
||||
@@ -243,8 +176,6 @@ MODEL_BAUDRATE_TABLE = {
|
||||
"xm430-w350": X_SERIES_BAUDRATE_TABLE,
|
||||
"xm540-w270": X_SERIES_BAUDRATE_TABLE,
|
||||
"xc430-w150": X_SERIES_BAUDRATE_TABLE,
|
||||
"ax_series": AX_SERIES_BAUDRATE_TABLE,
|
||||
"ax-12a": AX_SERIES_BAUDRATE_TABLE,
|
||||
}
|
||||
|
||||
AVAILABLE_BAUDRATES = [
|
||||
|
||||
@@ -83,50 +83,6 @@ class VQBeTSchedulerConfig(LRSchedulerConfig):
|
||||
return LambdaLR(optimizer, lr_lambda, -1)
|
||||
|
||||
|
||||
@LRSchedulerConfig.register_subclass("constant_with_warmup")
|
||||
@dataclass
|
||||
class ConstantWithWarmupSchedulerConfig(LRSchedulerConfig):
|
||||
"""Linear warmup followed by a constant learning rate.
|
||||
|
||||
Mirrors the ``warmup_constant_lambda`` used by LingBot-VA (upstream ``wan_va/train.py``):
|
||||
the LR ramps linearly from 0 to the peak over ``num_warmup_steps`` steps, then stays flat.
|
||||
"""
|
||||
|
||||
num_warmup_steps: int = 1000
|
||||
|
||||
def build(self, optimizer: Optimizer, num_training_steps: int) -> LambdaLR:
|
||||
warmup_steps = self.num_warmup_steps or 0
|
||||
|
||||
def lr_lambda(current_step):
|
||||
if current_step < warmup_steps:
|
||||
return float(current_step) / float(max(1, warmup_steps))
|
||||
return 1.0
|
||||
|
||||
return LambdaLR(optimizer, lr_lambda, -1)
|
||||
|
||||
|
||||
@LRSchedulerConfig.register_subclass("cosine_annealing_with_warmup")
|
||||
@dataclass
|
||||
class CosineAnnealingWithWarmupSchedulerConfig(LRSchedulerConfig):
|
||||
"""Linear warmup followed by cosine annealing from the peak LR to zero.
|
||||
|
||||
Used by EVO1; the annealing phase always spans the remaining training steps.
|
||||
"""
|
||||
|
||||
num_warmup_steps: int
|
||||
|
||||
def build(self, optimizer: Optimizer, num_training_steps: int) -> LambdaLR:
|
||||
def lr_lambda(current_step: int) -> float:
|
||||
if current_step < self.num_warmup_steps:
|
||||
return current_step / max(1, self.num_warmup_steps)
|
||||
progress = (current_step - self.num_warmup_steps) / max(
|
||||
1, num_training_steps - self.num_warmup_steps
|
||||
)
|
||||
return max(0.0, 0.5 * (1.0 + math.cos(math.pi * progress)))
|
||||
|
||||
return LambdaLR(optimizer, lr_lambda, -1)
|
||||
|
||||
|
||||
@LRSchedulerConfig.register_subclass("cosine_decay_with_warmup")
|
||||
@dataclass
|
||||
class CosineDecayWithWarmupSchedulerConfig(LRSchedulerConfig):
|
||||
|
||||
@@ -17,12 +17,10 @@ from lerobot.utils.action_interpolator import ActionInterpolator as ActionInterp
|
||||
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 .evo1.configuration_evo1 import Evo1Config as Evo1Config
|
||||
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 .lingbot_va.configuration_lingbot_va import LingBotVAConfig as LingBotVAConfig
|
||||
from .molmoact2.configuration_molmoact2 import MolmoAct2Config as MolmoAct2Config
|
||||
from .multi_task_dit.configuration_multi_task_dit import MultiTaskDiTConfig as MultiTaskDiTConfig
|
||||
from .pi0.configuration_pi0 import PI0Config as PI0Config
|
||||
@@ -47,9 +45,7 @@ __all__ = [
|
||||
"EO1Config",
|
||||
"FastWAMConfig",
|
||||
"GaussianActorConfig",
|
||||
"Evo1Config",
|
||||
"GrootConfig",
|
||||
"LingBotVAConfig",
|
||||
"MolmoAct2Config",
|
||||
"MultiTaskDiTConfig",
|
||||
"PI0Config",
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
../../../../docs/source/policy_evo1_README.md
|
||||
@@ -1,19 +0,0 @@
|
||||
# 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.
|
||||
|
||||
from .configuration_evo1 import Evo1Config
|
||||
from .modeling_evo1 import Evo1Policy
|
||||
from .processor_evo1 import make_evo1_pre_post_processors
|
||||
|
||||
__all__ = ["Evo1Config", "Evo1Policy", "make_evo1_pre_post_processors"]
|
||||
@@ -1,252 +0,0 @@
|
||||
# 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.
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
from lerobot.configs.policies import PreTrainedConfig
|
||||
from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
|
||||
from lerobot.optim.optimizers import AdamWConfig
|
||||
from lerobot.optim.schedulers import CosineAnnealingWithWarmupSchedulerConfig
|
||||
from lerobot.utils.constants import ACTION, OBS_IMAGES, OBS_STATE
|
||||
|
||||
from ..rtc.configuration_rtc import RTCConfig
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@PreTrainedConfig.register_subclass("evo1")
|
||||
@dataclass
|
||||
class Evo1Config(PreTrainedConfig):
|
||||
training_stage: str = "stage1"
|
||||
# When True and the policy runs on CUDA, EVO1 wraps its own forward passes (training and
|
||||
# inference) in a bfloat16 autocast block, so its numerics do not depend on the dtype of any
|
||||
# outer autocast context opened by lerobot-train/lerobot-eval.
|
||||
use_amp: bool = True
|
||||
|
||||
n_obs_steps: int = 1
|
||||
chunk_size: int = 50
|
||||
n_action_steps: int = 50
|
||||
|
||||
max_state_dim: int = 24
|
||||
max_action_dim: int = 24
|
||||
max_views: int = 3
|
||||
image_resolution: tuple[int, int] = (448, 448)
|
||||
empty_cameras: int = 0
|
||||
postprocess_action_dim: int | None = None
|
||||
binarize_gripper: bool = False
|
||||
gripper_index: int = 6
|
||||
gripper_threshold: float = 0.5
|
||||
gripper_below_threshold_value: float = 1.0
|
||||
gripper_above_threshold_value: float = -1.0
|
||||
|
||||
normalization_mapping: dict[str, NormalizationMode] = field(
|
||||
default_factory=lambda: {
|
||||
"VISUAL": NormalizationMode.IDENTITY,
|
||||
"STATE": NormalizationMode.MIN_MAX,
|
||||
"ACTION": NormalizationMode.MIN_MAX,
|
||||
}
|
||||
)
|
||||
|
||||
vlm_model_name: str = "OpenGVLab/InternVL3-1B-hf"
|
||||
vlm_num_layers: int | None = 14
|
||||
vlm_dtype: str = "bfloat16"
|
||||
# Max token length for tokenizing the (image placeholders + instruction) prompt. Prompts longer
|
||||
# than this are right-truncated, so raise it for tasks with long language instructions or many views.
|
||||
max_text_length: int = 1024
|
||||
use_flash_attn: bool = True
|
||||
action_head: str = "flowmatching"
|
||||
embed_dim: int = 896
|
||||
hidden_dim: int = 1024
|
||||
state_hidden_dim: int = 1024
|
||||
num_heads: int = 8
|
||||
num_layers: int = 8
|
||||
dropout: float = 0.0
|
||||
num_inference_timesteps: int = 32
|
||||
num_categories: int = 1
|
||||
# When True, the action head is conditioned on a single pooled VL token (the last non-padding
|
||||
# token of the causal decoder) instead of the full fused token sequence.
|
||||
return_cls_only: bool = False
|
||||
enable_gradient_checkpointing: bool = True
|
||||
gradient_checkpointing_use_reentrant: bool = False
|
||||
|
||||
finetune_vlm: bool | None = None
|
||||
finetune_language_model: bool | None = None
|
||||
finetune_vision_model: bool | None = None
|
||||
finetune_action_head: bool | None = None
|
||||
# Reapply stage defaults after loading checkpoint configs so stage2 cannot
|
||||
# accidentally inherit the frozen VLM flags stored by a stage1 checkpoint.
|
||||
apply_training_stage_defaults: bool = True
|
||||
|
||||
task_field: str = "task"
|
||||
embodiment_id_field: str | None = None
|
||||
default_embodiment_id: int = 0
|
||||
|
||||
# Real-Time Chunking guidance for asynchronous inference (lerobot-rollout --inference.type=rtc
|
||||
# sets this and calls init_rtc_processor()); None disables RTC.
|
||||
rtc_config: RTCConfig | None = None
|
||||
|
||||
optimizer_lr: float = 1e-5
|
||||
optimizer_betas: tuple[float, float] = (0.9, 0.999)
|
||||
optimizer_eps: float = 1e-8
|
||||
optimizer_weight_decay: float = 1e-5
|
||||
optimizer_grad_clip_norm: float = 1.0
|
||||
|
||||
scheduler_warmup_steps: int = 300
|
||||
|
||||
def __post_init__(self):
|
||||
super().__post_init__()
|
||||
if self.training_stage not in {"stage1", "stage2"}:
|
||||
raise ValueError(
|
||||
f"Unsupported EVO1 training_stage '{self.training_stage}', expected 'stage1' or 'stage2'"
|
||||
)
|
||||
|
||||
if self.apply_training_stage_defaults:
|
||||
stage_defaults = {
|
||||
"stage1": {
|
||||
"finetune_vlm": False,
|
||||
"finetune_language_model": False,
|
||||
"finetune_vision_model": False,
|
||||
"finetune_action_head": True,
|
||||
},
|
||||
"stage2": {
|
||||
"finetune_vlm": True,
|
||||
"finetune_language_model": True,
|
||||
"finetune_vision_model": True,
|
||||
"finetune_action_head": True,
|
||||
},
|
||||
}[self.training_stage]
|
||||
for flag_name, default_value in stage_defaults.items():
|
||||
current_value = getattr(self, flag_name)
|
||||
if current_value is not None and current_value != default_value:
|
||||
logger.warning(
|
||||
"EVO1 %s=%s is overridden by training_stage=%s default %s. "
|
||||
"Set apply_training_stage_defaults=false to keep explicit finetuning flags.",
|
||||
flag_name,
|
||||
current_value,
|
||||
self.training_stage,
|
||||
default_value,
|
||||
)
|
||||
setattr(self, flag_name, default_value)
|
||||
elif self.training_stage == "stage1":
|
||||
if self.finetune_vlm is None:
|
||||
self.finetune_vlm = False
|
||||
if self.finetune_language_model is None:
|
||||
self.finetune_language_model = False
|
||||
if self.finetune_vision_model is None:
|
||||
self.finetune_vision_model = False
|
||||
if self.finetune_action_head is None:
|
||||
self.finetune_action_head = True
|
||||
elif self.training_stage == "stage2":
|
||||
has_explicit_branch_flags = any(
|
||||
flag is not None for flag in (self.finetune_language_model, self.finetune_vision_model)
|
||||
)
|
||||
if not has_explicit_branch_flags:
|
||||
# An explicit finetune_vlm decides both branches; otherwise stage2 defaults to a
|
||||
# full-VLM finetune.
|
||||
vlm_finetune = self.finetune_vlm if self.finetune_vlm is not None else True
|
||||
self.finetune_vlm = vlm_finetune
|
||||
self.finetune_language_model = vlm_finetune
|
||||
self.finetune_vision_model = vlm_finetune
|
||||
elif self.finetune_vlm is None:
|
||||
self.finetune_vlm = bool(self.finetune_language_model or self.finetune_vision_model)
|
||||
if self.finetune_action_head is None:
|
||||
self.finetune_action_head = True
|
||||
|
||||
if self.finetune_vlm is None:
|
||||
self.finetune_vlm = False
|
||||
if self.finetune_language_model is None:
|
||||
self.finetune_language_model = False
|
||||
if self.finetune_vision_model is None:
|
||||
self.finetune_vision_model = False
|
||||
if self.finetune_action_head is None:
|
||||
self.finetune_action_head = False
|
||||
|
||||
branch_vlm = self.finetune_language_model or self.finetune_vision_model
|
||||
if self.finetune_vlm != branch_vlm:
|
||||
raise ValueError(
|
||||
"Inconsistent EVO1 finetune config: "
|
||||
f"finetune_vlm={self.finetune_vlm} but "
|
||||
f"(finetune_language_model or finetune_vision_model)={branch_vlm}. "
|
||||
"When branch-level flags are used, finetune_vlm must match their effective union."
|
||||
)
|
||||
|
||||
if self.n_action_steps > self.chunk_size:
|
||||
raise ValueError(
|
||||
f"n_action_steps ({self.n_action_steps}) must be <= chunk_size ({self.chunk_size})"
|
||||
)
|
||||
if len(self.image_resolution) != 2 or self.image_resolution[0] != self.image_resolution[1]:
|
||||
raise ValueError(
|
||||
"EVO1 currently expects a square image_resolution because InternVL3 preprocessing "
|
||||
f"uses a scalar image_size, got {self.image_resolution}."
|
||||
)
|
||||
if not 0 <= self.default_embodiment_id < self.num_categories:
|
||||
raise ValueError(
|
||||
f"default_embodiment_id ({self.default_embodiment_id}) must be in "
|
||||
f"[0, num_categories={self.num_categories})"
|
||||
)
|
||||
|
||||
def validate_features(self) -> None:
|
||||
if self.input_features is None:
|
||||
self.input_features = {}
|
||||
if self.output_features is None:
|
||||
self.output_features = {}
|
||||
|
||||
for i in range(self.empty_cameras):
|
||||
key = OBS_IMAGES + f".empty_camera_{i}"
|
||||
if key not in self.input_features:
|
||||
self.input_features[key] = PolicyFeature(
|
||||
type=FeatureType.VISUAL,
|
||||
shape=(3, *self.image_resolution),
|
||||
)
|
||||
|
||||
if OBS_STATE not in self.input_features:
|
||||
self.input_features[OBS_STATE] = PolicyFeature(
|
||||
type=FeatureType.STATE,
|
||||
shape=(self.max_state_dim,),
|
||||
)
|
||||
|
||||
if ACTION not in self.output_features:
|
||||
self.output_features[ACTION] = PolicyFeature(
|
||||
type=FeatureType.ACTION,
|
||||
shape=(self.max_action_dim,),
|
||||
)
|
||||
|
||||
def get_optimizer_preset(self) -> AdamWConfig:
|
||||
return AdamWConfig(
|
||||
lr=self.optimizer_lr,
|
||||
betas=self.optimizer_betas,
|
||||
eps=self.optimizer_eps,
|
||||
weight_decay=self.optimizer_weight_decay,
|
||||
grad_clip_norm=self.optimizer_grad_clip_norm,
|
||||
)
|
||||
|
||||
def get_scheduler_preset(self):
|
||||
return CosineAnnealingWithWarmupSchedulerConfig(
|
||||
num_warmup_steps=self.scheduler_warmup_steps,
|
||||
)
|
||||
|
||||
@property
|
||||
def observation_delta_indices(self) -> list[int]:
|
||||
return [0]
|
||||
|
||||
@property
|
||||
def action_delta_indices(self) -> list[int]:
|
||||
return list(range(self.chunk_size))
|
||||
|
||||
@property
|
||||
def reward_delta_indices(self) -> None:
|
||||
return None
|
||||
@@ -1,210 +0,0 @@
|
||||
# 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.
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from .configuration_evo1 import Evo1Config
|
||||
from .flow_matching import FlowmatchingActionHead
|
||||
from .internvl3_embedder import InternVL3Embedder
|
||||
|
||||
|
||||
class Evo1Model(nn.Module):
|
||||
def __init__(self, config: Evo1Config, vlm_hub_kwargs: dict | None = None):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self._device = config.device
|
||||
self.return_cls_only = config.return_cls_only
|
||||
# Set by Evo1Policy.init_rtc_processor() when config.rtc_config is provided.
|
||||
self.rtc_processor = None
|
||||
|
||||
# Gradient checkpointing only pays off when the VLM is actually being trained; keep it off
|
||||
# whenever every VLM branch is frozen so the frozen forward stays cheap.
|
||||
tracks_vlm_gradients = bool(
|
||||
config.finetune_vlm or config.finetune_language_model or config.finetune_vision_model
|
||||
)
|
||||
enable_gradient_checkpointing = config.enable_gradient_checkpointing and tracks_vlm_gradients
|
||||
|
||||
self.embedder = InternVL3Embedder(
|
||||
model_name=config.vlm_model_name,
|
||||
image_size=int(config.image_resolution[0]),
|
||||
device=self._device,
|
||||
num_language_layers=config.vlm_num_layers,
|
||||
model_dtype=config.vlm_dtype,
|
||||
use_flash_attn=config.use_flash_attn,
|
||||
max_text_length=config.max_text_length,
|
||||
enable_gradient_checkpointing=enable_gradient_checkpointing,
|
||||
gradient_checkpointing_use_reentrant=config.gradient_checkpointing_use_reentrant,
|
||||
hub_kwargs=vlm_hub_kwargs,
|
||||
)
|
||||
|
||||
action_head_type = config.action_head.lower()
|
||||
if action_head_type != "flowmatching":
|
||||
raise NotImplementedError(f"Unknown action_head: {action_head_type}")
|
||||
|
||||
horizon = config.chunk_size
|
||||
per_action_dim = config.max_action_dim
|
||||
action_dim = horizon * per_action_dim
|
||||
|
||||
self.horizon = horizon
|
||||
self.per_action_dim = per_action_dim
|
||||
self.action_head = FlowmatchingActionHead(
|
||||
embed_dim=config.embed_dim,
|
||||
hidden_dim=config.hidden_dim,
|
||||
action_dim=action_dim,
|
||||
horizon=horizon,
|
||||
per_action_dim=per_action_dim,
|
||||
num_heads=config.num_heads,
|
||||
num_layers=config.num_layers,
|
||||
dropout=config.dropout,
|
||||
num_inference_timesteps=config.num_inference_timesteps,
|
||||
num_categories=config.num_categories,
|
||||
state_dim=config.max_state_dim,
|
||||
state_hidden_dim=config.state_hidden_dim,
|
||||
).to(self._device)
|
||||
|
||||
def get_vl_embeddings(
|
||||
self,
|
||||
images: list[torch.Tensor],
|
||||
image_mask: torch.Tensor,
|
||||
prompt: str | list[str] | None = None,
|
||||
return_cls_only: bool | None = None,
|
||||
) -> tuple[torch.Tensor, torch.Tensor | None]:
|
||||
"""Fused VL embeddings from per-camera image batches.
|
||||
|
||||
Args:
|
||||
images: list of per-camera tensors, each shaped ``(B, C, H, W)`` with values in ``[0, 1]``.
|
||||
image_mask: bool tensor ``(B, max_views)`` marking present views.
|
||||
|
||||
Returns:
|
||||
``(embeddings, valid_mask)``: the fused tokens and the bool mask of attendable context
|
||||
positions (None when a single pooled token is returned).
|
||||
"""
|
||||
if return_cls_only is None:
|
||||
return_cls_only = self.return_cls_only
|
||||
if not images:
|
||||
raise ValueError("EVO1 expects at least one image per sample.")
|
||||
|
||||
batch_size = images[0].shape[0]
|
||||
if prompt is None:
|
||||
prompts = [""] * batch_size
|
||||
elif isinstance(prompt, str):
|
||||
prompts = [prompt] * batch_size
|
||||
else:
|
||||
prompts = [str(p) for p in prompt]
|
||||
if len(prompts) != batch_size:
|
||||
raise ValueError(
|
||||
f"Prompt batch size {len(prompts)} does not match image batch size {batch_size}"
|
||||
)
|
||||
|
||||
if image_mask.dim() == 1:
|
||||
image_mask = image_mask.unsqueeze(0)
|
||||
if image_mask.shape[0] != batch_size:
|
||||
raise ValueError(
|
||||
f"image_mask batch size {image_mask.shape[0]} does not match image batch size {batch_size}"
|
||||
)
|
||||
|
||||
return self.embedder.get_fused_image_text_embedding_batched(
|
||||
camera_images=images,
|
||||
image_masks=image_mask,
|
||||
text_prompts=prompts,
|
||||
return_cls_only=return_cls_only,
|
||||
)
|
||||
|
||||
def predict_action(
|
||||
self,
|
||||
fused_tokens: torch.Tensor,
|
||||
state: torch.Tensor,
|
||||
actions_gt: torch.Tensor | None = None,
|
||||
action_mask: torch.Tensor | None = None,
|
||||
embodiment_ids: torch.Tensor | None = None,
|
||||
context_mask: torch.Tensor | None = None,
|
||||
inference_delay: int | None = None,
|
||||
prev_chunk_left_over: torch.Tensor | None = None,
|
||||
execution_horizon: int | None = None,
|
||||
):
|
||||
if actions_gt is None:
|
||||
return self.action_head.get_action(
|
||||
fused_tokens,
|
||||
state=state,
|
||||
action_mask=action_mask,
|
||||
embodiment_id=embodiment_ids,
|
||||
context_mask=context_mask,
|
||||
inference_delay=inference_delay,
|
||||
prev_chunk_left_over=prev_chunk_left_over,
|
||||
execution_horizon=execution_horizon,
|
||||
rtc_processor=self.rtc_processor,
|
||||
)
|
||||
return self.action_head(
|
||||
fused_tokens,
|
||||
state=state,
|
||||
actions_gt=actions_gt,
|
||||
action_mask=action_mask,
|
||||
embodiment_id=embodiment_ids,
|
||||
context_mask=context_mask,
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
fused_tokens: torch.Tensor,
|
||||
state: torch.Tensor | None = None,
|
||||
actions_gt: torch.Tensor | None = None,
|
||||
action_mask: torch.Tensor | None = None,
|
||||
embodiment_ids: torch.Tensor | None = None,
|
||||
context_mask: torch.Tensor | None = None,
|
||||
inference_delay: int | None = None,
|
||||
prev_chunk_left_over: torch.Tensor | None = None,
|
||||
execution_horizon: int | None = None,
|
||||
):
|
||||
return self.predict_action(
|
||||
fused_tokens,
|
||||
state,
|
||||
actions_gt,
|
||||
action_mask,
|
||||
embodiment_ids,
|
||||
context_mask,
|
||||
inference_delay,
|
||||
prev_chunk_left_over,
|
||||
execution_horizon,
|
||||
)
|
||||
|
||||
def _set_module_trainable(self, module: nn.Module, trainable: bool):
|
||||
for param in module.parameters():
|
||||
param.requires_grad = trainable
|
||||
|
||||
def _vlm_submodule(self, name: str) -> nn.Module:
|
||||
module = getattr(self.embedder.model, name, None)
|
||||
if not isinstance(module, nn.Module):
|
||||
raise AttributeError(
|
||||
f"InternVL model {type(self.embedder.model).__name__} has no '{name}' submodule; "
|
||||
"the native HF InternVL layout (language_model / vision_tower / "
|
||||
"multi_modal_projector) is required to apply the EVO1 finetune flags."
|
||||
)
|
||||
return module
|
||||
|
||||
def set_finetune_flags(self):
|
||||
# __post_init__ resolves every finetune flag to a concrete boolean, so branch-level flags
|
||||
# are authoritative here. Freeze everything first, then re-enable the requested branches.
|
||||
self._set_module_trainable(self.embedder, False)
|
||||
self._set_module_trainable(
|
||||
self._vlm_submodule("language_model"), bool(self.config.finetune_language_model)
|
||||
)
|
||||
finetune_vision = bool(self.config.finetune_vision_model)
|
||||
self._set_module_trainable(self._vlm_submodule("vision_tower"), finetune_vision)
|
||||
self._set_module_trainable(self._vlm_submodule("multi_modal_projector"), finetune_vision)
|
||||
|
||||
if not self.config.finetune_action_head:
|
||||
self._set_module_trainable(self.action_head, False)
|
||||
@@ -1,483 +0,0 @@
|
||||
# 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.
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import math
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class SinusoidalPositionalEncoding(nn.Module):
|
||||
def __init__(self, dim: int, max_len: int = 1000):
|
||||
super().__init__()
|
||||
pe = torch.zeros(max_len, dim)
|
||||
position = torch.arange(0, max_len).unsqueeze(1)
|
||||
div_term = torch.exp(torch.arange(0, dim, 2) * -(math.log(10000.0) / dim))
|
||||
pe[:, 0::2] = torch.sin(position * div_term)
|
||||
pe[:, 1::2] = torch.cos(position * div_term)
|
||||
pe = pe.unsqueeze(0)
|
||||
self.register_buffer("pe", pe)
|
||||
|
||||
def forward(self, seq_len: int):
|
||||
if seq_len > self.pe.size(1):
|
||||
self._extend_pe(seq_len)
|
||||
return self.pe[:, :seq_len, :]
|
||||
|
||||
def _extend_pe(self, new_max_len):
|
||||
old_max_len, dim = self.pe.size(1), self.pe.size(2)
|
||||
if new_max_len <= old_max_len:
|
||||
return
|
||||
extra_positions = torch.arange(old_max_len, new_max_len, dtype=torch.float).unsqueeze(1)
|
||||
div_term = torch.exp(torch.arange(0, dim, 2, dtype=torch.float) * -(math.log(10000.0) / dim))
|
||||
extra_pe = torch.zeros(new_max_len - old_max_len, dim)
|
||||
extra_pe[:, 0::2] = torch.sin(extra_positions * div_term)
|
||||
extra_pe[:, 1::2] = torch.cos(extra_positions * div_term)
|
||||
extra_pe = extra_pe.unsqueeze(0)
|
||||
new_pe = torch.cat([self.pe, extra_pe.to(self.pe.device)], dim=1)
|
||||
self.pe = new_pe
|
||||
|
||||
|
||||
class CategorySpecificLinear(nn.Module):
|
||||
def __init__(self, in_dim: int, out_dim: int, num_categories: int = 1):
|
||||
super().__init__()
|
||||
self.num_categories = num_categories
|
||||
if num_categories <= 1:
|
||||
self.linear = nn.Linear(in_dim, out_dim)
|
||||
else:
|
||||
self.weight = nn.Parameter(torch.empty(num_categories, in_dim, out_dim))
|
||||
self.bias = nn.Parameter(torch.zeros(num_categories, out_dim))
|
||||
# Initialize each per-category (in_dim, out_dim) matrix separately: xavier on the full
|
||||
# 3D tensor would compute fan_in = in_dim * out_dim and badly under-scale the weights.
|
||||
for category in range(num_categories):
|
||||
nn.init.xavier_uniform_(self.weight[category])
|
||||
|
||||
def forward(self, x: torch.Tensor, category_id: torch.LongTensor):
|
||||
if self.num_categories <= 1:
|
||||
if x.dtype != self.linear.weight.dtype:
|
||||
x = x.to(dtype=self.linear.weight.dtype)
|
||||
return self.linear(x)
|
||||
|
||||
if x.dtype != self.weight.dtype:
|
||||
x = x.to(dtype=self.weight.dtype)
|
||||
|
||||
orig_shape = x.shape
|
||||
x_flat = x.reshape(-1, orig_shape[-1])
|
||||
if category_id.dim() == 0:
|
||||
cid = category_id.item()
|
||||
out = x_flat @ self.weight[cid] + self.bias[cid]
|
||||
else:
|
||||
category_id = category_id.reshape(-1)
|
||||
if category_id.numel() != x_flat.size(0):
|
||||
raise ValueError(
|
||||
f"category_id length {category_id.numel()} does not match flattened batch {x_flat.size(0)}"
|
||||
)
|
||||
weight_selected = self.weight[category_id]
|
||||
bias_selected = self.bias[category_id]
|
||||
out = torch.bmm(x_flat.unsqueeze(1), weight_selected).squeeze(1) + bias_selected
|
||||
out_shape = orig_shape[:-1] + (out.shape[-1],)
|
||||
return out.view(out_shape)
|
||||
|
||||
|
||||
class CategorySpecificMLP(nn.Module):
|
||||
def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, num_categories: int = 1):
|
||||
super().__init__()
|
||||
self.fc1 = CategorySpecificLinear(input_dim, hidden_dim, num_categories)
|
||||
self.fc2 = CategorySpecificLinear(hidden_dim, output_dim, num_categories)
|
||||
self.activation = nn.ReLU(inplace=True)
|
||||
|
||||
def forward(self, x: torch.Tensor, category_id: torch.LongTensor):
|
||||
out = self.activation(self.fc1(x, category_id))
|
||||
out = self.fc2(out, category_id)
|
||||
return out
|
||||
|
||||
|
||||
class MultiEmbodimentActionEncoder(nn.Module):
|
||||
def __init__(
|
||||
self, action_dim: int, embed_dim: int, hidden_dim: int, horizon: int, num_categories: int = 1
|
||||
):
|
||||
super().__init__()
|
||||
self.horizon = horizon
|
||||
self.embed_dim = embed_dim
|
||||
self.num_categories = num_categories
|
||||
|
||||
self.W1 = CategorySpecificLinear(action_dim, hidden_dim, num_categories)
|
||||
self.W2 = CategorySpecificLinear(hidden_dim, hidden_dim, num_categories)
|
||||
self.W3 = CategorySpecificLinear(hidden_dim, embed_dim, num_categories)
|
||||
|
||||
self.pos_encoding = SinusoidalPositionalEncoding(hidden_dim, max_len=horizon)
|
||||
self.activation = nn.ReLU(inplace=True)
|
||||
|
||||
def forward(self, action_seq: torch.Tensor, category_id: torch.LongTensor):
|
||||
batch_size, horizon, action_dim = action_seq.shape
|
||||
if self.horizon != horizon:
|
||||
raise ValueError(
|
||||
f"Action sequence length must match horizon: got {horizon}, expected {self.horizon}."
|
||||
)
|
||||
|
||||
x = action_seq.reshape(batch_size * horizon, action_dim)
|
||||
if category_id.dim() == 0:
|
||||
cat_ids = category_id.expand(horizon * batch_size)
|
||||
else:
|
||||
cat_ids = category_id.unsqueeze(1).expand(batch_size, horizon).reshape(batch_size * horizon)
|
||||
|
||||
out = self.activation(self.W1(x, cat_ids))
|
||||
pos_enc = self.pos_encoding(horizon).to(device=out.device, dtype=out.dtype)
|
||||
out = out.view(batch_size, horizon, -1) + pos_enc
|
||||
out = out.view(batch_size * horizon, -1)
|
||||
out = self.activation(self.W2(out, cat_ids))
|
||||
out = self.W3(out, cat_ids)
|
||||
return out.view(batch_size, horizon, self.embed_dim)
|
||||
|
||||
|
||||
class BasicTransformerBlock(nn.Module):
|
||||
def __init__(self, embed_dim: int, num_heads: int, hidden_dim: int, dropout: float = 0.0):
|
||||
super().__init__()
|
||||
self.attn = nn.MultiheadAttention(embed_dim, num_heads, dropout=dropout, batch_first=True)
|
||||
self.norm1 = nn.LayerNorm(embed_dim)
|
||||
self.norm2 = nn.LayerNorm(embed_dim)
|
||||
self.ff = nn.Sequential(nn.Linear(embed_dim, hidden_dim), nn.GELU(), nn.Linear(hidden_dim, embed_dim))
|
||||
|
||||
def forward(
|
||||
self,
|
||||
action_tokens: torch.Tensor,
|
||||
context_tokens: torch.Tensor,
|
||||
time_emb: torch.Tensor,
|
||||
context_key_padding_mask: torch.Tensor | None = None,
|
||||
):
|
||||
x = self.norm1(action_tokens)
|
||||
attn_out, _ = self.attn(x, context_tokens, context_tokens, key_padding_mask=context_key_padding_mask)
|
||||
x = action_tokens + attn_out
|
||||
x2 = self.norm2(x)
|
||||
if time_emb is not None:
|
||||
x2 = x2 + time_emb.unsqueeze(1)
|
||||
ff_out = self.ff(x2)
|
||||
return x + ff_out
|
||||
|
||||
|
||||
class FlowmatchingActionHead(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
embed_dim: int = 896,
|
||||
hidden_dim: int = 1024,
|
||||
action_dim: int = 16 * 7,
|
||||
horizon: int = 16,
|
||||
per_action_dim: int = 7,
|
||||
num_heads: int = 8,
|
||||
num_layers: int = 8,
|
||||
dropout: float = 0.0,
|
||||
num_inference_timesteps: int = 20,
|
||||
num_categories: int = 1,
|
||||
state_dim: int | None = None,
|
||||
state_hidden_dim: int | None = None,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
logger.info("FlowmatchingActionHead num_inference_timesteps=%s", num_inference_timesteps)
|
||||
self.embed_dim = embed_dim
|
||||
self.horizon = horizon
|
||||
self.per_action_dim = per_action_dim
|
||||
self.action_dim = action_dim
|
||||
self.num_inference_timesteps = num_inference_timesteps
|
||||
self.num_categories = num_categories
|
||||
|
||||
self.time_pos_enc = SinusoidalPositionalEncoding(embed_dim, max_len=1000)
|
||||
self.transformer_blocks = nn.ModuleList(
|
||||
[
|
||||
BasicTransformerBlock(
|
||||
embed_dim=embed_dim,
|
||||
num_heads=num_heads,
|
||||
hidden_dim=embed_dim * 4,
|
||||
dropout=dropout,
|
||||
)
|
||||
for _ in range(num_layers)
|
||||
]
|
||||
)
|
||||
self.norm_out = nn.LayerNorm(embed_dim)
|
||||
self.seq_pool_proj = nn.Linear(self.horizon * self.embed_dim, self.embed_dim)
|
||||
self.mlp_head = CategorySpecificMLP(
|
||||
input_dim=embed_dim,
|
||||
hidden_dim=hidden_dim,
|
||||
output_dim=action_dim,
|
||||
num_categories=num_categories,
|
||||
)
|
||||
|
||||
self.state_encoder = None
|
||||
if state_dim is not None:
|
||||
state_hidden = state_hidden_dim if state_hidden_dim is not None else embed_dim
|
||||
self.state_encoder = CategorySpecificMLP(
|
||||
input_dim=state_dim,
|
||||
hidden_dim=state_hidden,
|
||||
output_dim=embed_dim,
|
||||
num_categories=num_categories,
|
||||
)
|
||||
|
||||
if horizon > 1:
|
||||
self.action_encoder = MultiEmbodimentActionEncoder(
|
||||
action_dim=self.per_action_dim,
|
||||
embed_dim=embed_dim,
|
||||
hidden_dim=embed_dim,
|
||||
horizon=horizon,
|
||||
num_categories=num_categories,
|
||||
)
|
||||
self.single_action_proj = None
|
||||
else:
|
||||
self.action_encoder = None
|
||||
self.single_action_proj = nn.Linear(self.per_action_dim, self.embed_dim)
|
||||
|
||||
def _project_actions(self, action_seq: torch.Tensor, embodiment_id: torch.LongTensor) -> torch.Tensor:
|
||||
if self.horizon > 1 and self.action_encoder is not None:
|
||||
return self.action_encoder(action_seq, embodiment_id)
|
||||
if self.single_action_proj is None:
|
||||
raise RuntimeError("single_action_proj is not initialized for horizon <= 1.")
|
||||
return self.single_action_proj(action_seq)
|
||||
|
||||
def _expand_action_mask(
|
||||
self,
|
||||
action_mask: torch.Tensor,
|
||||
batch_size: int,
|
||||
per_action_dim: int,
|
||||
device: torch.device,
|
||||
dtype: torch.dtype,
|
||||
) -> torch.Tensor:
|
||||
if action_mask is None:
|
||||
raise ValueError("action_mask must be provided for flow matching inference.")
|
||||
|
||||
if action_mask.dim() == 2:
|
||||
expected_last_dim = self.horizon * per_action_dim
|
||||
if action_mask.shape == (batch_size, expected_last_dim):
|
||||
expanded_mask = action_mask.reshape(batch_size, self.horizon, per_action_dim)
|
||||
elif action_mask.shape == (batch_size, per_action_dim):
|
||||
expanded_mask = action_mask.unsqueeze(1).expand(batch_size, self.horizon, per_action_dim)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Expected action_mask shape {(batch_size, expected_last_dim)} or "
|
||||
f"{(batch_size, per_action_dim)}, got {tuple(action_mask.shape)}"
|
||||
)
|
||||
elif action_mask.dim() == 3:
|
||||
expected_shape = (batch_size, self.horizon, per_action_dim)
|
||||
if tuple(action_mask.shape) != expected_shape:
|
||||
raise ValueError(
|
||||
f"Expected action_mask shape {expected_shape}, got {tuple(action_mask.shape)}"
|
||||
)
|
||||
expanded_mask = action_mask
|
||||
else:
|
||||
raise ValueError(f"Unsupported action_mask rank: {action_mask.dim()}")
|
||||
|
||||
return expanded_mask.to(device=device, dtype=dtype)
|
||||
|
||||
def _prepare_context(
|
||||
self,
|
||||
fused_tokens: torch.Tensor,
|
||||
state: torch.Tensor | None,
|
||||
embodiment_id: torch.LongTensor | None,
|
||||
context_mask: torch.Tensor | None,
|
||||
) -> tuple[torch.Tensor, torch.Tensor | None, torch.LongTensor]:
|
||||
"""Normalize the VL context and embodiment ids shared by training and inference.
|
||||
|
||||
Returns the context tokens ``(B, S, E)``, a key_padding_mask for
|
||||
``nn.MultiheadAttention`` (True = ignore) or None, and the resolved embodiment ids.
|
||||
"""
|
||||
batch_size = fused_tokens.size(0)
|
||||
device = fused_tokens.device
|
||||
if embodiment_id is None:
|
||||
embodiment_id = torch.zeros(batch_size, dtype=torch.long, device=device)
|
||||
elif self.num_categories > 1 and (
|
||||
int(embodiment_id.min()) < 0 or int(embodiment_id.max()) >= self.num_categories
|
||||
):
|
||||
raise ValueError(
|
||||
f"embodiment ids must be in [0, num_categories={self.num_categories}), "
|
||||
f"got range [{int(embodiment_id.min())}, {int(embodiment_id.max())}]"
|
||||
)
|
||||
|
||||
context_tokens = fused_tokens
|
||||
if context_tokens.dim() == 2:
|
||||
# A single pooled VL token (return_cls_only): give it a sequence dim of 1.
|
||||
context_tokens = context_tokens.unsqueeze(1)
|
||||
context_mask = None
|
||||
if state is not None and self.state_encoder is not None:
|
||||
state_emb = self.state_encoder(state, embodiment_id).unsqueeze(1)
|
||||
context_tokens = torch.cat([context_tokens, state_emb], dim=1)
|
||||
if context_mask is not None:
|
||||
state_valid = torch.ones(batch_size, 1, dtype=torch.bool, device=context_mask.device)
|
||||
context_mask = torch.cat([context_mask.to(torch.bool), state_valid], dim=1)
|
||||
|
||||
key_padding_mask = None if context_mask is None else ~context_mask.to(torch.bool)
|
||||
return context_tokens, key_padding_mask, embodiment_id
|
||||
|
||||
def forward(
|
||||
self,
|
||||
fused_tokens: torch.Tensor,
|
||||
state: torch.Tensor = None,
|
||||
actions_gt: torch.Tensor = None,
|
||||
embodiment_id: torch.LongTensor = None,
|
||||
action_mask: torch.Tensor = None,
|
||||
context_mask: torch.Tensor = None,
|
||||
):
|
||||
if actions_gt is None:
|
||||
return self.get_action(
|
||||
fused_tokens,
|
||||
state=state,
|
||||
embodiment_id=embodiment_id,
|
||||
action_mask=action_mask,
|
||||
context_mask=context_mask,
|
||||
)
|
||||
|
||||
batch_size = fused_tokens.size(0)
|
||||
device = fused_tokens.device
|
||||
context_tokens, key_padding_mask, embodiment_id = self._prepare_context(
|
||||
fused_tokens, state, embodiment_id, context_mask
|
||||
)
|
||||
|
||||
t = (
|
||||
torch.distributions.Beta(2, 2)
|
||||
.sample((batch_size,))
|
||||
.clamp(0.02, 0.98)
|
||||
.to(device)
|
||||
.to(dtype=self.dtype)
|
||||
)
|
||||
time_index = (t * 999).long().clamp_(0, 999)
|
||||
time_emb = self.time_pos_enc(1000)[:, time_index, :].squeeze(0).to(dtype=context_tokens.dtype)
|
||||
|
||||
actions_gt_seq = actions_gt
|
||||
noise = torch.rand_like(actions_gt) * 2 - 1
|
||||
if action_mask is not None:
|
||||
action_mask = action_mask.to(dtype=noise.dtype, device=noise.device)
|
||||
if action_mask.shape != noise.shape:
|
||||
raise ValueError(f"action_mask shape {action_mask.shape} != noise shape {noise.shape}")
|
||||
actions_gt_seq = actions_gt_seq * action_mask
|
||||
noise = noise * action_mask
|
||||
|
||||
if self.horizon > 1:
|
||||
noise_seq = noise.view(batch_size, self.horizon, self.per_action_dim)
|
||||
else:
|
||||
noise_seq = noise if noise.dim() == 3 else noise.unsqueeze(1)
|
||||
t_broadcast = t.view(batch_size, 1, 1)
|
||||
action_intermediate_seq = (1 - t_broadcast) * noise_seq + t_broadcast * actions_gt_seq
|
||||
|
||||
action_tokens = self._project_actions(action_intermediate_seq, embodiment_id)
|
||||
target_dtype = self.dtype
|
||||
action_tokens = action_tokens.to(dtype=target_dtype)
|
||||
context_tokens = context_tokens.to(dtype=target_dtype)
|
||||
time_emb = time_emb.to(dtype=target_dtype)
|
||||
|
||||
x = action_tokens
|
||||
for block in self.transformer_blocks:
|
||||
x = block(x, context_tokens, time_emb, key_padding_mask)
|
||||
x = self.norm_out(x)
|
||||
|
||||
if self.horizon > 1:
|
||||
x_flat = x.reshape(batch_size, -1)
|
||||
x_pooled = self.seq_pool_proj(x_flat)
|
||||
else:
|
||||
x_pooled = x.squeeze(1)
|
||||
|
||||
pred_velocity = self.mlp_head(x_pooled, embodiment_id)
|
||||
return pred_velocity, noise
|
||||
|
||||
def get_action(
|
||||
self,
|
||||
fused_tokens: torch.Tensor,
|
||||
state: torch.Tensor = None,
|
||||
embodiment_id: torch.LongTensor = None,
|
||||
action_mask: torch.Tensor = None,
|
||||
context_mask: torch.Tensor = None,
|
||||
inference_delay: int | None = None,
|
||||
prev_chunk_left_over: torch.Tensor | None = None,
|
||||
execution_horizon: int | None = None,
|
||||
rtc_processor=None,
|
||||
):
|
||||
batch_size = fused_tokens.size(0)
|
||||
device = fused_tokens.device
|
||||
context_tokens, key_padding_mask, embodiment_id = self._prepare_context(
|
||||
fused_tokens, state, embodiment_id, context_mask
|
||||
)
|
||||
|
||||
action_dim_total = self.action_dim
|
||||
per_action_dim = self.per_action_dim
|
||||
|
||||
action = torch.rand(batch_size, action_dim_total, device=device, dtype=context_tokens.dtype) * 2 - 1
|
||||
action_seq = action.view(batch_size, self.horizon, per_action_dim)
|
||||
action_mask = self._expand_action_mask(
|
||||
action_mask,
|
||||
batch_size=batch_size,
|
||||
per_action_dim=per_action_dim,
|
||||
device=action_seq.device,
|
||||
dtype=action_seq.dtype,
|
||||
)
|
||||
action_seq = action_seq * action_mask
|
||||
|
||||
target_dtype = self.dtype
|
||||
context_tokens = context_tokens.to(dtype=target_dtype)
|
||||
|
||||
num_steps = int(self.num_inference_timesteps)
|
||||
if num_steps <= 0:
|
||||
raise ValueError(f"num_inference_timesteps must be positive, got {num_steps}")
|
||||
dt = 1.0 / num_steps
|
||||
|
||||
use_rtc = rtc_processor is not None and (
|
||||
inference_delay is not None or prev_chunk_left_over is not None
|
||||
)
|
||||
|
||||
def predict_velocity(seq: torch.Tensor, step_time_emb: torch.Tensor) -> torch.Tensor:
|
||||
"""Predict the masked flow velocity (x1 - x0 convention) for one integration step."""
|
||||
seq = seq * action_mask
|
||||
action_tokens = self._project_actions(seq, embodiment_id).to(dtype=target_dtype)
|
||||
x = action_tokens
|
||||
for block in self.transformer_blocks:
|
||||
x = block(x, context_tokens, step_time_emb, key_padding_mask)
|
||||
x = self.norm_out(x)
|
||||
x_pooled = self.seq_pool_proj(x.reshape(batch_size, -1)) if self.horizon > 1 else x.squeeze(1)
|
||||
pred = self.mlp_head(x_pooled, embodiment_id)
|
||||
return pred.view(batch_size, self.horizon, per_action_dim) * action_mask
|
||||
|
||||
for i in range(num_steps):
|
||||
t = i / num_steps
|
||||
time_index = min(int(t * 999), 999)
|
||||
time_emb = self.time_pos_enc(1000)[:, time_index, :].to(device).squeeze(0).to(dtype=target_dtype)
|
||||
time_emb = time_emb.unsqueeze(0).repeat(batch_size, 1)
|
||||
|
||||
if use_rtc:
|
||||
# RTCProcessor assumes the pi0 flow convention: its `time` runs 1 -> 0 and the
|
||||
# clean-action estimate is x1 = x_t - time * v. EVO1 integrates t: 0 -> 1 with
|
||||
# velocity v = x1 - x0 (so x1 = x_t + (1 - t) * v); passing time = 1 - t and
|
||||
# flipping the velocity sign in both directions maps one convention onto the other.
|
||||
guided = rtc_processor.denoise_step(
|
||||
x_t=action_seq,
|
||||
prev_chunk_left_over=prev_chunk_left_over,
|
||||
inference_delay=inference_delay,
|
||||
time=1.0 - t,
|
||||
original_denoise_step_partial=lambda seq, emb=time_emb: -predict_velocity(seq, emb),
|
||||
execution_horizon=execution_horizon,
|
||||
)
|
||||
velocity = -guided
|
||||
else:
|
||||
velocity = predict_velocity(action_seq, time_emb)
|
||||
|
||||
action_seq = action_seq + dt * velocity
|
||||
|
||||
action_seq = action_seq * action_mask
|
||||
return action_seq.reshape(batch_size, -1)
|
||||
|
||||
@property
|
||||
def device(self):
|
||||
return next(self.parameters()).device
|
||||
|
||||
@property
|
||||
def dtype(self):
|
||||
return next(self.parameters()).dtype
|
||||
@@ -1,369 +0,0 @@
|
||||
# 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.
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from collections.abc import Sequence
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torchvision.transforms.functional as tvf
|
||||
from torchvision.transforms.functional import InterpolationMode
|
||||
|
||||
from lerobot.utils.import_utils import _transformers_available, require_package
|
||||
|
||||
if TYPE_CHECKING or _transformers_available:
|
||||
from transformers import AutoModel, AutoTokenizer
|
||||
else:
|
||||
AutoModel = None
|
||||
AutoTokenizer = None
|
||||
|
||||
IMAGENET_MEAN = (0.485, 0.456, 0.406)
|
||||
IMAGENET_STD = (0.229, 0.224, 0.225)
|
||||
IMG_CONTEXT_TOKEN = "<IMG_CONTEXT>" # nosec B105
|
||||
IMG_START_TOKEN = "<img>" # nosec B105
|
||||
IMG_END_TOKEN = "</img>" # nosec B105
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _batched_resize_01(images: torch.Tensor, image_size: int) -> torch.Tensor:
|
||||
"""Resize a batch of ``[0, 1]`` images to ``(image_size, image_size)`` on-device.
|
||||
|
||||
Numerically mirrors InternVL3's reference PIL preprocessing
|
||||
(``to_pil_image`` -> ``Image.resize`` -> ``to_tensor``): the float input is quantized to uint8
|
||||
exactly as ``to_pil_image`` does, then resized with bicubic interpolation and antialiasing,
|
||||
which matches PIL's default resampler. Matching the reference pixel-for-pixel keeps the policy
|
||||
interchangeable with checkpoints produced by the upstream EVO1 preprocessing.
|
||||
|
||||
Args:
|
||||
images: float tensor of shape ``(N, C, H, W)`` with values in ``[0, 1]``.
|
||||
|
||||
Returns:
|
||||
float32 tensor of shape ``(N, C, image_size, image_size)`` with values in ``[0, 1]``.
|
||||
"""
|
||||
# to_pil_image() quantizes float [0, 1] to uint8 (x * 255, truncated); replicate that so the
|
||||
# bicubic resample sees the same integer pixels PIL would.
|
||||
pixels_u8 = (images * 255.0).clamp(0, 255).to(torch.uint8)
|
||||
resized = tvf.resize(
|
||||
pixels_u8, [image_size, image_size], interpolation=InterpolationMode.BICUBIC, antialias=True
|
||||
)
|
||||
return resized.to(torch.float32) / 255.0
|
||||
|
||||
|
||||
def _batched_pixel_values(
|
||||
camera_images: Sequence[torch.Tensor],
|
||||
max_views: int,
|
||||
image_size: int,
|
||||
mean: torch.Tensor,
|
||||
std: torch.Tensor,
|
||||
dtype: torch.dtype,
|
||||
device: torch.device | str,
|
||||
) -> torch.Tensor:
|
||||
"""Build InternVL3 ``pixel_values`` from per-camera ``[0, 1]`` image batches without leaving the device.
|
||||
|
||||
Each image is resized, converted to ``dtype``, and ImageNet-normalized (a single tile per
|
||||
image), batched across the whole minibatch. Absent views (fewer cameras than ``max_views``)
|
||||
are filled with zero images; their placeholder tokens are masked out of attention downstream
|
||||
via ``_mask_absent_image_tokens``.
|
||||
|
||||
Returns:
|
||||
``pixel_values`` of shape ``(B * max_views, C, image_size, image_size)``, ordered row-major
|
||||
over ``(sample, view)`` to line up with the per-view image placeholders in the prompt.
|
||||
"""
|
||||
resized: list[torch.Tensor] = []
|
||||
for image in camera_images:
|
||||
resized.append(_batched_resize_01(image.to(device=device), image_size).to(dtype))
|
||||
|
||||
batch_size = resized[0].shape[0]
|
||||
channels = resized[0].shape[1]
|
||||
while len(resized) < max_views:
|
||||
resized.append(torch.zeros(batch_size, channels, image_size, image_size, dtype=dtype, device=device))
|
||||
|
||||
stacked = torch.stack(resized[:max_views], dim=1) # (B, V, C, H, W)
|
||||
mean = mean.to(device=device, dtype=dtype).view(1, 1, -1, 1, 1)
|
||||
std = std.to(device=device, dtype=dtype).view(1, 1, -1, 1, 1)
|
||||
normalized = (stacked - mean) / std
|
||||
return normalized.reshape(batch_size * max_views, channels, image_size, image_size)
|
||||
|
||||
|
||||
class InternVL3Embedder(nn.Module):
|
||||
"""Vision-language embedder using the native HF InternVL3 model (no trust_remote_code)."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_name="OpenGVLab/InternVL3-1B-hf",
|
||||
image_size=448,
|
||||
device="cuda",
|
||||
num_language_layers: int | None = 14,
|
||||
model_dtype: str | torch.dtype = "bfloat16",
|
||||
use_flash_attn: bool = True,
|
||||
max_text_length: int = 1024,
|
||||
enable_gradient_checkpointing: bool = True,
|
||||
gradient_checkpointing_use_reentrant: bool = False,
|
||||
hub_kwargs: dict | None = None,
|
||||
):
|
||||
super().__init__()
|
||||
self._requested_device = device
|
||||
self.image_size = image_size
|
||||
self.num_language_layers = num_language_layers
|
||||
self.max_text_length = max_text_length
|
||||
self.enable_gradient_checkpointing = bool(enable_gradient_checkpointing)
|
||||
self.gradient_checkpointing_use_reentrant = bool(gradient_checkpointing_use_reentrant)
|
||||
hub_kwargs = hub_kwargs or {}
|
||||
|
||||
require_package("transformers", extra="evo1")
|
||||
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(model_name, **hub_kwargs)
|
||||
if isinstance(model_dtype, str):
|
||||
try:
|
||||
model_dtype = getattr(torch, model_dtype)
|
||||
except AttributeError as exc:
|
||||
raise ValueError(f"Unsupported EVO1 vlm_dtype '{model_dtype}'") from exc
|
||||
self.model_dtype = model_dtype
|
||||
|
||||
attn_implementation = "flash_attention_2" if (use_flash_attn and _flash_attn_available()) else "eager"
|
||||
if use_flash_attn and attn_implementation == "eager":
|
||||
logger.warning("flash_attn is not installed. Falling back to eager attention.")
|
||||
|
||||
self.model = AutoModel.from_pretrained(
|
||||
model_name,
|
||||
torch_dtype=model_dtype,
|
||||
attn_implementation=attn_implementation,
|
||||
low_cpu_mem_usage=True,
|
||||
**hub_kwargs,
|
||||
).to(self._requested_device)
|
||||
|
||||
checkpoint_image_size = getattr(self.model.config.vision_config, "image_size", None)
|
||||
if isinstance(checkpoint_image_size, (list, tuple)):
|
||||
checkpoint_image_size = checkpoint_image_size[0]
|
||||
if checkpoint_image_size is not None and int(checkpoint_image_size) != int(image_size):
|
||||
raise ValueError(
|
||||
f"EVO1 image_resolution ({image_size}) must match the InternVL checkpoint's native "
|
||||
f"image size ({checkpoint_image_size}): the checkpoint's image_seq_length assumes "
|
||||
"its native resolution, so other sizes would desync the image placeholder tokens "
|
||||
"from the vision features."
|
||||
)
|
||||
|
||||
self.num_image_token = self.model.config.image_seq_length
|
||||
|
||||
# Truncate language model to the requested number of layers
|
||||
layers = self.model.language_model.layers
|
||||
if self.num_language_layers is not None:
|
||||
layers = layers[: self.num_language_layers]
|
||||
self.model.language_model.layers = torch.nn.ModuleList(layers)
|
||||
|
||||
self._configure_memory_features()
|
||||
self.img_context_token_id = self.tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
|
||||
|
||||
def _configure_memory_features(self) -> None:
|
||||
checkpoint_kwargs = {"use_reentrant": self.gradient_checkpointing_use_reentrant}
|
||||
|
||||
if not self.enable_gradient_checkpointing:
|
||||
language_model = self.model.language_model
|
||||
if hasattr(language_model, "gradient_checkpointing_disable"):
|
||||
language_model.gradient_checkpointing_disable()
|
||||
vision_tower = getattr(self.model, "vision_tower", None)
|
||||
if vision_tower is not None and hasattr(vision_tower, "encoder"):
|
||||
vision_tower.encoder.gradient_checkpointing = False
|
||||
return
|
||||
|
||||
def _enable_ckpt(module: nn.Module | None) -> bool:
|
||||
if module is None:
|
||||
return False
|
||||
if hasattr(module, "gradient_checkpointing_enable"):
|
||||
try:
|
||||
module.gradient_checkpointing_enable(gradient_checkpointing_kwargs=checkpoint_kwargs)
|
||||
except TypeError:
|
||||
module.gradient_checkpointing_enable()
|
||||
return True
|
||||
if hasattr(module, "gradient_checkpointing"):
|
||||
module.gradient_checkpointing = True
|
||||
return True
|
||||
return False
|
||||
|
||||
enabled_any = _enable_ckpt(self.model)
|
||||
|
||||
vision_tower = getattr(self.model, "vision_tower", None)
|
||||
if vision_tower is not None:
|
||||
enabled_any = _enable_ckpt(vision_tower) or enabled_any
|
||||
|
||||
language_model = self.model.language_model
|
||||
enabled_any = _enable_ckpt(language_model) or enabled_any
|
||||
if hasattr(language_model, "config"):
|
||||
language_model.config.use_cache = False
|
||||
|
||||
if hasattr(self.model, "config"):
|
||||
self.model.config.use_cache = False
|
||||
if hasattr(self.model, "enable_input_require_grads"):
|
||||
self.model.enable_input_require_grads()
|
||||
|
||||
if enabled_any:
|
||||
logger.info("Gradient checkpointing enabled for InternVL3 embedder.")
|
||||
else:
|
||||
logger.warning(
|
||||
"Requested gradient checkpointing, but model does not expose checkpointing controls."
|
||||
)
|
||||
|
||||
def _build_multimodal_prompts(
|
||||
self,
|
||||
batch_num_tiles_list: list[list[int]],
|
||||
text_prompts: Sequence[str],
|
||||
) -> list[str]:
|
||||
prompts = []
|
||||
for num_tiles_list, text_prompt in zip(batch_num_tiles_list, text_prompts, strict=True):
|
||||
prompt_segments = []
|
||||
for i, tile_count in enumerate(num_tiles_list):
|
||||
token_count = self.num_image_token * tile_count
|
||||
image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * token_count + IMG_END_TOKEN
|
||||
prompt_segments.append(f"Image-{i + 1}: {image_tokens}\n")
|
||||
prompts.append("".join(prompt_segments) + text_prompt.strip())
|
||||
return prompts
|
||||
|
||||
def get_fused_image_text_embedding_batched(
|
||||
self,
|
||||
camera_images: Sequence[torch.Tensor],
|
||||
image_masks: torch.Tensor,
|
||||
text_prompts: Sequence[str],
|
||||
return_cls_only: bool = True,
|
||||
):
|
||||
"""Fused VL embedding from per-camera ``[0, 1]`` image batches (no PIL, no host round-trip).
|
||||
|
||||
Args:
|
||||
camera_images: list of per-camera tensors, each shaped ``(B, C, H, W)`` in ``[0, 1]``.
|
||||
image_masks: bool tensor ``(B, max_views)`` marking present views.
|
||||
|
||||
Returns:
|
||||
A ``(embeddings, valid_mask)`` tuple. With ``return_cls_only=False``, ``embeddings`` is
|
||||
``(B, L, H)`` and ``valid_mask`` is a ``(B, L)`` bool tensor marking tokens downstream
|
||||
attention may attend to (padding and absent-view tokens are False). With
|
||||
``return_cls_only=True``, ``embeddings`` is the pooled ``(B, H)`` last-valid-token state
|
||||
and ``valid_mask`` is None.
|
||||
"""
|
||||
max_views = int(image_masks.shape[1])
|
||||
batch_size = int(image_masks.shape[0])
|
||||
mean = torch.tensor(IMAGENET_MEAN, device=self.device, dtype=self.model_dtype)
|
||||
std = torch.tensor(IMAGENET_STD, device=self.device, dtype=self.model_dtype)
|
||||
pixel_values = _batched_pixel_values(
|
||||
camera_images, max_views, self.image_size, mean, std, self.model_dtype, self.device
|
||||
)
|
||||
# InternVL3 preprocessing uses a single tile per image (max_num=1).
|
||||
batch_num_tiles_list = [[1] * max_views for _ in range(batch_size)]
|
||||
return self._forward_vlm(
|
||||
pixel_values, batch_num_tiles_list, image_masks, text_prompts, return_cls_only
|
||||
)
|
||||
|
||||
def _mask_absent_image_tokens(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
attention_mask: torch.Tensor,
|
||||
image_masks: torch.Tensor,
|
||||
batch_num_tiles_list: list[list[int]],
|
||||
) -> torch.Tensor:
|
||||
"""Zero attention over the image-context tokens of absent (zero-padded) views.
|
||||
|
||||
Fully vectorized: runs without any host<->device synchronization.
|
||||
"""
|
||||
# A single tile per image (max_num=1), so every image occupies the same number of
|
||||
# context tokens.
|
||||
tiles_per_image = (
|
||||
batch_num_tiles_list[0][0] if batch_num_tiles_list and batch_num_tiles_list[0] else 1
|
||||
)
|
||||
tokens_per_image = self.num_image_token * tiles_per_image
|
||||
|
||||
image_masks = image_masks.to(device=input_ids.device).bool()
|
||||
img_token_mask = input_ids == self.img_context_token_id # (B, L)
|
||||
# keep[b, k] tells whether the k-th image-context token (ordered view0, view1, ...) survives.
|
||||
per_token_keep = image_masks.repeat_interleave(tokens_per_image, dim=1) # (B, V * tokens_per_image)
|
||||
# Rank each context token by its running position among the row's context tokens.
|
||||
ctx_index = img_token_mask.to(torch.long).cumsum(dim=1) - 1
|
||||
ctx_index = ctx_index.clamp(min=0, max=per_token_keep.shape[1] - 1)
|
||||
keep_here = torch.gather(per_token_keep, 1, ctx_index) # (B, L)
|
||||
drop = img_token_mask & ~keep_here
|
||||
return attention_mask.masked_fill(drop, 0)
|
||||
|
||||
def _forward_vlm(
|
||||
self,
|
||||
pixel_values: torch.Tensor,
|
||||
batch_num_tiles_list: list[list[int]],
|
||||
image_masks: torch.Tensor,
|
||||
text_prompts: Sequence[str],
|
||||
return_cls_only: bool,
|
||||
):
|
||||
if pixel_values.shape[0] == 0:
|
||||
logger.warning("InternVL3 received an empty image batch after preprocessing.")
|
||||
hidden_size = getattr(self.model.config, "hidden_size", None)
|
||||
if hidden_size is None:
|
||||
hidden_size = getattr(self.model.config.text_config, "hidden_size", None)
|
||||
if hidden_size is None:
|
||||
raise RuntimeError("Unable to infer hidden size for empty InternVL3 batch.")
|
||||
return torch.empty(0, hidden_size, device=self.device, dtype=torch.float32), None
|
||||
|
||||
prompts = self._build_multimodal_prompts(batch_num_tiles_list, text_prompts)
|
||||
|
||||
model_inputs = self.tokenizer(
|
||||
list(prompts),
|
||||
return_tensors="pt",
|
||||
padding=True,
|
||||
truncation=True,
|
||||
max_length=self.max_text_length,
|
||||
).to(self.device)
|
||||
input_ids = model_inputs["input_ids"]
|
||||
if input_ids.shape[1] >= self.max_text_length:
|
||||
# Truncation cuts from the right, so text is dropped before image placeholders — but a
|
||||
# large max_views * image_seq_length budget can still eat into them. Fail loudly instead
|
||||
# of letting the VLM crash on a placeholder/vision-feature count mismatch.
|
||||
expected_image_tokens = self.num_image_token * sum(batch_num_tiles_list[0])
|
||||
image_token_counts = (input_ids == self.img_context_token_id).sum(dim=1)
|
||||
if not bool((image_token_counts == expected_image_tokens).all()):
|
||||
raise ValueError(
|
||||
f"Prompt truncation at max_text_length={self.max_text_length} cut into the "
|
||||
f"image placeholder tokens ({expected_image_tokens} expected per sample). "
|
||||
"Increase max_text_length or reduce max_views."
|
||||
)
|
||||
attention_mask = self._mask_absent_image_tokens(
|
||||
input_ids, model_inputs["attention_mask"], image_masks, batch_num_tiles_list
|
||||
)
|
||||
|
||||
outputs = self.model(
|
||||
input_ids=input_ids,
|
||||
pixel_values=pixel_values,
|
||||
attention_mask=attention_mask,
|
||||
output_hidden_states=True,
|
||||
return_dict=True,
|
||||
)
|
||||
fused_hidden = outputs.hidden_states[-1].to(torch.float32)
|
||||
valid_mask = attention_mask.to(torch.bool)
|
||||
if return_cls_only:
|
||||
# Right-padded causal decoder: the last valid token is the only one that has attended
|
||||
# to the full image + text prompt.
|
||||
positions = torch.arange(valid_mask.shape[1], device=valid_mask.device)
|
||||
last_valid = (valid_mask.long() * positions).argmax(dim=1)
|
||||
batch_index = torch.arange(fused_hidden.shape[0], device=fused_hidden.device)
|
||||
return fused_hidden[batch_index, last_valid], None
|
||||
return fused_hidden, valid_mask
|
||||
|
||||
@property
|
||||
def device(self) -> torch.device:
|
||||
return next(self.model.parameters()).device
|
||||
|
||||
|
||||
def _flash_attn_available() -> bool:
|
||||
try:
|
||||
import flash_attn # noqa: F401
|
||||
except ModuleNotFoundError:
|
||||
return False
|
||||
return True
|
||||
@@ -1,532 +0,0 @@
|
||||
# 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.
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import builtins
|
||||
from collections import deque
|
||||
from contextlib import nullcontext
|
||||
from pathlib import Path
|
||||
from typing import TypedDict, Unpack
|
||||
|
||||
import torch
|
||||
from torch import Tensor
|
||||
|
||||
from lerobot.configs.policies import PreTrainedConfig
|
||||
from lerobot.policies.pretrained import PreTrainedPolicy, T
|
||||
from lerobot.utils.constants import ACTION, OBS_IMAGES, OBS_STATE
|
||||
|
||||
from ..rtc.modeling_rtc import RTCProcessor
|
||||
from .configuration_evo1 import Evo1Config
|
||||
from .evo1_model import Evo1Model
|
||||
|
||||
|
||||
class ActionSelectKwargs(TypedDict, total=False):
|
||||
inference_delay: int | None
|
||||
prev_chunk_left_over: Tensor | None
|
||||
execution_horizon: int | None
|
||||
|
||||
|
||||
class Evo1Policy(PreTrainedPolicy):
|
||||
config_class = Evo1Config
|
||||
name = "evo1"
|
||||
|
||||
def __init__(self, config: Evo1Config, *, vlm_hub_kwargs: dict | None = None, **kwargs):
|
||||
super().__init__(config)
|
||||
config.validate_features()
|
||||
|
||||
if len(config.image_features) > config.max_views:
|
||||
raise ValueError(
|
||||
f"EVO1 supports at most {config.max_views} camera streams, got {len(config.image_features)}"
|
||||
)
|
||||
|
||||
self.config = config
|
||||
self.model = Evo1Model(config, vlm_hub_kwargs=vlm_hub_kwargs)
|
||||
self.model.set_finetune_flags()
|
||||
self._keep_frozen_embedder_eval()
|
||||
self.init_rtc_processor()
|
||||
self.reset()
|
||||
|
||||
def init_rtc_processor(self):
|
||||
"""Create the RTC processor when config.rtc_config is set.
|
||||
|
||||
The RTC rollout backend assigns config.rtc_config after loading the policy and re-invokes
|
||||
this method.
|
||||
"""
|
||||
self.rtc_processor = None
|
||||
if self.config.rtc_config is not None:
|
||||
self.rtc_processor = RTCProcessor(self.config.rtc_config)
|
||||
model = getattr(self, "model", None)
|
||||
if model is not None:
|
||||
model.rtc_processor = self.rtc_processor
|
||||
|
||||
def _rtc_enabled(self) -> bool:
|
||||
return self.config.rtc_config is not None and self.config.rtc_config.enabled
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(
|
||||
cls: builtins.type[T],
|
||||
pretrained_name_or_path: str | Path,
|
||||
*,
|
||||
config: PreTrainedConfig | None = None,
|
||||
force_download: bool = False,
|
||||
resume_download: bool | None = None,
|
||||
proxies: dict | None = None,
|
||||
token: str | bool | None = None,
|
||||
cache_dir: str | Path | None = None,
|
||||
local_files_only: bool = False,
|
||||
revision: str | None = None,
|
||||
strict: bool | None = None,
|
||||
**kwargs,
|
||||
) -> T:
|
||||
if strict is None:
|
||||
strict = True
|
||||
vlm_hub_kwargs = kwargs.pop("vlm_hub_kwargs", None)
|
||||
if config is None:
|
||||
config = PreTrainedConfig.from_pretrained(
|
||||
pretrained_name_or_path=pretrained_name_or_path,
|
||||
force_download=force_download,
|
||||
resume_download=resume_download,
|
||||
proxies=proxies,
|
||||
token=token,
|
||||
cache_dir=cache_dir,
|
||||
local_files_only=local_files_only,
|
||||
revision=revision,
|
||||
**kwargs,
|
||||
)
|
||||
if vlm_hub_kwargs is None:
|
||||
# Forward the hub download options to the base-VLM download as well; `revision` is not
|
||||
# forwarded because it identifies the policy repo, not the VLM repo.
|
||||
vlm_hub_kwargs = {
|
||||
key: value
|
||||
for key, value in (
|
||||
("token", token),
|
||||
("cache_dir", cache_dir),
|
||||
("local_files_only", local_files_only),
|
||||
("proxies", proxies),
|
||||
)
|
||||
if value not in (None, False)
|
||||
}
|
||||
kwargs["vlm_hub_kwargs"] = vlm_hub_kwargs
|
||||
return super().from_pretrained(
|
||||
pretrained_name_or_path=pretrained_name_or_path,
|
||||
config=config,
|
||||
force_download=force_download,
|
||||
resume_download=resume_download,
|
||||
proxies=proxies,
|
||||
token=token,
|
||||
cache_dir=cache_dir,
|
||||
local_files_only=local_files_only,
|
||||
revision=revision,
|
||||
strict=strict,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@property
|
||||
def _camera_keys(self) -> list[str]:
|
||||
return list(self.config.image_features)
|
||||
|
||||
@property
|
||||
def _env_action_dim(self) -> int:
|
||||
action_feature = self.config.action_feature
|
||||
if action_feature is None:
|
||||
return self.config.max_action_dim
|
||||
return int(action_feature.shape[0])
|
||||
|
||||
@property
|
||||
def _compute_dtype(self) -> torch.dtype:
|
||||
return next(self.model.action_head.parameters()).dtype
|
||||
|
||||
@property
|
||||
def _device(self) -> torch.device:
|
||||
# The device the policy actually lives on. Derived from the parameters rather than
|
||||
# config.device so the policy keeps working after accelerate (or a plain .to()) moves it.
|
||||
return next(self.model.action_head.parameters()).device
|
||||
|
||||
@property
|
||||
def _amp_enabled(self) -> bool:
|
||||
return bool(self.config.use_amp) and self._device.type == "cuda"
|
||||
|
||||
def _maybe_autocast(self):
|
||||
# EVO1 manages its own mixed precision: an explicit bf16 autocast that also overrides any
|
||||
# outer autocast context (e.g. lerobot-eval's fp16 default), keeping train and eval
|
||||
# numerics identical.
|
||||
if self._amp_enabled:
|
||||
return torch.autocast(device_type="cuda", dtype=torch.bfloat16)
|
||||
return nullcontext()
|
||||
|
||||
def get_optim_params(self) -> list[dict]:
|
||||
decay, no_decay = [], []
|
||||
for name, param in self.named_parameters():
|
||||
if not param.requires_grad:
|
||||
continue
|
||||
is_bias = name.endswith("bias") or ".bias" in name
|
||||
is_norm = param.dim() == 1 or "norm" in name.lower()
|
||||
if is_bias or is_norm:
|
||||
no_decay.append(param)
|
||||
else:
|
||||
decay.append(param)
|
||||
return [
|
||||
{"params": decay, "weight_decay": self.config.optimizer_weight_decay},
|
||||
{"params": no_decay, "weight_decay": 0.0},
|
||||
]
|
||||
|
||||
def reset(self):
|
||||
self._action_queue = deque([], maxlen=self.config.n_action_steps)
|
||||
|
||||
def _normalize_task_batch(self, batch: dict[str, Tensor | list[str] | str]) -> list[str]:
|
||||
prompts = batch.get(self.config.task_field)
|
||||
if prompts is None and self.config.task_field != "task":
|
||||
prompts = batch.get("task")
|
||||
if prompts is None:
|
||||
raise ValueError(f"EVO1 expects a '{self.config.task_field}' text field in the batch.")
|
||||
if isinstance(prompts, str):
|
||||
return [prompts]
|
||||
if isinstance(prompts, (list, tuple)):
|
||||
return [str(prompt) for prompt in prompts]
|
||||
raise TypeError(f"Unsupported prompt batch type: {type(prompts)}")
|
||||
|
||||
def _prepare_state(self, batch: dict[str, Tensor]) -> tuple[Tensor, Tensor]:
|
||||
if OBS_STATE not in batch:
|
||||
raise ValueError(f"EVO1 requires '{OBS_STATE}' in the batch.")
|
||||
state = batch[OBS_STATE]
|
||||
if state.dim() == 1:
|
||||
state = state.unsqueeze(0)
|
||||
elif state.dim() == 3:
|
||||
state = state[:, -1]
|
||||
elif state.dim() != 2:
|
||||
raise ValueError(f"Unsupported state tensor shape for EVO1: {tuple(state.shape)}")
|
||||
batch_size, state_dim = state.shape
|
||||
if state_dim > self.config.max_state_dim:
|
||||
raise ValueError(
|
||||
f"State dim {state_dim} exceeds configured max_state_dim {self.config.max_state_dim}"
|
||||
)
|
||||
explicit_mask = batch.get("state_mask")
|
||||
if explicit_mask is not None:
|
||||
if explicit_mask.dim() == 1:
|
||||
explicit_mask = explicit_mask.unsqueeze(0)
|
||||
elif explicit_mask.dim() == 3:
|
||||
explicit_mask = explicit_mask[:, -1]
|
||||
elif explicit_mask.dim() != 2:
|
||||
raise ValueError(
|
||||
f"Unsupported state_mask tensor shape for EVO1: {tuple(explicit_mask.shape)}"
|
||||
)
|
||||
if explicit_mask.shape != (batch_size, state_dim):
|
||||
raise ValueError(
|
||||
f"state_mask shape {tuple(explicit_mask.shape)} does not match state shape {(batch_size, state_dim)}"
|
||||
)
|
||||
device = self._device
|
||||
padded = torch.zeros(
|
||||
batch_size,
|
||||
self.config.max_state_dim,
|
||||
dtype=state.dtype,
|
||||
device=device,
|
||||
)
|
||||
padded[:, :state_dim] = state.to(device=device)
|
||||
mask = torch.zeros(
|
||||
batch_size,
|
||||
self.config.max_state_dim,
|
||||
dtype=torch.bool,
|
||||
device=device,
|
||||
)
|
||||
if explicit_mask is None:
|
||||
mask[:, :state_dim] = True
|
||||
else:
|
||||
mask[:, :state_dim] = explicit_mask.to(device=device, dtype=torch.bool)
|
||||
# Zero out masked state dims so an explicit state_mask actually affects the model input
|
||||
# (the state encoder has no mask argument of its own).
|
||||
padded = padded * mask.to(dtype=padded.dtype)
|
||||
return padded.to(dtype=self._compute_dtype), mask
|
||||
|
||||
def _prepare_actions(self, batch: dict[str, Tensor]) -> tuple[Tensor, Tensor]:
|
||||
if ACTION not in batch:
|
||||
raise ValueError(f"EVO1 requires '{ACTION}' in the batch for training.")
|
||||
action = batch[ACTION]
|
||||
if action.dim() == 2:
|
||||
action = action.unsqueeze(1)
|
||||
batch_size, horizon, action_dim = action.shape
|
||||
if horizon != self.config.chunk_size:
|
||||
raise ValueError(
|
||||
f"EVO1 expects chunk_size={self.config.chunk_size}, got action horizon {horizon}"
|
||||
)
|
||||
if action_dim > self.config.max_action_dim:
|
||||
raise ValueError(
|
||||
f"Action dim {action_dim} exceeds configured max_action_dim {self.config.max_action_dim}"
|
||||
)
|
||||
explicit_mask = batch.get("action_mask")
|
||||
if explicit_mask is not None:
|
||||
if explicit_mask.dim() == 2:
|
||||
if horizon == 1:
|
||||
explicit_mask = explicit_mask.unsqueeze(1)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"2D action_mask is only supported when chunk_size=1, got action horizon {horizon}"
|
||||
)
|
||||
elif explicit_mask.dim() != 3:
|
||||
raise ValueError(
|
||||
f"Unsupported action_mask tensor shape for EVO1: {tuple(explicit_mask.shape)}"
|
||||
)
|
||||
if explicit_mask.shape != (batch_size, horizon, action_dim):
|
||||
raise ValueError(
|
||||
"action_mask shape "
|
||||
f"{tuple(explicit_mask.shape)} does not match action shape {(batch_size, horizon, action_dim)}"
|
||||
)
|
||||
device = self._device
|
||||
padded = torch.zeros(
|
||||
batch_size,
|
||||
horizon,
|
||||
self.config.max_action_dim,
|
||||
dtype=action.dtype,
|
||||
device=device,
|
||||
)
|
||||
padded[:, :, :action_dim] = action.to(device=device)
|
||||
mask = torch.zeros(
|
||||
batch_size,
|
||||
horizon,
|
||||
self.config.max_action_dim,
|
||||
dtype=torch.bool,
|
||||
device=device,
|
||||
)
|
||||
if explicit_mask is None:
|
||||
mask[:, :, :action_dim] = True
|
||||
else:
|
||||
mask[:, :, :action_dim] = explicit_mask.to(device=device, dtype=torch.bool)
|
||||
|
||||
# Timesteps beyond the episode end hold fabricated (repeated) actions; exclude them from
|
||||
# the loss like the other chunked policies do.
|
||||
action_is_pad = batch.get("action_is_pad")
|
||||
if action_is_pad is not None:
|
||||
if action_is_pad.shape != (batch_size, horizon):
|
||||
raise ValueError(
|
||||
f"action_is_pad shape {tuple(action_is_pad.shape)} does not match "
|
||||
f"(batch_size, chunk_size)={(batch_size, horizon)}"
|
||||
)
|
||||
in_episode = ~action_is_pad.to(device=device, dtype=torch.bool)
|
||||
mask = mask & in_episode.unsqueeze(-1)
|
||||
return padded.to(dtype=self._compute_dtype), mask
|
||||
|
||||
def _prepare_inference_action_mask(self, batch_size: int) -> Tensor:
|
||||
mask = torch.zeros(
|
||||
batch_size,
|
||||
self.config.max_action_dim,
|
||||
dtype=torch.bool,
|
||||
device=self._device,
|
||||
)
|
||||
mask[:, : self._env_action_dim] = True
|
||||
return mask
|
||||
|
||||
def _get_embodiment_ids(self, batch: dict[str, Tensor], batch_size: int) -> Tensor:
|
||||
embodiment_ids = batch.get("embodiment_id")
|
||||
if embodiment_ids is None and self.config.embodiment_id_field:
|
||||
embodiment_ids = batch.get(self.config.embodiment_id_field)
|
||||
if embodiment_ids is None:
|
||||
return torch.full(
|
||||
(batch_size,),
|
||||
self.config.default_embodiment_id,
|
||||
dtype=torch.long,
|
||||
device=self._device,
|
||||
)
|
||||
if embodiment_ids.dim() == 0:
|
||||
embodiment_ids = embodiment_ids.unsqueeze(0)
|
||||
elif embodiment_ids.dim() > 1:
|
||||
embodiment_ids = embodiment_ids[:, -1]
|
||||
return embodiment_ids.to(device=self._device, dtype=torch.long)
|
||||
|
||||
@property
|
||||
def _tracks_vlm_gradients(self) -> bool:
|
||||
return bool(
|
||||
self.config.finetune_vlm
|
||||
or self.config.finetune_language_model
|
||||
or self.config.finetune_vision_model
|
||||
)
|
||||
|
||||
def _keep_frozen_embedder_eval(self) -> None:
|
||||
if self._tracks_vlm_gradients:
|
||||
return
|
||||
embedder = getattr(self.model, "embedder", None)
|
||||
if embedder is not None:
|
||||
embedder.eval()
|
||||
|
||||
def train(self, mode: bool = True):
|
||||
super().train(mode)
|
||||
self._keep_frozen_embedder_eval()
|
||||
return self
|
||||
|
||||
def _collect_image_batches(self, batch: dict[str, Tensor]) -> tuple[list[Tensor], Tensor]:
|
||||
camera_keys = self._camera_keys or sorted(key for key in batch if key.startswith(f"{OBS_IMAGES}."))
|
||||
if not camera_keys:
|
||||
raise ValueError("EVO1 requires at least one visual observation feature.")
|
||||
camera_keys = list(camera_keys)[: self.config.max_views]
|
||||
|
||||
# Configured cameras may be absent from the batch up to the empty_cameras budget (e.g. the
|
||||
# placeholder features added by validate_features); they become masked-out views that the
|
||||
# embedder zero-pads. Any other absent camera is an error.
|
||||
present_keys = [key for key in camera_keys if key in batch]
|
||||
missing_keys = [key for key in camera_keys if key not in batch]
|
||||
if len(missing_keys) > self.config.empty_cameras:
|
||||
raise ValueError(
|
||||
f"Missing camera features {missing_keys} in batch; at most "
|
||||
f"empty_cameras={self.config.empty_cameras} may be absent."
|
||||
)
|
||||
if not present_keys:
|
||||
raise ValueError("EVO1 requires at least one visual observation in the batch.")
|
||||
|
||||
# Keep each present camera as a batched (B, C, H, W) tensor on its current (GPU) device.
|
||||
# Resizing/normalization and zero-padding of absent views happen batched inside the
|
||||
# embedder, so images never leave the device here.
|
||||
camera_images: list[Tensor] = []
|
||||
for camera_key in present_keys:
|
||||
image = batch[camera_key]
|
||||
if image.dim() == 3:
|
||||
# Promote an unbatched (C, H, W) frame so batch_size is read from a real batch dim.
|
||||
image = image.unsqueeze(0)
|
||||
elif image.dim() == 5:
|
||||
image = image[:, -1]
|
||||
elif image.dim() != 4:
|
||||
raise ValueError(
|
||||
f"Unsupported image tensor shape for EVO1: key={camera_key} shape={tuple(image.shape)}"
|
||||
)
|
||||
camera_images.append(image)
|
||||
|
||||
batch_size = camera_images[0].shape[0]
|
||||
n_present = len(camera_images)
|
||||
image_masks = torch.zeros(
|
||||
batch_size, self.config.max_views, dtype=torch.bool, device=camera_images[0].device
|
||||
)
|
||||
image_masks[:, :n_present] = True
|
||||
|
||||
return camera_images, image_masks
|
||||
|
||||
def _compute_fused_tokens(
|
||||
self,
|
||||
prompts: list[str],
|
||||
image_batches: list[Tensor],
|
||||
image_masks: Tensor,
|
||||
) -> tuple[Tensor, Tensor | None]:
|
||||
track_vlm_gradients = self._tracks_vlm_gradients
|
||||
grad_context = nullcontext() if track_vlm_gradients else torch.no_grad()
|
||||
with grad_context:
|
||||
fused_tokens, context_mask = self.model.get_vl_embeddings(
|
||||
images=image_batches,
|
||||
image_mask=image_masks,
|
||||
prompt=prompts,
|
||||
return_cls_only=self.config.return_cls_only,
|
||||
)
|
||||
|
||||
if not track_vlm_gradients:
|
||||
fused_tokens = fused_tokens.detach()
|
||||
fused_tokens = fused_tokens.to(device=self._device, dtype=self._compute_dtype)
|
||||
if context_mask is not None:
|
||||
context_mask = context_mask.to(device=self._device)
|
||||
return fused_tokens, context_mask
|
||||
|
||||
def _compute_masked_loss(
|
||||
self,
|
||||
pred_velocity: Tensor,
|
||||
target_velocity: Tensor,
|
||||
action_mask: Tensor,
|
||||
reduction: str,
|
||||
) -> Tensor:
|
||||
flat_mask = action_mask.view(action_mask.shape[0], -1).to(dtype=pred_velocity.dtype)
|
||||
sq_error = ((pred_velocity - target_velocity) * flat_mask).pow(2)
|
||||
active = flat_mask.sum(dim=1).clamp_min(1.0)
|
||||
per_sample_loss = sq_error.sum(dim=1) / active
|
||||
if reduction == "none":
|
||||
return per_sample_loss
|
||||
if reduction != "mean":
|
||||
raise ValueError(f"Unsupported reduction '{reduction}'")
|
||||
return sq_error.sum() / active.sum()
|
||||
|
||||
def forward(self, batch: dict[str, Tensor], reduction: str = "mean") -> tuple[Tensor, dict]:
|
||||
prompts = self._normalize_task_batch(batch)
|
||||
image_batches, image_masks = self._collect_image_batches(batch)
|
||||
states, _state_mask = self._prepare_state(batch)
|
||||
actions_gt, action_mask = self._prepare_actions(batch)
|
||||
embodiment_ids = self._get_embodiment_ids(batch, states.shape[0])
|
||||
|
||||
with self._maybe_autocast():
|
||||
fused_tokens, context_mask = self._compute_fused_tokens(prompts, image_batches, image_masks)
|
||||
pred_velocity, noise = self.model(
|
||||
fused_tokens,
|
||||
state=states,
|
||||
actions_gt=actions_gt,
|
||||
action_mask=action_mask.to(device=self._device, dtype=self._compute_dtype),
|
||||
embodiment_ids=embodiment_ids,
|
||||
context_mask=context_mask,
|
||||
)
|
||||
|
||||
# Compute the flow-matching regression loss in fp32, outside the autocast block.
|
||||
pred_velocity = pred_velocity.float()
|
||||
noise = noise.float()
|
||||
flat_action_mask = action_mask.view(action_mask.shape[0], -1).to(dtype=torch.float32)
|
||||
# Flow-matching velocity target. Padded (masked-out) action dims are already zero on both sides
|
||||
# here (`actions_gt` is zero-padded in `_prepare_actions`, and `noise` is masked inside the head),
|
||||
# and the whole difference is multiplied by `flat_action_mask`, so padded dims contribute nothing.
|
||||
target_velocity = (actions_gt.float() - noise).view(actions_gt.shape[0], -1) * flat_action_mask
|
||||
loss = self._compute_masked_loss(pred_velocity, target_velocity, action_mask, reduction)
|
||||
loss_mean = loss.mean().item() if loss.ndim > 0 else loss.item()
|
||||
return loss, {
|
||||
"loss": loss_mean,
|
||||
"active_action_dims": float(action_mask.sum(dim=(1, 2)).float().mean().item()),
|
||||
}
|
||||
|
||||
@torch.no_grad()
|
||||
def predict_action_chunk(self, batch: dict[str, Tensor], **kwargs: Unpack[ActionSelectKwargs]) -> Tensor:
|
||||
inference_delay = kwargs.get("inference_delay")
|
||||
prev_chunk_left_over = kwargs.get("prev_chunk_left_over")
|
||||
execution_horizon = kwargs.get("execution_horizon")
|
||||
if (inference_delay is not None or prev_chunk_left_over is not None) and not self._rtc_enabled():
|
||||
raise RuntimeError(
|
||||
"Received RTC arguments but RTC is not configured for this EVO1 policy: set "
|
||||
"config.rtc_config and call init_rtc_processor() (lerobot-rollout does this for "
|
||||
"--inference.type=rtc)."
|
||||
)
|
||||
self.eval()
|
||||
|
||||
prompts = self._normalize_task_batch(batch)
|
||||
image_batches, image_masks = self._collect_image_batches(batch)
|
||||
states, _state_mask = self._prepare_state(batch)
|
||||
embodiment_ids = self._get_embodiment_ids(batch, states.shape[0])
|
||||
action_mask = self._prepare_inference_action_mask(states.shape[0])
|
||||
if prev_chunk_left_over is not None:
|
||||
prev_chunk_left_over = prev_chunk_left_over.to(device=self._device)
|
||||
|
||||
with self._maybe_autocast():
|
||||
fused_tokens, context_mask = self._compute_fused_tokens(prompts, image_batches, image_masks)
|
||||
actions = self.model(
|
||||
fused_tokens,
|
||||
state=states,
|
||||
action_mask=action_mask,
|
||||
embodiment_ids=embodiment_ids,
|
||||
context_mask=context_mask,
|
||||
inference_delay=inference_delay,
|
||||
prev_chunk_left_over=prev_chunk_left_over,
|
||||
execution_horizon=execution_horizon,
|
||||
)
|
||||
actions = actions.view(states.shape[0], self.config.chunk_size, self.config.max_action_dim)
|
||||
return actions.to(dtype=torch.float32)
|
||||
|
||||
@torch.no_grad()
|
||||
def select_action(self, batch: dict[str, Tensor], **kwargs) -> Tensor:
|
||||
assert not self._rtc_enabled(), (
|
||||
"RTC is not supported for select_action, use it with predict_action_chunk"
|
||||
)
|
||||
self.eval()
|
||||
if len(self._action_queue) == 0:
|
||||
action_chunk = self.predict_action_chunk(batch)[:, : self.config.n_action_steps]
|
||||
self._action_queue.extend(action_chunk.transpose(0, 1))
|
||||
# Returns one step of shape (B, max_action_dim): actions are emitted at the padded max_action_dim
|
||||
# width and cropped to the real action dim downstream by the postprocessor (Evo1ActionProcessorStep).
|
||||
# Callers that bypass the postprocessor receive the padded width.
|
||||
return self._action_queue.popleft()
|
||||
@@ -1,400 +0,0 @@
|
||||
# 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.
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from copy import deepcopy
|
||||
from dataclasses import dataclass
|
||||
from typing import Any
|
||||
|
||||
import torch
|
||||
|
||||
from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
|
||||
from lerobot.processor import (
|
||||
AddBatchDimensionProcessorStep,
|
||||
DeviceProcessorStep,
|
||||
NormalizerProcessorStep,
|
||||
ObservationProcessorStep,
|
||||
PolicyAction,
|
||||
PolicyActionProcessorStep,
|
||||
PolicyProcessorPipeline,
|
||||
ProcessorStep,
|
||||
ProcessorStepRegistry,
|
||||
RenameObservationsProcessorStep,
|
||||
UnnormalizerProcessorStep,
|
||||
)
|
||||
from lerobot.processor.converters import (
|
||||
batch_to_transition,
|
||||
create_transition,
|
||||
policy_action_to_transition,
|
||||
transition_to_policy_action,
|
||||
)
|
||||
from lerobot.types import EnvTransition, TransitionKey
|
||||
from lerobot.utils.constants import (
|
||||
ACTION,
|
||||
DONE,
|
||||
INFO,
|
||||
OBS_PREFIX,
|
||||
OBS_STATE,
|
||||
POLICY_POSTPROCESSOR_DEFAULT_NAME,
|
||||
POLICY_PREPROCESSOR_DEFAULT_NAME,
|
||||
REWARD,
|
||||
TRUNCATED,
|
||||
)
|
||||
|
||||
from .configuration_evo1 import Evo1Config
|
||||
|
||||
|
||||
def evo1_batch_to_transition(batch: dict[str, Any]):
|
||||
transition = batch_to_transition(batch)
|
||||
complementary_data = dict(transition.get("complementary_data") or {})
|
||||
reserved = {ACTION, REWARD, DONE, TRUNCATED, INFO}
|
||||
for key, value in batch.items():
|
||||
if key in reserved or key.startswith(OBS_PREFIX):
|
||||
continue
|
||||
complementary_data.setdefault(key, value)
|
||||
return create_transition(
|
||||
observation=transition.get("observation"),
|
||||
action=transition.get("action"),
|
||||
reward=transition.get("reward", 0.0),
|
||||
done=transition.get("done", False),
|
||||
truncated=transition.get("truncated", False),
|
||||
info=transition.get("info", {}),
|
||||
complementary_data=complementary_data,
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
@ProcessorStepRegistry.register(name="evo1_pad_state_processor")
|
||||
class Evo1PadStateProcessorStep(ObservationProcessorStep):
|
||||
"""Pad policy observations to EVO1's fixed state width before normalization."""
|
||||
|
||||
max_state_dim: int = 24
|
||||
|
||||
def observation(self, observation: dict[str, Any]) -> dict[str, Any]:
|
||||
if OBS_STATE not in observation:
|
||||
return observation
|
||||
|
||||
state = observation[OBS_STATE]
|
||||
state_dim = state.shape[-1]
|
||||
if state_dim > self.max_state_dim:
|
||||
raise ValueError(
|
||||
f"EVO1 state has {state_dim} dims, which exceeds max_state_dim={self.max_state_dim}."
|
||||
)
|
||||
if state_dim < self.max_state_dim:
|
||||
observation = observation.copy()
|
||||
observation[OBS_STATE] = torch.nn.functional.pad(state, (0, self.max_state_dim - state_dim))
|
||||
return observation
|
||||
|
||||
def transform_features(
|
||||
self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
|
||||
) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
|
||||
new_features = {ft: feats.copy() for ft, feats in features.items()}
|
||||
obs_feats = new_features.setdefault(PipelineFeatureType.OBSERVATION, {})
|
||||
if OBS_STATE in obs_feats:
|
||||
obs_feats[OBS_STATE] = PolicyFeature(type=FeatureType.STATE, shape=(self.max_state_dim,))
|
||||
return new_features
|
||||
|
||||
def get_config(self) -> dict[str, Any]:
|
||||
return {"max_state_dim": self.max_state_dim}
|
||||
|
||||
|
||||
@dataclass
|
||||
@ProcessorStepRegistry.register(name="evo1_pad_action_processor")
|
||||
class Evo1PadActionProcessorStep(ProcessorStep):
|
||||
"""Pad training actions and preserve the active action dimensions with action_mask."""
|
||||
|
||||
max_action_dim: int = 24
|
||||
|
||||
def __call__(self, transition: EnvTransition) -> EnvTransition:
|
||||
action = transition.get(TransitionKey.ACTION)
|
||||
if action is None:
|
||||
return transition
|
||||
if not isinstance(action, PolicyAction):
|
||||
raise ValueError(f"EVO1 action should be a PolicyAction tensor, but got {type(action)}.")
|
||||
|
||||
action_dim = action.shape[-1]
|
||||
if action_dim > self.max_action_dim:
|
||||
raise ValueError(
|
||||
f"EVO1 action has {action_dim} dims, which exceeds max_action_dim={self.max_action_dim}."
|
||||
)
|
||||
|
||||
new_transition = transition.copy()
|
||||
new_action = action
|
||||
if action_dim < self.max_action_dim:
|
||||
new_action = torch.nn.functional.pad(action, (0, self.max_action_dim - action_dim))
|
||||
|
||||
complementary_data = dict(new_transition.get(TransitionKey.COMPLEMENTARY_DATA) or {})
|
||||
action_mask = complementary_data.get("action_mask")
|
||||
if action_mask is None:
|
||||
action_mask = torch.ones(action.shape, dtype=torch.bool, device=action.device)
|
||||
else:
|
||||
action_mask = torch.as_tensor(action_mask, dtype=torch.bool, device=action.device)
|
||||
if action_mask.shape != action.shape:
|
||||
raise ValueError(
|
||||
f"action_mask shape {tuple(action_mask.shape)} does not match action shape {tuple(action.shape)}."
|
||||
)
|
||||
if action_dim < self.max_action_dim:
|
||||
action_mask = torch.nn.functional.pad(action_mask, (0, self.max_action_dim - action_dim))
|
||||
|
||||
complementary_data["action_mask"] = action_mask
|
||||
new_transition[TransitionKey.ACTION] = new_action
|
||||
new_transition[TransitionKey.COMPLEMENTARY_DATA] = complementary_data
|
||||
return new_transition
|
||||
|
||||
def transform_features(
|
||||
self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
|
||||
) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
|
||||
new_features = {ft: feats.copy() for ft, feats in features.items()}
|
||||
action_feats = new_features.setdefault(PipelineFeatureType.ACTION, {})
|
||||
action_feats[ACTION] = PolicyFeature(type=FeatureType.ACTION, shape=(self.max_action_dim,))
|
||||
return new_features
|
||||
|
||||
def get_config(self) -> dict[str, Any]:
|
||||
return {"max_action_dim": self.max_action_dim}
|
||||
|
||||
|
||||
@dataclass
|
||||
@ProcessorStepRegistry.register(name="evo1_action_processor")
|
||||
class Evo1ActionProcessorStep(PolicyActionProcessorStep):
|
||||
"""Crop padded EVO1 actions and optionally binarize the LIBERO gripper channel."""
|
||||
|
||||
action_dim: int
|
||||
binarize_gripper: bool = False
|
||||
gripper_index: int = 6
|
||||
gripper_threshold: float = 0.5
|
||||
gripper_below_threshold_value: float = 1.0
|
||||
gripper_above_threshold_value: float = -1.0
|
||||
|
||||
def action(self, action: PolicyAction) -> PolicyAction:
|
||||
if action.shape[-1] < self.action_dim:
|
||||
raise ValueError(
|
||||
f"EVO1 action has {action.shape[-1]} dims, which is smaller than action_dim={self.action_dim}."
|
||||
)
|
||||
|
||||
action = action[..., : self.action_dim]
|
||||
if not self.binarize_gripper:
|
||||
return action
|
||||
|
||||
if not 0 <= self.gripper_index < self.action_dim:
|
||||
raise ValueError(
|
||||
f"gripper_index={self.gripper_index} must be within action_dim={self.action_dim}."
|
||||
)
|
||||
|
||||
action = action.clone()
|
||||
below = torch.as_tensor(
|
||||
self.gripper_below_threshold_value,
|
||||
dtype=action.dtype,
|
||||
device=action.device,
|
||||
)
|
||||
above = torch.as_tensor(
|
||||
self.gripper_above_threshold_value,
|
||||
dtype=action.dtype,
|
||||
device=action.device,
|
||||
)
|
||||
action[..., self.gripper_index] = torch.where(
|
||||
action[..., self.gripper_index] > self.gripper_threshold,
|
||||
above,
|
||||
below,
|
||||
)
|
||||
return action
|
||||
|
||||
def transform_features(
|
||||
self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
|
||||
) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
|
||||
new_features = {ft: feats.copy() for ft, feats in features.items()}
|
||||
action_feats = new_features.setdefault(PipelineFeatureType.ACTION, {})
|
||||
action_feats[ACTION] = PolicyFeature(type=FeatureType.ACTION, shape=(self.action_dim,))
|
||||
return new_features
|
||||
|
||||
def get_config(self) -> dict[str, Any]:
|
||||
return {
|
||||
"action_dim": self.action_dim,
|
||||
"binarize_gripper": self.binarize_gripper,
|
||||
"gripper_index": self.gripper_index,
|
||||
"gripper_threshold": self.gripper_threshold,
|
||||
"gripper_below_threshold_value": self.gripper_below_threshold_value,
|
||||
"gripper_above_threshold_value": self.gripper_above_threshold_value,
|
||||
}
|
||||
|
||||
|
||||
def _evo1_action_dim(config: Evo1Config) -> int:
|
||||
if config.postprocess_action_dim is not None:
|
||||
return config.postprocess_action_dim
|
||||
action_feature = config.action_feature
|
||||
if action_feature is None:
|
||||
return config.max_action_dim
|
||||
return int(action_feature.shape[0])
|
||||
|
||||
|
||||
def _evo1_normalization_features(config: Evo1Config) -> dict[str, PolicyFeature]:
|
||||
features = {**config.input_features, **config.output_features}
|
||||
features[OBS_STATE] = PolicyFeature(type=FeatureType.STATE, shape=(config.max_state_dim,))
|
||||
features[ACTION] = PolicyFeature(type=FeatureType.ACTION, shape=(config.max_action_dim,))
|
||||
return features
|
||||
|
||||
|
||||
def _evo1_action_features(config: Evo1Config) -> dict[str, PolicyFeature]:
|
||||
return {ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(config.max_action_dim,))}
|
||||
|
||||
|
||||
_STAT_PAD_VALUES = {
|
||||
"mean": 0.0,
|
||||
"std": 1.0,
|
||||
"min": -1.0,
|
||||
"max": 1.0,
|
||||
"q01": -1.0,
|
||||
"q99": 1.0,
|
||||
"q10": -1.0,
|
||||
"q90": 1.0,
|
||||
}
|
||||
|
||||
|
||||
def _pad_stat_value(value: Any, target_dim: int, stat_name: str) -> torch.Tensor:
|
||||
tensor = torch.as_tensor(value)
|
||||
if not tensor.is_floating_point():
|
||||
tensor = tensor.to(dtype=torch.float32)
|
||||
if tensor.ndim == 0 or tensor.shape[-1] >= target_dim:
|
||||
return tensor
|
||||
|
||||
pad_shape = (*tensor.shape[:-1], target_dim - tensor.shape[-1])
|
||||
pad_value = _STAT_PAD_VALUES.get(stat_name, 0.0)
|
||||
padding = torch.full(pad_shape, pad_value, dtype=tensor.dtype, device=tensor.device)
|
||||
return torch.cat([tensor, padding], dim=-1)
|
||||
|
||||
|
||||
def _pad_feature_stats(
|
||||
stats: dict[str, dict[str, Any]],
|
||||
feature_key: str,
|
||||
target_dim: int,
|
||||
) -> None:
|
||||
if feature_key not in stats:
|
||||
return
|
||||
stats[feature_key] = {
|
||||
stat_name: _pad_stat_value(stat_value, target_dim, stat_name)
|
||||
for stat_name, stat_value in stats[feature_key].items()
|
||||
}
|
||||
|
||||
|
||||
def _pad_evo1_stats(
|
||||
config: Evo1Config,
|
||||
stats: dict[str, dict[str, Any]] | None,
|
||||
) -> dict[str, dict[str, Any]] | None:
|
||||
if stats is None:
|
||||
return None
|
||||
|
||||
padded_stats = deepcopy(stats)
|
||||
# Added dimensions represent zero-padding inside EVO1. These neutral stats keep
|
||||
# padded observations at normalized zero and only provide shape compatibility.
|
||||
_pad_feature_stats(padded_stats, OBS_STATE, config.max_state_dim)
|
||||
_pad_feature_stats(padded_stats, ACTION, config.max_action_dim)
|
||||
return padded_stats
|
||||
|
||||
|
||||
def reconcile_evo1_processors(
|
||||
config: Evo1Config,
|
||||
preprocessor: PolicyProcessorPipeline,
|
||||
postprocessor: PolicyProcessorPipeline,
|
||||
) -> tuple[PolicyProcessorPipeline, PolicyProcessorPipeline]:
|
||||
"""Reconcile checkpoint-loaded pipelines with the current EVO1 config.
|
||||
|
||||
Two things cannot be restored from a serialized pipeline alone: the EVO1 batch converter
|
||||
(converters are plain functions and are never serialized), and eval-time CLI overrides of the
|
||||
action postprocessing flags (`postprocess_action_dim`, `binarize_gripper`, `gripper_*`). This
|
||||
restores the converter and rebuilds the action step from the current config so those overrides
|
||||
take effect.
|
||||
"""
|
||||
# Pipelines reloaded from a checkpoint come back with the default batch converter, which drops
|
||||
# non-observation extras (embodiment_id, state_mask, custom task fields) needed by EVO1.
|
||||
preprocessor.to_transition = evo1_batch_to_transition
|
||||
|
||||
action_step = Evo1ActionProcessorStep(
|
||||
action_dim=_evo1_action_dim(config),
|
||||
binarize_gripper=config.binarize_gripper,
|
||||
gripper_index=config.gripper_index,
|
||||
gripper_threshold=config.gripper_threshold,
|
||||
gripper_below_threshold_value=config.gripper_below_threshold_value,
|
||||
gripper_above_threshold_value=config.gripper_above_threshold_value,
|
||||
)
|
||||
steps = list(postprocessor.steps)
|
||||
action_step_idx = next(
|
||||
(idx for idx, step in enumerate(steps) if isinstance(step, Evo1ActionProcessorStep)), None
|
||||
)
|
||||
if action_step_idx is None:
|
||||
insert_idx = next(
|
||||
(idx + 1 for idx, step in enumerate(steps) if isinstance(step, UnnormalizerProcessorStep)),
|
||||
0,
|
||||
)
|
||||
steps.insert(insert_idx, action_step)
|
||||
else:
|
||||
steps[action_step_idx] = action_step
|
||||
postprocessor.steps = steps
|
||||
|
||||
return preprocessor, postprocessor
|
||||
|
||||
|
||||
def make_evo1_pre_post_processors(
|
||||
config: Evo1Config,
|
||||
dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
|
||||
) -> tuple[
|
||||
PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
|
||||
PolicyProcessorPipeline[PolicyAction, PolicyAction],
|
||||
]:
|
||||
normalization_features = _evo1_normalization_features(config)
|
||||
action_features = _evo1_action_features(config)
|
||||
normalization_stats = _pad_evo1_stats(config, dataset_stats)
|
||||
|
||||
input_steps = [
|
||||
RenameObservationsProcessorStep(rename_map={}),
|
||||
AddBatchDimensionProcessorStep(),
|
||||
Evo1PadStateProcessorStep(max_state_dim=config.max_state_dim),
|
||||
Evo1PadActionProcessorStep(max_action_dim=config.max_action_dim),
|
||||
NormalizerProcessorStep(
|
||||
features=normalization_features,
|
||||
norm_map=config.normalization_mapping,
|
||||
stats=normalization_stats,
|
||||
),
|
||||
DeviceProcessorStep(device=config.device),
|
||||
]
|
||||
output_steps = [
|
||||
UnnormalizerProcessorStep(
|
||||
features=action_features,
|
||||
norm_map=config.normalization_mapping,
|
||||
stats=normalization_stats,
|
||||
),
|
||||
Evo1ActionProcessorStep(
|
||||
action_dim=_evo1_action_dim(config),
|
||||
binarize_gripper=config.binarize_gripper,
|
||||
gripper_index=config.gripper_index,
|
||||
gripper_threshold=config.gripper_threshold,
|
||||
gripper_below_threshold_value=config.gripper_below_threshold_value,
|
||||
gripper_above_threshold_value=config.gripper_above_threshold_value,
|
||||
),
|
||||
# float32 so downstream numpy conversion works even when the policy computes in bf16.
|
||||
DeviceProcessorStep(device="cpu", float_dtype="float32"),
|
||||
]
|
||||
|
||||
return (
|
||||
PolicyProcessorPipeline[dict[str, Any], dict[str, Any]](
|
||||
steps=input_steps,
|
||||
name=POLICY_PREPROCESSOR_DEFAULT_NAME,
|
||||
to_transition=evo1_batch_to_transition,
|
||||
),
|
||||
PolicyProcessorPipeline[PolicyAction, PolicyAction](
|
||||
steps=output_steps,
|
||||
name=POLICY_POSTPROCESSOR_DEFAULT_NAME,
|
||||
to_transition=policy_action_to_transition,
|
||||
to_output=transition_to_policy_action,
|
||||
),
|
||||
)
|
||||
@@ -47,11 +47,9 @@ 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 .evo1.configuration_evo1 import Evo1Config
|
||||
from .fastwam.configuration_fastwam import FastWAMConfig
|
||||
from .gaussian_actor.configuration_gaussian_actor import GaussianActorConfig
|
||||
from .groot.configuration_groot import GrootConfig
|
||||
from .lingbot_va.configuration_lingbot_va import LingBotVAConfig
|
||||
from .molmoact2.configuration_molmoact2 import MolmoAct2Config
|
||||
from .multi_task_dit.configuration_multi_task_dit import MultiTaskDiTConfig
|
||||
from .pi0.configuration_pi0 import PI0Config
|
||||
@@ -94,7 +92,7 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:
|
||||
Args:
|
||||
name: The name of the policy. Supported names are "tdmpc", "diffusion", "act",
|
||||
"multi_task_dit", "vqbet", "pi0", "pi05", "gaussian_actor", "smolvla", "wall_x",
|
||||
"molmoact2", "eo1", "evo1".
|
||||
"molmoact2".
|
||||
Returns:
|
||||
The policy class corresponding to the given name.
|
||||
|
||||
@@ -165,18 +163,10 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:
|
||||
from .vla_jepa.modeling_vla_jepa import VLAJEPAPolicy
|
||||
|
||||
return VLAJEPAPolicy
|
||||
elif name == "lingbot_va":
|
||||
from .lingbot_va.modeling_lingbot_va import LingBotVAPolicy
|
||||
|
||||
return LingBotVAPolicy
|
||||
elif name == "fastwam":
|
||||
from .fastwam.modeling_fastwam import FastWAMPolicy
|
||||
|
||||
return FastWAMPolicy
|
||||
elif name == "evo1":
|
||||
from .evo1.modeling_evo1 import Evo1Policy
|
||||
|
||||
return Evo1Policy
|
||||
else:
|
||||
try:
|
||||
return _get_policy_cls_from_policy_name(name=name)
|
||||
@@ -194,7 +184,7 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
|
||||
Args:
|
||||
policy_type: The type of the policy. Supported types include "tdmpc",
|
||||
"multi_task_dit", "diffusion", "act", "vqbet", "pi0", "pi05", "gaussian_actor",
|
||||
"smolvla", "wall_x", "molmoact2", "eo1", "evo1".
|
||||
"smolvla", "wall_x", "molmoact2".
|
||||
**kwargs: Keyword arguments to be passed to the configuration class constructor.
|
||||
|
||||
Returns:
|
||||
@@ -233,12 +223,8 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
|
||||
return MolmoAct2Config(**kwargs)
|
||||
elif policy_type == "vla_jepa":
|
||||
return VLAJEPAConfig(**kwargs)
|
||||
elif policy_type == "lingbot_va":
|
||||
return LingBotVAConfig(**kwargs)
|
||||
elif policy_type == "fastwam":
|
||||
return FastWAMConfig(**kwargs)
|
||||
elif policy_type == "evo1":
|
||||
return Evo1Config(**kwargs)
|
||||
else:
|
||||
try:
|
||||
config_cls = PreTrainedConfig.get_choice_class(policy_type)
|
||||
@@ -302,23 +288,26 @@ 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):
|
||||
from .groot.processor_groot import make_groot_pre_post_processors_from_pretrained
|
||||
# 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,
|
||||
}
|
||||
|
||||
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"
|
||||
),
|
||||
)
|
||||
# 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
|
||||
|
||||
preprocessor = PolicyProcessorPipeline.from_pretrained(
|
||||
pretrained_model_name_or_path=pretrained_path,
|
||||
@@ -341,14 +330,6 @@ def make_pre_post_processors(
|
||||
revision=pretrained_revision,
|
||||
)
|
||||
_reconnect_relative_absolute_steps(preprocessor, postprocessor)
|
||||
if isinstance(policy_cfg, Evo1Config):
|
||||
from .evo1.processor_evo1 import reconcile_evo1_processors
|
||||
|
||||
preprocessor, postprocessor = reconcile_evo1_processors(
|
||||
policy_cfg,
|
||||
preprocessor,
|
||||
postprocessor,
|
||||
)
|
||||
return preprocessor, postprocessor
|
||||
|
||||
# Create a new processor based on policy type
|
||||
@@ -432,7 +413,6 @@ 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):
|
||||
@@ -460,13 +440,6 @@ def make_pre_post_processors(
|
||||
config=policy_cfg,
|
||||
dataset_stats=kwargs.get("dataset_stats"),
|
||||
)
|
||||
elif isinstance(policy_cfg, Evo1Config):
|
||||
from .evo1.processor_evo1 import make_evo1_pre_post_processors
|
||||
|
||||
processors = make_evo1_pre_post_processors(
|
||||
config=policy_cfg,
|
||||
dataset_stats=kwargs.get("dataset_stats"),
|
||||
)
|
||||
|
||||
elif isinstance(policy_cfg, MolmoAct2Config):
|
||||
from .molmoact2.processor_molmoact2 import make_molmoact2_pre_post_processors
|
||||
@@ -485,14 +458,6 @@ def make_pre_post_processors(
|
||||
dataset_stats=kwargs.get("dataset_stats"),
|
||||
)
|
||||
|
||||
elif isinstance(policy_cfg, LingBotVAConfig):
|
||||
from .lingbot_va.processor_lingbot_va import make_lingbot_va_pre_post_processors
|
||||
|
||||
processors = make_lingbot_va_pre_post_processors(
|
||||
config=policy_cfg,
|
||||
dataset_stats=kwargs.get("dataset_stats"),
|
||||
)
|
||||
|
||||
elif isinstance(policy_cfg, FastWAMConfig):
|
||||
from .fastwam.processor_fastwam import make_fastwam_pre_post_processors
|
||||
|
||||
@@ -590,7 +555,6 @@ 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
|
||||
|
||||
|
||||
@@ -0,0 +1,54 @@
|
||||
# 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
|
||||
@@ -1,12 +1,11 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2025 NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved.
|
||||
# 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
|
||||
# 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,
|
||||
@@ -15,7 +14,6 @@
|
||||
# limitations under the License.
|
||||
|
||||
|
||||
import logging
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
import torch
|
||||
@@ -44,9 +42,6 @@ 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")
|
||||
@@ -186,7 +181,8 @@ class BasicTransformerBlock(nn.Module):
|
||||
attn_output = self.attn1(
|
||||
norm_hidden_states,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
attention_mask=encoder_attention_mask if encoder_hidden_states is not None else attention_mask,
|
||||
attention_mask=attention_mask,
|
||||
# encoder_attention_mask=encoder_attention_mask,
|
||||
)
|
||||
if self.final_dropout:
|
||||
attn_output = self.final_dropout(attn_output)
|
||||
@@ -270,8 +266,8 @@ 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)
|
||||
logger.debug(
|
||||
"Total number of DiT parameters: %d",
|
||||
print(
|
||||
"Total number of DiT parameters: ",
|
||||
sum(p.numel() for p in self.parameters() if p.requires_grad),
|
||||
)
|
||||
|
||||
@@ -322,71 +318,6 @@ 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
|
||||
|
||||
@@ -431,8 +362,8 @@ class SelfAttentionTransformer(ModelMixin, ConfigMixin):
|
||||
for _ in range(self.config.num_layers)
|
||||
]
|
||||
)
|
||||
logger.debug(
|
||||
"Total number of SelfAttentionTransformer parameters: %d",
|
||||
print(
|
||||
"Total number of SelfAttentionTransformer parameters: ",
|
||||
sum(p.numel() for p in self.parameters() if p.requires_grad),
|
||||
)
|
||||
|
||||
|
||||
@@ -0,0 +1,408 @@
|
||||
# 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
|
||||
@@ -14,229 +14,12 @@
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import logging
|
||||
import math
|
||||
from dataclasses import dataclass, field
|
||||
from pathlib import Path
|
||||
|
||||
from lerobot.configs import FeatureType, NormalizationMode, PolicyFeature, PreTrainedConfig
|
||||
from lerobot.optim import AdamWConfig, DiffuserSchedulerConfig
|
||||
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
|
||||
@@ -245,44 +28,35 @@ class GrootConfig(PreTrainedConfig):
|
||||
|
||||
# Basic policy settings
|
||||
n_obs_steps: int = 1
|
||||
chunk_size: int = 40
|
||||
n_action_steps: int = 40
|
||||
chunk_size: int = 50
|
||||
n_action_steps: int = 50
|
||||
|
||||
# Dimension settings (must match pretrained GR00T model expectations)
|
||||
# Maximum state dimension. Shorter states will be zero-padded.
|
||||
max_state_dim: int = 132
|
||||
max_state_dim: int = 64
|
||||
|
||||
# Maximum action dimension. Shorter actions will be zero-padded.
|
||||
max_action_dim: int = 132
|
||||
max_action_dim: int = 32
|
||||
|
||||
# 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 (start with identity, adjust as needed)
|
||||
normalization_mapping: dict[str, NormalizationMode] = field(
|
||||
default_factory=lambda: {
|
||||
"VISUAL": NormalizationMode.IDENTITY,
|
||||
"STATE": NormalizationMode.IDENTITY,
|
||||
"ACTION": NormalizationMode.IDENTITY,
|
||||
"STATE": NormalizationMode.MEAN_STD,
|
||||
"ACTION": NormalizationMode.MEAN_STD,
|
||||
}
|
||||
)
|
||||
|
||||
# Groot-specific model parameters
|
||||
# Image preprocessing (adjust to match Groot's expected input)
|
||||
image_size: tuple[int, int] = (224, 224)
|
||||
|
||||
# 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
|
||||
# Groot-specific model parameters (from groot_finetune_script.py)
|
||||
|
||||
# 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
|
||||
# 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"
|
||||
|
||||
# Embodiment tag to use for training (e.g. 'new_embodiment', 'gr1')
|
||||
embodiment_tag: str = "new_embodiment"
|
||||
@@ -301,67 +75,38 @@ class GrootConfig(PreTrainedConfig):
|
||||
# Whether to fine-tune the diffusion model
|
||||
tune_diffusion_model: bool = True
|
||||
|
||||
# Whether to fine-tune the VL LayerNorm + VL self-attention projector in the action head.
|
||||
tune_vlln: 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
|
||||
|
||||
# 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
|
||||
# Alpha value for the LORA model
|
||||
lora_alpha: int = 16
|
||||
|
||||
# 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
|
||||
# Dropout rate for the LORA model
|
||||
lora_dropout: float = 0.1
|
||||
|
||||
# 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
|
||||
# Whether to use the full model for LORA
|
||||
lora_full_model: bool = False
|
||||
|
||||
# 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
|
||||
# Training parameters (matching groot_finetune_script.py)
|
||||
optimizer_lr: float = 1e-4
|
||||
# Isaac-GR00T N1.7 fine-tunes with AdamW betas (0.9, 0.999).
|
||||
optimizer_betas: tuple[float, float] = (0.9, 0.999)
|
||||
optimizer_betas: tuple[float, float] = (0.95, 0.999)
|
||||
optimizer_eps: float = 1e-8
|
||||
optimizer_weight_decay: float = 1e-5
|
||||
warmup_ratio: float = 0.05
|
||||
use_bf16: bool = True
|
||||
# The native N1.7 fine-tuning recipe keeps model parameters in FP32 and computes under BF16 autocast.
|
||||
model_params_fp32: bool = True
|
||||
|
||||
# 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
|
||||
# Dataset parameters
|
||||
# Video backend to use for training ('decord' or 'torchvision_av')
|
||||
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
|
||||
@@ -372,65 +117,6 @@ 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:
|
||||
@@ -438,6 +124,9 @@ 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]
|
||||
@@ -484,20 +173,15 @@ class GrootConfig(PreTrainedConfig):
|
||||
betas=self.optimizer_betas,
|
||||
eps=self.optimizer_eps,
|
||||
weight_decay=self.optimizer_weight_decay,
|
||||
grad_clip_norm=1.0,
|
||||
)
|
||||
|
||||
def get_scheduler_preset(self) -> DiffuserSchedulerConfig:
|
||||
"""Return scheduler configuration.
|
||||
|
||||
Isaac-GR00T uses the HF Trainer cosine schedule with ~5% warmup over the
|
||||
actual training update count; DiffuserSchedulerConfig wraps the same
|
||||
diffusers/transformers `get_scheduler("cosine")` implementation and
|
||||
derives num_training_steps from the outer --steps value at runtime.
|
||||
"""
|
||||
return DiffuserSchedulerConfig(
|
||||
name="cosine",
|
||||
num_warmup_steps=math.ceil(self.max_steps * self.warmup_ratio),
|
||||
def get_scheduler_preset(self) -> CosineDecayWithWarmupSchedulerConfig:
|
||||
"""Return scheduler configuration."""
|
||||
return CosineDecayWithWarmupSchedulerConfig(
|
||||
num_warmup_steps=int(10000 * self.warmup_ratio), # 5% warmup by default
|
||||
num_decay_steps=10000, # Adjust based on training steps
|
||||
peak_lr=self.optimizer_lr,
|
||||
decay_lr=self.optimizer_lr * 0.1,
|
||||
)
|
||||
|
||||
@property
|
||||
@@ -508,15 +192,7 @@ class GrootConfig(PreTrainedConfig):
|
||||
@property
|
||||
def action_delta_indices(self) -> list[int]:
|
||||
"""Return indices for delta actions."""
|
||||
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 drop_n_last_frames(self) -> int:
|
||||
"""Exclude episode tails that cannot supply a complete N1.7 action chunk."""
|
||||
return max(0, len(self.action_delta_indices) - 1)
|
||||
return list(range(min(self.chunk_size, 16)))
|
||||
|
||||
@property
|
||||
def reward_delta_indices(self) -> None:
|
||||
|
||||
@@ -0,0 +1,135 @@
|
||||
# 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
|
||||
@@ -0,0 +1,503 @@
|
||||
# --------------------------------------------------------
|
||||
# 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"]
|
||||
@@ -0,0 +1,396 @@
|
||||
# --------------------------------------------------------
|
||||
# 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()
|
||||
@@ -0,0 +1,541 @@
|
||||
# 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"]
|
||||
@@ -0,0 +1,380 @@
|
||||
# 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
|
||||
@@ -1,951 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2026 NVIDIA Corporation and 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 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__)
|
||||
|
||||
|
||||
def _tie_unused_qwen_lm_head(model: nn.Module) -> None:
|
||||
"""Restore the TF4 weight tie so the unused LM head stays frozen and is omitted on save."""
|
||||
lm_head = getattr(model, "lm_head", None)
|
||||
get_input_embeddings = getattr(model, "get_input_embeddings", None)
|
||||
if lm_head is None or not callable(get_input_embeddings):
|
||||
return
|
||||
input_embeddings = get_input_embeddings()
|
||||
embedding_weight = getattr(input_embeddings, "weight", None)
|
||||
if embedding_weight is None:
|
||||
return
|
||||
lm_head.weight = embedding_weight
|
||||
|
||||
|
||||
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()
|
||||
|
||||
_tie_unused_qwen_lm_head(self.model)
|
||||
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
|
||||
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:
|
||||
"""Hard-coded copy of the nvidia/Cosmos-Reason2-2B config.json (a Qwen3-VL-2B-Instruct layout)."""
|
||||
|
||||
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]:
|
||||
require_package("dm-tree", extra="groot", import_name="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)
|
||||
@@ -17,47 +17,38 @@
|
||||
"""
|
||||
Groot Policy Wrapper for LeRobot Integration
|
||||
|
||||
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.
|
||||
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.
|
||||
"""
|
||||
|
||||
import builtins
|
||||
import logging
|
||||
import os
|
||||
from collections import deque
|
||||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING, TypeVar
|
||||
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
|
||||
from lerobot.utils.constants import ACTION, OBS_IMAGES
|
||||
from lerobot.utils.import_utils import _transformers_available, require_package
|
||||
from lerobot.utils.device_utils import get_safe_autocast_context
|
||||
from lerobot.utils.import_utils import require_package
|
||||
|
||||
from ..pretrained import PreTrainedPolicy
|
||||
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, _tie_unused_qwen_lm_head
|
||||
|
||||
if TYPE_CHECKING or _transformers_available:
|
||||
from transformers.trainer_pt_utils import get_parameter_names
|
||||
else:
|
||||
get_parameter_names = None # type: ignore[assignment]
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
from .configuration_groot import GrootConfig
|
||||
from .groot_n1 import GR00TN15
|
||||
|
||||
T = TypeVar("T", bound="GrootPolicy")
|
||||
|
||||
@@ -77,77 +68,37 @@ 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 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
|
||||
"""Create and initialize the GR00T model using Isaac-GR00T API.
|
||||
|
||||
model = GR00TN17.from_pretrained(
|
||||
**model_kwargs,
|
||||
tune_vlln=self.config.tune_vlln,
|
||||
transformers_loading_kwargs={"trust_remote_code": True},
|
||||
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,
|
||||
)
|
||||
backbone = getattr(model, "backbone", None)
|
||||
qwen_model = getattr(backbone, "model", None)
|
||||
if qwen_model is not None:
|
||||
_tie_unused_qwen_lm_head(qwen_model)
|
||||
if self.config.model_params_fp32:
|
||||
self._cast_model_parameters_to_fp32(model)
|
||||
|
||||
model.compute_dtype = "bfloat16" if self.config.use_bf16 else model.compute_dtype
|
||||
model.config.compute_dtype = model.compute_dtype
|
||||
|
||||
return model
|
||||
|
||||
@staticmethod
|
||||
def _cast_model_parameters_to_fp32(model: torch.nn.Module) -> None:
|
||||
for parameter in model.parameters():
|
||||
if parameter.is_floating_point():
|
||||
parameter.data = parameter.data.to(torch.float32)
|
||||
|
||||
@staticmethod
|
||||
def _build_weight_decay_parameter_groups(model: torch.nn.Module) -> list[dict[str, object]]:
|
||||
forbidden_name_patterns = [
|
||||
r"bias",
|
||||
r"layernorm",
|
||||
r"rmsnorm",
|
||||
r"(?:^|\.)norm(?:$|\.)",
|
||||
r"_norm(?:$|\.)",
|
||||
]
|
||||
decay_names = set(get_parameter_names(model, [torch.nn.LayerNorm], forbidden_name_patterns))
|
||||
decay_params = [
|
||||
parameter
|
||||
for name, parameter in model.named_parameters()
|
||||
if parameter.requires_grad and name in decay_names
|
||||
]
|
||||
no_decay_params = [
|
||||
parameter
|
||||
for name, parameter in model.named_parameters()
|
||||
if parameter.requires_grad and name not in decay_names
|
||||
]
|
||||
return [
|
||||
{"params": decay_params},
|
||||
{"params": no_decay_params, "weight_decay": 0.0},
|
||||
]
|
||||
|
||||
def reset(self):
|
||||
"""Reset policy state when environment resets."""
|
||||
self._action_queue = deque([], maxlen=self._action_queue_steps)
|
||||
self._action_queue = deque([], maxlen=self.config.n_action_steps)
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(
|
||||
@@ -168,7 +119,7 @@ class GrootPolicy(PreTrainedPolicy):
|
||||
"""Load Groot policy from pretrained model.
|
||||
|
||||
Handles two cases:
|
||||
1. Base GR00T N1.7 models - loads the raw model
|
||||
1. Base GR00T models (e.g., 'nvidia/GR00T-N1.5-3B') - loads the raw model
|
||||
2. Fine-tuned LeRobot checkpoints - loads config and weights from safetensors
|
||||
|
||||
Args:
|
||||
@@ -187,11 +138,13 @@ class GrootPolicy(PreTrainedPolicy):
|
||||
Returns:
|
||||
Initialized GrootPolicy instance with loaded model
|
||||
"""
|
||||
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,
|
||||
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}"
|
||||
)
|
||||
|
||||
model_id = str(pretrained_name_or_path)
|
||||
@@ -222,7 +175,7 @@ class GrootPolicy(PreTrainedPolicy):
|
||||
|
||||
if is_finetuned_checkpoint:
|
||||
# This is a fine-tuned LeRobot checkpoint - use parent class loading
|
||||
logger.info("Detected fine-tuned LeRobot checkpoint, loading with state dict...")
|
||||
print("Detected fine-tuned LeRobot checkpoint, loading with state dict...")
|
||||
return super().from_pretrained(
|
||||
pretrained_name_or_path=pretrained_name_or_path,
|
||||
config=config,
|
||||
@@ -238,13 +191,11 @@ class GrootPolicy(PreTrainedPolicy):
|
||||
)
|
||||
|
||||
# This is a base GR00T model - load it fresh
|
||||
logger.info("Detected base GR00T model, loading from HuggingFace...")
|
||||
print("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
|
||||
@@ -265,15 +216,6 @@ 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)
|
||||
@@ -281,228 +223,64 @@ class GrootPolicy(PreTrainedPolicy):
|
||||
policy.eval()
|
||||
return policy
|
||||
|
||||
def get_optim_params(self): # type: ignore[override]
|
||||
"""Isaac-GR00T excludes biases and normalization parameters from weight decay."""
|
||||
return self._build_weight_decay_parameter_groups(self)
|
||||
|
||||
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", "action_mask", "embodiment_id"}
|
||||
if include_action:
|
||||
allowed_base.add("action")
|
||||
|
||||
allowed_base.update(
|
||||
{
|
||||
"input_ids",
|
||||
"attention_mask",
|
||||
"pixel_values",
|
||||
"image_grid_thw",
|
||||
"mm_token_type_ids",
|
||||
"pixel_values_videos",
|
||||
"video_grid_thw",
|
||||
}
|
||||
)
|
||||
|
||||
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 get_optim_params(self) -> dict:
|
||||
return self.parameters()
|
||||
|
||||
def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]:
|
||||
"""Training forward pass.
|
||||
|
||||
Delegates to Isaac-GR00T model.forward when inputs are compatible.
|
||||
"""
|
||||
groot_inputs = self._filter_groot_inputs(batch, include_action=True)
|
||||
# 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")
|
||||
}
|
||||
|
||||
# Get device from model parameters
|
||||
device = get_device_from_parameters(self)
|
||||
device = next(self.parameters()).device
|
||||
|
||||
# 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.
|
||||
with torch.autocast(device_type=device.type, dtype=torch.bfloat16, enabled=self.config.use_bf16):
|
||||
with get_safe_autocast_context(device, dtype=torch.bfloat16, enabled=self.config.use_bf16):
|
||||
outputs = self._groot_model.forward(groot_inputs)
|
||||
|
||||
# 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], **kwargs: object) -> Tensor:
|
||||
def predict_action_chunk(self, batch: dict[str, Tensor]) -> 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()
|
||||
|
||||
# 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"),
|
||||
)
|
||||
# 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")
|
||||
}
|
||||
|
||||
# Get device from model parameters
|
||||
device = get_device_from_parameters(self)
|
||||
device = next(self.parameters()).device
|
||||
|
||||
# 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):
|
||||
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)
|
||||
with get_safe_autocast_context(device, dtype=torch.bfloat16, enabled=self.config.use_bf16):
|
||||
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]
|
||||
|
||||
@@ -511,17 +289,44 @@ 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[:, : self._action_queue_steps].transpose(0, 1))
|
||||
self._action_queue.extend(actions.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")
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,264 +1,47 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2025 NVIDIA Corporation and 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.
|
||||
|
||||
"""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 typing import Any
|
||||
from shutil import copytree
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from huggingface_hub import hf_hub_download
|
||||
|
||||
|
||||
def read_json(path: Path) -> dict[str, Any]:
|
||||
"""Read a JSON object from ``path``, returning ``{}`` on any read/parse error."""
|
||||
try:
|
||||
with path.open() as f:
|
||||
data = json.load(f)
|
||||
except (OSError, json.JSONDecodeError):
|
||||
return {}
|
||||
return data if isinstance(data, dict) else {}
|
||||
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.
|
||||
|
||||
|
||||
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
|
||||
|
||||
|
||||
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.
|
||||
- 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)
|
||||
|
||||
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 {}
|
||||
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}")
|
||||
|
||||
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 = {}
|
||||
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",
|
||||
]
|
||||
|
||||
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"
|
||||
print(f"[GROOT] Assets repo: {assets_repo} \n Cache dir: {cache_dir}")
|
||||
|
||||
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}."
|
||||
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),
|
||||
)
|
||||
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
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
../../../../docs/source/lingbot_va.mdx
|
||||
@@ -1,21 +0,0 @@
|
||||
#!/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.
|
||||
|
||||
from .configuration_lingbot_va import LingBotVAConfig
|
||||
from .modeling_lingbot_va import LingBotVAPolicy
|
||||
from .processor_lingbot_va import make_lingbot_va_pre_post_processors
|
||||
|
||||
__all__ = ["LingBotVAConfig", "LingBotVAPolicy", "make_lingbot_va_pre_post_processors"]
|
||||
@@ -1,168 +0,0 @@
|
||||
# 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.
|
||||
|
||||
"""Configuration for the LingBot-VA policy.
|
||||
|
||||
LingBot-VA is an autoregressive video-action world-model policy built on the Wan2.2
|
||||
video-diffusion stack. It interleaves prediction of future video latents and robot
|
||||
actions in a single dual-stream transformer. See ``docs/source/lingbot_va.mdx`` and the
|
||||
upstream repository (https://github.com/Robbyant/lingbot-va).
|
||||
|
||||
Defaults below match the upstream LIBERO configuration (``wan_va/configs/va_libero_cfg.py``)
|
||||
and the ``transformer/config.json`` of the released checkpoints.
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
from lerobot.configs.policies import PreTrainedConfig
|
||||
from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
|
||||
from lerobot.optim.optimizers import AdamWConfig
|
||||
from lerobot.optim.schedulers import ConstantWithWarmupSchedulerConfig, LRSchedulerConfig
|
||||
from lerobot.utils.constants import ACTION
|
||||
|
||||
|
||||
@PreTrainedConfig.register_subclass("lingbot_va")
|
||||
@dataclass
|
||||
class LingBotVAConfig(PreTrainedConfig):
|
||||
"""Configuration for the native LingBot-VA policy integration in LeRobot."""
|
||||
|
||||
# Wan transformer architecture
|
||||
patch_size: tuple[int, int, int] = (1, 2, 2)
|
||||
num_attention_heads: int = 24
|
||||
attention_head_dim: int = 128
|
||||
in_channels: int = 48
|
||||
out_channels: int = 48
|
||||
action_dim: int = 30
|
||||
text_dim: int = 4096
|
||||
freq_dim: int = 256
|
||||
ffn_dim: int = 14336
|
||||
num_layers: int = 30
|
||||
cross_attn_norm: bool = True
|
||||
eps: float = 1e-6
|
||||
rope_max_seq_len: int = 1024
|
||||
# "flex" = training only (needs recent torch); inference uses "torch" SDPA or "flashattn".
|
||||
attn_mode: str = "torch"
|
||||
|
||||
# Frozen sub-models (VAE + UMT5 text encoder + tokenizer)
|
||||
# ~20 GB of frozen weights, NOT bundled in the checkpoint; lazily pulled from this HF repo /
|
||||
# local dir (must hold diffusers-style ``vae/``, ``text_encoder/``, ``tokenizer/`` sub-folders).
|
||||
wan_pretrained_path: str = "robbyant/lingbot-va-base"
|
||||
dtype: str = "bfloat16" # transformer / VAE / text-encoder dtype: "bfloat16", "float16", "float32"
|
||||
# Frozen UMT5-XXL encoder device; "cpu" frees ~11 GB VRAM (it runs once per episode).
|
||||
text_encoder_device: str = "cpu"
|
||||
|
||||
# Observation cameras (order matters: latents are concatenated on width; LIBERO defaults)
|
||||
obs_cam_keys: list[str] = field(
|
||||
default_factory=lambda: ["observation.images.image", "observation.images.image2"]
|
||||
)
|
||||
# Undo the LIBERO env processor's extra horizontal flip to match the model's training orientation.
|
||||
image_hflip: bool = False
|
||||
# Camera latent layout: "width_concat" (cameras concatenated on width; LIBERO) or
|
||||
# "robotwin_tshape" (full-res head + half-res wrists in a "T"; RoboTwin).
|
||||
camera_layout: str = "width_concat"
|
||||
|
||||
# Inference hyperparameters (LIBERO defaults)
|
||||
n_obs_steps: int = 1
|
||||
height: int = 128
|
||||
width: int = 128
|
||||
action_per_frame: int = 4
|
||||
frame_chunk_size: int = 4
|
||||
attn_window: int = 30
|
||||
num_inference_steps: int = 20
|
||||
video_exec_step: int = -1
|
||||
action_num_inference_steps: int = 50
|
||||
guidance_scale: float = 5.0
|
||||
action_guidance_scale: float = 1.0
|
||||
snr_shift: float = 5.0
|
||||
action_snr_shift: float = 0.05
|
||||
max_sequence_length: int = 512 # UMT5 prompt length
|
||||
|
||||
# Subset of the 30-d action space used by the benchmark (LIBERO = 7-DoF). The action
|
||||
# (un)normalization quantiles live in the checkpoint's ``policy_postprocessor.json``, not here.
|
||||
used_action_channel_ids: list[int] = field(default_factory=lambda: list(range(7)))
|
||||
|
||||
# Opt-in: VAE-decode predicted video latents to ``self.last_predicted_frames`` for saving MP4s.
|
||||
save_predicted_video: bool = False
|
||||
|
||||
# Normalization: IDENTITY here; images are scaled + VAE-encoded and actions are
|
||||
# quantile-(un)normalized inside the policy / dedicated processor steps.
|
||||
normalization_mapping: dict[str, NormalizationMode] = field(
|
||||
default_factory=lambda: {
|
||||
"VISUAL": NormalizationMode.IDENTITY,
|
||||
"STATE": NormalizationMode.IDENTITY,
|
||||
"ACTION": NormalizationMode.IDENTITY,
|
||||
}
|
||||
)
|
||||
|
||||
# Optimizer / scheduler (training; AdamW + warmup-constant per upstream train.py)
|
||||
optimizer_lr: float = 1e-5
|
||||
optimizer_betas: tuple[float, float] = (0.9, 0.95)
|
||||
optimizer_eps: float = 1e-8
|
||||
optimizer_weight_decay: float = 1e-4
|
||||
optimizer_grad_clip_norm: float = 1.0
|
||||
scheduler_warmup_steps: int = 1000
|
||||
|
||||
def __post_init__(self):
|
||||
super().__post_init__()
|
||||
if self.attn_mode not in ("torch", "flashattn", "flex"):
|
||||
raise ValueError(f"attn_mode must be one of 'torch', 'flashattn', 'flex'; got {self.attn_mode!r}")
|
||||
|
||||
@property
|
||||
def chunk_size(self) -> int:
|
||||
"""Number of single-step actions produced per autoregressive chunk."""
|
||||
return self.frame_chunk_size * self.action_per_frame
|
||||
|
||||
@property
|
||||
def n_action_steps(self) -> int:
|
||||
"""Number of actions executed before refilling (the whole chunk)."""
|
||||
return self.chunk_size
|
||||
|
||||
def validate_features(self) -> None:
|
||||
image_features = [key for key, feat in self.input_features.items() if feat.type == FeatureType.VISUAL]
|
||||
if not image_features:
|
||||
raise ValueError(
|
||||
"LingBot-VA requires at least one visual input feature. "
|
||||
"No features of type FeatureType.VISUAL found in input_features."
|
||||
)
|
||||
if ACTION not in self.output_features:
|
||||
self.output_features[ACTION] = PolicyFeature(
|
||||
type=FeatureType.ACTION, shape=(len(self.used_action_channel_ids),)
|
||||
)
|
||||
|
||||
def get_optimizer_preset(self) -> AdamWConfig:
|
||||
return AdamWConfig(
|
||||
lr=self.optimizer_lr,
|
||||
betas=self.optimizer_betas,
|
||||
eps=self.optimizer_eps,
|
||||
weight_decay=self.optimizer_weight_decay,
|
||||
grad_clip_norm=self.optimizer_grad_clip_norm,
|
||||
)
|
||||
|
||||
def get_scheduler_preset(self) -> LRSchedulerConfig | None:
|
||||
# Upstream uses a linear warmup followed by a constant LR (warmup_constant_lambda).
|
||||
return ConstantWithWarmupSchedulerConfig(num_warmup_steps=self.scheduler_warmup_steps)
|
||||
|
||||
@property
|
||||
def observation_delta_indices(self) -> list[int]:
|
||||
temporal_downsample = 4
|
||||
stride = max(1, self.action_per_frame // temporal_downsample)
|
||||
return list(range(0, self.frame_chunk_size * temporal_downsample * stride, stride))
|
||||
|
||||
@property
|
||||
def action_delta_indices(self) -> list[int]:
|
||||
return list(range(self.chunk_size))
|
||||
|
||||
@property
|
||||
def reward_delta_indices(self) -> None:
|
||||
return None
|
||||
@@ -1,853 +0,0 @@
|
||||
# Copyright 2024-2025 The Robbyant Team Authors. 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.
|
||||
# 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.
|
||||
|
||||
"""LingBot-VA policy: an autoregressive video-action world model on the Wan2.2 stack.
|
||||
|
||||
The sampling loop is a faithful re-implementation of the upstream streaming server
|
||||
(``wan_va/wan_va_server.py``) and LIBERO client (``evaluation/libero/client.py``), adapted
|
||||
to LeRobot's ``select_action`` interface:
|
||||
|
||||
* the trainable dual-stream transformer is owned as a sub-module and round-trips in the
|
||||
single ``model.safetensors`` checkpoint;
|
||||
* the frozen Wan VAE + UMT5 text encoder + tokenizer are *lazily pulled* from
|
||||
``config.wan_pretrained_path`` (not bundled), so the LeRobot checkpoint stays small;
|
||||
* ``predict_action_chunk`` runs one autoregressive chunk (video stream then action
|
||||
stream, each with CFG and its own flow-matching scheduler) and updates the KV cache;
|
||||
* ``select_action`` drains a per-step action queue and records the real observed
|
||||
keyframes that are fed back into the KV cache when the queue is refilled.
|
||||
|
||||
NOTE: The streaming path is written for single-environment eval (``--eval.batch_size=1``).
|
||||
"""
|
||||
|
||||
from collections import deque
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F # noqa: N812
|
||||
from einops import rearrange
|
||||
from torch import Tensor
|
||||
|
||||
from lerobot.policies.pretrained import PreTrainedPolicy
|
||||
from lerobot.utils.constants import ACTION
|
||||
from lerobot.utils.import_utils import require_package
|
||||
|
||||
from .configuration_lingbot_va import LingBotVAConfig
|
||||
from .utils import (
|
||||
FlowMatchScheduler,
|
||||
WanTransformer3DModel,
|
||||
WanVAEStreamingWrapper,
|
||||
_sample_timestep_id,
|
||||
_torch_dtype,
|
||||
clean_prompt,
|
||||
data_seq_to_patch,
|
||||
denormalize_latents,
|
||||
get_mesh_id,
|
||||
load_text_encoder,
|
||||
load_tokenizer,
|
||||
load_vae,
|
||||
)
|
||||
|
||||
|
||||
class LingBotVAPolicy(PreTrainedPolicy):
|
||||
"""LeRobot wrapper for the LingBot-VA autoregressive video-action world model."""
|
||||
|
||||
config_class = LingBotVAConfig
|
||||
name = "lingbot_va"
|
||||
|
||||
def __init__(self, config: LingBotVAConfig, **kwargs):
|
||||
require_package("diffusers", extra="lingbot_va")
|
||||
require_package("transformers", extra="lingbot_va")
|
||||
super().__init__(config)
|
||||
config.validate_features()
|
||||
self.config = config
|
||||
|
||||
self.dtype = _torch_dtype(config.dtype)
|
||||
|
||||
# Trainable dual-stream transformer (the only sub-module saved in the LeRobot checkpoint).
|
||||
self.transformer = WanTransformer3DModel(
|
||||
patch_size=tuple(config.patch_size),
|
||||
num_attention_heads=config.num_attention_heads,
|
||||
attention_head_dim=config.attention_head_dim,
|
||||
in_channels=config.in_channels,
|
||||
out_channels=config.out_channels,
|
||||
action_dim=config.action_dim,
|
||||
text_dim=config.text_dim,
|
||||
freq_dim=config.freq_dim,
|
||||
ffn_dim=config.ffn_dim,
|
||||
num_layers=config.num_layers,
|
||||
cross_attn_norm=config.cross_attn_norm,
|
||||
eps=config.eps,
|
||||
rope_max_seq_len=config.rope_max_seq_len,
|
||||
attn_mode=config.attn_mode,
|
||||
)
|
||||
# Run the transformer in config.dtype (bf16); norm/modulation paths upcast to fp32 internally.
|
||||
self.transformer = self.transformer.to(self.dtype)
|
||||
|
||||
# Frozen modules are stored OUTSIDE the nn.Module registry (plain dict) so they are
|
||||
# neither saved into model.safetensors nor moved by ``.to()``. They are lazily loaded
|
||||
# from ``config.wan_pretrained_path`` the first time inference runs.
|
||||
self._frozen: dict = {}
|
||||
|
||||
self.last_predicted_frames: Tensor | None = None
|
||||
self.last_predicted_latents: Tensor | None = None
|
||||
self.reset()
|
||||
|
||||
# Frozen-module lazy loading (VAE + UMT5 + tokenizer)
|
||||
def _ensure_frozen_modules(self):
|
||||
if self._frozen:
|
||||
return
|
||||
path = self.config.wan_pretrained_path
|
||||
device = self.config.device
|
||||
|
||||
# The frozen modules always live in ``vae/``, ``text_encoder/`` and ``tokenizer/``
|
||||
# sub-folders -- both in the released diffusers-style HF repos and in the local
|
||||
# ``--bundle-frozen`` output dir. ``from_pretrained(path, subfolder=...)`` resolves
|
||||
# them for either a HF repo id or a local directory.
|
||||
vae = load_vae(path, torch_dtype=self.dtype, torch_device=device, subfolder="vae")
|
||||
# The UMT5-XXL text encoder (~11 GB) runs once per episode; keep it on its own
|
||||
# (CPU by default) device so the 5B transformer + VAE fit on a single GPU.
|
||||
text_encoder = load_text_encoder(
|
||||
path,
|
||||
torch_dtype=self.dtype,
|
||||
torch_device=self.config.text_encoder_device,
|
||||
subfolder="text_encoder",
|
||||
)
|
||||
tokenizer = load_tokenizer(path, subfolder="tokenizer")
|
||||
self._frozen = {
|
||||
"vae": vae.eval(),
|
||||
"streaming_vae": WanVAEStreamingWrapper(vae),
|
||||
"text_encoder": text_encoder.eval(),
|
||||
"tokenizer": tokenizer,
|
||||
}
|
||||
# RoboTwin's T-shape layout encodes the half-resolution wrist cameras through a second
|
||||
# streaming VAE (separate causal cache) alongside the full-res head camera.
|
||||
if self.config.camera_layout == "robotwin_tshape":
|
||||
vae_half = load_vae(path, torch_dtype=self.dtype, torch_device=device, subfolder="vae")
|
||||
self._frozen["streaming_vae_half"] = WanVAEStreamingWrapper(vae_half.eval())
|
||||
|
||||
@property
|
||||
def _vae(self):
|
||||
return self._frozen["vae"]
|
||||
|
||||
@property
|
||||
def _streaming_vae(self):
|
||||
return self._frozen["streaming_vae"]
|
||||
|
||||
# PreTrainedPolicy API
|
||||
def get_optim_params(self) -> dict:
|
||||
# Only the transformer is trainable; the VAE / text encoder stay frozen (kept outside the
|
||||
# nn.Module registry). With PEFT/LoRA this naturally returns just the adapter params.
|
||||
return [p for p in self.transformer.parameters() if p.requires_grad]
|
||||
|
||||
def reset(self):
|
||||
"""Reset all per-episode streaming state (KV cache, queues, frame counter)."""
|
||||
cfg = self.config
|
||||
self._action_queue: deque = deque(maxlen=cfg.n_action_steps)
|
||||
self._obs_buffer: list = [] # raw keyframe obs (one per env substep) observed this chunk
|
||||
self._executed_actions: Tensor | None = (
|
||||
None # last chunk's actions (model-normalized) for KV feedback
|
||||
)
|
||||
self._started = False # first select_action call uses the obs as the conditioning frame
|
||||
self._exec_step = 0 # index of the action being executed within the current chunk
|
||||
self._prev_j = 0 # sub-step index (within a predicted frame) of the last executed action
|
||||
# Sample one keyframe every ``action_per_frame / temporal_downsample`` executed sub-steps so
|
||||
# that exactly ``frame_chunk_size * temporal_downsample`` frames are VAE-encoded per chunk
|
||||
# (the Wan2.2 VAE temporal downsample is 4 -> ``frame_chunk_size`` latent frames).
|
||||
self._keyframe_stride = max(1, cfg.action_per_frame // 4)
|
||||
self._frame_st_id = 0
|
||||
self._first_chunk = True
|
||||
self._prompt: str | None = None
|
||||
self._prompt_embeds = None
|
||||
self._negative_prompt_embeds = None
|
||||
self.last_predicted_frames = None
|
||||
self.last_predicted_latents = None
|
||||
self._use_cfg = (cfg.guidance_scale > 1) or (cfg.action_guidance_scale > 1)
|
||||
# Two independent flow-matching schedulers (video latent + action streams).
|
||||
self._scheduler = FlowMatchScheduler(shift=cfg.snr_shift, sigma_min=0.0, extra_one_step=True)
|
||||
self._action_scheduler = FlowMatchScheduler(
|
||||
shift=cfg.action_snr_shift, sigma_min=0.0, extra_one_step=True
|
||||
)
|
||||
self._scheduler.set_timesteps(1000, training=True)
|
||||
self._action_scheduler.set_timesteps(1000, training=True)
|
||||
self._cache_initialised = False
|
||||
# Clear KV cache on the (already-built) transformer, if present.
|
||||
if hasattr(self, "transformer"):
|
||||
self.transformer.clear_cache("pos")
|
||||
# Reset the causal streaming-VAE feat cache between episodes (mirrors upstream ``_reset``).
|
||||
# Without this the encoder carries over the previous episode's temporal state, corrupting the
|
||||
# latent frame counts on the next episode's first encode.
|
||||
if self._frozen:
|
||||
self._frozen["streaming_vae"].clear_cache()
|
||||
if "streaming_vae_half" in self._frozen:
|
||||
self._frozen["streaming_vae_half"].clear_cache()
|
||||
|
||||
# Training (flow-matching dual-stream loss). Requires attn_mode="flex".
|
||||
def _ensure_train_schedulers(self):
|
||||
if getattr(self, "_train_sched_latent", None) is None:
|
||||
cfg = self.config
|
||||
self._train_sched_latent = FlowMatchScheduler(
|
||||
shift=cfg.snr_shift, sigma_min=0.0, extra_one_step=True
|
||||
)
|
||||
self._train_sched_latent.set_timesteps(1000, training=True)
|
||||
self._train_sched_action = FlowMatchScheduler(
|
||||
shift=cfg.action_snr_shift, sigma_min=0.0, extra_one_step=True
|
||||
)
|
||||
self._train_sched_action.set_timesteps(1000, training=True)
|
||||
|
||||
@torch.no_grad()
|
||||
def _add_noise_stream(self, latent, scheduler, action_mask, action_mode, noisy_cond_prob):
|
||||
"""Flow-matching noising of one stream (port of upstream ``Trainer._add_noise``)."""
|
||||
device = latent.device
|
||||
b, _c, f, _h, _w = latent.shape
|
||||
p = self.config.patch_size
|
||||
patch_f, patch_h, patch_w = (1, 1, 1) if action_mode else (p[0], p[1], p[2])
|
||||
|
||||
ts_ids = _sample_timestep_id(f, num_train_timesteps=scheduler.num_train_timesteps)
|
||||
noise = torch.zeros_like(latent).normal_()
|
||||
timesteps = scheduler.timesteps[ts_ids].to(device)
|
||||
noisy_latents = scheduler.add_noise(latent, noise, timesteps, t_dim=2)
|
||||
targets = scheduler.training_target(latent, noise, timesteps)
|
||||
|
||||
grid_id = (
|
||||
get_mesh_id(
|
||||
latent.shape[-3] // patch_f,
|
||||
latent.shape[-2] // patch_h,
|
||||
latent.shape[-1] // patch_w,
|
||||
t=1 if action_mode else 0,
|
||||
f_w=1,
|
||||
f_shift=0,
|
||||
action=action_mode,
|
||||
)
|
||||
.to(device)[None]
|
||||
.repeat(b, 1, 1)
|
||||
)
|
||||
|
||||
if torch.rand(1).item() < noisy_cond_prob:
|
||||
cond_ids = _sample_timestep_id(
|
||||
f, min_timestep_bd=0.5, max_timestep_bd=1.0, num_train_timesteps=scheduler.num_train_timesteps
|
||||
)
|
||||
cond_noise = torch.zeros_like(latent).normal_()
|
||||
cond_timesteps = scheduler.timesteps[cond_ids].to(device)
|
||||
latent = scheduler.add_noise(latent, cond_noise, cond_timesteps, t_dim=2)
|
||||
else:
|
||||
cond_timesteps = torch.zeros_like(timesteps)
|
||||
|
||||
if action_mask is not None:
|
||||
noisy_latents = noisy_latents * action_mask.float()
|
||||
targets = targets * action_mask.float()
|
||||
latent = latent * action_mask.float()
|
||||
|
||||
return {
|
||||
"timesteps": timesteps[None].repeat(b, 1),
|
||||
"noisy_latents": noisy_latents,
|
||||
"targets": targets,
|
||||
"latent": latent,
|
||||
"cond_timesteps": cond_timesteps[None].repeat(b, 1),
|
||||
"grid_id": grid_id,
|
||||
}
|
||||
|
||||
def _flow_matching_loss(self, input_dict, pred):
|
||||
"""Dual-stream flow-matching loss (port of upstream ``Trainer.compute_loss``)."""
|
||||
latent_pred, action_pred = pred
|
||||
ld, ad = input_dict["latent_dict"], input_dict["action_dict"]
|
||||
action_pred = rearrange(action_pred, "b (f n) c -> b c f n 1", f=ad["targets"].shape[-3])
|
||||
latent_pred = data_seq_to_patch(
|
||||
self.config.patch_size,
|
||||
latent_pred,
|
||||
ld["targets"].shape[-3],
|
||||
ld["targets"].shape[-2],
|
||||
ld["targets"].shape[-1],
|
||||
batch_size=latent_pred.shape[0],
|
||||
)
|
||||
bn, fn = ld["timesteps"].shape
|
||||
lw = self._train_sched_latent.training_weight(ld["timesteps"].flatten()).reshape(bn, fn)
|
||||
aw = self._train_sched_action.training_weight(ad["timesteps"].flatten()).reshape(bn, fn)
|
||||
|
||||
latent_loss = F.mse_loss(latent_pred.float(), ld["targets"].float().detach(), reduction="none")
|
||||
latent_loss = (
|
||||
(latent_loss * lw[:, None, :, None, None]).permute(0, 2, 3, 4, 1).flatten(0, 1).flatten(1)
|
||||
)
|
||||
latent_loss = (latent_loss.sum(dim=1) / (torch.ones_like(latent_loss).sum(dim=1) + 1e-6)).mean()
|
||||
|
||||
amask = ad["actions_mask"].float()
|
||||
action_loss = F.mse_loss(action_pred.float(), ad["targets"].float().detach(), reduction="none")
|
||||
action_loss = (
|
||||
(action_loss * aw[:, None, :, None, None] * amask).permute(0, 2, 3, 4, 1).flatten(0, 1).flatten(1)
|
||||
)
|
||||
amask_f = amask.permute(0, 2, 3, 4, 1).flatten(0, 1).flatten(1)
|
||||
action_loss = (action_loss.sum(dim=1) / (amask_f.sum(dim=1) + 1e-6)).mean()
|
||||
return latent_loss, action_loss
|
||||
|
||||
def training_loss_from_streams(self, latents, actions, actions_mask, text_emb):
|
||||
"""Core dual-stream training loss given prepared latents / actions / text embeddings.
|
||||
|
||||
``latents``: ``[B, in_channels, F, h, w]`` (normalized video latents).
|
||||
``actions`` / ``actions_mask``: ``[B, action_dim, F, action_per_frame, 1]``.
|
||||
``text_emb``: ``[B, seq_len, text_dim]``. Returns ``(loss, {latent_loss, action_loss})``.
|
||||
"""
|
||||
if self.config.attn_mode != "flex":
|
||||
raise ValueError(
|
||||
"LingBot-VA training requires attn_mode='flex' (block-causal flow-matching masks). "
|
||||
"Load/convert the policy with --policy.attn_mode=flex for training/fine-tuning."
|
||||
)
|
||||
self._ensure_train_schedulers()
|
||||
latent_dict = self._add_noise_stream(
|
||||
latents, self._train_sched_latent, action_mask=None, action_mode=False, noisy_cond_prob=0.5
|
||||
)
|
||||
action_dict = self._add_noise_stream(
|
||||
actions, self._train_sched_action, action_mask=actions_mask, action_mode=True, noisy_cond_prob=0.0
|
||||
)
|
||||
latent_dict["text_emb"] = text_emb
|
||||
action_dict["text_emb"] = text_emb
|
||||
action_dict["actions_mask"] = actions_mask
|
||||
input_dict = {
|
||||
"latent_dict": latent_dict,
|
||||
"action_dict": action_dict,
|
||||
"chunk_size": int(torch.randint(1, 5, (1,)).item()),
|
||||
"window_size": int(torch.randint(4, 65, (1,)).item()),
|
||||
}
|
||||
pred = self.transformer(input_dict, train_mode=True)
|
||||
latent_loss, action_loss = self._flow_matching_loss(input_dict, pred)
|
||||
loss = latent_loss + action_loss
|
||||
return loss, {"latent_loss": latent_loss.item(), "action_loss": action_loss.item()}
|
||||
|
||||
def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict | None]:
|
||||
"""Training forward: dual-stream flow-matching loss.
|
||||
|
||||
Builds the (video-latent, action, text) training streams from a LeRobot batch
|
||||
(VAE-encoding the camera frames and UMT5-encoding the task), then runs the flow-matching
|
||||
dual-stream loss. Requires the policy to be built with ``attn_mode='flex'``.
|
||||
"""
|
||||
self._ensure_frozen_modules()
|
||||
latents, actions, actions_mask, text_emb = self._build_training_streams(batch)
|
||||
return self.training_loss_from_streams(latents, actions, actions_mask, text_emb)
|
||||
|
||||
@torch.no_grad()
|
||||
def _build_training_streams(self, batch):
|
||||
"""Build (latents, actions, actions_mask, text_emb) from a LeRobot training batch.
|
||||
|
||||
Camera frames per ``obs_cam_keys`` are expected as a temporal clip ``[B, C, T, H, W]`` (or
|
||||
``[B, T, C, H, W]``); they are VAE-encoded into ``F = T / temporal_downsample`` latent frames.
|
||||
Actions ``[B, F*action_per_frame, n_used]`` are scattered into the model's ``action_dim`` space.
|
||||
"""
|
||||
cfg = self.config
|
||||
device = cfg.device
|
||||
# text embeddings
|
||||
task = batch.get("task")
|
||||
if isinstance(task, str):
|
||||
task = [task]
|
||||
text_emb = self._get_t5_prompt_embeds(list(task), cfg.max_sequence_length)
|
||||
|
||||
# video latents (VAE-encode the camera clips)
|
||||
latents = self._encode_training_latents(batch)
|
||||
|
||||
# actions -> [B, action_dim, F, action_per_frame, 1]
|
||||
act = batch[ACTION].to(device) # [B, F*apf, n_used]
|
||||
b = act.shape[0]
|
||||
used = cfg.used_action_channel_ids
|
||||
apf, fc = cfg.action_per_frame, cfg.frame_chunk_size
|
||||
act = act[:, : fc * apf].reshape(b, fc, apf, len(used)).permute(0, 3, 1, 2) # [B, n_used, F, apf]
|
||||
full = act.new_zeros(b, cfg.action_dim, fc, apf)
|
||||
idx = torch.as_tensor(used, device=device)
|
||||
full[:, idx] = act
|
||||
actions = full.unsqueeze(-1).to(self.dtype) # [B, action_dim, F, apf, 1]
|
||||
mask = torch.zeros(cfg.action_dim, device=device, dtype=self.dtype)
|
||||
mask[idx] = 1.0
|
||||
actions_mask = mask.view(1, -1, 1, 1, 1).expand_as(actions)
|
||||
return latents, actions, actions_mask, text_emb
|
||||
|
||||
@torch.no_grad()
|
||||
def _encode_training_latents(self, batch) -> Tensor:
|
||||
"""VAE-encode the per-camera training clips into normalized video latents [B, C, F, h, w]."""
|
||||
vae_device = next(self._vae.parameters()).device
|
||||
|
||||
def _clip(key):
|
||||
x = batch[key].to(vae_device)
|
||||
if x.dim() == 4: # [B, C, H, W] -> single frame clip
|
||||
x = x.unsqueeze(2)
|
||||
elif x.shape[1] not in (1, 3) and x.shape[2] in (1, 3): # [B, T, C, H, W] -> [B, C, T, H, W]
|
||||
x = x.permute(0, 2, 1, 3, 4)
|
||||
return x.contiguous()
|
||||
|
||||
def _encode(x, size):
|
||||
b, c, t = x.shape[:3]
|
||||
x = F.interpolate(x.flatten(0, 1).float(), size=size, mode="bilinear", align_corners=False)
|
||||
x = (x.view(b, c, t, *size) * 2.0 - 1.0).to(self.dtype)
|
||||
mu = self._vae.encode(x).latent_dist.mode() # [B, z_dim, F, h, w]
|
||||
mean = torch.tensor(self._vae.config.latents_mean).view(1, -1, 1, 1, 1).to(mu.device)
|
||||
inv_std = (1.0 / torch.tensor(self._vae.config.latents_std)).view(1, -1, 1, 1, 1).to(mu.device)
|
||||
return ((mu.float() - mean) * inv_std).to(mu)
|
||||
|
||||
keys = self.config.obs_cam_keys
|
||||
if self.config.camera_layout == "robotwin_tshape":
|
||||
h, w = self.config.height, self.config.width
|
||||
head = _encode(_clip(keys[0]), (h, w))
|
||||
left = _encode(_clip(keys[1]), (h // 2, w // 2))
|
||||
right = _encode(_clip(keys[2]), (h // 2, w // 2))
|
||||
return torch.cat([torch.cat([left, right], dim=-1), head], dim=-2).to(self.config.device)
|
||||
per_cam = [_encode(_clip(k), (self.config.height, self.config.width)) for k in keys]
|
||||
return torch.cat(per_cam, dim=-1).to(self.config.device)
|
||||
|
||||
@torch.no_grad()
|
||||
def select_action(self, batch: dict[str, Tensor], **kwargs) -> Tensor:
|
||||
"""Return one action, refilling the chunk (and feeding back observed keyframes) as needed.
|
||||
|
||||
Mirrors the upstream LIBERO client loop (``evaluation/libero/client.py``): the first obs is
|
||||
the conditioning frame; every observation produced afterwards is buffered as a keyframe and,
|
||||
once the chunk's actions are exhausted, the buffered frames + executed actions are fed back
|
||||
into the KV cache before the next chunk is predicted.
|
||||
"""
|
||||
self.eval()
|
||||
self._ensure_frozen_modules()
|
||||
self._maybe_init_prompt(batch)
|
||||
|
||||
if not self._started:
|
||||
# First call: this observation conditions the first chunk (it is *not* a keyframe).
|
||||
self._started = True
|
||||
actions = self.predict_action_chunk(batch) # [B, chunk_size, n_used]
|
||||
self._action_queue.extend(actions.transpose(0, 1)) # [chunk_size, B, n_used]
|
||||
self._obs_buffer = []
|
||||
self._exec_step = 0
|
||||
else:
|
||||
# This observation is the result of the previously executed action -> a candidate
|
||||
# keyframe. Buffer it on the sub-step boundary the upstream client samples on.
|
||||
if (self._prev_j + 1) % self._keyframe_stride == 0:
|
||||
self._obs_buffer.append(self._extract_raw_obs(batch))
|
||||
if len(self._action_queue) == 0:
|
||||
# All actions for the current chunk have been executed; feed the observed
|
||||
# keyframes + executed actions back and predict the next chunk.
|
||||
actions = self.predict_action_chunk(None)
|
||||
self._action_queue.extend(actions.transpose(0, 1))
|
||||
self._exec_step = 0
|
||||
|
||||
self._prev_j = self._exec_step % self.config.action_per_frame
|
||||
self._exec_step += 1
|
||||
return self._action_queue.popleft()
|
||||
|
||||
@torch.no_grad()
|
||||
def predict_action_chunk(self, batch: dict[str, Tensor], **kwargs) -> Tensor:
|
||||
"""Run one autoregressive chunk and return actions ``[B, chunk_size, n_used]`` (normalized)."""
|
||||
self.eval()
|
||||
self._ensure_frozen_modules()
|
||||
self._maybe_init_prompt(batch)
|
||||
|
||||
is_first = self._first_chunk
|
||||
if is_first:
|
||||
init_latent = self._encode_frames([self._extract_raw_obs(batch)])
|
||||
self._init_latent = init_latent
|
||||
self._init_streaming_cache(init_latent)
|
||||
self._obs_buffer = [] # frame 0 (the init obs) conditions the chunk; it is not fed back
|
||||
actions, latents = self._infer(init_latent, frame_st_id=0)
|
||||
self._first_chunk = False
|
||||
else:
|
||||
# Feed the real observed keyframes + the executed actions back into the KV cache.
|
||||
self._compute_kv_cache(self._obs_buffer, self._executed_actions)
|
||||
self._obs_buffer = []
|
||||
actions, latents = self._infer(None, frame_st_id=self._frame_st_id)
|
||||
|
||||
# actions: [B, action_dim, F, action_per_frame, 1] (model-normalized). Keep for KV feedback.
|
||||
self._executed_actions = actions
|
||||
|
||||
if self.config.save_predicted_video:
|
||||
# Match upstream LingBot-VA visualization: collect chunk latents and decode the
|
||||
# concatenated latent sequence once after the rollout finishes.
|
||||
self.last_predicted_frames = None
|
||||
self.last_predicted_latents = latents.detach().to("cpu")
|
||||
|
||||
# On the first chunk, frame 0 is the conditioning frame (already "known"): the upstream
|
||||
# LIBERO client skips it (start_idx=1), so we drop the first frame's actions here.
|
||||
used = self.config.used_action_channel_ids
|
||||
a = actions[:, used] # [B, n_used, F, action_per_frame, 1]
|
||||
if is_first:
|
||||
a = a[:, :, 1:] # drop frame 0 -> (F-1) frames of actions
|
||||
a = a.squeeze(-1).flatten(2) # [B, n_used, n_steps]
|
||||
a = a.transpose(1, 2).contiguous() # [B, n_steps, n_used]
|
||||
return a.to(torch.float32)
|
||||
|
||||
# Prompt / text encoding
|
||||
def _maybe_init_prompt(self, batch):
|
||||
if self._prompt_embeds is not None or batch is None:
|
||||
return
|
||||
task = batch.get("task")
|
||||
prompt = task[0] if isinstance(task, list | tuple) else task
|
||||
self._prompt = prompt or ""
|
||||
self._prompt_embeds, self._negative_prompt_embeds = self._encode_prompt(self._prompt)
|
||||
|
||||
def _get_t5_prompt_embeds(self, prompt, max_sequence_length):
|
||||
tokenizer = self._frozen["tokenizer"]
|
||||
text_encoder = self._frozen["text_encoder"]
|
||||
device = self.config.device
|
||||
|
||||
prompt = [prompt] if isinstance(prompt, str) else prompt
|
||||
prompt = [clean_prompt(u) for u in prompt]
|
||||
|
||||
text_inputs = tokenizer(
|
||||
prompt,
|
||||
padding="max_length",
|
||||
max_length=max_sequence_length,
|
||||
truncation=True,
|
||||
add_special_tokens=True,
|
||||
return_attention_mask=True,
|
||||
return_tensors="pt",
|
||||
)
|
||||
text_input_ids, mask = text_inputs.input_ids, text_inputs.attention_mask
|
||||
seq_lens = mask.gt(0).sum(dim=1).long()
|
||||
|
||||
te_device = next(text_encoder.parameters()).device
|
||||
prompt_embeds = text_encoder(text_input_ids.to(te_device), mask.to(te_device)).last_hidden_state
|
||||
prompt_embeds = prompt_embeds.to(dtype=self.dtype, device=device)
|
||||
prompt_embeds = [u[:v] for u, v in zip(prompt_embeds, seq_lens, strict=False)]
|
||||
prompt_embeds = torch.stack(
|
||||
[torch.cat([u, u.new_zeros(max_sequence_length - u.size(0), u.size(1))]) for u in prompt_embeds],
|
||||
dim=0,
|
||||
)
|
||||
return prompt_embeds.to(device)
|
||||
|
||||
def _encode_prompt(self, prompt):
|
||||
max_len = self.config.max_sequence_length
|
||||
prompt_embeds = self._get_t5_prompt_embeds(prompt, max_len)
|
||||
negative_prompt_embeds = None
|
||||
if self._use_cfg:
|
||||
negative_prompt_embeds = self._get_t5_prompt_embeds("", max_len)
|
||||
return prompt_embeds, negative_prompt_embeds
|
||||
|
||||
# Observation (image) encoding -> normalized video latents
|
||||
def _extract_raw_obs(self, batch) -> dict[str, Tensor]:
|
||||
"""Snapshot the configured camera images from a batch (kept raw for later VAE encoding)."""
|
||||
return {k: batch[k].detach() for k in self.config.obs_cam_keys}
|
||||
|
||||
def _camera_frame(self, raw_obs, key, size=None) -> Tensor:
|
||||
"""Return a single-frame camera tensor [1, C, 1, H, W] resized + scaled to [-1, 1]."""
|
||||
img = raw_obs[key]
|
||||
if img.dim() == 3: # [C, H, W]
|
||||
img = img.unsqueeze(0)
|
||||
# LeRobot images arrive as float in [0, 1], shape [B, C, H, W].
|
||||
img = img.to(self.config.device, torch.float32)
|
||||
if self.config.image_hflip:
|
||||
img = torch.flip(img, dims=[-1]) # undo the env processor's horizontal flip
|
||||
if size is None:
|
||||
size = (self.config.height, self.config.width)
|
||||
img = F.interpolate(img, size=size, mode="bilinear", align_corners=False)
|
||||
img = img * 2.0 - 1.0
|
||||
return img.unsqueeze(2).to(self.dtype) # [1, C, F=1, H, W]
|
||||
|
||||
def _normalize_vae_latent(self, enc_out: Tensor) -> Tensor:
|
||||
"""Take the mean of a VAE encoder output and channel-normalize it (matches upstream)."""
|
||||
mu, _logvar = torch.chunk(enc_out, 2, dim=1)
|
||||
latents_mean = torch.tensor(self._vae.config.latents_mean).to(mu.device)
|
||||
latents_std = torch.tensor(self._vae.config.latents_std).to(mu.device)
|
||||
mean = latents_mean.view(1, -1, 1, 1, 1)
|
||||
inv_std = (1.0 / latents_std).view(1, -1, 1, 1, 1)
|
||||
return ((mu.float() - mean) * inv_std).to(mu)
|
||||
|
||||
@torch.no_grad()
|
||||
def _encode_frames(self, raw_frames: list) -> Tensor:
|
||||
"""VAE-encode a temporal clip of observed frames and concat the per-camera latents on width.
|
||||
|
||||
``raw_frames`` is a list of per-frame obs dicts (one per env sub-step). Each configured
|
||||
camera is stacked along the temporal axis into a ``[1, C, F, H, W]`` clip and encoded in a
|
||||
single streaming ``encode_chunk`` call so the VAE temporal downsample (x4) collapses the F
|
||||
input frames into ``F / 4`` latent frames, with the causal ``feat_cache`` carried across
|
||||
chunks (mirrors upstream ``_encode_obs``).
|
||||
"""
|
||||
vae_device = next(self._vae.parameters()).device
|
||||
if self.config.camera_layout == "robotwin_tshape":
|
||||
return self._encode_frames_tshape(raw_frames, vae_device)
|
||||
per_cam_videos = []
|
||||
for k in self.config.obs_cam_keys:
|
||||
frames = [self._camera_frame(fb, k) for fb in raw_frames]
|
||||
per_cam_videos.append(torch.cat(frames, dim=2)) # [1, C, F, H, W]
|
||||
videos = torch.cat(per_cam_videos, dim=0) # [num_cam, C, F, H, W]
|
||||
enc_out = self._streaming_vae.encode_chunk(videos.to(vae_device).to(self.dtype))
|
||||
mu_norm = self._normalize_vae_latent(enc_out)
|
||||
# Concatenate the per-camera latents along width.
|
||||
video_latent = torch.cat(mu_norm.split(1, dim=0), dim=-1)
|
||||
return video_latent.to(self.config.device)
|
||||
|
||||
@torch.no_grad()
|
||||
def _encode_frames_tshape(self, raw_frames: list, vae_device) -> Tensor:
|
||||
"""RoboTwin T-shape latent assembly: full-res head + half-res wrists (second streaming VAE).
|
||||
|
||||
The two wrist latents are concatenated on width and stacked (on the height axis) on top of
|
||||
the head latent, mirroring upstream ``_encode_obs`` for ``env_type='robotwin_tshape'``.
|
||||
"""
|
||||
cfg = self.config
|
||||
h, w = cfg.height, cfg.width
|
||||
head_key, left_key, right_key = cfg.obs_cam_keys[0], cfg.obs_cam_keys[1], cfg.obs_cam_keys[2]
|
||||
head = torch.cat([self._camera_frame(fb, head_key, size=(h, w)) for fb in raw_frames], dim=2)
|
||||
left = torch.cat(
|
||||
[self._camera_frame(fb, left_key, size=(h // 2, w // 2)) for fb in raw_frames], dim=2
|
||||
)
|
||||
right = torch.cat(
|
||||
[self._camera_frame(fb, right_key, size=(h // 2, w // 2)) for fb in raw_frames], dim=2
|
||||
)
|
||||
wrists = torch.cat([left, right], dim=0) # [2, C, F, H/2, W/2]
|
||||
enc_high = self._streaming_vae.encode_chunk(head.to(vae_device).to(self.dtype))
|
||||
enc_lr = self._frozen["streaming_vae_half"].encode_chunk(wrists.to(vae_device).to(self.dtype))
|
||||
# wrists side-by-side on width, then stacked on top of the head latent on the height axis.
|
||||
enc_out = torch.cat([torch.cat(enc_lr.split(1, dim=0), dim=-1), enc_high], dim=-2)
|
||||
video_latent = self._normalize_vae_latent(enc_out)
|
||||
return video_latent.to(self.config.device)
|
||||
|
||||
# KV cache management
|
||||
@property
|
||||
def _latent_hw(self):
|
||||
if self.config.camera_layout == "robotwin_tshape":
|
||||
# head (full) on the bottom, two half-res wrists side-by-side on top -> 1.5x height.
|
||||
return ((self.config.height // 16) * 3) // 2, self.config.width // 16
|
||||
h = self.config.height // 16
|
||||
w = (self.config.width // 16) * len(self.config.obs_cam_keys)
|
||||
return h, w
|
||||
|
||||
def _init_streaming_cache(self, init_latent):
|
||||
cfg = self.config
|
||||
latent_h, latent_w = self._latent_hw
|
||||
p = cfg.patch_size
|
||||
latent_token_per_chunk = (cfg.frame_chunk_size * latent_h * latent_w) // (p[0] * p[1] * p[2])
|
||||
action_token_per_chunk = cfg.frame_chunk_size * cfg.action_per_frame
|
||||
self.transformer.create_empty_cache(
|
||||
"pos",
|
||||
cfg.attn_window,
|
||||
latent_token_per_chunk,
|
||||
action_token_per_chunk,
|
||||
device=self.config.device,
|
||||
dtype=self.dtype,
|
||||
batch_size=2 if self._use_cfg else 1,
|
||||
)
|
||||
self._cache_initialised = True
|
||||
|
||||
def _repeat_input_for_cfg(self, input_dict):
|
||||
if self._use_cfg:
|
||||
input_dict["noisy_latents"] = input_dict["noisy_latents"].repeat(2, 1, 1, 1, 1)
|
||||
input_dict["text_emb"] = torch.cat(
|
||||
[
|
||||
self._prompt_embeds.to(self.dtype).clone(),
|
||||
self._negative_prompt_embeds.to(self.dtype).clone(),
|
||||
],
|
||||
dim=0,
|
||||
)
|
||||
input_dict["grid_id"] = input_dict["grid_id"][None].repeat(2, 1, 1)
|
||||
input_dict["timesteps"] = input_dict["timesteps"][None].repeat(2, 1)
|
||||
else:
|
||||
input_dict["grid_id"] = input_dict["grid_id"][None]
|
||||
input_dict["timesteps"] = input_dict["timesteps"][None]
|
||||
return input_dict
|
||||
|
||||
def _prepare_latent_input(
|
||||
self,
|
||||
latent_model_input,
|
||||
action_model_input,
|
||||
latent_t=0,
|
||||
action_t=0,
|
||||
latent_cond=None,
|
||||
action_cond=None,
|
||||
frame_st_id=0,
|
||||
):
|
||||
cfg = self.config
|
||||
device = self.config.device
|
||||
p = cfg.patch_size
|
||||
out = {}
|
||||
if latent_model_input is not None:
|
||||
out["latent_res_lst"] = {
|
||||
"noisy_latents": latent_model_input,
|
||||
"timesteps": torch.ones([latent_model_input.shape[2]], dtype=torch.float32, device=device)
|
||||
* latent_t,
|
||||
"grid_id": get_mesh_id(
|
||||
latent_model_input.shape[-3] // p[0],
|
||||
latent_model_input.shape[-2] // p[1],
|
||||
latent_model_input.shape[-1] // p[2],
|
||||
0,
|
||||
1,
|
||||
frame_st_id,
|
||||
).to(device),
|
||||
"text_emb": self._prompt_embeds.to(self.dtype).clone(),
|
||||
}
|
||||
if latent_cond is not None:
|
||||
out["latent_res_lst"]["noisy_latents"][:, :, 0:1] = latent_cond[:, :, 0:1]
|
||||
out["latent_res_lst"]["timesteps"][0:1] *= 0
|
||||
if action_model_input is not None:
|
||||
out["action_res_lst"] = {
|
||||
"noisy_latents": action_model_input,
|
||||
"timesteps": torch.ones([action_model_input.shape[2]], dtype=torch.float32, device=device)
|
||||
* action_t,
|
||||
"grid_id": get_mesh_id(
|
||||
action_model_input.shape[-3],
|
||||
action_model_input.shape[-2],
|
||||
action_model_input.shape[-1],
|
||||
1,
|
||||
1,
|
||||
frame_st_id,
|
||||
action=True,
|
||||
).to(device),
|
||||
"text_emb": self._prompt_embeds.to(self.dtype).clone(),
|
||||
}
|
||||
if action_cond is not None:
|
||||
out["action_res_lst"]["noisy_latents"][:, :, 0:1] = action_cond[:, :, 0:1]
|
||||
out["action_res_lst"]["timesteps"][0:1] *= 0
|
||||
out["action_res_lst"]["noisy_latents"][:, ~self._action_mask] *= 0
|
||||
return out
|
||||
|
||||
@property
|
||||
def _action_mask(self):
|
||||
mask = torch.zeros([self.config.action_dim], dtype=torch.bool)
|
||||
mask[self.config.used_action_channel_ids] = True
|
||||
return mask
|
||||
|
||||
# Action conditioning (executed action history) (de)normalization
|
||||
def _preprocess_action_state(self, action_norm: Tensor) -> Tensor:
|
||||
"""Build the action-conditioning tensor from the already-normalized executed actions.
|
||||
|
||||
``action_norm`` is the model-space action chunk ``[B, action_dim, F, action_per_frame, 1]``.
|
||||
Upstream re-derives the conditioning from the raw executed action via quantile norm; here
|
||||
the executed actions are already in the model-normalized space, so we pass them through.
|
||||
"""
|
||||
return action_norm.to(self.config.device, self.dtype)
|
||||
|
||||
def _compute_kv_cache(self, obs_buffer, executed_actions):
|
||||
"""Feed real observed keyframes + executed actions back into the KV cache."""
|
||||
if not obs_buffer or executed_actions is None:
|
||||
return
|
||||
self.transformer.clear_pred_cache("pos")
|
||||
# Encode the buffered keyframe clip in one streaming call (carries the causal VAE cache).
|
||||
latent_model_input = self._encode_frames(obs_buffer)
|
||||
# On the first feedback, prepend the init latent so the latent/action frame counts align
|
||||
# (upstream prepends ``init_latent`` to the observed keyframes when frame_st_id == 0).
|
||||
if self._frame_st_id == 0 and getattr(self, "_init_latent", None) is not None:
|
||||
latent_model_input = torch.cat([self._init_latent, latent_model_input], dim=2)
|
||||
action_model_input = self._preprocess_action_state(executed_actions)
|
||||
action_model_input = action_model_input.to(latent_model_input)
|
||||
input_dict = self._prepare_latent_input(
|
||||
latent_model_input, action_model_input, frame_st_id=self._frame_st_id
|
||||
)
|
||||
with torch.no_grad():
|
||||
self.transformer(
|
||||
self._repeat_input_for_cfg(input_dict["latent_res_lst"]),
|
||||
update_cache=2,
|
||||
cache_name="pos",
|
||||
action_mode=False,
|
||||
)
|
||||
self.transformer(
|
||||
self._repeat_input_for_cfg(input_dict["action_res_lst"]),
|
||||
update_cache=2,
|
||||
cache_name="pos",
|
||||
action_mode=True,
|
||||
)
|
||||
self._frame_st_id += latent_model_input.shape[2]
|
||||
|
||||
# The core dual-stream denoising loop (one chunk)
|
||||
@torch.no_grad()
|
||||
def _infer(self, init_latent, frame_st_id=0):
|
||||
cfg = self.config
|
||||
device = self.config.device
|
||||
latent_h, latent_w = self._latent_hw
|
||||
frame_chunk_size = cfg.frame_chunk_size
|
||||
|
||||
latents = torch.randn(1, 48, frame_chunk_size, latent_h, latent_w, device=device, dtype=self.dtype)
|
||||
actions = torch.randn(
|
||||
1, cfg.action_dim, frame_chunk_size, cfg.action_per_frame, 1, device=device, dtype=self.dtype
|
||||
)
|
||||
|
||||
self._scheduler.set_timesteps(cfg.num_inference_steps)
|
||||
self._action_scheduler.set_timesteps(cfg.action_num_inference_steps)
|
||||
timesteps = F.pad(self._scheduler.timesteps, (0, 1), mode="constant", value=0)
|
||||
if cfg.video_exec_step != -1:
|
||||
timesteps = timesteps[: cfg.video_exec_step]
|
||||
action_timesteps = F.pad(self._action_scheduler.timesteps, (0, 1), mode="constant", value=0)
|
||||
|
||||
# 1. Video-latent denoising loop
|
||||
for i, t in enumerate(timesteps):
|
||||
last_step = i == len(timesteps) - 1
|
||||
latent_cond = (
|
||||
init_latent[:, :, 0:1].to(self.dtype)
|
||||
if frame_st_id == 0 and init_latent is not None
|
||||
else None
|
||||
)
|
||||
input_dict = self._prepare_latent_input(
|
||||
latents, None, t, t, latent_cond, None, frame_st_id=frame_st_id
|
||||
)
|
||||
video_noise_pred = self.transformer(
|
||||
self._repeat_input_for_cfg(input_dict["latent_res_lst"]),
|
||||
update_cache=1 if last_step else 0,
|
||||
cache_name="pos",
|
||||
action_mode=False,
|
||||
)
|
||||
if not last_step or cfg.video_exec_step != -1:
|
||||
video_noise_pred = data_seq_to_patch(
|
||||
cfg.patch_size,
|
||||
video_noise_pred,
|
||||
frame_chunk_size,
|
||||
latent_h,
|
||||
latent_w,
|
||||
batch_size=2 if self._use_cfg else 1,
|
||||
)
|
||||
if cfg.guidance_scale > 1:
|
||||
video_noise_pred = video_noise_pred[1:] + cfg.guidance_scale * (
|
||||
video_noise_pred[:1] - video_noise_pred[1:]
|
||||
)
|
||||
else:
|
||||
video_noise_pred = video_noise_pred[:1]
|
||||
latents = self._scheduler.step(video_noise_pred, t, latents, return_dict=False)
|
||||
if frame_st_id == 0 and latent_cond is not None:
|
||||
latents[:, :, 0:1] = latent_cond
|
||||
|
||||
# 2. Action denoising loop
|
||||
for i, t in enumerate(action_timesteps):
|
||||
last_step = i == len(action_timesteps) - 1
|
||||
action_cond = (
|
||||
torch.zeros([1, cfg.action_dim, 1, cfg.action_per_frame, 1], device=device, dtype=self.dtype)
|
||||
if frame_st_id == 0
|
||||
else None
|
||||
)
|
||||
input_dict = self._prepare_latent_input(
|
||||
None, actions, t, t, None, action_cond, frame_st_id=frame_st_id
|
||||
)
|
||||
action_noise_pred = self.transformer(
|
||||
self._repeat_input_for_cfg(input_dict["action_res_lst"]),
|
||||
update_cache=1 if last_step else 0,
|
||||
cache_name="pos",
|
||||
action_mode=True,
|
||||
)
|
||||
if not last_step:
|
||||
action_noise_pred = rearrange(action_noise_pred, "b (f n) c -> b c f n 1", f=frame_chunk_size)
|
||||
if cfg.action_guidance_scale > 1:
|
||||
action_noise_pred = action_noise_pred[1:] + cfg.action_guidance_scale * (
|
||||
action_noise_pred[:1] - action_noise_pred[1:]
|
||||
)
|
||||
else:
|
||||
action_noise_pred = action_noise_pred[:1]
|
||||
actions = self._action_scheduler.step(action_noise_pred, t, actions, return_dict=False)
|
||||
if frame_st_id == 0 and action_cond is not None:
|
||||
actions[:, :, 0:1] = action_cond
|
||||
|
||||
actions[:, ~self._action_mask] *= 0
|
||||
return actions, latents
|
||||
|
||||
# Predicted-video decoding (opt-in)
|
||||
@torch.no_grad()
|
||||
def decode_predicted_latents(self, latents) -> Tensor:
|
||||
"""Decode a concatenated predicted-latent sequence into ``[T, H, W, 3]`` uint8 frames."""
|
||||
return self._decode_predicted_video(latents)
|
||||
|
||||
@torch.no_grad()
|
||||
def _decode_predicted_video(self, latents) -> Tensor:
|
||||
"""VAE-decode predicted latents into a uint8 frame stack ``[T, H, W, 3]`` on CPU."""
|
||||
vae = self._vae
|
||||
z_dim = vae.config.z_dim
|
||||
vae_device = next(vae.parameters()).device
|
||||
latents = latents.to(device=vae_device, dtype=vae.dtype)
|
||||
latents = denormalize_latents(latents, vae.config.latents_mean, vae.config.latents_std, z_dim)
|
||||
video = vae.decode(latents, return_dict=False)[0] # [B, C, F, H, W] in [-1, 1]
|
||||
video = (video.float().clamp(-1, 1) + 1.0) / 2.0
|
||||
video = (video[0].permute(1, 2, 3, 0) * 255.0).round().to(torch.uint8) # [F, H, W, C]
|
||||
return video.cpu()
|
||||
@@ -1,87 +0,0 @@
|
||||
# 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.
|
||||
|
||||
"""Pre/post-processor pipelines for the LingBot-VA policy.
|
||||
|
||||
The preprocessor passes inputs through (IDENTITY) and the postprocessor maps the policy's
|
||||
``[-1, 1]`` actions back to physical units with the built-in ``UnnormalizerProcessorStep``
|
||||
(QUANTILES) using per-channel q01/q99 restored from the checkpoint.
|
||||
"""
|
||||
|
||||
from typing import Any
|
||||
|
||||
import torch
|
||||
|
||||
from lerobot.configs.types import FeatureType, NormalizationMode
|
||||
from lerobot.processor import (
|
||||
AddBatchDimensionProcessorStep,
|
||||
DeviceProcessorStep,
|
||||
NormalizerProcessorStep,
|
||||
PolicyAction,
|
||||
PolicyProcessorPipeline,
|
||||
ProcessorStep,
|
||||
RenameObservationsProcessorStep,
|
||||
UnnormalizerProcessorStep,
|
||||
)
|
||||
from lerobot.processor.converters import policy_action_to_transition, transition_to_policy_action
|
||||
from lerobot.utils.constants import (
|
||||
POLICY_POSTPROCESSOR_DEFAULT_NAME,
|
||||
POLICY_PREPROCESSOR_DEFAULT_NAME,
|
||||
)
|
||||
|
||||
from .configuration_lingbot_va import LingBotVAConfig
|
||||
|
||||
|
||||
def make_lingbot_va_pre_post_processors(
|
||||
config: LingBotVAConfig,
|
||||
dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
|
||||
) -> tuple[
|
||||
PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
|
||||
PolicyProcessorPipeline[PolicyAction, PolicyAction],
|
||||
]:
|
||||
"""Build the pre/post processor pipelines for LingBot-VA."""
|
||||
|
||||
input_steps: list[ProcessorStep] = [
|
||||
RenameObservationsProcessorStep(rename_map={}),
|
||||
AddBatchDimensionProcessorStep(),
|
||||
NormalizerProcessorStep(
|
||||
features={**config.input_features, **config.output_features},
|
||||
norm_map=config.normalization_mapping,
|
||||
stats=dataset_stats,
|
||||
),
|
||||
DeviceProcessorStep(device=config.device),
|
||||
]
|
||||
|
||||
# Unnormalize actions from [-1, 1] to physical units (QUANTILES) using q01/q99 restored from the checkpoint.
|
||||
output_steps: list[ProcessorStep] = [
|
||||
UnnormalizerProcessorStep(
|
||||
features=config.output_features,
|
||||
norm_map={FeatureType.ACTION: NormalizationMode.QUANTILES},
|
||||
stats=dataset_stats,
|
||||
),
|
||||
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,
|
||||
),
|
||||
)
|
||||
File diff suppressed because it is too large
Load Diff
@@ -31,7 +31,6 @@ import logging
|
||||
import os
|
||||
import types
|
||||
from collections import deque
|
||||
from contextlib import nullcontext
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
import numpy as np
|
||||
@@ -43,6 +42,7 @@ from torch.distributions import Beta
|
||||
|
||||
from lerobot.policies.pretrained import PreTrainedPolicy
|
||||
from lerobot.utils.constants import ACTION
|
||||
from lerobot.utils.device_utils import get_safe_autocast_context
|
||||
from lerobot.utils.import_utils import _scipy_available, _transformers_available, require_package
|
||||
|
||||
from ..rtc.modeling_rtc import RTCProcessor
|
||||
@@ -1644,10 +1644,8 @@ class MolmoAct2Policy(PreTrainedPolicy):
|
||||
device=device,
|
||||
)
|
||||
action_dim = self._output_action_dim(batch)
|
||||
autocast_context = (
|
||||
torch.autocast(device_type=device.type, dtype=model_dtype)
|
||||
if device.type in {"cuda", "cpu"} and model_dtype in {torch.bfloat16, torch.float16}
|
||||
else nullcontext()
|
||||
autocast_context = get_safe_autocast_context(
|
||||
device, dtype=model_dtype, enabled=model_dtype in {torch.bfloat16, torch.float16}
|
||||
)
|
||||
with autocast_context:
|
||||
if inference_action_mode == "discrete":
|
||||
|
||||
@@ -26,6 +26,7 @@ from torch import Tensor, nn
|
||||
from lerobot.policies.pretrained import PreTrainedPolicy, T
|
||||
from lerobot.policies.utils import populate_queues
|
||||
from lerobot.utils.constants import ACTION, OBS_STATE
|
||||
from lerobot.utils.device_utils import get_safe_autocast_context
|
||||
from lerobot.utils.import_utils import _transformers_available, require_package
|
||||
|
||||
if TYPE_CHECKING or _transformers_available:
|
||||
@@ -183,7 +184,7 @@ class VLAJEPAModel(nn.Module):
|
||||
action_idx = action_mask.nonzero(as_tuple=True)
|
||||
|
||||
device_type = next(self.parameters()).device.type
|
||||
with torch.autocast(device_type=device_type, dtype=torch.bfloat16):
|
||||
with get_safe_autocast_context(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)
|
||||
@@ -250,7 +251,7 @@ class VLAJEPAModel(nn.Module):
|
||||
) -> 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):
|
||||
with get_safe_autocast_context(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)
|
||||
|
||||
@@ -17,7 +17,6 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from contextlib import nullcontext
|
||||
from copy import copy
|
||||
|
||||
import torch
|
||||
@@ -25,6 +24,7 @@ import torch
|
||||
from lerobot.policies.pretrained import PreTrainedPolicy
|
||||
from lerobot.policies.utils import make_robot_action, prepare_observation_for_inference
|
||||
from lerobot.processor import PolicyProcessorPipeline
|
||||
from lerobot.utils.device_utils import get_safe_autocast_context
|
||||
|
||||
from .base import InferenceEngine
|
||||
|
||||
@@ -102,11 +102,7 @@ class SyncInferenceEngine(InferenceEngine):
|
||||
# ``obs_frame`` fresh per tick via ``build_dataset_frame``, so the
|
||||
# tensor/array values are not shared with any other reader.
|
||||
observation = copy(obs_frame)
|
||||
autocast_ctx = (
|
||||
torch.autocast(device_type=self._device.type)
|
||||
if self._device.type == "cuda" and self._policy.config.use_amp
|
||||
else nullcontext()
|
||||
)
|
||||
autocast_ctx = get_safe_autocast_context(self._device, enabled=self._policy.config.use_amp)
|
||||
with torch.inference_mode(), autocast_ctx:
|
||||
observation = prepare_observation_for_inference(
|
||||
observation, self._device, self._task, self._robot_type
|
||||
|
||||
@@ -56,7 +56,6 @@ import threading
|
||||
import time
|
||||
from collections import defaultdict
|
||||
from collections.abc import Callable
|
||||
from contextlib import nullcontext
|
||||
from copy import deepcopy
|
||||
from dataclasses import asdict
|
||||
from functools import partial
|
||||
@@ -86,7 +85,7 @@ from lerobot.policies import PreTrainedPolicy, make_policy, make_pre_post_proces
|
||||
from lerobot.processor import PolicyProcessorPipeline
|
||||
from lerobot.types import PolicyAction
|
||||
from lerobot.utils.constants import ACTION, DONE, OBS_IMAGE, OBS_IMAGES, OBS_STR, REWARD
|
||||
from lerobot.utils.device_utils import get_safe_torch_device
|
||||
from lerobot.utils.device_utils import get_safe_autocast_context, get_safe_torch_device
|
||||
from lerobot.utils.import_utils import register_third_party_plugins
|
||||
from lerobot.utils.io_utils import write_video
|
||||
from lerobot.utils.random_utils import set_seed
|
||||
@@ -169,7 +168,6 @@ def rollout(
|
||||
env_features: dict | None = None,
|
||||
recording_repo_id: str | None = None,
|
||||
recording_private: bool = False,
|
||||
predicted_latents_callback: Callable[[PreTrainedPolicy], None] | None = None,
|
||||
) -> dict:
|
||||
"""Run a batched policy rollout once through a batch of environments.
|
||||
|
||||
@@ -199,9 +197,6 @@ def rollout(
|
||||
are returned optionally because they typically take more memory to cache. Defaults to False.
|
||||
render_callback: Optional rendering callback to be used after the environments are reset, and after
|
||||
every step.
|
||||
predicted_latents_callback: Optional callback invoked after every ``select_action`` with the policy
|
||||
itself. World-model policies (e.g. LingBot-VA) stash predicted video latents on
|
||||
``policy.last_predicted_latents``; this lets the caller concatenate chunks and decode once.
|
||||
Returns:
|
||||
The dictionary described above.
|
||||
"""
|
||||
@@ -280,8 +275,6 @@ def rollout(
|
||||
observation = preprocessor(observation)
|
||||
with torch.inference_mode():
|
||||
action = policy.select_action(observation)
|
||||
if predicted_latents_callback is not None:
|
||||
predicted_latents_callback(policy)
|
||||
action = postprocessor(action)
|
||||
|
||||
action_transition = {ACTION: action}
|
||||
@@ -301,22 +294,12 @@ def rollout(
|
||||
# available if none of the envs finished.
|
||||
if "final_info" in info:
|
||||
final_info = info["final_info"]
|
||||
if isinstance(final_info, dict):
|
||||
is_success = final_info.get("is_success", [False] * env.num_envs)
|
||||
successes = (
|
||||
is_success.tolist()
|
||||
if hasattr(is_success, "tolist")
|
||||
else [bool(is_success)] * env.num_envs
|
||||
if not isinstance(final_info, dict):
|
||||
raise RuntimeError(
|
||||
"Unsupported `final_info` format: expected dict (Gymnasium >= 1.0). "
|
||||
"You're likely using an older version of gymnasium (< 1.0). Please upgrade."
|
||||
)
|
||||
else:
|
||||
# Gymnasium < 1.0 returns final_info as a per-env sequence/object array,
|
||||
# with entries set to a dict only for envs that just finished.
|
||||
successes = []
|
||||
for item in final_info:
|
||||
if isinstance(item, dict) and "is_success" in item:
|
||||
successes.append(bool(item["is_success"]))
|
||||
else:
|
||||
successes.append(False)
|
||||
successes = final_info["is_success"].tolist()
|
||||
elif "is_success" in info:
|
||||
is_success = info["is_success"]
|
||||
successes = (
|
||||
@@ -416,7 +399,6 @@ def eval_policy(
|
||||
env_features: dict | None = None,
|
||||
recording_repo_id: str | None = None,
|
||||
recording_private: bool = False,
|
||||
save_predicted_video: bool = False,
|
||||
) -> dict:
|
||||
"""
|
||||
Args:
|
||||
@@ -435,11 +417,6 @@ def eval_policy(
|
||||
if max_episodes_rendered > 0 and not videos_dir:
|
||||
raise ValueError("If max_episodes_rendered > 0, videos_dir must be provided.")
|
||||
|
||||
# World-model policies (e.g. LingBot-VA) opt into predicted-video saving via their config.
|
||||
save_predicted_video = save_predicted_video or bool(
|
||||
getattr(getattr(policy, "config", None), "save_predicted_video", False)
|
||||
)
|
||||
|
||||
if not isinstance(policy, PreTrainedPolicy):
|
||||
exc = ValueError(
|
||||
f"Policy of type 'PreTrainedPolicy' is expected, but type '{type(policy)}' was provided."
|
||||
@@ -483,22 +460,6 @@ def eval_policy(
|
||||
if max_episodes_rendered > 0:
|
||||
video_paths: list[str] = []
|
||||
|
||||
if save_predicted_video:
|
||||
if not videos_dir:
|
||||
raise ValueError("If save_predicted_video is True, videos_dir must be provided.")
|
||||
predicted_video_paths: list[str] = []
|
||||
n_predicted_rendered = 0
|
||||
|
||||
# Collect predicted-video latents across a rollout (world-model policies only). The latents are
|
||||
# concatenated and decoded once after the rollout, matching upstream LingBot-VA's visualization path.
|
||||
def collect_predicted_latents(policy: PreTrainedPolicy):
|
||||
latents = getattr(policy, "last_predicted_latents", None)
|
||||
if latents is not None:
|
||||
pred_latents.append(
|
||||
latents.detach().to("cpu") if hasattr(latents, "detach") else torch.as_tensor(latents).cpu()
|
||||
)
|
||||
policy.last_predicted_latents = None
|
||||
|
||||
if return_episode_data:
|
||||
episode_data: dict | None = None
|
||||
|
||||
@@ -510,9 +471,6 @@ def eval_policy(
|
||||
if max_episodes_rendered > 0:
|
||||
ep_frames: list[np.ndarray] = []
|
||||
|
||||
if save_predicted_video:
|
||||
pred_latents: list[torch.Tensor] = []
|
||||
|
||||
if start_seed is None:
|
||||
seeds = None
|
||||
else:
|
||||
@@ -533,7 +491,6 @@ def eval_policy(
|
||||
env_features=env_features,
|
||||
recording_repo_id=recording_repo_id,
|
||||
recording_private=recording_private,
|
||||
predicted_latents_callback=collect_predicted_latents if save_predicted_video else None,
|
||||
)
|
||||
|
||||
# Figure out where in each rollout sequence the first done condition was encountered (results after
|
||||
@@ -599,35 +556,6 @@ def eval_policy(
|
||||
threads.append(thread)
|
||||
n_episodes_rendered += 1
|
||||
|
||||
# Maybe save the policy's predicted (imagined) video for this batch's rollout.
|
||||
if save_predicted_video and len(pred_latents) > 0:
|
||||
predicted_latent = torch.cat(pred_latents, dim=2)
|
||||
decoder = getattr(policy, "decode_predicted_latents", None) or getattr(
|
||||
policy, "_decode_predicted_video", None
|
||||
)
|
||||
if decoder is None:
|
||||
raise AttributeError(
|
||||
"Policy config requested predicted-video saving, but the policy does not expose "
|
||||
"`decode_predicted_latents` or `_decode_predicted_video`."
|
||||
)
|
||||
predicted_video = decoder(predicted_latent)
|
||||
if hasattr(predicted_video, "detach"):
|
||||
predicted_video = predicted_video.detach().to("cpu").numpy()
|
||||
videos_dir.mkdir(parents=True, exist_ok=True)
|
||||
predicted_video_path = videos_dir / f"pred_episode_{n_predicted_rendered}.mp4"
|
||||
predicted_video_paths.append(str(predicted_video_path))
|
||||
thread = threading.Thread(
|
||||
target=write_video,
|
||||
args=(
|
||||
str(predicted_video_path),
|
||||
predicted_video,
|
||||
env.unwrapped.metadata["render_fps"],
|
||||
),
|
||||
)
|
||||
thread.start()
|
||||
threads.append(thread)
|
||||
n_predicted_rendered += 1
|
||||
|
||||
progbar.set_postfix(
|
||||
{"running_success_rate": f"{np.mean(all_successes[:n_episodes]).item() * 100:.1f}%"}
|
||||
)
|
||||
@@ -671,9 +599,6 @@ def eval_policy(
|
||||
if max_episodes_rendered > 0:
|
||||
info["video_paths"] = video_paths
|
||||
|
||||
if save_predicted_video:
|
||||
info["predicted_video_paths"] = predicted_video_paths
|
||||
|
||||
return info
|
||||
|
||||
|
||||
@@ -772,7 +697,7 @@ def eval_main(cfg: EvalPipelineConfig):
|
||||
max_episodes_rendered = 0 if cfg.eval.recording else 10
|
||||
videos_dir = None if cfg.eval.recording else Path(cfg.output_dir) / "videos"
|
||||
|
||||
with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.policy.use_amp else nullcontext():
|
||||
with torch.no_grad(), get_safe_autocast_context(device, enabled=cfg.policy.use_amp):
|
||||
info = eval_policy_all(
|
||||
envs=envs,
|
||||
policy=policy,
|
||||
@@ -814,10 +739,9 @@ class TaskMetrics(TypedDict):
|
||||
max_rewards: list[float]
|
||||
successes: list[bool]
|
||||
video_paths: list[str]
|
||||
predicted_video_paths: list[str]
|
||||
|
||||
|
||||
ACC_KEYS = ("sum_rewards", "max_rewards", "successes", "video_paths", "predicted_video_paths")
|
||||
ACC_KEYS = ("sum_rewards", "max_rewards", "successes", "video_paths")
|
||||
|
||||
|
||||
def eval_one(
|
||||
@@ -866,7 +790,6 @@ def eval_one(
|
||||
max_rewards=[ep["max_reward"] for ep in per_episode],
|
||||
successes=[ep["success"] for ep in per_episode],
|
||||
video_paths=task_result.get("video_paths", []),
|
||||
predicted_video_paths=task_result.get("predicted_video_paths", []),
|
||||
)
|
||||
|
||||
|
||||
@@ -927,7 +850,6 @@ def run_one(
|
||||
|
||||
if max_episodes_rendered > 0:
|
||||
metrics.setdefault("video_paths", [])
|
||||
metrics.setdefault("predicted_video_paths", [])
|
||||
return task_group, task_id, metrics
|
||||
|
||||
|
||||
@@ -985,11 +907,11 @@ def eval_policy_all(
|
||||
_append("sum_rewards", metrics.get("sum_rewards"))
|
||||
_append("max_rewards", metrics.get("max_rewards"))
|
||||
_append("successes", metrics.get("successes"))
|
||||
for key in ("video_paths", "predicted_video_paths"):
|
||||
paths = metrics.get(key, [])
|
||||
if paths:
|
||||
group_acc[group][key].extend(paths)
|
||||
overall[key].extend(paths)
|
||||
# video_paths is list-like
|
||||
paths = metrics.get("video_paths", [])
|
||||
if paths:
|
||||
group_acc[group]["video_paths"].extend(paths)
|
||||
overall["video_paths"].extend(paths)
|
||||
|
||||
# Choose runner (sequential vs threaded)
|
||||
task_runner = partial(
|
||||
@@ -1061,7 +983,6 @@ def eval_policy_all(
|
||||
"pc_success": _agg_from_list(acc["successes"]) * 100 if acc["successes"] else float("nan"),
|
||||
"n_episodes": len(acc["sum_rewards"]),
|
||||
"video_paths": list(acc["video_paths"]),
|
||||
"predicted_video_paths": list(acc["predicted_video_paths"]),
|
||||
}
|
||||
|
||||
# overall aggregates
|
||||
@@ -1073,7 +994,6 @@ def eval_policy_all(
|
||||
"eval_s": time.time() - start_t,
|
||||
"eval_ep_s": (time.time() - start_t) / max(1, len(overall["sum_rewards"])),
|
||||
"video_paths": list(overall["video_paths"]),
|
||||
"predicted_video_paths": list(overall["predicted_video_paths"]),
|
||||
}
|
||||
|
||||
return {
|
||||
|
||||
@@ -33,7 +33,12 @@ from .constants import (
|
||||
REWARD,
|
||||
)
|
||||
from .decorators import check_if_already_connected, check_if_not_connected
|
||||
from .device_utils import auto_select_torch_device, get_safe_torch_device, is_torch_device_available
|
||||
from .device_utils import (
|
||||
auto_select_torch_device,
|
||||
get_safe_autocast_context,
|
||||
get_safe_torch_device,
|
||||
is_torch_device_available,
|
||||
)
|
||||
from .errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
|
||||
from .import_utils import is_package_available, require_package
|
||||
|
||||
@@ -51,6 +56,7 @@ __all__ = [
|
||||
"REWARD",
|
||||
# Device utilities
|
||||
"auto_select_torch_device",
|
||||
"get_safe_autocast_context",
|
||||
"get_safe_torch_device",
|
||||
"is_torch_device_available",
|
||||
# Import guards
|
||||
|
||||
@@ -15,6 +15,7 @@
|
||||
# limitations under the License.
|
||||
|
||||
import logging
|
||||
from contextlib import AbstractContextManager, nullcontext
|
||||
|
||||
import torch
|
||||
|
||||
@@ -107,3 +108,25 @@ def is_amp_available(device: str):
|
||||
return False
|
||||
else:
|
||||
raise ValueError(f"Unknown device '{device}.")
|
||||
|
||||
|
||||
def get_safe_autocast_context(
|
||||
device: str | torch.device,
|
||||
*,
|
||||
dtype: torch.dtype | None = None,
|
||||
enabled: bool = True,
|
||||
) -> AbstractContextManager:
|
||||
"""Return a ``torch.autocast`` context, or a no-op when AMP is unsupported on ``device``.
|
||||
|
||||
Autocast is only entered on devices that support AMP (cuda, xpu, cpu); on mps and any
|
||||
unknown device this falls back to ``nullcontext()`` so callers can request autocast
|
||||
unconditionally without breaking on unsupported backends.
|
||||
"""
|
||||
device_type = device.type if isinstance(device, torch.device) else str(device).split(":", 1)[0]
|
||||
try:
|
||||
amp_ok = is_amp_available(device_type)
|
||||
except ValueError:
|
||||
amp_ok = False
|
||||
if not enabled or not amp_ok:
|
||||
return nullcontext()
|
||||
return torch.autocast(device_type=device_type, dtype=dtype)
|
||||
|
||||
@@ -129,7 +129,6 @@ _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")
|
||||
|
||||
|
||||
@@ -7,14 +7,11 @@ from dataclasses import dataclass, field
|
||||
|
||||
import gymnasium as gym
|
||||
import pytest
|
||||
import torch
|
||||
from gymnasium.envs.registration import register, registry as gym_registry
|
||||
|
||||
from lerobot.configs.types import PolicyFeature
|
||||
from lerobot.envs.configs import EnvConfig, LiberoEnv
|
||||
from lerobot.envs.configs import EnvConfig
|
||||
from lerobot.envs.factory import make_env, make_env_config, make_env_pre_post_processors
|
||||
from lerobot.processor import LiberoProcessorStep
|
||||
from lerobot.utils.constants import OBS_PREFIX, OBS_STATE
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -64,31 +61,6 @@ def test_processors_delegation():
|
||||
assert len(pre.steps) == 0
|
||||
|
||||
|
||||
def test_libero_processors_are_policy_agnostic():
|
||||
cfg = LiberoEnv()
|
||||
pre, post = make_env_pre_post_processors(cfg, policy_cfg=object())
|
||||
|
||||
assert isinstance(pre.steps[0], LiberoProcessorStep)
|
||||
assert len(post.steps) == 0
|
||||
|
||||
|
||||
def test_libero_processor_flattens_state_to_raw_8_dim():
|
||||
step = LiberoProcessorStep()
|
||||
observation = {
|
||||
OBS_PREFIX + "robot_state": {
|
||||
"eef": {
|
||||
"pos": torch.tensor([[1.0, 2.0, 3.0]]),
|
||||
"quat": torch.tensor([[0.0, 0.0, 0.0, 1.0]]),
|
||||
},
|
||||
"gripper": {"qpos": torch.tensor([[4.0, 5.0]])},
|
||||
}
|
||||
}
|
||||
|
||||
state = step.observation(observation)[OBS_STATE]
|
||||
assert state.shape == (1, 8)
|
||||
assert torch.allclose(state, torch.tensor([[1.0, 2.0, 3.0, 0.0, 0.0, 0.0, 4.0, 5.0]]))
|
||||
|
||||
|
||||
def test_base_create_envs():
|
||||
"""Base class create_envs() should build a single-task VectorEnv via gym.make()."""
|
||||
gym_id = "_dispatch_test/CartPole-v99"
|
||||
|
||||
@@ -1,840 +0,0 @@
|
||||
#!/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.
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import pytest
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
import lerobot.policies.evo1.evo1_model as evo1_model
|
||||
import lerobot.policies.evo1.modeling_evo1 as modeling_evo1
|
||||
from lerobot.configs.types import FeatureType, PolicyFeature
|
||||
from lerobot.policies.evo1.configuration_evo1 import Evo1Config
|
||||
from lerobot.policies.evo1.flow_matching import FlowmatchingActionHead
|
||||
from lerobot.policies.evo1.internvl3_embedder import (
|
||||
IMAGENET_MEAN,
|
||||
IMAGENET_STD,
|
||||
_batched_pixel_values,
|
||||
)
|
||||
from lerobot.policies.evo1.processor_evo1 import (
|
||||
Evo1ActionProcessorStep,
|
||||
Evo1PadActionProcessorStep,
|
||||
Evo1PadStateProcessorStep,
|
||||
evo1_batch_to_transition,
|
||||
make_evo1_pre_post_processors,
|
||||
reconcile_evo1_processors,
|
||||
)
|
||||
from lerobot.policies.factory import get_policy_class, make_policy_config
|
||||
from lerobot.policies.rtc.configuration_rtc import RTCConfig
|
||||
from lerobot.policies.rtc.modeling_rtc import RTCProcessor
|
||||
from lerobot.processor import (
|
||||
DeviceProcessorStep,
|
||||
NormalizerProcessorStep,
|
||||
PolicyProcessorPipeline,
|
||||
UnnormalizerProcessorStep,
|
||||
)
|
||||
from lerobot.processor.converters import (
|
||||
batch_to_transition,
|
||||
policy_action_to_transition,
|
||||
transition_to_batch,
|
||||
transition_to_policy_action,
|
||||
)
|
||||
from lerobot.utils.constants import (
|
||||
ACTION,
|
||||
OBS_IMAGES,
|
||||
OBS_STATE,
|
||||
POLICY_POSTPROCESSOR_DEFAULT_NAME,
|
||||
POLICY_PREPROCESSOR_DEFAULT_NAME,
|
||||
)
|
||||
|
||||
STATE_DIM = 4
|
||||
ACTION_DIM = 3
|
||||
MAX_STATE_DIM = 6
|
||||
MAX_ACTION_DIM = 5
|
||||
CHUNK_SIZE = 2
|
||||
EMBED_DIM = 8
|
||||
|
||||
|
||||
class DummyEvo1Model(nn.Module):
|
||||
def __init__(self, config, vlm_hub_kwargs=None):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.embedder = nn.Dropout(p=0.0)
|
||||
self.action_head = nn.Linear(1, 1)
|
||||
self.get_vl_embeddings_calls = 0
|
||||
self.grad_enabled_calls = []
|
||||
self.embedder_training_calls = []
|
||||
|
||||
def set_finetune_flags(self):
|
||||
return None
|
||||
|
||||
def get_vl_embeddings(self, images, image_mask, prompt=None, return_cls_only=False):
|
||||
self.get_vl_embeddings_calls += 1
|
||||
self.grad_enabled_calls.append(torch.is_grad_enabled())
|
||||
self.embedder_training_calls.append(self.embedder.training)
|
||||
# images is a list of per-camera (B, C, H, W) tensors, so the batch dim is images[0].shape[0].
|
||||
batch_size = images[0].shape[0]
|
||||
tokens = torch.ones(batch_size, 4, EMBED_DIM, requires_grad=torch.is_grad_enabled())
|
||||
valid_mask = torch.ones(batch_size, 4, dtype=torch.bool)
|
||||
return tokens, valid_mask
|
||||
|
||||
def forward(
|
||||
self,
|
||||
fused_tokens,
|
||||
state=None,
|
||||
actions_gt=None,
|
||||
action_mask=None,
|
||||
embodiment_ids=None,
|
||||
context_mask=None,
|
||||
**kwargs,
|
||||
):
|
||||
batch_size = fused_tokens.shape[0]
|
||||
if actions_gt is None:
|
||||
return torch.ones(batch_size, CHUNK_SIZE * MAX_ACTION_DIM)
|
||||
pred_velocity = torch.zeros(batch_size, CHUNK_SIZE * MAX_ACTION_DIM)
|
||||
noise = torch.zeros_like(actions_gt)
|
||||
return pred_velocity, noise
|
||||
|
||||
|
||||
class ChunkCountingDummyModel(DummyEvo1Model):
|
||||
"""Emits per-step distinguishable actions so queue ordering and re-prediction are observable."""
|
||||
|
||||
def __init__(self, config, vlm_hub_kwargs=None):
|
||||
super().__init__(config, vlm_hub_kwargs)
|
||||
self.chunks_predicted = 0
|
||||
|
||||
def forward(
|
||||
self,
|
||||
fused_tokens,
|
||||
state=None,
|
||||
actions_gt=None,
|
||||
action_mask=None,
|
||||
embodiment_ids=None,
|
||||
context_mask=None,
|
||||
**kwargs,
|
||||
):
|
||||
if actions_gt is not None:
|
||||
return super().forward(fused_tokens, state, actions_gt, action_mask, embodiment_ids, context_mask)
|
||||
self.chunks_predicted += 1
|
||||
batch_size = fused_tokens.shape[0]
|
||||
step_values = torch.arange(CHUNK_SIZE, dtype=torch.float32) + 10.0 * self.chunks_predicted
|
||||
chunk = step_values.repeat_interleave(MAX_ACTION_DIM).unsqueeze(0).repeat(batch_size, 1)
|
||||
return chunk
|
||||
|
||||
|
||||
def make_config(training_stage="stage1", **kwargs):
|
||||
config_kwargs = {
|
||||
"device": "cpu",
|
||||
"vlm_model_name": "dummy-internvl3",
|
||||
"training_stage": training_stage,
|
||||
"chunk_size": CHUNK_SIZE,
|
||||
"n_action_steps": 1,
|
||||
"max_state_dim": MAX_STATE_DIM,
|
||||
"max_action_dim": MAX_ACTION_DIM,
|
||||
"max_views": 2,
|
||||
"embed_dim": EMBED_DIM,
|
||||
"hidden_dim": 16,
|
||||
"state_hidden_dim": 16,
|
||||
"num_heads": 2,
|
||||
"num_layers": 1,
|
||||
"num_inference_timesteps": 2,
|
||||
"input_features": {
|
||||
OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(STATE_DIM,)),
|
||||
f"{OBS_IMAGES}.front": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 16, 16)),
|
||||
},
|
||||
"output_features": {
|
||||
ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(ACTION_DIM,)),
|
||||
},
|
||||
}
|
||||
config_kwargs.update(kwargs)
|
||||
return Evo1Config(**config_kwargs)
|
||||
|
||||
|
||||
def make_batch(include_action=True):
|
||||
batch = {
|
||||
"task": ["pick the block", "place the block"],
|
||||
OBS_STATE: torch.randn(2, STATE_DIM),
|
||||
f"{OBS_IMAGES}.front": torch.rand(2, 3, 16, 16),
|
||||
}
|
||||
if include_action:
|
||||
batch[ACTION] = torch.randn(2, CHUNK_SIZE, ACTION_DIM)
|
||||
return batch
|
||||
|
||||
|
||||
def make_stats(state_dim=STATE_DIM, action_dim=ACTION_DIM):
|
||||
return {
|
||||
OBS_STATE: {
|
||||
"min": torch.full((state_dim,), -2.0),
|
||||
"max": torch.full((state_dim,), 2.0),
|
||||
},
|
||||
ACTION: {
|
||||
"min": torch.full((action_dim,), -1.0),
|
||||
"max": torch.full((action_dim,), 1.0),
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def make_flowmatching_head(**overrides):
|
||||
kwargs = {
|
||||
"embed_dim": EMBED_DIM,
|
||||
"hidden_dim": 16,
|
||||
"action_dim": CHUNK_SIZE * ACTION_DIM,
|
||||
"horizon": CHUNK_SIZE,
|
||||
"per_action_dim": ACTION_DIM,
|
||||
"num_heads": 2,
|
||||
"num_layers": 1,
|
||||
"num_inference_timesteps": 2,
|
||||
"state_dim": STATE_DIM,
|
||||
"state_hidden_dim": 16,
|
||||
"num_categories": 1,
|
||||
}
|
||||
kwargs.update(overrides)
|
||||
return FlowmatchingActionHead(**kwargs)
|
||||
|
||||
|
||||
def test_evo1_factory_registration():
|
||||
cfg = make_policy_config(
|
||||
"evo1",
|
||||
device="cpu",
|
||||
vlm_model_name="dummy-internvl3",
|
||||
input_features={
|
||||
OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(STATE_DIM,)),
|
||||
f"{OBS_IMAGES}.front": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 16, 16)),
|
||||
},
|
||||
output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(ACTION_DIM,))},
|
||||
)
|
||||
|
||||
assert isinstance(cfg, Evo1Config)
|
||||
assert get_policy_class("evo1") is modeling_evo1.Evo1Policy
|
||||
|
||||
|
||||
def test_evo1_stage_defaults_and_consistency():
|
||||
stage1 = make_config(training_stage="stage1")
|
||||
assert (stage1.finetune_vlm, stage1.finetune_language_model, stage1.finetune_vision_model) == (
|
||||
False,
|
||||
False,
|
||||
False,
|
||||
)
|
||||
assert stage1.finetune_action_head is True
|
||||
|
||||
stage2 = make_config(training_stage="stage2")
|
||||
assert (stage2.finetune_vlm, stage2.finetune_language_model, stage2.finetune_vision_model) == (
|
||||
True,
|
||||
True,
|
||||
True,
|
||||
)
|
||||
assert stage2.finetune_action_head is True
|
||||
|
||||
stage2_from_stage1_checkpoint_flags = make_config(
|
||||
training_stage="stage2",
|
||||
finetune_vlm=False,
|
||||
finetune_language_model=False,
|
||||
finetune_vision_model=False,
|
||||
finetune_action_head=False,
|
||||
)
|
||||
assert (
|
||||
stage2_from_stage1_checkpoint_flags.finetune_vlm,
|
||||
stage2_from_stage1_checkpoint_flags.finetune_language_model,
|
||||
stage2_from_stage1_checkpoint_flags.finetune_vision_model,
|
||||
) == (
|
||||
True,
|
||||
True,
|
||||
True,
|
||||
)
|
||||
assert stage2_from_stage1_checkpoint_flags.finetune_action_head is True
|
||||
|
||||
explicit_off = make_config(
|
||||
training_stage="stage2",
|
||||
apply_training_stage_defaults=False,
|
||||
finetune_vlm=False,
|
||||
finetune_language_model=False,
|
||||
finetune_vision_model=False,
|
||||
finetune_action_head=False,
|
||||
)
|
||||
assert (
|
||||
explicit_off.finetune_vlm,
|
||||
explicit_off.finetune_language_model,
|
||||
explicit_off.finetune_vision_model,
|
||||
) == (
|
||||
False,
|
||||
False,
|
||||
False,
|
||||
)
|
||||
assert explicit_off.finetune_action_head is False
|
||||
|
||||
# An explicit finetune_vlm=False without branch-level flags freezes both branches instead of
|
||||
# raising an inconsistency error.
|
||||
frozen_vlm = make_config(
|
||||
training_stage="stage2",
|
||||
apply_training_stage_defaults=False,
|
||||
finetune_vlm=False,
|
||||
)
|
||||
assert (
|
||||
frozen_vlm.finetune_vlm,
|
||||
frozen_vlm.finetune_language_model,
|
||||
frozen_vlm.finetune_vision_model,
|
||||
) == (False, False, False)
|
||||
|
||||
try:
|
||||
make_config(
|
||||
training_stage="stage2",
|
||||
apply_training_stage_defaults=False,
|
||||
finetune_vlm=True,
|
||||
finetune_language_model=False,
|
||||
)
|
||||
except ValueError as exc:
|
||||
assert "Inconsistent EVO1 finetune config" in str(exc)
|
||||
else:
|
||||
raise AssertionError("Expected inconsistent finetune config to raise ValueError")
|
||||
|
||||
|
||||
def test_evo1_rejects_non_square_image_resolution():
|
||||
with pytest.raises(ValueError, match="square image_resolution"):
|
||||
make_config(image_resolution=(448, 320))
|
||||
|
||||
|
||||
def test_evo1_rejects_out_of_range_default_embodiment_id():
|
||||
with pytest.raises(ValueError, match="default_embodiment_id"):
|
||||
make_config(default_embodiment_id=3, num_categories=2)
|
||||
|
||||
|
||||
def test_evo1_model_uses_image_resolution_and_trainable_checkpointing(monkeypatch):
|
||||
captured: dict = {}
|
||||
|
||||
class SpyEmbedder(nn.Module):
|
||||
def __init__(self, **kwargs):
|
||||
super().__init__()
|
||||
captured.clear()
|
||||
captured.update(kwargs)
|
||||
|
||||
monkeypatch.setattr(evo1_model, "InternVL3Embedder", SpyEmbedder)
|
||||
|
||||
stage1 = make_config(training_stage="stage1", image_resolution=(224, 224))
|
||||
evo1_model.Evo1Model(stage1)
|
||||
assert captured["image_size"] == 224
|
||||
# VLM is frozen in stage1, so gradient checkpointing is gated off.
|
||||
assert captured["enable_gradient_checkpointing"] is False
|
||||
|
||||
stage2 = make_config(training_stage="stage2", image_resolution=(224, 224))
|
||||
evo1_model.Evo1Model(stage2)
|
||||
assert captured["enable_gradient_checkpointing"] is True
|
||||
|
||||
|
||||
class FakeInternVLModel(nn.Module):
|
||||
"""Minimal stand-in with the native HF InternVL submodule layout."""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.language_model = nn.Linear(2, 2)
|
||||
self.vision_tower = nn.Linear(2, 2)
|
||||
self.multi_modal_projector = nn.Linear(2, 2)
|
||||
|
||||
|
||||
class FakeEmbedder(nn.Module):
|
||||
def __init__(self, **kwargs):
|
||||
super().__init__()
|
||||
self.model = FakeInternVLModel()
|
||||
|
||||
|
||||
def test_set_finetune_flags_targets_native_hf_internvl_submodules(monkeypatch):
|
||||
monkeypatch.setattr(evo1_model, "InternVL3Embedder", FakeEmbedder)
|
||||
|
||||
stage2_model = evo1_model.Evo1Model(make_config(training_stage="stage2"))
|
||||
stage2_model.set_finetune_flags()
|
||||
vlm = stage2_model.embedder.model
|
||||
assert all(p.requires_grad for p in vlm.language_model.parameters())
|
||||
assert all(p.requires_grad for p in vlm.vision_tower.parameters())
|
||||
assert all(p.requires_grad for p in vlm.multi_modal_projector.parameters())
|
||||
assert all(p.requires_grad for p in stage2_model.action_head.parameters())
|
||||
|
||||
stage1_model = evo1_model.Evo1Model(make_config(training_stage="stage1"))
|
||||
stage1_model.set_finetune_flags()
|
||||
vlm = stage1_model.embedder.model
|
||||
assert not any(p.requires_grad for p in vlm.parameters())
|
||||
assert all(p.requires_grad for p in stage1_model.action_head.parameters())
|
||||
|
||||
|
||||
def test_set_finetune_flags_fails_loudly_on_unknown_vlm_layout(monkeypatch):
|
||||
class LegacyLayoutModel(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.language_model = nn.Linear(2, 2)
|
||||
self.vision_model = nn.Linear(2, 2) # trust_remote_code-era attribute name
|
||||
self.mlp1 = nn.Linear(2, 2)
|
||||
|
||||
class FakeEmbedder(nn.Module):
|
||||
def __init__(self, **kwargs):
|
||||
super().__init__()
|
||||
self.model = LegacyLayoutModel()
|
||||
|
||||
monkeypatch.setattr(evo1_model, "InternVL3Embedder", FakeEmbedder)
|
||||
model = evo1_model.Evo1Model(make_config(training_stage="stage2"))
|
||||
with pytest.raises(AttributeError, match="vision_tower"):
|
||||
model.set_finetune_flags()
|
||||
|
||||
|
||||
def test_evo1_policy_processors_pad_state_crop_action_and_binarize_gripper():
|
||||
libero_action_dim = 7
|
||||
config = make_config(
|
||||
max_state_dim=MAX_STATE_DIM,
|
||||
max_action_dim=8,
|
||||
postprocess_action_dim=libero_action_dim,
|
||||
binarize_gripper=True,
|
||||
output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(libero_action_dim,))},
|
||||
)
|
||||
stats = make_stats(action_dim=libero_action_dim)
|
||||
|
||||
preprocessor, postprocessor = make_evo1_pre_post_processors(config, dataset_stats=stats)
|
||||
|
||||
assert isinstance(preprocessor.steps[2], Evo1PadStateProcessorStep)
|
||||
assert isinstance(preprocessor.steps[3], Evo1PadActionProcessorStep)
|
||||
assert isinstance(preprocessor.steps[4], NormalizerProcessorStep)
|
||||
assert isinstance(postprocessor.steps[0], UnnormalizerProcessorStep)
|
||||
assert isinstance(postprocessor.steps[1], Evo1ActionProcessorStep)
|
||||
|
||||
normalizer = preprocessor.steps[4]
|
||||
assert normalizer.features[OBS_STATE].shape == (MAX_STATE_DIM,)
|
||||
assert normalizer.features[ACTION].shape == (8,)
|
||||
assert normalizer._tensor_stats[OBS_STATE]["min"].shape == (MAX_STATE_DIM,)
|
||||
assert normalizer._tensor_stats[ACTION]["min"].shape == (8,)
|
||||
|
||||
processed_batch = preprocessor(
|
||||
{
|
||||
"task": "pick the block",
|
||||
OBS_STATE: torch.zeros(STATE_DIM),
|
||||
ACTION: torch.zeros(libero_action_dim),
|
||||
f"{OBS_IMAGES}.front": torch.rand(3, 16, 16),
|
||||
}
|
||||
)
|
||||
processed_state = processed_batch[OBS_STATE]
|
||||
assert processed_state.shape == (1, MAX_STATE_DIM)
|
||||
assert torch.allclose(processed_state, torch.zeros_like(processed_state))
|
||||
assert processed_batch[ACTION].shape == (1, 8)
|
||||
assert torch.allclose(processed_batch[ACTION], torch.zeros_like(processed_batch[ACTION]))
|
||||
assert processed_batch["action_mask"].shape == (1, 8)
|
||||
assert processed_batch["action_mask"][:, :libero_action_dim].all()
|
||||
assert not processed_batch["action_mask"][:, libero_action_dim:].any()
|
||||
|
||||
action = torch.tensor(
|
||||
[
|
||||
[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.5, 0.7],
|
||||
[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7],
|
||||
],
|
||||
dtype=torch.float32,
|
||||
)
|
||||
processed = postprocessor(action)
|
||||
|
||||
assert processed.shape == (2, 7)
|
||||
assert processed.dtype == torch.float32
|
||||
assert torch.allclose(processed[:, :6], action[:, :6])
|
||||
assert torch.equal(processed[:, 6], torch.tensor([1.0, -1.0]))
|
||||
|
||||
|
||||
def test_evo1_postprocessor_returns_float32_for_bf16_actions():
|
||||
config = make_config()
|
||||
_preprocessor, postprocessor = make_evo1_pre_post_processors(config, dataset_stats=make_stats())
|
||||
|
||||
processed = postprocessor(torch.zeros(2, MAX_ACTION_DIM, dtype=torch.bfloat16))
|
||||
assert processed.dtype == torch.float32
|
||||
|
||||
|
||||
def test_evo1_processor_save_load_round_trip_applies_config_overrides(tmp_path):
|
||||
train_config = make_config()
|
||||
preprocessor, postprocessor = make_evo1_pre_post_processors(train_config, dataset_stats=make_stats())
|
||||
preprocessor.save_pretrained(tmp_path)
|
||||
postprocessor.save_pretrained(tmp_path)
|
||||
|
||||
loaded_pre = PolicyProcessorPipeline.from_pretrained(
|
||||
tmp_path,
|
||||
config_filename=f"{POLICY_PREPROCESSOR_DEFAULT_NAME}.json",
|
||||
to_transition=batch_to_transition,
|
||||
to_output=transition_to_batch,
|
||||
)
|
||||
loaded_post = PolicyProcessorPipeline.from_pretrained(
|
||||
tmp_path,
|
||||
config_filename=f"{POLICY_POSTPROCESSOR_DEFAULT_NAME}.json",
|
||||
to_transition=policy_action_to_transition,
|
||||
to_output=transition_to_policy_action,
|
||||
)
|
||||
|
||||
# Simulate eval-time CLI overrides applied on top of the loaded pipelines.
|
||||
eval_config = make_config(binarize_gripper=True, postprocess_action_dim=ACTION_DIM)
|
||||
loaded_pre, loaded_post = reconcile_evo1_processors(eval_config, loaded_pre, loaded_post)
|
||||
|
||||
assert loaded_pre.to_transition is evo1_batch_to_transition
|
||||
assert sum(isinstance(step, Evo1ActionProcessorStep) for step in loaded_post.steps) == 1
|
||||
action_step = next(step for step in loaded_post.steps if isinstance(step, Evo1ActionProcessorStep))
|
||||
assert action_step.binarize_gripper is True
|
||||
assert action_step.action_dim == ACTION_DIM
|
||||
# The float32 output dtype is part of the serialized pipeline itself.
|
||||
device_step = next(step for step in loaded_post.steps if isinstance(step, DeviceProcessorStep))
|
||||
assert device_step.float_dtype == "float32"
|
||||
|
||||
# Non-observation extras (embodiment_id, ...) must survive the reloaded preprocessor.
|
||||
processed = loaded_pre(
|
||||
{
|
||||
"task": "pick the block",
|
||||
OBS_STATE: torch.zeros(STATE_DIM),
|
||||
f"{OBS_IMAGES}.front": torch.rand(3, 16, 16),
|
||||
"embodiment_id": torch.tensor([0]),
|
||||
}
|
||||
)
|
||||
assert "embodiment_id" in processed
|
||||
|
||||
|
||||
def test_evo1_policy_forward_and_inference_use_batched_embedding(monkeypatch):
|
||||
monkeypatch.setattr(modeling_evo1, "Evo1Model", DummyEvo1Model)
|
||||
policy = modeling_evo1.Evo1Policy(make_config())
|
||||
preprocessor, _postprocessor = make_evo1_pre_post_processors(policy.config, dataset_stats=make_stats())
|
||||
training_batch = preprocessor(make_batch(include_action=True))
|
||||
|
||||
assert training_batch[ACTION].shape == (2, CHUNK_SIZE, MAX_ACTION_DIM)
|
||||
assert training_batch["action_mask"].shape == (2, CHUNK_SIZE, MAX_ACTION_DIM)
|
||||
assert training_batch["action_mask"][:, :, :ACTION_DIM].all()
|
||||
assert not training_batch["action_mask"][:, :, ACTION_DIM:].any()
|
||||
|
||||
loss, metrics = policy.forward(training_batch)
|
||||
assert loss.ndim == 0
|
||||
assert torch.isfinite(loss)
|
||||
assert metrics["active_action_dims"] == ACTION_DIM * CHUNK_SIZE
|
||||
assert policy.model.get_vl_embeddings_calls == 1
|
||||
|
||||
action_chunk = policy.predict_action_chunk(make_batch(include_action=False))
|
||||
assert action_chunk.shape == (2, CHUNK_SIZE, MAX_ACTION_DIM)
|
||||
assert action_chunk.dtype == torch.float32
|
||||
|
||||
policy.reset()
|
||||
selected = policy.select_action(make_batch(include_action=False))
|
||||
assert selected.shape == (2, MAX_ACTION_DIM)
|
||||
|
||||
|
||||
def test_evo1_forward_masks_padded_action_timesteps(monkeypatch):
|
||||
monkeypatch.setattr(modeling_evo1, "Evo1Model", DummyEvo1Model)
|
||||
policy = modeling_evo1.Evo1Policy(make_config())
|
||||
|
||||
batch = make_batch(include_action=True)
|
||||
batch[ACTION] = torch.ones(2, CHUNK_SIZE, ACTION_DIM)
|
||||
# Give the padded (past-episode-end) timestep a huge value: if it leaked into the loss, the
|
||||
# loss would blow up far beyond 1.0.
|
||||
batch[ACTION][:, -1, :] = 100.0
|
||||
batch["action_is_pad"] = torch.zeros(2, CHUNK_SIZE, dtype=torch.bool)
|
||||
batch["action_is_pad"][:, -1] = True
|
||||
|
||||
loss, metrics = policy.forward(batch)
|
||||
|
||||
# DummyEvo1Model predicts zero velocity and zero noise, so each active element contributes
|
||||
# (0 - action)^2 = 1.0 for the in-episode ones-valued actions.
|
||||
assert metrics["active_action_dims"] == ACTION_DIM * (CHUNK_SIZE - 1)
|
||||
assert torch.isclose(loss, torch.tensor(1.0))
|
||||
|
||||
|
||||
def test_evo1_select_action_queue_orders_steps_and_repredicts(monkeypatch):
|
||||
monkeypatch.setattr(modeling_evo1, "Evo1Model", ChunkCountingDummyModel)
|
||||
policy = modeling_evo1.Evo1Policy(make_config(n_action_steps=CHUNK_SIZE))
|
||||
|
||||
batch = make_batch(include_action=False)
|
||||
first = policy.select_action(batch)
|
||||
second = policy.select_action(batch)
|
||||
third = policy.select_action(batch)
|
||||
|
||||
# First chunk provides steps 10, 11 in order; the third call triggers a fresh prediction (20).
|
||||
assert torch.all(first == 10.0)
|
||||
assert torch.all(second == 11.0)
|
||||
assert torch.all(third == 20.0)
|
||||
assert policy.model.chunks_predicted == 2
|
||||
|
||||
|
||||
def test_evo1_predict_action_chunk_rejects_rtc_kwargs_without_rtc_config(monkeypatch):
|
||||
monkeypatch.setattr(modeling_evo1, "Evo1Model", DummyEvo1Model)
|
||||
policy = modeling_evo1.Evo1Policy(make_config())
|
||||
with pytest.raises(RuntimeError, match="RTC"):
|
||||
policy.predict_action_chunk(make_batch(include_action=False), inference_delay=2)
|
||||
|
||||
|
||||
def test_evo1_rtc_processor_wiring(monkeypatch):
|
||||
monkeypatch.setattr(evo1_model, "InternVL3Embedder", FakeEmbedder)
|
||||
policy = modeling_evo1.Evo1Policy(make_config())
|
||||
assert policy.rtc_processor is None
|
||||
assert policy.model.rtc_processor is None
|
||||
|
||||
# The RTC rollout backend assigns rtc_config after loading and re-inits the processor.
|
||||
policy.config.rtc_config = RTCConfig(execution_horizon=CHUNK_SIZE)
|
||||
policy.init_rtc_processor()
|
||||
assert isinstance(policy.rtc_processor, RTCProcessor)
|
||||
assert policy.model.rtc_processor is policy.rtc_processor
|
||||
|
||||
# RTC drives predict_action_chunk directly; the select_action queue path is unsupported.
|
||||
with pytest.raises(AssertionError, match="select_action"):
|
||||
policy.select_action(make_batch(include_action=False))
|
||||
|
||||
|
||||
def test_flowmatching_rtc_guidance_pulls_prefix_toward_previous_chunk():
|
||||
head = make_flowmatching_head(num_inference_timesteps=16)
|
||||
processor = RTCProcessor(RTCConfig(execution_horizon=CHUNK_SIZE))
|
||||
fused = torch.randn(2, 4, EMBED_DIM)
|
||||
state = torch.randn(2, STATE_DIM)
|
||||
action_mask = torch.ones(2, ACTION_DIM, dtype=torch.bool)
|
||||
prev_chunk = torch.tensor([0.7, -0.4, 0.2]).expand(2, CHUNK_SIZE, ACTION_DIM).contiguous()
|
||||
|
||||
torch.manual_seed(0)
|
||||
unguided = head.get_action(fused, state=state, action_mask=action_mask)
|
||||
unguided = unguided.view(2, CHUNK_SIZE, ACTION_DIM)
|
||||
torch.manual_seed(0)
|
||||
guided = head.get_action(
|
||||
fused,
|
||||
state=state,
|
||||
action_mask=action_mask,
|
||||
inference_delay=1,
|
||||
prev_chunk_left_over=prev_chunk,
|
||||
rtc_processor=processor,
|
||||
)
|
||||
guided = guided.view(2, CHUNK_SIZE, ACTION_DIM)
|
||||
|
||||
# The frozen prefix (first inference_delay steps) must land far closer to the previous chunk
|
||||
# than the unguided sample from the same noise does.
|
||||
guided_dist = (guided[:, 0] - prev_chunk[:, 0]).abs().mean()
|
||||
unguided_dist = (unguided[:, 0] - prev_chunk[:, 0]).abs().mean()
|
||||
assert guided_dist < 0.5 * unguided_dist
|
||||
assert torch.isfinite(guided).all()
|
||||
|
||||
|
||||
def test_flowmatching_rtc_first_chunk_without_leftover_matches_unguided():
|
||||
head = make_flowmatching_head(num_inference_timesteps=4)
|
||||
processor = RTCProcessor(RTCConfig(execution_horizon=CHUNK_SIZE))
|
||||
fused = torch.randn(2, 4, EMBED_DIM)
|
||||
state = torch.randn(2, STATE_DIM)
|
||||
action_mask = torch.ones(2, ACTION_DIM, dtype=torch.bool)
|
||||
|
||||
torch.manual_seed(0)
|
||||
unguided = head.get_action(fused, state=state, action_mask=action_mask)
|
||||
torch.manual_seed(0)
|
||||
first_chunk = head.get_action(
|
||||
fused,
|
||||
state=state,
|
||||
action_mask=action_mask,
|
||||
inference_delay=2,
|
||||
prev_chunk_left_over=None,
|
||||
rtc_processor=processor,
|
||||
)
|
||||
|
||||
assert torch.allclose(unguided, first_chunk)
|
||||
|
||||
|
||||
def test_evo1_missing_configured_camera_needs_empty_cameras_budget(monkeypatch):
|
||||
monkeypatch.setattr(modeling_evo1, "Evo1Model", DummyEvo1Model)
|
||||
batch = make_batch(include_action=False) # only provides the front camera
|
||||
|
||||
two_camera_features = {
|
||||
OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(STATE_DIM,)),
|
||||
f"{OBS_IMAGES}.front": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 16, 16)),
|
||||
f"{OBS_IMAGES}.wrist": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 16, 16)),
|
||||
}
|
||||
strict_policy = modeling_evo1.Evo1Policy(make_config(input_features=dict(two_camera_features)))
|
||||
with pytest.raises(ValueError, match="empty_cameras"):
|
||||
strict_policy._collect_image_batches(batch)
|
||||
|
||||
# empty_cameras adds placeholder camera features that are never present in the batch; they
|
||||
# become masked-out views instead of crashing with a KeyError.
|
||||
padded_policy = modeling_evo1.Evo1Policy(make_config(empty_cameras=1))
|
||||
assert len(padded_policy.config.image_features) == 2
|
||||
camera_images, image_masks = padded_policy._collect_image_batches(batch)
|
||||
assert len(camera_images) == 1
|
||||
assert image_masks.tolist() == [[True, False], [True, False]]
|
||||
|
||||
|
||||
def test_stage1_frozen_vlm_embeddings_do_not_track_gradients(monkeypatch):
|
||||
monkeypatch.setattr(modeling_evo1, "Evo1Model", DummyEvo1Model)
|
||||
policy = modeling_evo1.Evo1Policy(make_config(training_stage="stage1"))
|
||||
policy.train()
|
||||
|
||||
image_batches, image_masks = policy._collect_image_batches(make_batch(include_action=False))
|
||||
fused_tokens, context_mask = policy._compute_fused_tokens(["pick", "place"], image_batches, image_masks)
|
||||
|
||||
assert policy.model.grad_enabled_calls == [False]
|
||||
assert policy.model.embedder_training_calls == [False]
|
||||
assert not fused_tokens.requires_grad
|
||||
assert context_mask is not None
|
||||
assert policy.model.embedder.training is False
|
||||
|
||||
|
||||
def test_stage2_vlm_embeddings_track_gradients(monkeypatch):
|
||||
monkeypatch.setattr(modeling_evo1, "Evo1Model", DummyEvo1Model)
|
||||
policy = modeling_evo1.Evo1Policy(make_config(training_stage="stage2"))
|
||||
policy.train()
|
||||
|
||||
image_batches, image_masks = policy._collect_image_batches(make_batch(include_action=False))
|
||||
fused_tokens, _context_mask = policy._compute_fused_tokens(["pick", "place"], image_batches, image_masks)
|
||||
|
||||
assert policy.model.grad_enabled_calls == [True]
|
||||
assert policy.model.embedder_training_calls == [True]
|
||||
assert fused_tokens.requires_grad
|
||||
|
||||
|
||||
def test_collect_image_batches_handles_unbatched_chw(monkeypatch):
|
||||
# Regression for an issue where batch_size was read from shape[0] before normalizing
|
||||
# per-camera tensor dims, so an unbatched (C, H, W) input was treated as batch_size=C.
|
||||
monkeypatch.setattr(modeling_evo1, "Evo1Model", DummyEvo1Model)
|
||||
policy = modeling_evo1.Evo1Policy(make_config())
|
||||
batch = {
|
||||
OBS_STATE: torch.randn(1, STATE_DIM),
|
||||
f"{OBS_IMAGES}.front": torch.rand(3, 16, 16),
|
||||
}
|
||||
|
||||
camera_images, image_masks = policy._collect_image_batches(batch)
|
||||
|
||||
# One present camera, returned as a batched (B, C, H, W) tensor with the unbatched CHW frame
|
||||
# promoted to batch_size=1 (not read as batch_size=C).
|
||||
assert len(camera_images) == 1
|
||||
assert camera_images[0].shape == (1, 3, 16, 16)
|
||||
assert image_masks.tolist() == [[True, False]]
|
||||
|
||||
|
||||
def test_evo1_state_mask_zeroes_masked_dims(monkeypatch):
|
||||
monkeypatch.setattr(modeling_evo1, "Evo1Model", DummyEvo1Model)
|
||||
policy = modeling_evo1.Evo1Policy(make_config())
|
||||
batch = {
|
||||
OBS_STATE: torch.ones(2, STATE_DIM),
|
||||
"state_mask": torch.tensor([[True, True, False, False]] * 2),
|
||||
}
|
||||
|
||||
states, mask = policy._prepare_state(batch)
|
||||
|
||||
assert torch.all(states[:, :2] == 1.0)
|
||||
assert torch.all(states[:, 2:] == 0.0)
|
||||
assert mask[:, :2].all()
|
||||
assert not mask[:, 2:].any()
|
||||
|
||||
|
||||
def test_evo1_action_mask_accepts_chunk_size_one(monkeypatch):
|
||||
monkeypatch.setattr(modeling_evo1, "Evo1Model", DummyEvo1Model)
|
||||
config = make_config(chunk_size=1, n_action_steps=1)
|
||||
policy = modeling_evo1.Evo1Policy(config)
|
||||
batch = make_batch(include_action=True)
|
||||
batch[ACTION] = torch.randn(2, ACTION_DIM)
|
||||
batch["action_mask"] = torch.ones(2, ACTION_DIM, dtype=torch.bool)
|
||||
|
||||
actions, action_mask = policy._prepare_actions(batch)
|
||||
|
||||
assert actions.shape == (2, 1, MAX_ACTION_DIM)
|
||||
assert action_mask.shape == (2, 1, MAX_ACTION_DIM)
|
||||
assert action_mask[:, :, :ACTION_DIM].all()
|
||||
assert not action_mask[:, :, ACTION_DIM:].any()
|
||||
|
||||
|
||||
def test_flowmatching_state_encoder_for_horizon_one():
|
||||
head = make_flowmatching_head(action_dim=ACTION_DIM, horizon=1)
|
||||
|
||||
assert head.state_encoder is not None
|
||||
pred_velocity, noise = head(
|
||||
torch.randn(2, 4, EMBED_DIM),
|
||||
state=torch.randn(2, STATE_DIM),
|
||||
actions_gt=torch.randn(2, 1, ACTION_DIM),
|
||||
action_mask=torch.ones(2, 1, ACTION_DIM, dtype=torch.bool),
|
||||
)
|
||||
|
||||
assert pred_velocity.shape == (2, ACTION_DIM)
|
||||
assert noise.shape == (2, 1, ACTION_DIM)
|
||||
|
||||
|
||||
def test_flowmatching_get_action_real_path_respects_action_mask():
|
||||
torch.manual_seed(0)
|
||||
head = make_flowmatching_head()
|
||||
|
||||
action_mask = torch.zeros(2, ACTION_DIM, dtype=torch.bool)
|
||||
action_mask[:, :2] = True
|
||||
actions = head.get_action(
|
||||
torch.randn(2, 4, EMBED_DIM),
|
||||
state=torch.randn(2, STATE_DIM),
|
||||
action_mask=action_mask,
|
||||
)
|
||||
|
||||
assert actions.shape == (2, CHUNK_SIZE * ACTION_DIM)
|
||||
assert torch.isfinite(actions).all()
|
||||
action_seq = actions.view(2, CHUNK_SIZE, ACTION_DIM)
|
||||
assert torch.all(action_seq[..., 2] == 0.0)
|
||||
|
||||
|
||||
def test_flowmatching_context_mask_blocks_masked_context_tokens():
|
||||
head = make_flowmatching_head()
|
||||
state = torch.randn(2, STATE_DIM)
|
||||
action_mask = torch.ones(2, ACTION_DIM, dtype=torch.bool)
|
||||
fused = torch.randn(2, 4, EMBED_DIM)
|
||||
context_mask = torch.ones(2, 4, dtype=torch.bool)
|
||||
context_mask[:, -1] = False
|
||||
corrupted = fused.clone()
|
||||
corrupted[:, -1] = 1e4
|
||||
|
||||
torch.manual_seed(0)
|
||||
reference = head.get_action(fused, state=state, action_mask=action_mask, context_mask=context_mask)
|
||||
torch.manual_seed(0)
|
||||
with_garbage = head.get_action(corrupted, state=state, action_mask=action_mask, context_mask=context_mask)
|
||||
|
||||
assert torch.allclose(reference, with_garbage)
|
||||
|
||||
|
||||
def test_flowmatching_head_accepts_pooled_2d_context():
|
||||
head = make_flowmatching_head()
|
||||
pred_velocity, noise = head(
|
||||
torch.randn(2, EMBED_DIM), # pooled (B, E) context from return_cls_only
|
||||
state=torch.randn(2, STATE_DIM),
|
||||
actions_gt=torch.randn(2, CHUNK_SIZE, ACTION_DIM),
|
||||
action_mask=torch.ones(2, CHUNK_SIZE, ACTION_DIM, dtype=torch.bool),
|
||||
)
|
||||
assert pred_velocity.shape == (2, CHUNK_SIZE * ACTION_DIM)
|
||||
|
||||
actions = head.get_action(
|
||||
torch.randn(2, EMBED_DIM),
|
||||
state=torch.randn(2, STATE_DIM),
|
||||
action_mask=torch.ones(2, ACTION_DIM, dtype=torch.bool),
|
||||
)
|
||||
assert actions.shape == (2, CHUNK_SIZE * ACTION_DIM)
|
||||
|
||||
|
||||
def test_flowmatching_rejects_out_of_range_embodiment_ids():
|
||||
head = make_flowmatching_head(num_categories=2)
|
||||
with pytest.raises(ValueError, match="num_categories"):
|
||||
head.get_action(
|
||||
torch.randn(2, 4, EMBED_DIM),
|
||||
state=torch.randn(2, STATE_DIM),
|
||||
action_mask=torch.ones(2, ACTION_DIM, dtype=torch.bool),
|
||||
embodiment_id=torch.tensor([0, 5]),
|
||||
)
|
||||
|
||||
|
||||
def test_evo1_batched_pixel_values_shape_and_zero_padding():
|
||||
torch.manual_seed(0)
|
||||
batch_size, image_size, max_views = 2, 448, 3
|
||||
camera_images = [torch.rand(batch_size, 3, 40, 50)] # a single present camera
|
||||
mean = torch.tensor(IMAGENET_MEAN)
|
||||
std = torch.tensor(IMAGENET_STD)
|
||||
|
||||
pixel_values = _batched_pixel_values(
|
||||
camera_images, max_views, image_size, mean, std, torch.float32, torch.device("cpu")
|
||||
)
|
||||
|
||||
assert pixel_values.shape == (batch_size * max_views, 3, image_size, image_size)
|
||||
grouped = pixel_values.reshape(batch_size, max_views, 3, image_size, image_size)
|
||||
# Absent views (indices 1, 2) are zero images, normalized to the constant -mean/std.
|
||||
expected_pad = (-mean / std).view(1, 3, 1, 1)
|
||||
for view in (1, 2):
|
||||
assert torch.allclose(
|
||||
grouped[:, view], expected_pad.expand(batch_size, 3, image_size, image_size), atol=1e-5
|
||||
)
|
||||
# The present view is genuinely different from the constant pad value.
|
||||
assert not torch.allclose(
|
||||
grouped[:, 0], expected_pad.expand(batch_size, 3, image_size, image_size), atol=1e-3
|
||||
)
|
||||
@@ -14,7 +14,7 @@
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
"""Test script for LeRobot's GR00T N1.7 policy forward and inference passes."""
|
||||
"""Test script for LeRobot's Groot policy forward and inference passes."""
|
||||
|
||||
import gc
|
||||
import os
|
||||
@@ -25,8 +25,6 @@ import numpy as np
|
||||
import pytest
|
||||
import torch
|
||||
|
||||
pytest.importorskip("transformers", reason="groot requires the `groot` extra (transformers)")
|
||||
|
||||
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
|
||||
@@ -35,26 +33,21 @@ from lerobot.types import PolicyAction
|
||||
from lerobot.utils.device_utils import auto_select_torch_device
|
||||
from tests.utils import require_cuda
|
||||
|
||||
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",
|
||||
)
|
||||
|
||||
|
||||
# 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.
|
||||
# Define constants for dummy data
|
||||
DUMMY_STATE_DIM = 44
|
||||
DUMMY_ACTION_DIM = 44
|
||||
DUMMY_ACTION_HORIZON = 40
|
||||
DUMMY_ACTION_HORIZON = 16
|
||||
IMAGE_SIZE = 256
|
||||
DEVICE = auto_select_torch_device()
|
||||
# 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"
|
||||
MODEL_PATH = "aractingi/bimanual-handover-groot-10k"
|
||||
|
||||
|
||||
def cleanup_memory():
|
||||
@@ -95,13 +88,13 @@ def instantiate_lerobot_groot(
|
||||
PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
|
||||
PolicyProcessorPipeline[PolicyAction, PolicyAction],
|
||||
]:
|
||||
"""Instantiate LeRobot GR00T N1.7 policy with preprocessor and postprocessor."""
|
||||
"""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 = EMBODIMENT_TAG
|
||||
policy.config.embodiment_tag = "gr1"
|
||||
else:
|
||||
config = GrootConfig(
|
||||
base_model_path=model_path,
|
||||
@@ -109,7 +102,7 @@ def instantiate_lerobot_groot(
|
||||
chunk_size=DUMMY_ACTION_HORIZON,
|
||||
image_size=[IMAGE_SIZE, IMAGE_SIZE],
|
||||
device=DEVICE,
|
||||
embodiment_tag=EMBODIMENT_TAG,
|
||||
embodiment_tag="gr1",
|
||||
)
|
||||
policy = GrootPolicy(config)
|
||||
|
||||
@@ -155,8 +148,8 @@ def create_dummy_data(device=DEVICE):
|
||||
|
||||
@require_cuda
|
||||
def test_lerobot_groot_inference():
|
||||
"""Test the inference pass (select_action) of LeRobot's GR00T N1.7 policy."""
|
||||
print("Test: LeRobot GR00T N1.7 Inference Pass")
|
||||
"""Test the inference pass (select_action) of LeRobot's Groot policy."""
|
||||
print("Test: LeRobot Groot Inference Pass")
|
||||
|
||||
set_seed_all(42)
|
||||
|
||||
@@ -188,9 +181,9 @@ def test_lerobot_groot_inference():
|
||||
|
||||
@require_cuda
|
||||
def test_lerobot_groot_forward_pass():
|
||||
"""Test the forward pass of LeRobot's GR00T N1.7 policy."""
|
||||
"""Test the forward pass of LeRobot's Groot policy."""
|
||||
print("\n" + "=" * 50)
|
||||
print("Test: LeRobot GR00T N1.7 Forward Pass (Training Mode)")
|
||||
print("Test: LeRobot Groot Forward Pass (Training Mode)")
|
||||
|
||||
set_seed_all(42)
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,259 +0,0 @@
|
||||
#!/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"])
|
||||
@@ -1,100 +0,0 @@
|
||||
#!/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.
|
||||
|
||||
"""Isaac-GR00T N1.7 raw-state dropout training contract.
|
||||
|
||||
Isaac-GR00T zeroes the entire proprioceptive state of a sample with probability
|
||||
``state_dropout_prob`` (configured in the checkpoint's processor sidecar) during
|
||||
training only. Baseline LeRobot kept the processor deterministic, so this
|
||||
regularization never activated. These tests pin the train/eval split.
|
||||
"""
|
||||
|
||||
import torch
|
||||
|
||||
from lerobot.policies.groot.processor_groot import GrootN17PackInputsStep
|
||||
from lerobot.types import TransitionKey
|
||||
from lerobot.utils.constants import OBS_STATE
|
||||
|
||||
|
||||
def _make_transition():
|
||||
return {
|
||||
TransitionKey.OBSERVATION: {OBS_STATE: torch.tensor([[1.0, 2.0], [3.0, 4.0]])},
|
||||
TransitionKey.COMPLEMENTARY_DATA: {"task": ["Move", "Move"]},
|
||||
}
|
||||
|
||||
|
||||
def test_groot_n1_7_training_applies_raw_state_dropout_before_encoder():
|
||||
step = GrootN17PackInputsStep(
|
||||
max_state_dim=4,
|
||||
max_action_dim=4,
|
||||
normalize_min_max=False,
|
||||
training=True,
|
||||
state_dropout_prob=1.0,
|
||||
)
|
||||
|
||||
output = step(_make_transition())
|
||||
|
||||
expected = torch.zeros(2, 1, 4)
|
||||
torch.testing.assert_close(output[TransitionKey.OBSERVATION]["state"], expected)
|
||||
|
||||
|
||||
def test_groot_n1_7_training_state_dropout_is_disabled_under_no_grad():
|
||||
step = GrootN17PackInputsStep(
|
||||
max_state_dim=4,
|
||||
max_action_dim=4,
|
||||
normalize_min_max=False,
|
||||
training=True,
|
||||
state_dropout_prob=1.0,
|
||||
)
|
||||
|
||||
with torch.no_grad():
|
||||
output = step(_make_transition())
|
||||
|
||||
expected = torch.tensor([[[1.0, 2.0, 0.0, 0.0]], [[3.0, 4.0, 0.0, 0.0]]])
|
||||
torch.testing.assert_close(output[TransitionKey.OBSERVATION]["state"], expected)
|
||||
|
||||
|
||||
def test_groot_n1_7_eval_mode_state_dropout_is_inactive():
|
||||
step = GrootN17PackInputsStep(
|
||||
max_state_dim=4,
|
||||
max_action_dim=4,
|
||||
normalize_min_max=False,
|
||||
training=False,
|
||||
state_dropout_prob=1.0,
|
||||
)
|
||||
|
||||
output = step(_make_transition())
|
||||
|
||||
expected = torch.tensor([[[1.0, 2.0, 0.0, 0.0]], [[3.0, 4.0, 0.0, 0.0]]])
|
||||
torch.testing.assert_close(output[TransitionKey.OBSERVATION]["state"], expected)
|
||||
|
||||
|
||||
def test_groot_n1_7_pack_step_serializes_dropout_prob_but_not_training_mode():
|
||||
step = GrootN17PackInputsStep(
|
||||
max_state_dim=4,
|
||||
max_action_dim=4,
|
||||
normalize_min_max=False,
|
||||
training=True,
|
||||
state_dropout_prob=0.2,
|
||||
)
|
||||
|
||||
serialized = step.get_config()
|
||||
restored = GrootN17PackInputsStep(**serialized)
|
||||
|
||||
assert "training" not in serialized
|
||||
assert serialized["state_dropout_prob"] == 0.2
|
||||
assert restored.training is False
|
||||
assert restored.state_dropout_prob == 0.2
|
||||
@@ -1,169 +0,0 @@
|
||||
#!/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.
|
||||
|
||||
"""Isaac-GR00T N1.7 train-time random crop contract (crop geometry only).
|
||||
|
||||
Isaac-GR00T crops a random ``crop_fraction`` window during training and the
|
||||
deterministic center window at eval, replaying the sampled window across all
|
||||
camera views of a sample (gr00t/data/transform/video.py, n1.5-release onward:
|
||||
"If mode is 'train', return a random crop transform. If mode is 'eval', return
|
||||
a center crop transform."). This mirrors LeRobot's own Diffusion/VQBeT
|
||||
``crop_is_random`` pattern. Color jitter is intentionally out of scope here.
|
||||
"""
|
||||
|
||||
import random
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
from lerobot.policies.groot.processor_groot import (
|
||||
GrootN17VLMEncodeStep,
|
||||
_transform_n1_7_image_for_vlm_albumentations,
|
||||
)
|
||||
|
||||
|
||||
def _structured_image(h=480, w=640):
|
||||
yy, xx = np.mgrid[0:h, 0:w]
|
||||
return np.stack([(xx * 255 / w), (yy * 255 / h), ((xx + yy) * 255 / (h + w))], axis=-1).astype(np.uint8)
|
||||
|
||||
|
||||
def test_crop_position_none_is_bitexact_center_crop():
|
||||
"""crop_position=None must remain byte-identical to the pre-change eval path."""
|
||||
img = _structured_image()
|
||||
ref = _transform_n1_7_image_for_vlm_albumentations(
|
||||
img,
|
||||
image_crop_size=None,
|
||||
image_target_size=[256, 256],
|
||||
shortest_image_edge=256,
|
||||
crop_fraction=0.95,
|
||||
)
|
||||
out = _transform_n1_7_image_for_vlm_albumentations(
|
||||
img,
|
||||
image_crop_size=None,
|
||||
image_target_size=[256, 256],
|
||||
shortest_image_edge=256,
|
||||
crop_fraction=0.95,
|
||||
crop_position=None,
|
||||
)
|
||||
np.testing.assert_array_equal(ref, out)
|
||||
|
||||
|
||||
def test_crop_position_center_matches_center_crop():
|
||||
img = _structured_image()
|
||||
center = _transform_n1_7_image_for_vlm_albumentations(
|
||||
img,
|
||||
image_crop_size=None,
|
||||
image_target_size=[256, 256],
|
||||
shortest_image_edge=256,
|
||||
crop_fraction=0.95,
|
||||
crop_position=None,
|
||||
)
|
||||
explicit = _transform_n1_7_image_for_vlm_albumentations(
|
||||
img,
|
||||
image_crop_size=None,
|
||||
image_target_size=[256, 256],
|
||||
shortest_image_edge=256,
|
||||
crop_fraction=0.95,
|
||||
crop_position=(0.5, 0.5),
|
||||
)
|
||||
# int-floor center vs rounded positional center may differ by <=1 px of grid
|
||||
assert center.shape == explicit.shape
|
||||
diff = np.abs(center.astype(np.int16) - explicit.astype(np.int16))
|
||||
assert diff.mean() < 3.0
|
||||
|
||||
|
||||
def test_crop_position_corners_differ_from_center():
|
||||
img = _structured_image()
|
||||
|
||||
def crop_at(position):
|
||||
return _transform_n1_7_image_for_vlm_albumentations(
|
||||
img,
|
||||
image_crop_size=None,
|
||||
image_target_size=[256, 256],
|
||||
shortest_image_edge=256,
|
||||
crop_fraction=0.95,
|
||||
crop_position=position,
|
||||
)
|
||||
|
||||
center = crop_at(None)
|
||||
tl = crop_at((0.0, 0.0))
|
||||
br = crop_at((1.0, 1.0))
|
||||
assert not np.array_equal(center, tl)
|
||||
assert not np.array_equal(tl, br)
|
||||
|
||||
|
||||
def _video(img, views=2):
|
||||
return np.stack([img] * views, axis=0).reshape(1, 1, views, *img.shape)
|
||||
|
||||
|
||||
def _step(training):
|
||||
return GrootN17VLMEncodeStep(
|
||||
image_target_size=[256, 256],
|
||||
shortest_image_edge=256,
|
||||
crop_fraction=0.95,
|
||||
use_albumentations=True,
|
||||
training=training,
|
||||
)
|
||||
|
||||
|
||||
def test_training_crop_replays_one_window_across_views():
|
||||
video = _video(_structured_image())
|
||||
frames = _step(training=True)._build_sample_images(video, batch_size=1, target_device=None)[0]
|
||||
np.testing.assert_array_equal(np.asarray(frames[0]), np.asarray(frames[1]))
|
||||
|
||||
|
||||
def test_training_crop_differs_from_eval_center_crop():
|
||||
video = _video(_structured_image())
|
||||
random.seed(3) # a draw that is not the exact center
|
||||
train_frame = np.asarray(
|
||||
_step(training=True)._build_sample_images(video, batch_size=1, target_device=None)[0][0]
|
||||
)
|
||||
eval_frame = np.asarray(
|
||||
_step(training=False)._build_sample_images(video, batch_size=1, target_device=None)[0][0]
|
||||
)
|
||||
assert not np.array_equal(train_frame, eval_frame)
|
||||
|
||||
|
||||
def test_training_crop_is_disabled_under_no_grad():
|
||||
video = _video(_structured_image())
|
||||
with torch.no_grad():
|
||||
no_grad_frame = np.asarray(
|
||||
_step(training=True)._build_sample_images(video, batch_size=1, target_device=None)[0][0]
|
||||
)
|
||||
eval_frame = np.asarray(
|
||||
_step(training=False)._build_sample_images(video, batch_size=1, target_device=None)[0][0]
|
||||
)
|
||||
np.testing.assert_array_equal(no_grad_frame, eval_frame)
|
||||
|
||||
|
||||
def test_training_mode_is_not_serialized():
|
||||
step = _step(training=True)
|
||||
serialized = step.get_config()
|
||||
assert "training" not in serialized
|
||||
restored = GrootN17VLMEncodeStep(**serialized)
|
||||
assert restored.training is False
|
||||
|
||||
|
||||
def test_training_crop_respects_global_seed():
|
||||
video = _video(_structured_image())
|
||||
|
||||
def draw():
|
||||
random.seed(11)
|
||||
return np.asarray(
|
||||
_step(training=True)._build_sample_images(video, batch_size=1, target_device=None)[0][0]
|
||||
)
|
||||
|
||||
np.testing.assert_array_equal(draw(), draw())
|
||||
@@ -1,125 +0,0 @@
|
||||
#!/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.
|
||||
|
||||
"""Isaac-GR00T N1.7 optimizer/scheduler/precision training contract.
|
||||
|
||||
Pins the LeRobot GR00T fine-tuning recipe to the native Isaac-GR00T contract:
|
||||
AdamW(lr=1e-4, betas=(0.9, 0.999), eps=1e-8, weight_decay=1e-5, grad clip 1.0),
|
||||
HF cosine schedule with ~5% warmup over the actual update count, FP32 master
|
||||
parameters under BF16 autocast, transformers-style weight-decay grouping, the
|
||||
frozen LM-head weight tie, and episode-tail exclusion for incomplete chunks.
|
||||
"""
|
||||
|
||||
import pytest
|
||||
import torch
|
||||
|
||||
from lerobot.optim.schedulers import DiffuserSchedulerConfig
|
||||
from lerobot.policies.groot.configuration_groot import GrootConfig
|
||||
from lerobot.policies.groot.groot_n1_7 import _tie_unused_qwen_lm_head
|
||||
from lerobot.policies.groot.modeling_groot import GrootPolicy
|
||||
|
||||
|
||||
def test_groot_n1_7_optimizer_matches_isaac_training_contract():
|
||||
optimizer = GrootConfig().get_optimizer_preset()
|
||||
|
||||
assert optimizer.lr == pytest.approx(1e-4)
|
||||
assert optimizer.betas == pytest.approx((0.9, 0.999))
|
||||
assert optimizer.eps == pytest.approx(1e-8)
|
||||
assert optimizer.weight_decay == pytest.approx(1e-5)
|
||||
assert optimizer.grad_clip_norm == pytest.approx(1.0)
|
||||
|
||||
|
||||
def test_groot_n1_7_sampler_excludes_incomplete_action_tails():
|
||||
config = GrootConfig(chunk_size=16, n_action_steps=16)
|
||||
|
||||
assert len(config.action_delta_indices) == 16
|
||||
assert config.drop_n_last_frames == 15
|
||||
|
||||
|
||||
def test_groot_n1_7_scheduler_matches_isaac_hf_cosine_contract():
|
||||
pytest.importorskip("diffusers", reason="the scheduler preset requires the `groot` extra (diffusers)")
|
||||
config = GrootConfig(max_steps=20_000)
|
||||
scheduler_config = config.get_scheduler_preset()
|
||||
|
||||
assert isinstance(scheduler_config, DiffuserSchedulerConfig)
|
||||
assert scheduler_config.name == "cosine"
|
||||
assert scheduler_config.num_warmup_steps == 1_000
|
||||
|
||||
parameter = torch.nn.Parameter(torch.ones(()))
|
||||
optimizer = torch.optim.AdamW([parameter], lr=config.optimizer_lr)
|
||||
scheduler = scheduler_config.build(optimizer, num_training_steps=20_000)
|
||||
lr_factor = scheduler.lr_lambdas[0]
|
||||
|
||||
assert lr_factor(0) == pytest.approx(0.0)
|
||||
assert lr_factor(1_000) == pytest.approx(1.0)
|
||||
assert lr_factor(10_500) == pytest.approx(0.5)
|
||||
assert lr_factor(20_000) == pytest.approx(0.0, abs=1e-12)
|
||||
|
||||
|
||||
def test_groot_n1_7_scheduler_rounds_fractional_warmup_up_like_transformers():
|
||||
scheduler_config = GrootConfig(max_steps=777).get_scheduler_preset()
|
||||
|
||||
assert scheduler_config.num_warmup_steps == 39
|
||||
|
||||
|
||||
def test_groot_n1_7_model_parameters_use_fp32_checkpoint_and_optimizer_precision():
|
||||
module = torch.nn.Module()
|
||||
module.trainable = torch.nn.Parameter(torch.ones(3, dtype=torch.bfloat16))
|
||||
module.frozen = torch.nn.Parameter(torch.ones(3, dtype=torch.bfloat16), requires_grad=False)
|
||||
|
||||
GrootPolicy._cast_model_parameters_to_fp32(module)
|
||||
|
||||
assert module.trainable.dtype == torch.float32
|
||||
assert module.frozen.dtype == torch.float32
|
||||
|
||||
|
||||
def test_groot_n1_7_ties_unused_qwen_lm_head_to_frozen_input_embeddings():
|
||||
class DummyQwen(torch.nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.embed_tokens = torch.nn.Embedding(7, 3)
|
||||
self.lm_head = torch.nn.Linear(3, 7, bias=False)
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.embed_tokens
|
||||
|
||||
model = DummyQwen()
|
||||
_tie_unused_qwen_lm_head(model)
|
||||
|
||||
assert model.lm_head.weight is model.embed_tokens.weight
|
||||
assert len(list(model.parameters())) == 1
|
||||
|
||||
|
||||
def test_groot_n1_7_optimizer_groups_match_transformers_weight_decay_rules():
|
||||
pytest.importorskip(
|
||||
"transformers", reason="weight-decay grouping requires the `groot` extra (transformers)"
|
||||
)
|
||||
module = torch.nn.Module()
|
||||
module.linear = torch.nn.Linear(3, 2)
|
||||
module.norm = torch.nn.LayerNorm(2)
|
||||
module.frozen = torch.nn.Parameter(torch.ones(1), requires_grad=False)
|
||||
|
||||
groups = GrootPolicy._build_weight_decay_parameter_groups(module)
|
||||
|
||||
assert len(groups) == 2
|
||||
assert "weight_decay" not in groups[0]
|
||||
assert groups[1]["weight_decay"] == 0.0
|
||||
assert groups[0]["params"] == [module.linear.weight]
|
||||
assert {id(parameter) for parameter in groups[1]["params"]} == {
|
||||
id(module.linear.bias),
|
||||
id(module.norm.weight),
|
||||
id(module.norm.bias),
|
||||
}
|
||||
@@ -1,6 +1,6 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
|
||||
# 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.
|
||||
@@ -14,194 +14,431 @@
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
"""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.
|
||||
"""
|
||||
"""Test script to verify Groot policy integration with LeRobot vs the original implementation, only meant to be run locally!"""
|
||||
|
||||
import gc
|
||||
import os
|
||||
from pathlib import Path
|
||||
from copy import deepcopy
|
||||
from typing import Any
|
||||
|
||||
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="Requires a local GR00T N1.7 checkpoint + pre-generated artifacts; not for CI.",
|
||||
reason="This test requires local Groot installation and is not meant for CI",
|
||||
)
|
||||
|
||||
from lerobot.policies.groot.configuration_groot import GROOT_N1_7 # noqa: E402,F401
|
||||
|
||||
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"))
|
||||
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
|
||||
|
||||
# Artifact filenames are original_n1_7_<embodiment_tag>.npz
|
||||
_ARTIFACT_PREFIX = "original_n1_7_"
|
||||
_ARTIFACT_SUFFIX = ".npz"
|
||||
# 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,
|
||||
}
|
||||
|
||||
|
||||
def _artifact_dir() -> Path:
|
||||
"""Directory holding the per-embodiment .npz artifacts.
|
||||
|
||||
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.
|
||||
"""
|
||||
env = os.environ.get("GROOT_N1_7_PARITY_DIR")
|
||||
if env:
|
||||
return Path(env)
|
||||
return Path(__file__).resolve().parent / "artifacts"
|
||||
|
||||
|
||||
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
|
||||
|
||||
|
||||
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},
|
||||
)
|
||||
# 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
|
||||
|
||||
|
||||
_ARTIFACTS = _discover_artifacts()
|
||||
|
||||
|
||||
@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)
|
||||
|
||||
# Align the flow-matching RNG exactly as the producer did (seed right before sampling).
|
||||
torch.manual_seed(SEED)
|
||||
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.manual_seed_all(SEED)
|
||||
with torch.inference_mode():
|
||||
out = lerobot_model.get_action(model_inputs)
|
||||
lerobot_action = out["action_pred"].float().cpu()
|
||||
torch.cuda.empty_cache()
|
||||
torch.cuda.synchronize()
|
||||
if torch.backends.mps.is_available():
|
||||
torch.mps.empty_cache()
|
||||
print("Memory cleanup complete.")
|
||||
|
||||
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]
|
||||
|
||||
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}"
|
||||
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)
|
||||
)
|
||||
|
||||
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}"
|
||||
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]
|
||||
}
|
||||
"""
|
||||
# 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
|
||||
|
||||
|
||||
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)")
|
||||
|
||||
set_seed_all(42)
|
||||
|
||||
lerobot_policy, lerobot_preprocessor, lerobot_postprocessor = instantiate_lerobot_groot(
|
||||
from_pretrained=True
|
||||
)
|
||||
original_policy, modality_config, modality_transform = instantiate_original_groot(from_pretrained=True)
|
||||
|
||||
batch = create_dummy_data()
|
||||
batch_lerobot = deepcopy(batch)
|
||||
|
||||
print("\n[LeRobot] Running inference...")
|
||||
lerobot_policy.eval()
|
||||
batch_lerobot_processed = lerobot_preprocessor(batch_lerobot)
|
||||
|
||||
# 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)
|
||||
|
||||
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))
|
||||
|
||||
# Important: Reset seed immediately before inference to ensure identical RNG state
|
||||
torch.manual_seed(42)
|
||||
|
||||
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}"
|
||||
)
|
||||
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
|
||||
)
|
||||
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()
|
||||
|
||||
@@ -1,212 +0,0 @@
|
||||
#!/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.
|
||||
"""Producer (run in the ORIGINAL gr00t env): dump original GR00T N1.7 outputs + inputs.
|
||||
|
||||
The original NVIDIA ``gr00t`` package pins ``transformers==4.57.3`` (py3.10) and its
|
||||
model-config dataclasses are incompatible with the ``transformers==5.x`` that the
|
||||
LeRobot GR00T N1.7 integration requires. The two implementations therefore cannot be
|
||||
imported in the same Python process. To keep the parity comparison FAIR, we run the
|
||||
original model in its native env here and serialize, PER EMBODIMENT TAG:
|
||||
|
||||
* the exact pre-processed/collated model inputs (so the LeRobot side consumes the
|
||||
byte-identical tensors -- same image preprocessing, tokenization, normalization),
|
||||
* the random seed used right before the flow-matching sampler,
|
||||
* the raw ``action_pred`` tensor returned by ``model.get_action`` (normalized space,
|
||||
before any per-implementation action decoding).
|
||||
|
||||
Inputs are built GENERICALLY from the checkpoint metadata (no per-tag hardcoding):
|
||||
state keys + dims come from ``statistics.json``; video + language keys come from the
|
||||
processor's per-embodiment modality configs. This lets us test many embodiment tags
|
||||
from the SAME checkpoint and confirm the LeRobot integration is not overfit to
|
||||
``libero_sim``.
|
||||
|
||||
The companion pytest (run in the LeRobot env) loads each .npz, replays the identical
|
||||
inputs + seed through the LeRobot GR00T N1.7 model, and asserts the outputs match.
|
||||
|
||||
Usage:
|
||||
.venv-original/bin/python tests/policies/groot/utils/dump_original_n1_7.py \
|
||||
--ckpt <path-to-GR00T-N1.7-LIBERO/libero_10> \
|
||||
--out-dir tests/policies/groot/artifacts \
|
||||
[--tags libero_sim,oxe_droid_relative_eef_relative_joint,...] \
|
||||
[--device cuda] [--seed 42]
|
||||
|
||||
If --tags is omitted, every embodiment present in the checkpoint statistics is dumped.
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
IMAGE_SIZE = 256
|
||||
BATCH_SIZE = 2
|
||||
PROMPT = "pick up the black bowl and place it on the plate"
|
||||
|
||||
|
||||
def load_statistics(ckpt: str) -> dict:
|
||||
with open(os.path.join(ckpt, "statistics.json")) as f:
|
||||
return json.load(f)
|
||||
|
||||
|
||||
def make_observation(seed: int, video_keys, lang_key, state_spec):
|
||||
"""Build a dummy observation dict generically from the embodiment metadata."""
|
||||
rng = np.random.default_rng(seed)
|
||||
video = {
|
||||
k: rng.integers(0, 256, (BATCH_SIZE, 1, IMAGE_SIZE, IMAGE_SIZE, 3), dtype=np.uint8)
|
||||
for k in video_keys
|
||||
}
|
||||
# One ndarray per state key, shape (B, T=1, key_dim); dim taken from statistics.
|
||||
# Keys with dim 0 (e.g. disabled eef on some embodiments) are still emitted as
|
||||
# present-but-empty so the processor's state transform finds every expected key.
|
||||
state = {k: rng.standard_normal((BATCH_SIZE, 1, dim)).astype(np.float32) for k, dim in state_spec}
|
||||
language = {lang_key: [[PROMPT] for _ in range(BATCH_SIZE)]}
|
||||
return {"video": video, "state": state, "language": language}
|
||||
|
||||
|
||||
def dump_one_tag(policy, fair_model, tag, modality_cfg, state_spec, args, out_path):
|
||||
from gr00t.data.types import MessageType
|
||||
|
||||
video_keys = modality_cfg["video"].modality_keys
|
||||
lang_key = modality_cfg["language"].modality_keys[0]
|
||||
observation = make_observation(args.seed, video_keys, lang_key, state_spec)
|
||||
|
||||
# Point the policy preprocessing at this embodiment (mirrors Gr00tPolicy.__init__).
|
||||
policy.embodiment_tag = type(policy.embodiment_tag)(tag)
|
||||
policy.modality_configs = {
|
||||
k: v for k, v in policy.processor.get_modality_configs()[tag].items() if k != "rl_info"
|
||||
}
|
||||
policy.language_key = policy.modality_configs["language"].modality_keys[0]
|
||||
|
||||
torch.manual_seed(args.seed)
|
||||
np.random.seed(args.seed)
|
||||
|
||||
unbatched = policy._unbatch_observation(observation)
|
||||
processed = []
|
||||
for obs in unbatched:
|
||||
vla = policy._to_vla_step_data(obs)
|
||||
processed.append(policy.processor([{"type": MessageType.EPISODE_STEP.value, "content": vla}]))
|
||||
collated = policy.collate_fn(processed)
|
||||
|
||||
def to_dev(x):
|
||||
if isinstance(x, torch.Tensor) and torch.is_floating_point(x):
|
||||
return x.to(args.device, torch.float32)
|
||||
if isinstance(x, torch.Tensor):
|
||||
return x.to(args.device)
|
||||
if isinstance(x, dict):
|
||||
return {k: to_dev(v) for k, v in x.items()}
|
||||
return x
|
||||
|
||||
collated = {k: to_dev(v) for k, v in collated.items()}
|
||||
|
||||
torch.manual_seed(args.seed)
|
||||
with torch.inference_mode():
|
||||
out = fair_model.get_action(**collated)
|
||||
action_pred = out["action_pred"].float().cpu().numpy()
|
||||
|
||||
flat, meta = {}, {}
|
||||
|
||||
def flatten(prefix, obj):
|
||||
if isinstance(obj, torch.Tensor):
|
||||
arr = obj.float().cpu().numpy() if torch.is_floating_point(obj) else obj.cpu().numpy()
|
||||
flat[f"in::{prefix}"] = arr
|
||||
meta[f"in::{prefix}"] = str(obj.dtype)
|
||||
elif isinstance(obj, dict):
|
||||
for k, v in obj.items():
|
||||
flatten(f"{prefix}.{k}" if prefix else k, v)
|
||||
elif isinstance(obj, (list, tuple)):
|
||||
flat[f"in::{prefix}"] = np.array(obj, dtype=object)
|
||||
else:
|
||||
flat[f"in::{prefix}"] = np.array(obj)
|
||||
|
||||
flatten("", collated)
|
||||
|
||||
out_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
np.savez(
|
||||
out_path,
|
||||
action_pred=action_pred,
|
||||
seed=np.array(args.seed),
|
||||
device=np.array(args.device),
|
||||
embodiment_tag=np.array(tag),
|
||||
meta_keys=np.array(list(meta.keys()), dtype=object),
|
||||
meta_dtypes=np.array(list(meta.values()), dtype=object),
|
||||
**flat,
|
||||
)
|
||||
print(f"[{tag}] action_pred {action_pred.shape} -> {out_path.name} ({os.path.getsize(out_path)} B)")
|
||||
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("--ckpt", required=True)
|
||||
ap.add_argument("--out-dir", required=True, help="directory for per-tag .npz files")
|
||||
ap.add_argument("--tags", default="", help="comma-separated embodiment tags (default: all in stats)")
|
||||
ap.add_argument("--device", default="cuda")
|
||||
ap.add_argument("--seed", type=int, default=42)
|
||||
args = ap.parse_args()
|
||||
|
||||
from gr00t.policy.gr00t_policy import Gr00tPolicy
|
||||
from transformers import AutoConfig, AutoModel
|
||||
|
||||
stats = load_statistics(args.ckpt)
|
||||
requested = [t.strip() for t in args.tags.split(",") if t.strip()] or list(stats.keys())
|
||||
|
||||
# Load the policy once (for its processor/preprocessing) on any valid tag.
|
||||
bootstrap_tag = "libero_sim" if "libero_sim" in stats else requested[0]
|
||||
policy = Gr00tPolicy(embodiment_tag=bootstrap_tag, model_path=args.ckpt, device=args.device)
|
||||
all_modality = policy.processor.get_modality_configs()
|
||||
|
||||
# Load a FAIR model (SDPA + fp32) once and reuse across tags. Otherwise the
|
||||
# original checkpoint default (flash_attention_2 + bf16) introduces kernel/rounding
|
||||
# noise vs the LeRobot env (which has no flash_attn and runs SDPA).
|
||||
cfg = AutoConfig.from_pretrained(args.ckpt, trust_remote_code=True)
|
||||
cfg.use_flash_attention = False
|
||||
cfg.load_bf16 = False
|
||||
fair_model = AutoModel.from_pretrained(args.ckpt, config=cfg, trust_remote_code=True)
|
||||
fair_model.to(device=args.device, dtype=torch.float32)
|
||||
fair_model.eval()
|
||||
|
||||
out_dir = Path(args.out_dir)
|
||||
done, skipped = [], []
|
||||
for tag in requested:
|
||||
if tag not in stats or tag not in all_modality:
|
||||
print(f"[skip] {tag}: not present in checkpoint statistics/modality configs")
|
||||
skipped.append(tag)
|
||||
continue
|
||||
state_spec = [(k, len(v["min"])) for k, v in stats[tag]["state"].items()]
|
||||
try:
|
||||
dump_one_tag(
|
||||
policy,
|
||||
fair_model,
|
||||
tag,
|
||||
all_modality[tag],
|
||||
state_spec,
|
||||
args,
|
||||
out_dir / f"original_n1_7_{tag}.npz",
|
||||
)
|
||||
done.append(tag)
|
||||
except Exception as exc: # noqa: BLE001
|
||||
print(f"[fail] {tag}: {type(exc).__name__}: {exc}")
|
||||
skipped.append(tag)
|
||||
|
||||
print(f"\nDumped {len(done)} tags: {done}")
|
||||
if skipped:
|
||||
print(f"Skipped/failed {len(skipped)} tags: {skipped}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,78 +0,0 @@
|
||||
#!/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.
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import pytest
|
||||
|
||||
from lerobot.configs.policies import PreTrainedConfig
|
||||
from lerobot.configs.types import FeatureType, PolicyFeature
|
||||
from lerobot.policies.lingbot_va.configuration_lingbot_va import LingBotVAConfig
|
||||
from lerobot.utils.constants import ACTION, OBS_IMAGES
|
||||
|
||||
|
||||
def make_config(**overrides) -> LingBotVAConfig:
|
||||
kwargs = {"device": "cpu"}
|
||||
kwargs.update(overrides)
|
||||
return LingBotVAConfig(**kwargs)
|
||||
|
||||
|
||||
def test_registered_in_choice_registry() -> None:
|
||||
assert "lingbot_va" in PreTrainedConfig.get_known_choices()
|
||||
assert PreTrainedConfig.get_choice_class("lingbot_va") is LingBotVAConfig
|
||||
|
||||
|
||||
def test_type_property() -> None:
|
||||
assert make_config().type == "lingbot_va"
|
||||
|
||||
|
||||
def test_chunk_size_and_action_steps() -> None:
|
||||
cfg = make_config(frame_chunk_size=4, action_per_frame=4)
|
||||
assert cfg.chunk_size == 16
|
||||
assert cfg.n_action_steps == 16
|
||||
assert cfg.action_delta_indices == list(range(16))
|
||||
assert cfg.observation_delta_indices == list(range(16))
|
||||
assert cfg.reward_delta_indices is None
|
||||
|
||||
|
||||
def test_optimizer_and_scheduler_presets() -> None:
|
||||
cfg = make_config()
|
||||
opt = cfg.get_optimizer_preset()
|
||||
assert opt.lr == cfg.optimizer_lr
|
||||
sched = cfg.get_scheduler_preset()
|
||||
assert sched.num_warmup_steps == cfg.scheduler_warmup_steps
|
||||
|
||||
|
||||
def test_validate_features_sets_action_feature() -> None:
|
||||
cfg = make_config()
|
||||
cfg.input_features = {f"{OBS_IMAGES}.image": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128))}
|
||||
cfg.output_features = {}
|
||||
cfg.validate_features()
|
||||
assert ACTION in cfg.output_features
|
||||
assert cfg.output_features[ACTION].shape == (len(cfg.used_action_channel_ids),)
|
||||
|
||||
|
||||
def test_validate_features_no_visual_raises() -> None:
|
||||
cfg = make_config()
|
||||
cfg.input_features = {}
|
||||
cfg.output_features = {}
|
||||
with pytest.raises(ValueError, match="at least one visual input feature"):
|
||||
cfg.validate_features()
|
||||
|
||||
|
||||
def test_invalid_attn_mode_raises() -> None:
|
||||
with pytest.raises(ValueError, match="attn_mode"):
|
||||
make_config(attn_mode="banana")
|
||||
@@ -1,38 +0,0 @@
|
||||
#!/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.
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import pytest
|
||||
|
||||
from lerobot.policies.factory import make_policy_config
|
||||
from lerobot.policies.lingbot_va.configuration_lingbot_va import LingBotVAConfig
|
||||
|
||||
|
||||
def test_make_policy_config_returns_lingbot_va() -> None:
|
||||
cfg = make_policy_config("lingbot_va", device="cpu")
|
||||
assert isinstance(cfg, LingBotVAConfig)
|
||||
|
||||
|
||||
def test_get_policy_class_resolves_lazily() -> None:
|
||||
# Importing the policy class pulls in diffusers (Wan2.2 stack); skip if unavailable.
|
||||
pytest.importorskip("diffusers")
|
||||
pytest.importorskip("transformers")
|
||||
from lerobot.policies.factory import get_policy_class
|
||||
|
||||
cls = get_policy_class("lingbot_va")
|
||||
assert cls.name == "lingbot_va"
|
||||
assert cls.config_class is LingBotVAConfig
|
||||
@@ -1,128 +0,0 @@
|
||||
#!/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.
|
||||
|
||||
"""Unit tests for the vendored LingBot-VA helper code (scheduler + grid utilities)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import pytest
|
||||
import torch
|
||||
|
||||
pytest.importorskip("diffusers") # the model code lives in modeling_lingbot_va, which imports diffusers
|
||||
|
||||
from lerobot.policies.lingbot_va.modeling_lingbot_va import FlowMatchScheduler
|
||||
from lerobot.policies.lingbot_va.utils import data_seq_to_patch, get_mesh_id
|
||||
|
||||
|
||||
def test_flow_match_scheduler_timesteps_monotone_decreasing() -> None:
|
||||
sch = FlowMatchScheduler(shift=5.0, sigma_min=0.0, extra_one_step=True)
|
||||
sch.set_timesteps(20)
|
||||
assert sch.timesteps.shape == (20,)
|
||||
diffs = sch.timesteps[1:] - sch.timesteps[:-1]
|
||||
assert torch.all(diffs <= 0) # decreasing
|
||||
|
||||
|
||||
def test_flow_match_scheduler_step_preserves_shape() -> None:
|
||||
sch = FlowMatchScheduler(shift=5.0, sigma_min=0.0, extra_one_step=True)
|
||||
sch.set_timesteps(20)
|
||||
sample = torch.zeros(1, 48, 4, 8, 16)
|
||||
out = sch.step(torch.ones_like(sample), sch.timesteps[0], sample)
|
||||
assert out.shape == sample.shape
|
||||
|
||||
|
||||
def test_flow_match_scheduler_add_noise() -> None:
|
||||
sch = FlowMatchScheduler(shift=5.0, sigma_min=0.0, extra_one_step=True)
|
||||
sch.set_timesteps(20)
|
||||
sample = torch.randn(1, 48, 4, 8, 16)
|
||||
noise = torch.randn_like(sample)
|
||||
noisy = sch.add_noise(sample, noise, sch.timesteps[:4], t_dim=2)
|
||||
assert noisy.shape == sample.shape
|
||||
|
||||
|
||||
def test_get_mesh_id_latent_shape() -> None:
|
||||
grid = get_mesh_id(4, 8, 16, 0, 1, 0)
|
||||
assert grid.shape == (4, 4 * 8 * 16) # (f, h, w, stream) x tokens
|
||||
|
||||
|
||||
def test_get_mesh_id_action_shape() -> None:
|
||||
grid = get_mesh_id(4, 4, 1, 1, 1, 0, action=True)
|
||||
assert grid.shape == (4, 4 * 4 * 1)
|
||||
# Action rows for h/w are sentinel -1.
|
||||
assert torch.all(grid[1] < 0)
|
||||
assert torch.all(grid[2] < 0)
|
||||
|
||||
|
||||
def test_data_seq_to_patch_roundtrip_shape() -> None:
|
||||
b, f, h, w, c = 1, 4, 8, 16, 48
|
||||
seq = torch.arange(b * f * h * w * c, dtype=torch.float32).reshape(b, f * h * w, c)
|
||||
out = data_seq_to_patch((1, 2, 2), seq, f, h, w, batch_size=b)
|
||||
assert out.shape == (b, c, f, h, w)
|
||||
|
||||
|
||||
def test_training_step_reduces_loss_tiny_flex() -> None:
|
||||
"""End-to-end single training step (flow-matching loss -> backward -> AdamW) on a tiny config.
|
||||
|
||||
Exercises the flex-attention training path; requires a CUDA GPU with flex-attention support.
|
||||
"""
|
||||
if not torch.cuda.is_available():
|
||||
import pytest
|
||||
|
||||
pytest.skip("training step test requires a CUDA GPU (flex-attention)")
|
||||
|
||||
from lerobot.configs.types import FeatureType, PolicyFeature
|
||||
from lerobot.policies.lingbot_va.configuration_lingbot_va import LingBotVAConfig
|
||||
from lerobot.policies.lingbot_va.modeling_lingbot_va import LingBotVAPolicy
|
||||
from lerobot.utils.constants import ACTION, OBS_IMAGES
|
||||
|
||||
cfg = LingBotVAConfig(
|
||||
attn_mode="flex",
|
||||
dtype="bfloat16",
|
||||
in_channels=16,
|
||||
out_channels=16,
|
||||
action_dim=8,
|
||||
text_dim=32,
|
||||
freq_dim=64,
|
||||
ffn_dim=64,
|
||||
num_attention_heads=2,
|
||||
attention_head_dim=24,
|
||||
num_layers=2,
|
||||
frame_chunk_size=2,
|
||||
action_per_frame=4,
|
||||
used_action_channel_ids=[0, 1, 2, 3],
|
||||
obs_cam_keys=[f"{OBS_IMAGES}.image"],
|
||||
device="cuda",
|
||||
)
|
||||
cfg.input_features = {f"{OBS_IMAGES}.image": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 64, 64))}
|
||||
cfg.output_features = {ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(4,))}
|
||||
cfg.validate_features()
|
||||
|
||||
policy = LingBotVAPolicy(cfg).to("cuda")
|
||||
policy.train()
|
||||
opt = torch.optim.AdamW(policy.get_optim_params(), lr=1e-4)
|
||||
|
||||
b, fc, apf = 1, cfg.frame_chunk_size, cfg.action_per_frame
|
||||
latents = torch.randn(b, cfg.in_channels, fc, 4, 4, device="cuda", dtype=torch.bfloat16)
|
||||
actions = torch.randn(b, cfg.action_dim, fc, apf, 1, device="cuda", dtype=torch.bfloat16)
|
||||
amask = torch.zeros(cfg.action_dim, device="cuda")
|
||||
amask[cfg.used_action_channel_ids] = 1.0
|
||||
actions_mask = amask.view(1, -1, 1, 1, 1).expand_as(actions)
|
||||
text_emb = torch.randn(b, cfg.max_sequence_length, cfg.text_dim, device="cuda", dtype=torch.bfloat16)
|
||||
|
||||
loss, metrics = policy.training_loss_from_streams(latents, actions, actions_mask, text_emb)
|
||||
assert torch.isfinite(loss) and {"latent_loss", "action_loss"} <= set(metrics)
|
||||
loss.backward()
|
||||
assert any(p.grad is not None and torch.isfinite(p.grad).all() for p in policy.get_optim_params())
|
||||
opt.step()
|
||||
@@ -1,88 +0,0 @@
|
||||
#!/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.
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import torch
|
||||
|
||||
from lerobot.configs.types import FeatureType, PolicyFeature
|
||||
from lerobot.policies.lingbot_va.configuration_lingbot_va import LingBotVAConfig
|
||||
from lerobot.policies.lingbot_va.processor_lingbot_va import make_lingbot_va_pre_post_processors
|
||||
from lerobot.processor import PolicyProcessorPipeline, UnnormalizerProcessorStep
|
||||
from lerobot.processor.converters import policy_action_to_transition, transition_to_policy_action
|
||||
from lerobot.utils.constants import (
|
||||
ACTION,
|
||||
OBS_IMAGES,
|
||||
POLICY_POSTPROCESSOR_DEFAULT_NAME,
|
||||
POLICY_PREPROCESSOR_DEFAULT_NAME,
|
||||
)
|
||||
|
||||
|
||||
def _make_config() -> LingBotVAConfig:
|
||||
cfg = LingBotVAConfig(device="cpu")
|
||||
cfg.input_features = {f"{OBS_IMAGES}.image": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128))}
|
||||
cfg.output_features = {}
|
||||
cfg.validate_features()
|
||||
return cfg
|
||||
|
||||
|
||||
def test_make_pre_post_processors_names_and_steps() -> None:
|
||||
cfg = _make_config()
|
||||
pre, post = make_lingbot_va_pre_post_processors(cfg, dataset_stats=None)
|
||||
assert pre.name == POLICY_PREPROCESSOR_DEFAULT_NAME
|
||||
assert post.name == POLICY_POSTPROCESSOR_DEFAULT_NAME
|
||||
# Actions are unnormalized by the standard built-in quantile unnormalizer.
|
||||
assert any(isinstance(s, UnnormalizerProcessorStep) for s in post.steps)
|
||||
|
||||
|
||||
def test_freshly_built_postprocessor_is_identity() -> None:
|
||||
# Without action stats the quantile unnormalizer is a no-op (identity passthrough): the real
|
||||
# per-benchmark q01/q99 are restored from the saved checkpoint on load, not hardcoded here.
|
||||
cfg = _make_config()
|
||||
_, post = make_lingbot_va_pre_post_processors(cfg, dataset_stats=None)
|
||||
normed = torch.tensor([[0.3, -0.5, 1.0, -1.0, 0.0, 0.7, -0.2]])
|
||||
assert torch.allclose(post(normed), normed, atol=1e-6)
|
||||
|
||||
|
||||
def test_postprocessor_quantile_unnormalization() -> None:
|
||||
# QUANTILES unnormalize maps [-1, 1] -> [q01, q99]: -1 -> q01, +1 -> q99.
|
||||
cfg = _make_config()
|
||||
q01 = [-1.0, -0.5, 0.0, -1.0, -1.0, -1.0, -1.0]
|
||||
q99 = [1.0, 0.5, 2.0, 1.0, 1.0, 1.0, 1.0]
|
||||
stats = {ACTION: {"q01": q01, "q99": q99}}
|
||||
_, post = make_lingbot_va_pre_post_processors(cfg, dataset_stats=stats)
|
||||
out_lo = post(torch.full((1, 7), -1.0))
|
||||
out_hi = post(torch.full((1, 7), 1.0))
|
||||
assert torch.allclose(out_lo, torch.tensor(q01).unsqueeze(0), atol=1e-4)
|
||||
assert torch.allclose(out_hi, torch.tensor(q99).unsqueeze(0), atol=1e-4)
|
||||
|
||||
|
||||
def test_postprocessor_stats_survive_save_load(tmp_path) -> None:
|
||||
# Regression guard for the Hub mechanism: the q01/q99 stats live in the saved post-processor
|
||||
# state and must round-trip through save_pretrained / from_pretrained.
|
||||
cfg = _make_config()
|
||||
q01 = [-0.6, -0.8, -0.9, -0.1, -0.15, -0.25, -1.0]
|
||||
q99 = [0.9, 0.85, 0.9, 0.17, 0.18, 0.34, 1.0]
|
||||
_, post = make_lingbot_va_pre_post_processors(cfg, dataset_stats={ACTION: {"q01": q01, "q99": q99}})
|
||||
post.save_pretrained(tmp_path)
|
||||
loaded = PolicyProcessorPipeline.from_pretrained(
|
||||
tmp_path,
|
||||
config_filename=f"{POLICY_POSTPROCESSOR_DEFAULT_NAME}.json",
|
||||
to_transition=policy_action_to_transition,
|
||||
to_output=transition_to_policy_action,
|
||||
)
|
||||
out = loaded(torch.full((1, 7), -1.0))
|
||||
assert torch.allclose(out, torch.tensor(q01).unsqueeze(0), atol=1e-4)
|
||||
@@ -0,0 +1,40 @@
|
||||
# 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 contextlib import nullcontext
|
||||
|
||||
import pytest
|
||||
import torch
|
||||
|
||||
from lerobot.utils.device_utils import get_safe_autocast_context
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
("device", "enabled", "expect_autocast"),
|
||||
[
|
||||
("cpu", True, True), # AMP-capable device -> real autocast
|
||||
(torch.device("cpu"), True, True), # accepts torch.device
|
||||
("cpu", False, False), # explicitly disabled -> no-op
|
||||
("mps", True, False), # AMP unsupported on mps -> no-op
|
||||
("privateuseone", True, False), # unknown device -> safe no-op
|
||||
],
|
||||
)
|
||||
def test_get_safe_autocast_context(device, enabled, expect_autocast):
|
||||
ctx = get_safe_autocast_context(device, dtype=torch.bfloat16, enabled=enabled)
|
||||
if expect_autocast:
|
||||
assert isinstance(ctx, torch.autocast)
|
||||
with ctx:
|
||||
assert torch.is_autocast_enabled("cpu")
|
||||
else:
|
||||
assert isinstance(ctx, nullcontext)
|
||||
Reference in New Issue
Block a user