mirror of
https://github.com/huggingface/lerobot.git
synced 2026-05-16 00:59:46 +00:00
nemo
dc67b2ff3f
Before this change the PEFT-wrapped policy did not save the policy's config alongside the adapter config / weights which prevented us from changing the policy config. Now the policy config is saved both in full training and PEFT training. This change makes loading the PEFT policy adapter much easier as well.
112 lines
3.8 KiB
Python
112 lines
3.8 KiB
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.
|
|
from pathlib import Path
|
|
from unittest.mock import Mock, patch
|
|
|
|
from lerobot.common.constants import (
|
|
CHECKPOINTS_DIR,
|
|
LAST_CHECKPOINT_LINK,
|
|
OPTIMIZER_PARAM_GROUPS,
|
|
OPTIMIZER_STATE,
|
|
RNG_STATE,
|
|
SCHEDULER_STATE,
|
|
TRAINING_STATE_DIR,
|
|
TRAINING_STEP,
|
|
)
|
|
from lerobot.common.utils.train_utils import (
|
|
get_step_checkpoint_dir,
|
|
get_step_identifier,
|
|
load_training_state,
|
|
load_training_step,
|
|
save_checkpoint,
|
|
save_training_state,
|
|
save_training_step,
|
|
update_last_checkpoint,
|
|
)
|
|
|
|
|
|
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_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.utils.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.utils.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
|