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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.
from pathlib import Path
from unittest.mock import MagicMock, patch
import pytest
@pytest.fixture
def trainer():
return MagicMock()
@patch('nemo.collections.llm.gpt.data.core.GPTSFTDataset.__init__', return_value=None)
def test_finetuning_module(mock_gpt_sft_dataset, trainer) -> None:
from nemo.collections.llm.gpt.data import FineTuningDataModule
dataset_root = 'random_root'
datamodule = FineTuningDataModule(
dataset_root,
seq_length=2048,
micro_batch_size=4,
global_batch_size=8,
seed=1234,
)
datamodule.trainer = trainer
datamodule.setup(stage='train')
datamodule.train_dataloader()
mock_gpt_sft_dataset.assert_called_once()
@patch('nemo.collections.llm.gpt.data.core.GPTSFTDataset.__init__', return_value=None)
def test_dolly_module(mock_gpt_sft_dataset, trainer) -> None:
from nemo.collections.llm.gpt.data import DollyDataModule
datamodule = DollyDataModule(
seq_length=2048,
micro_batch_size=4,
global_batch_size=8,
seed=1234,
)
datamodule.trainer = trainer
datamodule.setup(stage='train')
datamodule.train_dataloader()
mock_gpt_sft_dataset.assert_called_once()
@patch('nemo.collections.llm.gpt.data.core.GPTSFTDataset.__init__', return_value=None)
def test_squad_module(mock_gpt_sft_dataset, trainer) -> None:
from nemo.collections.llm.gpt.data import SquadDataModule
datamodule = SquadDataModule(
seq_length=2048,
micro_batch_size=4,
global_batch_size=8,
seed=1234,
)
datamodule.trainer = trainer
datamodule.setup(stage='train')
datamodule.train_dataloader()
mock_gpt_sft_dataset.assert_called_once()
# TODO @chcui fix test for pretrain data module
# @patch('megatron.core.datasets.blended_megatron_dataset_builder.BlendedMegatronDatasetBuilder')
# @patch('nemo.lightning.pytorch.trainer.Trainer')
# def test_pretraining_module(mock_pretraining_dataset_builder, mock_trainer) -> None:
# from nemo.collections.llm.gpt.data import PreTrainingDataModule
#
# datamodule = PreTrainingDataModule(
# path=Path('random_path'),
# seq_length=2048,
# micro_batch_size=4,
# global_batch_size=8,
# seed=1234,
# )
# mock_trainer.max_steps = 100
# mock_trainer.val_check_interval = 5
# mock_trainer.limit_val_batches = 10
# mock_trainer.limit_test_batches = 10
# datamodule.trainer = mock_trainer
#
# datamodule.setup()
# datamodule.train_dataloader()
# mock_pretraining_dataset_builder.assert_called_once()
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