62 lines
2.7 KiB
Python
62 lines
2.7 KiB
Python
#!/usr/bin/env python
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# Copyright 2024 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 pathlib import Path
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import torch
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from torch.optim import Optimizer
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from torch.optim.lr_scheduler import LRScheduler
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from lerobot.common.logger import TRAINING_STATE
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from lerobot.common.policies.pretrained import PreTrainedPolicy
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from lerobot.common.utils.utils import get_global_random_state, set_global_random_state
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from lerobot.configs.train import TrainPipelineConfig
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def make_optimizer_and_scheduler(
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cfg: TrainPipelineConfig, policy: PreTrainedPolicy
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) -> tuple[Optimizer, LRScheduler | None]:
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"""Generates the optimizer and scheduler based on configs.
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Args:
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cfg (TrainPipelineConfig): The training config that contains optimizer and scheduler configs
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policy (PreTrainedPolicy): The policy config from which parameters and presets must be taken from.
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Returns:
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tuple[Optimizer, LRScheduler | None]: The couple (Optimizer, Scheduler). Scheduler can be `None`.
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"""
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params = policy.get_optim_params() if cfg.use_policy_training_preset else policy.parameters()
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optimizer = cfg.optimizer.build(params)
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lr_scheduler = cfg.scheduler.build(optimizer, cfg.offline.steps) if cfg.scheduler is not None else None
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return optimizer, lr_scheduler
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def load_training_state(checkpoint_dir: Path, optimizer: Optimizer, scheduler: LRScheduler | None) -> int:
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"""
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Given the checkpoint directory, load the optimizer state, scheduler state, and random state, and
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return the global training step.
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"""
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# TODO(aliberts): use safetensors instead as weights_only=False is unsafe
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training_state = torch.load(checkpoint_dir / TRAINING_STATE, weights_only=False)
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optimizer.load_state_dict(training_state["optimizer"])
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if scheduler is not None:
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scheduler.load_state_dict(training_state["scheduler"])
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elif "scheduler" in training_state:
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raise ValueError("The checkpoint contains a scheduler state_dict, but no LRScheduler was provided.")
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# Small HACK to get the expected keys: use `get_global_random_state`.
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set_global_random_state({k: training_state[k] for k in get_global_random_state()})
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return training_state["step"], optimizer, scheduler
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