mirror of
https://github.com/huggingface/lerobot.git
synced 2026-06-02 03:41:25 +00:00
dbce707db5
- Added `init_logging` calls to ensure proper logging setup when using the accelerator and in standard training mode. - This change enhances the clarity and consistency of logging during training sessions.
448 lines
17 KiB
Python
448 lines
17 KiB
Python
#!/usr/bin/env python
|
|
|
|
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import logging
|
|
import time
|
|
from contextlib import nullcontext
|
|
from pprint import pformat
|
|
from typing import Any
|
|
from collections.abc import Callable
|
|
|
|
import torch
|
|
from termcolor import colored
|
|
from torch.amp import GradScaler
|
|
from torch.optim import Optimizer
|
|
|
|
from lerobot.configs import parser
|
|
from lerobot.configs.train import TrainPipelineConfig
|
|
from lerobot.datasets.factory import make_dataset
|
|
from lerobot.datasets.sampler import EpisodeAwareSampler
|
|
from lerobot.datasets.utils import cycle
|
|
from lerobot.envs.factory import make_env
|
|
from lerobot.envs.utils import close_envs
|
|
from lerobot.optim.factory import make_optimizer_and_scheduler
|
|
from lerobot.policies.factory import make_policy, make_pre_post_processors
|
|
from lerobot.policies.pretrained import PreTrainedPolicy
|
|
from lerobot.policies.utils import get_device_from_parameters
|
|
from lerobot.rl.wandb_utils import WandBLogger
|
|
from lerobot.scripts.lerobot_eval import eval_policy_all
|
|
from lerobot.utils.logging_utils import AverageMeter, MetricsTracker
|
|
from lerobot.utils.random_utils import set_seed
|
|
from lerobot.utils.train_utils import (
|
|
get_step_checkpoint_dir,
|
|
get_step_identifier,
|
|
load_training_state,
|
|
save_checkpoint,
|
|
update_last_checkpoint,
|
|
)
|
|
from lerobot.utils.utils import (
|
|
format_big_number,
|
|
get_safe_torch_device,
|
|
has_method,
|
|
init_logging,
|
|
is_launched_with_accelerate,
|
|
)
|
|
|
|
|
|
def update_policy(
|
|
train_metrics: MetricsTracker,
|
|
policy: PreTrainedPolicy,
|
|
batch: Any,
|
|
optimizer: Optimizer,
|
|
grad_clip_norm: float,
|
|
grad_scaler: GradScaler,
|
|
lr_scheduler=None,
|
|
use_amp: bool = False,
|
|
lock=None,
|
|
accelerator: Callable | None = None,
|
|
) -> tuple[MetricsTracker, dict]:
|
|
"""
|
|
Performs a single training step to update the policy's weights.
|
|
|
|
This function executes the forward and backward passes, clips gradients, and steps the optimizer and
|
|
learning rate scheduler. It also handles mixed-precision training via a GradScaler.
|
|
|
|
Args:
|
|
train_metrics: A MetricsTracker instance to record training statistics.
|
|
policy: The policy model to be trained.
|
|
batch: A batch of training data.
|
|
optimizer: The optimizer used to update the policy's parameters.
|
|
grad_clip_norm: The maximum norm for gradient clipping.
|
|
grad_scaler: The GradScaler for automatic mixed-precision training.
|
|
lr_scheduler: An optional learning rate scheduler.
|
|
use_amp: A boolean indicating whether to use automatic mixed precision.
|
|
lock: An optional lock for thread-safe optimizer updates.
|
|
accelerator: An optional accelerator, for multi-gpu training.
|
|
|
|
Returns:
|
|
A tuple containing:
|
|
- The updated MetricsTracker with new statistics for this step.
|
|
- A dictionary of outputs from the policy's forward pass, for logging purposes.
|
|
"""
|
|
start_time = time.perf_counter()
|
|
device = get_device_from_parameters(policy)
|
|
policy.train()
|
|
with torch.autocast(device_type=device.type) if use_amp and accelerator is None else nullcontext():
|
|
loss, output_dict = policy.forward(batch)
|
|
# TODO(rcadene): policy.unnormalize_outputs(out_dict)
|
|
|
|
if accelerator:
|
|
accelerator.backward(loss)
|
|
accelerator.unscale_gradients(optimizer=optimizer)
|
|
grad_norm = torch.nn.utils.clip_grad_norm_(
|
|
policy.parameters(),
|
|
grad_clip_norm,
|
|
error_if_nonfinite=False,
|
|
)
|
|
optimizer.step()
|
|
else:
|
|
grad_scaler.scale(loss).backward()
|
|
# Unscale the gradient of the optimizer's assigned params in-place **prior to gradient clipping**.
|
|
grad_scaler.unscale_(optimizer)
|
|
|
|
grad_norm = torch.nn.utils.clip_grad_norm_(
|
|
policy.parameters(),
|
|
grad_clip_norm,
|
|
error_if_nonfinite=False,
|
|
)
|
|
|
|
# Optimizer's gradients are already unscaled, so scaler.step does not unscale them,
|
|
# although it still skips optimizer.step() if the gradients contain infs or NaNs.
|
|
with lock if lock is not None else nullcontext():
|
|
grad_scaler.step(optimizer)
|
|
# Updates the scale for next iteration.
|
|
grad_scaler.update()
|
|
|
|
optimizer.zero_grad()
|
|
|
|
# Step through pytorch scheduler at every batch instead of epoch
|
|
if lr_scheduler is not None:
|
|
lr_scheduler.step()
|
|
|
|
if accelerator:
|
|
if has_method(accelerator.unwrap_model(policy, keep_fp32_wrapper=True), "update"):
|
|
accelerator.unwrap_model(policy, keep_fp32_wrapper=True).update()
|
|
else:
|
|
if has_method(policy, "update"):
|
|
# To possibly update an internal buffer (for instance an Exponential Moving Average like in TDMPC).
|
|
policy.update()
|
|
|
|
train_metrics.loss = loss.item()
|
|
train_metrics.grad_norm = grad_norm.item()
|
|
train_metrics.lr = optimizer.param_groups[0]["lr"]
|
|
train_metrics.update_s = time.perf_counter() - start_time
|
|
return train_metrics, output_dict
|
|
|
|
|
|
@parser.wrap()
|
|
def train(cfg: TrainPipelineConfig, accelerator: Callable | None = None):
|
|
"""
|
|
Main function to train a policy.
|
|
|
|
This function orchestrates the entire training pipeline, including:
|
|
- Setting up logging, seeding, and device configuration.
|
|
- Creating the dataset, evaluation environment (if applicable), policy, and optimizer.
|
|
- Handling resumption from a checkpoint.
|
|
- Running the main training loop, which involves fetching data batches and calling `update_policy`.
|
|
- Periodically logging metrics, saving model checkpoints, and evaluating the policy.
|
|
- Pushing the final trained model to the Hugging Face Hub if configured.
|
|
|
|
Args:
|
|
cfg: A `TrainPipelineConfig` object containing all training configurations.
|
|
"""
|
|
cfg.validate()
|
|
logging.info(pformat(cfg.to_dict()))
|
|
|
|
if accelerator and not accelerator.is_main_process:
|
|
# Disable logging on non-main processes.
|
|
cfg.wandb.enable = False
|
|
|
|
if cfg.wandb.enable and cfg.wandb.project:
|
|
wandb_logger = WandBLogger(cfg)
|
|
else:
|
|
wandb_logger = None
|
|
logging.info(colored("Logs will be saved locally.", "yellow", attrs=["bold"]))
|
|
|
|
if cfg.seed is not None:
|
|
set_seed(cfg.seed, accelerator=accelerator)
|
|
|
|
# Check device is available
|
|
device = get_safe_torch_device(cfg.policy.device, log=True, accelerator=accelerator)
|
|
torch.backends.cudnn.benchmark = True
|
|
torch.backends.cuda.matmul.allow_tf32 = True
|
|
|
|
logging.info("Creating dataset")
|
|
dataset = make_dataset(cfg)
|
|
|
|
# Create environment used for evaluating checkpoints during training on simulation data.
|
|
# On real-world data, no need to create an environment as evaluations are done outside train.py,
|
|
# using the eval.py instead, with gym_dora environment and dora-rs.
|
|
eval_env = None
|
|
if cfg.eval_freq > 0 and cfg.env is not None:
|
|
logging.info("Creating env")
|
|
eval_env = make_env(cfg.env, n_envs=cfg.eval.batch_size, use_async_envs=cfg.eval.use_async_envs)
|
|
|
|
logging.info("Creating policy")
|
|
policy = make_policy(
|
|
cfg=cfg.policy,
|
|
ds_meta=dataset.meta,
|
|
)
|
|
policy.to(device)
|
|
|
|
# Create processors - only provide dataset_stats if not resuming from saved processors
|
|
processor_kwargs = {}
|
|
postprocessor_kwargs = {}
|
|
if (cfg.policy.pretrained_path and not cfg.resume) or not cfg.policy.pretrained_path:
|
|
# Only provide dataset_stats when not resuming from saved processor state
|
|
processor_kwargs["dataset_stats"] = dataset.meta.stats
|
|
|
|
if cfg.policy.pretrained_path is not None:
|
|
processor_kwargs["preprocessor_overrides"] = {
|
|
"device_processor": {"device": device.type},
|
|
"normalizer_processor": {
|
|
"stats": dataset.meta.stats,
|
|
"features": {**policy.config.input_features, **policy.config.output_features},
|
|
"norm_map": policy.config.normalization_mapping,
|
|
},
|
|
}
|
|
postprocessor_kwargs["postprocessor_overrides"] = {
|
|
"unnormalizer_processor": {
|
|
"stats": dataset.meta.stats,
|
|
"features": policy.config.output_features,
|
|
"norm_map": policy.config.normalization_mapping,
|
|
},
|
|
}
|
|
|
|
preprocessor, postprocessor = make_pre_post_processors(
|
|
policy_cfg=cfg.policy,
|
|
pretrained_path=cfg.policy.pretrained_path,
|
|
**processor_kwargs,
|
|
**postprocessor_kwargs,
|
|
)
|
|
|
|
logging.info("Creating optimizer and scheduler")
|
|
optimizer, lr_scheduler = make_optimizer_and_scheduler(cfg, policy)
|
|
grad_scaler = GradScaler(device.type, enabled=cfg.policy.use_amp)
|
|
|
|
step = 0 # number of policy updates (forward + backward + optim)
|
|
|
|
if cfg.resume:
|
|
step, optimizer, lr_scheduler = load_training_state(cfg.checkpoint_path, optimizer, lr_scheduler)
|
|
|
|
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())
|
|
|
|
if not accelerator or accelerator.is_main_process:
|
|
logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {cfg.output_dir}")
|
|
if cfg.env is not None:
|
|
logging.info(f"{cfg.env.task=}")
|
|
logging.info(f"{cfg.steps=} ({format_big_number(cfg.steps)})")
|
|
logging.info(f"{dataset.num_frames=} ({format_big_number(dataset.num_frames)})")
|
|
logging.info(f"{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)})")
|
|
|
|
# create dataloader for offline training
|
|
if hasattr(cfg.policy, "drop_n_last_frames"):
|
|
shuffle = False
|
|
sampler = EpisodeAwareSampler(
|
|
dataset.meta.episodes["dataset_from_index"],
|
|
dataset.meta.episodes["dataset_to_index"],
|
|
drop_n_last_frames=cfg.policy.drop_n_last_frames,
|
|
shuffle=True,
|
|
)
|
|
else:
|
|
shuffle = True
|
|
sampler = None
|
|
|
|
dataloader = torch.utils.data.DataLoader(
|
|
dataset,
|
|
num_workers=cfg.num_workers,
|
|
batch_size=cfg.batch_size,
|
|
shuffle=shuffle and not cfg.dataset.streaming,
|
|
sampler=sampler,
|
|
pin_memory=device.type == "cuda",
|
|
drop_last=False,
|
|
prefetch_factor=2,
|
|
)
|
|
if accelerator:
|
|
policy, optimizer, dataloader, lr_scheduler = accelerator.prepare(
|
|
policy, optimizer, dataloader, lr_scheduler
|
|
)
|
|
dl_iter = cycle(dataloader)
|
|
|
|
policy.train()
|
|
|
|
train_metrics = {
|
|
"loss": AverageMeter("loss", ":.3f"),
|
|
"grad_norm": AverageMeter("grdn", ":.3f"),
|
|
"lr": AverageMeter("lr", ":0.1e"),
|
|
"update_s": AverageMeter("updt_s", ":.3f"),
|
|
"dataloading_s": AverageMeter("data_s", ":.3f"),
|
|
}
|
|
|
|
train_tracker = MetricsTracker(
|
|
cfg.batch_size,
|
|
dataset.num_frames,
|
|
dataset.num_episodes,
|
|
train_metrics,
|
|
initial_step=step,
|
|
accelerator=accelerator,
|
|
)
|
|
|
|
if not accelerator or accelerator.is_main_process:
|
|
logging.info("Start offline training on a fixed dataset")
|
|
for _ in range(step, cfg.steps):
|
|
start_time = time.perf_counter()
|
|
batch = next(dl_iter)
|
|
batch = preprocessor(batch)
|
|
train_tracker.dataloading_s = time.perf_counter() - start_time
|
|
|
|
train_tracker, output_dict = update_policy(
|
|
train_tracker,
|
|
policy,
|
|
batch,
|
|
optimizer,
|
|
cfg.optimizer.grad_clip_norm,
|
|
grad_scaler=grad_scaler,
|
|
lr_scheduler=lr_scheduler,
|
|
use_amp=cfg.policy.use_amp,
|
|
accelerator=accelerator,
|
|
)
|
|
|
|
# Note: eval and checkpoint happens *after* the `step`th training update has completed, so we
|
|
# increment `step` here.
|
|
step += 1
|
|
train_tracker.step()
|
|
is_log_step = (
|
|
cfg.log_freq > 0 and step % cfg.log_freq == 0 and (not accelerator or accelerator.is_main_process)
|
|
)
|
|
is_saving_step = (
|
|
step % cfg.save_freq == 0
|
|
or step == cfg.steps
|
|
and (not accelerator or accelerator.is_main_process)
|
|
)
|
|
is_eval_step = (
|
|
cfg.eval_freq > 0
|
|
and step % cfg.eval_freq == 0
|
|
and (not accelerator or accelerator.is_main_process)
|
|
)
|
|
|
|
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)
|
|
wandb_logger.log_dict(wandb_log_dict, step)
|
|
train_tracker.reset_averages()
|
|
|
|
if cfg.save_checkpoint and is_saving_step:
|
|
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=policy if not accelerator else accelerator.unwrap_model(policy),
|
|
optimizer=optimizer,
|
|
scheduler=lr_scheduler,
|
|
)
|
|
update_last_checkpoint(checkpoint_dir)
|
|
if wandb_logger:
|
|
wandb_logger.log_policy(checkpoint_dir)
|
|
|
|
if accelerator:
|
|
accelerator.wait_for_everyone()
|
|
if cfg.env and is_eval_step:
|
|
step_id = get_step_identifier(step, cfg.steps)
|
|
logging.info(f"Eval policy at step {step}")
|
|
with (
|
|
torch.no_grad(),
|
|
torch.autocast(device_type=device.type)
|
|
if cfg.policy.use_amp and not accelerator
|
|
else nullcontext(),
|
|
):
|
|
eval_info = eval_policy_all(
|
|
envs=eval_env, # dict[suite][task_id] -> vec_env
|
|
policy=policy if not accelerator else accelerator.unwrap_model(policy),
|
|
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")
|
|
|
|
if eval_env:
|
|
close_envs(eval_env)
|
|
|
|
if not accelerator or accelerator.is_main_process:
|
|
logging.info("End of training")
|
|
|
|
if cfg.policy.push_to_hub:
|
|
policy.push_model_to_hub(cfg)
|
|
preprocessor.push_to_hub(cfg.policy.repo_id)
|
|
postprocessor.push_to_hub(cfg.policy.repo_id)
|
|
|
|
|
|
def main():
|
|
init_logging()
|
|
train()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
if is_launched_with_accelerate():
|
|
import accelerate
|
|
|
|
# We set step_scheduler_with_optimizer False to prevent accelerate from
|
|
# adjusting the lr_scheduler steps based on the num_processes
|
|
accelerator = accelerate.Accelerator(step_scheduler_with_optimizer=False)
|
|
|
|
init_logging(accelerator=accelerator)
|
|
train(accelerator=accelerator)
|
|
else:
|
|
init_logging()
|
|
train()
|