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feat(rewards): add RewardModelConfig and PreTrainedRewardModel base classes
This commit is contained in:
Khalil Meftah
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# Copyright 2026 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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import abc
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import builtins
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import json
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import logging
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import os
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import tempfile
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Any, TypeVar
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import draccus
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from huggingface_hub import hf_hub_download
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from huggingface_hub.constants import CONFIG_NAME
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from huggingface_hub.errors import HfHubHTTPError
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from lerobot.configs.types import PolicyFeature
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from lerobot.optim.optimizers import OptimizerConfig
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from lerobot.optim.schedulers import LRSchedulerConfig
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from lerobot.utils.hub import HubMixin
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T = TypeVar("T", bound="RewardModelConfig")
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logger = logging.getLogger(__name__)
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@dataclass
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class RewardModelConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):
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"""Base configuration for reward models.
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Args:
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input_features: A dictionary defining the PolicyFeature of the input data for the reward. The key represents
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the input data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes.
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output_features: A dictionary defining the PolicyFeature of the output data for the reward. The key represents
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the output data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes.
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"""
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# Reuses PolicyFeature
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input_features: dict[str, PolicyFeature] = field(default_factory=dict)
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output_features: dict[str, PolicyFeature] = field(default_factory=dict)
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device: str | None = None
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pretrained_path: str | None = None
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@property
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def type(self) -> str:
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choice_name = self.get_choice_name(self.__class__)
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if not isinstance(choice_name, str):
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raise TypeError(f"Expected string from get_choice_name, got {type(choice_name)}")
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return choice_name
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@abc.abstractmethod
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def get_optimizer_preset(self) -> OptimizerConfig:
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raise NotImplementedError
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def get_scheduler_preset(self) -> LRSchedulerConfig | None:
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return None
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def validate_features(self) -> None:
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pass
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def _save_pretrained(self, save_directory: Path) -> None:
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with open(save_directory / CONFIG_NAME, "w") as f, draccus.config_type("json"):
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draccus.dump(self, f, indent=4)
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@classmethod
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def from_pretrained(
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cls: builtins.type[T],
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pretrained_name_or_path: str | Path,
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*,
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force_download: bool = False,
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resume_download: bool | None = None,
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proxies: dict[Any, Any] | None = None,
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token: str | bool | None = None,
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cache_dir: str | Path | None = None,
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local_files_only: bool = False,
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revision: str | None = None,
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**reward_kwargs: Any,
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) -> T:
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model_id = str(pretrained_name_or_path)
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config_file: str | None = None
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if Path(model_id).is_dir():
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if CONFIG_NAME in os.listdir(model_id):
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config_file = os.path.join(model_id, CONFIG_NAME)
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else:
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logger.error(f"{CONFIG_NAME} not found in {Path(model_id).resolve()}")
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else:
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try:
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config_file = hf_hub_download(
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repo_id=model_id,
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filename=CONFIG_NAME,
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revision=revision,
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cache_dir=cache_dir,
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force_download=force_download,
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proxies=proxies,
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resume_download=resume_download,
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token=token,
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local_files_only=local_files_only,
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)
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except HfHubHTTPError as e:
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raise FileNotFoundError(
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f"{CONFIG_NAME} not found on the HuggingFace Hub in {model_id}"
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) from e
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# HACK: Parse the original config to get the config subclass, so that we can
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# apply cli overrides.
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with draccus.config_type("json"):
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orig_config = draccus.parse(cls, config_file, args=[])
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if config_file is None:
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raise FileNotFoundError(f"{CONFIG_NAME} not found in {model_id}")
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with open(config_file) as f:
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config = json.load(f)
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config.pop("type", None)
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with tempfile.NamedTemporaryFile("w+", delete=False, suffix=".json") as f:
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json.dump(config, f)
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config_file = f.name
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cli_overrides = reward_kwargs.pop("cli_overrides", [])
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with draccus.config_type("json"):
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return draccus.parse(orig_config.__class__, config_file, args=cli_overrides)
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@@ -0,0 +1,21 @@
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# Copyright 2026 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 .classifier.configuration_classifier import RewardClassifierConfig as RewardClassifierConfig
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from .sarm.configuration_sarm import SARMConfig as SARMConfig
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__all__ = [
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"RewardClassifierConfig",
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"SARMConfig",
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]
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@@ -0,0 +1,178 @@
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# Copyright 2026 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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import abc
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import builtins
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import logging
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import os
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from pathlib import Path
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from typing import Any, TypeVar
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import packaging
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import safetensors
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from huggingface_hub import hf_hub_download
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from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE
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from huggingface_hub.errors import HfHubHTTPError
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from safetensors.torch import load_model as load_model_as_safetensor, save_model as save_model_as_safetensor
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from torch import Tensor, nn
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from lerobot.configs.rewards import RewardModelConfig
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from lerobot.utils.hub import HubMixin
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logger = logging.getLogger(__name__)
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T = TypeVar("T", bound="PreTrainedRewardModel")
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class PreTrainedRewardModel(nn.Module, HubMixin, abc.ABC):
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"""Base class for reward models."""
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config_class: None
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name: None
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def __init__(self, config: RewardModelConfig, *inputs, **kwargs):
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super().__init__()
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if not isinstance(config, RewardModelConfig):
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raise ValueError(
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f"Parameter config in `{self.__class__.__name__}(config)` should be an instance of class "
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"`RewardModelConfig`. To create a model from a pretrained model use "
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f"`model = {self.__class__.__name__}.from_pretrained(PRETRAINED_MODEL_NAME)`"
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)
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self.config = config
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def __init_subclass__(cls, **kwargs):
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super().__init_subclass__(**kwargs)
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if not getattr(cls, "config_class", None):
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raise TypeError(f"Class {cls.__name__} must define 'config_class'")
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if not getattr(cls, "name", None):
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raise TypeError(f"Class {cls.__name__} must define 'name'")
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def _save_pretrained(self, save_directory: Path) -> None:
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self.config._save_pretrained(save_directory)
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model_to_save = self.module if hasattr(self, "module") else self
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save_model_as_safetensor(model_to_save, str(Path(save_directory) / SAFETENSORS_SINGLE_FILE))
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@classmethod
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def from_pretrained(
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cls: builtins.type[T],
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pretrained_name_or_path: str | Path,
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*,
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config: RewardModelConfig | None = None,
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force_download: bool = False,
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resume_download: bool | None = None,
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proxies: dict | None = None,
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token: str | bool | None = None,
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cache_dir: str | Path | None = None,
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local_files_only: bool = False,
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revision: str | None = None,
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strict: bool = False,
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**kwargs,
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) -> T:
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if config is None:
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config = RewardModelConfig.from_pretrained(
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pretrained_name_or_path=pretrained_name_or_path,
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force_download=force_download,
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resume_download=resume_download,
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proxies=proxies,
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token=token,
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cache_dir=cache_dir,
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local_files_only=local_files_only,
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revision=revision,
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**kwargs,
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)
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model_id = str(pretrained_name_or_path)
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instance = cls(config, **kwargs)
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if os.path.isdir(model_id):
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logger.info("Loading reward model weights from local directory")
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model_file = os.path.join(model_id, SAFETENSORS_SINGLE_FILE)
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reward = cls._load_as_safetensor(instance, model_file, config.device or "cpu", strict)
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else:
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try:
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model_file = hf_hub_download(
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repo_id=model_id,
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filename=SAFETENSORS_SINGLE_FILE,
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revision=revision,
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cache_dir=cache_dir,
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force_download=force_download,
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proxies=proxies,
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resume_download=resume_download,
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token=token,
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local_files_only=local_files_only,
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)
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reward = cls._load_as_safetensor(instance, model_file, config.device or "cpu", strict)
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except HfHubHTTPError as e:
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raise FileNotFoundError(
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f"{SAFETENSORS_SINGLE_FILE} not found on the HuggingFace Hub in {model_id}"
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) from e
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reward.to(config.device)
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reward.eval()
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return reward
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@classmethod
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def _load_as_safetensor(cls, model: T, model_file: str, map_location: str, strict: bool) -> T:
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# Create base kwargs
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kwargs: dict[str, Any] = {"strict": strict}
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# Add device parameter for newer versions that support it
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if packaging.version.parse(safetensors.__version__) >= packaging.version.parse("0.4.3"):
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kwargs["device"] = map_location
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# Load the model with appropriate kwargs
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missing_keys, unexpected_keys = load_model_as_safetensor(model, model_file, **kwargs)
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if missing_keys:
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logger.warning(f"Missing keys when loading reward model: {missing_keys}")
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if unexpected_keys:
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logger.warning(f"Unexpected keys when loading reward model: {unexpected_keys}")
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# For older versions, manually move to device if needed
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if "device" not in kwargs and map_location != "cpu":
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logging.warning(
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"Loading model weights on other devices than 'cpu' is not supported natively in your version of safetensors."
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" This means that the model is loaded on 'cpu' first and then copied to the device."
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" This leads to a slower loading time."
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" Please update safetensors to version 0.4.3 or above for improved performance."
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)
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model.to(map_location)
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return model
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def reset(self) -> None:
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"""Reset any internal state."""
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pass
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def get_optim_params(self):
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"""
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Returns the reward-model-specific parameters dict to be passed on to the optimizer.
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"""
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return self.parameters()
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@abc.abstractmethod
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def compute_reward(self, batch: dict[str, Tensor]) -> Tensor:
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"""Compute a scalar reward signal for a batch of observations.
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Args:
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batch: Dictionary containing at minimum observation tensors.
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May also contain "action", "next_observation.*", etc.
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Returns:
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Tensor of shape ``(batch_size,)`` with reward values.
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"""
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...
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def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict[str, Any]]:
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"""Training forward pass — override for trainable reward models."""
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raise NotImplementedError(
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f"{self.__class__.__name__} is not trainable. Only use compute_reward() for inference."
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)
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