lerobot/tests/scripts/save_policy_to_safetensors.py

146 lines
5.5 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 shutil
from pathlib import Path
import torch
from safetensors.torch import save_file
from lerobot.common.datasets.factory import make_dataset
from lerobot.common.optim.factory import make_optimizer_and_scheduler
from lerobot.common.policies.factory import make_policy, make_policy_config
from lerobot.common.utils.random_utils import set_seed
from lerobot.configs.default import DatasetConfig
from lerobot.configs.train import TrainPipelineConfig
def get_policy_stats(ds_repo_id: str, policy_name: str, policy_kwargs: dict):
set_seed(1337)
train_cfg = TrainPipelineConfig(
# TODO(rcadene, aliberts): remove dataset download
dataset=DatasetConfig(repo_id=ds_repo_id, episodes=[0]),
policy=make_policy_config(policy_name, **policy_kwargs),
)
train_cfg.validate() # Needed for auto-setting some parameters
dataset = make_dataset(train_cfg)
policy = make_policy(train_cfg.policy, ds_meta=dataset.meta)
policy.train()
optimizer, _ = make_optimizer_and_scheduler(train_cfg, policy)
dataloader = torch.utils.data.DataLoader(
dataset,
num_workers=0,
batch_size=train_cfg.batch_size,
shuffle=False,
)
batch = next(iter(dataloader))
loss, output_dict = policy.forward(batch)
if output_dict is not None:
output_dict = {k: v for k, v in output_dict.items() if isinstance(v, torch.Tensor)}
output_dict["loss"] = loss
else:
output_dict = {"loss": loss}
loss.backward()
grad_stats = {}
for key, param in policy.named_parameters():
if param.requires_grad:
grad_stats[f"{key}_mean"] = param.grad.mean()
grad_stats[f"{key}_std"] = (
param.grad.std() if param.grad.numel() > 1 else torch.tensor(float(0.0))
)
optimizer.step()
param_stats = {}
for key, param in policy.named_parameters():
param_stats[f"{key}_mean"] = param.mean()
param_stats[f"{key}_std"] = param.std() if param.numel() > 1 else torch.tensor(float(0.0))
optimizer.zero_grad()
policy.reset()
# HACK: We reload a batch with no delta_indices as `select_action` won't expect a timestamps dimension
# We simulate having an environment using a dataset by setting delta_indices to None and dropping tensors
# indicating padding (those ending with "_is_pad")
dataset.delta_indices = None
batch = next(iter(dataloader))
obs = {}
for k in batch:
# TODO: regenerate the safetensors
# for backward compatibility
if k.endswith("_is_pad"):
continue
# for backward compatibility
if k == "task":
continue
if k.startswith("observation"):
obs[k] = batch[k]
if hasattr(train_cfg.policy, "n_action_steps"):
actions_queue = train_cfg.policy.n_action_steps
else:
actions_queue = train_cfg.policy.n_action_repeats
actions = {str(i): policy.select_action(obs).contiguous() for i in range(actions_queue)}
return output_dict, grad_stats, param_stats, actions
def save_policy_to_safetensors(output_dir: Path, ds_repo_id: str, policy_name: str, policy_kwargs: dict):
if output_dir.exists():
print(f"Overwrite existing safetensors in '{output_dir}':")
print(f" - Validate with: `git add {output_dir}`")
print(f" - Revert with: `git checkout -- {output_dir}`")
shutil.rmtree(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
output_dict, grad_stats, param_stats, actions = get_policy_stats(ds_repo_id, policy_name, policy_kwargs)
save_file(output_dict, output_dir / "output_dict.safetensors")
save_file(grad_stats, output_dir / "grad_stats.safetensors")
save_file(param_stats, output_dir / "param_stats.safetensors")
save_file(actions, output_dir / "actions.safetensors")
if __name__ == "__main__":
artifacts_cfg = [
("lerobot/xarm_lift_medium", "tdmpc", {"use_mpc": False}, "use_policy"),
("lerobot/xarm_lift_medium", "tdmpc", {"use_mpc": True}, "use_mpc"),
(
"lerobot/pusht",
"diffusion",
{
"n_action_steps": 8,
"num_inference_steps": 10,
"down_dims": [128, 256, 512],
},
"",
),
("lerobot/aloha_sim_insertion_human", "act", {"n_action_steps": 10}, ""),
(
"lerobot/aloha_sim_insertion_human",
"act",
{"n_action_steps": 1000, "chunk_size": 1000},
"1000_steps",
),
]
if len(artifacts_cfg) == 0:
raise RuntimeError("No policies were provided!")
for ds_repo_id, policy, policy_kwargs, file_name_extra in artifacts_cfg:
ds_name = ds_repo_id.split("/")[-1]
output_dir = Path("tests/data/save_policy_to_safetensors") / f"{ds_name}_{policy}_{file_name_extra}"
save_policy_to_safetensors(output_dir, ds_repo_id, policy, policy_kwargs)