lightning/tests/trainer/loops/test_training_loop_flow_sca...

365 lines
12 KiB
Python

# Copyright The PyTorch Lightning 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.
import pytest
import torch
from torch.utils.data import DataLoader
from torch.utils.data._utils.collate import default_collate
from pytorch_lightning import Trainer
from pytorch_lightning.core.lightning import LightningModule
from pytorch_lightning.trainer.states import RunningStage
from tests.helpers.boring_model import BoringModel, RandomDataset
from tests.helpers.deterministic_model import DeterministicModel
from tests.helpers.utils import no_warning_call
def test__training_step__flow_scalar(tmpdir):
""" Tests that only training_step can be used. """
class TestModel(DeterministicModel):
def training_step(self, batch, batch_idx):
acc = self.step(batch, batch_idx)
acc = acc + batch_idx
self.training_step_called = True
return acc
def backward(self, loss, optimizer, optimizer_idx):
return LightningModule.backward(self, loss, optimizer, optimizer_idx)
model = TestModel()
model.val_dataloader = None
trainer = Trainer(
default_root_dir=tmpdir,
limit_train_batches=2,
limit_val_batches=2,
max_epochs=2,
log_every_n_steps=1,
weights_summary=None,
)
trainer.fit(model)
# make sure correct steps were called
assert model.training_step_called
assert not model.training_step_end_called
assert not model.training_epoch_end_called
def test__training_step__tr_step_end__flow_scalar(tmpdir):
""" Tests that only training_step can be used. """
class TestModel(DeterministicModel):
def training_step(self, batch, batch_idx):
acc = self.step(batch, batch_idx)
acc = acc + batch_idx
self.training_step_called = True
self.out = acc
return acc
def training_step_end(self, tr_step_output):
assert self.out == tr_step_output
assert self.count_num_graphs({'loss': tr_step_output}) == 1
self.training_step_end_called = True
return tr_step_output
def backward(self, loss, optimizer, optimizer_idx):
return LightningModule.backward(self, loss, optimizer, optimizer_idx)
model = TestModel()
model.val_dataloader = None
trainer = Trainer(
default_root_dir=tmpdir,
limit_train_batches=2,
limit_val_batches=2,
max_epochs=2,
log_every_n_steps=1,
weights_summary=None,
)
trainer.fit(model)
# make sure correct steps were called
assert model.training_step_called
assert model.training_step_end_called
assert not model.training_epoch_end_called
def test__training_step__epoch_end__flow_scalar(tmpdir):
""" Tests that only training_step can be used. """
class TestModel(DeterministicModel):
def training_step(self, batch, batch_idx):
acc = self.step(batch, batch_idx)
acc = acc + batch_idx
self.training_step_called = True
return acc
def training_epoch_end(self, outputs):
self.training_epoch_end_called = True
# verify we saw the current num of batches
assert len(outputs) == 2
for b in outputs:
# time = 1
assert len(b) == 1
assert 'loss' in b
assert isinstance(b, dict)
def backward(self, loss, optimizer, optimizer_idx):
return LightningModule.backward(self, loss, optimizer, optimizer_idx)
model = TestModel()
model.val_dataloader = None
trainer = Trainer(
default_root_dir=tmpdir,
limit_train_batches=2,
limit_val_batches=2,
max_epochs=2,
log_every_n_steps=1,
weights_summary=None,
)
trainer.fit(model)
# make sure correct steps were called
assert model.training_step_called
assert not model.training_step_end_called
assert model.training_epoch_end_called
# assert epoch end metrics were added
assert len(trainer.logger_connector.callback_metrics) == 0
assert len(trainer.logger_connector.progress_bar_metrics) == 0
trainer.state.stage = RunningStage.TRAINING
# make sure training outputs what is expected
batch_idx, batch = 0, next(iter(model.train_dataloader()))
out = trainer.fit_loop.training_loop.batch_loop.run(batch, batch_idx, 0)
assert out.signal == 0
train_step_out = out.training_step_output
assert len(train_step_out) == 1
train_step_out = train_step_out[0][0]
assert isinstance(train_step_out.minimize, torch.Tensor)
assert train_step_out.minimize.item() == 171
# make sure the optimizer closure returns the correct things
opt_closure_result = trainer.fit_loop.training_loop.batch_loop.training_step_and_backward(
batch,
batch_idx,
0,
trainer.optimizers[0],
hiddens=None,
)
assert opt_closure_result['loss'].item() == 171
def test__training_step__step_end__epoch_end__flow_scalar(tmpdir):
""" Checks train_step + training_step_end + training_epoch_end (all with scalar return from train_step). """
class TestModel(DeterministicModel):
def training_step(self, batch, batch_idx):
acc = self.step(batch, batch_idx)
acc = acc + batch_idx
self.training_step_called = True
return acc
def training_step_end(self, tr_step_output):
assert isinstance(tr_step_output, torch.Tensor)
assert self.count_num_graphs({'loss': tr_step_output}) == 1
self.training_step_end_called = True
return tr_step_output
def training_epoch_end(self, outputs):
self.training_epoch_end_called = True
# verify we saw the current num of batches
assert len(outputs) == 2
for b in outputs:
# time = 1
assert len(b) == 1
assert 'loss' in b
assert isinstance(b, dict)
def backward(self, loss, optimizer, optimizer_idx):
return LightningModule.backward(self, loss, optimizer, optimizer_idx)
model = TestModel()
model.val_dataloader = None
trainer = Trainer(
default_root_dir=tmpdir,
limit_train_batches=2,
limit_val_batches=2,
max_epochs=2,
log_every_n_steps=1,
weights_summary=None,
)
trainer.fit(model)
# make sure correct steps were called
assert model.training_step_called
assert model.training_step_end_called
assert model.training_epoch_end_called
# assert epoch end metrics were added
assert len(trainer.logger_connector.callback_metrics) == 0
assert len(trainer.logger_connector.progress_bar_metrics) == 0
trainer.state.stage = RunningStage.TRAINING
# make sure training outputs what is expected
batch_idx, batch = 0, next(iter(model.train_dataloader()))
out = trainer.fit_loop.training_loop.batch_loop.run(batch, batch_idx, 0)
assert out.signal == 0
train_step_out = out.training_step_output
assert len(train_step_out) == 1
train_step_out = train_step_out[0][0]
assert isinstance(train_step_out.minimize, torch.Tensor)
assert train_step_out.minimize.item() == 171
# make sure the optimizer closure returns the correct things
opt_closure_result = trainer.fit_loop.training_loop.batch_loop.training_step_and_backward(
batch, batch_idx, 0, trainer.optimizers[0], hiddens=None
)
assert opt_closure_result['loss'].item() == 171
def test_train_step_no_return(tmpdir):
""" Tests that only training_step raises a warning when nothing is returned in case of automatic_optimization. """
class TestModel(BoringModel):
def training_step(self, batch, batch_idx):
self.training_step_called = True
loss = self.step(batch[0])
self.log('a', loss, on_step=True, on_epoch=True)
def training_epoch_end(self, outputs) -> None:
assert len(outputs) == 0
def validation_step(self, batch, batch_idx):
self.validation_step_called = True
def validation_epoch_end(self, outputs):
assert len(outputs) == 0
model = TestModel()
trainer_args = dict(
default_root_dir=tmpdir,
fast_dev_run=2,
)
trainer = Trainer(**trainer_args)
with pytest.warns(UserWarning, match=r'training_step returned None.*'):
trainer.fit(model)
assert model.training_step_called
assert model.validation_step_called
model = TestModel()
model.automatic_optimization = False
trainer = Trainer(**trainer_args)
with no_warning_call(UserWarning, match=r'training_step returned None.*'):
trainer.fit(model)
def test_training_step_no_return_when_even(tmpdir):
""" Tests correctness when some training steps have been skipped. """
class TestModel(BoringModel):
def training_step(self, batch, batch_idx):
self.training_step_called = True
loss = self.step(batch[0])
self.log('a', loss, on_step=True, on_epoch=True)
return loss if batch_idx % 2 else None
model = TestModel()
trainer = Trainer(
default_root_dir=tmpdir,
limit_train_batches=4,
limit_val_batches=1,
max_epochs=4,
weights_summary=None,
logger=False,
checkpoint_callback=False,
)
with pytest.warns(UserWarning, match=r'.*training_step returned None.*'):
trainer.fit(model)
trainer.state.stage = RunningStage.TRAINING
# manually check a few batches
for batch_idx, batch in enumerate(model.train_dataloader()):
out = trainer.fit_loop.training_loop.batch_loop.run(batch, batch_idx, 0)
if not batch_idx % 2:
assert out.training_step_output == [[]]
assert out.signal == 0
def test_training_step_none_batches(tmpdir):
""" Tests correctness when the train dataloader gives None for some steps. """
class TestModel(BoringModel):
def __init__(self):
super().__init__()
self.counter = 0
def collate_none_when_even(self, batch):
if self.counter % 2 == 0:
result = None
else:
result = default_collate(batch)
self.counter += 1
return result
def train_dataloader(self):
return DataLoader(RandomDataset(32, 64), collate_fn=self.collate_none_when_even)
model = TestModel()
trainer = Trainer(
default_root_dir=tmpdir,
limit_train_batches=4,
limit_val_batches=1,
max_epochs=4,
weights_summary=None,
logger=False,
checkpoint_callback=False,
)
with pytest.warns(UserWarning, match=r'.*train_dataloader yielded None.*'):
trainer.fit(model)
trainer.state.stage = RunningStage.TRAINING
# manually check a few batches
for batch_idx, batch in enumerate(model.train_dataloader()):
out = trainer.fit_loop.training_loop.batch_loop.run(batch, batch_idx, 0)
if not batch_idx % 2:
assert out.training_step_output == [[]]
assert out.signal == 0