333 lines
10 KiB
Python
333 lines
10 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Tests to ensure that the training loop works with a dict (1.0)
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"""
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import pytest
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import torch
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from pytorch_lightning import Trainer
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from pytorch_lightning.core.lightning import LightningModule
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from tests.helpers.boring_model import BoringModel
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from tests.helpers.deterministic_model import DeterministicModel
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from tests.helpers.utils import no_warning_call
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def test__training_step__flow_scalar(tmpdir):
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"""
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Tests that only training_step can be used
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"""
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class TestModel(DeterministicModel):
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def training_step(self, batch, batch_idx):
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acc = self.step(batch, batch_idx)
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acc = acc + batch_idx
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self.training_step_called = True
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return acc
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def backward(self, loss, optimizer, optimizer_idx):
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return LightningModule.backward(self, loss, optimizer, optimizer_idx)
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model = TestModel()
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model.val_dataloader = None
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trainer = Trainer(
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default_root_dir=tmpdir,
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limit_train_batches=2,
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limit_val_batches=2,
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max_epochs=2,
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log_every_n_steps=1,
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weights_summary=None,
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)
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trainer.fit(model)
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# make sure correct steps were called
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assert model.training_step_called
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assert not model.training_step_end_called
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assert not model.training_epoch_end_called
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def test__training_step__tr_step_end__flow_scalar(tmpdir):
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"""
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Tests that only training_step can be used
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"""
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class TestModel(DeterministicModel):
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def training_step(self, batch, batch_idx):
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acc = self.step(batch, batch_idx)
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acc = acc + batch_idx
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self.training_step_called = True
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self.out = acc
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return acc
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def training_step_end(self, tr_step_output):
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assert self.out == tr_step_output
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assert self.count_num_graphs({'loss': tr_step_output}) == 1
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self.training_step_end_called = True
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return tr_step_output
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def backward(self, loss, optimizer, optimizer_idx):
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return LightningModule.backward(self, loss, optimizer, optimizer_idx)
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model = TestModel()
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model.val_dataloader = None
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trainer = Trainer(
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default_root_dir=tmpdir,
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limit_train_batches=2,
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limit_val_batches=2,
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max_epochs=2,
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log_every_n_steps=1,
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weights_summary=None,
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)
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trainer.fit(model)
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# make sure correct steps were called
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assert model.training_step_called
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assert model.training_step_end_called
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assert not model.training_epoch_end_called
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def test__training_step__epoch_end__flow_scalar(tmpdir):
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"""
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Tests that only training_step can be used
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"""
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class TestModel(DeterministicModel):
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def training_step(self, batch, batch_idx):
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acc = self.step(batch, batch_idx)
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acc = acc + batch_idx
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self.training_step_called = True
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return acc
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def training_epoch_end(self, outputs):
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self.training_epoch_end_called = True
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# verify we saw the current num of batches
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assert len(outputs) == 2
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for b in outputs:
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# time = 1
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assert len(b) == 1
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assert 'loss' in b
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assert isinstance(b, dict)
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def backward(self, loss, optimizer, optimizer_idx):
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return LightningModule.backward(self, loss, optimizer, optimizer_idx)
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model = TestModel()
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model.val_dataloader = None
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trainer = Trainer(
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default_root_dir=tmpdir,
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limit_train_batches=2,
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limit_val_batches=2,
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max_epochs=2,
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log_every_n_steps=1,
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weights_summary=None,
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)
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trainer.fit(model)
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# make sure correct steps were called
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assert model.training_step_called
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assert not model.training_step_end_called
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assert model.training_epoch_end_called
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# assert epoch end metrics were added
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assert len(trainer.logger_connector.callback_metrics) == 0
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assert len(trainer.logger_connector.progress_bar_metrics) == 0
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# make sure training outputs what is expected
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for batch_idx, batch in enumerate(model.train_dataloader()):
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break
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out = trainer.train_loop.run_training_batch(batch, batch_idx, 0)
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assert out.signal == 0
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assert len(out.grad_norm_dic) == 0 and isinstance(out.grad_norm_dic, dict)
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train_step_out = out.training_step_output_for_epoch_end
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assert len(train_step_out) == 1
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train_step_out = train_step_out[0][0]
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assert isinstance(train_step_out['minimize'], torch.Tensor)
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assert train_step_out['minimize'].item() == 171
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# make sure the optimizer closure returns the correct things
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opt_closure_result = trainer.train_loop.training_step_and_backward(
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batch, batch_idx, 0, trainer.optimizers[0], trainer.hiddens
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)
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assert opt_closure_result['loss'].item() == 171
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def test__training_step__step_end__epoch_end__flow_scalar(tmpdir):
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"""
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Checks train_step + training_step_end + training_epoch_end
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(all with scalar return from train_step)
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"""
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class TestModel(DeterministicModel):
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def training_step(self, batch, batch_idx):
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acc = self.step(batch, batch_idx)
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acc = acc + batch_idx
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self.training_step_called = True
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return acc
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def training_step_end(self, tr_step_output):
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assert isinstance(tr_step_output, torch.Tensor)
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assert self.count_num_graphs({'loss': tr_step_output}) == 1
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self.training_step_end_called = True
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return tr_step_output
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def training_epoch_end(self, outputs):
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self.training_epoch_end_called = True
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# verify we saw the current num of batches
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assert len(outputs) == 2
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for b in outputs:
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# time = 1
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assert len(b) == 1
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assert 'loss' in b
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assert isinstance(b, dict)
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def backward(self, loss, optimizer, optimizer_idx):
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return LightningModule.backward(self, loss, optimizer, optimizer_idx)
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model = TestModel()
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model.val_dataloader = None
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trainer = Trainer(
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default_root_dir=tmpdir,
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limit_train_batches=2,
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limit_val_batches=2,
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max_epochs=2,
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log_every_n_steps=1,
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weights_summary=None,
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)
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trainer.fit(model)
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# make sure correct steps were called
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assert model.training_step_called
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assert model.training_step_end_called
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assert model.training_epoch_end_called
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# assert epoch end metrics were added
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assert len(trainer.logger_connector.callback_metrics) == 0
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assert len(trainer.logger_connector.progress_bar_metrics) == 0
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# make sure training outputs what is expected
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for batch_idx, batch in enumerate(model.train_dataloader()):
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break
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out = trainer.train_loop.run_training_batch(batch, batch_idx, 0)
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assert out.signal == 0
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assert len(out.grad_norm_dic) == 0 and isinstance(out.grad_norm_dic, dict)
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train_step_out = out.training_step_output_for_epoch_end
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assert len(train_step_out) == 1
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train_step_out = train_step_out[0][0]
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assert isinstance(train_step_out['minimize'], torch.Tensor)
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assert train_step_out['minimize'].item() == 171
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# make sure the optimizer closure returns the correct things
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opt_closure_result = trainer.train_loop.training_step_and_backward(
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batch, batch_idx, 0, trainer.optimizers[0], trainer.hiddens
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)
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assert opt_closure_result['loss'].item() == 171
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def test_train_step_no_return(tmpdir):
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"""
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Tests that only training_step raises a warning when
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nothing is returned in case of automatic_optimization
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"""
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class TestModel(BoringModel):
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def training_step(self, batch, batch_idx):
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self.training_step_called = True
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loss = self.step(batch[0])
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self.log('a', loss, on_step=True, on_epoch=True)
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def training_epoch_end(self, outputs) -> None:
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assert len(outputs) == 0
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def validation_step(self, batch, batch_idx):
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self.validation_step_called = True
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def validation_epoch_end(self, outputs):
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assert len(outputs) == 0
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model = TestModel()
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trainer_args = dict(
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default_root_dir=tmpdir,
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fast_dev_run=2,
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)
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trainer = Trainer(**trainer_args)
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with pytest.warns(UserWarning, match=r'training_step returned None .*'):
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trainer.fit(model)
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assert model.training_step_called
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assert model.validation_step_called
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model = TestModel()
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model.automatic_optimization = False
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trainer = Trainer(**trainer_args)
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with no_warning_call(UserWarning, match=r'training_step returned None .*'):
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trainer.fit(model)
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def test_training_step_no_return_when_even(tmpdir):
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"""
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Tests correctness when some training steps have been skipped
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"""
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class TestModel(BoringModel):
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def training_step(self, batch, batch_idx):
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self.training_step_called = True
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loss = self.step(batch[0])
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self.log('a', loss, on_step=True, on_epoch=True)
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return loss if batch_idx % 2 else None
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model = TestModel()
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trainer = Trainer(
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default_root_dir=tmpdir,
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limit_train_batches=4,
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limit_val_batches=1,
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max_epochs=4,
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weights_summary=None,
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logger=False,
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checkpoint_callback=False,
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)
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with pytest.warns(UserWarning, match=r'.*training_step returned None.*'):
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trainer.fit(model)
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# manually check a few batches
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for batch_idx, batch in enumerate(model.train_dataloader()):
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out = trainer.train_loop.run_training_batch(batch, batch_idx, 0)
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if not batch_idx % 2:
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assert out.training_step_output_for_epoch_end == [[]]
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assert out.signal == 0
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