src/lerobot/optim/optimizers.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
# limitations under the License.
import abc
from dataclasses import asdict, dataclass, field
from pathlib import Path
from typing import Any

import draccus
import torch
from safetensors.torch import load_file, save_file

from lerobot.datasets.utils import flatten_dict, unflatten_dict, write_json
from lerobot.utils.constants import (
    OPTIMIZER_PARAM_GROUPS,
    OPTIMIZER_STATE,
)
from lerobot.utils.io_utils import deserialize_json_into_object


@dataclass
class OptimizerConfig(draccus.ChoiceRegistry, abc.ABC):
    lr: float
    weight_decay: float
    grad_clip_norm: float

    @property
    def type(self) -> str:
        return self.get_choice_name(self.__class__)

    @classmethod
    def default_choice_name(cls) -> str | None:
        return "adam"

    @abc.abstractmethod
    def build(self) -> torch.optim.Optimizer | dict[str, torch.optim.Optimizer]:
        """
        Build the optimizer. It can be a single optimizer or a dictionary of optimizers.
        NOTE: Multiple optimizers are useful when you have different models to optimize.
        For example, you can have one optimizer for the policy and another one for the value function
        in reinforcement learning settings.

        Returns:
            The optimizer or a dictionary of optimizers.
        """
        raise NotImplementedError


@OptimizerConfig.register_subclass("adam")
@dataclass
class AdamConfig(OptimizerConfig):
    lr: float = 1e-3
    betas: tuple[float, float] = (0.9, 0.999)
    eps: float = 1e-8
    weight_decay: float = 0.0
    grad_clip_norm: float = 10.0

    def build(self, params: dict) -> torch.optim.Optimizer:
        kwargs = asdict(self)
        kwargs.pop("grad_clip_norm")
        return torch.optim.Adam(params, **kwargs)


@OptimizerConfig.register_subclass("adamw")
@dataclass
class AdamWConfig(OptimizerConfig):
    lr: float = 1e-3
    betas: tuple[float, float] = (0.9, 0.999)
    eps: float = 1e-8
    weight_decay: float = 1e-2
    grad_clip_norm: float = 10.0

    def build(self, params: dict) -> torch.optim.Optimizer:
        kwargs = asdict(self)
        kwargs.pop("grad_clip_norm")
        return torch.optim.AdamW(params, **kwargs)


@OptimizerConfig.register_subclass("sgd")
@dataclass
class SGDConfig(OptimizerConfig):
    lr: float = 1e-3
    momentum: float = 0.0
    dampening: float = 0.0
    nesterov: bool = False
    weight_decay: float = 0.0
    grad_clip_norm: float = 10.0

    def build(self, params: dict) -> torch.optim.Optimizer:
        kwargs = asdict(self)
        kwargs.pop("grad_clip_norm")
        return torch.optim.SGD(params, **kwargs)


@OptimizerConfig.register_subclass("multi_adam")
@dataclass
class MultiAdamConfig(OptimizerConfig):
    """Configuration for multiple Adam optimizers with different parameter groups.

    This creates a dictionary of Adam optimizers, each with its own hyperparameters.

    Args:
        lr: Default learning rate (used if not specified for a group)
        weight_decay: Default weight decay (used if not specified for a group)
        optimizer_groups: Dictionary mapping parameter group names to their hyperparameters
        grad_clip_norm: Gradient clipping norm
    """

    lr: float = 1e-3
    weight_decay: float = 0.0
    grad_clip_norm: float = 10.0
    optimizer_groups: dict[str, dict[str, Any]] = field(default_factory=dict)

    def build(self, params_dict: dict[str, list]) -> dict[str, torch.optim.Optimizer]:
        """Build multiple Adam optimizers.

        Args:
            params_dict: Dictionary mapping parameter group names to lists of parameters
                         The keys should match the keys in optimizer_groups

        Returns:
            Dictionary mapping parameter group names to their optimizers
        """
        optimizers = {}

        for name, params in params_dict.items():
            # Get group-specific hyperparameters or use defaults
            group_config = self.optimizer_groups.get(name, {})

            # Create optimizer with merged parameters (defaults + group-specific)
            optimizer_kwargs = {
                "lr": group_config.get("lr", self.lr),
                "betas": group_config.get("betas", (0.9, 0.999)),
                "eps": group_config.get("eps", 1e-5),
                "weight_decay": group_config.get("weight_decay", self.weight_decay),
            }

            optimizers[name] = torch.optim.Adam(params, **optimizer_kwargs)

        return optimizers


def save_optimizer_state(
    optimizer: torch.optim.Optimizer | dict[str, torch.optim.Optimizer], save_dir: Path
) -> None:
    """Save optimizer state to disk.

    Args:
        optimizer: Either a single optimizer or a dictionary of optimizers.
        save_dir: Directory to save the optimizer state.
    """
    if isinstance(optimizer, dict):
        # Handle dictionary of optimizers
        for name, opt in optimizer.items():
            optimizer_dir = save_dir / name
            optimizer_dir.mkdir(exist_ok=True, parents=True)
            _save_single_optimizer_state(opt, optimizer_dir)
    else:
        # Handle single optimizer
        _save_single_optimizer_state(optimizer, save_dir)


def _save_single_optimizer_state(optimizer: torch.optim.Optimizer, save_dir: Path) -> None:
    """Save a single optimizer's state to disk."""
    state = optimizer.state_dict()
    param_groups = state.pop("param_groups")
    flat_state = flatten_dict(state)
    save_file(flat_state, save_dir / OPTIMIZER_STATE)
    write_json(param_groups, save_dir / OPTIMIZER_PARAM_GROUPS)


def load_optimizer_state(
    optimizer: torch.optim.Optimizer | dict[str, torch.optim.Optimizer], save_dir: Path
) -> torch.optim.Optimizer | dict[str, torch.optim.Optimizer]:
    """Load optimizer state from disk.

    Args:
        optimizer: Either a single optimizer or a dictionary of optimizers.
        save_dir: Directory to load the optimizer state from.

    Returns:
        The updated optimizer(s) with loaded state.
    """
    if isinstance(optimizer, dict):
        # Handle dictionary of optimizers
        loaded_optimizers = {}
        for name, opt in optimizer.items():
            optimizer_dir = save_dir / name
            if optimizer_dir.exists():
                loaded_optimizers[name] = _load_single_optimizer_state(opt, optimizer_dir)
            else:
                loaded_optimizers[name] = opt
        return loaded_optimizers
    else:
        # Handle single optimizer
        return _load_single_optimizer_state(optimizer, save_dir)


def _load_single_optimizer_state(optimizer: torch.optim.Optimizer, save_dir: Path) -> torch.optim.Optimizer:
    """Load a single optimizer's state from disk."""
    current_state_dict = optimizer.state_dict()
    flat_state = load_file(save_dir / OPTIMIZER_STATE)
    state = unflatten_dict(flat_state)

    # Handle case where 'state' key might not exist (for newly created optimizers)
    if "state" in state:
        loaded_state_dict = {"state": {int(k): v for k, v in state["state"].items()}}
    else:
        loaded_state_dict = {"state": {}}

    if "param_groups" in current_state_dict:
        param_groups = deserialize_json_into_object(
            save_dir / OPTIMIZER_PARAM_GROUPS, current_state_dict["param_groups"]
        )
        loaded_state_dict["param_groups"] = param_groups

    optimizer.load_state_dict(loaded_state_dict)
    return optimizer