61 lines
2.2 KiB
Python
61 lines
2.2 KiB
Python
# Copyright The Lightning AI team.
|
|
#
|
|
# 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 copy import deepcopy
|
|
|
|
import torch
|
|
|
|
from lightning.fabric import Fabric
|
|
from lightning.pytorch.demos.boring_classes import BoringModel, ManualOptimBoringModel
|
|
|
|
|
|
def test_fabric_boring_lightning_module_automatic():
|
|
"""Test that basic LightningModules written for 'automatic optimization' work with Fabric."""
|
|
fabric = Fabric(accelerator="cpu", devices=1)
|
|
|
|
module = BoringModel()
|
|
parameters_before = deepcopy(list(module.parameters()))
|
|
|
|
optimizers, _ = module.configure_optimizers()
|
|
dataloader = module.train_dataloader()
|
|
|
|
model, optimizer = fabric.setup(module, optimizers[0])
|
|
dataloader = fabric.setup_dataloaders(dataloader)
|
|
|
|
batch = next(iter(dataloader))
|
|
output = model.training_step(batch, 0)
|
|
fabric.backward(output["loss"])
|
|
optimizer.step()
|
|
|
|
assert all(not torch.equal(before, after) for before, after in zip(parameters_before, model.parameters()))
|
|
|
|
|
|
def test_fabric_boring_lightning_module_manual():
|
|
"""Test that basic LightningModules written for 'manual optimization' work with Fabric."""
|
|
fabric = Fabric(accelerator="cpu", devices=1)
|
|
|
|
module = ManualOptimBoringModel()
|
|
parameters_before = deepcopy(list(module.parameters()))
|
|
|
|
optimizers, _ = module.configure_optimizers()
|
|
dataloader = module.train_dataloader()
|
|
|
|
model, optimizer = fabric.setup(module, optimizers[0])
|
|
dataloader = fabric.setup_dataloaders(dataloader)
|
|
|
|
batch = next(iter(dataloader))
|
|
model.training_step(batch, 0) # .backward() and optimizer.step() happen inside training_step()
|
|
|
|
assert all(not torch.equal(before, after) for before, after in zip(parameters_before, model.parameters()))
|