88 lines
2.9 KiB
Python
88 lines
2.9 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.
|
|
"""Tests to ensure that the behaviours related to multiple optimizers works."""
|
|
|
|
import lightning.pytorch as pl
|
|
import pytest
|
|
import torch
|
|
from lightning.pytorch.demos.boring_classes import BoringModel
|
|
|
|
|
|
class MultiOptModel(BoringModel):
|
|
def configure_optimizers(self):
|
|
opt_a = torch.optim.SGD(self.layer.parameters(), lr=0.001)
|
|
opt_b = torch.optim.SGD(self.layer.parameters(), lr=0.001)
|
|
return opt_a, opt_b
|
|
|
|
|
|
def test_multiple_optimizers_automatic_optimization_raises():
|
|
"""Test that multiple optimizers in automatic optimization is not allowed."""
|
|
|
|
class TestModel(BoringModel):
|
|
def training_step(self, batch, batch_idx, optimizer_idx):
|
|
return super().training_step(batch, batch_idx)
|
|
|
|
model = TestModel()
|
|
model.automatic_optimization = True
|
|
|
|
trainer = pl.Trainer()
|
|
with pytest.raises(RuntimeError, match="Remove the `optimizer_idx` argument from `training_step`"):
|
|
trainer.fit(model)
|
|
|
|
class TestModel(BoringModel):
|
|
def configure_optimizers(self):
|
|
return torch.optim.Adam(self.parameters()), torch.optim.Adam(self.parameters())
|
|
|
|
model = TestModel()
|
|
model.automatic_optimization = True
|
|
|
|
trainer = pl.Trainer()
|
|
with pytest.raises(RuntimeError, match="multiple optimizers is only supported with manual optimization"):
|
|
trainer.fit(model)
|
|
|
|
|
|
def test_multiple_optimizers_manual(tmpdir):
|
|
class TestModel(MultiOptModel):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.automatic_optimization = False
|
|
|
|
def training_step(self, batch, batch_idx):
|
|
self.training_step_called = True
|
|
|
|
# manual optimization
|
|
opt_a, opt_b = self.optimizers()
|
|
loss_1 = self.step(batch[0])
|
|
|
|
# fake generator
|
|
self.manual_backward(loss_1)
|
|
opt_a.step()
|
|
opt_a.zero_grad()
|
|
|
|
# fake discriminator
|
|
loss_2 = self.step(batch[0])
|
|
self.manual_backward(loss_2)
|
|
opt_b.step()
|
|
opt_b.zero_grad()
|
|
|
|
model = TestModel()
|
|
model.val_dataloader = None
|
|
|
|
trainer = pl.Trainer(
|
|
default_root_dir=tmpdir, limit_train_batches=2, max_epochs=1, log_every_n_steps=1, enable_model_summary=False
|
|
)
|
|
trainer.fit(model)
|
|
|
|
assert model.training_step_called
|