import logging from copy import deepcopy from pathlib import Path import hydra import torch from lerobot.common.datasets.factory import make_dataset from lerobot.common.datasets.utils import cycle from lerobot.common.envs.factory import make_env from lerobot.common.logger import Logger, log_output_dir from lerobot.common.policies.factory import make_policy from lerobot.common.utils import ( format_big_number, get_safe_torch_device, init_logging, set_global_seed, ) from lerobot.scripts.eval import eval_policy @hydra.main(version_base=None, config_name="default", config_path="../configs") def train_cli(cfg: dict): train( cfg, out_dir=hydra.core.hydra_config.HydraConfig.get().run.dir, job_name=hydra.core.hydra_config.HydraConfig.get().job.name, ) def train_notebook(out_dir=None, job_name=None, config_name="default", config_path="../configs"): from hydra import compose, initialize hydra.core.global_hydra.GlobalHydra.instance().clear() initialize(config_path=config_path) cfg = compose(config_name=config_name) train(cfg, out_dir=out_dir, job_name=job_name) def log_train_info(logger, info, step, cfg, dataset, is_offline): loss = info["loss"] grad_norm = info["grad_norm"] lr = info["lr"] update_s = info["update_s"] # A sample is an (observation,action) pair, where observation and action # can be on multiple timestamps. In a batch, we have `batch_size`` number of samples. num_samples = (step + 1) * cfg.policy.batch_size avg_samples_per_ep = dataset.num_samples / dataset.num_episodes num_episodes = num_samples / avg_samples_per_ep num_epochs = num_samples / dataset.num_samples log_items = [ f"step:{format_big_number(step)}", # number of samples seen during training f"smpl:{format_big_number(num_samples)}", # number of episodes seen during training f"ep:{format_big_number(num_episodes)}", # number of time all unique samples are seen f"epch:{num_epochs:.2f}", f"loss:{loss:.3f}", f"grdn:{grad_norm:.3f}", f"lr:{lr:0.1e}", # in seconds f"updt_s:{update_s:.3f}", ] logging.info(" ".join(log_items)) info["step"] = step info["num_samples"] = num_samples info["num_episodes"] = num_episodes info["num_epochs"] = num_epochs info["is_offline"] = is_offline logger.log_dict(info, step, mode="train") def log_eval_info(logger, info, step, cfg, dataset, is_offline): eval_s = info["eval_s"] avg_sum_reward = info["avg_sum_reward"] pc_success = info["pc_success"] # A sample is an (observation,action) pair, where observation and action # can be on multiple timestamps. In a batch, we have `batch_size`` number of samples. num_samples = (step + 1) * cfg.policy.batch_size avg_samples_per_ep = dataset.num_samples / dataset.num_episodes num_episodes = num_samples / avg_samples_per_ep num_epochs = num_samples / dataset.num_samples log_items = [ f"step:{format_big_number(step)}", # number of samples seen during training f"smpl:{format_big_number(num_samples)}", # number of episodes seen during training f"ep:{format_big_number(num_episodes)}", # number of time all unique samples are seen f"epch:{num_epochs:.2f}", f"∑rwrd:{avg_sum_reward:.3f}", f"success:{pc_success:.1f}%", f"eval_s:{eval_s:.3f}", ] logging.info(" ".join(log_items)) info["step"] = step info["num_samples"] = num_samples info["num_episodes"] = num_episodes info["num_epochs"] = num_epochs info["is_offline"] = is_offline logger.log_dict(info, step, mode="eval") def calculate_online_sample_weight(n_off: int, n_on: int, pc_on: float): """ Calculate the sampling weight to be assigned to samples so that a specified percentage of the batch comes from online dataset (on average). Parameters: - n_off (int): Number of offline samples, each with a sampling weight of 1. - n_on (int): Number of online samples. - pc_on (float): Desired percentage of online samples in decimal form (e.g., 50% as 0.5). The total weight of offline samples is n_off * 1.0. The total weight of offline samples is n_on * w. The total combined weight of all samples is n_off + n_on * w. The fraction of the weight that is online is n_on * w / (n_off + n_on * w). We want this fraction to equal pc_on, so we set up the equation n_on * w / (n_off + n_on * w) = pc_on. The solution is w = - (n_off * pc_on) / (n_on * (pc_on - 1)) """ assert 0.0 <= pc_on <= 1.0 return -(n_off * pc_on) / (n_on * (pc_on - 1)) def add_episodes_inplace(episodes, online_dataset, concat_dataset, sampler, pc_online_samples): data_dict = episodes["data_dict"] data_ids_per_episode = episodes["data_ids_per_episode"] if len(online_dataset) == 0: # initialize online dataset online_dataset.data_dict = data_dict online_dataset.data_ids_per_episode = data_ids_per_episode else: # find episode index and data frame indices according to previous episode in online_dataset start_episode = max(online_dataset.data_ids_per_episode.keys()) + 1 start_index = online_dataset.data_dict["index"][-1].item() + 1 data_dict["episode"] += start_episode data_dict["index"] += start_index # extend online dataset for key in data_dict: # TODO(rcadene): avoid reallocating memory at every step by preallocating memory or changing our data structure online_dataset.data_dict[key] = torch.cat([online_dataset.data_dict[key], data_dict[key]]) for ep_id in data_ids_per_episode: online_dataset.data_ids_per_episode[ep_id + start_episode] = ( data_ids_per_episode[ep_id] + start_index ) # update the concatenated dataset length used during sampling concat_dataset.cumulative_sizes = concat_dataset.cumsum(concat_dataset.datasets) # update the sampling weights for each frame so that online frames get sampled a certain percentage of times len_online = len(online_dataset) len_offline = len(concat_dataset) - len_online weight_offline = 1.0 weight_online = calculate_online_sample_weight(len_offline, len_online, pc_online_samples) sampler.weights = torch.tensor([weight_offline] * len_offline + [weight_online] * len(online_dataset)) # update the total number of samples used during sampling sampler.num_samples = len(concat_dataset) def train(cfg: dict, out_dir=None, job_name=None): if out_dir is None: raise NotImplementedError() if job_name is None: raise NotImplementedError() if cfg.online_steps > 0: assert cfg.rollout_batch_size == 1, "rollout_batch_size > 1 not supported for online training steps" init_logging() # Check device is available get_safe_torch_device(cfg.device, log=True) torch.backends.cudnn.benchmark = True torch.backends.cuda.matmul.allow_tf32 = True set_global_seed(cfg.seed) logging.info("make_dataset") offline_dataset = make_dataset(cfg) logging.info("make_env") env = make_env(cfg, num_parallel_envs=cfg.eval_episodes) logging.info("make_policy") policy = make_policy(cfg) num_learnable_params = sum(p.numel() for p in policy.parameters() if p.requires_grad) num_total_params = sum(p.numel() for p in policy.parameters()) # log metrics to terminal and wandb logger = Logger(out_dir, job_name, cfg) log_output_dir(out_dir) logging.info(f"{cfg.env.task=}") logging.info(f"{cfg.offline_steps=} ({format_big_number(cfg.offline_steps)})") logging.info(f"{cfg.online_steps=}") logging.info(f"{offline_dataset.num_samples=} ({format_big_number(offline_dataset.num_samples)})") logging.info(f"{offline_dataset.num_episodes=}") logging.info(f"{num_learnable_params=} ({format_big_number(num_learnable_params)})") logging.info(f"{num_total_params=} ({format_big_number(num_total_params)})") # Note: this helper will be used in offline and online training loops. def _maybe_eval_and_maybe_save(step): if step % cfg.eval_freq == 0: logging.info(f"Eval policy at step {step}") eval_info = eval_policy( env, policy, video_dir=Path(out_dir) / "eval", max_episodes_rendered=4, transform=offline_dataset.transform, seed=cfg.seed, ) log_eval_info(logger, eval_info["aggregated"], step, cfg, offline_dataset, is_offline) if cfg.wandb.enable: logger.log_video(eval_info["videos"][0], step, mode="eval") logging.info("Resume training") if cfg.save_model and step % cfg.save_freq == 0: logging.info(f"Checkpoint policy after step {step}") logger.save_model(policy, identifier=step) logging.info("Resume training") # create dataloader for offline training dataloader = torch.utils.data.DataLoader( offline_dataset, num_workers=4, batch_size=cfg.policy.batch_size, shuffle=True, pin_memory=cfg.device != "cpu", drop_last=False, ) dl_iter = cycle(dataloader) step = 0 # number of policy update (forward + backward + optim) is_offline = True for offline_step in range(cfg.offline_steps): if offline_step == 0: logging.info("Start offline training on a fixed dataset") policy.train() batch = next(dl_iter) for key in batch: batch[key] = batch[key].to(cfg.device, non_blocking=True) train_info = policy(batch, step=step) # TODO(rcadene): is it ok if step_t=0 = 0 and not 1 as previously done? if step % cfg.log_freq == 0: log_train_info(logger, train_info, step, cfg, offline_dataset, is_offline) # Note: _maybe_eval_and_maybe_save happens **after** the `step`th training update has completed, so we pass in # step + 1. _maybe_eval_and_maybe_save(step + 1) step += 1 # create an env dedicated to online episodes collection from policy rollout rollout_env = make_env(cfg, num_parallel_envs=1) # create an empty online dataset similar to offline dataset online_dataset = deepcopy(offline_dataset) online_dataset.data_dict = {} online_dataset.data_ids_per_episode = {} # create dataloader for online training concat_dataset = torch.utils.data.ConcatDataset([offline_dataset, online_dataset]) weights = [1.0] * len(concat_dataset) sampler = torch.utils.data.WeightedRandomSampler( weights, num_samples=len(concat_dataset), replacement=True ) dataloader = torch.utils.data.DataLoader( concat_dataset, num_workers=4, batch_size=cfg.policy.batch_size, sampler=sampler, pin_memory=cfg.device != "cpu", drop_last=False, ) dl_iter = cycle(dataloader) online_step = 0 is_offline = False for env_step in range(cfg.online_steps): if env_step == 0: logging.info("Start online training by interacting with environment") with torch.no_grad(): eval_info = eval_policy( rollout_env, policy, transform=offline_dataset.transform, seed=cfg.seed, ) online_pc_sampling = cfg.get("demo_schedule", 0.5) add_episodes_inplace( eval_info["episodes"], online_dataset, concat_dataset, sampler, online_pc_sampling ) for _ in range(cfg.policy.utd): policy.train() batch = next(dl_iter) for key in batch: batch[key] = batch[key].to(cfg.device, non_blocking=True) train_info = policy(batch, step) if step % cfg.log_freq == 0: log_train_info(logger, train_info, step, cfg, online_dataset, is_offline) # Note: _maybe_eval_and_maybe_save happens **after** the `step`th training update has completed, so we pass # in step + 1. _maybe_eval_and_maybe_save(step + 1) step += 1 online_step += 1 logging.info("End of training") if __name__ == "__main__": train_cli()