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import os
from unittest.mock import MagicMock, patch
import pytest
from safetensors.torch import load_file
from .utils import require_package
# Skip this entire module in CI
pytestmark = pytest.mark.skipif(
os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true",
reason="This test requires peft and is very slow, not meant for CI",
)
def run_command(cmd, module, args):
module = importlib.import_module(f"lerobot.scripts.{module}")
with patch("sys.argv", [cmd] + args):
module.main()
def lerobot_train(args):
return run_command(cmd="lerobot-train", module="lerobot_train", args=args)
def lerobot_record(args):
return run_command(cmd="lerobot-record", module="lerobot_record", args=args)
def resolve_model_id_for_peft_training(policy_type):
"""PEFT training needs pretrained models, this finds the pretrained model of a policy type for PEFT training."""
if policy_type == "smolvla":
return "lerobot/smolvla_base"
raise ValueError(f"No pretrained model known for {policy_type}. PEFT training will not work.")
@pytest.mark.parametrize("policy_type", ["smolvla"])
@require_package("peft")
def test_peft_training_push_to_hub_works(policy_type, tmp_path):
"""Ensure that push to hub stores PEFT only the adapter, not the full model weights."""
output_dir = tmp_path / f"output_{policy_type}"
upload_folder_contents = set()
model_id = resolve_model_id_for_peft_training(policy_type)
def mock_upload_folder(*args, **kwargs):
folder_path = kwargs["folder_path"]
# we include more than is actually uploaded since we ignore {allow,ignore}_patterns of upload_folders()
upload_folder_contents.update(os.listdir(folder_path))
return MagicMock()
with (
patch("huggingface_hub.HfApi.create_repo"),
patch("huggingface_hub.HfApi.upload_folder", mock_upload_folder),
):
lerobot_train(
[
f"--policy.path={model_id}",
"--policy.push_to_hub=true",
"--policy.repo_id=foo/bar",
"--policy.input_features=null",
"--policy.output_features=null",
"--peft.method=LORA",
"--dataset.repo_id=lerobot/pusht",
"--dataset.episodes=[0, 1]",
"--steps=1",
f"--output_dir={output_dir}",
]
)
assert "adapter_model.safetensors" in upload_folder_contents
assert "config.json" in upload_folder_contents
assert "adapter_config.json" in upload_folder_contents
@pytest.mark.parametrize("policy_type", ["smolvla"])
@require_package("peft")
def test_peft_training_works(policy_type, tmp_path):
"""Check whether the standard case of fine-tuning a (partially) pre-trained policy with PEFT works."""
output_dir = tmp_path / f"output_{policy_type}"
model_id = resolve_model_id_for_peft_training(policy_type)
lerobot_train(
[
f"--policy.path={model_id}",
"--policy.push_to_hub=false",
"--policy.input_features=null",
"--policy.output_features=null",
"--peft.method=LORA",
"--dataset.repo_id=lerobot/pusht",
"--dataset.episodes=[0, 1]",
"--steps=1",
f"--output_dir={output_dir}",
]
)
policy_dir = output_dir / "checkpoints" / "last" / "pretrained_model"
for file in ["adapter_config.json", "adapter_model.safetensors", "config.json"]:
assert (policy_dir / file).exists()
# This is the default case where we train a pre-trained policy from scratch with new data.
# We assume that we target policy-specific modules but fully fine-tune action and state projections
# so these must be part of the trained state dict.
state_dict = load_file(policy_dir / "adapter_model.safetensors")
adapted_keys = [
"state_proj",
"action_in_proj",
"action_out_proj",
"action_time_mlp_in",
"action_time_mlp_out",
]
found_keys = [
module_key
for module_key in adapted_keys
for state_dict_key in state_dict
if f".{module_key}." in state_dict_key
]
assert set(found_keys) == set(adapted_keys)
@pytest.mark.parametrize("policy_type", ["smolvla"])
@require_package("peft")
def test_peft_training_params_are_fewer(policy_type, tmp_path):
"""Check whether the standard case of fine-tuning a (partially) pre-trained policy with PEFT works."""
output_dir = tmp_path / f"output_{policy_type}"
model_id = resolve_model_id_for_peft_training(policy_type)
def dummy_update_policy(
train_metrics, policy, batch, optimizer, grad_clip_norm: float, accelerator, **kwargs
):
params_total = sum(p.numel() for p in policy.parameters())
params_trainable = sum(p.numel() for p in policy.parameters() if p.requires_grad)
assert params_total > params_trainable
return train_metrics, {}
with patch("lerobot.scripts.lerobot_train.update_policy", dummy_update_policy):
lerobot_train(
[
f"--policy.path={model_id}",
"--policy.push_to_hub=false",
"--policy.input_features=null",
"--policy.output_features=null",
"--peft.method=LORA",
"--dataset.repo_id=lerobot/pusht",
"--dataset.episodes=[0, 1]",
"--steps=1",
f"--output_dir={output_dir}",
]
)
class DummyRobot:
name = "dummy"
cameras = []
action_features = {"foo": 1.0, "bar": 2.0}
observation_features = {"obs1": 1.0, "obs2": 2.0}
is_connected = True
def connect(self, *args):
pass
def disconnect(self):
pass
def dummy_make_robot_from_config(*args, **kwargs):
return DummyRobot()
@pytest.mark.parametrize("policy_type", ["smolvla"])
@require_package("peft")
def test_peft_record_loads_policy(policy_type, tmp_path):
"""Train a policy with PEFT and attempt to load it with `lerobot-record`."""
from peft import PeftModel
output_dir = tmp_path / f"output_{policy_type}"
model_id = resolve_model_id_for_peft_training(policy_type)
lerobot_train(
[
f"--policy.path={model_id}",
"--policy.push_to_hub=false",
"--policy.input_features=null",
"--policy.output_features=null",
"--peft.method=LORA",
"--dataset.repo_id=lerobot/pusht",
"--dataset.episodes=[0, 1]",
"--steps=1",
f"--output_dir={output_dir}",
]
)
policy_dir = output_dir / "checkpoints" / "last" / "pretrained_model"
dataset_dir = tmp_path / "eval_pusht"
single_task = "move the table"
loaded_policy = None
def dummy_record_loop(*args, **kwargs):
nonlocal loaded_policy
if "dataset" not in kwargs:
return
dataset = kwargs["dataset"]
dataset.add_frame({"task": single_task})
loaded_policy = kwargs["policy"]
with (
patch("lerobot.scripts.lerobot_record.make_robot_from_config", dummy_make_robot_from_config),
# disable record loop since we're only interested in successful loading of the policy.
patch("lerobot.scripts.lerobot_record.record_loop", dummy_record_loop),
# disable speech output
patch("lerobot.utils.utils.say"),
):
lerobot_record(
[
f"--policy.path={policy_dir}",
"--robot.type=so101_follower",
"--robot.port=/dev/null",
"--dataset.repo_id=lerobot/eval_pusht",
f'--dataset.single_task="{single_task}"',
f"--dataset.root={dataset_dir}",
"--dataset.push_to_hub=false",
]
)
assert isinstance(loaded_policy, PeftModel)
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