cosmos_predict1/diffusion/training/config/base/optim.py

# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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 cosmos_predict1.diffusion.training.functional.lr_scheduler import LambdaLinearScheduler
from cosmos_predict1.diffusion.training.utils.optim_instantiate import get_base_optimizer
from cosmos_predict1.utils.lazy_config import PLACEHOLDER
from cosmos_predict1.utils.lazy_config import LazyCall as L
from cosmos_predict1.utils.lazy_config import LazyDict

FusedAdamWConfig: LazyDict = L(get_base_optimizer)(
    model=PLACEHOLDER,
    lr=1e-4,
    weight_decay=0.3,
    betas=[0.9, 0.999],
    optim_type="fusedadam",
    eps=1e-8,
    sharding=False,
    master_weights=True,
    capturable=True,
)

LambdaLinearSchedulerConfig: LazyDict = L(LambdaLinearScheduler)(
    warm_up_steps=[1000],
    cycle_lengths=[10000000000000],
    f_start=[1.0e-6],
    f_max=[1.0],
    f_min=[1.0],
)