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# 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 torch.optim.lr_scheduler import LambdaLR
from lerobot.optim.schedulers import (
CosineDecayWithWarmupSchedulerConfig,
DiffuserSchedulerConfig,
VQBeTSchedulerConfig,
load_scheduler_state,
save_scheduler_state,
)
from lerobot.utils.constants import SCHEDULER_STATE
def test_diffuser_scheduler(optimizer):
config = DiffuserSchedulerConfig(name="cosine", num_warmup_steps=5)
scheduler = config.build(optimizer, num_training_steps=100)
assert isinstance(scheduler, LambdaLR)
optimizer.step() # so that we don't get torch warning
scheduler.step()
expected_state_dict = {
"_get_lr_called_within_step": False,
"_last_lr": [0.0002],
"_step_count": 2,
"base_lrs": [0.001],
"last_epoch": 1,
"lr_lambdas": [None],
}
assert scheduler.state_dict() == expected_state_dict
def test_vqbet_scheduler(optimizer):
config = VQBeTSchedulerConfig(num_warmup_steps=10, num_vqvae_training_steps=20, num_cycles=0.5)
scheduler = config.build(optimizer, num_training_steps=100)
assert isinstance(scheduler, LambdaLR)
optimizer.step()
scheduler.step()
expected_state_dict = {
"_get_lr_called_within_step": False,
"_last_lr": [0.001],
"_step_count": 2,
"base_lrs": [0.001],
"last_epoch": 1,
"lr_lambdas": [None],
}
assert scheduler.state_dict() == expected_state_dict
def test_cosine_decay_with_warmup_scheduler(optimizer):
config = CosineDecayWithWarmupSchedulerConfig(
num_warmup_steps=10, num_decay_steps=90, peak_lr=0.01, decay_lr=0.001
)
scheduler = config.build(optimizer, num_training_steps=100)
assert isinstance(scheduler, LambdaLR)
optimizer.step()
scheduler.step()
expected_state_dict = {
"_get_lr_called_within_step": False,
"_last_lr": [0.0001818181818181819],
"_step_count": 2,
"base_lrs": [0.001],
"last_epoch": 1,
"lr_lambdas": [None],
}
assert scheduler.state_dict() == expected_state_dict
def test_save_scheduler_state(scheduler, tmp_path):
save_scheduler_state(scheduler, tmp_path)
assert (tmp_path / SCHEDULER_STATE).is_file()
def test_save_load_scheduler_state(scheduler, tmp_path):
save_scheduler_state(scheduler, tmp_path)
loaded_scheduler = load_scheduler_state(scheduler, tmp_path)
assert scheduler.state_dict() == loaded_scheduler.state_dict()
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