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Disable online training (#202) 

Co-authored-by: Remi <re.cadene@gmail.com>
This commit is contained in:
Alexander Soare 2024-05-20 18:27:54 +01:00 committed by GitHub
parent c4da689171
commit 2b270d085b
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4 changed files with 9 additions and 160 deletions
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3
Makefile
View File

@ -74,6 +74,7 @@ test-diffusion-ete-eval:
env.episode_length=8 \
device=cpu \
# TODO(alexander-soare): Restore online_steps to 2 when it is reinstated.
test-tdmpc-ete-train:
python lerobot/scripts/train.py \
policy=tdmpc \
@ -82,7 +83,7 @@ test-tdmpc-ete-train:
dataset_repo_id=lerobot/xarm_lift_medium \
wandb.enable=False \
training.offline_steps=2 \
training.online_steps=2 \
training.online_steps=0 \
eval.n_episodes=1 \
eval.batch_size=1 \
env.episode_length=2 \

1
lerobot/configs/default.yaml
View File

@ -17,6 +17,7 @@ dataset_repo_id: lerobot/pusht
training:
offline_steps: ???
# NOTE: `online_steps` is not implemented yet. It's here as a placeholder.
online_steps: ???
online_steps_between_rollouts: ???
online_sampling_ratio: 0.5

3
lerobot/configs/policy/tdmpc.yaml
View File

@ -5,7 +5,8 @@ dataset_repo_id: lerobot/xarm_lift_medium
training:
offline_steps: 25000
online_steps: 25000
# TODO(alexander-soare): uncomment when online training gets reinstated
online_steps: 0 # 25000 not implemented yet
eval_freq: 5000
online_steps_between_rollouts: 1
online_sampling_ratio: 0.5

162
lerobot/scripts/train.py
View File

@ -18,11 +18,8 @@ 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 omegaconf import DictConfig
from lerobot.common.datasets.factory import make_dataset
@ -69,7 +66,6 @@ def make_optimizer_and_scheduler(cfg, policy):
cfg.training.adam_eps,
cfg.training.adam_weight_decay,
)
assert cfg.training.online_steps == 0, "Diffusion Policy does not handle online training."
from diffusers.optimization import get_scheduler
lr_scheduler = get_scheduler(
@ -211,103 +207,6 @@ def log_eval_info(logger, info, step, cfg, dataset, 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: DictConfig, out_dir: str | None = None, job_name: str | None = None):
if out_dir is None:
raise NotImplementedError()
@ -316,8 +215,8 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
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")
if cfg.training.online_steps > 0:
raise NotImplementedError("Online training is not implemented yet.")
# Check device is available
get_safe_torch_device(cfg.device, log=True)
@ -395,10 +294,9 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
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:
for step in range(cfg.training.offline_steps):
if step == 0:
logging.info("Start offline training on a fixed dataset")
batch = next(dl_iter)
@ -415,11 +313,6 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
# so we pass in step + 1.
evaluate_and_checkpoint_if_needed(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 = {}
@ -439,55 +332,8 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
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: evaluate_and_checkpoint_if_needed happens **after** the `step`th training update has completed,
# so we pass in step + 1.
evaluate_and_checkpoint_if_needed(step + 1)
step += 1
online_step += 1
eval_env.close()
online_training_env.close()
logging.info("End of training")