Remove offline training, refactor train.py and logging/checkpointing (#670)

Co-authored-by: Remi <remi.cadene@huggingface.co>

lerobot/common/utils/train_utils.py

@@ -0,0 +1,161 @@
+#!/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 logging
+from pathlib import Path
+
+from termcolor import colored
+from torch.optim import Optimizer
+from torch.optim.lr_scheduler import LRScheduler
+
+from lerobot.common.constants import (
+    CHECKPOINTS_DIR,
+    LAST_CHECKPOINT_LINK,
+    PRETRAINED_MODEL_DIR,
+    TRAINING_STATE_DIR,
+    TRAINING_STEP,
+)
+from lerobot.common.datasets.utils import load_json, write_json
+from lerobot.common.optim.optimizers import load_optimizer_state, save_optimizer_state
+from lerobot.common.optim.schedulers import load_scheduler_state, save_scheduler_state
+from lerobot.common.policies.pretrained import PreTrainedPolicy
+from lerobot.common.utils.random_utils import load_rng_state, save_rng_state
+from lerobot.configs.train import TrainPipelineConfig
+
+
+def log_output_dir(out_dir):
+    logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {out_dir}")
+
+
+def get_step_identifier(step: int, total_steps: int) -> str:
+    num_digits = max(6, len(str(total_steps)))
+    return f"{step:0{num_digits}d}"
+
+
+def get_step_checkpoint_dir(output_dir: Path, total_steps: int, step: int) -> Path:
+    """Returns the checkpoint sub-directory corresponding to the step number."""
+    step_identifier = get_step_identifier(step, total_steps)
+    return output_dir / CHECKPOINTS_DIR / step_identifier
+
+
+def save_training_step(step: int, save_dir: Path) -> None:
+    write_json({"step": step}, save_dir / TRAINING_STEP)
+
+
+def load_training_step(save_dir: Path) -> int:
+    training_step = load_json(save_dir / TRAINING_STEP)
+    return training_step["step"]
+
+
+def update_last_checkpoint(checkpoint_dir: Path) -> Path:
+    last_checkpoint_dir = checkpoint_dir.parent / LAST_CHECKPOINT_LINK
+    if last_checkpoint_dir.is_symlink():
+        last_checkpoint_dir.unlink()
+    relative_target = checkpoint_dir.relative_to(checkpoint_dir.parent)
+    last_checkpoint_dir.symlink_to(relative_target)
+
+
+def save_checkpoint(
+    checkpoint_dir: Path,
+    step: int,
+    cfg: TrainPipelineConfig,
+    policy: PreTrainedPolicy,
+    optimizer: Optimizer,
+    scheduler: LRScheduler | None = None,
+) -> None:
+    """This function creates the following directory structure:
+
+    005000/  #  training step at checkpoint
+    ├── pretrained_model/
+    │   ├── config.json  # policy config
+    │   ├── model.safetensors  # policy weights
+    │   └── train_config.json  # train config
+    └── training_state/
+        ├── optimizer_param_groups.json  #  optimizer param groups
+        ├── optimizer_state.safetensors  # optimizer state
+        ├── rng_state.safetensors  # rng states
+        ├── scheduler_state.json  # scheduler state
+        └── training_step.json  # training step
+
+    Args:
+        cfg (TrainPipelineConfig): The training config used for this run.
+        step (int): The training step at that checkpoint.
+        policy (PreTrainedPolicy): The policy to save.
+        optimizer (Optimizer | None, optional): The optimizer to save the state from. Defaults to None.
+        scheduler (LRScheduler | None, optional): The scheduler to save the state from. Defaults to None.
+    """
+    pretrained_dir = checkpoint_dir / PRETRAINED_MODEL_DIR
+    policy.save_pretrained(pretrained_dir)
+    cfg.save_pretrained(pretrained_dir)
+    save_training_state(checkpoint_dir, step, optimizer, scheduler)
+
+
+def save_training_state(
+    checkpoint_dir: Path,
+    train_step: int,
+    optimizer: Optimizer | None = None,
+    scheduler: LRScheduler | None = None,
+) -> None:
+    """
+    Saves the training step, optimizer state, scheduler state, and rng state.
+
+    Args:
+        save_dir (Path): The directory to save artifacts to.
+        train_step (int): Current training step.
+        optimizer (Optimizer | None, optional): The optimizer from which to save the state_dict.
+            Defaults to None.
+        scheduler (LRScheduler | None, optional): The scheduler from which to save the state_dict.
+            Defaults to None.
+    """
+    save_dir = checkpoint_dir / TRAINING_STATE_DIR
+    save_dir.mkdir(parents=True, exist_ok=True)
+    save_training_step(train_step, save_dir)
+    save_rng_state(save_dir)
+    if optimizer is not None:
+        save_optimizer_state(optimizer, save_dir)
+    if scheduler is not None:
+        save_scheduler_state(scheduler, save_dir)
+
+
+def load_training_state(
+    checkpoint_dir: Path, optimizer: Optimizer, scheduler: LRScheduler | None
+) -> tuple[int, Optimizer, LRScheduler | None]:
+    """
+    Loads the training step, optimizer state, scheduler state, and rng state.
+    This is used to resume a training run.
+
+    Args:
+        checkpoint_dir (Path): The checkpoint directory. Should contain a 'training_state' dir.
+        optimizer (Optimizer): The optimizer to load the state_dict to.
+        scheduler (LRScheduler | None): The scheduler to load the state_dict to (can be None).
+
+    Raises:
+        NotADirectoryError: If 'checkpoint_dir' doesn't contain a 'training_state' dir
+
+    Returns:
+        tuple[int, Optimizer, LRScheduler | None]: training step, optimizer and scheduler with their
+            state_dict loaded.
+    """
+    training_state_dir = checkpoint_dir / TRAINING_STATE_DIR
+    if not training_state_dir.is_dir():
+        raise NotADirectoryError(training_state_dir)
+
+    load_rng_state(training_state_dir)
+    step = load_training_step(training_state_dir)
+    optimizer = load_optimizer_state(optimizer, training_state_dir)
+    if scheduler is not None:
+        scheduler = load_scheduler_state(scheduler, training_state_dir)
+
+    return step, optimizer, scheduler