Simplify configs (#550)

Co-authored-by: Remi <remi.cadene@huggingface.co>
Co-authored-by: HUANG TZU-CHUN <137322177+tc-huang@users.noreply.github.com>

lerobot/common/optim/factory.py

@@ -0,0 +1,61 @@
+#!/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.
+
+from pathlib import Path
+
+import torch
+from torch.optim import Optimizer
+from torch.optim.lr_scheduler import LRScheduler
+
+from lerobot.common.logger import TRAINING_STATE
+from lerobot.common.policies.pretrained import PreTrainedPolicy
+from lerobot.common.utils.utils import get_global_random_state, set_global_random_state
+from lerobot.configs.train import TrainPipelineConfig
+
+
+def make_optimizer_and_scheduler(
+    cfg: TrainPipelineConfig, policy: PreTrainedPolicy
+) -> tuple[Optimizer, LRScheduler | None]:
+    """Generates the optimizer and scheduler based on configs.
+
+    Args:
+        cfg (TrainPipelineConfig): The training config that contains optimizer and scheduler configs
+        policy (PreTrainedPolicy): The policy config from which parameters and presets must be taken from.
+
+    Returns:
+        tuple[Optimizer, LRScheduler | None]: The couple (Optimizer, Scheduler). Scheduler can be `None`.
+    """
+    params = policy.get_optim_params() if cfg.use_policy_training_preset else policy.parameters()
+    optimizer = cfg.optimizer.build(params)
+    lr_scheduler = cfg.scheduler.build(optimizer, cfg.offline.steps) if cfg.scheduler is not None else None
+    return optimizer, lr_scheduler
+
+
+def load_training_state(checkpoint_dir: Path, optimizer: Optimizer, scheduler: LRScheduler | None) -> int:
+    """
+    Given the checkpoint directory, load the optimizer state, scheduler state, and random state, and
+    return the global training step.
+    """
+    # TODO(aliberts): use safetensors instead as weights_only=False is unsafe
+    training_state = torch.load(checkpoint_dir / TRAINING_STATE, weights_only=False)
+    optimizer.load_state_dict(training_state["optimizer"])
+    if scheduler is not None:
+        scheduler.load_state_dict(training_state["scheduler"])
+    elif "scheduler" in training_state:
+        raise ValueError("The checkpoint contains a scheduler state_dict, but no LRScheduler was provided.")
+    # Small HACK to get the expected keys: use `get_global_random_state`.
+    set_global_random_state({k: training_state[k] for k in get_global_random_state()})
+    return training_state["step"], optimizer, scheduler