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# Copyright (c) 2025, NVIDIA CORPORATION. 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.
import nemo_run as run
from nemo.collections import llm
from nemo.collections.diffusion.vae.train_vae import train_vae
@run.cli.factory(target=llm.validate)
def validate_vae() -> run.Partial:
"""
Create a partial function for validating a VAE (Variational Autoencoder) model.
This function uses the training recipe defined in `train_vae()` to set up
the model, data, trainer, logging, and optimization configurations for
validation. It returns a Partial object that can be used by the NeMo run CLI
to execute the validation procedure on the provided model and data.
Returns:
run.Partial: A partial object configured with llm.validate target
and all necessary arguments extracted from the VAE training recipe.
"""
recipe = train_vae()
return run.Partial(
llm.validate,
model=recipe.model,
data=recipe.data,
trainer=recipe.trainer,
log=recipe.log,
optim=recipe.optim,
tokenizer=None,
resume=recipe.resume,
model_transform=None,
)
if __name__ == "__main__":
run.cli.main(llm.validate, default_factory=validate_vae)
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