mirror of
https://github.com/huggingface/lerobot.git
synced 2026-05-28 06:59:44 +00:00
Remove previous pi0 and rename pi0_openpi and pi05_openpi
This commit is contained in:
Pepijn
@@ -124,3 +124,7 @@ python src/lerobot/scripts/train.py \
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LeRobot uses MuJoCo for simulation. You need to set the rendering backend before training or evaluation:
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- `export MUJOCO_GL=egl` → for headless servers (e.g. HPC, cloud)
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## Reproducing π₀ and π₀.₅ results
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We can also reproduce the results of π₀ and π₀.₅ on the Libero benchmark by using the finetuned libero models.
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+3
-3
@@ -35,7 +35,7 @@ As described by Physical Intelligence, while AI has achieved remarkable success
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2. Apply the custom patches:
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```bash
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cp -r ./src/lerobot/policies/pi0_openpi/transformers_replace/* \
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cp -r ./src/lerobot/policies/pi0/transformers_replace/* \
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$(python -c "import transformers, os; print(os.path.dirname(transformers.__file__))")
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```
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@@ -72,7 +72,7 @@ pip install transformers==4.53.2
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To use π₀ in LeRobot, specify the policy type as:
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```python
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policy.type=pi0_openpi
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policy.type=pi0
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```
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## Training
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@@ -82,7 +82,7 @@ For training π₀, you can use the standard LeRobot training script with the ap
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```bash
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python src/lerobot/scripts/train.py \
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--dataset.repo_id=your_dataset \
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--policy.type=pi0_openpi \
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--policy.type=pi0 \
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--output_dir=./outputs/pi0_training \
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--job_name=pi0_training \
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--policy.pretrained_path=pepijn223/pi0_base_fp32 \
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@@ -43,7 +43,7 @@ This diverse training mixture creates a "curriculum" that enables generalization
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2. Apply the custom patches:
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```bash
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cp -r ./src/lerobot/policies/pi05_openpi/transformers_replace/* \
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cp -r ./src/lerobot/policies/pi05/transformers_replace/* \
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$(python -c "import transformers, os; print(os.path.dirname(transformers.__file__))")
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```
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@@ -72,7 +72,7 @@ pip install transformers==4.53.2
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To use π₀.₅ in your LeRobot configuration, specify the policy type as:
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```python
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policy.type=pi05_openpi
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policy.type=pi05
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```
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## Training
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@@ -84,7 +84,7 @@ Here's a complete training command for finetuning the base π₀.₅ model on yo
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```bash
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python src/lerobot/scripts/train.py \
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--dataset.repo_id=your_dataset \
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--policy.type=pi05_openpi \
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--policy.type=pi05 \
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--output_dir=./outputs/pi0_training \
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--job_name=pi0_training \
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--policy.repo_id=pepijn223/pi05_base_fp32 \
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+1
-2
@@ -147,8 +147,7 @@ all = [
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"lerobot[reachy2]",
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"lerobot[kinematics]",
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"lerobot[intelrealsense]",
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"lerobot[pi0]",
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"lerobot[pi05]",
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"lerobot[pi]",
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"lerobot[smolvla]",
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"lerobot[hilserl]",
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"lerobot[async]",
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@@ -14,10 +14,8 @@
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from .act.configuration_act import ACTConfig as ACTConfig
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from .diffusion.configuration_diffusion import DiffusionConfig as DiffusionConfig
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from .pi0.configuration_pi0 import PI0Config as PI0Config
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from .pi0.processor_pi0 import Pi0NewLineProcessor
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from .pi0_openpi.configuration_pi0openpi import PI0OpenPIConfig as PI0OpenPIConfig
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from .pi05_openpi.configuration_pi05openpi import PI05OpenPIConfig as PI05OpenPIConfig
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from .pi0.configuration_pi0openpi import PI0OpenPIConfig as PI0OpenPIConfig
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from .pi05.configuration_pi05openpi import PI05OpenPIConfig as PI05OpenPIConfig
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from .smolvla.configuration_smolvla import SmolVLAConfig as SmolVLAConfig
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from .smolvla.processor_smolvla import SmolVLANewLineProcessor
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from .tdmpc.configuration_tdmpc import TDMPCConfig as TDMPCConfig
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@@ -26,7 +24,8 @@ from .vqbet.configuration_vqbet import VQBeTConfig as VQBeTConfig
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__all__ = [
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"ACTConfig",
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"DiffusionConfig",
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"PI0Config",
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"PI0OpenPIConfig",
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"PI05OpenPIConfig",
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"SmolVLAConfig",
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"TDMPCConfig",
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"VQBeTConfig",
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@@ -31,10 +31,9 @@ from lerobot.envs.configs import EnvConfig
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from lerobot.envs.utils import env_to_policy_features
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from lerobot.policies.act.configuration_act import ACTConfig
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from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
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from lerobot.policies.pi0.configuration_pi0 import PI0Config
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from lerobot.policies.pi0_openpi.configuration_pi0openpi import PI0OpenPIConfig
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from lerobot.policies.pi0.configuration_pi0openpi import PI0OpenPIConfig
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from lerobot.policies.pi0fast.configuration_pi0fast import PI0FASTConfig
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from lerobot.policies.pi05_openpi.configuration_pi05openpi import PI05OpenPIConfig
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from lerobot.policies.pi05.configuration_pi05openpi import PI05OpenPIConfig
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from lerobot.policies.pretrained import PreTrainedPolicy
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from lerobot.policies.sac.configuration_sac import SACConfig
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from lerobot.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig
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@@ -83,20 +82,16 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:
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from lerobot.policies.vqbet.modeling_vqbet import VQBeTPolicy
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return VQBeTPolicy
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elif name == "pi0":
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from lerobot.policies.pi0.modeling_pi0 import PI0Policy
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return PI0Policy
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elif name == "pi0fast":
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from lerobot.policies.pi0fast.modeling_pi0fast import PI0FASTPolicy
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return PI0FASTPolicy
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elif name == "pi0_openpi":
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from lerobot.policies.pi0_openpi.modeling_pi0openpi import PI0OpenPIPolicy
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elif name == "pi0":
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from lerobot.policies.pi0.modeling_pi0openpi import PI0OpenPIPolicy
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return PI0OpenPIPolicy
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elif name == "pi05_openpi":
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from lerobot.policies.pi05_openpi.modeling_pi05openpi import PI05OpenPIPolicy
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elif name == "pi05":
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from lerobot.policies.pi05.modeling_pi05openpi import PI05OpenPIPolicy
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return PI05OpenPIPolicy
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elif name == "sac":
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@@ -142,13 +137,11 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
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return ACTConfig(**kwargs)
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elif policy_type == "vqbet":
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return VQBeTConfig(**kwargs)
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elif policy_type == "pi0":
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return PI0Config(**kwargs)
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elif policy_type == "pi0fast":
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return PI0FASTConfig(**kwargs)
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elif policy_type == "pi0_openpi":
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elif policy_type == "pi0":
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return PI0OpenPIConfig(**kwargs)
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elif policy_type == "pi05_openpi":
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elif policy_type == "pi05":
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return PI05OpenPIConfig(**kwargs)
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elif policy_type == "sac":
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return SACConfig(**kwargs)
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@@ -267,14 +260,6 @@ def make_pre_post_processors(
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dataset_stats=kwargs.get("dataset_stats"),
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)
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elif isinstance(policy_cfg, PI0Config):
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from lerobot.policies.pi0.processor_pi0 import make_pi0_pre_post_processors
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processors = make_pi0_pre_post_processors(
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config=policy_cfg,
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dataset_stats=kwargs.get("dataset_stats"),
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)
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elif isinstance(policy_cfg, PI0FASTConfig):
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from lerobot.policies.pi0fast.processor_pi0fast import make_pi0fast_pre_post_processors
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@@ -1,149 +0,0 @@
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from dataclasses import dataclass, field
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from lerobot.configs.policies import PreTrainedConfig
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from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
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from lerobot.optim.optimizers import AdamWConfig
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from lerobot.optim.schedulers import (
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CosineDecayWithWarmupSchedulerConfig,
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)
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@PreTrainedConfig.register_subclass("pi0")
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@dataclass
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class PI0Config(PreTrainedConfig):
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# Input / output structure.
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n_obs_steps: int = 1
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chunk_size: int = 50
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n_action_steps: int = 50
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normalization_mapping: dict[str, NormalizationMode] = field(
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default_factory=lambda: {
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"VISUAL": NormalizationMode.IDENTITY,
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"STATE": NormalizationMode.MEAN_STD,
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"ACTION": NormalizationMode.MEAN_STD,
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}
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)
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# Shorter state and action vectors will be padded
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max_state_dim: int = 32
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max_action_dim: int = 32
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# Image preprocessing
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resize_imgs_with_padding: tuple[int, int] = (224, 224)
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# Add empty images. Used by pi0_aloha_sim which adds the empty
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# left and right wrist cameras in addition to the top camera.
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empty_cameras: int = 0
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# Converts the joint and gripper values from the standard Aloha space to
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# the space used by the pi internal runtime which was used to train the base model.
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adapt_to_pi_aloha: bool = False
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# Converts joint dimensions to deltas with respect to the current state before passing to the model.
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# Gripper dimensions will remain in absolute values.
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use_delta_joint_actions_aloha: bool = False
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# Tokenizer
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tokenizer_max_length: int = 48
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# Projector
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proj_width: int = 1024
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# Decoding
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num_steps: int = 10
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# Attention utils
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use_cache: bool = True
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attention_implementation: str = "eager" # or fa2, flex
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# Finetuning settings
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freeze_vision_encoder: bool = True
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train_expert_only: bool = False
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train_state_proj: bool = True
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# Training presets
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optimizer_lr: float = 2.5e-5
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optimizer_betas: tuple[float, float] = (0.9, 0.95)
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optimizer_eps: float = 1e-8
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optimizer_weight_decay: float = 1e-10
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scheduler_warmup_steps: int = 1_000
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scheduler_decay_steps: int = 30_000
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scheduler_decay_lr: float = 2.5e-6
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# TODO: Add EMA
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def __post_init__(self):
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super().__post_init__()
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# TODO(Steven): Validate device and amp? in all policy configs?
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"""Input validation (not exhaustive)."""
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if self.n_action_steps > self.chunk_size:
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raise ValueError(
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f"The chunk size is the upper bound for the number of action steps per model invocation. Got "
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f"{self.n_action_steps} for `n_action_steps` and {self.chunk_size} for `chunk_size`."
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)
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if self.n_obs_steps != 1:
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raise ValueError(
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f"Multiple observation steps not handled yet. Got `nobs_steps={self.n_obs_steps}`"
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)
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if self.use_delta_joint_actions_aloha:
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raise NotImplementedError(
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"`use_delta_joint_actions_aloha` is used by pi0 for aloha real models. It is not ported yet in LeRobot."
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)
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def validate_features(self) -> None:
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# TODO: implement value error
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# if not self.image_features and not self.env_state_feature:
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# raise ValueError("You must provide at least one image or the environment state among the inputs.")
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for i in range(self.empty_cameras):
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key = f"observation.images.empty_camera_{i}"
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empty_camera = PolicyFeature(
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type=FeatureType.VISUAL,
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shape=(3, 480, 640),
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)
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self.input_features[key] = empty_camera
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def get_optimizer_preset(self) -> AdamWConfig:
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return AdamWConfig(
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lr=self.optimizer_lr,
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betas=self.optimizer_betas,
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eps=self.optimizer_eps,
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weight_decay=self.optimizer_weight_decay,
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)
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def get_scheduler_preset(self):
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return CosineDecayWithWarmupSchedulerConfig(
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peak_lr=self.optimizer_lr,
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decay_lr=self.scheduler_decay_lr,
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num_warmup_steps=self.scheduler_warmup_steps,
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num_decay_steps=self.scheduler_decay_steps,
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)
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@property
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def observation_delta_indices(self) -> None:
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return None
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@property
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def action_delta_indices(self) -> list:
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return list(range(self.chunk_size))
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@property
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def reward_delta_indices(self) -> None:
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return None
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+1
-1
@@ -22,7 +22,7 @@ from lerobot.optim.optimizers import AdamWConfig
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from lerobot.optim.schedulers import CosineDecayWithWarmupSchedulerConfig
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@PreTrainedConfig.register_subclass("pi0_openpi")
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@PreTrainedConfig.register_subclass("pi0")
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@dataclass
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class PI0OpenPIConfig(PreTrainedConfig):
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# Model architecture
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@@ -1,82 +0,0 @@
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
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#
|
||||
# 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
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|
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from lerobot.configs.policies import PreTrainedConfig
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from lerobot.datasets.lerobot_dataset import LeRobotDataset
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from lerobot.policies.factory import make_policy
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torch.backends.cudnn.benchmark = True
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def main():
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device = "cuda"
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dataset_repo_id = "danaaubakirova/koch_test"
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# model_name = "pi0_base"
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# ckpt_torch_dir = Path.home() / f".cache/openpi/openpi-assets/checkpoints/{model_name}_pytorch"
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ckpt_torch_dir = "lerobot/pi0"
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dataset = LeRobotDataset(dataset_repo_id, episodes=[0])
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|
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dataloader = torch.utils.data.DataLoader(
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dataset,
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num_workers=0,
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batch_size=1,
|
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)
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|
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batch = next(iter(dataloader))
|
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|
||||
# To device
|
||||
for k in batch:
|
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if isinstance(batch[k], torch.Tensor):
|
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batch[k] = batch[k].to(device=device, dtype=torch.float32)
|
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|
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cfg = PreTrainedConfig.from_pretrained(ckpt_torch_dir)
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cfg.pretrained_path = ckpt_torch_dir
|
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policy = make_policy(cfg, ds_meta=dataset.meta)
|
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|
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# policy = torch.compile(policy, mode="reduce-overhead")
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|
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warmup_iters = 10
|
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benchmark_iters = 30
|
||||
|
||||
# Warmup
|
||||
for _ in range(warmup_iters):
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torch.cuda.synchronize()
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policy.select_action(batch)
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policy.reset()
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torch.cuda.synchronize()
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||||
|
||||
# Benchmark
|
||||
start_event = torch.cuda.Event(enable_timing=True)
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end_event = torch.cuda.Event(enable_timing=True)
|
||||
|
||||
start_event.record()
|
||||
for _ in range(benchmark_iters):
|
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policy.select_action(batch)
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policy.reset()
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||||
end_event.record()
|
||||
|
||||
# Synchronize and measure time
|
||||
torch.cuda.synchronize()
|
||||
elapsed_time_ms = start_event.elapsed_time(end_event)
|
||||
|
||||
avg_time_per_iter = elapsed_time_ms / benchmark_iters
|
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print(f"Average execution time per iteration: {avg_time_per_iter:.3f} ms")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
with torch.inference_mode():
|
||||
main()
|
||||
@@ -1,131 +0,0 @@
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import json
|
||||
import pickle
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
|
||||
from lerobot.configs.policies import PreTrainedConfig
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata
|
||||
from lerobot.policies.factory import make_policy
|
||||
|
||||
|
||||
def display(tensor: torch.Tensor):
|
||||
if tensor.dtype == torch.bool:
|
||||
tensor = tensor.float()
|
||||
print(f"Shape: {tensor.shape}")
|
||||
print(f"Mean: {tensor.mean().item()}")
|
||||
print(f"Std: {tensor.std().item()}")
|
||||
print(f"Min: {tensor.min().item()}")
|
||||
print(f"Max: {tensor.max().item()}")
|
||||
|
||||
|
||||
def main():
|
||||
num_motors = 14
|
||||
device = "cuda"
|
||||
# model_name = "pi0_aloha_towel"
|
||||
model_name = "pi0_aloha_sim"
|
||||
|
||||
if model_name == "pi0_aloha_towel":
|
||||
dataset_repo_id = "lerobot/aloha_static_towel"
|
||||
else:
|
||||
dataset_repo_id = "lerobot/aloha_sim_transfer_cube_human"
|
||||
|
||||
ckpt_torch_dir = Path.home() / f".cache/openpi/openpi-assets/checkpoints/{model_name}_pytorch"
|
||||
ckpt_jax_dir = Path.home() / f".cache/openpi/openpi-assets/checkpoints/{model_name}"
|
||||
save_dir = Path(f"../openpi/data/{model_name}/save")
|
||||
|
||||
with open(save_dir / "example.pkl", "rb") as f:
|
||||
example = pickle.load(f)
|
||||
with open(save_dir / "outputs.pkl", "rb") as f:
|
||||
outputs = pickle.load(f)
|
||||
with open(save_dir / "noise.pkl", "rb") as f:
|
||||
noise = pickle.load(f)
|
||||
|
||||
with open(ckpt_jax_dir / "assets/norm_stats.json") as f:
|
||||
norm_stats = json.load(f)
|
||||
|
||||
# Override stats
|
||||
dataset_meta = LeRobotDatasetMetadata(dataset_repo_id)
|
||||
dataset_meta.stats["observation.state"]["mean"] = torch.tensor(
|
||||
norm_stats["norm_stats"]["state"]["mean"][:num_motors], dtype=torch.float32
|
||||
)
|
||||
dataset_meta.stats["observation.state"]["std"] = torch.tensor(
|
||||
norm_stats["norm_stats"]["state"]["std"][:num_motors], dtype=torch.float32
|
||||
)
|
||||
|
||||
# Create LeRobot batch from Jax
|
||||
batch = {}
|
||||
for cam_key, uint_chw_array in example["images"].items():
|
||||
batch[f"observation.images.{cam_key}"] = torch.from_numpy(uint_chw_array) / 255.0
|
||||
batch["observation.state"] = torch.from_numpy(example["state"])
|
||||
batch["action"] = torch.from_numpy(outputs["actions"])
|
||||
batch["task"] = example["prompt"]
|
||||
|
||||
if model_name == "pi0_aloha_towel":
|
||||
del batch["observation.images.cam_low"]
|
||||
elif model_name == "pi0_aloha_sim":
|
||||
batch["observation.images.top"] = batch["observation.images.cam_high"]
|
||||
del batch["observation.images.cam_high"]
|
||||
|
||||
# Batchify
|
||||
for key in batch:
|
||||
if isinstance(batch[key], torch.Tensor):
|
||||
batch[key] = batch[key].unsqueeze(0)
|
||||
elif isinstance(batch[key], str):
|
||||
batch[key] = [batch[key]]
|
||||
else:
|
||||
raise ValueError(f"{key}, {batch[key]}")
|
||||
|
||||
# To device
|
||||
for k in batch:
|
||||
if isinstance(batch[k], torch.Tensor):
|
||||
batch[k] = batch[k].to(device=device, dtype=torch.float32)
|
||||
|
||||
noise = torch.from_numpy(noise).to(device=device, dtype=torch.float32)
|
||||
|
||||
from lerobot import policies # noqa
|
||||
|
||||
cfg = PreTrainedConfig.from_pretrained(ckpt_torch_dir)
|
||||
cfg.pretrained_path = ckpt_torch_dir
|
||||
policy = make_policy(cfg, dataset_meta)
|
||||
|
||||
# loss_dict = policy.forward(batch, noise=noise, time=time_beta)
|
||||
# loss_dict["loss"].backward()
|
||||
# print("losses")
|
||||
# display(loss_dict["losses_after_forward"])
|
||||
# print("pi_losses")
|
||||
# display(pi_losses)
|
||||
|
||||
actions = []
|
||||
for _ in range(50):
|
||||
action = policy.select_action(batch, noise=noise)
|
||||
actions.append(action)
|
||||
|
||||
actions = torch.stack(actions, dim=1)
|
||||
pi_actions = batch["action"]
|
||||
print("actions")
|
||||
display(actions)
|
||||
print()
|
||||
print("pi_actions")
|
||||
display(pi_actions)
|
||||
print("atol=3e-2", torch.allclose(actions, pi_actions, atol=3e-2))
|
||||
print("atol=2e-2", torch.allclose(actions, pi_actions, atol=2e-2))
|
||||
print("atol=1e-2", torch.allclose(actions, pi_actions, atol=1e-2))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,84 +0,0 @@
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from transformers import GemmaConfig, PaliGemmaConfig
|
||||
|
||||
|
||||
def get_paligemma_config(precision: str):
|
||||
config = {
|
||||
"image_token_index": None,
|
||||
"pad_token_id": 0,
|
||||
"bos_token_id": 2,
|
||||
"eos_token_id": 1,
|
||||
}
|
||||
|
||||
# image_sizes = {"2b-test": 224, "3b-224px": 224, "3b-448px": 448, "3b-896px": 896}
|
||||
|
||||
image_size = 224 # image_sizes[variant]
|
||||
patch_size = 14
|
||||
num_image_tokens = (image_size**2) // (patch_size**2)
|
||||
|
||||
config["image_token_index"] = 257152
|
||||
text_config = {
|
||||
"vocab_size": 257152,
|
||||
"num_hidden_layers": 18,
|
||||
"num_key_value_heads": 1,
|
||||
"head_dim": 256,
|
||||
"torch_dtype": precision,
|
||||
"hidden_size": 2048,
|
||||
"hidden_activation": "gelu_pytorch_tanh",
|
||||
"num_attention_heads": 8,
|
||||
"intermediate_size": 16384,
|
||||
"is_encoder_decoder": False,
|
||||
}
|
||||
vision_config = {
|
||||
"torch_dtype": precision,
|
||||
"image_size": image_size,
|
||||
"patch_size": patch_size,
|
||||
"num_image_tokens": num_image_tokens,
|
||||
"hidden_size": 1152,
|
||||
"intermediate_size": 4304,
|
||||
"num_hidden_layers": 27,
|
||||
"num_attention_heads": 16,
|
||||
"projector_hidden_act": "gelu_fast",
|
||||
"vision_use_head": False,
|
||||
}
|
||||
final_config = PaliGemmaConfig(text_config=text_config, vision_config=vision_config, **config)
|
||||
return final_config
|
||||
|
||||
|
||||
def get_gemma_config(precision: str):
|
||||
config = {
|
||||
"image_token_index": None,
|
||||
"pad_token_id": 0,
|
||||
"bos_token_id": 2,
|
||||
"eos_token_id": 1,
|
||||
}
|
||||
|
||||
config["image_token_index"] = 257152
|
||||
text_config = {
|
||||
"vocab_size": 257152,
|
||||
"num_hidden_layers": 18,
|
||||
"num_key_value_heads": 1,
|
||||
"head_dim": 256,
|
||||
"torch_dtype": precision,
|
||||
"hidden_size": 1024,
|
||||
"hidden_activation": "gelu_pytorch_tanh",
|
||||
"num_attention_heads": 8,
|
||||
"intermediate_size": 4096,
|
||||
"is_encoder_decoder": False,
|
||||
}
|
||||
final_config = GemmaConfig()
|
||||
final_config.update(text_config)
|
||||
return final_config
|
||||
@@ -1,437 +0,0 @@
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
"""
|
||||
Convert pi0 parameters from Jax to Pytorch
|
||||
|
||||
Follow [README of openpi](https://github.com/Physical-Intelligence/openpi) to create a new environment
|
||||
and install the required libraries.
|
||||
|
||||
```bash
|
||||
cd ~/code/openpi
|
||||
source .venv/bin/activate
|
||||
```
|
||||
|
||||
Example downloading parameters:
|
||||
```bash
|
||||
python
|
||||
>>> import openpi.shared.download as download
|
||||
>>> path='s3://openpi-assets/checkpoints/pi0_base/params'
|
||||
>>> download.maybe_download(path)
|
||||
```
|
||||
|
||||
Converting pi0_base:
|
||||
```python
|
||||
python -m lerobot.policies.pi0.conversion_scripts.convert_pi0_to_hf_lerobot \
|
||||
--checkpoint_dir /home/remi_cadene/.cache/openpi/openpi-assets/checkpoints/pi0_base/params \
|
||||
--output_path /home/remi_cadene/.cache/openpi/openpi-assets/checkpoints/pi0_base_pytorch
|
||||
```
|
||||
|
||||
```python
|
||||
python -m lerobot.policies.pi0.conversion_scripts.convert_pi0_to_hf_lerobot \
|
||||
--checkpoint_dir /home/remi_cadene/.cache/openpi/openpi-assets/checkpoints/pi0_aloha_sim/params \
|
||||
--output_path /home/remi_cadene/.cache/openpi/openpi-assets/checkpoints/pi0_aloha_sim_pytorch
|
||||
```
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import pathlib
|
||||
|
||||
import jax
|
||||
import numpy as np
|
||||
import orbax.checkpoint as ocp
|
||||
import torch
|
||||
from jax.sharding import SingleDeviceSharding
|
||||
|
||||
from lerobot.policies.pi0.configuration_pi0 import PI0Config
|
||||
from lerobot.policies.pi0.conversion_scripts.conversion_utils import (
|
||||
get_gemma_config,
|
||||
get_paligemma_config,
|
||||
)
|
||||
from lerobot.policies.pi0.modeling_pi0 import PI0Policy
|
||||
|
||||
PRECISIONS = {"bfloat16": torch.bfloat16, "float32": torch.float32, "float16": torch.float16}
|
||||
|
||||
|
||||
def slice_paligemma_state_dict(state_dict, config):
|
||||
suffix = "/value" if "img/embedding/kernel/value" in state_dict else ""
|
||||
|
||||
# fmt: off
|
||||
# patch embeddings
|
||||
state_dict["paligemma.vision_tower.vision_model.embeddings.patch_embedding.weight"] = state_dict.pop(f"img/embedding/kernel{suffix}").transpose(
|
||||
3, 2, 0, 1
|
||||
)
|
||||
state_dict["paligemma.vision_tower.vision_model.embeddings.patch_embedding.bias"] = state_dict.pop(f"img/embedding/bias{suffix}")
|
||||
# positional embeddings
|
||||
state_dict["paligemma.vision_tower.vision_model.embeddings.position_embedding.weight"] = state_dict.pop(f"img/pos_embedding{suffix}").reshape(
|
||||
-1, config.vision_config.hidden_size
|
||||
)
|
||||
|
||||
# extract vision layers to be sliced at index 0. There are 27 layers in the base model.
|
||||
encoderblock_layernorm0_scale = state_dict.pop(f"img/Transformer/encoderblock/LayerNorm_0/scale{suffix}")
|
||||
encoderblock_layernorm0_bias = state_dict.pop(f"img/Transformer/encoderblock/LayerNorm_0/bias{suffix}")
|
||||
encoderblock_layernorm1_scale = state_dict.pop(f"img/Transformer/encoderblock/LayerNorm_1/scale{suffix}")
|
||||
encoderblock_layernorm1_bias = state_dict.pop(f"img/Transformer/encoderblock/LayerNorm_1/bias{suffix}")
|
||||
|
||||
encoderblock_mlp_dense0_kernel= state_dict.pop(f"img/Transformer/encoderblock/MlpBlock_0/Dense_0/kernel{suffix}")
|
||||
encoderblock_mlp_dense0_bias= state_dict.pop(f"img/Transformer/encoderblock/MlpBlock_0/Dense_0/bias{suffix}")
|
||||
encoderblock_mlp_dense1_kernel= state_dict.pop(f"img/Transformer/encoderblock/MlpBlock_0/Dense_1/kernel{suffix}")
|
||||
encoderblock_mlp_dense1_bias= state_dict.pop(f"img/Transformer/encoderblock/MlpBlock_0/Dense_1/bias{suffix}")
|
||||
|
||||
encoderblock_attention_0_key_kernel = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/key/kernel{suffix}")
|
||||
encoderblock_attention_0_key_bias = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/key/bias{suffix}")
|
||||
encoderblock_attention_0_value_kernel = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/value/kernel{suffix}")
|
||||
encoderblock_attention_0_value_bias = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/value/bias{suffix}")
|
||||
encoderblock_attention_0_query_kernel = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/query/kernel{suffix}")
|
||||
encoderblock_attention_0_query_bias = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/query/bias{suffix}")
|
||||
encoderblock_attention_0_out_kernel = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/out/kernel{suffix}")
|
||||
encoderblock_attention_0_out_bias = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/out/bias{suffix}")
|
||||
|
||||
for i in range(config.vision_config.num_hidden_layers):
|
||||
state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.layer_norm1.weight"] = encoderblock_layernorm0_scale[i].transpose()
|
||||
state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.layer_norm1.bias"] = encoderblock_layernorm0_bias[i]
|
||||
state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.layer_norm2.weight"] = encoderblock_layernorm1_scale[i].transpose()
|
||||
state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.layer_norm2.bias"] = encoderblock_layernorm1_bias[i]
|
||||
|
||||
state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.mlp.fc1.weight"] = encoderblock_mlp_dense0_kernel[i].transpose()
|
||||
state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.mlp.fc1.bias"] = encoderblock_mlp_dense0_bias[i]
|
||||
state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.mlp.fc2.weight"] = encoderblock_mlp_dense1_kernel[i].transpose()
|
||||
state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.mlp.fc2.bias"] = encoderblock_mlp_dense1_bias[i]
|
||||
state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.k_proj.weight"] = encoderblock_attention_0_key_kernel[i].reshape(-1, config.vision_config.hidden_size).transpose()
|
||||
state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.k_proj.bias"] = encoderblock_attention_0_key_bias[i].reshape(-1, config.vision_config.hidden_size).reshape(-1)
|
||||
state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.v_proj.weight"] = encoderblock_attention_0_value_kernel[i].reshape(-1, config.vision_config.hidden_size).transpose()
|
||||
state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.v_proj.bias"] = encoderblock_attention_0_value_bias[i].reshape(-1, config.vision_config.hidden_size).reshape(-1)
|
||||
state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.q_proj.weight"] = encoderblock_attention_0_query_kernel[i].reshape(-1, config.vision_config.hidden_size).transpose()
|
||||
state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.q_proj.bias"] = encoderblock_attention_0_query_bias[i].reshape(-1, config.vision_config.hidden_size).reshape(-1)
|
||||
state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.out_proj.weight"] = encoderblock_attention_0_out_kernel[i].reshape(-1, config.vision_config.hidden_size).transpose()
|
||||
state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.out_proj.bias"] = encoderblock_attention_0_out_bias[i].reshape(-1, config.vision_config.hidden_size).reshape(-1)
|
||||
|
||||
state_dict["paligemma.vision_tower.vision_model.post_layernorm.weight"] = state_dict.pop(f"img/Transformer/encoder_norm/scale{suffix}").transpose()
|
||||
state_dict["paligemma.vision_tower.vision_model.post_layernorm.bias"] = state_dict.pop(f"img/Transformer/encoder_norm/bias{suffix}")
|
||||
|
||||
# multimodal projector
|
||||
|
||||
state_dict['paligemma.multi_modal_projector.linear.weight'] = state_dict.pop(f"img/head/kernel{suffix}").transpose()
|
||||
state_dict['paligemma.multi_modal_projector.linear.bias'] = state_dict.pop(f"img/head/bias{suffix}")
|
||||
|
||||
# text decoder (gemma)
|
||||
embedding_vector = state_dict.pop(f"llm/embedder/input_embedding{suffix}")
|
||||
state_dict["paligemma.language_model.model.embed_tokens.weight"] = embedding_vector
|
||||
|
||||
# pop the einsum attention + mlp representations. There are 18 layers in gemma-2b.
|
||||
|
||||
llm_attention_attn_vec_einsum = state_dict.pop(f"llm/layers/attn/attn_vec_einsum/w{suffix}")
|
||||
llm_attention_kv_einsum = state_dict.pop(f"llm/layers/attn/kv_einsum/w{suffix}")
|
||||
llm_attention_q_einsum = state_dict.pop(f"llm/layers/attn/q_einsum/w{suffix}")
|
||||
|
||||
llm_mlp_gating_einsum = state_dict.pop(f"llm/layers/mlp/gating_einsum{suffix}")
|
||||
llm_mlp_linear = state_dict.pop(f"llm/layers/mlp/linear{suffix}")
|
||||
# TODO verify correctness of layer norm loading
|
||||
|
||||
llm_input_layernorm = state_dict.pop(f"llm/layers/pre_attention_norm/scale{suffix}")
|
||||
llm_post_attention_layernorm = state_dict.pop(f"llm/layers/pre_ffw_norm/scale{suffix}")
|
||||
|
||||
for i in range(config.text_config.num_hidden_layers):
|
||||
# llm_attention_q_einsum[i].shape = (8, 2048, 256)
|
||||
q_proj_weight_reshaped = llm_attention_q_einsum[i].transpose(0, 2, 1).reshape(config.text_config.num_attention_heads * config.text_config.head_dim, config.text_config.hidden_size)
|
||||
|
||||
state_dict[f"paligemma.language_model.model.layers.{i}.self_attn.q_proj.weight"] = q_proj_weight_reshaped
|
||||
|
||||
# llm_attention_kv_einsum[i, 0, 0].shape = (2048, 256)
|
||||
k_proj_weight_reshaped = llm_attention_kv_einsum[i, 0, 0].transpose()
|
||||
state_dict[f"paligemma.language_model.model.layers.{i}.self_attn.k_proj.weight"] = k_proj_weight_reshaped
|
||||
# llm_attention_kv_einsum[i, 1, 0].shape = (2048, 256)
|
||||
v_proj_weight_reshaped = llm_attention_kv_einsum[i, 1, 0].transpose()
|
||||
state_dict[f"paligemma.language_model.model.layers.{i}.self_attn.v_proj.weight"] = v_proj_weight_reshaped
|
||||
|
||||
# output projection.
|
||||
|
||||
# llm_attention_attn_vec_einsum[i].shape = (8, 256, 2048)
|
||||
o_proj_weight_reshaped = llm_attention_attn_vec_einsum[i].transpose(2, 0, 1).reshape(config.text_config.num_attention_heads * config.text_config.head_dim, config.text_config.hidden_size)
|
||||
|
||||
state_dict[f"paligemma.language_model.model.layers.{i}.self_attn.o_proj.weight"] = o_proj_weight_reshaped
|
||||
# mlp layers
|
||||
gate_proj_weight = llm_mlp_gating_einsum[i, 0]
|
||||
state_dict[f"paligemma.language_model.model.layers.{i}.mlp.gate_proj.weight"] = gate_proj_weight.transpose()
|
||||
up_proj_weight = llm_mlp_gating_einsum[i, 1]
|
||||
state_dict[f"paligemma.language_model.model.layers.{i}.mlp.up_proj.weight"] = up_proj_weight.transpose()
|
||||
state_dict[f"paligemma.language_model.model.layers.{i}.mlp.down_proj.weight"] = llm_mlp_linear[i].transpose()
|
||||
state_dict[f"paligemma.language_model.model.layers.{i}.input_layernorm.weight"] = llm_input_layernorm[i]
|
||||
state_dict[f"paligemma.language_model.model.layers.{i}.post_attention_layernorm.weight"] = llm_post_attention_layernorm[i]
|
||||
|
||||
state_dict["paligemma.language_model.model.norm.weight"] = state_dict.pop(f"llm/final_norm/scale{suffix}")
|
||||
state_dict["paligemma.language_model.lm_head.weight"] = embedding_vector # weights are tied.
|
||||
|
||||
# fmt: on
|
||||
expert_dict = {}
|
||||
final_state_dict = {}
|
||||
for key, value in state_dict.items():
|
||||
if key not in [
|
||||
f"llm/final_norm_1/scale{suffix}",
|
||||
f"llm/layers/attn/attn_vec_einsum_1/w{suffix}",
|
||||
f"llm/layers/attn/kv_einsum_1/w{suffix}",
|
||||
f"llm/layers/attn/q_einsum_1/w{suffix}",
|
||||
f"llm/layers/mlp_1/gating_einsum{suffix}",
|
||||
f"llm/layers/mlp_1/linear{suffix}",
|
||||
f"llm/layers/pre_attention_norm_1/scale{suffix}",
|
||||
f"llm/layers/pre_ffw_norm_1/scale{suffix}",
|
||||
]:
|
||||
final_state_dict[key] = torch.from_numpy(value)
|
||||
else:
|
||||
expert_dict[key] = value
|
||||
|
||||
return final_state_dict, expert_dict
|
||||
|
||||
|
||||
def slice_gemma_state_dict(state_dict, config, num_expert=1):
|
||||
# fmt: off
|
||||
# text decoder (gemma)
|
||||
# no embedding vector, the expert just has the decoder layers
|
||||
|
||||
embedding_vector = torch.zeros([config.vocab_size, config.hidden_size])
|
||||
state_dict["gemma_expert.model.embed_tokens.weight"] = embedding_vector
|
||||
|
||||
# pop the einsum attention + mlp representations. There are 18 layers in gemma-2b.
|
||||
|
||||
suffix = "/value" if f"llm/layers/attn/attn_vec_einsum_{num_expert}/w/value" in state_dict else ""
|
||||
|
||||
llm_attention_attn_vec_einsum = state_dict.pop(f"llm/layers/attn/attn_vec_einsum_{num_expert}/w{suffix}")
|
||||
llm_attention_kv_einsum = state_dict.pop(f"llm/layers/attn/kv_einsum_{num_expert}/w{suffix}")
|
||||
llm_attention_q_einsum = state_dict.pop(f"llm/layers/attn/q_einsum_{num_expert}/w{suffix}")
|
||||
|
||||
llm_mlp_gating_einsum = state_dict.pop(f"llm/layers/mlp_{num_expert}/gating_einsum{suffix}")
|
||||
llm_mlp_linear = state_dict.pop(f"llm/layers/mlp_{num_expert}/linear{suffix}")
|
||||
# TODO verify correctness of layer norm loading
|
||||
|
||||
llm_input_layernorm = state_dict.pop(f"llm/layers/pre_attention_norm_{num_expert}/scale{suffix}")
|
||||
llm_post_attention_layernorm = state_dict.pop(f"llm/layers/pre_ffw_norm_{num_expert}/scale{suffix}")
|
||||
|
||||
for i in range(config.num_hidden_layers):
|
||||
q_proj_weight_reshaped = llm_attention_q_einsum[i].transpose(0, 2, 1).reshape(config.num_attention_heads * config.head_dim, config.hidden_size)
|
||||
|
||||
state_dict[f"gemma_expert.model.layers.{i}.self_attn.q_proj.weight"] = q_proj_weight_reshaped
|
||||
|
||||
k_proj_weight_reshaped = llm_attention_kv_einsum[i, 0, 0].transpose()
|
||||
state_dict[f"gemma_expert.model.layers.{i}.self_attn.k_proj.weight"] = k_proj_weight_reshaped
|
||||
v_proj_weight_reshaped = llm_attention_kv_einsum[i, 1, 0].transpose()
|
||||
state_dict[f"gemma_expert.model.layers.{i}.self_attn.v_proj.weight"] = v_proj_weight_reshaped
|
||||
|
||||
# output projection.
|
||||
|
||||
# llm_attention_attn_vec_einsum[i].shape = (8, 256, 1024)
|
||||
o_proj_weight_reshaped = llm_attention_attn_vec_einsum[i].reshape(config.num_attention_heads * config.head_dim, config.hidden_size).transpose(1,0)# .transpose(2, 0, 1).reshape(config.num_attention_heads * config.head_dim, config.hidden_size).transpose(1, 0)
|
||||
|
||||
state_dict[f"gemma_expert.model.layers.{i}.self_attn.o_proj.weight"] = o_proj_weight_reshaped
|
||||
# mlp layers
|
||||
gate_proj_weight = llm_mlp_gating_einsum[i, 0]
|
||||
state_dict[f"gemma_expert.model.layers.{i}.mlp.gate_proj.weight"] = gate_proj_weight.transpose()
|
||||
up_proj_weight = llm_mlp_gating_einsum[i, 1]
|
||||
state_dict[f"gemma_expert.model.layers.{i}.mlp.up_proj.weight"] = up_proj_weight.transpose()
|
||||
state_dict[f"gemma_expert.model.layers.{i}.mlp.down_proj.weight"] = llm_mlp_linear[i].transpose()
|
||||
state_dict[f"gemma_expert.model.layers.{i}.input_layernorm.weight"] = llm_input_layernorm[i]
|
||||
state_dict[f"gemma_expert.model.layers.{i}.post_attention_layernorm.weight"] = llm_post_attention_layernorm[i]
|
||||
|
||||
state_dict["gemma_expert.model.norm.weight"] = state_dict.pop(f"llm/final_norm_{num_expert}/scale{suffix}")
|
||||
state_dict["gemma_expert.lm_head.weight"] = embedding_vector # weights are tied. (and zeros here)
|
||||
|
||||
# fmt: on
|
||||
final_state_dict = {}
|
||||
for key, value in state_dict.items():
|
||||
if not isinstance(value, torch.Tensor):
|
||||
final_state_dict[key] = torch.from_numpy(value)
|
||||
else:
|
||||
final_state_dict[key] = value
|
||||
return final_state_dict
|
||||
|
||||
|
||||
def flatten_for_memory(tree, parent_key=""):
|
||||
out = {}
|
||||
for k, v in tree.items():
|
||||
new_key = f"{parent_key}/{k}" if parent_key else k
|
||||
if isinstance(v, dict):
|
||||
out.update(flatten_for_memory(v, new_key))
|
||||
else:
|
||||
out[new_key] = np.array(v) # Ensure conversion to np.array for consistency
|
||||
return out
|
||||
|
||||
|
||||
def flatten_for_npz(tree, parent_key=""):
|
||||
out = {}
|
||||
for k, v in tree.items():
|
||||
new_key = f"{parent_key}/{k}" if parent_key else k
|
||||
if isinstance(v, dict):
|
||||
out.update(flatten_for_npz(v, new_key))
|
||||
else:
|
||||
# bf16/f32 here?
|
||||
out[new_key] = np.array(v)
|
||||
return out
|
||||
|
||||
|
||||
def slice_initial_orbax_checkpoint(checkpoint_dir: str):
|
||||
params_path = pathlib.Path(checkpoint_dir).resolve()
|
||||
checkpointer = ocp.PyTreeCheckpointer()
|
||||
|
||||
metadata = checkpointer.metadata(params_path)
|
||||
print("Metadata keys:", list(metadata.keys()))
|
||||
|
||||
params_name = "params"
|
||||
|
||||
item = {params_name: metadata[params_name]}
|
||||
device = jax.local_devices()[0] # Use the first local device
|
||||
sharding = SingleDeviceSharding(device)
|
||||
restored = checkpointer.restore(
|
||||
params_path,
|
||||
ocp.args.PyTreeRestore(
|
||||
item=item,
|
||||
restore_args=jax.tree_util.tree_map(
|
||||
lambda _: ocp.ArrayRestoreArgs(
|
||||
restore_type=jax.Array, # or np.ndarray, but bf16 is annoying about it
|
||||
sharding=sharding,
|
||||
),
|
||||
item,
|
||||
),
|
||||
transforms={},
|
||||
),
|
||||
)
|
||||
params = restored[params_name]
|
||||
|
||||
# get params for PaliGemma
|
||||
pali_params = params["PaliGemma"]
|
||||
del params["PaliGemma"]
|
||||
pali_params_flat = flatten_for_npz(pali_params)
|
||||
return {"paligemma_params": pali_params_flat, "projection_params": params}
|
||||
|
||||
|
||||
def update_keys_with_prefix(d: dict, prefix: str) -> dict:
|
||||
"""Update dictionary keys by adding a prefix."""
|
||||
return {f"{prefix}{key}": value for key, value in d.items()}
|
||||
|
||||
|
||||
def convert_pi0_checkpoint(checkpoint_dir: str, precision: str, tokenizer_id: str, output_path: str):
|
||||
# Break down orbax ckpts - they are in OCDBT
|
||||
initial_params = slice_initial_orbax_checkpoint(checkpoint_dir=checkpoint_dir)
|
||||
# process projection params
|
||||
keys = [
|
||||
"state_proj",
|
||||
"action_in_proj",
|
||||
"action_out_proj",
|
||||
"action_time_mlp_in",
|
||||
"action_time_mlp_out",
|
||||
]
|
||||
|
||||
projection_params = {}
|
||||
for key in keys:
|
||||
kernel_params = initial_params["projection_params"][key]["kernel"]
|
||||
bias_params = initial_params["projection_params"][key]["bias"]
|
||||
if isinstance(kernel_params, dict):
|
||||
weight = kernel_params["value"]
|
||||
bias = bias_params["value"]
|
||||
else:
|
||||
weight = kernel_params
|
||||
bias = bias_params
|
||||
projection_params[f"{key}.weight"] = torch.from_numpy(np.array(weight)).T
|
||||
projection_params[f"{key}.bias"] = torch.from_numpy(np.array(bias))
|
||||
|
||||
# Process PaliGemma weights
|
||||
paligemma_config = get_paligemma_config(precision)
|
||||
paligemma_params, gemma_raw_dictionary = slice_paligemma_state_dict(
|
||||
initial_params["paligemma_params"], paligemma_config
|
||||
)
|
||||
|
||||
# Process Gemma weights (at this stage they are unused)
|
||||
gemma_config = get_gemma_config(precision)
|
||||
gemma_params = slice_gemma_state_dict(gemma_raw_dictionary, config=gemma_config)
|
||||
|
||||
# Instantiate model from configs
|
||||
|
||||
if "pi0_aloha_sim" in checkpoint_dir:
|
||||
pi0_config = PI0Config(
|
||||
empty_cameras=2,
|
||||
adapt_to_pi_aloha=True,
|
||||
use_delta_joint_actions_aloha=False,
|
||||
)
|
||||
elif "pi0_aloha_towel" in checkpoint_dir:
|
||||
pi0_config = PI0Config(
|
||||
adapt_to_pi_aloha=True,
|
||||
use_delta_joint_actions_aloha=True,
|
||||
)
|
||||
elif "pi0_base" in checkpoint_dir:
|
||||
pi0_config = PI0Config(
|
||||
empty_cameras=0,
|
||||
adapt_to_pi_aloha=False,
|
||||
use_delta_joint_actions_aloha=False,
|
||||
)
|
||||
else:
|
||||
raise ValueError()
|
||||
|
||||
# gemma_config=gemma_config, paligemma_config=paligemma_config)
|
||||
pi0_model = PI0Policy(pi0_config)
|
||||
|
||||
paligemma_params = update_keys_with_prefix(paligemma_params, "model.paligemma_with_expert.")
|
||||
gemma_params = update_keys_with_prefix(gemma_params, "model.paligemma_with_expert.")
|
||||
projection_params = update_keys_with_prefix(projection_params, "model.")
|
||||
|
||||
# load state dict
|
||||
torch_dtype = PRECISIONS[precision]
|
||||
pi0_model.load_state_dict({**paligemma_params, **gemma_params, **projection_params})
|
||||
pi0_model = pi0_model.to(torch_dtype)
|
||||
# pi0_tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
|
||||
|
||||
pi0_model.save_pretrained(output_path, safe_serialization=True)
|
||||
# pi0_tokenizer.save_pretrained(output_path, dtype=torch_dtype)
|
||||
|
||||
# assert that model loads properly
|
||||
del pi0_model
|
||||
PI0Policy.from_pretrained(output_path)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"--checkpoint_dir",
|
||||
default="/raid/pablo/.cache/openpi/openpi-assets/checkpoints/pi0_aloha_sim/params",
|
||||
type=str,
|
||||
help="Path to the ocdbt checkpoint",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--precision",
|
||||
choices=["float32", "bfloat16", "float16"],
|
||||
default="float32",
|
||||
type=str,
|
||||
help="Precision identifier for model conversion - should match the base checkpoint precision.",
|
||||
)
|
||||
# tokenizer is identical to paligemma, it appears
|
||||
|
||||
parser.add_argument(
|
||||
"--tokenizer_hub_id",
|
||||
default="google/paligemma-3b-pt-224",
|
||||
type=str,
|
||||
help="Hub path to the tokenizer to save",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--output_path",
|
||||
required=True,
|
||||
type=str,
|
||||
help="Path to save converted weights to",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
convert_pi0_checkpoint(
|
||||
checkpoint_dir=args.checkpoint_dir,
|
||||
precision=args.precision,
|
||||
tokenizer_id=args.tokenizer_hub_id,
|
||||
output_path=args.output_path,
|
||||
)
|
||||
@@ -1,141 +0,0 @@
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F # noqa: N812
|
||||
from packaging.version import Version
|
||||
|
||||
if Version(torch.__version__) > Version("2.5.0"):
|
||||
# Ffex attention is only available from torch 2.5 onwards
|
||||
from torch.nn.attention.flex_attention import (
|
||||
_mask_mod_signature,
|
||||
_round_up_to_multiple,
|
||||
create_block_mask,
|
||||
create_mask,
|
||||
flex_attention,
|
||||
)
|
||||
|
||||
|
||||
# @torch.compile(dynamic=False)
|
||||
def flex_attention_forward(
|
||||
attention_mask: torch.Tensor,
|
||||
batch_size: int,
|
||||
head_dim: int,
|
||||
query_states: torch.Tensor,
|
||||
key_states: torch.Tensor,
|
||||
value_states: torch.Tensor,
|
||||
scaling=None,
|
||||
):
|
||||
"""
|
||||
This is defined out of classes to make compile happy.
|
||||
"""
|
||||
|
||||
original_dtype = query_states.dtype
|
||||
num_att_heads = 8
|
||||
num_key_value_heads = 1
|
||||
num_key_value_groups = num_att_heads // num_key_value_heads
|
||||
|
||||
key_states = key_states[:, :, :, None, :]
|
||||
key_states = key_states.expand(
|
||||
batch_size, key_states.shape[1], num_key_value_heads, num_key_value_groups, head_dim
|
||||
)
|
||||
key_states = key_states.reshape(
|
||||
batch_size, key_states.shape[1], num_key_value_heads * num_key_value_groups, head_dim
|
||||
)
|
||||
|
||||
value_states = value_states[:, :, :, None, :]
|
||||
value_states = value_states.expand(
|
||||
batch_size, value_states.shape[1], num_key_value_heads, num_key_value_groups, head_dim
|
||||
)
|
||||
value_states = value_states.reshape(
|
||||
batch_size, value_states.shape[1], num_key_value_heads * num_key_value_groups, head_dim
|
||||
)
|
||||
|
||||
query_states = query_states.transpose(1, 2)
|
||||
key_states = key_states.transpose(1, 2)
|
||||
value_states = value_states.transpose(1, 2)
|
||||
|
||||
query_states = query_states.to(torch.float32)
|
||||
key_states = key_states.to(torch.float32)
|
||||
value_states = value_states.to(torch.float32)
|
||||
|
||||
causal_mask = attention_mask
|
||||
if causal_mask is not None:
|
||||
causal_mask = causal_mask[:, None, :, : key_states.shape[2]]
|
||||
|
||||
if causal_mask.shape[1] == 1 and query_states.shape[1] > 1:
|
||||
causal_mask = causal_mask.expand(-1, query_states.shape[1], -1, -1)
|
||||
|
||||
def precomputed_mask_factory(precomputed_mask: torch.Tensor) -> _mask_mod_signature:
|
||||
def mask_mod(b, h, q_idx, kv_idx):
|
||||
# Danger zone: if b,h,q_idx,kv_idx exceed the shape, device-side assert occurs.
|
||||
return precomputed_mask[b][h][q_idx][kv_idx]
|
||||
|
||||
return mask_mod
|
||||
|
||||
b_mask, h_mask, q_len, kv_len = causal_mask.shape # The shape of your mask
|
||||
|
||||
block_size = 128
|
||||
q_len_rounded = _round_up_to_multiple(q_len, block_size)
|
||||
kv_len_rounded = _round_up_to_multiple(kv_len, block_size)
|
||||
|
||||
# *CRITICAL* we do need to expand here, else we get a CUDA index error
|
||||
|
||||
pad_q = q_len_rounded - q_len
|
||||
pad_k = kv_len_rounded - kv_len
|
||||
|
||||
padded_causal_mask = F.pad(causal_mask, (0, pad_k, 0, pad_q), value=0.0)
|
||||
mask_mod_fn_orig = precomputed_mask_factory(padded_causal_mask)
|
||||
|
||||
mask_4d = create_mask(
|
||||
mod_fn=mask_mod_fn_orig,
|
||||
B=b_mask,
|
||||
H=h_mask,
|
||||
Q_LEN=q_len_rounded,
|
||||
KV_LEN=kv_len_rounded,
|
||||
device=causal_mask.device,
|
||||
_compile=False,
|
||||
)
|
||||
|
||||
mask_mod_fn_padded = precomputed_mask_factory(mask_4d)
|
||||
block_mask = create_block_mask(
|
||||
mask_mod=mask_mod_fn_padded,
|
||||
B=b_mask,
|
||||
H=h_mask,
|
||||
Q_LEN=q_len_rounded,
|
||||
KV_LEN=kv_len_rounded,
|
||||
BLOCK_SIZE=block_size,
|
||||
device=causal_mask.device,
|
||||
_compile=False,
|
||||
)
|
||||
|
||||
# mask is applied inside the kernel, ideally more efficiently than score_mod.
|
||||
attn_output, attention_weights = flex_attention(
|
||||
query_states,
|
||||
key_states,
|
||||
value_states,
|
||||
block_mask=block_mask,
|
||||
enable_gqa=True, # because we shaped query/key states for GQA
|
||||
scale=head_dim**-0.5 if scaling is None else scaling,
|
||||
return_lse=True,
|
||||
)
|
||||
|
||||
attn_output = attn_output.to(dtype=original_dtype)
|
||||
attn_output = attn_output.transpose(1, 2).contiguous() # [B, Q_LEN, H, head_dim]
|
||||
attn_output = attn_output.reshape(
|
||||
batch_size,
|
||||
-1,
|
||||
attn_output.shape[2] * attn_output.shape[3], # merges [H, head_dim]
|
||||
)
|
||||
return attn_output
|
||||
@@ -1,705 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2025 Physical Intelligence 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.
|
||||
|
||||
"""
|
||||
π0: A Vision-Language-Action Flow Model for General Robot Control
|
||||
|
||||
[Paper](https://www.physicalintelligence.company/download/pi0.pdf)
|
||||
[Jax code](https://github.com/Physical-Intelligence/openpi)
|
||||
|
||||
Designed by Physical Intelligence. Ported from Jax by Hugging Face.
|
||||
Disclaimer: It is not expected to perform as well as the original implementation.
|
||||
|
||||
Install pi0 extra dependencies:
|
||||
```bash
|
||||
pip install -e ".[pi0]"
|
||||
```
|
||||
|
||||
Example of finetuning the pi0 pretrained model (`pi0_base` in `openpi`):
|
||||
```bash
|
||||
lerobot-train \
|
||||
--policy.path=lerobot/pi0 \
|
||||
--dataset.repo_id=danaaubakirova/koch_test
|
||||
```
|
||||
|
||||
Example of finetuning the pi0 neural network with PaliGemma and expert Gemma
|
||||
pretrained with VLM default parameters before pi0 finetuning:
|
||||
```bash
|
||||
lerobot-train \
|
||||
--policy.type=pi0 \
|
||||
--dataset.repo_id=danaaubakirova/koch_test
|
||||
```
|
||||
|
||||
Example of using the pi0 pretrained model outside LeRobot training framework:
|
||||
```python
|
||||
policy = Pi0Policy.from_pretrained("lerobot/pi0")
|
||||
```
|
||||
|
||||
"""
|
||||
|
||||
import math
|
||||
from collections import deque
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F # noqa: N812
|
||||
from torch import Tensor, nn
|
||||
|
||||
from lerobot.constants import ACTION, OBS_LANGUAGE_ATTENTION_MASK, OBS_LANGUAGE_TOKENS, OBS_STATE
|
||||
from lerobot.policies.pi0.configuration_pi0 import PI0Config
|
||||
from lerobot.policies.pi0.paligemma_with_expert import (
|
||||
PaliGemmaWithExpertConfig,
|
||||
PaliGemmaWithExpertModel,
|
||||
)
|
||||
from lerobot.policies.pretrained import PreTrainedPolicy
|
||||
from lerobot.utils.utils import get_safe_dtype
|
||||
|
||||
|
||||
def create_sinusoidal_pos_embedding(
|
||||
time: torch.tensor, dimension: int, min_period: float, max_period: float, device="cpu"
|
||||
) -> Tensor:
|
||||
"""Computes sine-cosine positional embedding vectors for scalar positions."""
|
||||
if dimension % 2 != 0:
|
||||
raise ValueError(f"dimension ({dimension}) must be divisible by 2")
|
||||
|
||||
if time.ndim != 1:
|
||||
raise ValueError("The time tensor is expected to be of shape `(batch_size, )`.")
|
||||
|
||||
dtype = get_safe_dtype(torch.float64, device.type)
|
||||
fraction = torch.linspace(0.0, 1.0, dimension // 2, dtype=dtype, device=device)
|
||||
period = min_period * (max_period / min_period) ** fraction
|
||||
|
||||
# Compute the outer product
|
||||
scaling_factor = 1.0 / period * 2 * math.pi
|
||||
sin_input = scaling_factor[None, :] * time[:, None]
|
||||
pos_emb = torch.cat([torch.sin(sin_input), torch.cos(sin_input)], dim=1)
|
||||
return pos_emb
|
||||
|
||||
|
||||
def make_att_2d_masks(pad_masks, att_masks):
|
||||
"""Copied from big_vision.
|
||||
|
||||
Tokens can attend to valid inputs tokens which have a cumulative mask_ar
|
||||
smaller or equal to theirs. This way `mask_ar` int[B, N] can be used to
|
||||
setup several types of attention, for example:
|
||||
|
||||
[[1 1 1 1 1 1]]: pure causal attention.
|
||||
|
||||
[[0 0 0 1 1 1]]: prefix-lm attention. The first 3 tokens can attend between
|
||||
themselves and the last 3 tokens have a causal attention. The first
|
||||
entry could also be a 1 without changing behaviour.
|
||||
|
||||
[[1 0 1 0 1 0 0 1 0 0]]: causal attention between 4 blocks. Tokens of a
|
||||
block can attend all previous blocks and all tokens on the same block.
|
||||
|
||||
Args:
|
||||
input_mask: bool[B, N] true if its part of the input, false if padding.
|
||||
mask_ar: int32[B, N] mask that's 1 where previous tokens cannot depend on
|
||||
it and 0 where it shares the same attention mask as the previous token.
|
||||
"""
|
||||
if att_masks.ndim != 2:
|
||||
raise ValueError(att_masks.ndim)
|
||||
if pad_masks.ndim != 2:
|
||||
raise ValueError(pad_masks.ndim)
|
||||
|
||||
cumsum = torch.cumsum(att_masks, dim=1)
|
||||
att_2d_masks = cumsum[:, None, :] <= cumsum[:, :, None]
|
||||
pad_2d_masks = pad_masks[:, None, :] * pad_masks[:, :, None]
|
||||
att_2d_masks = att_2d_masks & pad_2d_masks
|
||||
return att_2d_masks
|
||||
|
||||
|
||||
def resize_with_pad(img, width, height, pad_value=-1):
|
||||
# assume no-op when width height fits already
|
||||
if img.ndim != 4:
|
||||
raise ValueError(f"(b,c,h,w) expected, but {img.shape}")
|
||||
|
||||
cur_height, cur_width = img.shape[2:]
|
||||
|
||||
ratio = max(cur_width / width, cur_height / height)
|
||||
resized_height = int(cur_height / ratio)
|
||||
resized_width = int(cur_width / ratio)
|
||||
resized_img = F.interpolate(
|
||||
img, size=(resized_height, resized_width), mode="bilinear", align_corners=False
|
||||
)
|
||||
|
||||
pad_height = max(0, int(height - resized_height))
|
||||
pad_width = max(0, int(width - resized_width))
|
||||
|
||||
# pad on left and top of image
|
||||
padded_img = F.pad(resized_img, (pad_width, 0, pad_height, 0), value=pad_value)
|
||||
return padded_img
|
||||
|
||||
|
||||
def pad_vector(vector, new_dim):
|
||||
"""Can be (batch_size x sequence_length x features_dimension)
|
||||
or (batch_size x features_dimension)
|
||||
"""
|
||||
if vector.shape[-1] == new_dim:
|
||||
return vector
|
||||
shape = list(vector.shape)
|
||||
current_dim = shape[-1]
|
||||
shape[-1] = new_dim
|
||||
new_vector = torch.zeros(*shape, dtype=vector.dtype, device=vector.device)
|
||||
new_vector[..., :current_dim] = vector
|
||||
return new_vector
|
||||
|
||||
|
||||
def normalize(x, min_val, max_val):
|
||||
return (x - min_val) / (max_val - min_val)
|
||||
|
||||
|
||||
def unnormalize(x, min_val, max_val):
|
||||
return x * (max_val - min_val) + min_val
|
||||
|
||||
|
||||
def safe_arcsin(value):
|
||||
# This ensures that the input stays within
|
||||
# [−1,1] to avoid invalid values for arcsin
|
||||
return torch.arcsin(torch.clamp(value, -1.0, 1.0))
|
||||
|
||||
|
||||
def aloha_gripper_to_angular(value):
|
||||
# Aloha transforms the gripper positions into a linear space. The following code
|
||||
# reverses this transformation to be consistent with pi0 which is pretrained in
|
||||
# angular space.
|
||||
#
|
||||
# These values are coming from the Aloha code:
|
||||
# PUPPET_GRIPPER_POSITION_OPEN, PUPPET_GRIPPER_POSITION_CLOSED
|
||||
value = unnormalize(value, min_val=0.01844, max_val=0.05800)
|
||||
|
||||
# This is the inverse of the angular to linear transformation inside the Interbotix code.
|
||||
def linear_to_radian(linear_position, arm_length, horn_radius):
|
||||
value = (horn_radius**2 + linear_position**2 - arm_length**2) / (2 * horn_radius * linear_position)
|
||||
return safe_arcsin(value)
|
||||
|
||||
# The constants are taken from the Interbotix code.
|
||||
value = linear_to_radian(value, arm_length=0.036, horn_radius=0.022)
|
||||
|
||||
# Normalize to [0, 1].
|
||||
# The values 0.4 and 1.5 were measured on an actual Trossen robot.
|
||||
return normalize(value, min_val=0.4, max_val=1.5)
|
||||
|
||||
|
||||
def aloha_gripper_from_angular(value):
|
||||
# Convert from the gripper position used by pi0 to the gripper position that is used by Aloha.
|
||||
# Note that the units are still angular but the range is different.
|
||||
|
||||
# The values 0.4 and 1.5 were measured on an actual Trossen robot.
|
||||
value = unnormalize(value, min_val=0.4, max_val=1.5)
|
||||
|
||||
# These values are coming from the Aloha code:
|
||||
# PUPPET_GRIPPER_JOINT_OPEN, PUPPET_GRIPPER_JOINT_CLOSE
|
||||
return normalize(value, min_val=-0.6213, max_val=1.4910)
|
||||
|
||||
|
||||
def aloha_gripper_from_angular_inv(value):
|
||||
# Directly inverts the gripper_from_angular function.
|
||||
value = unnormalize(value, min_val=-0.6213, max_val=1.4910)
|
||||
return normalize(value, min_val=0.4, max_val=1.5)
|
||||
|
||||
|
||||
class PI0Policy(PreTrainedPolicy):
|
||||
"""Wrapper class around PI0FlowMatching model to train and run inference within LeRobot."""
|
||||
|
||||
config_class = PI0Config
|
||||
name = "pi0"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: PI0Config,
|
||||
):
|
||||
"""
|
||||
Args:
|
||||
config: Policy configuration class instance or None, in which case the default instantiation of
|
||||
the configuration class is used.
|
||||
"""
|
||||
|
||||
super().__init__(config)
|
||||
config.validate_features()
|
||||
self.config = config
|
||||
|
||||
self.model = PI0FlowMatching(config)
|
||||
|
||||
self.reset()
|
||||
|
||||
def reset(self):
|
||||
"""This should be called whenever the environment is reset."""
|
||||
self._action_queue = deque([], maxlen=self.config.n_action_steps)
|
||||
|
||||
def get_optim_params(self) -> dict:
|
||||
return self.parameters()
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, *args, **kwargs):
|
||||
"""Override the from_pretrained method to display important disclaimer."""
|
||||
print(
|
||||
"⚠️ DISCLAIMER: The PI0 model is ported from JAX by the Hugging Face team. \n"
|
||||
" It is not expected to perform as well as the original implementation. \n"
|
||||
" Original implementation: https://github.com/Physical-Intelligence/openpi"
|
||||
)
|
||||
return super().from_pretrained(*args, **kwargs)
|
||||
|
||||
@torch.no_grad()
|
||||
def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor:
|
||||
"""Predict a chunk of actions given environment observations."""
|
||||
raise NotImplementedError("Currently not implemented for PI0")
|
||||
|
||||
@torch.no_grad()
|
||||
def select_action(self, batch: dict[str, Tensor], noise: Tensor | None = None) -> Tensor:
|
||||
"""Select a single action given environment observations.
|
||||
|
||||
This method wraps `select_actions` in order to return one action at a time for execution in the
|
||||
environment. It works by managing the actions in a queue and only calling `select_actions` when the
|
||||
queue is empty.
|
||||
"""
|
||||
self.eval()
|
||||
|
||||
if self.config.adapt_to_pi_aloha:
|
||||
batch[OBS_STATE] = self._pi_aloha_decode_state(batch[OBS_STATE])
|
||||
|
||||
# Action queue logic for n_action_steps > 1. When the action_queue is depleted, populate it by
|
||||
# querying the policy.
|
||||
if len(self._action_queue) == 0:
|
||||
images, img_masks = self.prepare_images(batch)
|
||||
state = self.prepare_state(batch)
|
||||
lang_tokens = batch[f"{OBS_LANGUAGE_TOKENS}"]
|
||||
lang_masks = batch[f"{OBS_LANGUAGE_ATTENTION_MASK}"]
|
||||
|
||||
actions = self.model.sample_actions(
|
||||
images, img_masks, lang_tokens, lang_masks, state, noise=noise
|
||||
)
|
||||
|
||||
# Unpad actions
|
||||
original_action_dim = self.config.action_feature.shape[0]
|
||||
actions = actions[:, :, :original_action_dim]
|
||||
|
||||
if self.config.adapt_to_pi_aloha:
|
||||
actions = self._pi_aloha_encode_actions(actions)
|
||||
|
||||
# `self.model.forward` returns a (batch_size, n_action_steps, action_dim) tensor, but the queue
|
||||
# effectively has shape (n_action_steps, batch_size, *), hence the transpose.
|
||||
self._action_queue.extend(actions.transpose(0, 1))
|
||||
return self._action_queue.popleft()
|
||||
|
||||
def forward(self, batch: dict[str, Tensor], noise=None, time=None) -> tuple[Tensor, dict[str, Tensor]]:
|
||||
"""Do a full training forward pass to compute the loss"""
|
||||
if self.config.adapt_to_pi_aloha:
|
||||
batch[OBS_STATE] = self._pi_aloha_decode_state(batch[OBS_STATE])
|
||||
batch[ACTION] = self._pi_aloha_encode_actions_inv(batch[ACTION])
|
||||
|
||||
images, img_masks = self.prepare_images(batch)
|
||||
state = self.prepare_state(batch)
|
||||
lang_tokens = batch[f"{OBS_LANGUAGE_TOKENS}"]
|
||||
lang_masks = batch[f"{OBS_LANGUAGE_ATTENTION_MASK}"]
|
||||
actions = self.prepare_action(batch)
|
||||
actions_is_pad = batch.get("action_is_pad")
|
||||
|
||||
loss_dict = {}
|
||||
losses = self.model.forward(images, img_masks, lang_tokens, lang_masks, state, actions, noise, time)
|
||||
loss_dict["losses_after_forward"] = losses.clone()
|
||||
|
||||
if actions_is_pad is not None:
|
||||
in_episode_bound = ~actions_is_pad
|
||||
losses = losses * in_episode_bound.unsqueeze(-1)
|
||||
loss_dict["losses_after_in_ep_bound"] = losses.clone()
|
||||
|
||||
# Remove padding
|
||||
losses = losses[:, :, : self.config.max_action_dim]
|
||||
loss_dict["losses_after_rm_padding"] = losses.clone()
|
||||
|
||||
# For backward pass
|
||||
loss = losses.mean()
|
||||
# For logging
|
||||
loss_dict["l2_loss"] = loss.item()
|
||||
|
||||
return loss, loss_dict
|
||||
|
||||
def prepare_images(self, batch):
|
||||
"""Apply Pi0 preprocessing to the images, like resizing to 224x224 and padding to keep aspect ratio, and
|
||||
convert pixel range from [0.0, 1.0] to [-1.0, 1.0] as requested by SigLIP.
|
||||
"""
|
||||
images = []
|
||||
img_masks = []
|
||||
|
||||
present_img_keys = [key for key in self.config.image_features if key in batch]
|
||||
missing_img_keys = [key for key in self.config.image_features if key not in batch]
|
||||
|
||||
if len(present_img_keys) == 0:
|
||||
raise ValueError(
|
||||
f"All image features are missing from the batch. At least one expected. (batch: {batch.keys()}) (image_features:{self.config.image_features})"
|
||||
)
|
||||
|
||||
# Preprocess image features present in the batch
|
||||
for key in present_img_keys:
|
||||
img = batch[key]
|
||||
|
||||
if self.config.resize_imgs_with_padding is not None:
|
||||
img = resize_with_pad(img, *self.config.resize_imgs_with_padding, pad_value=0)
|
||||
|
||||
# Normalize from range [0,1] to [-1,1] as expected by siglip
|
||||
img = img * 2.0 - 1.0
|
||||
|
||||
bsize = img.shape[0]
|
||||
device = img.device
|
||||
mask = torch.ones(bsize, dtype=torch.bool, device=device)
|
||||
images.append(img)
|
||||
img_masks.append(mask)
|
||||
|
||||
# Create image features not present in the batch
|
||||
# as fully 0 padded images.
|
||||
for num_empty_cameras in range(len(missing_img_keys)):
|
||||
if num_empty_cameras >= self.config.empty_cameras:
|
||||
break
|
||||
img = torch.ones_like(img) * -1
|
||||
mask = torch.zeros_like(mask)
|
||||
images.append(img)
|
||||
img_masks.append(mask)
|
||||
|
||||
return images, img_masks
|
||||
|
||||
def _pi_aloha_decode_state(self, state):
|
||||
# Flip the joints.
|
||||
for motor_idx in [1, 2, 8, 9]:
|
||||
state[:, motor_idx] *= -1
|
||||
# Reverse the gripper transformation that is being applied by the Aloha runtime.
|
||||
for motor_idx in [6, 13]:
|
||||
state[:, motor_idx] = aloha_gripper_to_angular(state[:, motor_idx])
|
||||
return state
|
||||
|
||||
def _pi_aloha_encode_actions(self, actions):
|
||||
# Flip the joints.
|
||||
for motor_idx in [1, 2, 8, 9]:
|
||||
actions[:, :, motor_idx] *= -1
|
||||
# Reverse the gripper transformation that is being applied by the Aloha runtime.
|
||||
for motor_idx in [6, 13]:
|
||||
actions[:, :, motor_idx] = aloha_gripper_from_angular(actions[:, :, motor_idx])
|
||||
return actions
|
||||
|
||||
def _pi_aloha_encode_actions_inv(self, actions):
|
||||
# Flip the joints again.
|
||||
for motor_idx in [1, 2, 8, 9]:
|
||||
actions[:, :, motor_idx] *= -1
|
||||
# Reverse the gripper transformation that is being applied by the Aloha runtime.
|
||||
for motor_idx in [6, 13]:
|
||||
actions[:, :, motor_idx] = aloha_gripper_from_angular_inv(actions[:, :, motor_idx])
|
||||
return actions
|
||||
|
||||
def prepare_state(self, batch):
|
||||
"""Pad state"""
|
||||
state = pad_vector(batch[OBS_STATE], self.config.max_state_dim)
|
||||
return state
|
||||
|
||||
def prepare_action(self, batch):
|
||||
"""Pad action"""
|
||||
actions = pad_vector(batch[ACTION], self.config.max_action_dim)
|
||||
return actions
|
||||
|
||||
|
||||
class PI0FlowMatching(nn.Module):
|
||||
"""
|
||||
π0: A Vision-Language-Action Flow Model for General Robot Control
|
||||
|
||||
[Paper](https://www.physicalintelligence.company/download/pi0.pdf)
|
||||
[Jax code](https://github.com/Physical-Intelligence/openpi)
|
||||
|
||||
Designed by Physical Intelligence. Ported from Jax by Hugging Face.
|
||||
┌──────────────────────────────┐
|
||||
│ actions │
|
||||
│ ▲ │
|
||||
│ ┌┴─────┐ │
|
||||
│ kv cache │Gemma │ │
|
||||
│ ┌──────────►│Expert│ │
|
||||
│ │ │ │ │
|
||||
│ ┌┴────────┐ │x 10 │ │
|
||||
│ │ │ └▲──▲──┘ │
|
||||
│ │PaliGemma│ │ │ │
|
||||
│ │ │ │ robot state │
|
||||
│ │ │ noise │
|
||||
│ └▲──▲─────┘ │
|
||||
│ │ │ │
|
||||
│ │ image(s) │
|
||||
│ language tokens │
|
||||
└──────────────────────────────┘
|
||||
"""
|
||||
|
||||
def __init__(self, config: PI0Config):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
|
||||
paligemma_with_export_config = PaliGemmaWithExpertConfig(
|
||||
freeze_vision_encoder=self.config.freeze_vision_encoder,
|
||||
train_expert_only=self.config.train_expert_only,
|
||||
attention_implementation=self.config.attention_implementation,
|
||||
)
|
||||
self.paligemma_with_expert = PaliGemmaWithExpertModel(paligemma_with_export_config)
|
||||
|
||||
# Projections are float32
|
||||
self.state_proj = nn.Linear(self.config.max_state_dim, self.config.proj_width)
|
||||
self.action_in_proj = nn.Linear(self.config.max_action_dim, self.config.proj_width)
|
||||
self.action_out_proj = nn.Linear(self.config.proj_width, self.config.max_action_dim)
|
||||
|
||||
self.action_time_mlp_in = nn.Linear(self.config.proj_width * 2, self.config.proj_width)
|
||||
self.action_time_mlp_out = nn.Linear(self.config.proj_width, self.config.proj_width)
|
||||
|
||||
self.set_requires_grad()
|
||||
|
||||
def set_requires_grad(self):
|
||||
for params in self.state_proj.parameters():
|
||||
params.requires_grad = self.config.train_state_proj
|
||||
|
||||
def sample_noise(self, shape, device):
|
||||
noise = torch.normal(
|
||||
mean=0.0,
|
||||
std=1.0,
|
||||
size=shape,
|
||||
dtype=torch.float32,
|
||||
device=device,
|
||||
)
|
||||
return noise
|
||||
|
||||
def sample_time(self, bsize, device):
|
||||
beta_dist = torch.distributions.Beta(concentration1=1.5, concentration0=1.0)
|
||||
time_beta = beta_dist.sample((bsize,)).to(device=device, dtype=torch.float32)
|
||||
time = time_beta * 0.999 + 0.001
|
||||
return time
|
||||
|
||||
def embed_prefix(
|
||||
self, images, img_masks, lang_tokens, lang_masks
|
||||
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
"""Embed images with SigLIP and language tokens with embedding layer to prepare
|
||||
for PaliGemma transformer processing.
|
||||
"""
|
||||
# TODO: avoid list in python and torch.cat ; prefer pre-allocation with torch.empty
|
||||
embs = []
|
||||
pad_masks = []
|
||||
att_masks = []
|
||||
|
||||
# TODO: remove for loop
|
||||
for (
|
||||
img,
|
||||
img_mask,
|
||||
) in zip(images, img_masks, strict=False):
|
||||
img_emb = self.paligemma_with_expert.embed_image(img)
|
||||
img_emb = img_emb.to(dtype=torch.bfloat16)
|
||||
|
||||
# Normalize image embeddings
|
||||
img_emb_dim = img_emb.shape[-1]
|
||||
img_emb = img_emb * torch.tensor(img_emb_dim**0.5, dtype=img_emb.dtype, device=img_emb.device)
|
||||
|
||||
bsize, num_img_embs = img_emb.shape[:2]
|
||||
img_mask = img_mask[:, None].expand(bsize, num_img_embs)
|
||||
|
||||
embs.append(img_emb)
|
||||
pad_masks.append(img_mask)
|
||||
|
||||
# Create attention masks so that image tokens attend to each other
|
||||
att_masks += [0] * num_img_embs
|
||||
|
||||
lang_emb = self.paligemma_with_expert.embed_language_tokens(lang_tokens)
|
||||
|
||||
# Normalize language embeddings
|
||||
lang_emb_dim = lang_emb.shape[-1]
|
||||
lang_emb = lang_emb * math.sqrt(lang_emb_dim)
|
||||
|
||||
embs.append(lang_emb)
|
||||
pad_masks.append(lang_masks)
|
||||
|
||||
# full attention between image and language inputs
|
||||
num_lang_embs = lang_emb.shape[1]
|
||||
att_masks += [0] * num_lang_embs
|
||||
|
||||
embs = torch.cat(embs, dim=1)
|
||||
pad_masks = torch.cat(pad_masks, dim=1)
|
||||
att_masks = torch.tensor(att_masks, dtype=torch.bool, device=pad_masks.device)
|
||||
att_masks = att_masks[None, :].expand(bsize, len(att_masks))
|
||||
|
||||
return embs, pad_masks, att_masks
|
||||
|
||||
def embed_suffix(self, state, noisy_actions, timestep):
|
||||
"""Embed state, noisy_actions, timestep to prepare for Expert Gemma processing."""
|
||||
embs = []
|
||||
pad_masks = []
|
||||
att_masks = []
|
||||
|
||||
# Embed state
|
||||
state_emb = self.state_proj(state)
|
||||
state_emb = state_emb.to(dtype=torch.bfloat16)
|
||||
embs.append(state_emb[:, None, :])
|
||||
bsize = state_emb.shape[0]
|
||||
dtype = state_emb.dtype
|
||||
device = state_emb.device
|
||||
|
||||
state_mask = torch.ones(bsize, 1, dtype=torch.bool, device=device)
|
||||
pad_masks.append(state_mask)
|
||||
|
||||
# Set attention masks so that image and language inputs do not attend to state or actions
|
||||
att_masks += [1]
|
||||
|
||||
# Embed timestep using sine-cosine positional encoding with sensitivity in the range [0, 1]
|
||||
time_emb = create_sinusoidal_pos_embedding(
|
||||
timestep, self.config.proj_width, min_period=4e-3, max_period=4.0, device=device
|
||||
)
|
||||
time_emb = time_emb.type(dtype=dtype)
|
||||
|
||||
# Fuse timestep + action information using an MLP
|
||||
action_emb = self.action_in_proj(noisy_actions)
|
||||
|
||||
time_emb = time_emb[:, None, :].expand_as(action_emb)
|
||||
action_time_emb = torch.cat([action_emb, time_emb], dim=2)
|
||||
|
||||
action_time_emb = self.action_time_mlp_in(action_time_emb)
|
||||
action_time_emb = F.silu(action_time_emb) # swish == silu
|
||||
action_time_emb = self.action_time_mlp_out(action_time_emb)
|
||||
|
||||
# Add to input tokens
|
||||
embs.append(action_time_emb)
|
||||
|
||||
bsize, action_time_dim = action_time_emb.shape[:2]
|
||||
action_time_mask = torch.ones(bsize, action_time_dim, dtype=torch.bool, device=device)
|
||||
pad_masks.append(action_time_mask)
|
||||
|
||||
# Set attention masks so that image, language and state inputs do not attend to action tokens
|
||||
att_masks += [1] + ([0] * (self.config.n_action_steps - 1))
|
||||
|
||||
embs = torch.cat(embs, dim=1)
|
||||
pad_masks = torch.cat(pad_masks, dim=1)
|
||||
att_masks = torch.tensor(att_masks, dtype=embs.dtype, device=embs.device)
|
||||
att_masks = att_masks[None, :].expand(bsize, len(att_masks))
|
||||
|
||||
return embs, pad_masks, att_masks
|
||||
|
||||
def forward(
|
||||
self, images, img_masks, lang_tokens, lang_masks, state, actions, noise=None, time=None
|
||||
) -> Tensor:
|
||||
"""Do a full training forward pass and compute the loss (batch_size x num_steps x num_motors)"""
|
||||
if noise is None:
|
||||
noise = self.sample_noise(actions.shape, actions.device)
|
||||
|
||||
if time is None:
|
||||
time = self.sample_time(actions.shape[0], actions.device)
|
||||
|
||||
time_expanded = time[:, None, None]
|
||||
x_t = time_expanded * noise + (1 - time_expanded) * actions
|
||||
u_t = noise - actions
|
||||
|
||||
prefix_embs, prefix_pad_masks, prefix_att_masks = self.embed_prefix(
|
||||
images, img_masks, lang_tokens, lang_masks
|
||||
)
|
||||
suffix_embs, suffix_pad_masks, suffix_att_masks = self.embed_suffix(state, x_t, time)
|
||||
|
||||
pad_masks = torch.cat([prefix_pad_masks, suffix_pad_masks], dim=1)
|
||||
att_masks = torch.cat([prefix_att_masks, suffix_att_masks], dim=1)
|
||||
|
||||
att_2d_masks = make_att_2d_masks(pad_masks, att_masks)
|
||||
position_ids = torch.cumsum(pad_masks, dim=1) - 1
|
||||
|
||||
(_, suffix_out), _ = self.paligemma_with_expert.forward(
|
||||
attention_mask=att_2d_masks,
|
||||
position_ids=position_ids,
|
||||
past_key_values=None,
|
||||
inputs_embeds=[prefix_embs, suffix_embs],
|
||||
use_cache=False,
|
||||
fill_kv_cache=False,
|
||||
)
|
||||
suffix_out = suffix_out[:, -self.config.n_action_steps :]
|
||||
# Original openpi code, upcast attention output
|
||||
suffix_out = suffix_out.to(dtype=torch.float32)
|
||||
v_t = self.action_out_proj(suffix_out)
|
||||
|
||||
losses = F.mse_loss(u_t, v_t, reduction="none")
|
||||
return losses
|
||||
|
||||
def sample_actions(self, images, img_masks, lang_tokens, lang_masks, state, noise=None) -> Tensor:
|
||||
"""Do a full inference forward and compute the action (batch_size x num_steps x num_motors)"""
|
||||
bsize = state.shape[0]
|
||||
device = state.device
|
||||
|
||||
if noise is None:
|
||||
actions_shape = (bsize, self.config.n_action_steps, self.config.max_action_dim)
|
||||
noise = self.sample_noise(actions_shape, device)
|
||||
|
||||
prefix_embs, prefix_pad_masks, prefix_att_masks = self.embed_prefix(
|
||||
images, img_masks, lang_tokens, lang_masks
|
||||
)
|
||||
prefix_att_2d_masks = make_att_2d_masks(prefix_pad_masks, prefix_att_masks)
|
||||
prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1
|
||||
|
||||
# Compute image and language key value cache
|
||||
_, past_key_values = self.paligemma_with_expert.forward(
|
||||
attention_mask=prefix_att_2d_masks,
|
||||
position_ids=prefix_position_ids,
|
||||
past_key_values=None,
|
||||
inputs_embeds=[prefix_embs, None],
|
||||
use_cache=self.config.use_cache,
|
||||
fill_kv_cache=True,
|
||||
)
|
||||
|
||||
dt = -1.0 / self.config.num_steps
|
||||
dt = torch.tensor(dt, dtype=torch.float32, device=device)
|
||||
|
||||
x_t = noise
|
||||
time = torch.tensor(1.0, dtype=torch.float32, device=device)
|
||||
while time >= -dt / 2:
|
||||
expanded_time = time.expand(bsize)
|
||||
v_t = self.denoise_step(
|
||||
state,
|
||||
prefix_pad_masks,
|
||||
past_key_values,
|
||||
x_t,
|
||||
expanded_time,
|
||||
)
|
||||
|
||||
# Euler step
|
||||
x_t += dt * v_t
|
||||
time += dt
|
||||
return x_t
|
||||
|
||||
def denoise_step(
|
||||
self,
|
||||
state,
|
||||
prefix_pad_masks,
|
||||
past_key_values,
|
||||
x_t,
|
||||
timestep,
|
||||
):
|
||||
"""Apply one denoising step of the noise `x_t` at a given timestep."""
|
||||
suffix_embs, suffix_pad_masks, suffix_att_masks = self.embed_suffix(state, x_t, timestep)
|
||||
|
||||
suffix_len = suffix_pad_masks.shape[1]
|
||||
batch_size = prefix_pad_masks.shape[0]
|
||||
prefix_len = prefix_pad_masks.shape[1]
|
||||
prefix_pad_2d_masks = prefix_pad_masks[:, None, :].expand(batch_size, suffix_len, prefix_len)
|
||||
|
||||
suffix_att_2d_masks = make_att_2d_masks(suffix_pad_masks, suffix_att_masks)
|
||||
|
||||
full_att_2d_masks = torch.cat([prefix_pad_2d_masks, suffix_att_2d_masks], dim=2)
|
||||
|
||||
prefix_offsets = torch.sum(prefix_pad_masks, dim=-1)[:, None]
|
||||
position_ids = prefix_offsets + torch.cumsum(suffix_pad_masks, dim=1) - 1
|
||||
|
||||
outputs_embeds, _ = self.paligemma_with_expert.forward(
|
||||
attention_mask=full_att_2d_masks,
|
||||
position_ids=position_ids,
|
||||
past_key_values=past_key_values,
|
||||
inputs_embeds=[None, suffix_embs],
|
||||
use_cache=self.config.use_cache,
|
||||
fill_kv_cache=False,
|
||||
)
|
||||
suffix_out = outputs_embeds[1]
|
||||
suffix_out = suffix_out[:, -self.config.n_action_steps :]
|
||||
suffix_out = suffix_out.to(dtype=torch.float32)
|
||||
v_t = self.action_out_proj(suffix_out)
|
||||
return v_t
|
||||
+3
-3
@@ -33,7 +33,7 @@ from transformers.models.paligemma.modeling_paligemma import PaliGemmaForConditi
|
||||
from lerobot.configs.policies import PreTrainedConfig
|
||||
from lerobot.constants import ACTION, OBS_STATE
|
||||
from lerobot.policies.normalize import Normalize, Unnormalize
|
||||
from lerobot.policies.pi0_openpi.configuration_pi0openpi import PI0OpenPIConfig
|
||||
from lerobot.policies.pi0.configuration_pi0openpi import PI0OpenPIConfig
|
||||
from lerobot.policies.pretrained import PreTrainedPolicy, T
|
||||
|
||||
|
||||
@@ -525,7 +525,7 @@ class PI0Pytorch(nn.Module): # see openpi `PI0Pytorch`
|
||||
|
||||
msg = """transformers_replace is not installed correctly.
|
||||
Please install it with `pip install transformers==4.53.2`
|
||||
and `cp -r ./src/lerobot/policies/pi0_openpi/transformers_replace/* \
|
||||
and `cp -r ./src/lerobot/policies/pi0/transformers_replace/* \
|
||||
$(python -c "import transformers, os; print(os.path.dirname(transformers.__file__))")`"""
|
||||
|
||||
try:
|
||||
@@ -846,7 +846,7 @@ class PI0OpenPIPolicy(PreTrainedPolicy):
|
||||
"""PI0 OpenPI Policy for LeRobot."""
|
||||
|
||||
config_class = PI0OpenPIConfig
|
||||
name = "pi0_openpi"
|
||||
name = "pi0"
|
||||
|
||||
def __init__( # see lerobot pi0 `__init__`
|
||||
self,
|
||||
@@ -1,420 +0,0 @@
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
|
||||
import torch
|
||||
import torch.version
|
||||
from pytest import Cache
|
||||
from torch import nn
|
||||
from transformers import (
|
||||
AutoConfig,
|
||||
GemmaForCausalLM,
|
||||
PaliGemmaForConditionalGeneration,
|
||||
PretrainedConfig,
|
||||
PreTrainedModel,
|
||||
)
|
||||
from transformers.models.auto import CONFIG_MAPPING
|
||||
|
||||
from lerobot.policies.pi0.flex_attention import flex_attention_forward
|
||||
|
||||
|
||||
def apply_rope(x, positions, max_wavelength=10_000):
|
||||
"""
|
||||
Applies RoPE positions [B, L] to x [B, L, H, D].
|
||||
"""
|
||||
d_half = x.shape[-1] // 2
|
||||
device = x.device
|
||||
dtype = x.dtype
|
||||
x = x.to(torch.float32)
|
||||
|
||||
freq_exponents = (2.0 / x.shape[-1]) * torch.arange(d_half, dtype=torch.float32, device=device)
|
||||
timescale = max_wavelength**freq_exponents
|
||||
radians = positions[..., None].to(torch.float32) / timescale[None, None, :].to(torch.float32)
|
||||
|
||||
radians = radians[..., None, :]
|
||||
|
||||
sin = torch.sin(radians) # .to(dtype=dtype)
|
||||
cos = torch.cos(radians) # .to(dtype=dtype)
|
||||
|
||||
x1, x2 = x.split(d_half, dim=-1)
|
||||
res = torch.empty_like(x)
|
||||
res[..., :d_half] = x1 * cos - x2 * sin
|
||||
res[..., d_half:] = x2 * cos + x1 * sin
|
||||
|
||||
return res.to(dtype)
|
||||
|
||||
|
||||
class PaliGemmaWithExpertConfig(PretrainedConfig):
|
||||
model_type = "PaliGemmaWithExpertModel"
|
||||
sub_configs = {"paligemma_config": AutoConfig, "gemma_expert_config": AutoConfig}
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
paligemma_config: dict | None = None,
|
||||
gemma_expert_config: dict | None = None,
|
||||
freeze_vision_encoder: bool = True,
|
||||
train_expert_only: bool = True,
|
||||
attention_implementation: str = "eager",
|
||||
**kwargs,
|
||||
):
|
||||
self.freeze_vision_encoder = freeze_vision_encoder
|
||||
self.train_expert_only = train_expert_only
|
||||
self.attention_implementation = attention_implementation
|
||||
|
||||
if paligemma_config is None:
|
||||
# Default config from Pi0
|
||||
self.paligemma_config = CONFIG_MAPPING["paligemma"](
|
||||
transformers_version="4.48.1",
|
||||
_vocab_size=257152,
|
||||
bos_token_id=2,
|
||||
eos_token_id=1,
|
||||
hidden_size=2048,
|
||||
image_token_index=257152,
|
||||
model_type="paligemma",
|
||||
pad_token_id=0,
|
||||
projection_dim=2048,
|
||||
text_config={
|
||||
"hidden_activation": "gelu_pytorch_tanh",
|
||||
"hidden_size": 2048,
|
||||
"intermediate_size": 16384,
|
||||
"model_type": "gemma",
|
||||
"num_attention_heads": 8,
|
||||
"num_hidden_layers": 18,
|
||||
"num_image_tokens": 256,
|
||||
"num_key_value_heads": 1,
|
||||
"torch_dtype": "float32",
|
||||
"vocab_size": 257152,
|
||||
},
|
||||
vision_config={
|
||||
"hidden_size": 1152,
|
||||
"intermediate_size": 4304,
|
||||
"model_type": "siglip_vision_model",
|
||||
"num_attention_heads": 16,
|
||||
"num_hidden_layers": 27,
|
||||
"num_image_tokens": 256,
|
||||
"patch_size": 14,
|
||||
"projection_dim": 2048,
|
||||
"projector_hidden_act": "gelu_fast",
|
||||
"torch_dtype": "float32",
|
||||
"vision_use_head": False,
|
||||
},
|
||||
)
|
||||
elif isinstance(self.paligemma_config, dict):
|
||||
# Override Pi0 default config for PaliGemma
|
||||
if "model_type" not in gemma_expert_config:
|
||||
paligemma_config["model_type"] = "paligemma"
|
||||
|
||||
cfg_cls = CONFIG_MAPPING[paligemma_config["model_type"]]
|
||||
self.paligemma_config = cfg_cls(**paligemma_config)
|
||||
|
||||
if gemma_expert_config is None:
|
||||
# Default config from Pi0
|
||||
self.gemma_expert_config = CONFIG_MAPPING["gemma"](
|
||||
attention_bias=False,
|
||||
attention_dropout=0.0,
|
||||
bos_token_id=2,
|
||||
eos_token_id=1,
|
||||
head_dim=256,
|
||||
hidden_act="gelu_pytorch_tanh",
|
||||
hidden_activation="gelu_pytorch_tanh",
|
||||
hidden_size=1024,
|
||||
initializer_range=0.02,
|
||||
intermediate_size=4096,
|
||||
max_position_embeddings=8192,
|
||||
model_type="gemma",
|
||||
num_attention_heads=8,
|
||||
num_hidden_layers=18,
|
||||
num_key_value_heads=1,
|
||||
pad_token_id=0,
|
||||
rms_norm_eps=1e-06,
|
||||
rope_theta=10000.0,
|
||||
torch_dtype="float32",
|
||||
transformers_version="4.48.1",
|
||||
use_cache=True,
|
||||
vocab_size=257152,
|
||||
)
|
||||
elif isinstance(self.gemma_expert_config, dict):
|
||||
# Override Pi0 default config for Gemma Expert
|
||||
if "model_type" not in gemma_expert_config:
|
||||
gemma_expert_config["model_type"] = "gemma"
|
||||
|
||||
cfg_cls = CONFIG_MAPPING[paligemma_config["model_type"]]
|
||||
self.gemma_expert_config = cfg_cls(**gemma_expert_config)
|
||||
|
||||
super().__init__(**kwargs)
|
||||
|
||||
def __post_init__(self):
|
||||
super().__post_init__()
|
||||
if self.train_expert_only and not self.freeze_vision_encoder:
|
||||
raise ValueError(
|
||||
"You set `freeze_vision_encoder=False` and `train_expert_only=True` which are not compatible."
|
||||
)
|
||||
|
||||
if self.attention_implementation not in ["eager", "fa2", "flex"]:
|
||||
raise ValueError(
|
||||
f"Wrong value provided for `attention_implementation` ({self.attention_implementation}). Expected 'eager', 'fa2' or 'flex'."
|
||||
)
|
||||
|
||||
|
||||
class PaliGemmaWithExpertModel(PreTrainedModel):
|
||||
config_class = PaliGemmaWithExpertConfig
|
||||
|
||||
def __init__(self, config: PaliGemmaWithExpertConfig):
|
||||
super().__init__(config=config)
|
||||
self.config = config
|
||||
self.paligemma = PaliGemmaForConditionalGeneration(config=config.paligemma_config)
|
||||
self.gemma_expert = GemmaForCausalLM(config=config.gemma_expert_config)
|
||||
# Remove unused embed_tokens
|
||||
self.gemma_expert.model.embed_tokens = None
|
||||
|
||||
self.to_bfloat16_like_physical_intelligence()
|
||||
self.set_requires_grad()
|
||||
|
||||
def set_requires_grad(self):
|
||||
if self.config.freeze_vision_encoder:
|
||||
self.paligemma.vision_tower.eval()
|
||||
for params in self.paligemma.vision_tower.parameters():
|
||||
params.requires_grad = False
|
||||
|
||||
if self.config.train_expert_only:
|
||||
self.paligemma.eval()
|
||||
for params in self.paligemma.parameters():
|
||||
params.requires_grad = False
|
||||
|
||||
def train(self, mode: bool = True):
|
||||
super().train(mode)
|
||||
|
||||
if self.config.freeze_vision_encoder:
|
||||
self.paligemma.vision_tower.eval()
|
||||
|
||||
if self.config.train_expert_only:
|
||||
self.paligemma.eval()
|
||||
|
||||
def to_bfloat16_like_physical_intelligence(self):
|
||||
self.paligemma = self.paligemma.to(dtype=torch.bfloat16)
|
||||
|
||||
params_to_change_dtype = [
|
||||
"language_model.model.layers",
|
||||
"gemma_expert.model.layers",
|
||||
"vision_tower",
|
||||
"multi_modal",
|
||||
]
|
||||
for name, param in self.named_parameters():
|
||||
if any(selector in name for selector in params_to_change_dtype):
|
||||
param.data = param.data.to(dtype=torch.bfloat16)
|
||||
|
||||
def embed_image(self, image: torch.Tensor):
|
||||
# Handle different transformers versions
|
||||
if hasattr(self.paligemma, "get_image_features"):
|
||||
return self.paligemma.get_image_features(image)
|
||||
else:
|
||||
return self.paligemma.model.get_image_features(image)
|
||||
|
||||
def embed_language_tokens(self, tokens: torch.Tensor):
|
||||
return self.paligemma.language_model.embed_tokens(tokens)
|
||||
|
||||
# TODO: break down this huge forward into modules or functions
|
||||
def forward(
|
||||
self,
|
||||
attention_mask: torch.Tensor | None = None,
|
||||
position_ids: torch.LongTensor | None = None,
|
||||
past_key_values: list[torch.FloatTensor] | Cache | None = None,
|
||||
inputs_embeds: list[torch.FloatTensor] = None,
|
||||
use_cache: bool | None = None,
|
||||
fill_kv_cache: bool | None = None,
|
||||
):
|
||||
models = [self.paligemma.language_model, self.gemma_expert.model]
|
||||
|
||||
for hidden_states in inputs_embeds:
|
||||
# TODO this is very inefficient
|
||||
# dtype is always the same, batch size too (if > 1 len)
|
||||
# device could be trickier in multi gpu edge cases but that's it
|
||||
if hidden_states is None:
|
||||
continue
|
||||
batch_size = hidden_states.shape[0]
|
||||
|
||||
# RMSNorm
|
||||
num_layers = self.paligemma.config.text_config.num_hidden_layers
|
||||
head_dim = self.paligemma.config.text_config.head_dim
|
||||
for layer_idx in range(num_layers):
|
||||
query_states = []
|
||||
key_states = []
|
||||
value_states = []
|
||||
for i, hidden_states in enumerate(inputs_embeds):
|
||||
if hidden_states is None:
|
||||
continue
|
||||
layer = models[i].layers[layer_idx]
|
||||
# normalizer = torch.tensor(models[i].config.hidden_size**0.5, dtype=hidden_states.dtype)
|
||||
# hidden_states = hidden_states * normalizer
|
||||
hidden_states = layer.input_layernorm(hidden_states)
|
||||
|
||||
input_shape = hidden_states.shape[:-1]
|
||||
hidden_shape = (*input_shape, -1, layer.self_attn.head_dim)
|
||||
|
||||
hidden_states = hidden_states.to(dtype=torch.bfloat16)
|
||||
query_state = layer.self_attn.q_proj(hidden_states).view(hidden_shape)
|
||||
key_state = layer.self_attn.k_proj(hidden_states).view(hidden_shape)
|
||||
value_state = layer.self_attn.v_proj(hidden_states).view(hidden_shape)
|
||||
|
||||
query_states.append(query_state)
|
||||
key_states.append(key_state)
|
||||
value_states.append(value_state)
|
||||
|
||||
# B,L,H,D with L sequence length, H number of heads, D head dim
|
||||
# concatenate on the number of embeddings/tokens
|
||||
query_states = torch.cat(query_states, dim=1)
|
||||
key_states = torch.cat(key_states, dim=1)
|
||||
value_states = torch.cat(value_states, dim=1)
|
||||
|
||||
query_states = apply_rope(query_states, position_ids)
|
||||
key_states = apply_rope(key_states, position_ids)
|
||||
|
||||
if use_cache and past_key_values is None:
|
||||
past_key_values = {}
|
||||
|
||||
if use_cache:
|
||||
if fill_kv_cache:
|
||||
past_key_values[layer_idx] = {
|
||||
"key_states": key_states,
|
||||
"value_states": value_states,
|
||||
}
|
||||
else:
|
||||
# TODO here, some optimization can be done - similar to a `StaticCache` we can declare the `max_len` before.
|
||||
# so we create an empty cache, with just one cuda malloc, and if (in autoregressive case) we reach
|
||||
# the max len, then we (for instance) double the cache size. This implementation already exists
|
||||
# in `transformers`. (molbap)
|
||||
key_states = torch.cat([past_key_values[layer_idx]["key_states"], key_states], dim=1)
|
||||
value_states = torch.cat(
|
||||
[past_key_values[layer_idx]["value_states"], value_states], dim=1
|
||||
)
|
||||
|
||||
attention_interface = self.get_attention_interface()
|
||||
att_output = attention_interface(
|
||||
attention_mask, batch_size, head_dim, query_states, key_states, value_states
|
||||
)
|
||||
att_output = att_output.to(dtype=torch.bfloat16)
|
||||
|
||||
# first part of att_output is prefix (up to sequence length, [:, 0:prefix_seq_len])
|
||||
outputs_embeds = []
|
||||
start = 0
|
||||
for i, hidden_states in enumerate(inputs_embeds):
|
||||
layer = models[i].layers[layer_idx]
|
||||
|
||||
if hidden_states is not None:
|
||||
end = start + hidden_states.shape[1]
|
||||
|
||||
if att_output.dtype != layer.self_attn.o_proj.weight.dtype:
|
||||
att_output = att_output.to(layer.self_attn.o_proj.weight.dtype)
|
||||
out_emb = layer.self_attn.o_proj(att_output[:, start:end])
|
||||
|
||||
# TODO: first dropout (by default 0.0)
|
||||
|
||||
# first residual
|
||||
out_emb += hidden_states
|
||||
after_first_residual = out_emb.clone()
|
||||
|
||||
out_emb = layer.post_attention_layernorm(out_emb)
|
||||
out_emb = layer.mlp(out_emb)
|
||||
|
||||
# TODO: second dropout (by default 0.0)
|
||||
|
||||
# second residual
|
||||
out_emb += after_first_residual
|
||||
|
||||
outputs_embeds.append(out_emb)
|
||||
|
||||
start = end
|
||||
else:
|
||||
outputs_embeds.append(None)
|
||||
|
||||
inputs_embeds = outputs_embeds
|
||||
|
||||
# final norm
|
||||
outputs_embeds = []
|
||||
for i, hidden_states in enumerate(inputs_embeds):
|
||||
if hidden_states is not None:
|
||||
out_emb = models[i].norm(hidden_states)
|
||||
outputs_embeds.append(out_emb)
|
||||
else:
|
||||
outputs_embeds.append(None)
|
||||
|
||||
return outputs_embeds, past_key_values
|
||||
|
||||
def get_attention_interface(self):
|
||||
if self.config.attention_implementation == "fa2":
|
||||
attention_interface = self.flash_attention_forward
|
||||
elif self.config.attention_implementation == "flex":
|
||||
attention_interface = flex_attention_forward
|
||||
else:
|
||||
attention_interface = self.eager_attention_forward
|
||||
return attention_interface
|
||||
|
||||
def flash_attention_forward(
|
||||
self, attention_mask, batch_size, head_dim, query_states, key_states, value_states
|
||||
):
|
||||
raise NotImplementedError("FA2 is not implemented (yet)")
|
||||
|
||||
def eager_attention_forward(
|
||||
self, attention_mask, batch_size, head_dim, query_states, key_states, value_states
|
||||
):
|
||||
num_att_heads = self.config.paligemma_config.text_config.num_attention_heads
|
||||
num_key_value_heads = self.config.paligemma_config.text_config.num_key_value_heads
|
||||
num_key_value_groups = num_att_heads // num_key_value_heads
|
||||
|
||||
# query_states: batch_size, sequence_length, num_att_head, head_dim
|
||||
# key_states: batch_size, sequence_length, num_key_value_head, head_dim
|
||||
# value_states: batch_size, sequence_length, num_key_value_head, head_dim
|
||||
sequence_length = key_states.shape[1]
|
||||
|
||||
key_states = key_states[:, :, :, None, :].expand(
|
||||
batch_size, sequence_length, num_key_value_heads, num_key_value_groups, head_dim
|
||||
)
|
||||
key_states = key_states.reshape(
|
||||
batch_size, sequence_length, num_key_value_heads * num_key_value_groups, head_dim
|
||||
)
|
||||
|
||||
value_states = value_states[:, :, :, None, :].expand(
|
||||
batch_size, sequence_length, num_key_value_heads, num_key_value_groups, head_dim
|
||||
)
|
||||
value_states = value_states.reshape(
|
||||
batch_size, sequence_length, num_key_value_heads * num_key_value_groups, head_dim
|
||||
)
|
||||
|
||||
# Attention here is upcasted to float32 to match the original eager implementation.
|
||||
|
||||
query_states = query_states.to(dtype=torch.float32)
|
||||
key_states = key_states.to(dtype=torch.float32)
|
||||
|
||||
query_states = query_states.transpose(1, 2)
|
||||
key_states = key_states.transpose(1, 2)
|
||||
|
||||
att_weights = torch.matmul(query_states, key_states.transpose(2, 3))
|
||||
att_weights *= head_dim**-0.5
|
||||
big_neg = -2.3819763e38 # See gemma/modules.py
|
||||
|
||||
masked_att_weights = torch.where(attention_mask[:, None, :, :], att_weights, big_neg)
|
||||
|
||||
probs = nn.functional.softmax(masked_att_weights, dim=-1)
|
||||
probs = probs.to(dtype=value_states.dtype)
|
||||
|
||||
# probs: batch_size, num_key_value_head, num_att_head, sequence_length, sequence_length
|
||||
# value_states: batch_size, sequence_length, num_att_heads, head_dim
|
||||
|
||||
att_output = torch.matmul(probs, value_states.permute(0, 2, 1, 3))
|
||||
|
||||
att_output = att_output.permute(0, 2, 1, 3)
|
||||
# we use -1 because sequence length can change
|
||||
att_output = att_output.reshape(batch_size, -1, num_key_value_heads * num_key_value_groups * head_dim)
|
||||
|
||||
return att_output
|
||||
@@ -1,166 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2025 Physical Intelligence 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 typing import Any
|
||||
|
||||
import torch
|
||||
|
||||
from lerobot.configs.types import PipelineFeatureType, PolicyFeature
|
||||
from lerobot.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME
|
||||
from lerobot.policies.pi0.configuration_pi0 import PI0Config
|
||||
from lerobot.processor import (
|
||||
AddBatchDimensionProcessorStep,
|
||||
ComplementaryDataProcessorStep,
|
||||
DeviceProcessorStep,
|
||||
NormalizerProcessorStep,
|
||||
PolicyAction,
|
||||
PolicyProcessorPipeline,
|
||||
ProcessorStep,
|
||||
ProcessorStepRegistry,
|
||||
RenameObservationsProcessorStep,
|
||||
TokenizerProcessorStep,
|
||||
UnnormalizerProcessorStep,
|
||||
)
|
||||
from lerobot.processor.converters import policy_action_to_transition, transition_to_policy_action
|
||||
|
||||
|
||||
@ProcessorStepRegistry.register(name="pi0_new_line_processor")
|
||||
class Pi0NewLineProcessor(ComplementaryDataProcessorStep):
|
||||
"""
|
||||
Ensures that the task description string ends with a newline character.
|
||||
|
||||
This processing step is required for compatibility with the PaliGemma tokenizer,
|
||||
which expects a newline at the end of the text prompt. It handles both single
|
||||
strings and lists of strings for the 'task' key in complementary data.
|
||||
"""
|
||||
|
||||
def complementary_data(self, complementary_data):
|
||||
"""
|
||||
Adds a newline to the 'task' field if it doesn't already have one.
|
||||
|
||||
Args:
|
||||
complementary_data: A dictionary that may contain a 'task' key with a
|
||||
string or list of strings.
|
||||
|
||||
Returns:
|
||||
A new dictionary with the modified 'task' field.
|
||||
"""
|
||||
if "task" not in complementary_data:
|
||||
return complementary_data
|
||||
|
||||
task = complementary_data["task"]
|
||||
if task is None:
|
||||
return complementary_data
|
||||
|
||||
new_complementary_data = dict(complementary_data)
|
||||
|
||||
# Handle both string and list of strings
|
||||
if isinstance(task, str):
|
||||
# Single string: add newline if not present
|
||||
if not task.endswith("\n"):
|
||||
new_complementary_data["task"] = f"{task}\n"
|
||||
elif isinstance(task, list) and all(isinstance(t, str) for t in task):
|
||||
# List of strings: add newline to each if not present
|
||||
new_complementary_data["task"] = [t if t.endswith("\n") else f"{t}\n" for t in task]
|
||||
# If task is neither string nor list of strings, leave unchanged
|
||||
|
||||
return new_complementary_data
|
||||
|
||||
def transform_features(
|
||||
self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
|
||||
) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
|
||||
"""
|
||||
This step does not alter the feature definitions.
|
||||
|
||||
Args:
|
||||
features: The input feature dictionary.
|
||||
|
||||
Returns:
|
||||
The unchanged feature dictionary.
|
||||
"""
|
||||
return features
|
||||
|
||||
|
||||
def make_pi0_pre_post_processors(
|
||||
config: PI0Config,
|
||||
dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
|
||||
) -> tuple[
|
||||
PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
|
||||
PolicyProcessorPipeline[PolicyAction, PolicyAction],
|
||||
]:
|
||||
"""
|
||||
Constructs pre-processor and post-processor pipelines for the PI0 policy.
|
||||
|
||||
The pre-processing pipeline prepares input data for the model by:
|
||||
1. Renaming features to match pretrained configurations.
|
||||
2. Normalizing input and output features based on dataset statistics.
|
||||
3. Adding a batch dimension.
|
||||
4. Appending a newline character to the task description for tokenizer compatibility.
|
||||
5. Tokenizing the text prompt using the PaliGemma tokenizer.
|
||||
6. Moving all data to the specified device.
|
||||
|
||||
The post-processing pipeline handles the model's output by:
|
||||
1. Moving data to the CPU.
|
||||
2. Unnormalizing the output features to their original scale.
|
||||
|
||||
Args:
|
||||
config: The configuration object for the PI0 policy.
|
||||
dataset_stats: A dictionary of statistics for normalization.
|
||||
preprocessor_kwargs: Additional arguments for the pre-processor pipeline.
|
||||
postprocessor_kwargs: Additional arguments for the post-processor pipeline.
|
||||
|
||||
Returns:
|
||||
A tuple containing the configured pre-processor and post-processor pipelines.
|
||||
"""
|
||||
|
||||
# Add remaining processors
|
||||
input_steps: list[ProcessorStep] = [
|
||||
RenameObservationsProcessorStep(rename_map={}), # To mimic the same processor as pretrained one
|
||||
AddBatchDimensionProcessorStep(),
|
||||
Pi0NewLineProcessor(), # Add newlines before tokenization for PaliGemma
|
||||
TokenizerProcessorStep(
|
||||
tokenizer_name="google/paligemma-3b-pt-224",
|
||||
max_length=config.tokenizer_max_length,
|
||||
padding_side="right",
|
||||
padding="max_length",
|
||||
),
|
||||
DeviceProcessorStep(device=config.device),
|
||||
NormalizerProcessorStep(
|
||||
features={**config.input_features, **config.output_features},
|
||||
norm_map=config.normalization_mapping,
|
||||
stats=dataset_stats,
|
||||
),
|
||||
]
|
||||
|
||||
output_steps: list[ProcessorStep] = [
|
||||
UnnormalizerProcessorStep(
|
||||
features=config.output_features, norm_map=config.normalization_mapping, 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,
|
||||
),
|
||||
)
|
||||
+1
-1
@@ -22,7 +22,7 @@ from lerobot.optim.optimizers import AdamWConfig
|
||||
from lerobot.optim.schedulers import CosineDecayWithWarmupSchedulerConfig
|
||||
|
||||
|
||||
@PreTrainedConfig.register_subclass("pi05_openpi")
|
||||
@PreTrainedConfig.register_subclass("pi05")
|
||||
@dataclass
|
||||
class PI05OpenPIConfig(PreTrainedConfig):
|
||||
# Model architecture
|
||||
+3
-3
@@ -34,7 +34,7 @@ from transformers.models.paligemma.modeling_paligemma import PaliGemmaForConditi
|
||||
from lerobot.configs.policies import PreTrainedConfig
|
||||
from lerobot.constants import ACTION, OBS_STATE
|
||||
from lerobot.policies.normalize import Normalize, Unnormalize
|
||||
from lerobot.policies.pi05_openpi.configuration_pi05openpi import PI05OpenPIConfig
|
||||
from lerobot.policies.pi05.configuration_pi05openpi import PI05OpenPIConfig
|
||||
from lerobot.policies.pretrained import PreTrainedPolicy, T
|
||||
|
||||
|
||||
@@ -525,7 +525,7 @@ class PI05Pytorch(nn.Module): # see openpi `PI0Pytorch`
|
||||
|
||||
msg = """transformers_replace is not installed correctly.
|
||||
Please install it with `pip install transformers==4.53.2`
|
||||
and `cp -r ./src/lerobot/policies/pi0_openpi/transformers_replace/* \
|
||||
and `cp -r ./src/lerobot/policies/pi0/transformers_replace/* \
|
||||
$(python -c "import transformers, os; print(os.path.dirname(transformers.__file__))")`"""
|
||||
|
||||
try:
|
||||
@@ -820,7 +820,7 @@ class PI05OpenPIPolicy(PreTrainedPolicy):
|
||||
"""PI05 OpenPI Policy for LeRobot."""
|
||||
|
||||
config_class = PI05OpenPIConfig
|
||||
name = "pi05_openpi"
|
||||
name = "pi05"
|
||||
|
||||
def __init__( # see lerobot pi0 `__init__`
|
||||
self,
|
||||
@@ -23,7 +23,7 @@ DEFAULT_INFERENCE_LATENCY = 1 / DEFAULT_FPS
|
||||
DEFAULT_OBS_QUEUE_TIMEOUT = 2
|
||||
|
||||
# All action chunking policies
|
||||
SUPPORTED_POLICIES = ["act", "smolvla", "diffusion", "pi0", "tdmpc", "vqbet", "pi0_openpi", "pi05_openpi"]
|
||||
SUPPORTED_POLICIES = ["act", "smolvla", "diffusion", "tdmpc", "vqbet", "pi0", "pi05"]
|
||||
|
||||
# TODO: Add all other robots
|
||||
SUPPORTED_ROBOTS = ["so100_follower", "so101_follower"]
|
||||
|
||||
@@ -26,7 +26,6 @@ from lerobot.constants import OBS_IMAGES, OBS_STATE
|
||||
from lerobot.datasets.utils import build_dataset_frame, hw_to_dataset_features
|
||||
|
||||
# NOTE: Configs need to be loaded for the client to be able to instantiate the policy config
|
||||
from lerobot.policies import ACTConfig, DiffusionConfig, PI0Config, SmolVLAConfig, VQBeTConfig # noqa: F401
|
||||
from lerobot.robots.robot import Robot
|
||||
from lerobot.utils.utils import init_logging
|
||||
|
||||
|
||||
@@ -19,11 +19,9 @@
|
||||
[Diffusion Policy](https://huggingface.co/papers/2303.04137) treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation.
|
||||
{% elif model_name == "vqbet" %}
|
||||
[VQ-BET](https://huggingface.co/papers/2403.03181) combines vector-quantised action tokens with Behaviour Transformers to discretise control and achieve data-efficient imitation across diverse skills.
|
||||
{% elif model_name == "pi0" %}
|
||||
[Pi0](https://huggingface.co/papers/2410.24164) is a generalist vision-language-action transformer that converts multimodal observations and text instructions into robot actions for zero-shot task transfer.
|
||||
{% elif model_name == "pi0fast" %}
|
||||
[Pi0-Fast](https://huggingface.co/papers/2501.09747) is a variant of Pi0 that uses a new tokenization method called FAST, which enables training of an autoregressive vision-language-action policy for high-frequency robotic tasks with improved performance and reduced training time.
|
||||
{% elif model_name == "pi0_openpi" %}
|
||||
{% elif model_name == "pi0" %}
|
||||
**π₀ (Pi0)**
|
||||
|
||||
π₀ is a Vision-Language-Action model for general robot control, from Physical Intelligence. The LeRobot implementation is adapted from their open source OpenPI repository.
|
||||
@@ -33,7 +31,7 @@
|
||||
π₀ represents a breakthrough in robotics as the first general-purpose robot foundation model developed by Physical Intelligence. Unlike traditional robots that are narrow specialists programmed for repetitive motions, π₀ is designed to be a generalist policy that can understand visual inputs, interpret natural language instructions, and control a variety of different robots across diverse tasks.
|
||||
|
||||
For more details, see the [Physical Intelligence π₀ blog post](https://www.physicalintelligence.company/blog/pi0).
|
||||
{% elif model_name == "pi05_openpi" %}
|
||||
{% elif model_name == "pi05" %}
|
||||
**π₀.₅ (Pi05) Policy**
|
||||
|
||||
π₀.₅ is a Vision-Language-Action model with open-world generalization, from Physical Intelligence. The LeRobot implementation is adapted from their open source OpenPI repository.
|
||||
|
||||
@@ -13,7 +13,7 @@ pytestmark = pytest.mark.skipif(
|
||||
reason="This test requires local OpenPI installation and is not meant for CI",
|
||||
)
|
||||
|
||||
from lerobot.policies.pi05_openpi import PI05OpenPIConfig, PI05OpenPIPolicy # noqa: E402
|
||||
from lerobot.policies.pi05 import PI05Config, PI05Policy # noqa: E402
|
||||
from tests.utils import require_cuda # noqa: E402
|
||||
|
||||
|
||||
@@ -22,7 +22,7 @@ def test_pi05_model_architecture():
|
||||
"""Test that pi05=True creates the correct model architecture."""
|
||||
|
||||
# Create config
|
||||
config = PI05OpenPIConfig(
|
||||
config = PI05Config(
|
||||
max_action_dim=7,
|
||||
max_state_dim=14,
|
||||
dtype="float32",
|
||||
@@ -73,7 +73,7 @@ def test_pi05_model_architecture():
|
||||
}
|
||||
|
||||
# Instantiate policy
|
||||
policy = PI05OpenPIPolicy(config, dataset_stats)
|
||||
policy = PI05Policy(config, dataset_stats)
|
||||
|
||||
# Verify pi05 model components exist
|
||||
# Check that time_mlp layers exist (for AdaRMS conditioning)
|
||||
@@ -104,7 +104,7 @@ def test_pi05_forward_pass():
|
||||
"""Test forward pass with"""
|
||||
|
||||
# Create config
|
||||
config = PI05OpenPIConfig(
|
||||
config = PI05Config(
|
||||
max_action_dim=7,
|
||||
max_state_dim=14,
|
||||
dtype="float32",
|
||||
@@ -150,7 +150,7 @@ def test_pi05_forward_pass():
|
||||
}
|
||||
|
||||
# Instantiate policy
|
||||
policy = PI05OpenPIPolicy(config, dataset_stats)
|
||||
policy = PI05Policy(config, dataset_stats)
|
||||
|
||||
# Create test batch
|
||||
batch_size = 2
|
||||
|
||||
@@ -14,7 +14,7 @@ pytestmark = pytest.mark.skipif(
|
||||
)
|
||||
|
||||
from lerobot.policies.factory import make_policy_config # noqa: E402
|
||||
from lerobot.policies.pi0_openpi import PI0OpenPIConfig, PI0OpenPIPolicy # noqa: E402
|
||||
from lerobot.policies.pi0 import PI0OpenPIConfig, PI0OpenPIPolicy # noqa: E402
|
||||
from tests.utils import require_cuda # noqa: E402
|
||||
|
||||
|
||||
@@ -96,7 +96,7 @@ def test_config_creation():
|
||||
"""Test policy config creation through factory."""
|
||||
try:
|
||||
config = make_policy_config(
|
||||
policy_type="pi0_openpi",
|
||||
policy_type="pi0",
|
||||
max_action_dim=7,
|
||||
max_state_dim=14,
|
||||
)
|
||||
|
||||
@@ -21,7 +21,7 @@ from openpi.models_pytorch import preprocessing_pytorch as openpi_preprocessing
|
||||
from openpi.models_pytorch.pi0_pytorch import PI0Pytorch # noqa: E402
|
||||
from transformers import AutoTokenizer # noqa: E402
|
||||
|
||||
from lerobot.policies.pi0_openpi import PI0OpenPIConfig, PI0OpenPIPolicy # noqa: E402
|
||||
from lerobot.policies.pi0 import PI0Config, PI0Policy # noqa: E402
|
||||
|
||||
DUMMY_ACTION_DIM = 32
|
||||
DUMMY_STATE_DIM = 32
|
||||
@@ -68,9 +68,7 @@ class PI0BaseOriginalConfig:
|
||||
def instantiate_lerobot_pi0(from_pretrained: bool = False):
|
||||
if from_pretrained:
|
||||
# Load the policy first
|
||||
policy = PI0OpenPIPolicy.from_pretrained(
|
||||
pretrained_name_or_path="pepijn223/pi0_base_fp32", strict=True
|
||||
)
|
||||
policy = PI0Policy.from_pretrained(pretrained_name_or_path="pepijn223/pi0_base_fp32", strict=True)
|
||||
# Then reinitialize the normalization with proper stats
|
||||
from lerobot.policies.normalize import Normalize, Unnormalize
|
||||
|
||||
@@ -84,10 +82,8 @@ def instantiate_lerobot_pi0(from_pretrained: bool = False):
|
||||
policy.config.output_features, policy.config.normalization_mapping, DUMMY_DATASET_STATS
|
||||
)
|
||||
else:
|
||||
config = PI0OpenPIConfig(
|
||||
max_action_dim=DUMMY_ACTION_DIM, max_state_dim=DUMMY_STATE_DIM, dtype="float32"
|
||||
)
|
||||
policy = PI0OpenPIPolicy(config, DUMMY_DATASET_STATS)
|
||||
config = PI0Config(max_action_dim=DUMMY_ACTION_DIM, max_state_dim=DUMMY_STATE_DIM, dtype="float32")
|
||||
policy = PI0Policy(config, DUMMY_DATASET_STATS)
|
||||
policy.to(DEVICE)
|
||||
return policy
|
||||
|
||||
|
||||
@@ -18,8 +18,8 @@ pytestmark = pytest.mark.skipif(
|
||||
reason="This test requires HuggingFace authentication and is not meant for CI",
|
||||
)
|
||||
|
||||
from lerobot.policies.pi0_openpi import PI0OpenPIPolicy # noqa: E402
|
||||
from lerobot.policies.pi05_openpi.modeling_pi05openpi import PI05OpenPIPolicy # noqa: E402
|
||||
from lerobot.policies.pi0 import PI0Policy # noqa: E402
|
||||
from lerobot.policies.pi05.modeling_pi05openpi import PI05Policy # noqa: E402
|
||||
|
||||
|
||||
def create_dummy_stats(config):
|
||||
@@ -48,13 +48,13 @@ def create_dummy_stats(config):
|
||||
# Test data for all 6 base models
|
||||
MODEL_TEST_PARAMS = [
|
||||
# PI0 models
|
||||
("pepijn223/pi0_base_fp32", "PI0", PI0OpenPIPolicy),
|
||||
("pepijn223/pi0_droid_fp32", "PI0", PI0OpenPIPolicy),
|
||||
("pepijn223/pi0_libero_fp32", "PI0", PI0OpenPIPolicy),
|
||||
("pepijn223/pi0_base_fp32", "PI0", PI0Policy),
|
||||
("pepijn223/pi0_droid_fp32", "PI0", PI0Policy),
|
||||
("pepijn223/pi0_libero_fp32", "PI0", PI0Policy),
|
||||
# PI0.5 models
|
||||
("pepijn223/pi05_base_fp32", "PI0.5", PI05OpenPIPolicy),
|
||||
("pepijn223/pi05_droid_fp32", "PI0.5", PI05OpenPIPolicy),
|
||||
("pepijn223/pi05_libero_fp32", "PI0.5", PI05OpenPIPolicy),
|
||||
("pepijn223/pi05_base_fp32", "PI0.5", PI05Policy),
|
||||
("pepijn223/pi05_droid_fp32", "PI0.5", PI05Policy),
|
||||
("pepijn223/pi05_libero_fp32", "PI0.5", PI05Policy),
|
||||
]
|
||||
|
||||
|
||||
@@ -65,7 +65,7 @@ def test_all_base_models_hub_loading(model_id, model_type, policy_class):
|
||||
Args:
|
||||
model_id: HuggingFace model ID (e.g., "pepijn223/pi0_base_fp32")
|
||||
model_type: Model type ("PI0" or "PI0.5")
|
||||
policy_class: Policy class to use (PI0OpenPIPolicy or PI05OpenPIPolicy)
|
||||
policy_class: Policy class to use (PI0Policy or PI05Policy)
|
||||
"""
|
||||
print(f"\n{'=' * 80}")
|
||||
print(f"Testing {model_type} model: {model_id}")
|
||||
|
||||
@@ -1,424 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Tests for PI0 policy processor."""
|
||||
|
||||
from unittest.mock import patch
|
||||
|
||||
import pytest
|
||||
import torch
|
||||
|
||||
from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
|
||||
from lerobot.constants import ACTION, OBS_IMAGE, OBS_STATE
|
||||
from lerobot.policies.pi0.configuration_pi0 import PI0Config
|
||||
from lerobot.policies.pi0.processor_pi0 import Pi0NewLineProcessor, make_pi0_pre_post_processors
|
||||
from lerobot.processor import (
|
||||
AddBatchDimensionProcessorStep,
|
||||
DeviceProcessorStep,
|
||||
EnvTransition,
|
||||
NormalizerProcessorStep,
|
||||
ProcessorStep,
|
||||
RenameObservationsProcessorStep,
|
||||
TransitionKey,
|
||||
UnnormalizerProcessorStep,
|
||||
)
|
||||
from lerobot.processor.converters import create_transition, transition_to_batch
|
||||
|
||||
|
||||
class MockTokenizerProcessorStep(ProcessorStep):
|
||||
"""Mock tokenizer processor step for testing."""
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
# Accept any arguments to mimic the real TokenizerProcessorStep interface
|
||||
pass
|
||||
|
||||
def __call__(self, transition: EnvTransition) -> EnvTransition:
|
||||
# Pass through transition unchanged
|
||||
return transition
|
||||
|
||||
def transform_features(self, features):
|
||||
# Pass through features unchanged
|
||||
return features
|
||||
|
||||
|
||||
def create_default_config():
|
||||
"""Create a default PI0 configuration for testing."""
|
||||
config = PI0Config()
|
||||
config.input_features = {
|
||||
OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(10,)),
|
||||
OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 224, 224)),
|
||||
}
|
||||
config.output_features = {
|
||||
ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(6,)),
|
||||
}
|
||||
config.normalization_mapping = {
|
||||
FeatureType.STATE: NormalizationMode.MEAN_STD,
|
||||
FeatureType.VISUAL: NormalizationMode.IDENTITY,
|
||||
FeatureType.ACTION: NormalizationMode.MIN_MAX,
|
||||
}
|
||||
config.device = "cpu"
|
||||
config.tokenizer_max_length = 128
|
||||
return config
|
||||
|
||||
|
||||
def create_default_stats():
|
||||
"""Create default dataset statistics for testing."""
|
||||
return {
|
||||
OBS_STATE: {"mean": torch.zeros(10), "std": torch.ones(10)},
|
||||
OBS_IMAGE: {}, # No normalization for images
|
||||
ACTION: {"min": torch.full((6,), -1.0), "max": torch.ones(6)},
|
||||
}
|
||||
|
||||
|
||||
def test_make_pi0_processor_basic():
|
||||
"""Test basic creation of PI0 processor."""
|
||||
config = create_default_config()
|
||||
stats = create_default_stats()
|
||||
|
||||
with patch("lerobot.policies.pi0.processor_pi0.TokenizerProcessorStep", MockTokenizerProcessorStep):
|
||||
preprocessor, postprocessor = make_pi0_pre_post_processors(
|
||||
config,
|
||||
stats,
|
||||
)
|
||||
|
||||
# Check processor names
|
||||
assert preprocessor.name == "policy_preprocessor"
|
||||
assert postprocessor.name == "policy_postprocessor"
|
||||
|
||||
# Check steps in preprocessor
|
||||
assert len(preprocessor.steps) == 6
|
||||
assert isinstance(preprocessor.steps[0], RenameObservationsProcessorStep)
|
||||
assert isinstance(preprocessor.steps[1], AddBatchDimensionProcessorStep)
|
||||
assert isinstance(preprocessor.steps[2], Pi0NewLineProcessor)
|
||||
# Step 3 would be TokenizerProcessorStep but it's mocked
|
||||
assert isinstance(preprocessor.steps[4], DeviceProcessorStep)
|
||||
assert isinstance(preprocessor.steps[5], NormalizerProcessorStep)
|
||||
|
||||
# Check steps in postprocessor
|
||||
assert len(postprocessor.steps) == 2
|
||||
assert isinstance(postprocessor.steps[0], UnnormalizerProcessorStep)
|
||||
assert isinstance(postprocessor.steps[1], DeviceProcessorStep)
|
||||
|
||||
|
||||
def test_pi0_newline_processor_single_task():
|
||||
"""Test Pi0NewLineProcessor with single task string."""
|
||||
processor = Pi0NewLineProcessor()
|
||||
|
||||
# Test with task that doesn't have newline
|
||||
transition = create_transition(complementary_data={"task": "test task"})
|
||||
result = processor(transition)
|
||||
assert result[TransitionKey.COMPLEMENTARY_DATA]["task"] == "test task\n"
|
||||
|
||||
# Test with task that already has newline
|
||||
transition = create_transition(complementary_data={"task": "test task\n"})
|
||||
result = processor(transition)
|
||||
assert result[TransitionKey.COMPLEMENTARY_DATA]["task"] == "test task\n"
|
||||
|
||||
|
||||
def test_pi0_newline_processor_list_of_tasks():
|
||||
"""Test Pi0NewLineProcessor with list of task strings."""
|
||||
processor = Pi0NewLineProcessor()
|
||||
|
||||
# Test with list of tasks
|
||||
tasks = ["task1", "task2\n", "task3"]
|
||||
transition = create_transition(complementary_data={"task": tasks})
|
||||
result = processor(transition)
|
||||
expected = ["task1\n", "task2\n", "task3\n"]
|
||||
assert result[TransitionKey.COMPLEMENTARY_DATA]["task"] == expected
|
||||
|
||||
|
||||
def test_pi0_newline_processor_empty_transition():
|
||||
"""Test Pi0NewLineProcessor with empty transition."""
|
||||
processor = Pi0NewLineProcessor()
|
||||
|
||||
# Test with no complementary_data
|
||||
transition = create_transition()
|
||||
result = processor(transition)
|
||||
assert result == transition
|
||||
|
||||
# Test with complementary_data but no task
|
||||
transition = create_transition(complementary_data={"other": "data"})
|
||||
result = processor(transition)
|
||||
assert result == transition
|
||||
|
||||
# Test with None task
|
||||
transition = create_transition(complementary_data={"task": None})
|
||||
result = processor(transition)
|
||||
assert result == transition
|
||||
|
||||
|
||||
@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
|
||||
def test_pi0_processor_cuda():
|
||||
"""Test PI0 processor with CUDA device."""
|
||||
config = create_default_config()
|
||||
config.device = "cuda"
|
||||
stats = create_default_stats()
|
||||
|
||||
# Mock the tokenizer processor to act as pass-through
|
||||
class MockTokenizerProcessorStep(ProcessorStep):
|
||||
def __init__(self, *args, **kwargs):
|
||||
pass
|
||||
|
||||
def __call__(self, transition):
|
||||
return transition
|
||||
|
||||
def state_dict(self):
|
||||
return {}
|
||||
|
||||
def load_state_dict(self, state):
|
||||
pass
|
||||
|
||||
def reset(self):
|
||||
pass
|
||||
|
||||
def get_config(self):
|
||||
return {"tokenizer_name": "google/paligemma-3b-pt-224"}
|
||||
|
||||
def transform_features(self, features):
|
||||
return features
|
||||
|
||||
with patch("lerobot.policies.pi0.processor_pi0.TokenizerProcessorStep", MockTokenizerProcessorStep):
|
||||
preprocessor, postprocessor = make_pi0_pre_post_processors(
|
||||
config,
|
||||
stats,
|
||||
)
|
||||
|
||||
# Create CPU data
|
||||
observation = {
|
||||
OBS_STATE: torch.randn(10),
|
||||
OBS_IMAGE: torch.randn(3, 224, 224),
|
||||
}
|
||||
action = torch.randn(6)
|
||||
transition = create_transition(observation, action, complementary_data={"task": "test task"})
|
||||
batch = transition_to_batch(transition)
|
||||
|
||||
# Process through preprocessor
|
||||
processed = preprocessor(batch)
|
||||
|
||||
# Check that data is on CUDA
|
||||
assert processed[OBS_STATE].device.type == "cuda"
|
||||
assert processed[OBS_IMAGE].device.type == "cuda"
|
||||
assert processed[TransitionKey.ACTION.value].device.type == "cuda"
|
||||
|
||||
|
||||
@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
|
||||
def test_pi0_processor_accelerate_scenario():
|
||||
"""Test PI0 processor in simulated Accelerate scenario."""
|
||||
config = create_default_config()
|
||||
config.device = "cuda:0"
|
||||
stats = create_default_stats()
|
||||
|
||||
# Mock the tokenizer processor to act as pass-through
|
||||
class MockTokenizerProcessorStep(ProcessorStep):
|
||||
def __init__(self, *args, **kwargs):
|
||||
pass
|
||||
|
||||
def __call__(self, transition):
|
||||
return transition
|
||||
|
||||
def state_dict(self):
|
||||
return {}
|
||||
|
||||
def load_state_dict(self, state):
|
||||
pass
|
||||
|
||||
def reset(self):
|
||||
pass
|
||||
|
||||
def get_config(self):
|
||||
return {"tokenizer_name": "google/paligemma-3b-pt-224"}
|
||||
|
||||
def transform_features(self, features):
|
||||
return features
|
||||
|
||||
with patch("lerobot.policies.pi0.processor_pi0.TokenizerProcessorStep", MockTokenizerProcessorStep):
|
||||
preprocessor, postprocessor = make_pi0_pre_post_processors(
|
||||
config,
|
||||
stats,
|
||||
)
|
||||
|
||||
# Simulate Accelerate: data already on GPU and batched
|
||||
device = torch.device("cuda:0")
|
||||
observation = {
|
||||
OBS_STATE: torch.randn(1, 10).to(device),
|
||||
OBS_IMAGE: torch.randn(1, 3, 224, 224).to(device),
|
||||
}
|
||||
action = torch.randn(1, 6).to(device)
|
||||
transition = create_transition(observation, action, complementary_data={"task": ["test task"]})
|
||||
batch = transition_to_batch(transition)
|
||||
|
||||
# Process through preprocessor
|
||||
processed = preprocessor(batch)
|
||||
|
||||
# Check that data stays on same GPU
|
||||
assert processed[OBS_STATE].device == device
|
||||
assert processed[OBS_IMAGE].device == device
|
||||
assert processed[TransitionKey.ACTION.value].device == device
|
||||
|
||||
|
||||
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs")
|
||||
def test_pi0_processor_multi_gpu():
|
||||
"""Test PI0 processor with multi-GPU setup."""
|
||||
config = create_default_config()
|
||||
config.device = "cuda:0"
|
||||
stats = create_default_stats()
|
||||
|
||||
# Mock the tokenizer processor to act as pass-through
|
||||
class MockTokenizerProcessorStep(ProcessorStep):
|
||||
def __init__(self, *args, **kwargs):
|
||||
pass
|
||||
|
||||
def __call__(self, transition):
|
||||
return transition
|
||||
|
||||
def state_dict(self):
|
||||
return {}
|
||||
|
||||
def load_state_dict(self, state):
|
||||
pass
|
||||
|
||||
def reset(self):
|
||||
pass
|
||||
|
||||
def get_config(self):
|
||||
return {"tokenizer_name": "google/paligemma-3b-pt-224"}
|
||||
|
||||
def transform_features(self, features):
|
||||
return features
|
||||
|
||||
with patch("lerobot.policies.pi0.processor_pi0.TokenizerProcessorStep", MockTokenizerProcessorStep):
|
||||
preprocessor, postprocessor = make_pi0_pre_post_processors(
|
||||
config,
|
||||
stats,
|
||||
)
|
||||
|
||||
# Simulate data on different GPU
|
||||
device = torch.device("cuda:1")
|
||||
observation = {
|
||||
OBS_STATE: torch.randn(1, 10).to(device),
|
||||
OBS_IMAGE: torch.randn(1, 3, 224, 224).to(device),
|
||||
}
|
||||
action = torch.randn(1, 6).to(device)
|
||||
transition = create_transition(observation, action, complementary_data={"task": ["test task"]})
|
||||
batch = transition_to_batch(transition)
|
||||
|
||||
# Process through preprocessor
|
||||
processed = preprocessor(batch)
|
||||
|
||||
# Check that data stays on cuda:1
|
||||
assert processed[OBS_STATE].device == device
|
||||
assert processed[OBS_IMAGE].device == device
|
||||
assert processed[TransitionKey.ACTION.value].device == device
|
||||
|
||||
|
||||
def test_pi0_processor_without_stats():
|
||||
"""Test PI0 processor creation without dataset statistics."""
|
||||
config = create_default_config()
|
||||
|
||||
# Mock the tokenizer processor
|
||||
with patch("lerobot.policies.pi0.processor_pi0.TokenizerProcessorStep", MockTokenizerProcessorStep):
|
||||
preprocessor, postprocessor = make_pi0_pre_post_processors(
|
||||
config,
|
||||
dataset_stats=None,
|
||||
)
|
||||
|
||||
# Should still create processors
|
||||
assert preprocessor is not None
|
||||
assert postprocessor is not None
|
||||
|
||||
|
||||
def test_pi0_newline_processor_state_dict():
|
||||
"""Test Pi0NewLineProcessor state dict methods."""
|
||||
processor = Pi0NewLineProcessor()
|
||||
|
||||
# Test state_dict (should be empty)
|
||||
state = processor.state_dict()
|
||||
assert state == {}
|
||||
|
||||
# Test load_state_dict (should do nothing)
|
||||
processor.load_state_dict({})
|
||||
|
||||
# Test reset (should do nothing)
|
||||
processor.reset()
|
||||
|
||||
# Test get_config
|
||||
config = processor.get_config()
|
||||
assert config == {}
|
||||
|
||||
|
||||
@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
|
||||
def test_pi0_processor_bfloat16_device_float32_normalizer():
|
||||
"""Test: DeviceProcessor(bfloat16) + NormalizerProcessor(float32) → output bfloat16 via automatic adaptation"""
|
||||
config = create_default_config()
|
||||
stats = create_default_stats()
|
||||
config.device = "cuda"
|
||||
|
||||
with patch("lerobot.policies.pi0.processor_pi0.TokenizerProcessorStep", MockTokenizerProcessorStep):
|
||||
preprocessor, _ = make_pi0_pre_post_processors(
|
||||
config,
|
||||
stats,
|
||||
)
|
||||
|
||||
# Modify the pipeline to use bfloat16 device processor with float32 normalizer
|
||||
modified_steps = []
|
||||
for step in preprocessor.steps:
|
||||
if isinstance(step, DeviceProcessorStep):
|
||||
# Device processor converts to bfloat16
|
||||
modified_steps.append(DeviceProcessorStep(device=config.device, float_dtype="bfloat16"))
|
||||
elif isinstance(step, NormalizerProcessorStep):
|
||||
# Normalizer stays configured as float32 (will auto-adapt to bfloat16)
|
||||
norm_step = step # Now type checker knows this is NormalizerProcessorStep
|
||||
modified_steps.append(
|
||||
NormalizerProcessorStep(
|
||||
features=norm_step.features,
|
||||
norm_map=norm_step.norm_map,
|
||||
stats=norm_step.stats,
|
||||
device=config.device,
|
||||
dtype=torch.float32, # Deliberately configured as float32
|
||||
)
|
||||
)
|
||||
else:
|
||||
modified_steps.append(step)
|
||||
preprocessor.steps = modified_steps
|
||||
|
||||
# Verify initial normalizer configuration (PI0 has NormalizerProcessorStep at index 5)
|
||||
normalizer_step = preprocessor.steps[5] # NormalizerProcessorStep
|
||||
assert normalizer_step.dtype == torch.float32
|
||||
|
||||
# Create test data with both state and visual observations
|
||||
observation = {
|
||||
OBS_STATE: torch.randn(10, dtype=torch.float32), # PI0 expects size 10
|
||||
OBS_IMAGE: torch.randn(3, 224, 224, dtype=torch.float32),
|
||||
}
|
||||
action = torch.randn(6, dtype=torch.float32) # PI0 expects size 6
|
||||
transition = create_transition(
|
||||
observation, action, complementary_data={"task": "test bfloat16 adaptation"}
|
||||
)
|
||||
batch = transition_to_batch(transition)
|
||||
|
||||
# Process through full pipeline
|
||||
processed = preprocessor(batch)
|
||||
|
||||
# Verify: DeviceProcessor → bfloat16, NormalizerProcessor adapts → final output is bfloat16
|
||||
assert processed[OBS_STATE].dtype == torch.bfloat16
|
||||
assert processed[OBS_IMAGE].dtype == torch.bfloat16 # IDENTITY normalization still gets dtype conversion
|
||||
assert processed[TransitionKey.ACTION.value].dtype == torch.bfloat16
|
||||
|
||||
# Verify normalizer automatically adapted its internal state
|
||||
assert normalizer_step.dtype == torch.bfloat16
|
||||
# Check state stats (has normalization)
|
||||
for stat_tensor in normalizer_step._tensor_stats[OBS_STATE].values():
|
||||
assert stat_tensor.dtype == torch.bfloat16
|
||||
# OBS_IMAGE uses IDENTITY normalization, so no stats to check
|
||||
Reference in New Issue
Block a user