358 lines
14 KiB
Python
358 lines
14 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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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 Any
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import torch
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from termcolor import colored
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from torch.amp import GradScaler
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from torch.optim import Optimizer
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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.factory import make_dataset
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from lerobot.datasets.sampler import EpisodeAwareSampler
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from lerobot.datasets.utils import cycle
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from lerobot.envs.factory import make_env
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from lerobot.envs.utils import close_envs
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from lerobot.optim.factory import make_optimizer_and_scheduler
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from lerobot.policies.factory import make_policy, make_pre_post_processors
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from lerobot.policies.pretrained import PreTrainedPolicy
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from lerobot.policies.utils import get_device_from_parameters
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from lerobot.scripts.eval import eval_policy_all
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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.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.utils.utils import (
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format_big_number,
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get_safe_torch_device,
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has_method,
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init_logging,
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)
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from lerobot.utils.wandb_utils import WandBLogger
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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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grad_scaler: GradScaler,
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lr_scheduler=None,
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use_amp: bool = False,
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lock=None,
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) -> tuple[MetricsTracker, dict]:
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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. It also handles mixed-precision training via a GradScaler.
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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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grad_scaler: The GradScaler for automatic mixed-precision training.
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lr_scheduler: An optional learning rate scheduler.
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use_amp: A boolean indicating whether to use automatic mixed precision.
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lock: An optional lock for thread-safe optimizer updates.
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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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device = get_device_from_parameters(policy)
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policy.train()
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with torch.autocast(device_type=device.type) if use_amp else nullcontext():
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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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grad_scaler.scale(loss).backward()
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# Unscale the gradient of the optimizer's assigned params in-place **prior to gradient clipping**.
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grad_scaler.unscale_(optimizer)
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grad_norm = torch.nn.utils.clip_grad_norm_(
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policy.parameters(),
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grad_clip_norm,
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error_if_nonfinite=False,
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)
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# Optimizer's gradients are already unscaled, so scaler.step does not unscale them,
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# although it still skips optimizer.step() if the gradients contain infs or NaNs.
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with lock if lock is not None else nullcontext():
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grad_scaler.step(optimizer)
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# Updates the scale for next iteration.
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grad_scaler.update()
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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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if has_method(policy, "update"):
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# To possibly update an internal buffer (for instance an Exponential Moving Average like in TDMPC).
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policy.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):
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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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"""
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cfg.validate()
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logging.info(pformat(cfg.to_dict()))
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if cfg.wandb.enable and cfg.wandb.project:
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wandb_logger = WandBLogger(cfg)
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else:
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wandb_logger = None
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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)
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# Check device is available
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device = get_safe_torch_device(cfg.policy.device, log=True)
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torch.backends.cudnn.benchmark = True
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torch.backends.cuda.matmul.allow_tf32 = True
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logging.info("Creating dataset")
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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:
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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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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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)
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# Create processors - only provide dataset_stats if not resuming from saved processors
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processor_kwargs = {}
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if not (cfg.resume and cfg.policy.pretrained_path):
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# Only provide dataset_stats when not resuming from saved processor state
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processor_kwargs["dataset_stats"] = dataset.meta.stats
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if cfg.policy.pretrained_path is not None:
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processor_kwargs["preprocessor_overrides"] = {"device_processor": {"device": device.type}}
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preprocessor, postprocessor = make_pre_post_processors(
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policy_cfg=cfg.policy, pretrained_path=cfg.policy.pretrained_path, **processor_kwargs
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)
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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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grad_scaler = GradScaler(device.type, enabled=cfg.policy.use_amp)
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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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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(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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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(cfg.policy, "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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drop_n_last_frames=cfg.policy.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=2,
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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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train_tracker = MetricsTracker(
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cfg.batch_size, dataset.num_frames, dataset.num_episodes, train_metrics, initial_step=step
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)
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logging.info("Start offline training on a fixed dataset")
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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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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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grad_scaler=grad_scaler,
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lr_scheduler=lr_scheduler,
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use_amp=cfg.policy.use_amp,
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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
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train_tracker.step()
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is_log_step = cfg.log_freq > 0 and step % cfg.log_freq == 0
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is_saving_step = step % cfg.save_freq == 0 or step == cfg.steps
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is_eval_step = cfg.eval_freq > 0 and step % cfg.eval_freq == 0
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if is_log_step:
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logging.info(train_tracker)
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if wandb_logger:
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wandb_log_dict = train_tracker.to_dict()
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if output_dict:
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wandb_log_dict.update(output_dict)
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wandb_logger.log_dict(wandb_log_dict, step)
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train_tracker.reset_averages()
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if cfg.save_checkpoint and is_saving_step:
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logging.info(f"Checkpoint policy after step {step}")
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checkpoint_dir = get_step_checkpoint_dir(cfg.output_dir, cfg.steps, step)
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save_checkpoint(
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checkpoint_dir, step, cfg, policy, optimizer, lr_scheduler, preprocessor, postprocessor
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)
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update_last_checkpoint(checkpoint_dir)
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if wandb_logger:
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wandb_logger.log_policy(checkpoint_dir)
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if cfg.env and is_eval_step:
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step_id = get_step_identifier(step, cfg.steps)
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logging.info(f"Eval policy at step {step}")
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with (
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torch.no_grad(),
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torch.autocast(device_type=device.type) if cfg.policy.use_amp else nullcontext(),
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):
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eval_info = eval_policy_all(
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envs=eval_env, # dict[suite][task_id] -> vec_env
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policy=policy,
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preprocessor=preprocessor,
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postprocessor=postprocessor,
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n_episodes=cfg.eval.n_episodes,
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videos_dir=cfg.output_dir / "eval" / f"videos_step_{step_id}",
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max_episodes_rendered=4,
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start_seed=cfg.seed,
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max_parallel_tasks=cfg.env.max_parallel_tasks,
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)
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# overall metrics (suite-agnostic)
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aggregated = eval_info["overall"]
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# optional: per-suite logging
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for suite, suite_info in eval_info.items():
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logging.info("Suite %s aggregated: %s", suite, suite_info)
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# meters/tracker
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eval_metrics = {
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"avg_sum_reward": AverageMeter("∑rwrd", ":.3f"),
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"pc_success": AverageMeter("success", ":.1f"),
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"eval_s": AverageMeter("eval_s", ":.3f"),
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}
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eval_tracker = MetricsTracker(
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cfg.batch_size, dataset.num_frames, dataset.num_episodes, eval_metrics, initial_step=step
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)
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eval_tracker.eval_s = aggregated.pop("eval_s")
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eval_tracker.avg_sum_reward = aggregated.pop("avg_sum_reward")
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eval_tracker.pc_success = aggregated.pop("pc_success")
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if wandb_logger:
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wandb_log_dict = {**eval_tracker.to_dict(), **eval_info}
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wandb_logger.log_dict(wandb_log_dict, step, mode="eval")
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wandb_logger.log_video(eval_info["overall"]["video_paths"][0], step, mode="eval")
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if eval_env:
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close_envs(eval_env)
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logging.info("End of training")
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if cfg.policy.push_to_hub:
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policy.push_model_to_hub(cfg)
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preprocessor.push_to_hub(cfg.policy.repo_id)
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postprocessor.push_to_hub(cfg.policy.repo_id)
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def main():
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init_logging()
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train()
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if __name__ == "__main__":
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main()
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