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# Copyright 2025 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.
from pathlib import Path
from unittest.mock import MagicMock, Mock, patch
import pytest
from lerobot.common.train_utils import (
get_step_checkpoint_dir,
get_step_identifier,
load_training_batch_size,
load_training_num_processes,
load_training_state,
load_training_step,
push_checkpoint_to_hub,
save_checkpoint,
save_training_state,
save_training_step,
update_last_checkpoint,
)
from lerobot.utils.constants import (
CHECKPOINTS_DIR,
LAST_CHECKPOINT_LINK,
OPTIMIZER_PARAM_GROUPS,
OPTIMIZER_STATE,
RNG_STATE,
SCHEDULER_STATE,
TRAINING_STATE_DIR,
TRAINING_STEP,
)
def test_get_step_identifier():
assert get_step_identifier(5, 1000) == "000005"
assert get_step_identifier(123, 100_000) == "000123"
assert get_step_identifier(456789, 1_000_000) == "0456789"
def test_get_step_checkpoint_dir():
output_dir = Path("/checkpoints")
step_dir = get_step_checkpoint_dir(output_dir, 1000, 5)
assert step_dir == output_dir / CHECKPOINTS_DIR / "000005"
def test_save_load_training_step(tmp_path):
save_training_step(5000, tmp_path)
assert (tmp_path / TRAINING_STEP).is_file()
def test_load_training_step(tmp_path):
step = 5000
save_training_step(step, tmp_path)
loaded_step = load_training_step(tmp_path)
assert loaded_step == step
def test_save_training_state_records_num_processes(tmp_path, optimizer, scheduler):
save_training_state(tmp_path, 10, optimizer, scheduler, num_processes=4)
assert load_training_num_processes(tmp_path) == 4
def test_load_training_num_processes_absent_returns_none(tmp_path, optimizer, scheduler):
# Checkpoints written before the world size was recorded must still load (back-compat).
save_training_state(tmp_path, 10, optimizer, scheduler)
assert load_training_num_processes(tmp_path) is None
def test_save_training_state_records_batch_size(tmp_path, optimizer, scheduler):
save_training_state(tmp_path, 10, optimizer, scheduler, batch_size=32)
assert load_training_batch_size(tmp_path) == 32
def test_load_training_batch_size_absent_returns_none(tmp_path, optimizer, scheduler):
# Checkpoints written before the batch size was recorded must still load (back-compat).
save_training_state(tmp_path, 10, optimizer, scheduler)
assert load_training_batch_size(tmp_path) is None
def test_update_last_checkpoint(tmp_path):
checkpoint = tmp_path / "0005"
checkpoint.mkdir()
update_last_checkpoint(checkpoint)
last_checkpoint = tmp_path / LAST_CHECKPOINT_LINK
assert last_checkpoint.is_symlink()
assert last_checkpoint.resolve() == checkpoint
@patch("lerobot.common.train_utils.save_training_state")
def test_save_checkpoint(mock_save_training_state, tmp_path, optimizer):
policy = Mock()
cfg = Mock()
save_checkpoint(tmp_path, 10, cfg, policy, optimizer)
policy.save_pretrained.assert_called_once()
cfg.save_pretrained.assert_called_once()
mock_save_training_state.assert_called_once()
@patch("lerobot.common.train_utils.save_training_state")
def test_save_checkpoint_peft(mock_save_training_state, tmp_path, optimizer):
policy = Mock()
policy.config = Mock()
policy.config.save_pretrained = Mock()
cfg = Mock()
cfg.use_peft = True
save_checkpoint(tmp_path, 10, cfg, policy, optimizer)
policy.save_pretrained.assert_called_once()
cfg.save_pretrained.assert_called_once()
policy.config.save_pretrained.assert_called_once()
mock_save_training_state.assert_called_once()
def test_save_training_state(tmp_path, optimizer, scheduler):
save_training_state(tmp_path, 10, optimizer, scheduler)
assert (tmp_path / TRAINING_STATE_DIR).is_dir()
assert (tmp_path / TRAINING_STATE_DIR / TRAINING_STEP).is_file()
assert (tmp_path / TRAINING_STATE_DIR / RNG_STATE).is_file()
assert (tmp_path / TRAINING_STATE_DIR / OPTIMIZER_STATE).is_file()
assert (tmp_path / TRAINING_STATE_DIR / OPTIMIZER_PARAM_GROUPS).is_file()
assert (tmp_path / TRAINING_STATE_DIR / SCHEDULER_STATE).is_file()
def test_save_load_training_state(tmp_path, optimizer, scheduler):
save_training_state(tmp_path, 10, optimizer, scheduler)
loaded_step, loaded_optimizer, loaded_scheduler = load_training_state(tmp_path, optimizer, scheduler)
assert loaded_step == 10
assert loaded_optimizer is optimizer
assert loaded_scheduler is scheduler
def test_load_training_state_skip_optimizer(tmp_path, optimizer, scheduler):
# FSDP loads optimizer separately (after accelerator.prepare)
# load_training_state(load_optimizer=False) must restore step + scheduler but leave the
# optimizer untouched and never touch the on-disk optimizer state.
save_training_state(tmp_path, 10, optimizer, scheduler)
with patch("lerobot.common.train_utils.load_optimizer_state") as mock_load_optimizer_state:
loaded_step, loaded_optimizer, loaded_scheduler = load_training_state(
tmp_path, optimizer, scheduler, load_optimizer=False
)
mock_load_optimizer_state.assert_not_called()
assert loaded_step == 10
assert loaded_optimizer is optimizer
assert loaded_scheduler is scheduler
def test_push_checkpoint_to_hub_creates_repo_and_uploads(tmp_path, monkeypatch):
ckpt = tmp_path / "010000"
(ckpt / "pretrained_model").mkdir(parents=True)
api = MagicMock()
monkeypatch.setattr("lerobot.common.train_utils.HfApi", lambda *a, **k: api)
push_checkpoint_to_hub(ckpt, "user/run", private=True)
api.create_repo.assert_called_once()
assert api.create_repo.call_args.kwargs["private"] is True
assert api.create_repo.call_args.kwargs["repo_type"] == "model"
api.upload_folder.assert_called_once()
kwargs = api.upload_folder.call_args.kwargs
assert kwargs["repo_id"] == "user/run"
assert kwargs["repo_type"] == "model"
assert kwargs["path_in_repo"] == "checkpoints/010000"
assert kwargs["folder_path"] == str(ckpt)
assert kwargs["commit_message"] == "checkpoint 010000"
# A tag named after the checkpoint step is created so the checkpoint can be
# recovered with --policy.pretrained_revision instead of a commit sha.
api.create_tag.assert_called_once()
tag_kwargs = api.create_tag.call_args.kwargs
assert tag_kwargs["tag"] == "010000"
assert tag_kwargs["revision"] == api.upload_folder.return_value.oid
assert tag_kwargs["repo_type"] == "model"
assert tag_kwargs["exist_ok"] is True
def test_push_checkpoint_to_hub_defaults_to_hub_default_visibility(tmp_path, monkeypatch):
ckpt = tmp_path / "010000"
(ckpt / "pretrained_model").mkdir(parents=True)
api = MagicMock()
monkeypatch.setattr("lerobot.common.train_utils.HfApi", lambda *a, **k: api)
push_checkpoint_to_hub(ckpt, "user/run")
api.create_repo.assert_called_once()
assert api.create_repo.call_args.kwargs["private"] is None
def test_resolve_resume_checkpoint_downloads_latest_and_links(tmp_path, monkeypatch):
from lerobot.common import train_utils
out = tmp_path / "run"
def fake_snapshot_download(repo_id, repo_type, allow_patterns, local_dir):
# Mimic the Hub layout the real download materializes locally.
assert allow_patterns == "checkpoints/020000/*"
(Path(local_dir) / "checkpoints" / "020000" / "pretrained_model").mkdir(parents=True)
return local_dir
monkeypatch.setattr("lerobot.common.train_utils.snapshot_download", fake_snapshot_download)
monkeypatch.setattr(
"lerobot.common.train_utils.find_latest_hub_checkpoint", lambda repo_id: "checkpoints/020000"
)
checkpoint_dir = train_utils.resolve_resume_checkpoint("u/run", out)
assert checkpoint_dir == out / CHECKPOINTS_DIR / "020000"
last = out / CHECKPOINTS_DIR / LAST_CHECKPOINT_LINK
assert last.is_symlink()
# `last` points at the downloaded step dir.
assert (last.parent / last.readlink()).resolve() == checkpoint_dir.resolve()
def test_resolve_resume_checkpoint_raises_without_checkpoints(tmp_path, monkeypatch):
from lerobot.common import train_utils
monkeypatch.setattr("lerobot.common.train_utils.find_latest_hub_checkpoint", lambda repo_id: None)
with pytest.raises(FileNotFoundError, match="No checkpoint"):
train_utils.resolve_resume_checkpoint("u/run", tmp_path / "run")
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