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train.py
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train.py
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import logging
import time
from copy import deepcopy
from pathlib import Path
import datasets
import hydra
import torch
from datasets import concatenate_datasets
from datasets.utils import disable_progress_bars, enable_progress_bars
from diffusers.optimization import get_scheduler
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.policies.policy_protocol import PolicyWithUpdate
from lerobot.common.utils.utils import (
format_big_number,
get_safe_torch_device,
init_logging,
set_global_seed,
)
from lerobot.scripts.eval import eval_policy
def make_optimizer_and_scheduler(cfg, policy):
if cfg.policy.name == "act":
optimizer_params_dicts = [
{
"params": [
p
for n, p in policy.named_parameters()
if not n.startswith("backbone") and p.requires_grad
]
},
{
"params": [
p for n, p in policy.named_parameters() if n.startswith("backbone") and p.requires_grad
],
"lr": cfg.training.lr_backbone,
},
]
optimizer = torch.optim.AdamW(
optimizer_params_dicts, lr=cfg.training.lr, weight_decay=cfg.training.weight_decay
)
lr_scheduler = None
elif cfg.policy.name == "diffusion":
optimizer = torch.optim.Adam(
policy.diffusion.parameters(),
cfg.training.lr,
cfg.training.adam_betas,
cfg.training.adam_eps,
cfg.training.adam_weight_decay,
)
assert cfg.training.online_steps == 0, "Diffusion Policy does not handle online training."
lr_scheduler = get_scheduler(
cfg.training.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=cfg.training.lr_warmup_steps,
num_training_steps=cfg.training.offline_steps,
)
elif policy.name == "tdmpc":
optimizer = torch.optim.Adam(policy.parameters(), cfg.training.lr)
lr_scheduler = None
else:
raise NotImplementedError()
return optimizer, lr_scheduler
def update_policy(policy, batch, optimizer, grad_clip_norm, lr_scheduler=None):
start_time = time.time()
policy.train()
output_dict = policy.forward(batch)
# TODO(rcadene): policy.unnormalize_outputs(out_dict)
loss = output_dict["loss"]
loss.backward()
grad_norm = torch.nn.utils.clip_grad_norm_(
policy.parameters(),
grad_clip_norm,
error_if_nonfinite=False,
)
optimizer.step()
optimizer.zero_grad()
if lr_scheduler is not None:
lr_scheduler.step()
if hasattr(policy, "ema") and policy.ema is not None:
policy.ema.step(policy.diffusion)
if isinstance(policy, PolicyWithUpdate):
# To possibly update an internal buffer (for instance an Exponential Moving Average like in TDMPC).
policy.update()
info = {
"loss": loss.item(),
"grad_norm": float(grad_norm),
"lr": optimizer.param_groups[0]["lr"],
"update_s": time.time() - start_time,
}
return info
@hydra.main(version_base="1.2", 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.training.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.training.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"鈭憆wrd:{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(
online_dataset: torch.utils.data.Dataset,
concat_dataset: torch.utils.data.ConcatDataset,
sampler: torch.utils.data.WeightedRandomSampler,
hf_dataset: datasets.Dataset,
episode_data_index: dict[str, torch.Tensor],
pc_online_samples: float,
):
"""
Modifies the online_dataset, concat_dataset, and sampler in place by integrating
new episodes from hf_dataset into the online_dataset, updating the concatenated
dataset's structure and adjusting the sampling strategy based on the specified
percentage of online samples.
Parameters:
- online_dataset (torch.utils.data.Dataset): The existing online dataset to be updated.
- concat_dataset (torch.utils.data.ConcatDataset): The concatenated dataset that combines
offline and online datasets, used for sampling purposes.
- sampler (torch.utils.data.WeightedRandomSampler): A sampler that will be updated to
reflect changes in the dataset sizes and specified sampling weights.
- hf_dataset (datasets.Dataset): A Hugging Face dataset containing the new episodes to be added.
- episode_data_index (dict): A dictionary containing two keys ("from" and "to") associated to dataset indices.
They indicate the start index and end index of each episode in the dataset.
- pc_online_samples (float): The target percentage of samples that should come from
the online dataset during sampling operations.
Raises:
- AssertionError: If the first episode_id or index in hf_dataset is not 0
"""
first_episode_idx = hf_dataset.select_columns("episode_index")[0]["episode_index"].item()
last_episode_idx = hf_dataset.select_columns("episode_index")[-1]["episode_index"].item()
first_index = hf_dataset.select_columns("index")[0]["index"].item()
last_index = hf_dataset.select_columns("index")[-1]["index"].item()
# sanity check
assert first_episode_idx == 0, f"{first_episode_idx=} is not 0"
assert first_index == 0, f"{first_index=} is not 0"
assert first_index == episode_data_index["from"][first_episode_idx].item()
assert last_index == episode_data_index["to"][last_episode_idx].item() - 1
if len(online_dataset) == 0:
# initialize online dataset
online_dataset.hf_dataset = hf_dataset
online_dataset.episode_data_index = episode_data_index
else:
# get the starting indices of the new episodes and frames to be added
start_episode_idx = last_episode_idx + 1
start_index = last_index + 1
def shift_indices(episode_index, index):
# note: we dont shift "frame_index" since it represents the index of the frame in the episode it belongs to
example = {"episode_index": episode_index + start_episode_idx, "index": index + start_index}
return example
disable_progress_bars() # map has a tqdm progress bar
hf_dataset = hf_dataset.map(shift_indices, input_columns=["episode_index", "index"])
enable_progress_bars()
episode_data_index["from"] += start_index
episode_data_index["to"] += start_index
# extend online dataset
online_dataset.hf_dataset = concatenate_datasets([online_dataset.hf_dataset, hf_dataset])
# 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()
init_logging()
if cfg.training.online_steps > 0 and cfg.eval.batch_size > 1:
logging.warning("eval.batch_size > 1 not supported for online training steps")
# 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")
eval_env = make_env(cfg)
logging.info("make_policy")
policy = make_policy(hydra_cfg=cfg, dataset_stats=offline_dataset.stats)
# Create optimizer and scheduler
# Temporary hack to move optimizer out of policy
optimizer, lr_scheduler = make_optimizer_and_scheduler(cfg, policy)
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.training.offline_steps=} ({format_big_number(cfg.training.offline_steps)})")
logging.info(f"{cfg.training.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.training.eval_freq == 0:
logging.info(f"Eval policy at step {step}")
eval_info = eval_policy(
eval_env,
policy,
cfg.eval.n_episodes,
video_dir=Path(out_dir) / "eval",
max_episodes_rendered=4,
start_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["video_paths"][0], step, mode="eval")
logging.info("Resume training")
if cfg.training.save_model and step % cfg.training.save_freq == 0:
logging.info(f"Checkpoint policy after step {step}")
# Note: Save with step as the identifier, and format it to have at least 6 digits but more if
# needed (choose 6 as a minimum for consistency without being overkill).
logger.save_model(
policy,
identifier=str(step).zfill(
max(6, len(str(cfg.training.offline_steps + cfg.training.online_steps)))
),
)
logging.info("Resume training")
# create dataloader for offline training
dataloader = torch.utils.data.DataLoader(
offline_dataset,
num_workers=4,
batch_size=cfg.training.batch_size,
shuffle=True,
pin_memory=cfg.device != "cpu",
drop_last=False,
)
dl_iter = cycle(dataloader)
policy.train()
step = 0 # number of policy update (forward + backward + optim)
is_offline = True
for offline_step in range(cfg.training.offline_steps):
if offline_step == 0:
logging.info("Start offline training on a fixed dataset")
batch = next(dl_iter)
for key in batch:
batch[key] = batch[key].to(cfg.device, non_blocking=True)
train_info = update_policy(policy, batch, optimizer, cfg.training.grad_clip_norm, lr_scheduler)
# TODO(rcadene): is it ok if step_t=0 = 0 and not 1 as previously done?
if step % cfg.training.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
online_training_env = make_env(cfg, n_envs=1)
# create an empty online dataset similar to offline dataset
online_dataset = deepcopy(offline_dataset)
online_dataset.hf_dataset = {}
online_dataset.episode_data_index = {}
# 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.training.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.training.online_steps):
if env_step == 0:
logging.info("Start online training by interacting with environment")
policy.eval()
with torch.no_grad():
eval_info = eval_policy(
online_training_env,
policy,
n_episodes=1,
return_episode_data=True,
start_seed=cfg.training.online_env_seed,
enable_progbar=True,
)
add_episodes_inplace(
online_dataset,
concat_dataset,
sampler,
hf_dataset=eval_info["episodes"]["hf_dataset"],
episode_data_index=eval_info["episodes"]["episode_data_index"],
pc_online_samples=cfg.training.online_sampling_ratio,
)
policy.train()
for _ in range(cfg.training.online_steps_between_rollouts):
batch = next(dl_iter)
for key in batch:
batch[key] = batch[key].to(cfg.device, non_blocking=True)
train_info = update_policy(policy, batch, optimizer, cfg.training.grad_clip_norm, lr_scheduler)
if step % cfg.training.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
eval_env.close()
online_training_env.close()
logging.info("End of training")
if __name__ == "__main__":
train_cli()