mirror of
https://github.com/huggingface/lerobot.git
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600 lines
23 KiB
Python
600 lines
23 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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"""Train a policy.
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Requires: pip install 'lerobot[training]' (includes dataset + accelerate + wandb extras)
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"""
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import dataclasses
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import logging
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import time
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from contextlib import nullcontext
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from pprint import pformat
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from typing import TYPE_CHECKING, Any
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if TYPE_CHECKING:
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from accelerate import Accelerator
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import torch
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from termcolor import colored
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from torch.optim import Optimizer
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from tqdm import tqdm
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from lerobot.common.train_utils import (
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get_step_checkpoint_dir,
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get_step_identifier,
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load_training_state,
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save_checkpoint,
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update_last_checkpoint,
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)
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from lerobot.common.wandb_utils import WandBLogger
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from lerobot.configs import parser
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from lerobot.configs.train import TrainPipelineConfig
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from lerobot.datasets import EpisodeAwareSampler, make_dataset
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from lerobot.envs import close_envs, make_env, make_env_pre_post_processors
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from lerobot.optim.factory import make_optimizer_and_scheduler
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from lerobot.policies import PreTrainedPolicy, make_policy, make_pre_post_processors
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from lerobot.rewards import make_reward_pre_post_processors
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from lerobot.utils.import_utils import register_third_party_plugins
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from lerobot.utils.logging_utils import AverageMeter, MetricsTracker
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from lerobot.utils.random_utils import set_seed
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from lerobot.utils.utils import (
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cycle,
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format_big_number,
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has_method,
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init_logging,
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inside_slurm,
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)
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from .lerobot_eval import eval_policy_all
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def update_policy(
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train_metrics: MetricsTracker,
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policy: PreTrainedPolicy,
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batch: Any,
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optimizer: Optimizer,
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grad_clip_norm: float,
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accelerator: "Accelerator",
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lr_scheduler=None,
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lock=None,
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sample_weighter=None,
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) -> tuple[MetricsTracker, dict | None]:
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"""
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Performs a single training step to update the policy's weights.
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This function executes the forward and backward passes, clips gradients, and steps the optimizer and
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learning rate scheduler. Accelerator handles mixed-precision training automatically.
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Args:
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train_metrics: A MetricsTracker instance to record training statistics.
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policy: The policy model to be trained.
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batch: A batch of training data.
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optimizer: The optimizer used to update the policy's parameters.
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grad_clip_norm: The maximum norm for gradient clipping.
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accelerator: The Accelerator instance for distributed training and mixed precision.
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lr_scheduler: An optional learning rate scheduler.
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lock: An optional lock for thread-safe optimizer updates.
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sample_weighter: Optional SampleWeighter instance for per-sample loss weighting.
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Returns:
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A tuple containing:
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- The updated MetricsTracker with new statistics for this step.
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- A dictionary of outputs from the policy's forward pass, for logging purposes.
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"""
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start_time = time.perf_counter()
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policy.train()
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# Compute sample weights if a weighter is provided
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sample_weights = None
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weight_stats = None
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if sample_weighter is not None:
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sample_weights, weight_stats = sample_weighter.compute_batch_weights(batch)
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# Let accelerator handle mixed precision
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with accelerator.autocast():
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if sample_weights is not None:
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# Use per-sample loss for weighted training
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# Note: Policies supporting sample weighting must implement forward(batch, reduction="none")
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per_sample_loss, output_dict = policy.forward(batch, reduction="none")
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# Weighted loss: each sample's contribution is scaled by its weight.
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# We divide by weight sum (not batch size) so that if some weights are zero,
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# the remaining samples contribute proportionally more, preserving gradient scale.
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# Weights are pre-normalized to sum to batch_size for stable training dynamics.
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epsilon = 1e-6
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loss = (per_sample_loss * sample_weights).sum() / (sample_weights.sum() + epsilon)
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# Log weighting statistics
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if output_dict is None:
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output_dict = {}
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for key, value in weight_stats.items():
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output_dict[f"sample_weight_{key}"] = value
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else:
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loss, output_dict = policy.forward(batch)
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# TODO(rcadene): policy.unnormalize_outputs(out_dict)
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# Use accelerator's backward method
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accelerator.backward(loss)
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# Clip gradients if specified
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if grad_clip_norm > 0:
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grad_norm = accelerator.clip_grad_norm_(policy.parameters(), grad_clip_norm)
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else:
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grad_norm = torch.nn.utils.clip_grad_norm_(
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policy.parameters(), float("inf"), error_if_nonfinite=False
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)
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# Optimizer step
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with lock if lock is not None else nullcontext():
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optimizer.step()
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optimizer.zero_grad()
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# Step through pytorch scheduler at every batch instead of epoch
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if lr_scheduler is not None:
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lr_scheduler.step()
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# Update internal buffers if policy has update method
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if has_method(accelerator.unwrap_model(policy, keep_fp32_wrapper=True), "update"):
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accelerator.unwrap_model(policy, keep_fp32_wrapper=True).update()
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train_metrics.loss = loss.item()
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train_metrics.grad_norm = grad_norm.item()
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train_metrics.lr = optimizer.param_groups[0]["lr"]
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train_metrics.update_s = time.perf_counter() - start_time
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return train_metrics, output_dict
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@parser.wrap()
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def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
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"""
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Main function to train a policy.
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This function orchestrates the entire training pipeline, including:
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- Setting up logging, seeding, and device configuration.
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- Creating the dataset, evaluation environment (if applicable), policy, and optimizer.
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- Handling resumption from a checkpoint.
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- Running the main training loop, which involves fetching data batches and calling `update_policy`.
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- Periodically logging metrics, saving model checkpoints, and evaluating the policy.
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- Pushing the final trained model to the Hugging Face Hub if configured.
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Args:
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cfg: A `TrainPipelineConfig` object containing all training configurations.
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accelerator: Optional Accelerator instance. If None, one will be created automatically.
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"""
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from lerobot.utils.import_utils import require_package
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require_package("accelerate", extra="training")
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from accelerate import Accelerator
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cfg.validate()
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# Create Accelerator if not provided
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# It will automatically detect if running in distributed mode or single-process mode
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# We set step_scheduler_with_optimizer=False to prevent accelerate from adjusting the lr_scheduler steps based on the num_processes
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# We set find_unused_parameters=True to handle models with conditional computation
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if accelerator is None:
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from accelerate.utils import DistributedDataParallelKwargs
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ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
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# Accelerate auto-detects the device based on the available hardware and ignores the policy.device setting.
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# Force the device to be CPU when the active config's device is set to CPU (works for both policy and reward model training).
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force_cpu = cfg.trainable_config.device == "cpu"
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accelerator = Accelerator(
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step_scheduler_with_optimizer=False,
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kwargs_handlers=[ddp_kwargs],
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cpu=force_cpu,
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)
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init_logging(accelerator=accelerator)
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# Determine if this is the main process (for logging and checkpointing)
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# When using accelerate, only the main process should log to avoid duplicate outputs
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is_main_process = accelerator.is_main_process
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# Only log on main process
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if is_main_process:
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logging.info(pformat(cfg.to_dict()))
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# Initialize wandb only on main process
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if cfg.wandb.enable and cfg.wandb.project and is_main_process:
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wandb_logger = WandBLogger(cfg)
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else:
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wandb_logger = None
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if is_main_process:
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logging.info(colored("Logs will be saved locally.", "yellow", attrs=["bold"]))
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if cfg.seed is not None:
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set_seed(cfg.seed, accelerator=accelerator)
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# Use accelerator's device
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device = accelerator.device
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if cfg.cudnn_deterministic:
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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else:
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torch.backends.cudnn.benchmark = True
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torch.backends.cuda.matmul.allow_tf32 = True
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# Dataset loading synchronization: main process downloads first to avoid race conditions
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if is_main_process:
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logging.info("Creating dataset")
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dataset = make_dataset(cfg)
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accelerator.wait_for_everyone()
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# Now all other processes can safely load the dataset
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if not is_main_process:
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dataset = make_dataset(cfg)
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# Create environment used for evaluating checkpoints during training on simulation data.
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# On real-world data, no need to create an environment as evaluations are done outside train.py,
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# using the eval.py instead, with gym_dora environment and dora-rs.
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eval_env = None
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if cfg.eval_freq > 0 and cfg.env is not None and is_main_process:
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logging.info("Creating env")
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eval_env = make_env(cfg.env, n_envs=cfg.eval.batch_size, use_async_envs=cfg.eval.use_async_envs)
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if cfg.is_reward_model_training:
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if is_main_process:
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logging.info("Creating reward model")
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from lerobot.rewards import make_reward_model
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policy = make_reward_model(
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cfg=cfg.reward_model,
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dataset_stats=dataset.meta.stats,
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dataset_meta=dataset.meta,
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)
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if not policy.is_trainable:
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raise ValueError(
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f"Reward model '{policy.name}' is zero-shot and cannot be trained via lerobot-train. "
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"Use it directly for inference via compute_reward() (e.g. offline precompute)."
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)
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else:
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if is_main_process:
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logging.info("Creating policy")
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policy = make_policy(
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cfg=cfg.policy,
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ds_meta=dataset.meta,
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rename_map=cfg.rename_map,
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)
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if cfg.peft is not None:
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if cfg.is_reward_model_training:
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raise ValueError("PEFT is only supported for policy training. ")
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from peft import PeftModel
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if isinstance(policy, PeftModel):
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logging.info("PEFT adapter already loaded from checkpoint, skipping wrap_with_peft.")
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else:
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logging.info("Using PEFT! Wrapping model.")
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peft_cli_overrides = dataclasses.asdict(cfg.peft)
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policy = policy.wrap_with_peft(peft_cli_overrides=peft_cli_overrides)
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# Wait for all processes to finish model creation before continuing
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accelerator.wait_for_everyone()
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active_cfg = cfg.trainable_config
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processor_pretrained_path = active_cfg.pretrained_path
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if (
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getattr(active_cfg, "use_relative_actions", False)
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and processor_pretrained_path is not None
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and not cfg.resume
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):
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logging.warning(
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"use_relative_actions=true with pretrained processors can skip relative transforms if "
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"the checkpoint processors do not define them. Building processors from current policy config."
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)
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processor_pretrained_path = None
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processor_kwargs = {}
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postprocessor_kwargs = {}
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if (processor_pretrained_path and not cfg.resume) or not processor_pretrained_path:
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processor_kwargs["dataset_stats"] = dataset.meta.stats
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if cfg.is_reward_model_training:
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processor_kwargs["dataset_meta"] = dataset.meta
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if not cfg.is_reward_model_training and processor_pretrained_path is not None:
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processor_kwargs["preprocessor_overrides"] = {
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"device_processor": {"device": device.type},
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"normalizer_processor": {
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"stats": dataset.meta.stats,
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"features": {**policy.config.input_features, **policy.config.output_features},
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"norm_map": policy.config.normalization_mapping,
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},
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}
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processor_kwargs["preprocessor_overrides"]["rename_observations_processor"] = {
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"rename_map": cfg.rename_map
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}
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postprocessor_kwargs["postprocessor_overrides"] = {
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"unnormalizer_processor": {
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"stats": dataset.meta.stats,
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"features": policy.config.output_features,
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"norm_map": policy.config.normalization_mapping,
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},
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}
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if cfg.is_reward_model_training:
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preprocessor, postprocessor = make_reward_pre_post_processors(
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cfg.reward_model,
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**processor_kwargs,
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)
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else:
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preprocessor, postprocessor = make_pre_post_processors(
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policy_cfg=cfg.policy,
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pretrained_path=processor_pretrained_path,
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**processor_kwargs,
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**postprocessor_kwargs,
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)
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if is_main_process:
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logging.info("Creating optimizer and scheduler")
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optimizer, lr_scheduler = make_optimizer_and_scheduler(cfg, policy)
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# Create sample weighter if configured (e.g., for RA-BC training)
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sample_weighter = None
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if cfg.sample_weighting is not None:
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from lerobot.utils.sample_weighting import make_sample_weighter
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if is_main_process:
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logging.info(f"Creating sample weighter: {cfg.sample_weighting.type}")
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sample_weighter = make_sample_weighter(
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cfg.sample_weighting,
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policy,
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device,
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dataset_root=cfg.dataset.root,
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dataset_repo_id=cfg.dataset.repo_id,
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)
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step = 0 # number of policy updates (forward + backward + optim)
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if cfg.resume:
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step, optimizer, lr_scheduler = load_training_state(cfg.checkpoint_path, optimizer, lr_scheduler)
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num_learnable_params = sum(p.numel() for p in policy.parameters() if p.requires_grad)
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num_total_params = sum(p.numel() for p in policy.parameters())
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if is_main_process:
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logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {cfg.output_dir}")
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if cfg.env is not None:
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logging.info(f"{cfg.env.task=}")
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logging.info("Creating environment processors")
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env_preprocessor, env_postprocessor = make_env_pre_post_processors(
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env_cfg=cfg.env, policy_cfg=cfg.policy
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)
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logging.info(f"{cfg.steps=} ({format_big_number(cfg.steps)})")
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logging.info(f"{dataset.num_frames=} ({format_big_number(dataset.num_frames)})")
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logging.info(f"{dataset.num_episodes=}")
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num_processes = accelerator.num_processes
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effective_bs = cfg.batch_size * num_processes
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logging.info(f"Effective batch size: {cfg.batch_size} x {num_processes} = {effective_bs}")
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logging.info(f"{num_learnable_params=} ({format_big_number(num_learnable_params)})")
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logging.info(f"{num_total_params=} ({format_big_number(num_total_params)})")
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# create dataloader for offline training
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if hasattr(active_cfg, "drop_n_last_frames"):
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shuffle = False
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sampler = EpisodeAwareSampler(
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dataset.meta.episodes["dataset_from_index"],
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dataset.meta.episodes["dataset_to_index"],
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episode_indices_to_use=dataset.episodes,
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drop_n_last_frames=active_cfg.drop_n_last_frames,
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shuffle=True,
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)
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else:
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shuffle = True
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sampler = None
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dataloader = torch.utils.data.DataLoader(
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dataset,
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num_workers=cfg.num_workers,
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batch_size=cfg.batch_size,
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shuffle=shuffle and not cfg.dataset.streaming,
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sampler=sampler,
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pin_memory=device.type == "cuda",
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drop_last=False,
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prefetch_factor=cfg.prefetch_factor if cfg.num_workers > 0 else None,
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persistent_workers=cfg.persistent_workers and cfg.num_workers > 0,
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)
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# Prepare everything with accelerator
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accelerator.wait_for_everyone()
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policy, optimizer, dataloader, lr_scheduler = accelerator.prepare(
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policy, optimizer, dataloader, lr_scheduler
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)
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dl_iter = cycle(dataloader)
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policy.train()
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train_metrics = {
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"loss": AverageMeter("loss", ":.3f"),
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"grad_norm": AverageMeter("grdn", ":.3f"),
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"lr": AverageMeter("lr", ":0.1e"),
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"update_s": AverageMeter("updt_s", ":.3f"),
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"dataloading_s": AverageMeter("data_s", ":.3f"),
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}
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# Keep global batch size for logging; MetricsTracker handles world size internally.
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effective_batch_size = cfg.batch_size * accelerator.num_processes
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train_tracker = MetricsTracker(
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cfg.batch_size,
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dataset.num_frames,
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dataset.num_episodes,
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train_metrics,
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initial_step=step,
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accelerator=accelerator,
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)
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if is_main_process:
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progbar = tqdm(
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total=cfg.steps - step,
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desc="Training",
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unit="step",
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disable=inside_slurm(),
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position=0,
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leave=True,
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)
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logging.info(
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f"Start offline training on a fixed dataset, with effective batch size: {effective_batch_size}"
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)
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for _ in range(step, cfg.steps):
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start_time = time.perf_counter()
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batch = next(dl_iter)
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for cam_key in dataset.meta.camera_keys:
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if cam_key in batch and batch[cam_key].dtype == torch.uint8:
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batch[cam_key] = batch[cam_key].to(dtype=torch.float32) / 255.0
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batch = preprocessor(batch)
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train_tracker.dataloading_s = time.perf_counter() - start_time
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train_tracker, output_dict = update_policy(
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train_tracker,
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policy,
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batch,
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optimizer,
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cfg.optimizer.grad_clip_norm,
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accelerator=accelerator,
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lr_scheduler=lr_scheduler,
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sample_weighter=sample_weighter,
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)
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# Note: eval and checkpoint happens *after* the `step`th training update has completed, so we
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# increment `step` here.
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step += 1
|
|
if is_main_process:
|
|
progbar.update(1)
|
|
train_tracker.step()
|
|
is_log_step = cfg.log_freq > 0 and step % cfg.log_freq == 0 and is_main_process
|
|
is_saving_step = step % cfg.save_freq == 0 or step == cfg.steps
|
|
is_eval_step = cfg.eval_freq > 0 and step % cfg.eval_freq == 0
|
|
|
|
if is_log_step:
|
|
logging.info(train_tracker)
|
|
if wandb_logger:
|
|
wandb_log_dict = train_tracker.to_dict()
|
|
if output_dict:
|
|
wandb_log_dict.update(output_dict)
|
|
# Log sample weighting statistics if enabled
|
|
if sample_weighter is not None:
|
|
weighter_stats = sample_weighter.get_stats()
|
|
wandb_log_dict.update({f"sample_weighting/{k}": v for k, v in weighter_stats.items()})
|
|
wandb_logger.log_dict(wandb_log_dict, step)
|
|
train_tracker.reset_averages()
|
|
|
|
if cfg.save_checkpoint and is_saving_step:
|
|
if is_main_process:
|
|
logging.info(f"Checkpoint policy after step {step}")
|
|
checkpoint_dir = get_step_checkpoint_dir(cfg.output_dir, cfg.steps, step)
|
|
save_checkpoint(
|
|
checkpoint_dir=checkpoint_dir,
|
|
step=step,
|
|
cfg=cfg,
|
|
policy=accelerator.unwrap_model(policy),
|
|
optimizer=optimizer,
|
|
scheduler=lr_scheduler,
|
|
preprocessor=preprocessor,
|
|
postprocessor=postprocessor,
|
|
)
|
|
update_last_checkpoint(checkpoint_dir)
|
|
if wandb_logger:
|
|
wandb_logger.log_policy(checkpoint_dir)
|
|
|
|
accelerator.wait_for_everyone()
|
|
|
|
if cfg.env and is_eval_step:
|
|
if is_main_process:
|
|
step_id = get_step_identifier(step, cfg.steps)
|
|
logging.info(f"Eval policy at step {step}")
|
|
with torch.no_grad(), accelerator.autocast():
|
|
eval_info = eval_policy_all(
|
|
envs=eval_env, # dict[suite][task_id] -> vec_env
|
|
policy=accelerator.unwrap_model(policy),
|
|
env_preprocessor=env_preprocessor,
|
|
env_postprocessor=env_postprocessor,
|
|
preprocessor=preprocessor,
|
|
postprocessor=postprocessor,
|
|
n_episodes=cfg.eval.n_episodes,
|
|
videos_dir=cfg.output_dir / "eval" / f"videos_step_{step_id}",
|
|
max_episodes_rendered=4,
|
|
start_seed=cfg.seed,
|
|
max_parallel_tasks=cfg.env.max_parallel_tasks,
|
|
)
|
|
# overall metrics (suite-agnostic)
|
|
aggregated = eval_info["overall"]
|
|
|
|
# optional: per-suite logging
|
|
for suite, suite_info in eval_info.items():
|
|
logging.info("Suite %s aggregated: %s", suite, suite_info)
|
|
|
|
# meters/tracker
|
|
eval_metrics = {
|
|
"avg_sum_reward": AverageMeter("∑rwrd", ":.3f"),
|
|
"pc_success": AverageMeter("success", ":.1f"),
|
|
"eval_s": AverageMeter("eval_s", ":.3f"),
|
|
}
|
|
eval_tracker = MetricsTracker(
|
|
cfg.batch_size,
|
|
dataset.num_frames,
|
|
dataset.num_episodes,
|
|
eval_metrics,
|
|
initial_step=step,
|
|
accelerator=accelerator,
|
|
)
|
|
eval_tracker.eval_s = aggregated.pop("eval_s")
|
|
eval_tracker.avg_sum_reward = aggregated.pop("avg_sum_reward")
|
|
eval_tracker.pc_success = aggregated.pop("pc_success")
|
|
if wandb_logger:
|
|
wandb_log_dict = {**eval_tracker.to_dict(), **eval_info}
|
|
wandb_logger.log_dict(wandb_log_dict, step, mode="eval")
|
|
wandb_logger.log_video(eval_info["overall"]["video_paths"][0], step, mode="eval")
|
|
|
|
accelerator.wait_for_everyone()
|
|
|
|
if is_main_process:
|
|
progbar.close()
|
|
|
|
if eval_env:
|
|
close_envs(eval_env)
|
|
|
|
if is_main_process:
|
|
logging.info("End of training")
|
|
|
|
if getattr(active_cfg, "push_to_hub", False):
|
|
unwrapped_model = accelerator.unwrap_model(policy)
|
|
# PEFT only applies when training a policy — reward models use the plain path.
|
|
if not cfg.is_reward_model_training and cfg.policy.use_peft:
|
|
unwrapped_model.push_model_to_hub(cfg, peft_model=unwrapped_model)
|
|
else:
|
|
unwrapped_model.push_model_to_hub(cfg)
|
|
preprocessor.push_to_hub(active_cfg.repo_id)
|
|
postprocessor.push_to_hub(active_cfg.repo_id)
|
|
|
|
# Properly clean up the distributed process group
|
|
accelerator.wait_for_everyone()
|
|
accelerator.end_training()
|
|
|
|
|
|
def main():
|
|
register_third_party_plugins()
|
|
train()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|