lerobot/common/policies/pi0fast/configuration_pi0fast.py

from dataclasses import dataclass, field

from lerobot.common.optim.optimizers import AdamWConfig
from lerobot.common.optim.schedulers import (
    CosineDecayWithWarmupSchedulerConfig,
)
from lerobot.configs.policies import PreTrainedConfig
from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature


@PreTrainedConfig.register_subclass("pi0fast")
@dataclass
class PI0FASTConfig(PreTrainedConfig):
    # Input / output structure.
    n_obs_steps: int = 1
    chunk_size: int = 10
    n_action_steps: int = 5

    normalization_mapping: dict[str, NormalizationMode] = field(
        default_factory=lambda: {
            "VISUAL": NormalizationMode.IDENTITY,
            "STATE": NormalizationMode.MEAN_STD,
            "ACTION": NormalizationMode.MEAN_STD,
        }
    )

    # Shorter state and action vectors will be padded
    max_state_dim: int = 32  # 32
    max_action_dim: int = 32  # 32

    # Image preprocessing
    resize_imgs_with_padding: tuple[int, int] = (224, 224)
    interpolate_like_pi: bool = False

    # Add empty images. Used by pi0_aloha_sim which adds the empty
    # left and right wrist cameras in addition to the top camera.
    empty_cameras: int = 0

    # Converts the joint and gripper values from the standard Aloha space to
    # the space used by the pi internal runtime which was used to train the base model.
    adapt_to_pi_aloha: bool = False

    # Converts joint dimensions to deltas with respect to the current state before passing to the model.
    # Gripper dimensions will remain in absolute values.
    use_delta_joint_actions_aloha: bool = False

    # Tokenizer
    tokenizer_max_length: int = 48

    # Projector
    proj_width: int = 1024

    # Decoding
    max_decoding_steps: int = 256
    fast_skip_tokens: int = 128  # Skip last 128 tokens in PaliGemma vocab since they are special tokens
    max_input_seq_len: int = 256  # 512

    # Utils
    use_cache: bool = True

    # Frozen parameters
    freeze_vision_encoder: bool = True
    freeze_lm_head: bool = True

    # Training presets
    optimizer_lr: float = 1e-4
    optimizer_betas: tuple[float, float] = (0.9, 0.95)
    optimizer_eps: float = 1e-8
    optimizer_weight_decay: float = 1e-5

    scheduler_warmup_steps: int = 1_000
    scheduler_decay_steps: int = 30_000
    scheduler_decay_lr: float = 2.5e-6

    checkpoint_path: str = None

    padding_side: str = "right"

    precision: str = "bfloat16"
    grad_clip_norm: float = 1

    # Allows padding/truncation of generated action tokens during detokenization to ensure decoding.
    # In the original version, tensors of 0s were generated if shapes didn't match for stable decoding.
    relaxed_action_decoding: bool = True

    def __post_init__(self):
        super().__post_init__()

        """Input validation (not exhaustive)."""
        if self.n_action_steps > self.chunk_size:
            raise ValueError(
                f"The chunk size is the upper bound for the number of action steps per model invocation. Got "
                f"{self.n_action_steps} for `n_action_steps` and {self.chunk_size} for `chunk_size`."
            )
        if self.n_obs_steps != 1:
            raise ValueError(
                f"Multiple observation steps not handled yet. Got `nobs_steps={self.n_obs_steps}`"
            )

    def validate_features(self) -> None:
        for i in range(self.empty_cameras):
            key = f"observation.images.empty_camera_{i}"
            empty_camera = PolicyFeature(
                type=FeatureType.VISUAL,
                shape=(3, 480, 640),
            )
            self.input_features[key] = empty_camera

    def get_optimizer_preset(self) -> AdamWConfig:
        return AdamWConfig(
            lr=self.optimizer_lr,
            betas=self.optimizer_betas,
            eps=self.optimizer_eps,
            weight_decay=self.optimizer_weight_decay,
            grad_clip_norm=self.grad_clip_norm,
        )

    def get_scheduler_preset(self):
        return CosineDecayWithWarmupSchedulerConfig(
            peak_lr=self.optimizer_lr,
            decay_lr=self.scheduler_decay_lr,
            num_warmup_steps=self.scheduler_warmup_steps,
            num_decay_steps=self.scheduler_decay_steps,
        )

    @property
    def observation_delta_indices(self) -> None:
        return None

    @property
    def action_delta_indices(self) -> list:
        return list(range(self.chunk_size))

    @property
    def reward_delta_indices(self) -> None:
        return None