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318d421 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 | import importlib
import os
from unittest.mock import MagicMock, patch
import pytest
from safetensors.torch import load_file
from .utils import skip_if_package_missing
# 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 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"])
@skip_if_package_missing("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"])
@skip_if_package_missing("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"])
@skip_if_package_missing("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}",
]
)
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