src/lerobot/policies/factory.py

#!/usr/bin/env python

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

import logging
from typing import Any, TypedDict

import torch
from typing_extensions import Unpack

from lerobot.configs.policies import PreTrainedConfig
from lerobot.configs.types import FeatureType
from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata
from lerobot.datasets.utils import dataset_to_policy_features
from lerobot.envs.configs import EnvConfig
from lerobot.envs.utils import env_to_policy_features
from lerobot.policies.act.configuration_act import ACTConfig
from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
from lerobot.policies.groot.configuration_groot import GrootConfig
from lerobot.policies.pi0.configuration_pi0 import PI0Config
from lerobot.policies.pi05.configuration_pi05 import PI05Config
from lerobot.policies.pretrained import PreTrainedPolicy
from lerobot.policies.sac.configuration_sac import SACConfig
from lerobot.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig
from lerobot.policies.smolvla.configuration_smolvla import SmolVLAConfig
from lerobot.policies.tdmpc.configuration_tdmpc import TDMPCConfig
from lerobot.policies.vqbet.configuration_vqbet import VQBeTConfig
from lerobot.processor import PolicyAction, PolicyProcessorPipeline
from lerobot.processor.converters import (
    batch_to_transition,
    policy_action_to_transition,
    transition_to_batch,
    transition_to_policy_action,
)
from lerobot.utils.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME


def get_policy_class(name: str) -> type[PreTrainedPolicy]:
    """
    Retrieves a policy class by its registered name.

    This function uses dynamic imports to avoid loading all policy classes into memory
    at once, improving startup time and reducing dependencies.

    Args:
        name: The name of the policy. Supported names are "tdmpc", "diffusion", "act",
              "vqbet", "pi0", "pi05", "sac", "reward_classifier", "smolvla".

    Returns:
        The policy class corresponding to the given name.

    Raises:
        NotImplementedError: If the policy name is not recognized.
    """
    if name == "tdmpc":
        from lerobot.policies.tdmpc.modeling_tdmpc import TDMPCPolicy

        return TDMPCPolicy
    elif name == "diffusion":
        from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy

        return DiffusionPolicy
    elif name == "act":
        from lerobot.policies.act.modeling_act import ACTPolicy

        return ACTPolicy
    elif name == "vqbet":
        from lerobot.policies.vqbet.modeling_vqbet import VQBeTPolicy

        return VQBeTPolicy
    elif name == "pi0":
        from lerobot.policies.pi0.modeling_pi0 import PI0Policy

        return PI0Policy
    elif name == "pi05":
        from lerobot.policies.pi05.modeling_pi05 import PI05Policy

        return PI05Policy
    elif name == "sac":
        from lerobot.policies.sac.modeling_sac import SACPolicy

        return SACPolicy
    elif name == "reward_classifier":
        from lerobot.policies.sac.reward_model.modeling_classifier import Classifier

        return Classifier
    elif name == "smolvla":
        from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy

        return SmolVLAPolicy
    elif name == "groot":
        from lerobot.policies.groot.modeling_groot import GrootPolicy

        return GrootPolicy
    else:
        raise NotImplementedError(f"Policy with name {name} is not implemented.")


def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
    """
    Instantiates a policy configuration object based on the policy type.

    This factory function simplifies the creation of policy configuration objects by
    mapping a string identifier to the corresponding config class.

    Args:
        policy_type: The type of the policy. Supported types include "tdmpc",
                     "diffusion", "act", "vqbet", "pi0", "pi05", "sac", "smolvla",
                     "reward_classifier".
        **kwargs: Keyword arguments to be passed to the configuration class constructor.

    Returns:
        An instance of a `PreTrainedConfig` subclass.

    Raises:
        ValueError: If the `policy_type` is not recognized.
    """
    if policy_type == "tdmpc":
        return TDMPCConfig(**kwargs)
    elif policy_type == "diffusion":
        return DiffusionConfig(**kwargs)
    elif policy_type == "act":
        return ACTConfig(**kwargs)
    elif policy_type == "vqbet":
        return VQBeTConfig(**kwargs)
    elif policy_type == "pi0":
        return PI0Config(**kwargs)
    elif policy_type == "pi05":
        return PI05Config(**kwargs)
    elif policy_type == "sac":
        return SACConfig(**kwargs)
    elif policy_type == "smolvla":
        return SmolVLAConfig(**kwargs)
    elif policy_type == "reward_classifier":
        return RewardClassifierConfig(**kwargs)
    elif policy_type == "groot":
        return GrootConfig(**kwargs)
    else:
        raise ValueError(f"Policy type '{policy_type}' is not available.")


class ProcessorConfigKwargs(TypedDict, total=False):
    """
    A TypedDict defining the keyword arguments for processor configuration.

    This provides type hints for the optional arguments passed to `make_pre_post_processors`,
    improving code clarity and enabling static analysis.

    Attributes:
        preprocessor_config_filename: The filename for the preprocessor configuration.
        postprocessor_config_filename: The filename for the postprocessor configuration.
        preprocessor_overrides: A dictionary of overrides for the preprocessor configuration.
        postprocessor_overrides: A dictionary of overrides for the postprocessor configuration.
        dataset_stats: Dataset statistics for normalization.
    """

    preprocessor_config_filename: str | None
    postprocessor_config_filename: str | None
    preprocessor_overrides: dict[str, Any] | None
    postprocessor_overrides: dict[str, Any] | None
    dataset_stats: dict[str, dict[str, torch.Tensor]] | None


def make_pre_post_processors(
    policy_cfg: PreTrainedConfig,
    pretrained_path: str | None = None,
    **kwargs: Unpack[ProcessorConfigKwargs],
) -> tuple[
    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
    PolicyProcessorPipeline[PolicyAction, PolicyAction],
]:
    """
    Create or load pre- and post-processor pipelines for a given policy.

    This function acts as a factory. It can either load existing processor pipelines
    from a pretrained path or create new ones from scratch based on the policy
    configuration. Each policy type has a dedicated factory function for its
    processors (e.g., `make_tdmpc_pre_post_processors`).

    Args:
        policy_cfg: The configuration of the policy for which to create processors.
        pretrained_path: An optional path to load pretrained processor pipelines from.
            If provided, pipelines are loaded from this path.
        **kwargs: Keyword arguments for processor configuration, as defined in
            `ProcessorConfigKwargs`.

    Returns:
        A tuple containing the input (pre-processor) and output (post-processor) pipelines.

    Raises:
        NotImplementedError: If a processor factory is not implemented for the given
            policy configuration type.
    """
    if pretrained_path:
        # TODO(Steven): Temporary patch, implement correctly the processors for Gr00t
        if isinstance(policy_cfg, GrootConfig):
            # GROOT handles normalization in groot_pack_inputs_v3 step
            # Need to override both stats AND normalize_min_max since saved config might be empty
            preprocessor_overrides = {}
            postprocessor_overrides = {}
            preprocessor_overrides["groot_pack_inputs_v3"] = {
                "stats": kwargs.get("dataset_stats"),
                "normalize_min_max": True,
            }

            # Also ensure postprocessing slices to env action dim and unnormalizes with dataset stats
            env_action_dim = policy_cfg.output_features["action"].shape[0]
            postprocessor_overrides["groot_action_unpack_unnormalize_v1"] = {
                "stats": kwargs.get("dataset_stats"),
                "normalize_min_max": True,
                "env_action_dim": env_action_dim,
            }
            kwargs["preprocessor_overrides"] = preprocessor_overrides
            kwargs["postprocessor_overrides"] = postprocessor_overrides

        return (
            PolicyProcessorPipeline.from_pretrained(
                pretrained_model_name_or_path=pretrained_path,
                config_filename=kwargs.get(
                    "preprocessor_config_filename", f"{POLICY_PREPROCESSOR_DEFAULT_NAME}.json"
                ),
                overrides=kwargs.get("preprocessor_overrides", {}),
                to_transition=batch_to_transition,
                to_output=transition_to_batch,
            ),
            PolicyProcessorPipeline.from_pretrained(
                pretrained_model_name_or_path=pretrained_path,
                config_filename=kwargs.get(
                    "postprocessor_config_filename", f"{POLICY_POSTPROCESSOR_DEFAULT_NAME}.json"
                ),
                overrides=kwargs.get("postprocessor_overrides", {}),
                to_transition=policy_action_to_transition,
                to_output=transition_to_policy_action,
            ),
        )

    # Create a new processor based on policy type
    if isinstance(policy_cfg, TDMPCConfig):
        from lerobot.policies.tdmpc.processor_tdmpc import make_tdmpc_pre_post_processors

        processors = make_tdmpc_pre_post_processors(
            config=policy_cfg,
            dataset_stats=kwargs.get("dataset_stats"),
        )

    elif isinstance(policy_cfg, DiffusionConfig):
        from lerobot.policies.diffusion.processor_diffusion import make_diffusion_pre_post_processors

        processors = make_diffusion_pre_post_processors(
            config=policy_cfg,
            dataset_stats=kwargs.get("dataset_stats"),
        )

    elif isinstance(policy_cfg, ACTConfig):
        from lerobot.policies.act.processor_act import make_act_pre_post_processors

        processors = make_act_pre_post_processors(
            config=policy_cfg,
            dataset_stats=kwargs.get("dataset_stats"),
        )

    elif isinstance(policy_cfg, VQBeTConfig):
        from lerobot.policies.vqbet.processor_vqbet import make_vqbet_pre_post_processors

        processors = make_vqbet_pre_post_processors(
            config=policy_cfg,
            dataset_stats=kwargs.get("dataset_stats"),
        )

    elif isinstance(policy_cfg, PI0Config):
        from lerobot.policies.pi0.processor_pi0 import make_pi0_pre_post_processors

        processors = make_pi0_pre_post_processors(
            config=policy_cfg,
            dataset_stats=kwargs.get("dataset_stats"),
        )

    elif isinstance(policy_cfg, PI05Config):
        from lerobot.policies.pi05.processor_pi05 import make_pi05_pre_post_processors

        processors = make_pi05_pre_post_processors(
            config=policy_cfg,
            dataset_stats=kwargs.get("dataset_stats"),
        )

    elif isinstance(policy_cfg, SACConfig):
        from lerobot.policies.sac.processor_sac import make_sac_pre_post_processors

        processors = make_sac_pre_post_processors(
            config=policy_cfg,
            dataset_stats=kwargs.get("dataset_stats"),
        )

    elif isinstance(policy_cfg, RewardClassifierConfig):
        from lerobot.policies.sac.reward_model.processor_classifier import make_classifier_processor

        processors = make_classifier_processor(
            config=policy_cfg,
            dataset_stats=kwargs.get("dataset_stats"),
        )

    elif isinstance(policy_cfg, SmolVLAConfig):
        from lerobot.policies.smolvla.processor_smolvla import make_smolvla_pre_post_processors

        processors = make_smolvla_pre_post_processors(
            config=policy_cfg,
            dataset_stats=kwargs.get("dataset_stats"),
        )

    elif isinstance(policy_cfg, GrootConfig):
        from lerobot.policies.groot.processor_groot import make_groot_pre_post_processors

        processors = make_groot_pre_post_processors(
            config=policy_cfg,
            dataset_stats=kwargs.get("dataset_stats"),
        )

    else:
        raise NotImplementedError(f"Processor for policy type '{policy_cfg.type}' is not implemented.")

    return processors


def make_policy(
    cfg: PreTrainedConfig,
    ds_meta: LeRobotDatasetMetadata | None = None,
    env_cfg: EnvConfig | None = None,
    rename_map: dict[str, str] | None = None,
) -> PreTrainedPolicy:
    """
    Instantiate a policy model.

    This factory function handles the logic of creating a policy, which requires
    determining the input and output feature shapes. These shapes can be derived
    either from a `LeRobotDatasetMetadata` object or an `EnvConfig` object. The function
    can either initialize a new policy from scratch or load a pretrained one.

    Args:
        cfg: The configuration for the policy to be created. If `cfg.pretrained_path` is
             set, the policy will be loaded with weights from that path.
        ds_meta: Dataset metadata used to infer feature shapes and types. Also provides
                 statistics for normalization layers.
        env_cfg: Environment configuration used to infer feature shapes and types.
                 One of `ds_meta` or `env_cfg` must be provided.
        rename_map: Optional mapping of dataset or environment feature keys to match
                 expected policy feature names (e.g., `"left"` → `"camera1"`).

    Returns:
        An instantiated and device-placed policy model.

    Raises:
        ValueError: If both or neither of `ds_meta` and `env_cfg` are provided.
        NotImplementedError: If attempting to use an unsupported policy-backend
                             combination (e.g., VQBeT with 'mps').
    """
    if bool(ds_meta) == bool(env_cfg):
        raise ValueError("Either one of a dataset metadata or a sim env must be provided.")

    # NOTE: Currently, if you try to run vqbet with mps backend, you'll get this error.
    # TODO(aliberts, rcadene): Implement a check_backend_compatibility in policies?
    # NotImplementedError: The operator 'aten::unique_dim' is not currently implemented for the MPS device. If
    # you want this op to be added in priority during the prototype phase of this feature, please comment on
    # https://github.com/pytorch/pytorch/issues/77764. As a temporary fix, you can set the environment
    # variable `PYTORCH_ENABLE_MPS_FALLBACK=1` to use the CPU as a fallback for this op. WARNING: this will be
    # slower than running natively on MPS.
    if cfg.type == "vqbet" and cfg.device == "mps":
        raise NotImplementedError(
            "Current implementation of VQBeT does not support `mps` backend. "
            "Please use `cpu` or `cuda` backend."
        )

    policy_cls = get_policy_class(cfg.type)

    kwargs = {}
    if ds_meta is not None:
        features = dataset_to_policy_features(ds_meta.features)
    else:
        if not cfg.pretrained_path:
            logging.warning(
                "You are instantiating a policy from scratch and its features are parsed from an environment "
                "rather than a dataset. Normalization modules inside the policy will have infinite values "
                "by default without stats from a dataset."
            )
        if env_cfg is None:
            raise ValueError("env_cfg cannot be None when ds_meta is not provided")
        features = env_to_policy_features(env_cfg)

    if not cfg.output_features:
        cfg.output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION}
    if not cfg.input_features:
        cfg.input_features = {key: ft for key, ft in features.items() if key not in cfg.output_features}
    kwargs["config"] = cfg

    if cfg.pretrained_path:
        # Load a pretrained policy and override the config if needed (for example, if there are inference-time
        # hyperparameters that we want to vary).
        kwargs["pretrained_name_or_path"] = cfg.pretrained_path
        policy = policy_cls.from_pretrained(**kwargs)
    else:
        # Make a fresh policy.
        policy = policy_cls(**kwargs)

    policy.to(cfg.device)
    assert isinstance(policy, torch.nn.Module)

    # policy = torch.compile(policy, mode="reduce-overhead")

    if not rename_map:
        expected_features = set(cfg.input_features.keys()) | set(cfg.output_features.keys())
        provided_features = set(features.keys())
        if expected_features and provided_features != expected_features:
            missing = expected_features - provided_features
            extra = provided_features - expected_features
            # TODO (jadechoghari): provide a dynamic rename map suggestion to the user.
            raise ValueError(
                f"Feature mismatch between dataset/environment and policy config.\n"
                f"- Missing features: {sorted(missing) if missing else 'None'}\n"
                f"- Extra features: {sorted(extra) if extra else 'None'}\n\n"
                f"Please ensure your dataset and policy use consistent feature names.\n"
                f"If your dataset uses different observation keys (e.g., cameras named differently), "
                f"use the `--rename_map` argument, for example:\n"
                f'  --rename_map=\'{{"observation.images.left": "observation.images.camera1", '
                f'"observation.images.top": "observation.images.camera2"}}\''
            )
    return policy