tests/lightning/pytorch/callbacks/test_model_transform.py

# 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
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import lightning.pytorch as pl
import pytest
from torch import nn

from nemo.lightning.pytorch.callbacks.model_transform import ModelTransform


class TestModelTransformCallback:
    @pytest.fixture
    def callback(self):
        return ModelTransform()

    @pytest.fixture
    def pl_module(self):
        return MockLightningModule()

    @pytest.fixture
    def trainer(self):
        return pl.Trainer()

    def test_setup_stores_transform(self, callback, pl_module, trainer, caplog):
        callback.setup(trainer, pl_module, 'fit')

        assert callback.model_transform is not None, "callback.model_transform should be set after setup"
        assert hasattr(
            callback.model_transform, '__num_calls__'
        ), "callback.model_transform should have __num_calls__ attribute"
        assert callback.model_transform.__num_calls__ == 0, "callback.model_transform should not have been called yet"
        assert pl_module.model_transform == callback.model_transform, "pl_module.model_transform should be updated"


class MockModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(10, 10)

    def forward(self, x):
        return self.linear(x)


class MockLightningModule(pl.LightningModule):
    def __init__(self):
        super().__init__()
        self.model = MockModel()
        self.model_transform = lambda m: nn.Sequential(m, nn.ReLU())

    def forward(self, x):
        return self.model(x)